Keywords: Internet of Things (IoT), dynamic IoT networks, multi-purposed IoT networks, Integrated antennas, Reconfigurable antennas, Multi-band, Integration techniques, Low-power, Flexible antennas, Neural Networks (NN), Electronically Steerable Parasitic Array Radiator (ESPAR), Array synthesis, Parasitic antenna arrays, Particle Swarm Optimizer (PSO)

optimize the directivity, the front-to-back ratio, and the gain of compact parasitic array antennas for IoT devices. The impedance loads values identified with this approach are integrated into the antenna structures to realize the parasitic antenna array. Reconfiguration can then simply be obtained by rotating the load's values over the parasitic elements.

Abstract

Because of the unique characteristics of the Internet of things (IoT), such as massive volume of data, stringent resource constraints, and heterogeneous activity levels, future wireless IoT infrastructures must integrate smart self-organizing solutions to efficiently operate in the dynamic IoT environment. Within this context, this thesis presents the development of innovative integrated reconfigurable antenna systems capable of adapting to such dynamic and multi-purposed wireless IoT networks.

As a first step, the thesis covers the design of antennas suitable for integration in IoT terminals when specific radiation properties or multi-band operations are needed. Both antenna structures and integration techniques are presented for single and multi-band IoT applications. Next, a design methodology based on a multi-access single structure antenna and a miniaturized omnidirectional circularly polarized antenna for IoT applications are presented. Successively, an approach based on Neural Networks (NN) to estimate the performance of small antennas given practical constraints is presented. As proof of concept, the case of printed antennas integrated into compact terminals is considered.

In the second part, the thesis focuses on the development of compact and low-powerconsuming reconfigurable antennas. In order to enable new applications, such as wearable communications, and to deliver higher performance in a variety of integration environments, a flexible, reconfigurable antenna is presented. To guarantee reliable operation, flexible antennas must maintain proper electrical or radiation characteristics despite the variation of their structure given by the flexibility. Consequently, the reconfiguration mechanism of the proposed antenna allows the shift of the antenna resonance, making the antenna capable of maintaining an acceptable impedance matching over the operating band even when the antenna is folded.

A compact high gain and front-to-back ratio pattern reconfigurable antenna for spatial filtering in IoT networks is then proposed. The reconfiguration mechanism is based on the use of a low-insertion, low-on resistance SP4T switch, which can be numerically controlled by a microcontroller, as required by IoT applications. Successively, a slot-based electronically steerable parasitic array radiator (ESPAR) antenna suitable for IoT applications is presented. The work describes in-depth the design procedure of the antenna, addressing the practical implementation of the reconfiguration mechanism, and focusing on the realization of the prototype and its experimental evaluation.

Finally, a methodology for synthesizing parasitic antenna arrays is proposed. The method is based on the use of a Particle Swarm Optimizer (PSO), which iteratively optimizes the parasitic element loads until the desired pattern behavior is obtained. In order to assess the method's ability to address the desired goals, the methodology is used to

Résumé

De par les caractéristiques uniques de l'Internet des objets (IoT), telles qu'un volume massif de données, des contraintes de ressources strictes et des niveaux d'activité hétérogènes, les futures infrastructures IoT sans fil doivent intégrer des solutions d'auto-organisation intelligentes pour fonctionner efficacement dans l'environnement IoT dynamique.

Dans ce contexte, cette thèse présente le développement de systèmes d'antennes intégrés reconfigurables innovants capables de s'adapter à de tels réseaux IoT sans fil dynamiques et polyvalents.

Dans un premier temps, ce manuscrit de thèse traite de la conception d'antennes adaptées à une intégration dans des terminaux IoT lorsque des propriétés de rayonnement spécifiques ou des opérations multi-bandes sont nécessaires. Les structures d'antennes et les techniques d'intégration sont présentées pour les applications IoT à bande unique et multibande. Dans ce cadre, une méthodologie de conception basée sur une antenne à structure unique multi-accès et une antenne à polarisation circulaire omnidirectionnelle miniaturisée pour les applications IoT sont décrites. Ensuite, une approche basée sur les réseaux de neurones pour estimer les performances de petites antennes compte tenu des contraintes pratiques est présentée. Comme preuve de concept, le cas des antennes imprimées intégrées dans des terminaux compacts est étudié.

Dans une seconde partie, la thèse porte sur le développement d'antennes reconfigurables compactes et peu gourmandes en énergie. Afin de permettre de nouvelles applications, telles que les communications portables, et d'offrir des performances supérieures dans une variété d'environnements d'intégration, une antenne flexible et reconfigurable est présentée. Pour garantir un fonctionnement fiable, les antennes flexibles doivent en effet conserver des caractéristiques radioélectriques ou de rayonnement appropriées et ceci, malgré la modification de leur structure créée par la flexibilité. Le mécanisme de reconfiguration de l'antenne proposée pour les contraintes évoquées permet le décalage de sa résonance, la rendant ainsi capable de maintenir une adaptation d'impédance acceptable sur toute la bande de fonctionnement même lorsqu'elle est repliée.

Une antenne compacte reconfigurable à gain et rapport avant/arrière élevés pour le filtrage spatial dans les réseaux IoT est ensuite proposée. Le mécanisme de reconfiguration est basé sur l'utilisation d'un commutateur SP4T à faible pertes d'insertion et à faible résistance, qui peut être contrôlé numériquement par un microcontrôleur, comme l'exigent les applications IoT. Une antenne à réseau parasite, avec faisceau orientable électroniquement (ESPAR, pour electronically steerable parasitic array radiator), à base d'éléments rayonnants de type fentes adaptée aux applications IoT est ensuite présentée. Le travail décrit en profondeur la procédure de conception de l'antenne, en abordant Résumé également la mise en oeuvre pratique du mécanisme de reconfiguration, et se concentrant sur la réalisation du prototype et sa caractérisation expérimentale.

Enfin, une méthodologie de synthèse de réseaux d'antennes parasites est proposée. Cette méthode est basée sur l'utilisation d'un algorithme de type PSO (Particle Swarm Optimizer), qui optimise de manière itérative les impédances complexes des charges connectées sur les éléments parasites jusqu'à ce que le comportement en rayonnement souhaité soit obtenu. Afin d'évaluer la capacité de la méthode à atteindre les objectifs souhaités, la méthodologie est utilisée pour optimiser la directivité, le rapport avant/arrière et le gain d'antennes à réseau parasite compactes destinées aux appareils IoT. Les valeurs des impédances de charge identifiées avec cette approche sont intégrées dans les structures d'antenne pour réaliser le réseau d'antennes parasites. La reconfiguration peut alors être simplement obtenue en réalisant une permutation circulaire des valeurs de la charge sur les éléments parasites. 3-D isometric view of the pattern-reconfigurable, Huygens source antenna design together with the simulated 3-D radiation patterns, in all three diode states, at the resonance frequency 1.577 GHz from ref. [START_REF] Tang | Low-profile, electrically small, huygens source antenna with pattern-reconfigurability that covers the entire azimuthal plane[END_REF], (b) patternreconfigurable, vertically polarized NFRP antenna geometry from ref. [START_REF] Tang | Pattern reconfigurable, vertically polarized, low-profile, compact, near-field resonant parasitic antenna[END_REF], (c) layout of the reconfigurable inkjet-printed antenna on paper substrate presented in [START_REF] Abutarboush | A reconfigurable inkjet-printed antenna on paper substrate for wireless applications[END_REF], (e) the MIMO antenna system for CR platform proposed in [START_REF] Hussain | An integrated four-element slot-based mimo and a uwb sensing antenna system for cr platforms[END_REF] and the associated simulated reflection coefficient curves for Ant-1, (f) the integration of a DTC within the band-pass filter structure of a filtenna proposed in [START_REF] Ramadan | A new compact digitally tuned filtenna[END_REF] and the reflection coefficient of the proposed filtenna, and (g) schematic of parasitic layer-based radiation pattern reconfigurable antenna for 5G Communications from ref. [START_REF] Hossain | Parasitic layer-based radiation pattern reconfigurable antenna for 5g communications[END_REF]

List of Tables

CHAPTER 1

Context and Motivation

IoT paradigm

The "Internet of Things" concept can be described as an extension of the Internet to the world of objects and concrete places [START_REF] Han | Mobile cloud sensing, big data, and 5g networks make an intelligent and smart world[END_REF]. Nowadays, smart devices are present in any field. Networks of smart connected things (SCoT) may be perceived as a further stage to promotes new innovations and thus evolving the use of Internet to transform the economy, governance, living conditions, and mobility [START_REF] Cohen | Time harmonic electromagnetic fields[END_REF].

IoT technologies will dominate the market in the future. Innovative applications such as connected vehicles, traffic management, environmental monitoring, supply chain management, industrial, agricultural and commercial management, will make the interactions with the network an everyday use. Networks of smart connected objects, flexible electronics and flexible wireless systems could play a key role since they can enable new applications, such as wearable communications [3]. Smart retails, smart wearables reap the benefits of highly secure IoT data transmission. Industrial IoT is widening its application fields such as smart factories, smart buildings/smart homes, smart city, and smart grid [START_REF] Fan | Smart grid communications: Overview of research challenges, solutions, and standardization activities[END_REF], but it's not without challenges. These networks of 'things' provide seamless interoperability between a large variety of low-power wireless devices via the internet. Smart objects are continuously sensing the changes and gathering data from the environment. To establish connections between cyberspace and to remotely control intelligent sensors and actuators by users, efficient IoT wireless infrastructures are needed [START_REF] Al-Fuqaha | Internet of things: A survey on enabling technologies, protocols, and applications[END_REF], [START_REF] Lin | A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications[END_REF]. The ongoing rapid growth of the IoT will require billions of objects to be connected to the internet in the next future [START_REF] Frustaci | Evaluating critical security issues of the iot world: Present and future challenges[END_REF]. The huge traffic that will be generated by the communications between the objects and the network will pose demanding challenges in terms of reliability of the radio access [START_REF] Jiang | Analyzing random access collisions in massive iot networks[END_REF] and reduction of the energy consumption [START_REF] Liu | Toward green iot: Energy solutions and key challenges[END_REF].

1.1 IoT paradigm

IoT devices

Machines will consitute primary users of the IoT ecosystem in the future. IoT communications generally involve a wide range of machine-type devices (MTDs) and are constantly leveraged to gather information from sensors or to transmit data to the cloud. Machines connected to the internet are essential to realize smart environments (e.g, smart cities, smart homes, smart building, among others) as well as to collect and treat data to provide smart services [START_REF] Davis | Life with 50 billion connected devices[END_REF], [START_REF] Borkar | Application of 5g next generation network to internet of things[END_REF]. Machines can be both the collectors and distributors of information to create a microcosm of "objects", with which users will interact daily. IoT communication paradigm involves non delay-sensitive fragmented transmissions, high-density deployment, uplink-centric applications and small data volumes [START_REF] Shafiq | A first look at cellular machine-to-machine traffic: Large scale measurement and characterization[END_REF]. MTDs are usually expected to have a very large battery capacity and are expected to share data without human interaction. Collection of data in this ubiquitous environment demands massive coverage, and affordable connectivity across both rural and urban area. To react to the exchange of information, nodes need to become available anywhere and anytime. In spite of that, to face the limitations of low memory and limited resources, ad-hoc communication protocols are applied [START_REF] Wang | A lightweight xmpp publish/subscribe scheme for resource-constrained iot devices[END_REF].

The recent trend in device size reduction accompanied with the move to higher frequency ranges such as within the millimeter wave range and beyond is redefining the technological industry and is reshaping how information is exchanged. IoT realm is composed of miniaturized devices that are wirelessly connected in a network of everyday items. By providing more sensing and communication capabilities and more computing power, networks of smart small objects can be completely embedded into existing internet and mobile networks. On the other hand, electronics miniaturization is accompanied with a demand for increased speed and performance, as well as reduced weight. With the demand for enhanced quality of service (QoS) future networks would need more advanced communication solutions than those that are currently available.

IoT communication standards

As future IoT will offer a broad range of very different services, multifunctional devices will communicate with several standards and frequency allocations. The ability to handle more than one communication protocol allows the terminal to be used for various applications. Multiband operations would be therefore necessary.

Enterprises have begun to improve their personal wireless networks driven by the idea that technologies, such as low-energy BLE [START_REF] Jeon | Ble beacons for internet of things applications: Survey, challenges, and opportunities[END_REF], [START_REF] Prummel | A 10 mw bluetooth low-energy transceiver with on-chip matching[END_REF], supported by mesh networking technology are the most exciting innovation to create and develop IoT applications. Logic management through the RFID standard [START_REF] Vlacheas | Enabling smart cities through a cognitive management framework for the internet of things[END_REF], e-health [START_REF] Sawand | Toward energy-efficient and trustworthy ehealth monitoring system[END_REF], wireless sensor network [START_REF] Gungor | Industrial wireless sensor networks: Challenges, design principles, and technical approaches[END_REF] are other IoT killer applications. Wireless Networking of Things is exploiting device heterogeneity and new standard communication technologies (such as ISO/IEC 18000, IEEE 802.15.4 [START_REF] Davoli | From micro to macro iot: Challenges and solutions in the integration of ieee 802.15.4/802.11 and sub-ghz technologies[END_REF]).

However, due to 2.4 GHz's ubiquity, global deployments, greater capacity and the ability to cost-effectively connect a device, most IoT applications operate in the frequency ranges of 2.4 GHz (802.11b/g/n). In this IoT scenario, the high number of communication links lead to an overuse of 2.4 GHz radio spectrum. Moreover, the standard IEEE 802.11a/b/g/n suffer of multipath dispersion due to the influence of electromagnetic field generated by CHAPTER 1. CONTEXT AND MOTIVATION distinct signal reflectors and diffusions [START_REF] Sendra | Ieee 802.11a/b/g/n indoor coverage and performance comparison[END_REF]. For these same reason, the entire network may be prone to data packet losses or latency phenomenon.

A huge IoT challenge is represented by the expansion of the operating band especially for large wavelength systems (low frequency band). Transition from 4G to 5G networks is underway. Radical changes in the telecommunication infrastructure, as the one abovementioned, require some new frequency ranges. For 5G new bands are proposed such as: 410-430, 470-790, 1000-1700, 2025-2110, 2200-2290, 2700-5000, 5350-5470, and 5850-6425 MHz.

Other technologies such as the Low Power Wide Area Networks (LPWAN) offer longrange communication, which enables new types of services [START_REF] Raza | Low power wide area networks: An overview[END_REF]. This architecture provides network for ultra-high link distances (≤ 1 km), extremely high battery lifetimes (∼ 10 years), and low throughput on the order of bit per second (bps) transmissions for agricultural, industrial, medical, and smart city applications. Several LPWAN proposals have emerged in the past few years, including the commercial technology for the unlicensed sub-1GHz bands. LPWAN networks like LoRaWAN and Sigfox typically operate in 433 MHz, 868MHz and 902-928MHz bands. The main differences between these standards are the modulation type, the bit rate, and other communication parameters. LoRaWAN and Sig-Fox are the most adopted LPWAN protocols, even if SigFox is a proprietary Ultra Narrow Band (UNB) solution. It limits the number of packets per device that cannot exceed 140 packets/day with a bit rate of 100 bps and a maximum packet payload of 12 bytes. Together with the SigFox business model based on building and maintaining its own network, these restrictions have somewhat shifted the interest to LoRaWAN, which is considered more flexible and open. What makes LoRaWAN more interesting is its development as open standards in the unlicensed spectrum. It provides a medium access control mechanism, enabling many end devices to communicate with the gateways by using LoRa modulated transmission which is robust against disturbances and can be received across great distances [START_REF] Adelantado | Understanding the limits of lorawan[END_REF].

Cellular IoT devices are still largely using 2G technology. Operators in Europe would continue to use 2G GSM networks while spreading the refarming of their 3G networks. IoT commercial and industrial equipments still benefit from of the widespread coverage of GSM's networks. 2G networks, on the other hand, are not flexible enough to accommodate the wide variety of IoT applications that will be developed in the future and has been power-hungry standard. Two low power consumption standards have been proposed in recent years as open standards for reusing cellular infrastructure: Narrowband Internet of Things (NB-IoT), which is the direct replacement for low-bandwidth 2G applications, and LTE-M, which is planned for new applications needing mobility and higher bandwidth. Both cellular LTE-M/NB-IoT networks make common use of cellular bands below 1 GHz. 5G Massive Machine-Type Communications (mMTC), cellular LTE-M/NB-IoT and large scale LPWAN, will constitute in the next years the backbone for a fully connected world, in which everyday objects will enable new services by exchanging data with each other. The provision of new services is then followed by the question of how the next generation of IoT networks should embrace these unbounded capabilities to satisfy the user needs. Luca Santamaria 1

.1.3 IoT networks

The continuing exponential development of the IoT would necessitate the redefinition of the network capabilities to change the ways in which data is collected, processed, and analyzed [START_REF] Frustaci | Evaluating critical security issues of the iot world: Present and future challenges[END_REF]. The IoT is made up of devices that differ in functionality, technology and scope of use.

An environment able to sense, analyse and adapt to make our lives simpler, safer and more efficient.

Because of the unique characteristics of IoT, such as massive volume of data, stringent resources and heterogeneous activity levels, the actual trend is to move from a network with a specific function toward a massive multi-function dynamic IoT network. Fig. 1.1(a) shows a representation of a future IoT networks. While in Fig. 1.1(b) its characteristics are highlighted. This network must be:

• massive, because thousands of "things" will be connected with each other.

• dynamic, because it needs to integrate self-organizing solution which efficiently ensure dynamic communication between OSI layers and achieve data interoperability among heterogeneous IoT nodes.

• multifunctional, because each device will provide different features and would be able to handle with many applications.

Traditional communication systems have been designed to handle centralized data-flow models. However, to exploit massive IoT data opportunities and cope with many collaborative processes, there is a compelling need to modify this approach. IoT data generated will exceed the number of IoT devices and communication processes will typically be multidirectional and not anymore hierarchical. Because of the massive volumes of data created by intelligent objects, distributed approach is the only solution that fully enables the realization of efficient IoT systems since there is no single performance bottleneck and network overhead is minimized.

The installation of distributed and multifunction IoT networks brings many advantages but must face different problems:

• the robustness, i.e. the connection must be always available, flexible and avoid any faults. In IoT wireless networks constructive factors such as reinforced concrete walls, iron elements or other obstacles could disturb the signal transmission; CHAPTER 1. CONTEXT AND MOTIVATION

• the information transmitted must not be recoverable from third parties, unrelated to the transmission itself and must be monitored;

• the exchange of new data flows between data-centers and on-premises servers, between electronic devices and the Cloud need to coexist;

• the huge IoT data flows pose problems for the dimensions of technological infrastructures to be implemented and for the capacity to feed them (low power);

• since the available bandwidth for data transfer is variable, high latencies may be the norm. The availability of a broadband connection is essential for an increasing number of people and objects to take advantage of the services offered by the "Next Generation Networking "(NGN).

• the performance, in terms of transmission speed;

To enable fast, secure and reliable communications, and to fully exploit the potential offered by this new paradigm, IoT systems should integrate not only additional signal processing techniques but also revolutionary and "intelligent" hardware . Luca Santamaria The proliferation of small IoT cells offers direct connectivity among end-users and increases the effort to integrate and thus miniaturize communication equipment. As a result, nodes have become significantly smaller, resulting in a strong demand for compact antennas. Following this technological trend, the antenna's size must be reduced as much as possible to find space for other electronic components such as a screen, speaker, microphone, USB, battery, and so on. However, the urgent demand for compact modules does not come without any issues. The first concern is about the integration of the antenna into the IoT terminal. As seen in Sec. 1.1.2, most IoT applications operate in the sub-GHz band. By using this communications band, antennas would be too large to be integrated into a modern smartphone-sized device due to the 1 GHz free-space wavelength, which is 0.3 m. Antenna volume, geometry, and the strategy to embed the antenna into the terminal, possibly including the different passive and active components, become essential design factors.

Antenna performance is another concern. It's a well-known fact that the size of an antenna influences its radiation resistance and quality factor. For example by using the aforementioned standards, the wavelength is typically 50-100 times higher than the IoT device size. As a result, antenna radiation impedance is typically less than 1Ω, the efficiency becomes extremely low, the bandwidth very narrow. Lowering the antenna efficiency may reduce the communication range and affect the ability of the IoT terminal to communicate. To obtain a longer communication range, the transmitter must consume more power which means a shorter lifespan for the battery. In addition, for some applications, part of the radiated power is absorbed by the human body and the surrounding environment. Low propagation loss and high antenna efficiency, especially in battery-powered devices, are required. Integrating the antenna into IoT terminals while maintaining good performance still remains a major design problem.

Reconfigurable antennas

Future IoT wireless structures must integrate new technologies to overcome spectrum scarcity, interferences, fast-fading and shadowing effects, which affect the signal-to-noise ratio need to be implemented and efficiently handle with a high number of heterogeneous data. Reconfigurable antennas counteract these effects making the system capable of modifying its behaviour based on the requirements and conditions of the surrounding environment.

These antennas must meet the requirements of typical IoT devices and services. IoT services typically require to be integrated into a small, cost-effective device and running unattended by humans for a long time. On the other hand, the IoT device units must face extreme battery consumption limitations, and the remote control of their functionality is therefore required [START_REF] Cha | Trust in m2m communication[END_REF]. Consequently antennas must be compact and efficient despite their miniature size, and realized using low-cost materials, which are suitable for mass production. Compact reconfigurable antennas leverage the capabilities to be easily integrated into such complex systems and adapt to future multi-purpose, and ultra-dense wireless communication systems. Given the small amount of data needed to be transmitted by IoT devices, the antenna bandwidth reduction due to the miniaturization is usually not a problem. However, narrow band antennas are more sensitive to the close environment. Reconfigurable antennas can be conveniently used to compensate the shift of the antenna resonant frequency caused by variations of the antenna surroundings. Frequency reconfigurable antennas represent also a valid alternative when multiband antennas are difficult to be integrated in compact terminals. Frequency reconfigurable antennas respond to the rising user demand for access to spectrum while considering the shape factor limitation [START_REF] Abutarboush | A reconfigurable inkjet-printed antenna on paper substrate for wireless applications[END_REF]- [START_REF] Batel | Frequency reconfigurable antenna loaded with magneto dielectric materials at vhf band[END_REF].

Frequency reconfigurable antennas can operate at different frequencies by effectively exploring the spectrum and creating reliable communication links where the bands are left unused [START_REF] Srivastava | Compact reconfigurable uwb slot antenna for cognitive radio applications[END_REF]. These antennas are suitable to be integrated into cognitive radio systems (CRs) to learn and adapt to the surrounding environments.

Spatial reuse is one key technology for supporting IoT communications. Patternreconfigurable antennas can filter the interference by targeting the radiation pattern towards a specific direction and decrease the data collisions rate, the average energy for packet transmission, and the latency opportunistically. Nodes equipped with switched beam antennas would allow 88% lower energy consumption and 24% lower data collision than a solution based on omnidirectional antennas [START_REF] Le | Improving energy efficiency of mobile wsn using reconfigurable directional antennas[END_REF]. Reconfigurable radiation patterns with maximal gain in the azimuth plane are usually preferred for long range communication. transfer, the IoT network may integrate pattern reconfigurable antennas that maximize the signal-to-noise ratio (SNR), minimizing interferences and covering a high number of nodes. In a different IoT scenario, nodes would require to recover energy, as depicted in Fig. 1.2(c), whereas they are low in charge. In this context, the network changes its functioning to enable RF energy harvesting. Then the network might implement frequency reconfigurable antennas that allow nodes to harvest signals coming from e.g., Wi-fi sources; and continue transmitting to a different receiver, for example, a LoRa device. Fig. 1.3 shows the design of an intelligent IoT network in which reconfigurable antennas are employed. By integrating reconfigurable antennas into IoT networks, different features can be enabled. An example could be the implementation of algorithms for position and estimation using specific physical and MAC layers. Traditional MAC protocol based on omni-directional antennas causes excessive multi-user interference [START_REF] Abdullah | Wireless lan medium access control (mac) and physical layer (phy) specifications[END_REF]. Directional-MAC layer [START_REF] Ko | Medium access control protocols using directional antennas in ad hoc networks[END_REF] may use directional antennas protocol to reduce signal interferences in unnecessary directions, provide higher level of recorded received signal strength (RSS) and therefore an higher level of estimation accuracy while maintaining low energy consumption [START_REF] Bazan | An opportunistic directional mac protocol for multihop wireless networks with switched beam directional antennas[END_REF], [START_REF] Burtowy | Low-profile espar antenna for rss-based doa estimation in iot applications[END_REF]. In order to operate correctly, these intelligent Networks and Datalink layers need to be supported by an intelligent physical layer. Specifically, reconfigurable antennas need to be deployed into the networks and integrated into the smart objects. Transceiver and microcontrollers can be integrated into the same smart object to control the reconfiguration mechanism dynamically.

The design of antenna geometries suitable for integrating IoT devices is not a simple task to accomplish. Equally important is the accurate study on the best tunable mechanism CHAPTER 1. CONTEXT AND MOTIVATION in terms of power consumption, suitable for the reconfiguration used and for the feature's desired network. Antenna reconfigurability can be achieved by controlling mechanical, physical, or optical switches. Among them, electronic switches are the most popular because of their efficiency and easy integration with microwave circuits. Radio Frequency (RF PIN diodes and RF MEMS are typical components used to achieve dynamic tuning. PIN diodes have some disadvantages, like a high insertion loss and lower efficiency [START_REF] Asadallah | A multiband compact reconfigurable pifa based on nested slots[END_REF]. However, the lower costs make them attractive amongst researchers. RF MEMS switches are near-zero power consumption. They offer miniaturization with no loss of functionality, fast actuation techniques, very low insertion loss, and they are extremely linear. Even though MEMS have only a few drawbacks, such as the need for costly transformers to provide high DC-bias voltages (40 V to 100 V), they are still difficult to integrate on printed circuit boards (PCBs) and they are still expensive [START_REF] Wright | Mems reconfigurable broadband patch antenna for conformal applications[END_REF]. In contrast, Gallium Arsenide (GaAs) and CMOS switches are relatively low cost and easy to integrate with RF circuitry. GaAs switches are small, they consume low power, and they have very good switching speed performance. They are relatively low cost and easy to integrate into the antenna structure. Finally, the limited number of external components makes the interaction between the switch and the antenna kept to a minimum [START_REF] Aboufoul | Reconfiguring uwb monopole antenna for cognitive radio applications using gaas fet switches[END_REF]- [START_REF] Yang | Frequency reconfigurable antenna for wireless communications using gaas fet switch[END_REF]. However, GaAs switches require DC blocking capacitors at the RF inputs, an external driver, and are usually high-power (≥ 1 µA), making them unsuitable for power-constrained performance. CMOS switches are specifically designed to operate on a band of frequency up to 900 MHz. They can handle transmitted power up to 16 dBm. The low insertion loss, the high isolation between ports, the low distortion, and the low current consumption (1 µA max.) make these switches an excellent solution for low-power IoT applications [START_REF] Wang | Small-size reconfigurable loop antenna for mobile phone applications[END_REF], [START_REF] Santamaria | Flexible reconfigurable antenna robust to folding in wearable applications[END_REF].

Conclusion

Conclusion

This Chapter presents an introduction with supporting details of the context and motivation of this Thesis work. Sec. 1.1 focuses first on the characteristics of future IoT networks and their principal actors. The constraints faced by an IoT device have therefore been illustrated. Second, the compelling need for specific communication hardware to enable new IoT services and face network requirements has been presented.

Later on, the Chapter introduces in Sec. 1.2.1 an analysis of the necessity of integrating radiating structures in small IoT terminals, showing the challenges of such integration. The study of this topic will continue in Chapter 2, where integration techniques and examples of antennas suitable for integration into IoT devices will be presented.

In Sec. 1.2.2, it is then clarified that shape, dimension, and compactness are not the only factors to be considered when designing antennas for IoT networks. Consequently, the potential of reconfigurable antennas to provide dynamism and multi-functionality in future IoT networks is highlighted. Recent advances on pattern and frequency reconfigurable antennas for IoT devices will be presented in Chapter 3, where some of the antennas designed during this thesis work will also be analyzed in depth.

CHAPTER 2

Integrated Antennas for IoT Devices

Introduction

As seen in Sec. 1.2.1, designing antennas suitable for integration in IoT terminals is challenging. Even more complicated when enhancing the radiation properties of the antennas like the direction of antenna radiation, the polarization of the radiation emitted, or when multi-band operations are needed.

The design and integration of multi-band antennas for IoT applications have recently received more attention [START_REF] Islam | A modified meander line microstrip patch antenna with enhanced bandwidth for 2.4 ghz ism-band internet of things (iot) applications[END_REF]. The multi-function characteristics of IoT devices require the antennas to cover different frequency bands [START_REF] Rholam | Smart device for multi-band industrial iot communications[END_REF]. However, most of the portable IoT terminals have smaller sizes and must integrate a large number of electronic components, which brings challenges to the antenna design problem [START_REF] Atzori | The internet of things: A survey[END_REF].

From the antenna point of view different approaches to address multi-standard have been proposed. Firstly, the use of multi-band antennas is redundant in literature. The inverted-F antenna (IFA) is one of the most widespread antenna structures used for its space-saving properties. Tapered slot antennas (TSA) [START_REF] Niamien | A compact dual-band notch antenna for wireless multistandard terminals[END_REF] and other low-profile geometry such as the coupled patch antenna [START_REF] Stanley | A capacitive coupled patch antenna array with high gain and wide coverage for 5g smartphone applications[END_REF] are also desirable for miniaturizing the volume of the radiators in the compact portable device [START_REF] Yang | Siw multibeam array for 5g mobile devices[END_REF]. Substrate Integrated Waveguide (SIW) technology is considered as one of the suitable choices for compact IoT devices due to the non-planar component (waveguide) structure compatible for integration with planar circuits, such as a PCB. In [START_REF] Liu | Ism 433-mhz miniaturized antenna using the shielding box of mobile terminals[END_REF], the miniaturization requirement is achieved by connecting the metal shielding box of the terminal to a monopole antenna. IoT terminals are equipped with ground planes which further complicate the implementation of antennas while maintaining good performance. Due to the space constraint and the physical structure of IoT terminals, the ground plane needs to be considered during the antenna design process. Several approaches have been proposed to reduce the antenna's volume by maximizing radiation of currents from the terminal chassis or improving the antenna's performance by modifying the current distribution on the ground plane [START_REF] Islam | Ground current modification of mobile terminal antennas and its effects[END_REF], [START_REF] Szini | On small terminal mimo antennas, harmonizing characteristic modes with ground plane geometry[END_REF] In [START_REF] Liu | Electrically small loop antenna standing on compact ground in wireless sensor package[END_REF], a miniaturized loop antenna, a metal shielding box for the RF circuit, a wireless electric charging induction coil, and a compact ground plane for the antenna and circuit are integrated into a sensor package. The elements of the terminal mentioned above have been considered in the design process. However, despite the acceptable performance of the suggested solution, the use of a 3D geometry limits the antenna's suitability for mass , is to have multiple separated antennas [START_REF] Chen | Design of multiband antenna for lte wearable device with shared slots and radiators for smart watch[END_REF], which is not optimal for a miniature device due to the effective volume needed by each antenna to radiate correctly. In Sec. 2.2, by combining the previously mentioned solutions, a design methodology for multiple access antenna operating at different frequency bands is presented. This work has been published in [START_REF] Santamaria | 2-port antenna with matching network for dual-band iot terminal[END_REF]. The single antenna structure is successfully designed to radiate efficiently at both LoRa 863 MHz -870 MHz and GPS L1 1563 MHz -1587 MHz bands by exciting 2 different ports with the help of a suitable matching circuit. The study shows that by adjusting the antenna geometry, it is possible to achieve the desired total efficiency over multiple bands.

Circularly polarized (CP) antennas have become an attractive choice for tracking applications. They offer many performance advantages over linearly polarized antennas such as the jamming suppression, the mitigation of the 'Faraday rotation' effect due to the ionosphere or uncertainty in orientations of transceiver antennas [START_REF] Gao | Circularly polarized antennas[END_REF]. For co-polarized communications, CP antennas reduce multipath interferences due to the handedness modification of the wave polarization when single (or odd) bounce reflections take place.

Over the last few years, omnidirectional circularly polarized (OCP) antennas have attracted many researchers' attention [START_REF] Zheng | High-gain normal-mode omnidirectional circularly polarized antenna[END_REF], [START_REF] Li | Omnidirectional circularly polarized dielectric resonator antenna with top-loaded alford loop for pattern diversity design[END_REF]. By combining multiple directional CP antennas to cover the entire azimuth plane, it is possible to synthesize an omnidirectional CP antenna [START_REF] Quan | A novel broadband omni-directional circularly polarized antenna for mobile communications[END_REF]. This solutions, shown in Fig. 2.2(a), has a complex three-dimensional structure and it is large in size. In [START_REF] Lin | Compact, high directivity, omnidirectional circularly polarized antenna array[END_REF] an omnidirectional circularly polarized antenna system is reported, which achieves high directivity and operates within the 2.4 GHz WLAN band. The antenna structure is depicted in Fig. 2.2(b). Multiple stages of vertical strip and loop radiators are cascaded together to form multiple electric-type and magnetic-type radiators.

They act with a 90 o phase difference to produce the OCP radiation with an operating bandwidth of 110 MHz covering to 2.37-2.48 GHz. However, the significant length of the structure of 1.7λ at 2.37 GHz makes the antenna difficult to integrate into an IoT device. In [START_REF] Narbudowicz | Dual-band omnidirectional circularly polarized antenna[END_REF], an omnidirectional CP antenna realized on a single layer is presented, but the radiator and its power supply structure require a large area and the variation of the omnidirectional gain exceeds 3 dB. At 2.4 GHz PCB antenna printed on a RO5880 dielectric substrate with omnidirectional circularly polarized antenna is proposed in [START_REF] Wu | Compact omnidirectional circularly polarized antenna[END_REF]. The antenna achieves a compact size of 0.504λ 0 × 0.198λ 0 including a 90 • phase shifter to feed the v-polarized and h-polarized omnidirectional antennas. However, the latter solution is not suitable for IoT applications, because of the use of expensive substrate and intricate feeding networks (see Fig. 2.2(c)). In Sec. 2.3, a miniaturized omnidirectional circularly polarized (OCP) antenna for IoT applications is presented. This research work has been published in [START_REF] Mastrosimini | Miniaturized omnidirectional circularly polarized antenna for iot applications[END_REF]. The CP waveform is synthesized by combining two linearly polarized antenna elements which act as electric and magnetic dipole, respectively. The vertical and the horizontally polarized elements are integrated and fed by a single port. The OCP antenna achieves a CP bandwidth covering 80 MHz from 2.4 to 2.48 GHz. The results show that the antenna can provide at 2.44 GHz an axial ratio lower than 3 dB and gain variations of less than 1.1 dB over all azimuth angles. The OCP antenna is printed on two cost-effective FR-4 PCBs, and it is very low profile. Thanks to its radiation and geometric properties, the proposed antenna solution fits well in specific IoT use cases where a monopole-type radiation pattern is required.

In addition to the challenge of designing integrated antennas for IoT applications, estimating the best performance of antennas when integrated into an IoT terminal is also crucial. Over the years, several works have tried to estimate these performance bounds. In its classical work [START_REF] Chu | Physical limitations of omni-directional antennas[END_REF], Chu expressed the lower bound on the Q-factor of antennas in terms of their electrical size, defined as the radius of the smallest sphere circumscribing the antenna. More recent works [START_REF] Gustafsson | Physical limitations on antennas of arbitrary shape[END_REF], [START_REF]Illustrations of new physical bounds on linearly polarized antennas[END_REF] expressed antenna performance bounds in terms of 2.1 Introduction the of the structure polarizability of any antenna shape. These new bounds better apply to IoT and 5G terminal antennas, whose geometries are rarely spherical. Although these results are useful and provide a first step in analyzing small antennas, they are mostly purely theoretical and do not take into account practical aspects of the realization of antennas, such as the feeding method or the material losses.

In Sec. 2.4, an approach based on Machine Learning (ML) to estimate the performance of small antennas in practical applications is therefore proposed. This study has been published in [START_REF] Roqui | Estimation of small antenna performance using a machine learning approach[END_REF]. As a proof of concept, the case of printed antennas integrated into compact terminals, such as for IoT or 5G applications, is considered.

CHAPTER 2. INTEGRATED ANTENNAS FOR IOT DEVICES

2-Port antenna with matching network for dualband IoT terminal

In this Section, by combining the previously mentioned advantages of both multiple separated antennas and single access multiband antennas (see Sec. 2.1), a design methodology based on a multi-access single structure antenna is proposed. With the help of matching circuits, a simple antenna having two ports, each one generating a different resonance, is designed. This solution avoids using switches or duplexers in the PCB to connect different transceivers and enables simultaneous signal reception on various bands. Therefore, this switch-less approach provides cost, space-saving on the PCB, and 1 dB of SNR enhancement. A practical example can be found in an IoT terminal using wireless positioning services (e.g., based on GPS standard) and low-power connectivity for long-distance communication (e.g. LoRa) [START_REF] Figueiredo E Silva | Wireless positioning in iot: A look at current and future trends[END_REF].

Design methodology

The principle of the proposed approach is using multi-access on a single antenna to excite different frequencies. However, matching circuits are required to enhance isolation among ports and equally improve the matching properties. The Inverted-F Antenna (IFA) is a well-known single band resonator. It represents a good initial design choice by making use of the shorting point as an additional port.

The design procedure can be divided into the following steps: (a) A single port IFA is first designed and matched at a specific frequency band. (b) The shorting point of the antenna is replaced by the second excitation port. (c) Each port of the antenna is matched by using lumped circuits at the desired frequency band.

These steps can be repeated for different geometries matched at different bands of interest. The next subsection will show that the designer can optimize the radiation performance on each band by properly choosing the initial structure and therefore resonant frequency.

Results

The effectiveness of the proposed approach is evaluated in designing an antenna for LoRa and GPS standards. Three IFA-like radiating elements are used as initial geometries for the matching network optimization. These three configurations differ only for the number of meanders, shown in Fig. 2.4. They have been selected as following: the first one (N 1 ) and second one (N 9 ) are the ones exhibiting the resonances closest to the LoRa-868 MHz and GPS-1.575 GHz bands, respectively. The third antenna (N 3 ) resonates at 1.10 GHz which is an intermediate frequency between the two desired bands as illustrated in Fig. 2.5. N k represents the antenna with k meanders. The terminal size is 20 × 57.5 mm 2 with the ground plane occupying 20 × 37.5 mm 2 . All the free-ground area is used for the antenna. The substrate is a 1.6mm thick Epoxy FR-4 with a permittivity of 4.4.

To match the two antenna ports, namely P 1 and P 2 respectively, T-type matching (blue circle in Fig. 2.4a) circuits are utilized. This type of matching circuit could be modified to work as different types of filters [START_REF]Impedance matching[END_REF]. The obtained results are shown in Fig. In Table I.I, the minimum values of |Γ| and the total efficiency in both bands of the three antenna are reported. A trade-off is observed on the radiation efficiency at 868 MHz and 1575 MHz. As the number of meanders increases, the total efficiency improves in LoRa band, from -3.8 dB to -2.42 dB, while it decreases in the GPS band. The designer decision is based on specification of the system. Fig. 2.7 shows that if the device is designed to maximize GPS performance, the N 1 design is the best option as it obtains highest performance in terms of total efficiency for GPS, and LoRa has a total efficiency higher than -4 dB, which is sufficient for LP-WAN communication.

Finally, Fig. 2.10 depicts the simulated and measured antenna isolation. The measured antenna isolation measured 20 dB, while the simulated one is limited to 8. be notice from 2.9,2.10,2.11, the simulated and measured electric performances show minor discrepancies. This can be ascribed to the feeding cable used to connect the antenna to the measurement system. This feed cable couples to the currents on the antenna and affect the impedance matching. The small ground plane of the antenna causes the currents to flow back to the coaxial cable's outer surface, increasing the terminal's electrical size, and therefore increasing the bandwidth.

Based on the results shown previously, the N 1 design has been realized and measured. The antenna prototype is shown in Fig. 2.8. In addition, the electric characteristics of the prototype have been tested in a Satimo Starlab Station. Fig. 2.9 shows the simulated and measured impedance matching of the antenna over the frequency. It can be observed that the antenna achieves a measured impedance bandwidth of 15 MHz ranges from 865 to 880 MHz with an |S 11 | ≤ -10 dB. These data are in good agreement with the numerical ones considering the LoRa-868 MHz and the GPS-1575 MHz bands. The simulated antenna impedance bandwidth covers 11 MHz from 866 MHz to 877 MHz. It is worth noting that the antenna is well-matched with |S 11 | always below -10 dB, whatever the excited frequency.

Miniaturized omnidirectional circularly polarized

antenna for IoT applications

Introduction

This Section presents a 2.4 GHz miniaturized CP antenna with omnidirectional radiation pattern suitable for IoT applications. The omnidirectional CP radiation pattern in the azimuth plane is synthesized by combining, in the same geometry, two linearly polarized omnidirectional antennas fed by a 50Ω single port. The antenna does not require a power divider and a phase shifter to excite the far fields components E θ and E ϕ correctly which makes it suitable for low-power, low-cost IoT applications. The antenna has a compact volume of 0.252 × 0.252 × 0.081λ 3 0 = 0.0051λ 3 0 at 2.44 GHz and it is printed on two lowcost PCBs.

Antenna structure

The structure of the antenna is shown in Fig. 2.12. It is based on the well-known Alfordloop structure antenna adopted to design a planar printed omnidirectional horizontally polarized antenna [START_REF] Chuang | Omni-directional horizontally polarized alford loop strip antenna[END_REF]. However, the geometry is modified to combine a vertical (VP) and a horizontal (HP) linearly polarized excitations and to achieve the CP omnidirectional pattern.

Unlike the classic Alford-loop structure printed on a single PCB, the antenna proposed in this Section consists of two Z-shaped copper strips printed on two different FR-4 PCBs of 0.13 mm thickness and ϵ r = 3.4 dielectric permittivity.

VP wire-patch element design

The VP element has been realized as follows:

• Two slots are carved on the central 'trunk' of the top z-strip so that a rectangular patch and a truncated z-shaped metallic strip are formed on the top layer.

• A vertical feeding wire, which is attached to a 50Ω discrete port, is used to connect the central feed point of the top rectangular patch to the bottom z-shaped strip.

• Two short-circuit wires, located symmetrically on each side of the feeding wire, span from the top layer to the bottom layer.

The rectangular patch on the top PCB, the metallic z-shaped on the bottom PCB, together with the feeding and short-circuit wires constitute a wire-patch antenna, which provides an omnidirectional vertically polarized radiation pattern. Fig. 2.13 shows the surface current distribution at 2.44 GHz on the wire-patch and on the z-shaped strips of the top and bottom PCBs. These z-shaped strips form two capacitances which are placed in parallel with the two short-circuit wires considered as equivalent inductances. The equivalent circuit formed allows the excitation of the well-known mode of wire-patch antennas [START_REF] Delaveaud | New kind of microstrip antenna: The monopolar wire-patch antenna[END_REF]. The square loop E-field distribution in the branches of both strips generates an horizontally polarized omnidirectional radiation pattern.

The radius of feeding and the short-circuit wires are 0.9 mm and 0.4 mm respectively, whereas the distance between them is 2.3 mm. These three geometrical parameters are mainly responsible for ensuring a good impedance matching (|S 11 | ≤ -10 dB) from 2.4 to 2.48 GHz. The remain geometrical parameters, i.e., the wires height m h , the base t w and the length t l of the trunks, the length a l and width a w of the branches of both top and bottom strips, have been selected to achieve a CP bandwidth of 80 MHz covering the Wi-Fi channel bandwidth with axial ratio (AR) AR ≤ 3 dB and |S 11 | ≤ -10 dB. A balance between the amplitudes of the E θ and E ϕ far field components can be obtained by tuning the slots lengths s w and width c w . The optimized values of antenna geometric model parameters, including the length of the substrate v l , result to be as follow,: t w = 9.12, t l = 24.96, a l = 27, a w = 3.1, m h = 10, s w = 0.48, c w = 9.36 while v l = 31 mm.

The total height and the length of the antenna are 10.07 mm and 31 mm, respectively, corresponding to 0.081λ and 0.252λ at 2.44 GHz.

Numerical results

Fig. 2.14 shows the surface current distribution on the antenna at 2.44 GHz within one time period T . The efficient excitation of the segmented loop and the wire-patch generates the magnetic and electric dipoles, respectively. The main current is named J t ; t = 0, T /4, T /2, 3T /4. Each quarterly period J t is the combination of J HP , J V P distributed on both strips and vertical vias, respectively. The orientations of the surface currents at 0, T /4, T /2, 3T /4 are depicted by black arrows. The length of each arrow represents the current amplitude. Again, the currents on the top and bottom strip trunks, named J 1 , J 2 and J 3 , J 4 , are equal in magnitude with a phase difference of 90 degree, thus they cancel each other. When t = 0 a clockwise current distribution is produced by the segmented loop z-shaped radiator. Furthermore, the current flows in the negative z-direction on the short and feeding wires. When t = T /4 the currents on both the strips become weak, as seen in Fig. 2.14(b) whereas the currents in the short wires attain their maximum value. It is clearly that the z-strips currents reach their maxima again at t = T /2 but are moving in the reverse directions with respect to the ones at t = 0. At t = 3T /4 the currents reach the same magnitude but travel in the opposite direction to those obtained for t = T /4. Finally, the main surface current streamlines rotate counterclockwise (CCW) over the time which reveal that the OCP antenna is a source of left-hand circularly polarized waves.

The antenna impedance matching is shown in Fig. 2.15. The resonance is centered at 2.44 GHz and the matching achieves a return loss of better than 12 dB in the operating The realized LHCP and RHCP gain patterns evaluated in the XOY and XOZ planes at 2.44 GHz are reported in Fig. 2.17. The amplitude of the co-polarized field (LHCP) is at least 17 dB stronger than the corresponding cross polarized field (RHCP) in the XOY plane cut. It indicates that a counterclockwise polarization is achieved by the proposed antenna. In the XOY plane the average LHCP realized gain is about 0.8626 dBi with a maximum gain variation of 1.1 dB. The radiation pattern is omni-directional in the XOY plane, while it is symmetrical in the XOZ plane. In the XOY plane the omnidirectional pattern exhibits 1.9 dBi maximum realized gain and 2.37 dB AR at 2.44 GHz. The radiation patterns of the proposed antenna are stable in the operating band.

Finally, Fig. 2.18 shows the simulated antenna radiation and total efficiencies. The radiation efficiency is displayed in dotted black, while the total efficiency of the antenna in solid black. Numerical values are higher than -0.8 dB all over the band of interest with a radiated and total efficiency of -0.6 dB at 2.44 GHz.

2.4 Estimation of small antenna performance using a machine learning approach

Introduction

In this Section, a ML based approach is proposed to estimate the performance that can practically achieved by small antennas integrated in compact terminals. The objective is to create a model that, given the space available for the antenna and the terminal dimensions, provides the impedance matching and efficiency level that can be achieved. Such a tool can then be used by antenna designers to have (a) an a-priori estimation of the antenna performance given the terminal constraints (or viceversa), or (b) a comparison value for the relevance of a specific design.

Antenna structure and dataset

As a proof of concept of the proposed approach, we consider the case where the antenna is a printed folded IFA located in the corner of a PCB. The remaining part of the PCB is fully covered of metal, simulating the ground plane of the terminal (Fig. 2. [START_REF] Davoli | From micro to macro iot: Challenges and solutions in the integration of ieee 802.15.4/802.11 and sub-ghz technologies[END_REF]). This configuration is typical for most of modern compact wireless IoT devices. Starting from the structure in Fig. 2.19, we created a dataset to be used for the training and test of the ML algorithm. The dataset is created by varying the Ant-x and Ant-y dimensions of the space available for the antenna in the range [0.05λ, 0.12λ] and the PCB-x and PCB-y PCB dimensions in the range [0.12λ, 0.24λ]. For each configuration, the geometry of the antenna has been fixed to obtain |S 11 | ≤ -6 dB at the frequency f = c/λ, c being the speed of light. This is obtained by optimizing the antenna geometrical variables, i.e., the IFA line width and length and the distance between the feed and short. This constraint guarantees to consider only antenna configurations providing an acceptable impedance matching. Working in λ allows us to be independent on the working frequency.

In the dataset, the input variables are therefore (Ant-x, Ant-y, PCB-x, PCB-y) while the output variables are the considered antenna performance, that is, the S 11 magnitude and the total efficiency η at the resonance. These parameters have been obtained numerically using a commercial electromagnetic simulator. The entire dataset consists of 550 samples that we split with a K-Fold of 5, i.e. we used 80% for training and 20% for test. 10% of the training dataset were used for validation during training.

ML-based approach

From the ML perspective, our task is a multi-target regression problem as we want to predict two continuous values from a set of four inputs. We proposed a solution based on Artificial Neural Networks (ANNs), the most successful ML technique over the recent years. We designed a Multi-Layer Perceptron (MLP), a particular model of ANNs which consists of an input layer (4 neurons), three hidden layers (256 -512 -256 neurons) and an output layer (2 neurons). Each layer has a number of neurons called perceptrons, such that the neurons between the layers are fully connected via synapses, as shown in Fig. 2. [START_REF] Davoli | From micro to macro iot: Challenges and solutions in the integration of ieee 802.15.4/802.11 and sub-ghz technologies[END_REF]. Each perceptron computes the sum of the products of inputs × synaptic weights, followed by a non-linear activation function (ReLU). The MLP was trained with gradient backpropagation, a supervised learning algorithm that adjusts the neurons synaptic weights so that the entire MLP approximates the function which links the inputs to outputs. As shown in Fig. 2.19, the principle is to calculate the gradient of the Mean Squared Error (MSE) between the desired output and actual output and to back-propagate it to the neurons of the previous layers in order to update their synaptic weights [START_REF] Khacef | Confronting machine-learning with neuroscience for neuromorphic architectures design[END_REF]. The process is repeated through the whole training dataset for 1000 epochs.

Preliminary results

To assess the performance of the proposed model and training, we compute the Mean Absolute Error (MAE) between the predicted and the true values, a common metric for regression problems. We achieved a MAE of 1.0% ± 0.3 and 6.8% ± 0.9 for the training and test dataset, respectively. Fig. 2.20 and 2.21 show the prediction performance and the histogram of the prediction error respectively. The obtained results validate the learning convergence of the model and the generalization capability to new data, even though our model has some overfitting. The model indeed predicts better the training data than the test data, which is a common issue in ANNs training, especially for small datasets.

2.4 Estimation of small antenna performance using a machine learning approach

Conclusions

An overview of some of the most interesting antenna solutions suitable for integration into IoT devices has been introduced in this Chapter. In the first Section, some factors to consider when designing integrated antennas have been shown. Antenna structures, integration techniques are presented for single and multi-band IoT applications.

Later in the Chapter, some of the integrated antennas for IoT application designed during the thesis period are presented. Firstly, a miniature antenna configuration with a single radiating element and 2 feeding ports has been designed and simulated in Sec. 2.2. The proposed N 3 design, matched at 868 MHz and 1.575 GHz with efficiency higher than 50%, validates the proposed methodology. The simulated and measured performance indicates that the antenna is a good solution for multi-standard devices.

A circularly polarized antenna has been presented in Sec. 2.3. The antenna geometry has been miniaturized for easy integration in IoT terminals. The structure has a low profile and consists of only two FR4 PCBs, and 3 vias make the antenna easy to realize and cheap to produce. The antenna presents a good total efficiency of -0.8 dB at 2.44 GHz. The omnidirectional pattern exhibits 1.9 dBi maximum realized gain and AR ≤ 3 dB all over the 2.4 GHz band. Thus, the proposed antenna acquires good CP omnidirectional performances without an intricate feeding network, while keeping the stable omnidirectional radiation pattern for the IoT applications.

The previous Sections of the Chapter have shown how the design of small antennas is challenging. This is due to the fundamental physics, which limits their performance. In Sec. 2.4, a ML-based approach for the estimation of the performance achievable by small antennas in practical applications has been presented. The preliminary results indicate that, given the space available for the antenna in a compact terminal, the proposed model can estimate the impedance matching and the efficiency with an error lower than 7%.

CHAPTER 3

Reconfigurable Antennas for Dynamic IoT Networks

Introduction

This Chapter presents a detailed analysis of innovative reconfigurable antennas for dynamic wireless IoT networks. Some of the literature work on reconfigurable antennas for compact devices suitable for IoT applications are initially presented in this Section. Antenna concepts and designs, solutions in reconfigurability for IoT paradigm, and the techniques to accomplish it are detailed. In Sects. 3.2, 3.3, 3.4, the focus of the chapter is on the analysis of some of the reconfigurable antennas designed during my PhD research work.

Reconfigurable antennas represent a valid solution to overcome the challenges posed by these new wireless communication paradigms. Important advancements in reconfigurable antennas have been made in the last two decades. They have been focusing on the antenna performance characteristic to be reconfigured (frequency, polarization, or radiation pattern) as well as on the mechanism used to implement reconfiguration. However, only a few literature solutions are suitable for compact IoT devices.

Concerning frequency reconfigurable antennas, in [START_REF] Ramadan | A new compact digitally tuned filtenna[END_REF], a compact digital filtenna is designed for cognitive radio applications. The proposed antenna geometry is given in Fig. 3.1(f). This filtenna relies on a novel switch approach that is the integration of a digital tunable capacitor (DTC) in its filter section for frequency reconfiguration. In [START_REF] Hussain | An integrated four-element slot-based mimo and a uwb sensing antenna system for cr platforms[END_REF], an ultrawideband (UWB) sensing antenna was combined with a compact four-element annular slot-based MIMO antenna system. The measured reflection coefficient curves of the MIMO antenna system for the voltage values used for varactor diode from 0 to 10 V are shown in Fig. 3.1(e). The frequency reconfigurable slot dependent MIMO antenna system operates over a wide frequency range from 1.77 to 2.51 GHz, while the UWB sensing antenna operates between 0.75 and 7.65 GHz. The work in [START_REF] Tang | Compact, frequencyreconfigurable filtenna with sharply defined wideband and continuously tunable narrowband states[END_REF] relies on non-linear switches for the antenna continuously tunable narrowband states. The sensing state is a wideband state that operates between 2.35 and 4.98 GHz. The narrowband states are intended to cover communications in the 3.05-4.39 GHz range, which includes the WiMAX band and the satellite communications C-band. The operating frequencies are continuously shifted among the narrowband states using two varactor diodes.

Furthermore, [START_REF] Vamseekrishna | An iot controlled octahedron frequency reconfigurable multiband antenna for microwave sensing applications[END_REF] proposes a new octahedron frequency reconfigurable multiband antenna for microwave sensing-based IoT applications. This reconfigurable antenna can be utilized for wireless sensing, space, and IoT applications and operates in the ranges 1.57-2. [START_REF] Prummel | A 10 mw bluetooth low-energy transceiver with on-chip matching[END_REF] 3.1 Introduction GHz, 2.13-3.0 GHz, 3.17-3.43 GHz, 5.2-5.8 GHz, 6.3-6.78 GHz, 8.31-8.90 GHz, 9.04-9.58 GHz, and 12.03-13.14 GHz.The desired resonance frequencies are accomplished by using an omega shape slot in the radiating structure and switching PIN diodes to shift the current direction. An IoT device called NodeMcu is used to control the PIN diodes. The NodeMcu switches the PIN diodes on and off according to the software application. We can alter the application and keep track of it over the Internet. The S-parameter characteristics and radiation patterns have been measured, and there is good agreement between the simulated and measured results. The designed model is tested using a Cmote device from the Center for Development of Advanced Computing (CDAC) and operated over the IoT for data transmission and reception at appropriate operating bands.

In the era of smart connected objects, flexible electronics and flexible wireless systems could play a key role since they can enable new applications, such as wearable communications [START_REF] Hester | Additively manufactured nanotechnology and origami-enabled flexible microwave electronics[END_REF]. Flexible antennas have therefore received considerable attention in the last years. One of the main techniques for manufacturing flexible antennas is to use the injection printing techniques. The work in [START_REF] Abutarboush | A reconfigurable inkjet-printed antenna on paper substrate for wireless applications[END_REF] shows the design of a 30 × 40 × 0.44 mm 3 inkjetprinted reconfigurable multiband three-element antenna printed on a low-cost industrial photo paper substrate. The proposed antenna is shown in Fig. 3.1(d). The driven element consists of a rectangular patch operating around 3.4 GHz. Switching between the main radiator and an L-shaped and U-shaped elements using two PIN diodes allows the antenna to cover bands at 1.9 and 2.4 GHz with a maximum gain and efficiency of 2 dBi and 50 %, respectively. The antenna can be easily optimized to operate at different frequency bands. The additive fabrication technique of inkjet printing on a paper substrate reduces the antenna's total cost and makes it highly suited for IoT applications. Also, in [START_REF] Khalifeh | Reconfigurable multi-band antenna for miniature tracking terminal[END_REF], a miniature reconfigurable IFA spanning three separate frequency bands is proposed. IoT is the target application, which necessitates the use of a miniature antenna to obtain location data via the GPS standard and send it to a server via the LPWAN standard. In [START_REF] Batel | Frequency reconfigurable antenna loaded with magneto dielectric materials at vhf band[END_REF], the design of a frequency agile electrically small IFA loaded with tunable magneto-dielectric material is discussed. The antenna is loaded with a material optimized for VHF applications, resulting in a 20 % miniaturization factor and a 6 % frequency agility potential in the VHF band near 70 MHz.

In Sec. 3.2 a flexible reconfigurable antenna suitable for wearable applications is presented. This work has been published in [START_REF] Santamaria | Flexible reconfigurable antenna robust to folding in wearable applications[END_REF]. Thanks to the reconfiguration mechanism, the antenna is robust in terms of impedance matching to the folding of the structure.

Concerning pattern reconfigurable antennas, classical beam steering antennas like phased arrays hardly fit the hard constraints typical of modern compact IoT devices. In [START_REF] Chen | Pattern-reconfigurable antenna with five switchable beams in elevation plane[END_REF], pattern-reconfigurable antennas with multiple switchable beams, particularly with both boresight and endfire directions, are discussed. A pattern-reconfigurable antenna is suggested as a cost-effective solution. The antenna's main beam can be shifted to five directions in the elevation plane by reconfiguring parasitic striplines set around a radiating dipole using PIN diodes, roughly from 90 o (left endfire), to 45 o , to 0 o (boresight), tp +45 o , to +90 o (right endfire). The proposed antenna, which has dimensions of 0.57λ x 0.45λ x 0.28λ, works at 2.45 GHz. The calculated |S 11 | values for all five directional beams are less than -13 dB, and the measured realized gains vary from 5.2 to 6.5 dBi. Integrated devices such as PIN diodes [START_REF] Burtowy | Low-profile espar antenna for rss-based doa estimation in iot applications[END_REF], [START_REF] Yang | A wideband reconfigurable antenna with 360°beam steering for 802.11ac wlan applications[END_REF], or dimmers [START_REF] Akhoondzadeh-Asl | A novel low-profile monopole antenna with beam switching capabilities[END_REF] that can be turned on using external voltages, are also used in other approaches. The work in [START_REF] Tang | Low-profile, electrically small, huygens source antenna with pattern-reconfigurability that covers the entire azimuthal plane[END_REF] shows a Huygens source CHAPTER 3. RECONFIGURABLE ANTENNAS FOR DYNAMIC IOT NETWORKS Figure 3.1: Some of the reconfigurable antennas for IoT applications in literature. (a) 3-D isometric view of the pattern-reconfigurable, Huygens source antenna design together with the simulated 3-D radiation patterns, in all three diode states, at the resonance frequency 1.577 GHz from ref. [START_REF] Tang | Low-profile, electrically small, huygens source antenna with pattern-reconfigurability that covers the entire azimuthal plane[END_REF], (b) pattern-reconfigurable, vertically polarized NFRP antenna geometry from ref. [START_REF] Tang | Pattern reconfigurable, vertically polarized, low-profile, compact, near-field resonant parasitic antenna[END_REF], (c) layout of the reconfigurable inkjet-printed antenna on paper substrate presented in [START_REF] Abutarboush | A reconfigurable inkjet-printed antenna on paper substrate for wireless applications[END_REF], (e) the MIMO antenna system for CR platform proposed in [START_REF] Hussain | An integrated four-element slot-based mimo and a uwb sensing antenna system for cr platforms[END_REF] and the associated simulated reflection coefficient curves for Ant-1, (f) the integration of a DTC within the band-pass filter structure of a filtenna proposed in [START_REF] Ramadan | A new compact digitally tuned filtenna[END_REF] and the reflection coefficient of the proposed filtenna, and (g) schematic of parasitic layer-based radiation pattern reconfigurable antenna for 5G Communications from ref. [START_REF] Hossain | Parasitic layer-based radiation pattern reconfigurable antenna for 5g communications[END_REF].

3.1 Introduction pattern-reconfigurable electrically small antenna (ESA). The antenna consists of slot-based electric and capacitively loaded loop (CLL) magnetic near-field resonant parasitic (NFRP) components. These elements are coupled with a driven tripole electric antenna to achieve 3 Huygens sources, each one characterized by 120 o beamwidth. The antenna has a compact volume of 0.29 × 0.29 × 0.05λ 3 0 = 0.0042λ 3 0 at 1.564 GHz. The antenna geometry and the 3-D radiation pattern configurations provided by the antenna are reported in Fig. 3.1(a). The directive radiation patterns headed in 3 different directions and a maximum realized gain of 3.55 dBi, front to back ratio of 17.5 dB, and 84.9 % of radiation efficiency. PIN diodes have been integrated into the structure to accomplish the reconfiguration of the radiation pattern. Another example of low-profile, compact, NFRP antenna with pattern reconfigurability has been presented in [START_REF] Tang | Pattern reconfigurable, vertically polarized, low-profile, compact, near-field resonant parasitic antenna[END_REF]. The proposed antenna geometry is shown in Fig. 3.1(b). The antenna has dimensions of about 0.1λ × 0.1λ × 0.1λ and consists of three identical top-loaded folded monopoles and a reconfigurable feeding network. This solution requires only three PIN diodes to provide the same number of end-fire vertically polarized radiation configuration, each covering 120 % and with 6.6 dBi of peak realized gain and 158 o half-power beamwidth. The antenna achieves a 360 o beam-scanning ability and shows 11 % of fractional impedance bandwidth around 2.22 GHz.

The work in [START_REF] Yang | A wideband reconfigurable antenna with 360°beam steering for 802.11ac wlan applications[END_REF] presents a novel 360 o beam steering patch antenna with parasitic components. The antenna consists of a radiating patch and six parasitic components, each connected by a series of shorting vias operated by PIN diode switches. The electric field distribution within the substrate cavity appears at the desired beam direction by turning on the desired groups of shorting vias. The 360 o beam scanning is achieved through a rotational switching of the groups of shorting vias. The antenna is modeled with an analogous circuit in terms of the ON and OFF status of a sector of the antenna. This circuit can be used as a design guide for shorting-vias-controlled reconfigurable microstrip patch antennas. The fabricated antenna has a 14.5% bandwidth, a 10 dBi peak gain, and an efficiency of 80.5%. In the azimuth and elevation planes, the achieved beamwidths are 42 o and 97 o , respectively. Because of its ability to generate 360 o steered beams and its operation in the 5.1 -5.9 GHz frequency band, this antenna has major significance in IEEE 802.11ac wireless local area network applications.

In [START_REF] Bai | Pattern reconfigurable antenna with wide angle coverage[END_REF], by changing the eight PIN diodes-based switches' states, the antenna provides eight pattern-reconfigurable modes. In [START_REF] Chen | Pattern-reconfigurable antenna with five switchable beams in elevation plane[END_REF], the proposed antenna achieves beam agility by reconfiguring parasitic striplines with 20 PIN diodes. While effective, most of these reconfigurable design schemes are based on the use of several electronic components that do not fit the limited energy and computational resources usually available on autonomous IoT nodes. In [START_REF] Hossain | Parasitic layer-based radiation pattern reconfigurable antenna for 5g communications[END_REF], a 3-D parasitic layer-based antenna changes its geometry using switches, thus providing reconfigurability of the radiation pattern in both the azimuth and vertical planes. However, the complex three-dimensional structure makes the antenna challenging to fabricate and integrate into an IoT device.

In Sec. 3.3 an electronically pattern reconfigurable antenna for IoT applications is presented. The study has been published in [START_REF] Santamaria | Electronically pattern reconfigurable antenna for iot applications[END_REF]. The antenna consists of 4 wire-patches sharing the same ground plane. This radiating element can switch between four end-fire radiation states in less than 5 us and achieves a 290 MHz frequency bandwidth from 2.25 to 2.54 GHz. The results show that this antenna can realize a peak gain of 3.9 dBi at 2.44 GHz and a front-to-back ratio greater than 6.5 dB. A single low-power, low-insertion CHAPTER 3. RECONFIGURABLE ANTENNAS FOR DYNAMIC IOT NETWORKS loss SP4T switch enables the radiation pattern steering. This approach avoids the use of multiple electronic components in the reconfiguration mechanism, thus accommodating the IoT microcontroller's limited resource constraints. The antenna structure is compact and printed on two cost-effective FR-4 PCBs. Thanks to its high performance, small size, low cost, and low-power characteristics, the proposed structure is suitable for IoT applications.

Another promising solution to form a desired antenna beam and to electronically control the associated direction of maximum radiation without the use of expensive phase shifters, high power consuming components, and within a limited form factor, is represented by the ESPARs. Based on the theory proposed by Harrington [START_REF] Harrington | Reactively controlled antenna arrays[END_REF], an ESPAR antenna is constituted by a single driven element surrounded by passive elements connected to reactive loads. The overall antenna radiation pattern is formed by combining the contributions of both the active and the passive radiating elements, these latter being excited through the coupling mechanism. The pattern reconfiguration can therefore be obtained by properly varying the reactance values [START_REF] Lizzi | Differential pattern-reconfigurable antenna prototype for efficient wireless sensor networks[END_REF].

In [START_REF] Harrington | Reactively controlled antenna arrays[END_REF] both the driven and the parasitic elements were classical dipoles. Dipoles are good to perform theoretical studies [START_REF] Mitilineos | Design and optimization of espar antennas via impedance measurements and a genetic algorithm [antenna designer's notebook[END_REF], however they are difficult to be employed in practical IoT applications due to the lack of a ground plane. Monopole-based ESPAR structures are certainly more convenient since they allow the integration with the beam-steering electronics and the feeding cable without affecting the antenna radiation characteristics [START_REF] Shibata | Dual-band espar antenna for wireless lan applications[END_REF]- [START_REF] Liu | Compact-size electronically steerable parasitic array radiator antenna[END_REF].

In [START_REF] Burtowy | Low-profile espar antenna for rss-based doa estimation in iot applications[END_REF], a low-profile ESPAR antenna that can successfully estimate the direction-ofarrival (DoA) of incoming signals in a wireless sensor network (WSN) applications is presented. For DoA estimation, the proposed antenna is more than three times less costly than high-profile ESPAR antenna designs currently available in the literature. It can provide eight distinct main beam directions and relies on simplified beam steering, making it suitable for low-cost WSN nodes. Measurements of the fabricated ESPAR antenna prototype show that by relying solely on the obtained signal strength values reported at the antenna output port, accurate DoA estimation results can be obtained. The solution attains error levels that are comparable to those available for high-profile ESPAR antennas based on the same energy-efficient beam steering principle while having 12 distinct main beam directions. Therefore, using the proposed antenna, the overall time needed for DoA estimation can be reduced by 33%, which saves the total power required for the device. On the other hand, the limited dimension of the ground plane tilts the maximum of radiation in the elevation plane, which is a problem in dealing with IoT applications where all the connected objects are supposed to communicate in the same horizontal plane. Such an issue can be solved by modifying the antenna ground plane with a metallic skirt wound around it, as done in [START_REF] Kawakami | Electrically steerable passive array radiator (espar) antennas[END_REF]- [START_REF] Liu | Electrically small and low cost smart antenna for wireless communication[END_REF].

More recently, an alternative solution based on dipoles has been proposed in [START_REF] Batel | Superdirective and compact electronicallybeam-switchable antenna for smart communication objects[END_REF]. In this case, a floating ground plane decoupled at the RF has been used for the beam-steering electronics. Finally, in order to reduce the overall antenna height, the radiating elements can be folded [START_REF] Liu | Electrically small and low cost smart antenna for wireless communication[END_REF], capacitively loaded [START_REF] Batel | Superdirective and compact electronicallybeam-switchable antenna for smart communication objects[END_REF] or embedded in a dielectric material [START_REF] Lu | Dielectric embedded espar (de-espar) antenna array for wireless communications[END_REF].

In Sec. 3.4 a slot-based ESPAR antenna suitable for IoT applications is proposed. This research work has been published in [START_REF] Santamaria | Slot-based pattern reconfigurable espar antenna for iot applications[END_REF]. The antenna can provide 12 directive and 2 slightly different omnidirectional radiation states. The 12 directive patterns allow the antenna to focus the radiated energy towards 6 different directions uniformly distributed in the azimuth plane and with 2 different beamwidths. The reconfiguration of the radiation 3.1 Introduction pattern is based on the use of low-capacitance low-power consuming diodes that can be digitally controlled. This Section details the design of the antenna structure and the practical implementation of the reconfiguration mechanism, discussing the effects of real electronic components on the antenna performance. The antenna effectiveness is numerically and experimentally assessed. CHAPTER 3. RECONFIGURABLE ANTENNAS FOR DYNAMIC IOT NETWORKS 3.2 Flexible reconfigurable antenna robust to folding in wearable applications

Introduction

In order to guarantee effective and reliable operation, flexible antennas are required to maintain proper electrical or radiation characteristics despite the variation of their structure given by the flexibility [START_REF] Saeed | Inkjet-printed flexible reconfigurable antenna for conformal wlan/wimax wireless devices[END_REF], [START_REF] Tehrani | Inkjet printing of multilayer millimeter-wave yagi-uda antennas on flexible substrates[END_REF]. In this section, a flexible reconfigurable antenna suitable for wearable applications is proposed. The antenna geometry is a folded slot antenna in coplanar waveguide (CPW) feeding configuration. CPW feeding is preferred for flexible designs, since it simplifies the realization process by avoiding the use of vias and allowing all the antenna metallic parts to be printed on the same side of the flexible substrate.

The reconfiguration mechanism allows the shift of the antenna resonance through the modification of the slot size. This make the antenna capable of maintaining an acceptable impedance matching over the operating band even when the antenna is folded.

Reconfigurable antenna design

The structure of the flexible reconfigurable antenna is shown in Fig. 3.2. It is based on the well-known CPW-fed folded slot antenna concept [START_REF] Gheethan | Broadband and dual-band coplanar folded-slot antennas (cfsas) [antenna designer's notebook[END_REF]. However, the antenna is modified by integrating 4 parallel metallic lines L 1 , L 2 , L 3 , and L 4 that can be used to connect the two sides of the slot, thus varying its physical dimension and consequently the resonant frequency (Fig. The antenna is printed on a 50µ flex Kapton film with a 35µ of copper layer. The slot-based co-planar geometry allows the antenna to be printed on one side of PCB and avoid the use of the vias to connect to the antenna ground plane.

Since the antenna is intended for wearable applications, its geometry is initially optimized to obtain a good impedance matching when placed in front of a human body in a planar (unfolded) configuration (the optimized dimensions in mm are shown in Fig. 3.2). Towards this end, the antenna is required to exhibit |S 11 | ≤ -10 dB at 2.4 GHz when the C 4 reconfigurable state is activated. Fig. 3.3 shows the impedance matching behavior of the four antenna configurations in free space, which have been however optimized to operate at 2.4 GHz when close to human body.

Since the close environment and the folding effect usually lower the resonant frequency, three out of four antenna configurations resonate at higher frequencies. It can be observed that the resonance is increasing with the configuration number. To speed up the simulations, the human body is modeled as a 10 mm thick homogeneous layer having ϵ r = 44.82 and σ = 0.85 S/m and placed at a distance of 5 mm from the antenna. The antenna geometry is printed on a 50 µm flexible Kapton substrate. Luca Santamaria

Robustness to folding

The objective of the reconfiguration mechanism is to ensure the optimal impedance matching even when the antenna is folded, as it would happen in wearable applications. Consequently, the antenna performance have been evaluated in 4 different folding configurations whatever the folding case, a reconfiguration state that maximise the impedance matching of the antenna at 2.4 GHz can be always found. The optimal state for each folding case is highlighted with the corresponding |S 11 | curve in solid.

Antenna realization

The structure is realized through a photo-lithographic process by using 50 um Kapton polyimide film (ϵ r = 3.5, tanδ = 0.0027). Finally, the electric and radiating characteristics of the prototype have been tested in a Satimo Starlab Station. Fig. 3.7 shows the passive prototype of the antenna in which the switch is modeled as an ideal short/open circuit. tenna configurations in free space. Again, numerical results are in good agreement we the measured ones. As it can be noticed, the antenna configuration C 4 in free space does not allow to obtain the best performance at 2.4 GHz. Fig. 3.9 shows the measured results of the reflection coefficient for a folded test case. As can be seen for this specific test case, switching to C 4 provides better matching at 2.44 GHz.

Based on the results validated in the previous subsection, the reconfiguration mechanism has been practically implemented by using the PE613050 single-pole, four-throw (SP4T) Tuning Control Switch from Peregrine. In order to overcome the problem of integrating a large off-the-shelf reconfiguration circuit on a small flexible antenna, a miniaturized ded-3.2 Flexible reconfigurable antenna robust to folding in wearable applications

Electronically pattern reconfigurable antenna for

IoT applications

Introduction

In the previous Section the development of a flexible frequency reconfigurable antenna has been analyzed. The next two Sections will present the development of antennas with pattern reconfiguration capabilities.

In this Section a novel pattern reconfigurable antenna suitable for IoT applications is presented. A proof-of-concept of the antenna was proposed in [START_REF] Santamaria | Compact 4-element radiation pattern agile antenna for spatial filtering in iot networks[END_REF]. The antenna structure is composed of 4 wire-patch antennas and operates in the 2.4 GHz band. The radiating elements and the control circuit are printed on two different FR4 printed circuit boards (PCBs), thus facilitating the integration into an IoT device. Each pattern configuration is activated using a single 0.45 dB insertion loss single-pole four-throw (SP4T) switch. The radiation structure is optimized to provide a maximum gain in the azimuth plane. The simple reconfiguration mechanism, the small size, and the low-cost realization make the proposed solution an ideal candidate to fulfill the demand for IoT systems with spatial filtering capabilities.

Antenna design

Antenna structure

The proposed antenna geometry is shown in Fig. 3.13. It incorporates two layers, separated by an air gap. The top layer consists of a metal film that includes N = 4 fan-shaped microstrip patches while the bottom layer is a double side circular PCB. On one side of the board, the transmission lines used to feed the 4 antenna elements are printed. On the other side, a copper floor acts as a ground plane for the antenna. Each radiator is indicated with W P n , n = 1...N . The bottom PCB is printed on low-cost 0.8 mm thick FR-4 coppercladded substrate with a relative dielectric permittivity of ϵ r = 4.3, a dielectric loss tangent of tanδ = 0.025, and copper cladding thicknes of 0.035 mm.

Each patch is shorted to the ground plane by a copper pin S n , whose termination is indicated by T S n . The shorting pin is 8.835 mm long with a diameter of 0.5 mm. Each patch on the top copper film is connected to a second vertical wire marked as the feeding pin and indicated by F n . The feeding pin height is indicated by h p , and its diameter is 0.5 mm. The combination of the fan-shaped patch, the feeding pin and the shorting pin realizes a wire-patch radiating element. The feeding pins are connected through the pads T F n to N microstrip lines printed on the bottom board. The microstrip lines are used to transfer the RF energy from a 50Ω discrete port to the 4 patches. The length and width of the microstrip lines are indicated by l f and w f , respectively. The distance between shorting and feeding pins is indicated by d f .

A conductive skirt of height h s has been placed under the lower PCB. The skirt surrounds the ground plane's circumference forming an empty cylinder connected along the edge of the copper floor. The ground skirting increases the ground plane's electrical length without changing its radius and it reduces the tilt in the elevation plane of the radiation pattern when the ground-plane size is limited [START_REF] Schlub | Switched parasitic antenna on a finite ground plane with conductive sleeve[END_REF], [START_REF] Gao | Modified pifa and its array for mimo terminals[END_REF]. As a consequence the peak gain in the azimuth plane increases by reducing the elevation of the main lobe.

Antenna reconfiguration states

The reconfiguration mechanism of the antenna relies on the activation of one wire-patch radiating element at the time. The antenna can therefore provide N = 4 different states A n , n = 1...N . In each A n state, the n-th 50-Ohm port injects the RF signal to the T F n termination through the respective microstrip line, making the W P n wire-patch active.

The remaining ports P k , k = 1...N , k ̸ = n are left in open circuit, so that all W P k become parasitic elements and act as directors for W P n . The antenna's main beam direction is determined by the phase differences between the driven and the parasitic elements. These latter contribute to the overall radiation of the antenna through the coupling mechanism. In this sense, the antenna is not the simple switchable combination of four wire-patch antennas, which would, otherwise, exhibit their classical omnidirectional radiation behavior. The proposed antenna can steer its main beam in the azimuth plane to N different directions φ n with a discrete step of 90 degrees (see Table 3.1). Fig. 3.14 illustrates the simulated surface current distribution at 2.44 GHz for each antenna state A n . The strong current concentration on the standing pins distinguishes the antenna operating mode, which provides a vertically polarized radiation pattern. The inductances of both feeding and short-circuit wires act, together with the capacitance constituted by the fan-shaped patch and the ground plane, as a resonant circuit. The inductances are used to balance the antenna impedance matching [START_REF] Schaubert | Microstrip antennas with frequency agility and polarization diversity[END_REF], and the resulting equivalent circuit enables the T M 01 mode of wire-patch antennas [START_REF] Delaveaud | New kind of microstrip antenna: The monopolar wire-patch antenna[END_REF]. A change in patch height or size would therefore affect the resonant frequency of the antenna. Fig. 3.15 shows the effect of changing the patch height on the Voltage Standing Wave Ratio (VSWR) of the antenna. As expected, as the patch height decreases, the antenna resonant frequency increases. the impedance matching can be tuned by modifying the distance between the feeding wire and the short circuit. More specifically, the VSWR lowers as the short circuit gets closer to the feed. When d f = 1 mm, the resonance is centered at 2.44 GHz, and the matching achieves a VSWR lower than 2.4 in the operating band. It must be pointed out that, dealing with compact antennas, A VSWR lower than 3 is considered to be acceptable for IoT applications [START_REF] Luo | A novel automobile antenna for vehicles communication of iot systems in 5g network[END_REF]- [START_REF] Friedman | Wide-band matching of a small disk-loaded monopole[END_REF], since, as the antenna gets smaller, it becomes harder to have a matching with a high Q-factor. Moreover, given the low transmission powers of IoT devices, the losses due to a VSWR = 3 lead to a signal level reduction that is not noticeable over the air (if compared with the signal level when the antenna has a lower VSWR, e.g., equal The separation between each radiating element has been determined to optimize the radiation performance of the antenna in the state A 1 . The distance between the elements must be sufficient to ensure a good gain. On the other hand, such distance must not be too large so that the mutual coupling can be properly exploited to obtain the desired radiation Finally, the realized gain of the antenna in its 4 antenna states at 2.44 GHz is shown in Fig. 3.22. As it can be observed, the patterns have a constant angular spacing of 90 • and the antenna is capable of providing 360-degree coverage in the principal plane cut at θ = 90 • [see Fig. 3. 22(a)]. The peak realized gain in the direction of maximum radiation is almost 4.2 dBi. The half-power beamwidth (HPBW) of each pattern is about 135 • with a minimum gain between two consecutive patterns of 3.1 dBi. Moreover, each pattern has a high front-to-back ratio (FBR) of more than 9 dB, which can help in spatial filtering of interfering signals coming from the backward direction. For completeness, the radiation patterns in the vertical planes at the different φ n (see Table 3.1) are shown in Fig. 3.22(b). Because of the antenna symmetry, the vertical cuts look exactly the same.

Electronically pattern reconfigurable antenna for IoT applications

Reconfiguration circuit implementation

A reconfigurable network based on a SP4T switch is implemented to accomplish the reconfiguration of the radiation pattern. The numerical model of the antenna structure including the reconfiguration circuit is shown in Fig. 3.23. The copper film on the top layer is now printed on a FR-4 dielectric substrate that makes the antenna more rigid and easy to integrate into an IoT device. The 4 wire-patch radiators share a single DC control circuit. This results in savings power resources of the IoT node, which are usually limited (e.g., when the node is battery powered). The switching network is placed on the bottom PCB to limit the impact on the radiation performance when the reconfiguration is activated. The selected SP4T switch is the PE613050 SP4T from Peregrine, which is a CMOS-based tuning control switch with low on-resistance and insertion loss. Both decoding and biasing are integrated on-chip with the two-pin low voltage control. There is no need for external DC filtering or biasing components to prevent the DC and RF current's short circuit. The switch is characterized by 0.45 dB of insertion loss and a very low-on resistance of R = 1.6 Ω at 2.44 GHz. For low-power IoT services, a CMOS switch represents the most suitable means of reconfiguration amongst the many popular electronic switches proposed in literature. It is worth noting that the SP4T switch allows 4 directive radiation states that can satisfy the 360-deg coverage with a power consumption of 1 µA, which is approximately 10000 times lower than the typical current value (10 mA) required to control PIN diodes [START_REF] Santamaria | Slot-based pattern reconfigurable espar antenna for iot applications[END_REF]. Fig. 3.23(c) shows the schematic of the DC bias lines to control the SP4T switch. The switch input port is named P com , while the four outputs are indicated with P n , n = 1...N . Each output port is connected to the respective feeding pin F n via the pad T F n .

The RF signal is injected from a 50 Ω coaxial cable through the pad C com and then to the input port of the switch P com via a transmission line of dimension l com and w com . One output port of the switch is always in the ON state, and the other three are in the OFF state. The SP4T is used to practically activate one of four antenna configurations. When the A n from the dimension of a standard isolated 50 Ω line, the width of w n and w com lines have been optimized separately to obtain the best insertion loss considering the available space and the integration constraints. This resulted in two different line widths. The paths that link the RF input port to the selected RF output ports are modeled as an equivalent circuit composed of a parallel connection of a fixed capacitor C = 0.14 pF and a variable resistor R n . In each antenna state A n , R n = 1.6 Ω (low-on resistance) so that P n is in ON, while R k = 400 kΩ, k = 1...N , k ̸ = n (P k OFF). The switch requires only two voltage control bits to enable the four antenna configurations. The switch pads V 1 and V 2 receives the control voltages and put each output port P n either in low or highimpedance state. G and DG indicate respectively, the ground and digital ground. The optimized values of the final antenna model's geometric parameters, as well as the values of the reconfiguration circuit layout, are listed in Table 3.2.

Numerical and experimental results

The optimized pattern-reconfigurable antenna has been fabricated and tested. Fig. 3.24 shows the pre-assembly prototype elements of the antenna. Figs. 3.24(a),(b),(d),(e) show the top and bottom faces of the two PCBs. The plastic screws of Fig. 3.24(c) support the top PCB and keep it at the correct distance of 8.4 mm from the bottom PCB. The metallic wires have been used as feeding and shorting pins. The antenna allows suitable integration of the RF and DC electronic stages thanks to its natural protection created by the skirt and the bottom PCB's ground plane. The integration of the RF and DC electronic stages is shown in Fig. 3.24(f). The material of the skirt is an aluminum-manganese alloy. The alloy offers good strength and turns the skirt into natural protection to the antenna's RF and DC electronic stages accommodated inside it. The alloy allows the skirt to be easily soldered with the bottom layer's copper film to which it is attached and has good conductivity (σ = 3.5 × 10 7 S/m). On the bottom face of the lower PCB, the prototype has been equipped with a classical SMA connector to inject the RF signals and with wires soldered to the pads of the switching circuit. These electrical wires are connected to an Arduino Pro Mini powered by a 3.3 V battery. The Arduino provides the DC supply voltage to the switch through the V DD pad and the voltage control bits through the CB 1 and CB 2 pads (see Fig. 3.23). Finally, the microcontroller and antenna grounds are connected through the V GG pad. Both the electric and radiating characteristics of the prototype have been • is mainly due to the limited dimensions of the antenna ground plane. As shown in Fig. 3.20, when the antenna ground plane increases, the side lobe intensity in the vertical direction decreases. However, to keep the antenna size as small as possible, a smaller skirt radius has been selected (the minimum one for which the maximum of radiation was in the horizontal plane). It must be pointed out that, since IoT network nodes are usually considered to be communicating in the same horizontal plane, having a side lobe in the vertical direction will not increase the interference level.

The antenna exhibits a measured FBR higher than 6.5 dB. This value is acceptable in the majority of IoT applications. As demonstrated in [START_REF] Hossain | Parasitic layer-based radiation pattern reconfigurable antenna for 5g communications[END_REF], the spatial filtering capability given by a pattern reconfigurable antenna with a FBR approaching 7 dB provides up to 29% of network capacity gain and 13% coverage improvement compared to the standard case in which a legacy omnidirectional antenna is used. ranges from 2.25 to 2.54 GHz with a VSWR ≤ 3. These data are in good agreement with the numerical ones. The simulated antenna impedance bandwidth covers 170 MHz from 2.33 GHz to 2.5 GHz. The differences between the simulated and measured VSWR values outside the band of interest can be ascribed to the currents flowing on the coaxial cable used to feed the antenna during the measurement phase. These currents increase the electrical size of the antenna ground plane, resulting in a decrease of the antenna Q-factor and therefore in an increase of the impedance bandwidth. Finally, the simulated and measured antenna total efficiency is depicted in Fig. 3.28. The measured results well fit the simulated ones, especially in the band of interest, confirm- ing the effectiveness of the antenna numerical model. The differences in terms of quality factor can be ascribed to imperfections in the realization of the prototype. The measured antenna total efficiency varies between to 57% and 60% in the 2.4 GHz band. Table 3.3 presents the characteristics of the proposed solution compared with those of other works available in literature. The solutions in [START_REF] Yang | A wideband reconfigurable antenna with 360°beam steering for 802.11ac wlan applications[END_REF], [START_REF] Chen | Pattern-reconfigurable antenna with five switchable beams in elevation plane[END_REF], [START_REF] Hossain | Parasitic layer-based radiation pattern reconfigurable antenna for 5g communications[END_REF] have static power consumptions higher than the power handling capabilities of standard IoT receivers (usually 30 mW). In our solution, a monolithically integrated tuning solution based on CMOS technology reduces the overall power consumption, which is only 0.42 mW, less than the power consumption of the solutions presented in [START_REF] Lizzi | Differential pattern-reconfigurable antenna prototype for efficient wireless sensor networks[END_REF], [START_REF] Burtowy | Low-profile espar antenna for rss-based doa estimation in iot applications[END_REF], [START_REF] Yang | A wideband reconfigurable antenna with 360°beam steering for 802.11ac wlan applications[END_REF], [START_REF] Chen | Pattern-reconfigurable antenna with five switchable beams in elevation plane[END_REF], [START_REF] Hossain | Parasitic layer-based radiation pattern reconfigurable antenna for 5g communications[END_REF], [START_REF] Juan | Compact pattern-reconfigurable monopole antenna using parasitic strips[END_REF]. Only in [85], single pole single throw (SPST) switches have been used to activate the reconfiguration with a lower static power consumption (0.0013 mW). However the 4 radiation patterns that are enabled have a maximum gain of only 1.5 dBi in the azimuth plane which is less than the peak gain offered by the proposed solution (3.9 dBi).

Moreover, Table 3.3 shows that a ratio between the number of radiation states and the number of reconfigurable components of the proposed antenna is equal to 4, which is higher than the ratio obtained by the solutions in [START_REF] Burtowy | Low-profile espar antenna for rss-based doa estimation in iot applications[END_REF], [START_REF] Yang | A wideband reconfigurable antenna with 360°beam steering for 802.11ac wlan applications[END_REF], [START_REF] Bai | Pattern reconfigurable antenna with wide angle coverage[END_REF]- [START_REF] Hossain | Parasitic layer-based radiation pattern reconfigurable antenna for 5g communications[END_REF], [START_REF] Juan | Compact pattern-reconfigurable monopole antenna using parasitic strips[END_REF]. Using a single switch to reconfigure the radiation pattern eliminates the use of many circuit modules and, consequently, it well fits the microcontroller's limited resource constraints. An acceptable ratio of the number of the states over the number of components equal to 1 is obtained in [START_REF] Lizzi | Differential pattern-reconfigurable antenna prototype for efficient wireless sensor networks[END_REF]. However, the microcontroller's output voltage variations affect the capacitance values provided by the DTC, which also needs to be independently controlled. Finally, the solutions presented in [START_REF] Lizzi | Differential pattern-reconfigurable antenna prototype for efficient wireless sensor networks[END_REF], [START_REF] Chen | Pattern-reconfigurable antenna with five switchable beams in elevation plane[END_REF] have a larger ratio between the antenna realized gain in the principal cut θ = 90 • and its largest dimension when compared with the antenna proposed here. However, the solution in [START_REF] Lizzi | Differential pattern-reconfigurable antenna prototype for efficient wireless sensor networks[END_REF] is based on a differential antenna, and the lack of a ground plane makes the development and the integration with radio-frequency circuitry difficult. Besides, the gain performance of [START_REF] Chen | Pattern-reconfigurable antenna with five switchable beams in elevation plane[END_REF] relies on expensive substrate and complex biasing network. Instead, the proposed antenna solution is printed on two cost-effective FR-4 PCBs suitable for massive production.

Slot-based pattern reconfigurable ESPAR antenna

for IoT applications

Introduction

In this Section, a new slot-based ESPAR antenna is proposed. The antenna can provide 12 directive pattern configurations and 2 slightly different omnidirectional states. The directive patterns are uniformly distributed and cover the entire azimuth plane. Whatever the pattern configuration, the maximum of radiation lies in the horizontal plane without any tilt in the elevation plane. The reconfiguration mechanism is based on the use of lowcost low-power switches that can be digitally controlled. All these characteristics make the proposed antenna a perfect candidate for modern IoT applications where spatial filtering is crucial to handle a massive number of connected objects.

A preliminary concept was proposed in [START_REF] Trinh | A pattern-reconfigurable slot antenna for iot network concentrators[END_REF]. However, the proposed antenna could provide only 3 radiation states (which makes it unsuitable for large scale IoT network applications), its spatial filtering capability and its capacity of attenuating the interference was limited, and the realization process was more appropriate for a proof-of-concept rather than a practical application. The antenna design has been therefore successively improved, increasing the number of radiation configurations and optimizing the radiation characteristics as shown by numerical results presented in [START_REF] Santamaria | Radiation pattern agile antenna for smart iot gateways[END_REF]. This study expands and completes the work in [START_REF] Santamaria | Radiation pattern agile antenna for smart iot gateways[END_REF] explaining in detail the antenna design procedure, discussing the real implementation of the antenna and the reconfiguration mechanism and reporting the realization of the antenna prototype and its experimental assessment.

Antenna design

Antenna structure

The proposed antenna structure (Fig. 3.29) consists of a metallic closed cylinder, whose height is indicated by c h . On the lateral surface of the cylinder, 6 uniformly separated horizontal slots of dimension s l ×s w are carved on 2 different levels, whose vertical separation is indicated by s g . Each slot level is rotated by 60 o with respect to the other. The distance between two slots on the same level is indicated by s d . The radius of the cylinder changes when s d and s l vary and the circumference of the antenna structure is therefore equal to 3s d +3s l .

The 3 lower slots are located at a height of z = 18.25 mm, while the 3 upper slots are located at z = 25.75 mm. The slots are named S n ; n = 1, ...6 with the normal to the n-tn slot being directed towards the direction at φ = (n -1)60 o (Fig. 3.29(b)). A monopole of length m h is located at the center of the cylinder and fed through the bottom plate. The radius of the feeding monopole is 0.5 mm.

The working principle of the proposed antenna is that of an ESPAR antenna. In our solution, the driven element is the monopole at the center of the cylindrical cavity, while the passive elements are the slots.

In classical ESPAR antennas, the parasitic elements are base-loaded with varactor diodes whose voltage controlled reactances produce different antenna radiation patterns [START_REF] Ohira | Electronically steerable passive array radiator antennas for low-cost analog adaptive beamforming[END_REF], [119]. Varactors diodes, however, are not suitable for IoT applications, because of their limitations in terms of power handling and voltage level required to control the varactor state. In the proposed antenna, in order to obtain a solution suitable for IoT applications, the reconfigurable circuit is constituted by a switch W n ; n = 1, ...6 in series with a fixed inductor L. The circuit is located at the center of each slot S n connecting the two opposite sides (Fig. 3.29(c)). Such configuration allows only two possible impedance values to be put in parallel to each slot own impedance: Z o = ∞ when the switch is open and Z c = j2πf L, f being the working frequency, when the switch is closed. Having only two possible reconfiguration states at each slot, certainly limits the number of radiation pattern configurations that can be realized. However, this allows the antenna to be easily reconfigured using a low-power microcontroller, since each slot switch can be controlled by a 1-bit digital output. The antenna total radiation pattern is the result of the combination of the field radiated by the driven monopole and those radiated by the parasitic slots. Switching between Z o and 3.4 Slot-based pattern reconfigurable ESPAR antenna for IoT applications Z c modify the currents surrounding the slots, thus the fields they radiate and, consequently, the overall antenna radiation pattern. By properly selecting the value of the inductor L, and consequently of Z c , the energy radiated by the monopole and the slots can be combined to obtain globally a directive radiation pattern.

Antenna optimization

The antenna has been optimized by considering a reference configuration where only the switch W 1 (located on S 1 ) is open while all the others W n ; n = 2, ...6 are closed. Moreover, the switches are considered as ideal, i.e., they are modeled as an open circuit when they are open and as a short circuit when closed.

The antenna design procedure consists of two consecutive steps. The first one is aimed at optimizing only the antenna geometrical parameters, while the value of the inductors used in the reconfiguration circuits is set to L = 0 H. In the second step, the antenna structure is considered as fixed, and the inductance value L is optimized.

To obtain a solution suitable for IoT applications, the antenna is needed to fit the following electrical and radiating requirements. In terms of impedance matching, the antenna must exhibit

|S 11 (f )| ≤ S T 11 = -10 dB f 1 ≤ f ≤ f 2 (3.1)
where |S 11 (f )| indicates the magnitude of the complex reflection coefficient in dB, f 1 = 2.40 GHz and f 2 = 2.48 GHz are the limits of the 2.4 WiFi frequency band, and the superscript T denotes the target value imposed by the project specification. This will ensure the minimization of the impedance mismatch losses and consequently the optimization of the available energy on the IoT device. In terms of radiation behavior, the antenna is required to have a good front-to-back ratio F B at the central frequency of the operating band, i.e.,

F B(f c ) ≥ F B T = 15 dB f c = 2.44 GHz. (3.2)
F B is here calculated as the difference (in dB) between the gain in direction of maximum radiation φ mr in the horizontal plane (θ = 90 o ) and the gain in the opposite direction (φ mr -180 o , θ = 90 o ). As shown in [START_REF] Le | Improving energy efficiency of mobile wsn using reconfigurable directional antennas[END_REF], having a good F B allows the antenna to filter the signals coming from undesired directions, thus drastically reducing the number of packet collisions and the overall energy consumption of the IoT network.

The optimization performed during the first design step is mathematically reformulated as the problem of identifying the optimal values â of the antenna geometrical descriptors (see Fig.

Ψ S 11 = |S 11 (f )|-S T 11 S T 11 if Ψ S 11 > 0 0 if Ψ S 11 ≤ 0 (3.5)
and

Ψ F B = F B T -F B(fc) F B T if Ψ F B > 0 0 if Ψ F B ≤ 0. (3.6)
Finally, α and β are coefficients used to weigh the two cost function components. Since F B will be further enhanced through the correct choice of the inductors' value L (step 2 of the design procedure), in this first optimization phase the impedance matching is considered the priority, and consequently, α = 0.6 and β = 0.4.

Given the limited number of parameters to be optimized, the optimization has been carried out by analysing the behavior of each geometric variable with respect to the cost function Ψ. Towards this end, the value of the parameter under analysis has been varied while keeping the others to fixed values. Fig. 3.30 shows the results of the parametric analysis on the main geometric variables. The total cost function Ψ is displayed in solid black, while the two components Ψ S 11 and Ψ F B are shown in dotted red and dashed red, respectively. The effect of varying the slot 3.4 Slot-based pattern reconfigurable ESPAR antenna for IoT applications Table 3.4: Variation of the main antenna characteristics for different values of the inductor in the reconfiguration circuit (Fig. 3 length s l is shown in Fig. 3.30(a). As it can be noticed, a minimum of both the cost function terms, and consequently of the overall Ψ function, is obtained for s l = 56.5 mm. As expected, such a value corresponds to about half a wavelength at f c , as required by the slot to be resonant at the working frequency.

.29). L |S 11 | G max G back F B [nH] [dB] [dBi] [dBi] [dB] 1.5 -
By varying the monopole height (Fig. 3.30(b)), the overall cost function Ψ is minimized for m h = 27 mm. However, differently from the previous case, a variation of m h affects only the |S 11 | term, while the Ψ F B remains unchanged whatever the tested value. This can be ascribed to fact that the monopole height is mainly responsible for the variation of the impedance matching of the antenna, since it modifies the coupling with the slots.

As far as the cylinder height c h is concerned, Fig. 3.30(c) clearly shows that a trade-off between the antenna impedance matching at the working frequency and F B has to be found. As a matter of fact, increasing the cylinder height improves the antenna F B, as shown by the decreasing Ψ F B curve. However, because of the variation of the distance between the monopole and the slots, the impedance matching worsens. Moreover, it must be remembered that, as c h increases, the overall antenna dimension increases as well, making the integration with IoT devices more complicated. Fig. 3. 30(d) shows that varying the horizontal distance s d between two slots on the same level, an optimal value in terms of both Ψ S 11 and Ψ F B can be found. However, it must be pointed out that the effect of such a geometrical parameter on the antenna performance is much lower than that of s l , m h and c h .

Finally, in order to obtain a compact antenna structure, the slot width (s w ) and the vertical gap between the two layers of slots (s g ) are required to be as small as possible (but allowing for an easy realization). Consequently, variations of a few millimeters, which correspond to small fractions of the wavelength in the 2.4 GHz band, have a very little effect on the antenna impedance matching and front-to-back ratio performance.

After the optimization process, the final values of the antenna geometric parameters result to be as follows: â = {s l = 56.5, s w = 3.0, s g = 4.5, s d = 18.83, c h = 47.0, m h = 27.0} CHAPTER 3. RECONFIGURABLE ANTENNAS FOR DYNAMIC IOT NETWORKS mm. The radius and the height of the cylindrical cavity are therefore 36 mm (0.29λ at f c ) and 47 (0.38λ at f c ) mm respectively. The total volume occupied by the antenna is 191.26 cm 3 .

The second step of the antenna design procedure consists in the choice of the inductor to be placed in series to the switch on the slots. This inductor is used to further increase the overall F B of the antenna. This component, in fact, adds an additional degree of freedom to vary the radiating currents surrounding the slots and so to obtain the desired radiation behavior without modifying the antenna impedance matching. The resulting antenna characteristics for the different L values are summarized in Tab. 3.4. The switch configuration is the same as for the antenna geometry optimization, i.e., all the switches are closed but the one on S 1 . As it can be noticed, by varying the inductance value in the range 1.5 ≤ L ≤ 9.5 nH, the F B can be increased up to 30 dB for L = 3.9 nH. In Tab. 3.4, G max indicates the gain in the direction of maximum radiation (φ mr , θ = 90 o ), while G back is the one at (φ mr -180 o , θ = 90 o ). It is worth noting that a good impedance matching with |S 11 | lower than -10 dB is maintained whatever the inductor value.

Antenna performance

The simulated performance of the final antenna structure (optimized geometry and L = 3.9 nH) are reported in the following.

The radiation pattern configurations that are provided by the antenna are shown in Fig. 3.31, where the total realized gain cuts in the horizontal plane are reported. Their main characteristics are summarized in Tab. 3.5.

The first 6 configurations C 1i , i = 1, ...6 are obtained by sequentially reverse biasing each slot circuit, so that one switch at the time is open, while forward biasing all the others, so that they are closed. As it can be noticed, the patterns have a constant angular spacing of 60 o and they allow the 360 o coverage of the horizontal plane with a minimum gain between two consecutive patterns of 4.2 dBi. The realized gain in the direction of maximum radiation φ mr is almost 5 dBi, while the HPBW is about 110 o . Most importantly, the patterns exhibit a very high F B of more than 30 dB, which guarantees the filtering of the signals coming from the backward direction.

It must be pointed out that there are two main reasons for which it is important to use 6 pattern configurations to obtain the 360 o coverage.

First, when less radiation states are considered (e.g., by selecting C 11 , C 13 and C 15 ), the 360 o coverage is obtained with a minimum gain of 1.24 dBi. This corresponds to the minimum between two consecutive patterns, as shown in Fig. 3.32(a), where the combined forward gain G CF is highlighted in green. G CF is obtained by selecting, for each azimuth direction, the highest gain value among the different antenna configurations. On the other hand, when all the 6 C 1i configurations are considered, the 360 o coverage is obtained with a minimum gain of 4.2 dBi (as shown in Fig. 3.32(b)). Such a difference (about 3 dB) is not negligible, since it increases the node communication distance or, alternatively, reduces the transmission power and consequently the node power consumption.

Secondly, and probably most importantly, using 6 radiation states allows a better spatial filtering accuracy. As shown in Fig. 3.32(b), the combined backward gain G CB , defined as the lowest gain value among the different antenna configurations over the entire azimuth Finally, by simultaneously opening 3 slots on the upper (S 1 , S 3 and S 5 ) or lower level (S 2 , S 4 and S 6 ), an omnidirectional behavior can be obtained. In both cases, the average gain is about -0.39 dBi with a maximum variation of 1.9 dB. This mode is crucial in IoT applications since it allows network discovering procedures required during the deployment of the connected objects [START_REF] Le | Improving energy efficiency of mobile wsn using reconfigurable directional antennas[END_REF].

The antenna impedance matching in the different pattern configurations is shown in

Implementation

This subsection discusses the real implementation of the reconfiguration mechanism, that is, how the switches are realized, how they are electromagnetically modeled, and how the choice of the electronic components affects the performance of the antenna. Figs. 3.34 and 3.35 present two different electronic implementations of the switches that have been tested. These configurations have been compared to the ideal one in which the two switch's states are modeled as perfect open and short circuits.

The first configuration I 1 (Fig. 3.34(a)) is based on the use of one single PIN diode, which is DC biased through a choke inductor (L DC ) that prevents the RF current to flow to the ground on the polarization line. During the electromagnetic simulations, two different models have been used for the open and closed switches. When forward biased (closed switch), the diode is modeled as a series resistance R S accounting for the insertion losses of the component (Fig. 3.34(b)). The current flowing through the diode in the closed mode is indicated by I F . When reverse biased with a voltage V R (open switch), the diode is modeled as a capacitor C T accounting for the parasitic coupling between the diode's terminals (Fig.

3.34(c)).

The second configuration I 2 is based on the use of two diodes connected as shown in Fig. Table 3.6: Model parameters for the 2 considered diodes.

DIODE I F V R R S C T L DC L
SMP1345 0.5 mA 3.3 V 5Ω 0.14 pF 100 nH 3.9 nH SMP1322 0.5 mA 3.3 V 1.2Ω 0.85 pF 100 nH 3.9 nH to be halved, since it is given by the series of the two diodes' equivalent capacitors (Fig.

3.35(c))

. On the other hand, the overall resistance value of the circuit in the closed mode is therefore doubled (Fig. 3.35(b)). Moreover, this implementation avoids the use of the DC block C DC , since it automatically prevents the short circuit of the DC polarization current.

For each configuration, two different diodes from Skyworks (SMP1345 and SMP1322) are tested. The values of R S and C T as well as the required I F and V R used to control the two diodes are summarized in Tab. 3.6.

To evaluate the impact on the antenna performance, the 4 implementations have been simulated in the C 11 configuration and compared to the ideal case presented in Sec. 3.4.2. Fig. 3.36 shows the comparison of the antenna radiation pattern obtained in the ideal case (I 0 ) with the those obtained using the 4 implementations. As it can be noticed, when the SMP1345 diode is used in the I 1 configuration (I 1-1345 ), the maximum gain in the desired direction (0 o ) approaches the ideal value of 5 dBi. However, the F B is much lower (6.9 dB) than expected (30.7 dB). This can be ascribed to the 0.14 pF capacitance value modeling the diode when reverse biased, which limits the capacity of the diode to act as an open circuit. F B can be improved up to (8.5 dB) by adopting the two-diodes configuration (I 2-1345 ), in which the overall capacitance of the series of the two components is halved. Using higher-capacitance diodes, as the SMP1322 having C T = 0.85 pF, results in much worse performance, whatever the considered implementation. Low-capacitance diodes are, however, usually characterized by higher series resistances when forward biased, which will cause higher losses. In order to evaluate such a phe-CHAPTER 3. RECONFIGURABLE ANTENNAS FOR DYNAMIC IOT NETWORKS nomenon, Fig. 3.37 shows the antenna radiation efficiency for the 4 implementations and the ideal case. As expected, in the I 1 implementation, the SMP1322 diode, characterized by 1.2Ω series resistance provides higher efficiency values than the SMP1345 one (R S = 5Ω) in the required frequency band. This behavior is even more visible in the I 2 mode, where the overall circuit resistance is doubled.

Nevertheless, for the proposed antenna geometry, the I 2-1345 configuration seems to be the more suitable choice, since it allows the antenna to exhibit the best radiation performance (w.r.t. the ideal case), at the cost of a slight and acceptable efficiency reduction.

It is worth noticing that the practical implementation of the antenna, and consequently the choice of a specific commercially available electronic component has a strong impact on the antenna performance. These latter could be improved by a-priori selecting the commercial electronic components that will be used for the implementation and taking them into account during the antenna optimization phase.

Numerical and experimental results

Based on the results shown in the previous subsection, the two-diodes implementation using the SMP1345 component (I 2-1345 ) has been selected. The numerical model of the antenna structure including the reconfiguration circuit is shown in Fig. 3.38. Differently from Fig. 3.29, the lateral surface of the cylinder is no more made of a simple copper sheet but it is realized by folding a thin double-sided flex PCB made from a polyimide film of 0.13 mm thickness and ϵ r = 3.4 dielectric permittivity. The inner side of the substrate is totally covered with metal except for the 6 slots. The external side is instead used as a support to print the metallic pads to place the electronic components (diodes and inductors) as well as the polarization lines of the reconfiguration circuit. These lines start at the bottom of the cylinder and proceed upwards to join the first pad used for the electronic components. Vias are used to create the electrical interconnection between the two sides of the PCB. The vias have a diameter of 0.48 mm. The prototype of antenna, in which the diodes and the inductors have been soldered, is shown in Fig. 3.39. On the bottom face of the cylinder, the prototype has been equipped with a classical SMA connector to feed the internal monopole and with 6 electrical wires soldered on the polarization lines to control the switches.

When the DC current is applied to one of the polarization lines (through the green wires in Fig. 3.39), the corresponding two diodes D 1 and D 2 are forward biased, so that the RF current can flow through them. Because of the vias connecting the inner and the outer metallic layer, the RF current path makes the slot to be shorted out.

To guarantee the robustness of the antenna in practical applications, the structural foam ROHACELL HF from Evonik has been placed inside the cylindrical cavity. The foam has been shaped in such a way as to completely fill the cylinder while leaving a central hole, along the entire height, for the insertion of the monopole. The foam has an extremely low relative permittivity (ϵ r = 1.04), which makes it electromagnetically transparent. The fine cell structure of the ROHACELL HF ensures minimal resin uptake, and problem-free compatibility with the inside metallic layer of the polyimide film that constitutes the lateral surface of the antenna, due to the absence of corrosive effects.

Because of the symmetry of the antenna structure, only the experimental results of the configurations C 11 , C 21 , and C 31 are reported. These configurations correspond to Finally, Fig. 3.42 shows the simulated and measured antenna total efficiency. Numerical values are higher than -1.5 dB all over the band of interest. As for the experimental data, the minimum total efficiencies are equal to -1.5 dB, -2 and -3 dB in the C 11 , C 21 , and C 31 configurations, respectively. The differences between numerical and experimental data can be ascribed to imperfections in the realization of the prototype.

Conclusions

Conclusions

In this Chapter, the state-of-the-art on novel reconfigurable antennas for compact devices that are suitable for IoT communication paradigms has been proposed. A thorough examination of various frequency and pattern reconfigurable antennas principles and designs, as well as the methods for achieving it have been presented. A flexible reconfigurable antenna suitable for effective and reliable IoT communications has been proposed. Numerical results demonstrated the robustness of the antenna to structure folding.

In addition to the frequency reconfigurable antennas, two pattern reconfigurable antennas suitable for IoT applications have been also proposed. A novel pattern reconfigurable antenna suitable for IoT applications has been presented in this Chapter. The antenna structure consists of only two FR4 PCBs, vias, and a metallic skirt, making the antenna easy to realize and cheap to produce. The antenna can steer its radiation pattern to four overlapping endfire directions in the azimuth plane. The reconfiguration of the pattern is activated using a single low insertion loss, low on-resistance SP4T switch controlled by a 2-bit microcontroller-generated voltage combination.

Finally, a slot-based ESPAR antenna has been presented. Pattern reconfiguration is obtained by digitally controlling the states of PIN diodes located at the center of the slots. The antenna can provide 12 directive radiation patterns headed in 6 different directions and characterized by 2 different beamwidths as well as 2 omnidirectional patterns. The optimization of the antenna considering ideal components has been first presented and, successively, the practical implementation has been discussed. The importance of choosing low-capacitance PIN diodes to approach the ideal radiation behavior has been demonstrated. The effectiveness of the proposed solution has been experimentally validated.

Thanks to its characteristics, the proposed antenna solution well fits the needs of large scale IoT networks. The antenna front-to-back ratio allows the antenna to focus the energy towards the desired communication direction and, most importantly, to spatially filter the signal coming from undesired directions, thus reducing the interference. This will cause the reduction of packet collisions and needed retransmissions, and therefore the node power consumption. The maximum of radiation in the azimuth plane maximizes the communication performance in the majority of IoT application scenarios (e.g., environment monitoring, automotive, smart city, etc.), in which the nodes of the networks are considered to be all located on the same horizontal plane. The 360°azimuth coverage, obtained by switching among the directive radiation configurations, allows the antenna to easily adapt to changes in the network topology, while the available omnidirectional radiation behavior is important for specific IoT network setup processes, such as the node discovery. The compact and robust structure facilitates the integration with the IoT node electronics and the employment in harsh environments. Finally, the low-power (each diode consumes approximately 1.65 mW at 3.3 V) and easy-to-control reconfiguration mechanism (each slot can be activated using a simple digital output available on the IoT microcontroller) well fits the limited energy and computational resources usually available on autonomous IoT nodes CHAPTER 4

An Evolutionary-based Approach for Parasitic Antenna Arrays Synthesis

Introduction

Compact and electrically small antennas are of interest in a variety of IoT applications, which tend to employ tiny and low-power electronic components. As IoT ecosystems evolve, there is an intense interest in compact antennas with directional properties to accommodate users demands for multiple type of tasks.

High directivity and large front-to-back ratio antennas, which radiate most of the power towards specific directions, provide several benefits such as reduced interference and thus less packet collisions, better spatial reuse, and reduced multipath fading in massive IoT networks, such as for asset tracking (logistics) and smart city/smart home applications. High gain antennas are also suitable candidates to solve the poor signal strength problem in dead zones, ensure reliable and consistent communication between IoT end-devices, and provide real-time services such as real-time sensor monitoring, e-health monitoring, and agriculture control. To cope with the time-varying behavior of the IoT environment, directive antennas must be combined with electronic control mechanisms that allow the antenna main beam to be reconfigured towards different directions.

Broadly employed in communication systems, antenna arrays produce custom radiation characteristics, such as very narrow-focused beams. However, to enable pattern reconfiguration, each array element must be fed and necessitates a phase shifter for beam formation in the desired direction. This makes phased arrays unsuitable for integration into typical IoT end-nodes with restricted energy and space resources.

Compact parasitic antenna arrays, where only one element is driven, represent a more suitable solution to generate custom radiation characteristics within a small form factor and without the need for expensive phase shifters and high-power components.

Over the last years, extensive research has been done to improve directivity, FBR, and gain in compact parasitic antenna arrays. The introduction of a metal reflector behind 4.1 Introduction these arrays is a well-known technique for enhancing directivity. The distance required between the antenna and the reflector, however, is usually large. New metamaterial-based technologies have been used to provide high impedance ground planes when integrated into arrays, and develop very small directional antennas. In the parasitic array proposed in [START_REF] Jin | High-directivity, electrically small, low-profile nearfield resonant parasitic antennas[END_REF], metamaterials-inspired in-phase reflectors act as artificial magnetic conductor (AMC) ground planes to achieve high directivity. In [START_REF] Chamok | A thin switched beam parasitic antenna array on planar ebg for 2.4 ghz wireless application[END_REF] a thin parasitic antenna array integrates a non-uniform aperiodic electromagnetic band-gap (EBG) structure on the substrate to achieve a peak gain of 8.4 dBi. Another approach is based on the use of multiple low profile and planar electrically small parasitic antenna arrays in end-fire configuration [START_REF] Lim | Design of a closely spaced, folded yagi antenna[END_REF]- [START_REF] Zhou | A broadband and high-gain planar complementary yagi array antenna with circular polarization[END_REF]. However, in linear end-fire array designs, the PCB size limits the minimal separation between array elements. In [START_REF] Sentucq | Superdirective metamaterial-inspired electrically small antenna arrays[END_REF], [START_REF] Haskou | A compact circularly-polarized array based on superdirective elements[END_REF], a highly directive stacked broadside array has been proposed to improve compactness as required by IoT terminals.

A different solution is to integrate a slot-modified parasitic disk element to control amplitude and phase currents of the antenna and produce patterns with large FBR and high directivity [START_REF] Ziolkowski | An efficient, electrically small antenna with large impedance bandwidth simultaneously with high directivity and large front-to-back ratio[END_REF]. In [START_REF] Ahdi Rezaeieh | Unidirectional slot-loaded loop antenna with wideband performance and compact size for congestive heart failure detection[END_REF] the concept of inserting slots on a loop antenna is introduced to use the produced phase difference for directivity enhancement. Alternatively, Huygens sources antennas provide highly directive beams and an adequate FBR with an electrically small system [START_REF] Lee | Low-profile, electrically small planar huygens source antenna with an endfire radiation characteristic[END_REF].

The mentioned literature works show array designs with the associated performance results. However, there are still very limited works proposing complete methodologies to synthesize parasitic arrays to optimize directivity, FBR, or gain. Superdirective antennas with small inter-element spacing have been optimized by using procedures based on the Harrington maximum directivity definition [START_REF] Harrington | On the gain and beamwidth of directional antennas[END_REF] and spherical wave expansion [START_REF] Kim | Superdirective magnetic dipole array as a first-order probe for spherical near-field antenna measurements[END_REF]- [START_REF] Debard | Limitations and optimization of supergain end-fire arrays[END_REF]. In [START_REF] Kim | Superdirective magnetic dipole array as a first-order probe for spherical near-field antenna measurements[END_REF] a superdirective array of electrically small resonant magnetic dipole elements was designed and fully characterized. In [START_REF] Clemente | Design of a super directive four-element compact antenna array using spherical wave expansion[END_REF], a procedure to get the highest directivity with a compact array of four electric dipoles is proposed. However, the possibility to optimize other radiation characteristics such as the antenna gain and the antenna FBR is not investigated in these articles.

The work in [START_REF] Debard | Limitations and optimization of supergain end-fire arrays[END_REF] shows that the radiation efficiency can be optimized to provide an IEEE gain higher than the one obtained with classical directivity optimization procedures. Other approaches are based on the Yaghjian method [START_REF] Altshuler | A monopole superdirective array[END_REF] to compute the current excitation coefficients of an array in order to maximize the directivity in a given direction [START_REF] Haskou | Design of small parasitic loaded superdirective end-fire antenna arrays[END_REF], [START_REF] Dihissou | Technique to increase directivity of a reconfigurable array antenna for wireless sensor network[END_REF]. In [START_REF] Haskou | Design of small parasitic loaded superdirective end-fire antenna arrays[END_REF], the array excitation coefficients are used to deduce the loads to maximize the end-fire directivity of the parasitic antenna array. The method was also applied to design an array of miniaturized printed half-loop antennas. However, results reveal that as the element spacing increases, neglecting the negative loads' resistances obtained through the optimization cause an important degradation of the optimized directivity.

Other important practical aspects such as impedance mismatch losses and total efficiency are not yet investigated in literature. In addition, to the best of our knowledge, none of the available works have the versatility to simultaneously optimize multiple antenna radiation characteristics.

This research work presents a versatile approach for the synthesis of compact parasitic antenna arrays aimed at optimizing specific antenna radiation characteristics. The approach combines a full-wave modelling with an evolutionary-based optimizer. More spedifically, the evolutionary algorithm is used to iteratively optimize the loads to put on the parasitic CHAPTER 4. AN EVOLUTIONARY-BASED APPROACH FOR PARASITIC ANTENNA ARRAYS SYNTHESIS elements of the array until the desired radiation behavior is obtained. The key benefits of using this method are:

• It can be applied to any kind of parasitic antenna array geometries (e.g., non-endfire miniature structures, 3-D antenna arrays, etc.)

• It requires only one off-line electromagnetic simulation, making the optimization process very fast.

• It has the versatility to optimize different radiation characteristics, such as directivity, FBR, gain, even simultaneously.

• Practical aspects, such as impedance mismatch losses, can be evaluated during the iterative process and optimized to provide optimal realized gain values.

• Constraints on the interval of the final load values can be a-priori set, thus allowing specific loads (such as negatives resistance) to be avoided.

The methodology's effectiveness is demonstrated on three different test cases characterized by different optimization targets.

The rest of the Chapter is organized as follows: in Sects. 4.2, 4.3, the description of the methodology, highlighting each single steps procedure, is presented. In Sect. 4.4, the effectiveness of the proposed methodology is evaluated through test cases. Finally, some conclusions are drawn in Sect. 4.5.

Parasitic element antenna array characterization

Parasitic element antenna array characterization

Let us consider a parasitic element array constituted by P elements A p ; p = 1, ...P , in which the first element A 1 is the driven one, while all the others are parasitic elements loaded by a set of complex impedances z = {Z p ; p = 2, ...P }.

The total electric far-field of the array can be expressed as a weighted combination of the electric far-field E p (θ, ϕ) radiated by each element [START_REF] Thevenot | Synthesis of antenna arrays and parasitic antenna arrays with mutual couplings[END_REF], [START_REF] Fezai | Systematic design of parasitic element antennas-application to a wlan yagi design[END_REF]

E(θ, ϕ) = P p=1 α p E p (θ, ϕ) (4.1)
In order to compute the coefficients α p , let us consider the parasitc element array as a P -ports network. At each port, the reflection coefficient is, by definition,

Γ p = Z p -Z 0 Z p + Z 0 (4.2)
Z 0 being the characteristic impedance of the ports. Using the scattering parameters notation and including the excitation vector u, a P -ports network can be described by the following equations:

a p = Γ p b p + u p (4.3) b p = P j=1 S pj a j (4.4)
where u p is equal to 1 for p = 1 (driven port) and equal to 0 otherwise. By inserting (2) and ( 4) in (3), the input waves terms a p can be expressed as a function of the impedance Z p of each array element

a p = Z p -Z 0 Z p + Z 0 P j=1 S pj a j + u p (4.5)
From ( 5), the coefficients α p can be simply calculated as

α p = a p /a 1 (4.6)
If S pj and E p (θ, ϕ) are considered to be known, since they can be easily obtained with single offline electromagnetic simulation of the antenna structure (as explained in the next section), equations 4.1, 4.5, and 4.6 can be combined to compute the values of Z p ; p = 1, ...P providing the desired radiation pattern E(θ, ϕ). In the first step of the methodology, a full-wave offline simulation of the overall antenna array geometry is made using a commercial electromagnetic (EM) analysis software. The mutual coupling effects between array elements are completely accounted by exciting the array using the technique of the active element patterns [START_REF] Pozar | The active element pattern[END_REF]. Once at time, only the array element A f is excited with a normalized power (P s f , 0 o ), while the others array elements are terminated by matched loads with port impedances Z p = Z 0 , where p = 2, ...P, p ̸ = f and Z 0 = 50 Ω. At the end of the simulation, the electric far-field E p (θ, ϕ) and the elements of the scattering matrix S pj are retrieved.

Fitness function definition

The aim of the methodology is to synthesize an array which provides a custom radiation pattern. The radiation pattern characteristic to be optimised is expressed in the form of antenna parameter which depend on the total electric far-field defined in (4.1) of the array.

Ψ = f (E p (θ, ϕ)) (4.7)
According to such a representation, the optimization we want to perform is mathematically reformulated as the problem of identifying the optimal values z opt of the impedances that minimize Ψ, i.e.

z opt = argmin z {Ψ(z)} (4.8)
The impedance values are converted in input waves terms a p via (4.5) and are used, along with the information on the individual electric fields E p (θ, ϕ), to calculate the total radiation pattern of the parasitic antenna structure via (4.1).

The characteristics of the computed total radiation pattern are then compared with the established criteria by using (4.7).

Solution update

A particle swarm optimizer (PSO) has been designed for guiding the solution to the optimal value. The PSO uses the principle of the survival of the fittest, to quantitatively assess how close a given trial result is to the optimum solution. The cost function in (4.7) is used as a figure of merit to evaluate the quality of each trial solution. The methodology randomly generates a set of particles z In order to maximize the overall search efficiency, the velocity of the previous iteration is weighted with an inertia weight. Then, each particle's position is updated with the particle's new velocity.

The optimization terminates whether n = N or when Ψ z GB ≤ ψ conv , ψ conv being a user-defined threshold. Then, z GB = z opt .

It must be pointed out that, during the optimization process no EM simulations are needed, thus making the optimization process very fast.

Test cases

The effectiveness of the proposed procedure is evaluated through the synthesis of three different compact parasitic arrays.

Test case I -Directivity

In the first case, the evaluated antenna structure (Fig. 4.1) is a parasitic printed dipole array operating at f 0 = 0.868 GHz. The geometry consists of P = 4 identical electric dipoles A p ; p = 1, ...P , printed on a dieletric substrate of 0.451λ 0 × 0.358λ 0 . Each dipole has a thickness and an electric length of 0.01λ 0 and 0.4λ 0 respectively, and it is printed on 0.813 mm thick Roger 4003 substrate with a relative dielectric permittivity of ϵ r = 3.55, and a dielectric loss tangent of tanδ = 0.0027. The gap between each half dipole is 0.0029λ 0 , while the interelement distance is 0.1λ 0 .

In the first case, the goal is to find loads of the parasitic elements to approach the theoretical maximum directivity of a four-element electric dipole antenna array of D T = 12.6 dBi. This value was numerically estimated in [START_REF] Altshuler | A monopole superdirective array[END_REF] based on the Harrington maximum directivity definition. Towards this end, the following cost function has been minimized through the PSO:

Ψ D = D T -D D T if Ψ D > 0 0 if Ψ D ≤ 0. (4.11)
where D is the endifre directivity of each trial antenna solution.

In compliance with the methodology discussed in Sect. 4.3, a complete CST Microwave Studio wave simulation with each array unit connected to a 50Ω port is performed. The load impedance values Z p , p = 2, ...P are successively optimized by the PSO to achieve the desired radiation goal.

At each iteration of the PSO, the directivity of the trial solution required to evaluate the fitness function is calculated starting from E(θ, ϕ) as follows. First, the radiation intensity U (θ, ϕ) of the antenna is computed as [START_REF] Balanis | Antenna theory: analysis and design[END_REF] :

U (θ, ϕ) ≃ 1 2η 0 |E θ (θ, ϕ)| 2 + |E ϕ (θ, ϕ)| 2 (4.12)
where E θ , E ϕ are the far-zone electric-field components of E(θ, ϕ) and η 0 is the intrinsic impedance of the medium.

Then, the total radiated power is obtained by integrating the radiation intensity over the entire solid angle of 4π

P R = Ω U dΩ = 2π 0 π 0 U sin θdθdϕ (4.13)
where dΩ = sin θdθdϕ is the element of solid angle. Finally, the array directivity can be easily calculated as It must be pointed out that the mathematical steps to compute the directivity ( 12), ( 13), [START_REF] Jeon | Ble beacons for internet of things applications: Survey, challenges, and opportunities[END_REF], numerically applied at each iteration, do not require any electromagnetic simulations thus making the synthesis procedure very fast.

Let the particle swarm be a set of K = 20 particles, and the maximum number of iterations is equal to N = 300. Tab 4.1 summarizes the outcomes of the synthesis method at the end of the optimization phase. The optimal impedance loads obtained using the proposed approach have been integrated into each parasitic element, and the final parasitic antenna array has been fully simulated in CST Microwave Studio. Fig. 4.2 shows the simulated directivity patterns in both the azimuth and vertical planes, in the case of the initial active array (blue line) and the synthesized parasitic array (black line). The simulated maximum directivity for the initial array, is 6.01 dBi at 868 MHz . As expected, after the optimization procedure, the array provides a maximum of radiation in the endfire direction (θ = 90 • , ϕ = 0 • ). For the synthesized array, the maximum directivity is 12.59 dBi, 6.58 dB higher compared to the one obtained in the initial array, showing, therefore, the effectiveness of the procedure of optimization.

The evolution of the cost function Ψ D with the corresponding values of D over the iterations is shown in Fig. 4.3. As it can be observed, the cost function decreases along with the increase of the iterations, while the directivity increases, approaching increasingly the value D T . After 211 iterations, the directivity D has a value greater than 12.5 dBi. From Tab. 4.1, it is clear that these results are in agreement with the theoretical directivity computed in [START_REF] Altshuler | A monopole superdirective array[END_REF]. These results are also in good agreement with the maximum directivity calculated from the theoretical value (D T = 12.6 dBi) and full-wave simulations (12.5 dBi) for the four-element array shown in [START_REF] Clemente | Design of a super directive four-element compact antenna array using spherical wave expansion[END_REF] optimized using a procedure based on the spherical wave expansion. The antenna impedance matching before and after the optimization procedure is represented in Fig. 4 It is important to notice that during the optimization procedure, the real part of the impedance values Z p ; p = 2, ...P have been fixed to have values > 0. In other directivity optimization procedures, such as the ones presented in [START_REF] Clemente | Design of a super directive four-element compact antenna array using spherical wave expansion[END_REF], [START_REF] Haskou | Design of small parasitic loaded superdirective end-fire antenna arrays[END_REF], the values of the real impedance part of the loads can be negative. Replacing negative resistors with realistic components could cause differences between the simulated value of the target radiation characteristic and the value obtained from the real implementation. Alternatively, in order to implement such loads, bias circuits with active components are required, introducing additional losses and energy costs. Fig. 4.5 shows the comparison between the radiation efficiency of the initial and the synthesized end-fire array. As expected, at 868 MHz, as you can notice, the array, after the optimization, exhibits a radiation efficiency of 16 dB lower than the radiation efficiency obtained for the initial array (almost 0 dB). The impedance matching of both initial and synthesized array is shown in Fig. 4.9. As it can be seen, the antenna maintains a good matching (|S 11 | ≤ -10 dB) all over the LoRa band (863 -875 MHz), only for the initial array configuration. The synthesized array is not well matched at 868 MHz with an |S 11 | = 0 dB. This can be ascribed to the directivity optimization which is associated with small bandwidth.

The antenna radiation efficiency in the two different array configurations is plotted in Fig. 4.10. As it can be noticed an enhancement in the front-to-back ratio is associated with the increase of quality factor and a decrease in the radiation efficiency.

Test case III -Realized gain

This subsection demonstrates the possibility of optimizing the gain, taking mismatch losses into account. The objective of a methodology, therefore, is to find load impedance values that minimizes the following cost function

Ψ G = G T -G G T if Ψ G > 0 0 if Ψ G ≤ 0. (4.17)
where G is the realized gain of each trial antenna solution

G(θ, ϕ) = η * D(θ, ϕ) (4.18)
where η is the total efficiency of each trial antenna solution. For this test case, the realized gain target has been set to G T = 4.5 dBi. For this test case, the particle swarm become a set of K = 20 particles, and the maximum number of iterations is set to N = 400. The proposed method is used to optimize a compact slot-based parasitic antenna array working at 2.4 GHz. The antenna geometry is shown in Fig. 4.11. The antenna consists of a metallic (copper with a conductivity of σ = 5.8 × 10 7 S/m) concentric sectoral cylindrical cavity of thickness 0.035mm. The inner radius of the cylinder is r c . A monopole is placed internally on the cylinder's base, which constitutes its ground plane and serves as the driven element. The monopole has a length of m h , and it is fed through the bottom plate. 3 Slots have been etched on the lateral surface of the cylinder and are centered at (r c , ϕ = 210 o , z = 17.9 mm), (r c , ϕ = 90 o , z = 17.9 mm), (r c , ϕ = 330 o , z = 17.9 mm) respectively. All slots are circumferentially oriented and serve as parasitic elements excited by the monopole. The radius of the feeding monopole is 0.5 mm. Each slot is rotated around the z-axis by 120 o with respect to the other. The distance between two slots on the same level is indicated by s d . The values of proposed antenna geometric model parameters result to be as follow,: s l = 56, s w = 3, m h = 26, s d = 18.33, c h = 44 mm.

The load impedance values Z p optimized to minimize the cost function Ψ G defined in the eq. 4.17 are reported in Tab III. The realized gain pattern evaluated in the horizontal and elevation plane for the initial and the synthesized array at 2.4 GHz is reported in Fig. 4.12. The behavior of Ψ G and the realized gain value over the PSO iterations is shown in Fig. 4.13. The radiation pattern of the initial is omnidirectional in the horizontal plane, with a maximum realized gain in the horizontal plane of about 0.125 dBi and a maximum gain variation of 0.037 dB. Fig. 4.12(a) shows that before the optimization process, the pattern is clearly a quasi dipole pattern. In contrast, it can be observed that the pattern produced by the array after the synthesis process is a directive pattern. Once the array is synthesized, one slot acts as radiating slot, while the other two serve as coupling slots contributing to the overall radiation. Fig. 4.12(b) illustrates that the maximum radiation is directed at θ = 90 o which is useful in IoT applications, where nodes are often supposed to be all located in the same horizontal plane. The realized gain reaches its maximum at (θ = 90 o , ϕ = 210 o ) with a value of 4.796 dBi, 0.296 higher than G T proving that the target successfully reached. The corresponding directivity at 2.4 GHz is equal to 5.05 dBi.

Figs. 4.14 and 4.15 show the impedance matching and the radiation efficiency for both array configurations. As it can be noticed, the synthesis process allows, in this case, to optimize the directivity and simultaneously reduce the losses due to mismatch. The 4.15). Hence this proves the method's ability to enhance the return loss by 10 dB. Secondly, this test case shows that a better trade-off between the directivity and the total efficiency can be found with the direct optimization of the realized gain. Therefore, the method lets us also design high-gain array antennas that could be more suitable when integrated into IoT devices when losses are taken into account.

Conclusion

Conclusion

This Chapter presents an evolutionary-based approach for parasitic antenna arrays synthesis. The proposed technique allows the design of antenna arrays capable of fulfilling efficiently various criteria. The entire procedure is guided by a PSO which iteratively refine the load impedances of each antenna element in the array until the desired radiation characteristics are obtained.

The method is applied to three test cases, aimed at optimizing the antenna directivity, front-to-back ratio and realized gain for three different antenna geometries and applications, respectively.

CHAPTER 5 Conclusion and Perspectives

This Chapter will give a brief conclusion to the thesis and discuss some of the possible directions of future work related to the topic.

The thesis has made a detailed study of innovative integrated reconfigurable antennas systems capable of adapting to efficiently operate in dynamic and multi-purposed IoT environments.

• This first Chapter introduces the context and motivation of the thesis work. Initially, the Chapter aims to dissect the IoT paradigm by addressing the future connectivity challenges and the involved communication players. Therefore, the concerns faced by IoT devices to properly work have been presented. On the other hand, the Chapter provides an overview of the characteristics of future IoT networks which can rapidly adapt to better fit a specific target (e.g., mitigate the interference, improve the coverage, better monitoring a specific area, enhancing localization accuracy, etc.). After that, the existing IoT standards and services needed to create a heterogeneous IoT environment were listed. Finally, this dissertation highlights the compelling need for integrated reconfigurable antennas to adapt to changing system requirements or environmental conditions and provide additional levels of functionality for any IoT system.

• In Chapter 2, the state-of-the-art overview of the antenna integration problem for the next generation of IoT communication systems has been provided. The stateof-the-art antenna integration and miniaturization techniques are detailed, and the challenges and constraints for integration with IoT multi-standard devices or when specific antenna radiation properties are needed have been discussed. After that, a design technique for IoT applications based on a multi-access single structure antenna is provided. The study shows that it is possible to achieve the desired total efficiency over multiple bands by properly choosing the initial geometry and resonant frequency.

As future work, remote reflectometry measurements, based on the antenna's impulse responses when different loads are introduced, can be planned to avoid unwanted cable effects and eliminate discrepancies between simulated and measured results. Once the measurements are in perfect agreement with the simulated results, the antenna can be integrated with an IoT terminal used for communication or localization. For example, a solution could be the integration of the antenna with a LoRa tracker board to perform field testing such as communication range or localization accuracy.

Next, a miniaturized omnidirectional circularly polarized antenna for IoT applications has been presented. Thanks to its radiation and geometric properties, the proposed antenna solution fits well in specific IoT use cases where a monopole-type radiation pattern is required.

Possible development of this work investigates the possibility of providing multiple polarization configurations by implementing a reconfiguration mechanism. The idea is to enhance one single-polarization by implementing PIN diodes to electrically isolate the HP element from the LP element. Therefore, three polarization states, i.e., two orthogonal linear polarization and one circular polarization, can be enabled and controlled with the ON/OFF states of the PIN diodes.

Successively, a method based on Neural Networks is developed to predict the performance of small antennas given realistic constraints. A printed antenna incorporated into small terminals is considered as a proof of concept. The future objective of this work is the generalization of the prediction model for any planar antenna geometry. It will also be necessary to reduce the time needed to create the training set by using Data Augmentation techniques to enlarge the dataset artificially.

• The Chapter 3 initially reviews several integrated reconfigurable antenna concepts, designs, and techniques to accomplish the reconfiguration. The Chapter then focuses on the development of innovative reconfigurable antennas for compact devices suitable for IoT applications. A proof-of-concept of a flexible, reconfigurable antenna has been proposed to enable the next-generation of the self-adjustable integrated wearable systems in the IoT era. This flexible antenna maintains the correct electrical and radiation properties when close surroundings or structure modification caused by flexibility alter the effectiveness and the reliability of wearable communications. The implemented reconfiguration mechanism allows the antenna resonance to be shifted and achieve an adequate impedance matching across the operating 2.4 GHz ISM band even when folded. However, because of its geometry the antenna homogeneously radiates its power and part of it could be absorbed by the human body.

Therefore, possible future developments related to this work foresee the modification of the antenna structure to guarantee that the radiated power in the direction of the human body is reduced. Other future work includes optimizing the real implementation and integrating a control mechanism for self-adjusting. One solution envisages the integration of a reflectometer on the antenna structure to select the best configuration for each type of configuration. Finally, on-body measurements are required to prove the effectiveness of the proposed solution.

Dealing with IoT applications, attention has been given to the complexity and the cost of the developed solutions. Therefore, the focus will be on the development of miniature and low-power-consuming agile antennas. A new fan-shaped microstrip patch array printed on two cost-effective FR-4 PCBs has been presented. A reconfigurable network based on a low on-resistance and insertion loss CMOS SP4T switch is implemented to produce four directive radiation states that can satisfy the 360-CHAPTER 5. CONCLUSION AND PERSPECTIVES deg coverage with a power consumption of only 0.42 mA. The simple reconfiguration mechanism, the small size, and the low-cost realization make the proposed solution ideal to fulfill the IoT demands spatial filtering in crowded wireless IoT networks. Because of its low power consumption the developed antenna could be integrated with RF energy harvesting systems for IoT to recover part of the power needed to activate the reconfiguration mechanism.

Next, a slot-based ESPAR antenna suitable for IoT application is proposed. This parasitic antenna arrays rely on the exploitation of the mutual coupling between a unique driven element and a set of parasitic radiators. Since the overall radiation behavior is given by the contribution of both the active and passive elements, the desired radiation pattern can be obtained by reactively loading the parasitic elements. Reconfiguration can be, therefore, simply obtained by varying such loads. The presented work describes in-depth the design procedure of the antenna, addressing the practical mechanism of reconfiguration and the implementation of the antenna and focusing on the realization of the prototype and its experimental evaluation. The antenna can provide 16 directional radiation patterns as well as an omnidirectional behavior, thus increasing the degrees of freedom in the optimization of future IoT networks. Finally, to practically activate reconfiguration strategies, the antennas must be integrated with state-of-the-art electronic components (micro-controller, transceivers, etc.), thus realizing a complete system. The importance of choosing low-capacitance commercial off-the-shelf PIN diode models to approach the ideal radiation behavior of a slot-based pattern reconfigurable antenna has been demonstrated.

The integration of the developed pattern reconfigurable antennas into the networks, supported by a D-MAC protocol, to perform RSS and interference test may be a future research activity. In addition to fully exploit the larger reconfiguration capability offered by the developed ESPAR antenna, reconfiguration strategies can be developed in the foreseeable future. The control can be managed by the network, which could initially set the antenna to optimize the coverage and successively reconfigure it to improve the accuracy in localizing one of the network nodes. In this case, machine learning algorithms for the decision process will be evaluated, taking into account the reduced computation resources available on the end-node.

• In Chapter 4 an approach for the synthesis of reconfigurable parasitic antenna arrays has been presented. The method is based on using a Particle Swarm Optimizer, which iteratively optimizes the parasitic elements loads until the desired pattern behavior is obtained. In comparison with the approaches developed in Chapters 2 and 3 in which array design has been shown with relative performance results, the last Chapter shows a methodology that, given specific targets, explains how to synthesize compact arrays to fulfill such objectives. The proposed approach requires only one off-line full-wave simulation, thus making the optimization procedure very fast. Three test cases, aimed at optimizing the antenna directivity, front-to-back ratio, and realized gain respectively, have been presented. The proposed approach was initially set to obtain maximum directivity in 868 MHz compact end-fire dipole array. This case study demonstrates the effectiveness of the method in achieving maximum theoretical directivity in electrically small antennas. In order to assess the method's ability to address the desired goals, the methodology is used to maximize the front-to-back ratio of a compact PIFA-based planar array with low interelement distance. Finally, the method's efficacy has been demonstrated to optimize the realized gain of a compact array. In the last test case, the optimization of a compact slot-based parasitic antenna array relies on the synthesis of a cost function to obtain a tradeoff between the maximum directivity and total efficiency. Therefore, the method lets us also design high-gain array antennas that could be more suitable when integrated into IoT devices when losses are taking into account.

A possible future direction of the work done in this Chapter is to extend the methodology by including the practical implementation of the reconfiguration. Reconfiguration can simply be obtained by rotating the optimized loads values over the parasitic elements. The methodology results presented in this Chapter have referred to the optimization of features related to the antenna radiation pattern. On the other hand, the methodology could be used to optimize other features such as a specific polarization or radiation frequency. The forthcoming analysis will first concentrate on enhancing a specific polarization of compact antenna arrays by using the proposed approach. Finally, it could be interesting to use the optimizer to reduce the losses due to mismatch by synthesizing a matching network through the procedure.

The load impedances optimized to get the desired radiation pattern, polarization, and electric characteristics can then be implemented through a reconfiguration mechanism to realize a multifunctional antenna.

  Mots clés: Internet des objets (IoT), Environnement IoT dynamique, Antennes intégrées, Antennes reconfigurables, Multibandes, Techniques d'intégration, Antennes flexibles, Faible puissance, Réseaux de neurones, Electronically Steerable Parasitic Array Radiator (ES-PAR), Synthèse de réseaux d'antennes, Réseaux d'antennes parasites, Particle Swarm Optimizer (PSO).

List of Figures 1 . 1

 11 Massive IoT environment [23] . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Dynamic wireless IoT scenario. . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Illustration of a wireless IoT network when (a) MAC protocol assumes the use of omni-directional antenna at its physical layer and when (b) MAC protocol is using directional antennas. . . . . . . . . . . . . . . . . . . . . . . 2.1 Different approaches to synthesized multi-band antennas. (a) Miniature single access multiband antenna [51]. (b) Multi-access antenna structure for smart watch [52]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Different ways to synthesize an omnidirectional circularly polarized antenna. (a) Combination of multiple directional CP antennas [55]. (b), (c) Combination of 2 linearly polarized omnidirectional antennas with 90-degree phase different [56], [57]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Steps of the proposed design methodology. . . . . . . . . . . . . . . . . . . . 2.4 (a) 2-port antenna geometry for N 1 configuration with T-type matching networks. (b) N 3 antenna configuration. (c) N 9 antenna configuration. . . . . . 2.5 Imaginary part of antenna impedance of the single port IFA for (a) low band (N 1 ), middle band (N 3 ) and high band (N 9 ) antenna configurations. . . . . . 2.6 S-parameter for the three tested 2-port antenna configurations N 1 , N 3 , N 9 . . 2.7 Total efficiency of the 2-port antenna configuration N = 1. . . . . . . . . . . 2.8 Antenna prototype (configuration N = 1) and measurement test setup. . . . 2.9 Simulated and measured impedance matching of the 2-port antenna configuration N = 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10 Simulated and measured isolation of the 2-port antenna configuration N = 1. 2.11 Simulated and measured total efficiency of the 2-port antenna configuration N = 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.12 Antenna structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.13 Surface current distribution at 2.44 GHz on the (a) wire patch radiator, (b) branches of the top and bottom z-strips, (c) trunk of the top and (d) bottom strips. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.14 Current streamlines distribution on the antenna at 2.44 GHz over the period T. (a) t = 0, (b) t = T /4, (c) t = T /2, and (d) t = 3T /4. . . . . . . . . . . . 2.15 Reflection coefficient of the antenna. . . . . . . . . . . . . . . . . . . . . . . 2.16 AR over the frequency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List of Figures 2.17

  Simulated radiation pattern at 2.44 GHz in the (a) XOY plane and (b) XOZ plane. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.18 Radiated and total efficiency of the antenna. . . . . . . . . . . . . . . . . . . 2.19 Antenna structure and MLP topology and learning algorithm. . . . . . . . . 2.20 MLP predictions vs. true values on (a) S11-Train, (b) Eff-Train, (c) S11-Test and (d) Eff-Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.21 MLP predictions errors histogram on (a) training and (b) test datasets. . . . 3.1 Some of the reconfigurable antennas for IoT applications in literature. (a)

  . . . . . . . . . . . . . . . . . . . . . . 3.2 Antenna structure and reconfiguration mechanism. . . . . . . . . . . . . . . 3.3 Free space impedance matching of the optimized antenna in the 4 reconfiguration states. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Impedance matching of the optimized antenna in the 4 reconfiguration states when the antenna is placed close to the human body. . . . . . . . . . . . . . 3.5 The 4 different folding configurations: (a) F 1 , (b) F 2 , (c) F 3 , and (d) F 4 . . . 3.6 Antenna impedance matching in the (a) F 1 , (b) F 2 , (c) F 3 , and (d) F 4 folding configurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Passive antenna prototype. The switch is modeled as an ideal short/open circuit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Simulated an measured impedance matching of the antenna in free space in the (a) F 1 , (b) F 2 , (c) F 3 , and (d) F 4 folding configurations. . . . . . . . . . 3.9 Reflection coefficient of a folded antenna test case . . . . . . . . . . . . . . . 3.10 Numerical model of the antenna structure including the reconfiguration implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.11 Active prototype. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.12 Measured impedance matching of the active antenna prototype in free space in considering 4 radiation states. . . . . . . . . . . . . . . . . . . . . . . . . . 3.13 Initial antenna model: (a) 3-D isometric view and (b) zoom on the wire patch elements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.14 Current distribution on the antenna at 2.44 GHz. (a) A 1 , (b) A 2 , (c) A 3 , and (d) A 4 antenna states. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.15 Effect of varying the patch height h p (antenna in A 1 state). . . . . . . . . . . 3.16 Effect of varying the distance d f between the feeding wire and the short circuit (antenna in A 1 state). . . . . . . . . . . . . . . . . . . . . . . . . . . . List of Figures 3.17

  Effect of varying the distance d p between each radiating unit. (a) Antenna model, (b) and realized gain pattern in the A 1 state for 3 different values of d p . 3.18 Effect of varying the patch dimension. Analysis of (a) the radius r p , (b) and the angular aperture α p (antenna in A 1 state). . . . . . . . . . . . . . . . . . 3.19 Simulated total realized gain patterns in the A 1 configuration with and without the conductive skirt in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane at φ 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.20 Effect of varying the skirt's diameter. (a) Antenna model, (b) and realized gain pattern in the A 1 state for 3 different values of the diameter of the skirt r s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.21 Effect of varying the skirt's height. (a) Antenna model, (b) and realized gain pattern in the A 1 state for 3 different values of the height of the skirt h s . . . 3.22 The different radiation pattern configurations provided by the antenna. The patterns represent the total realized gain in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane at φ n , n = 1...N . . . . . . . . . . . . . . . . . . . 3.23 Final antenna structure: (a) 3-D isometric view, (b) zoom on the wire-patch, (c) bottom PCB including the SP4T switch and the reconfiguration circuit. . 3.24 Antenna prototype. Pre-assembly antenna elements: (a)-(b) top and bottom view of the top PCB, (c) vertical metallic pins and plastic screws, (d)-(e) top and bottom view of the bottom PCB, and (f) integration of the RF and DC stages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.25 Assembled antenna. 3-D radiation pattern and antenna under test (AUT) measurement test setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.26 Simulated and measured realized gain patterns of the antenna in the A 1 state. 3.27 Simulated and measured VSWR of the antenna in the A 1 state. . . . . . . . 3.28 Simulated and measured total efficiency of the antenna in the A 1 state. . . . 3.29 Proposed antenna structure: (a) geometrical parameters, (b) position of the 6 slots, and (c) reconfiguration circuit. . . . . . . . . . . . . . . . . . . . . . 3.30 Optimization of the antenna geometrical parameters. Analysis of (a) the slot length s l , (b) the monopole height m h , (c) the cylinder height c h , and (d) the distance between two adjacent slots on the same level s d . . . . . . . . . . 3.31 The different radiation pattern configurations provided by the antenna. The patterns represent the total realized gain in the azimuth plane. . . . . . . . . 3.32 360-degree coverage obtained considering (a) only 3 radiation states and (b) all the 6 radiation states C 11 -C 16 . . . . . . . . . . . . . . . . . . . . . . . . 3.33 Reflection coefficient of the antenna in the different configurations. . . . . . . 3.34 (a) Schematic of the one-diode electronic implementation I 1 of the reconfiguration mechanism. (b) Model of the circuit when forward biased with current I F . (c) Model of the circuit when reverse biased with voltage V R . . . 3.35 (a) Schematic of the two-diodes electronic implementation I 2 of the reconfiguration mechanism. (b) Model of the circuit when forward biased with current I F . (c) Model of the circuit when reverse biased with voltage V R . . . 3.36 Comparison between the simulated realized gain patterns (azimuth plane) for the 4 different implementations of the reconfiguration mechanism and the pattern obtained in the ideal case (I 0 ). . . . . . . . . . . . . . . . . . . . List of Figures 3.37 Simulated radiation efficiency obtained using the 4 different implementations of the reconfiguration mechanism. . . . . . . . . . . . . . . . . . . . . . . . . 3.38 Numerical model of the antenna structure including the reconfiguration circuit implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.39 Antenna prototype. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.40 Simulated and measured impedance matching of the antenna in the different pattern configurations: (a) C 11 , (b) C 21 and (c) C 31 . . . . . . . . . . . . . . . 3.41 Simulated and measured realized gain patterns of the antenna in both (left) horizontal and (right) vertical planes for (a)-(b) C 11 , (c)-(d) C 21 , and (e)-(f) C 31 pattern configurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.42 Simulated and measured antenna total efficiency in C 11 , C 21 , and C 31 pattern configurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Printed dipole array structure. . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 The radiation patterns provided by the initial and the synthesized dipole array as a result of the optimization procedure. The patterns represent the directivity in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane (ϕ = 0 • ). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Fitness trend evaluated for the test case I. . . . . . . . . . . . . . . . . . . . 4.4 Antenna impedance matching obtained for the dipole array (test case I). . . 4.5 Antenna radiation efficiency obtained for the dipole array (test case I). . . . 4.6 Compact and low profile 4-element PIFA array antenna. . . . . . . . . . . . 4.7 The radiation patterns provided by the initial array and the synthesized array as a result of the optimization procedure for the test case II. The patterns represent the directivity in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane (ϕ = 245 • ). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Fitness trend evaluated for the test case II. . . . . . . . . . . . . . . . . . . . 4.9 Antenna impedance matching for the PIFA array structure (test case II). . . 4.10 Antenna radiation efficiency for the test case II. . . . . . . . . . . . . . . . . 4.11 Slot-based parasitic antenna structure. . . . . . . . . . . . . . . . . . . . . . 4.12 The radiation patterns provided by the initial array and the synthesized array for the optimized test case III. The patterns represent the realized gain in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane (ϕ = 210 • ). . . . 4.13 Fitness trend evaluated for the test case III. . . . . . . . . . . . . . . . . . . 4.14 Antenna impedance matching for the test case III. . . . . . . . . . . . . . . . 4.15 Antenna radiation efficiency for the test case III. . . . . . . . . . . . . . . . .

Figure 1 . 1 :

 11 Figure 1.1: Massive IoT environment[START_REF]Massive iot environment[END_REF]

CHAPTER 1 .Figure 1 . 2 :

 112 Figure 1.2: Dynamic wireless IoT scenario.

Fig. 1 .Figure 1 . 3 :

 113 Figure 1.3: Illustration of a wireless IoT network when (a) MAC protocol assumes the use of omni-directional antenna at its physical layer and when (b) MAC protocol is using directional antennas.

Figure 2 . 1 :

 21 Figure 2.1: Different approaches to synthesized multi-band antennas. (a) Miniature single access multiband antenna [51]. (b) Multi-access antenna structure for smart watch [52].

CHAPTER 2 .Figure 2 . 2 :

 222 Figure 2.2: Different ways to synthesize an omnidirectional circularly polarized antenna. (a) Combination of multiple directional CP antennas [55]. (b), (c) Combination of 2 linearly polarized omnidirectional antennas with 90-degree phase different [56], [57].

Figure 2 . 3 :

 23 Figure 2.3: Steps of the proposed design methodology.

Figure 2 . 4 :

 24 Figure 2.4: (a) 2-port antenna geometry for N 1 configuration with T-type matching networks. (b) N 3 antenna configuration. (c) N 9 antenna configuration.

Figure 2 . 5 :

 25 Figure 2.5: Imaginary part of antenna impedance of the single port IFA for (a) low band (N 1 ), middle band (N 3 ) and high band (N 9 ) antenna configurations.

Figure 2 . 6 :

 26 Figure 2.6: S-parameter for the three tested 2-port antenna configurations N 1 , N 3 , N 9 .

Figure 2 . 7 :

 27 Figure 2.7: Total efficiency of the 2-port antenna configuration N = 1.

Figure 2 . 8 :

 28 Figure 2.8: Antenna prototype (configuration N = 1) and measurement test setup.

Figure 2 . 9 :

 29 Figure 2.9: Simulated and measured impedance matching of the 2-port antenna configuration N = 1.

CHAPTER 2 .

 2 Figure 2.10: Simulated and measured isolation of the 2-port antenna configuration N = 1.

Figure 2 . 11 :

 211 Figure 2.11: Simulated and measured total efficiency of the 2-port antenna configuration N = 1.

Figure 2 .

 2 Figure 2.12: Antenna structure.

Figure 2 . 13 :

 213 Figure 2.13: Surface current distribution at 2.44 GHz on the (a) wire patch radiator, (b) branches of the top and bottom z-strips, (c) trunk of the top and (d) bottom strips.

Figure 2 . 14 :

 214 Figure 2.14: Current streamlines distribution on the antenna at 2.44 GHz over the period T. (a) t = 0, (b) t = T /4, (c) t = T /2, and (d) t = 3T /4.

Figure 2 . 15 :

 215 Figure 2.15: Reflection coefficient of the antenna.

2. 3

 3 Figure 2.16: AR over the frequency.

Figure 2 . 17 :

 217 Figure 2.17: Simulated radiation pattern at 2.44 GHz in the (a) XOY plane and (b) XOZ plane.

Figure 2 . 18 :

 218 Figure 2.18: Radiated and total efficiency of the antenna.

CHAPTER 2 .

 2 Figure 2.19: Antenna structure and MLP topology and learning algorithm.

Figure 2 .Figure 2 . 21 :

 2221 Figure 2.20: MLP predictions vs. true values on (a) S11-Train, (b) Eff-Train, (c) S11-Test and (d) Eff-Test.

  3.2). The reconfiguration consists in simply selecting one of the lines through a switching mechanism. The antenna can provide therefore 4 different configuration states C 1 , C 2 , C 3 , and C 4 , C i representing the case in which L i is used to reduce the slot size, while the others are left in open circuit.

3. 2

 2 Figure 3.2: Antenna structure and reconfiguration mechanism.

Figure 3 . 3 :

 33 Figure 3.3: Free space impedance matching of the optimized antenna in the 4 reconfiguration states.

Fig. 3 .

 3 Fig. 3.4 shows the |S 11 | behavior for the optimized geometry in the C 4 configuration as well as in the other reconfiguration states. As expected, as the slot length is reduced through the selection of the different lines, the four antenna resonances are shifted towards the lower frequencies, compared to Fig. 3.3.

Figure 3 . 4 :

 34 Figure 3.4: Impedance matching of the optimized antenna in the 4 reconfiguration states when the antenna is placed close to the human body.

Figure 3 . 5 :

 35 Figure 3.5: The 4 different folding configurations: (a) F 1 , (b) F 2 , (c) F 3 , and (d) F 4 .

F 1 -Figure 3 . 6 :

 136 Figure 3.6: Antenna impedance matching in the (a) F 1 , (b) F 2 , (c) F 3 , and (d) F 4 folding configurations.

Figure 3 . 7 :

 37 Figure 3.7: Passive antenna prototype. The switch is modeled as an ideal short/open circuit.

Fig. 3 .Figure 3 . 8 :

 338 Figure 3.8: Simulated an measured impedance matching of the antenna in free space in the (a) F 1 , (b) F 2 , (c) F 3 , and (d) F 4 folding configurations.

Figure 3 . 9 :

 39 Figure 3.9: Reflection coefficient of a folded antenna test case

Figure 3 . 10 :

 310 Figure 3.10: Numerical model of the antenna structure including the reconfiguration implementation.

Figure 3 . 11 :

 311 Figure 3.11: Active prototype.

CHAPTER 3 .Figure 3 . 13 :

 3313 Figure 3.13: Initial antenna model: (a) 3-D isometric view and (b) zoom on the wire patch elements.

Figure 3 . 14 :

 314 Figure 3.14: Current distribution on the antenna at 2.44 GHz. (a) A 1 , (b) A 2 , (c) A 3 , and (d) A 4 antenna states.

Figure 3 . 15 :

 315 Figure 3.15: Effect of varying the patch height h p (antenna in A 1 state).

Fig. 3 .Figure 3 . 16 :

 3316 Figure 3.16: Effect of varying the distance d f between the feeding wire and the short circuit (antenna in A 1 state).

Figure 3 . 17 :

 317 Figure 3.17: Effect of varying the distance d p between each radiating unit. (a) Antenna model, (b) and realized gain pattern in the A 1 state for 3 different values of d p .

Figure 3 . 18 :

 318 Figure 3.18: Effect of varying the patch dimension. Analysis of (a) the radius r p , (b) and the angular aperture α p (antenna in A 1 state).

CHAPTER 3 .Figure 3 . 19 :Figure 3 . 20 :

 3319320 Figure 3.19: Simulated total realized gain patterns in the A 1 configuration with and without the conductive skirt in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane at φ 1 .

3. 3 Figure 3 . 21 :Figure 3 . 22 :

 3321322 Figure 3.21: Effect of varying the skirt's height. (a) Antenna model, (b) and realized gain pattern in the A 1 state for 3 different values of the height of the skirt h s .

3. 3 Figure 3 . 23 :

 3323 Figure 3.23: Final antenna structure: (a) 3-D isometric view, (b) zoom on the wire-patch, (c) bottom PCB including the SP4T switch and the reconfiguration circuit.

3. 3 Figure 3 . 24 :

 3324 Figure 3.24: Antenna prototype. Pre-assembly antenna elements: (a)-(b) top and bottom view of the top PCB, (c) vertical metallic pins and plastic screws, (d)-(e) top and bottom view of the bottom PCB, and (f) integration of the RF and DC stages.

Fig. 3 .

 3 Figure 3.25: Assembled antenna. 3-D radiation pattern and antenna under test (AUT) measurement test setup.

Figure 3 . 26 :

 326 Figure 3.26: Simulated and measured realized gain patterns of the antenna in the A 1 state.

4 4 . 6 *Figure 3 . 27 :

 46327 Figure 3.27: Simulated and measured VSWR of the antenna in the A 1 state.

CHAPTER 3 .Figure 3 . 28 :

 3328 Figure 3.28: Simulated and measured total efficiency of the antenna in the A 1 state.

CHAPTER 3 .Figure 3 . 29 :

 3329 Figure 3.29: Proposed antenna structure: (a) geometrical parameters, (b) position of the 6 slots, and (c) reconfiguration circuit.

Figure 3 . 30 :

 330 Figure 3.30: Optimization of the antenna geometrical parameters. Analysis of (a) the slot length s l , (b) the monopole height m h , (c) the cylinder height c h , and (d) the distance between two adjacent slots on the same level s d .

Figure 3 . 31 :Figure 3 .

 3313 Figure 3.31: The different radiation pattern configurations provided by the antenna. The patterns represent the total realized gain in the azimuth plane.

Figure 3 . 33 :

 333 Figure 3.33: Reflection coefficient of the antenna in the different configurations.

Figure 3 .Figure 3 .

 33 Figure 3.34: (a) Schematic of the one-diode electronic implementation I 1 of the reconfiguration mechanism. (b) Model of the circuit when forward biased with current I F . (c) Model of the circuit when reverse biased with voltage V R .

3. 4 Figure 3 . 36 :

 4336 Figure 3.36: Comparison between the simulated realized gain patterns (azimuth plane) for the 4 different implementations of the reconfiguration mechanism and the pattern obtained in the ideal case (I 0 ).

Figure 3 . 37 :

 337 Figure 3.37: Simulated radiation efficiency obtained using the 4 different implementations of the reconfiguration mechanism.

Figure 3 . 38 :

 338 Figure 3.38: Numerical model of the antenna structure including the reconfiguration circuit implementation.

Figure 3 . 39 :Figure 3 . 40 :Figure 3 . 41 :Figure 3 . 42 :

 339340341342 Figure 3.39: Antenna prototype.

CHAPTER 4 .

 4 AN EVOLUTIONARY-BASED APPROACH FOR PARASITIC ANTENNA ARRAYS SYNTHESIS 4.3 PSO-based optimization of the array loads 4.3.1 Offline simulation

;

  k = 1, ...K At each iteration n = 1 . . . N of the main PSO processing cycle, velocity magnitude and direction of each particle is updating to bring them closer to their best position defined as personal best z P B k = arg min n=1,...,N Ψ z (n) k (4.9) 4.3 PSO-based optimization of the array loads as well as the best position found by the neighborhood indicated as global best z GB = arg min k=1,...,K Ψ z P B k (4.10)

4. 4 Figure 4 . 1 :

 441 Figure 4.1: Printed dipole array structure.

Figure 4 . 2 :

 42 Figure 4.2: The radiation patterns provided by the initial and the synthesized dipole array as a result of the optimization procedure. The patterns represent the directivity in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane (ϕ = 0 • ).

Figure 4 . 3 :

 43 Figure 4.3: Fitness trend evaluated for the test case I.

. 4 .

 4 It can be seen that the initial array antenna achieves a simulated impedance bandwidth of 58 MHz ranges from 856 to 914 MHz with a S 11 ≤ -6 dB (S 11 = -6.81 dB). The S 11 curve reaches its minimum at 887 MHz with a S 11 = -7.79 dB. On the other hand, for the synthesized array it is clear that, as a result of the optimization operation, the antenna is no longer adapted (S 11 = -0.52 dB at 868 MHz).

4. 4 Figure 4 . 4 :

 444 Figure 4.4: Antenna impedance matching obtained for the dipole array (test case I).

Figure 4 . 5 :

 45 Figure 4.5: Antenna radiation efficiency obtained for the dipole array (test case I).

4. 4

 4 Figure 4.6: Compact and low profile 4-element PIFA array antenna.

Figure 4 . 7 :Figure 4 . 8 :

 4748 Figure 4.7: The radiation patterns provided by the initial array and the synthesized array as a result of the optimization procedure for the test case II. The patterns represent the directivity in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane (ϕ = 245 • ).

Figure 4 . 9 :

 49 Figure 4.9: Antenna impedance matching for the PIFA array structure (test case II).

4. 4 Figure 4 . 10 :

 4410 Figure 4.10: Antenna radiation efficiency for the test case II.

CHAPTER 4 .

 4 Figure 4.11: Slot-based parasitic antenna structure.

  Z p R p [Ω] C p [pF ] L p [nH] G(θ = 90 o , ϕ = 210 o )

Figure 4 . 12 :

 412 Figure 4.12: The radiation patterns provided by the initial array and the synthesized array for the optimized test case III. The patterns represent the realized gain in (a) the azimuth plane (θ = 90 • ), and (b) the elevation plane (ϕ = 210 • ).

4. 4 Figure 4 . 13 :

 4413 Figure 4.13: Fitness trend evaluated for the test case III.

Figure 4 . 14 :

 414 Figure 4.14: Antenna impedance matching for the test case III.

Figure 4 . 15 :

 415 Figure 4.15: Antenna radiation efficiency for the test case III.

  2.1 Total efficiency and reflection coefficients for the 2-port antenna configurations N 1 , N 3 , N 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model parameters for the 2 considered diodes. . . . . . . . . . . . . . . . . . 4.1 Test case I -Results Summary. . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Test case II -Results Summary. . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Test case III -Results Summary. . . . . . . . . . . . . . . . . . . . . . . . . Mastrosimini, L. Santamaria, M. Grande, et al., "Miniaturized omnidirectional circularly polarized antenna for iot applications," in 2021 15th European Conference on Antennas and Propagation (EuCAP), 2021, pp. 1-4. doi: 10.23919/EuCAP51087. 2021.9411046. • T. Q. Khai Nguyen, L. Santamaria, L. Lizzi, et al., "Measurement of finger effects on coverage efficiency for user equipment mm-wave antenna," in 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020, pp. 1371-1372. doi: 10.1109/IEEECONF35879.2020.9330275. • J. Roqui, L. Santamaria, L. Khacef, et al., "Estimation of small antenna performance using a machine learning approach," in 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020, pp. 581-582. doi: 10.1109/IEEECONF35879.2020.9330405.

	List of Abbreviations IoT Internet of Things NN Neural Networks ESPAR Electronically Stereable Parasitic Array Radiator PSO Paricle Swarm Optimizer SCoT Smart Connected of Things MTD Machine-type Devices QoS Quality of Service BLE Bluetooth Low Energy RFID Radio-Frequency IDentification LPWAN Low Power Wide Area Network NB-IoT Narrowband Internet of Things LTE-M Long Term Evolution-Machine-Type Communications mMTC massive Machines Type-Communications OSI Open Systems Interconnection NGN Next Generation Networking CRs Radio Cognitive systems SNR Signal-to Noise Ratio • V. for smart
	MAC iot gateways," in 2018 IEEE International Symposium on Antennas and Propagation Medium Access Control USNC/URSI National Radio Science Meeting, 2018, pp. 977-978. doi: 10 . 1109 /
	RSS	Received Signal Strength APUSNCURSINRSM.2018.8608985.
	RF		Radio Frequency
	PIN		Positive-Intrinsic-Negative
	MEMS	Micro Electro-Mechanical Systems

3.1 Radiation states provided by the antenna . . . . . . . . . . . . . . . . . . . . 3.2 Optimized parameters of the proposed antenna . . . . . . . . . . . . . . . . 3.3 Comparison to the State of the Art . . . . . . . . . . . . . . . . . . . . . . . 3.4 Variation of the main antenna characteristics for different values of the inductor in the reconfiguration circuit (Fig. 3.29). . . . . . . . . . . . . . . . . 3.5 Main characteristics of the different radiation pattern configurations provided by the antenna. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 • L. Santamaria, L. Lizzi, F. Ferrero, et al., "An optimization driven method for the synthesis of reconfigurable parasitic antenna arrays," in 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020, pp. 1659-1660. doi: 10.1109/IEEECONF35879.2020.9329627. • L. Santamaria, L. Lizzi, F. Ferrero, et al., "Compact 4-element radiation pattern agile antenna for spatial filtering in iot networks," in 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020, pp. 1-4. • L. Santamaria, T. Q. Khai Nguyen, F. Ferrero, et al., "2-port antenna with matching network for dual-band iot terminal," in 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019, pp. 1-2. • L. Santamaria, T. Q. Khai Nguyen, F. Ferrero, et al., "Flexible reconfigurable antenna robust to folding in wearable applications," in 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019, pp. 1-2. doi: 10.1109/APUSNCURSINRSM.2019.8888492. • L. Sadaoui, L. Santamaria, L. Lizzi, et al., "Study of a method for increasing isolation between closely spaced elements integrated in multi-standard multi-antenna systems," in 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019, pp. 1121-1122. doi: 10.1109/APUSNCURSINRSM. 2019.8889240. • L. Santamaria, F. Ferrero, and L. Lizzi, "Radiation pattern agile antenna

Table 2 .

 2 1: Total efficiency and reflection coefficients for the 2-port antenna configurations N 1 , N 3 , N 9 .

		(dB)	(dB)	(dB)	(dB)	(dB)	(dB)
	0.868 (P 2 )	-21	-3.8	-21.75	-2.47	-19	-2.42
	1.575 (P 1 )	-12.52	-0.64	-15.15	-2.09	-8.87	-7.7

Freq. (GHz) N = 1 N = 3 N = 9 |Γ| Tot. Eff. |Γ| Tot. Eff. |Γ| Tot. Eff.

Table 3 .

 3 1: Radiation states provided by the antenna

	State	φ n [deg.]	P 1	P 2	P 3	P 4
	A 1	225	fed	open	open	open
	A 2	315	open	fed	open	open
	A 3	45	open	open	fed	open
	A 4	135	open	open	open	fed

Table 3 .

 3

	2: Optimized parameters of the proposed antenna
	Parameter Size [mm] Parameter Size [mm]
	r p r s l f w f h p d f t s h s	16.41 35 9.1 1.55 8.4 1 0.8 20	d p l com w com l 1 l 2 l 3 l 4 w n	13.9 9.09 0.25 9.09 8.84 9.09 8.84 0.6

Table 3 .

 3 3: Comparison to the State of the Art

	80

  3.4 Slot-based pattern reconfigurable ESPAR antenna for IoT applicationsTable 3.5: Main characteristics of the different radiation pattern configurations provided by the antenna.Fig. 3.33. Because of the circular symmetry of the antenna, only the results concerning the configurations C 11 , C 12 , C 21 , C 22 , and C 31 , C 32 are reported. As it can be seen, the antenna maintains a good matching (|S 11 | ≤ -10 dB) all over the the WiFi band (2.40 -2.48 GHz), whatever the radiation pattern configuration.

	Conf.	Open switches	|S 11 | [dB]	G max F B HP BW φ mr [dBi] [dB] [deg] [deg]
	C 11 (dir)	W 1	-17.07 4.92 30.70	110.3	0
	C 12 (dir)	W 2	-15.21 4.89 31.79	108.8	60
	C 13 (dir)	W 3	-17.07 4.92 30.70	110.3	120
	C 14 (dir)	W 4	-15.21 4.89 31.79	108.8	180
	C 15 (dir)	W 5	-17.07 4.92 30.70	110.3	240
	C 16 (dir)	W 6	-15.21 4.89 31.79	108.8	300
	C 21 (dir)	W 1,3	-20.71 3.39 14.87	159.4	60
	C 22 (dir)	W 3,5	-20.71 3.39 14.87	159.4	180
	C 23 (dir)	W 5,1	-20.71 3.39 14.87	159.4	300
	C 24 (dir)	W 2,4	-18.76 3.41 15.74	160.6	120
	C 25 (dir)	W 4,6	-18.76 3.41 15.74	160.6	240
	C 26 (dir)	W 6,2	-18.76 3.41 15.74	160.6	0
	C 31 (omni)	W 1,3,5	-12.78 0.60	-	-	-
	C 32 (omni)	W 2,4,6	-12.77 0.57	-	-	-

Table 4 .

 4

	+i25.38	0	0	4.65
	0.63 + i12.44	0.62	0	2.28	12.59
	1.33 + i26.27	0	0	4.81
		D =	4πU (θ, ϕ) P R		(4.14)

1: Test case I -Results Summary.

Z p R p [Ω] C p [pF ] L p [nH] D(θ = 90 o , ϕ = 0 o ) [dBi]

Table 4 . 2 :

 42 Test case II -Results Summary. * 10 5 -i1.83 * 10 5 1.34 * 10 5

	Z 45.32	5.84	0
	-i66.5	0	2.75	0	42.78
	1.34

p R p [Ω] C p [pF ] L p [nH] F B [dB] 45.33 -i31.38

Table 4 . 3 :

 43 Test case III -Results Summary.

Luca Santamaria

PhD Thesis

Acknowledgements

Acknowledgements Luca Santamaria Nice, October 2021

List of Abbreviations

PCBs

based on the switch is represented. The selected SP4T switch is a CMOS-based tuning control switch with low on-resistance and insertion loss. Both decoding and biasing are integrated on-chip with the two-bit low voltage control. Thus, there is no need for external DC filtering or biasing components to prevent the DC and RF current's short circuit. The switch is characterized by 0.45 dB of insertion loss and a very low-on resistance of 1.6 Ohm at 2.4 GHz. For low-power IoT services, a CMOS switch represents the most suitable means of reconfiguration amongst the many popular electronic switches proposed in the literature. In Fig. 3.11 the realized PCB integrating the reconfiguration circuit is depicted. An Arduino Pro Mini powered by a 3.3 V battery provides the DC supply voltage to the switch. It is worth noting that the SP4T switch allows four resonance configurations with a power consumption of 1µ A, which is approximately 10000 times lower than the typical current value (10 mA) required to control PIN diodes.

Finally, Fig. 3.12 shows the reflection coefficient of the measured active antenna. The figure reveals that the winning design of the reconfiguration mechanism based on the SP4T properly works and retunes the antenna resonance in a real case. Further studies on the integration of the reconfiguration mechanism into the structure are required for the antenna to operate at the 2.4 GHz frequency in one of its allowed configurations.

Test case II -Front-to-back ratio

In order to test the method's versatility to deal with diverse objectives, the second test case aims to identify the load impedances to enhance the directivity front-to-back ratio performance of compact PIFA-based planar arrays with low interelement distance.

where F B is the front-to-back ratio of each trial antenna solution and it is identified as the ratio of directivity in the direction of the front/main radiation lobe (θ max , ϕ max ) and the directivity in the opposite direction (θ max , ϕ max -180 0 ):

and the target front-to-back ratio value is set to F B T = 40 dB. The evaluated antenna structure, operating at f 0 = 0.868 GHz, is shown in Fig. 4.6. The antenna array is formed by four identical PIFA elements are arranged on the same plane on four different PCBs and located very close to each other, with an inter-element distance of 7 mm (0.02λ 0 ). Each PCB is rotated, with respect to the adjacent of 90 o around the z-axis. The four PIFAs have the same dimensions. The PIFA elements are located at the left on each PCBs and occupy a clearance area of 35mm × 20mm corresponding to 0.1λ 0 × 0.057λ 0 , λ 0 being the wavelength at f 0 .

Each array element is printed on a FR-4 dielectric (ϵ r = 4.3). The size of the array is small, presenting an area of 0.25λ 0 × 0.25λ 0 , (87 × 87 mm 2 ). The values of the antenna model's geometric parameters, result to be as follows: t w = 9.12, t l = 24.96, a l = 27, a w = 3.1, m h = 10, s w = 0.48, c w = 9.36 while v l = 31 mm.

Tab. 4.2, shows the value of the loading impedance chosen so that the FB of the parasitic element array is maximized. Fig. 4.7 shows the comparison of the antenna radiation pattern obtained by the initial and the synthesized array fully simulated in CST Microwave Studio. The initial array has a maximum radiation at (θ = 90 o , ϕ = 245 o ). Therefore, the optimization is performed in this direction in order to verify how much the FB can be improved compared to the initial array. As it can be noticed, when the load value Z p of Tab. 4.2 are integrated into each parasitic elements, the maximum simulated directivity in the desired direction (θ = 90 o , ϕ = 245 o ) approaches the value of 4.35 dBi. This value is only few dB higher than the directivity value of obtained from the initial array (3.61 dB). Diversely, the synthesized array provides a much lower directivity value of -35.71 dBi in the backward direction (θ = 90 o , ϕ = 65 o ) than the initial array (-2.125 dBi).

Therefore, the synthesized pattern exhibits an extremely large FB of 40.05 dB, 34.32 dB higher than the one obtained for the initial array, ensuring that signals coming from the backward direction are filtered, thus minimizing interference. This reduces the number of packet collisions and required retransmissions, as well as the node's power consumption. The evolution of the cost function Ψ F B with the associated values of F B over the iterations is shown in Fig. 4.8. After 260 iterations, the cost function converges to 0, and the FB attain the value of 42.78, which is slightly different from the FB value obtained with fullwave simulation 40.05 dB.