Sihem Tlili Ut2j

Sami Mnasri
email: sami.mnasri@irit.fr

Thierry Val
email: val@irit.fr

The Internet of Things enabling communication technologies, applications and challenges: a survey

Keywords: Internet of Things, Communication Technologies, Main challenges, IoT domain applications, IoT uses cases

Recently, the IoT has gained great importance, which results in the evolution of communication technologies to meet the needs of different IoT applications. In addition, several domains integrate the IoT technologies in several applications of our professional life and daily activities. However, the IoT still presents many challenges and issues. This article describes in details the emerging communication technologies used in the IoT networks and enumerates the common domains of their application. It also describes the main challenges of the IoT and its use in order to exploit its advantages at most.

Introduction

The International Telecommunications Union defined the Internet of Things as that involving far-reaching technological and social implications to form a global infrastructure of the information society. IoT uses information and communication technologies to make physical and / or virtual objects interconnect in order to provide advanced services in our professional life and daily activities. Thanks to its advantages, the IoT is increasingly integrated in many application domains.

The IoT environments contain many types of sensors that measure the physical parameters of the environment and transform them, first, into electrical signals and, then, into digital messages. Furthermore, they produce a variety of data in different formats. In fact, the success of the IoT is attributed to an efficient management of these data that should be processed and communicated in real time and in a reliable and rapid manner in certain critical and time sensitive applications. Therefore, new technologies and new approaches must be introduced and integrated to manage efficiently these amounts of data. Moreover, communication technologies are constantly evolving in order to meet the needs of the IoT and provide solutions that can be adapted to different fields of application. Thus, a wide variety of IoT applications has been recently developed and deployed to significantly affect our daily and professional lives. Added to that, the emergence of new use cases has led to the appearance of several application domains of IoT. On the other hand, despite its success, the IoT still faces many challenges and problems. The major issues are: the management of Big Data (collection, transmission and analysis), the security and confidentiality of these data and the interoperability between the used heterogeneous technologies.

This paper provides an overview of the Internet of Things (IoT) by focusing on: i) the used communications technologies, ii) the most popular application domains of the IoT and iii) the main challenges faced by these applications. The reminder of this paper is organized as follows. Section 2 lists the emerging communications technologies used in the IoT networks. It also presents a comparative study between these technologies using some criteria while specifying their common use cases. Then, Section 3 describes a variety of application domains of the IoT and their common challenges. Finally, Section 4 concludes this paper.

Communication technologies for the IoT

Wired technologies

According to [START_REF] Dong-Seong | An Overview on Industrial Internet of Things: From Wired Technologies to Cloud Computing and the Internet of Things[END_REF] and [START_REF] Alparslan | Industrial Networks and IIoT: Now and Future Trends[END_REF], the wired communication technologies that are used in the IoT networks are: Ethernet, HomePlug GP, HomePNA, HomeGrid/G.hn and MoCA. These technologies are summarized in Table 1 and illustrated in the rest of this section.

 Ethernet (IEEE 802.3): It is one of the common local area network (LAN) technologies used in real life. Ethernet links several machines through twisted pair cables. The physical cabling makes this technology more secure and less vulnerable to disturbances. Table 1 contains a description of some characteristics of this technology. In [START_REF] Hoque | Smart home: An empirical analysis of communication technological challenges[END_REF], Ethernet was defined as a wired technologies that can be used in Smart Homes by providing reliable and secure transmissions. Besides, it is a low-cost technology with a high data rate (from 10 Mbps to several Gbps) [START_REF] Gianfranco | Chapter 4 -iot and sensor networks security[END_REF] based on IPv4 / IPv6 (Internet Protocol versions 4/6). Indeed, the latest versions of Ethernet (802.3cu-2021 and802.3cv-2021) offer up to 400 Gb/s on single-mode fiber (IEEE et al., 2021). On the other hand, its range is limited to 100m [START_REF] Hoque | Smart home: An empirical analysis of communication technological challenges[END_REF] for each cable linking two active elements (hub and switch). Authors, in [START_REF] Thompson | Ethernet: From office to data center to iot[END_REF], described the transition to the use of Ethernet in the IoT. [START_REF] Bahl | Wireless ethernet for iot: A case study[END_REF] and [START_REF] Pintilie | An i2c and ethernet based open-source solution for home automation in the iot context[END_REF] detailed the cases where Ethernet is used in IoT applications. [START_REF] Wu | Performance evaluation of industrial ethernet protocols for networked control application[END_REF] presented a performance evaluation of the use of Ethernet in an industrial IoT environment.

 HomePlug Green PHY (GP): it is a power-line communication (PLC) technology. This technology uses the existing electrical wiring in a building to connect network devices. HomePlug employs powerline adapters that plugs into a power outlets and use Ethernet cables to be connected to other devices. It can be utilized, for example, with an existing Ethernet network to connect equipment in a remote room without using wireless technology. The most important advantage of the HomePlug GP is its economical energy consumption (Dr. Yusuf et al., 2019). Besides, it offers a data rate between 4 Mbps and 200Mbps. Its range is close to 300m [START_REF] Hoque | Smart home: An empirical analysis of communication technological challenges[END_REF]. It is characterized by security, reliability, compatibility with existing infrastructure and interoperability with IEEE 1901 devices [START_REF] Md | Control of a Multilevel Inverter for Wind Energy Conversion System with Energy Storage and Condition Monitoring Options[END_REF]. Other characteristics of this technology are represented in Table1. HomePlug GP is designed for the IoT applications, mainly in Smart Homes and smart grid applications (Dr. Yusuf et al., 2019). In addition, due to the new power management and features offered by HomePlug GP 1.1, it has been a promising alternative for green communications for electric vehicles [START_REF] Pallander | Implementation of HomePlug Green Phy standard (ISO15118) into Electric Vehicle Supply Equipment[END_REF]. 1, offers a data rate up to 320 Mbps (HPNA 3.1) [START_REF] Gianfranco | Chapter 4 -iot and sensor networks security[END_REF]. 4.0 version of HomePNA includes support for the G.hn standard, which makes all connections possible regardless of the type of wiring.

 HomeGrid/G.hn: It is a technology that utilizes the existing types of domestic cabling (electrical cabling (PLC), coaxial cable and telephone lines) and plastic optical fibers [START_REF] Phatrakit | Review of the lightning electromagnetic impulse protection measure for smart home and building in smart grid system in thailand[END_REF], which makes it very cost effective. Indeed, a G.hn network uses adapters to connect its devices. It can be used in Smart Homes [START_REF] Hoque | Smart home: An empirical analysis of communication technological challenges[END_REF] and offices as well as in industrial applications [START_REF] Zeng | Use of G.hn in Industrial Applications[END_REF]. It is more beneficial than HomePNA as it provides a higher data rate (1 Gbps over power lines and 1.7 Gbps over phone lines and coaxial cables) with more robust performance. However, its range is limited to 100m [START_REF] Phatrakit | Review of the lightning electromagnetic impulse protection measure for smart home and building in smart grid system in thailand[END_REF]. Other characteristics of G.hn are represented in Table1.

Wireless technologies

The most widely used wireless communication technologies in the IoT networks are described in this section. Tables 2,3 and 4 detail their characteristics. Data illustrated in this section and these tables show the information presented in [START_REF] Dong-Seong | An Overview on Industrial Internet of Things: From Wired Technologies to Cloud Computing and the Internet of Things[END_REF][START_REF] Abdallah | Emergent iot wireless technologies beyond the year 2020 : A comprehensive comparative analysis[END_REF], [START_REF] Naik | Lpwan technologies for iot systems: Choice between ultra narrow band and spread spectrum[END_REF]  RFID (Radio Frequency Identification): It is a communication technology whose main objective is to store small data and, then, retrieve it remotely. It is used to transmit small amounts of data between an RFID tag and a reader (Figure 1(a)). RFID tag is a small device that plays the role of a transponder. It contains data that can be retrieved by being transmitted remotely after receiving a signal from the reader. RFID reader is a transceiver that generates radio waves through antenna to get information from the RFID tags. It also transfers the received data to a network.

Table 2 exposes the RFID characteristics. The range of this technology is short. In fact, the RFID is commonly used in retail applications and asset as well as in inventory tracking. It can also be used in an industrial environment to identify, in a simple and an efficient way, some measurements and information (physical location, offset measurements, identification and control of raw materials, etc.). ) by a wireless link at a short distance equal to 4 centimeters. Its basic structure components are the same as that of RFID. In fact, an end node in an NFC system can be: i) an NFC tag: a transponder that stocks data and sends it after receiving a signal from a reader, ii) an NFC Reader: a transceiver that reads data from tags, or iii) a device that combines both an NFC reader and an NFC tag.

The proximity between the devices that communicate with the NFC reduces the risk of hacking. For this reason, the NFC is commonly used in electronic payments and tickets. This technology is also supported by several devices that use Android and IOS systems.

Table 2 contains

Figure 2 Possible BLE Architectures

In fact, the BLE is supported by several operating systems (Linux, Windows, macOS, Android and IOS).

Thanks to the simplicity of its implementation, it is widely used to transmit sensor data. This technology is essentially applied in Smart Home, smart wearable devices, smart health and retail. Indeed, it makes it possible to transmit data from portable devices (fitness, medical, Smart Home, etc.) to smartphones. ZigBee Routers that act as intermediates between end devices and the coordinator. The ZigBee Coordinator is the only device that can form and start the network.

Other devices can join the network after sending requests. In addition, the coordinator may function as a bridge to other networks. Zigbee network must have one coordinator. Z-Wave is represented in Table 2. 3 shows the characteristics of WiFi. Figure 5 exposes the architecture of a use case of WiFi in an IoT network. The components represented in the figure are: i) End nodes (WiFi devices that ensure communication); ii) WiFi Mesh Routers (the routers that transmit data between end nodes and the gateway to another network (Internet). These routers can communicate with each other in a mesh topology); and iii) WiFi Mesh Router with Gateway (a router that acts also as a gateway to other networks). WiFi is efficiently used to transfer files thanks to its ability to handle large amounts of data. It is mainly characterized by its fast transfer speed. However, it has limits in terms of range and power consumption. Its best use cases are Smart Home and smart retail. 6 where the typical system architecture of a LPWAN is represented. Its main components are: i) end nodes (the end devices that communicate data); ii) network server (the central point of the network that aggregates data and acts as an intermediate with application servers); iii) LPWAN gateways (the devices that communicate data between terminal nodes and the central server); and iv) application servers (the servers that run applications using the network data).

LoRaWAN is represented in Table 4.

 Sigfox: It is an LPWAN technology that ensures the transmission of small amounts of data. Initially, Sigfox supports communications from terminal nodes to base stations. However, it is not efficient for communications in the opposite direction (from base stations to terminal nodes). Later versions support twoway communication between terminal nodes and base stations. The architecture of Sigfox is the same as that of a general LPWAN (Figure 6).

Yes

Direct Internet Access

Yes

Common Use Cases

smart home and smart retail long distances without consuming a lot of energy with them and relay data to IoT Evolved Packet Core); iii) IoT Evolved Packet Core (EPC) (the component that connects the network with IoT platform and application server); (iv) application server which is the server that hosts and runs applications; and v) IoT platform (a set of components that facilitate the interactions between the IoT EPCs and the application server and orchestrates the data movements). The characteristics of NB-IoT are illustrated in Table 4. , 2020). In fact, it avoids the negative impact of radio frequency (RF) on the human health and reduces carbon emissions. In addition, it can be used simultaneously for communication and lighting. However, this technology suffers from limited range and high vulnerability to interruptions (light cannot pass through obstacles). Therefore, it cannot be used in environments where there are other light sources. For this reason, the LiFi technology can be used only in specific fields where it coexists with other technologies since it cannot completely replace them [START_REF] Al | A survey on li fi technology and internet of things (iot)[END_REF]. Figure 9 presents a scenario that combines both the WiFi and the LiFi technologies. This network is composed of: i) end nodes (the end devices that communicate data); ii) Relay/Amplification devices (the components that relay data between end devices and the LiFi Access Point and can amplify the signals while transmitting the data); iii) LiFi Access Point (the device that aggregates data from LiFi devices and transmits it to WiFi Access Point); and iv) WiFi Access Point (the device that connects other devices to a public network). The characteristics of LiFi are represented in Table 4.

Figure 9 A topology that combines LiFi and WiFi

Wired or wireless

Since each communication technology has its own strengths and weaknesses in terms of several criteria, there is not a universal communication solution suitable for all the use cases and all the IoT applications. Every user must select the right technology according to the characteristics of the corresponding use case. The first step is to choose between the two main categories of communications technologies: wired and wireless.

Table 5 presents the advantages and disadvantages of each of these categories and compare them according to some criteria. This comparison demonstrates that wired technologies are characterized by lower power consumption and ensure faster and more secure transmissions, compared to the wireless technologies. Indeed, the use of wires makes it possible to create connections with high data rate and more reliability. However, wireless technologies reduce the data rate and can cause reliability problems in real time because of the errors caused by interference, noise and collisions. In addition, they are highly sensitive to intrusion and hacking, which makes wired technologies more secure. Despite these disadvantages, wireless technologies have some advantages. For instance, they create networks with longer ranges, compared to the wired technologies. These wireless networks are also more scalable and more flexible since new objects and mobile devices can be easily added and a greater number of connected objects can be supported using these technologies. In addition, they are less expensive and time-saving since they do not require the installation of many equipment and because of their easy remote maintenance. Moreover, without wires, the risk of failures and dangers can be minimized since their use can sometimes be dangerous, especially in high-temperature environments. All these advantages make wireless technologies a good choice for IoT networks. According to [START_REF] Dong-Seong | An Overview on Industrial Internet of Things: From Wired Technologies to Cloud Computing and the Internet of Things[END_REF], wireless communication technologies were behind the development of the IoT since they improve its system flexibility and scalability by providing long-range and low-power communications.

From the above-presented comparisons, we can deduce that wireless communications technologies are better suited for IoT environment. However, despite their importance, these technologies cause some problems. Indeed, due to the error resulting from interference, data to be transmitted can be lost. In addition, the transmission errors may lead to variable transmission delays, which can negatively affect the quality of the services provided by some applications. Moreover, the use of a wireless medium may limit the number of devices that share it depending on its bandwidth. Besides, compared to wired technologies, the data rate offered by wireless communication technologies is limited. For this reason, the deployed solutions must manage data efficiently and reduce these undesirable effects (losses and waste) by optimizing the use of the available resources.

Application domains of the IoT

Thanks to its benefits, the IoT becomes more integrated into different fields of application. In fact, its implementation is growing exponentially although it faces many challenges. In this section, we will present the most popular application domains of the IoT and their most important challenges.

 Smart City: is used in many domains such as public safety, greening the environment, smart lighting, traffic management, private and public transport traffic, efficient management of waste, etc. Thanks to this development, cities become smarter by offering more high-quality services and reducing their expenses (Dr. Yusuf et al., 2019).

 Smart Home: Authors, in (Dr. Yusuf et al., 2019), defined Smart Home as a residence equipped with IoT technologies. Its equipment is connected to the Internet in order to control continuously and remotely the various activities of the residents based on data collected from the deployed sensors. Indeed, it offers a better quality of life with more safety and comfort.

 Smart Building: The IoT can make buildings smart by benefiting from its solutions in terms of monitoring and management. Moreover, the integration of the IoT allows reducing the operating and maintenance costs of the buildings to better satisfy the occupants by guaranteeing their safety and minimizing the environmental impact of the buildings in terms of energy, water consumption and waste production.

 The Educational Internet of Things (EIoT): It is used to apply IoT technologies in education and learning. In fact, carrying out the learning process via the IoT makes it possible to build intelligent educational environments (intelligent technologies, intelligent objects and intelligent pedagogy) to make it more productive and more efficient.

 The Agricultural Internet of Things: Recently, the agriculture has become more industrialized to improve production quality and quantity by integrating the Agricultural Internet of Things (AIoT) that consists in using the IoT technologies to modernize and enhance the various application fields of the agricultural sector.

AIoT can improve the agricultural productivity and minimize the human intervention and workforce. , 2019). The GIoT technologies are also employed to significantly decrease the emissions of toxic pollutants such as carbon dioxide (CO2)) and the greenhouse effect caused by the IoT systems [START_REF] Albreem | Green internet of things (giot): Applications, practices, awareness, and challenges[END_REF].

In this section, the most popular application domains of the IoT were presented. Their important aspects, application domains, target environments, target customers and their most important objectives are shown in Table 6.

The common challenges and application issues for the different versions of the IoT can be summarized as follows:

 Management of large amounts of generated and communicated data by collecting, analyzing and transmitting them.

 Security and confidentiality of the transmitted and stored data: consist in protecting IoT environments against possible threats.

 Considering the heterogeneity of the used technologies:

The hardware and software installations implemented must guarantee compatibility and interoperability between the many used technologies.

 The scalability and connectivity of the IoT networks: are ensured by implementing scalable solutions to meet the future needs of networks where the number of connected objects is growing exponentially.

 Optimization of energy consumption: The deployed solutions and the used technologies must consume these resources in an optimized manner.

Conclusion

The success and benefits of the IoT lead to its rapid emergence in our domestic and professional life. Therefore, research works and technological innovations has recently evolved in order to address the different application domains. In fact, this paper presented a review describing the communication technologies used in the IoT networks. These technologies and their common use cases were compared. Subsequently, a variety of IoT application domains were listed. Moreover, each IoT application domain was depicted and examples of its use cases as well as its common challenges and problems were illustrated. Moreover, some recent research studies dealing with the IoT were summarized. We noticed that these application areas have the same challenges which consist in the management of Big Data (collection, transmission and analysis) as well as the security and confidentiality of these data and the interoperability between the heterogeneous used technologies. In this context, numerous intelligent methods can be applied to solve these problems. In fact, numerous intelligent and hybrid artificial intelligence-based methods were introduced and investigated the resolution of engineering real-world problems, especially IoT applications and issues [START_REF] Mnasri | The 3D Redeployment of Nodes in Wireless Sensor Networks with Real Testbed Prototyping[END_REF][START_REF] Alkasassbeh | On enhancing the performance of nearest neighbour classifiers using Hassanat distance metric[END_REF]) (MNASRI et al., 2020) (Hassanat, 2018) [START_REF] Tarawneh | Stability and reduction of statistical features for image classification and retrieval: Preliminary results[END_REF]) (MNASRI et al., 2018) (Hassanat et al., 2015). Most of these studies confirmed the complexity of IoT problems and highlighted the need for innovative methods to resolve IoT related challenges. As a continuation of this work, we will focus on the challenges and the unsolved problems of the IoT in order to explain them and propose promising ideas to find solutions.

  , (Bharat et al., 2020), (Saleem et al., 2019), (Joel et al., 2019), (Evgeny et al., 2018), (Kais et al., 2019), (Ayoub et al., 2019), (Joseph et al., 2018), (Franck et al., 2019), (Ahmad et al., 2020) and (Saurabh et al., 2020).

  There are three versions of the RFID technology: 1) a passive version: It is the most economical version where the tags use the energy generated by the waves of the RFID readers since they are not equipped with an internal battery; 2) an active version where the tags are powered by their own energy sources; 3) a semi-passive version in which the tags utilize their own energy sources and the energy generated by the RFID readers.

Figure 1

 1 Figure 1 RFID and NFC Systems Architectures

  a detailed description of NFC.  BLE (Bluetooth Low Energy): It is a short-range wireless network technology employed in Personal Area Networks (PAN). BLE is a version of Bluetooth standard designed to transmit / receive small amounts of data while consuming extremely low amounts of energy. Its possible architectures and end nodes (devices equipped with BLE technology) are represented in Figure 2. The first possible architecture, called Point-to-Point (Figure 2(a)), creates direct communications between two BLE nodes. In a star architecture (Figure 2(b)), the BLE network includes a number of end nodes connected to the BLE Central Master. It can represent a bridge to the outside of the network. In a mesh architecture (Figure 2(c)), the network contains full-interconnected BLE end nodes.

Figure 3

 3 Figure 3 ZigBee Architecture

Figure 4

 4 Figure 4 Z-Wave Architecture

Figure 5

 5 Figure 5 The architecture of the WiFi in an IoT network

Figure 6

 6 Figure 6 Typical LPWAN System Architecture

Figure 7

 7 Figure 7 NB-IoT Architecture

Figure 8

 8 Figure 8 The architecture of an example of using 5G for the IoT

Table 1

 1

	 HomePNA (Home Phone Networking Alliance): It is a
	technology applied in home networks by reusing
	telephone and coaxial cables. Indeed, HomePNA
	networks use phone jacks, HPNA adapters and bridges
	with coaxial cables and telephone wiring to connect
	devices. Using the existing cables does not interrupt
	voice or television services since different frequencies
	are used (

). HomePNA is mainly employed in Smart Homes with a range close to 300m

[START_REF] Hoque | Smart home: An empirical analysis of communication technological challenges[END_REF]

. HomePNA, described in Table

Table 1

 1 The characteristics of Ethernet, HomePlug PG, HomePNA, HomeGrid/G.hn and MoCA

	Technology	Ethernet	HomePlug PG	HomePNA	HomeGrid/G.hn	MoCA
	Used	Ethernet	Electrical wiring	Telephone and	Power lines, coaxial	Coaxial cables and
	infrastructure	cables and	and power outlets	coaxial cables,	cables and phone lines	jacks
		jacks		phone jacks	plastic optical fibers
	Frequencies	-	24 -500kHz	12 -44MHz	Power lines: 100MHz	500 -1650MHz
					Coaxial cables: 200MHz
					Phone Lines: 200MHz
	Range	100 m	300m	300m	100m	90m
	Data rate	400 Gb/s	between 4 Mbps	320Mbps	1 Gbps over power lines	10 Gbps
			and 200Mbps		1.7Gbps over phone lines
					and coaxial cables
	Latest version	802.3cv-	HomePlug GP 1.1	HomePNA 4.0	G.hn Wave-2	MoCA 3.0
		2021
	Common Use	smart home,	smart homes and	smart homes and	smart homes and offices,	smart homes, buildings,
	Cases	smart	offices, smart grid	offices	industrial applications	hospitals, hotels and
		offices	applications,			schools
			electric vehicles

  Table 2 presents the characteristics of BLE.

 ZigBee (IEEE802.15.4): It is a medium range technology designed for economical wireless networks with a low power consumption. This technology can put objects in a dormant state if necessary. ZigBee is used mainly in Smart Homes since it connects the nodes that are nearby the medium range. Its architecture is represented in Figure 3. Its main components are: i) ZigBee Coordinator, ii) ZigBee end nodes (which are devices equipped with ZigBee technology) and iii)

Table 2

 2 Comparison between the characteristics of BLE, NFC, RFID, Z-Wave and Zigbee

	Technology	BLE	NFC	RFID	Z-Wave	ZigBee
	Category	WPAN	Near Field	Near Field	WPAN	WPAN
	Frequency	2.4 Ghz	13.56 MHz	13.56 MHz	868 MHz	868 MHz (EU), 2.4 GHz
						(global)
	Power	Very low	Very low	Very low	Low	Low
	Consumption
	Range	From 50 to 150	10 cm (4cm is	100m (active	100m	From 10 to 100m
		m (70m typical)	the optimal	tags), 25m
			range)	(passive tags)
	Data rate	1 Mb/s	100-420 kbps	500 Kbps	100 Kbps	20 kbps (868 MHz), 250
						kbps (2.4 GHz)
	Maximum	251 bytes		16-64 Kbps	255 bits	100 bytes
	Transmission
	Unit
	Bidirectional	yes	yes	yes	yes	Yes
	Communication
	Topology	Point to point ,	Point to point Point to point, star	Mesh	Mesh
		star and mesh
		(Bluetooth
		MESH)
	Scalability	5917, 50 (mesh)	2	limited	232	65 000
	(devices)
	Mobility	yes	yes	yes	yes	Yes
	Support
		Smart home,	Electronic	Retail	Medium-range IoT	Medium-range IoT
		smart wearable	payments and	applications, asset	applications whose nodes	applications whose nodes
		devices, smart	tickets	and inventory	are nearby. For example:	are nearby. For example:
		health, retail		tracking	home automation	home automation

Table 3

 3 Comparison between the characteristics of the different WiFi versions

	WiFi version	802.11	802.11a	802.11b 802.11g	802.11n	802.11ac	802.11ad	802.11 ah	802.11ax
		Standard			(WiFi 3)	(WiFi 4)	(WiFi 5)	(Millimeter	(WiFi	(WiFi 6)
								wave)	Halow)
	Category	WLAN	WLAN	WLAN	WLAN	WLAN	WLAN	WLAN	WLAN	WLAN
	Frequency	2.4 GHz	5 GHz	2.4 GHz 2.4 GHz 2.4 GHz /	5 GHz	60 GHz	0.9 GHz	2.4 GHz / 5
						5 GHz				GHz
	Power	High	High	High	High	High	High	High	Low	Low
	Consumption
	Range	20m	100m	100m	100m	100m	100m	10m	1000m	100m
		(Indoor),
		100m
		(Outdoor)
	Data rate	1 Mbps-2	54	11	54	600 Mbps	6.9 Gbps	7 Gbps	347 Mbps	≈ 9.6 Gbps
		Mbps	Mbps	Mbps	Mbps
	Maximum	2304	2304	2304	2304	2304	2304	2304 bytes	100 bytes	100 bytes
	Transmission	bytes	bytes	bytes	bytes	bytes	bytes
	Unit
	Bidirectional					Yes
	Communication
	Topology					Star and Mesh
	Scalability	-	2007	2007	2007	2007	2007	2007	8191	A higher
	(devices)									number
	Mobility
	Support

Table 4

 4

	includes the
	characteristics of DASH7.
	 Cellular (3G/4G/5G): These technologies provide high
	data rate and reliable communications over long
	distances. Moreover, they require very high operating
	costs and energy requirements. An example of the
	architecture based on 5G used in the IoT is presented in
	Figure 8. It is composed of : i) end nodes (the end
	devices equipped with 5G technology; ii) Gateways
	(the devices that connect the 5G network to other IoT
	networks); and iii) 5G Base Station which is the
	component that connects other devices (end nodes and
	gateways) to the Internet. Table 4 enumerates the
	characteristics of the 5G. Although the 5G offers
	several advantages (e.g. considerable improvement of
	the data rate and the reduction of latency). The next
	version (the 6G) was developed to introduce a
	communication standard of new generation with more
	advantages and enhanced capacities.

Table 4

 4 Comparison between the characteristics of 5G, LiFi, LoRa, Dash7, Sigfox, NB-IoT and NB-Fi

	Technology	Cellular	LiFi	LoRa	Dash7	Sigfox	NB-IoT	NB-Fi
		(5G)
	Category	WWAN	WLAN			LPWAN
	Frequency	25-39	10000 times	125 KHz/	433 MHz	100Hz	200 kHz	868 MHz et 915
		GHz	of WiFi	250KHz				MHz
	Power	2 times	Low	low	low	low	low	low
	Consumption	less
		lower
		than 4G
	Range	from	Based on light	2-5 km (urban	Up to 5km	30-50 km	1 km (urban	16km (urban
		200m to	intensity	areas), 20 km		(rural	areas), 10 km	areas), 50km
		400m	(<=10m)	(rural areas)		areas), 3-10	(rural areas)	(rural areas)
						km (urban
						areas)
	Data rate	up to 36	About	from 300bps	up to 167 kbps	100 bps	200kbps	100bps
		Gbps	100Gbps1Tbp	to 50 kbps			(downlink),
			s				20kbps (uplink)
	Maximum	1420	-	250 bytes	256 bytes	≈ 12 bytes	1600 bytes	-
	Transmission	bytes
	Unit
	Bidirectional	yes	yes	yes	yes	yes	yes	yes
	Communication
	Topology	star	Point to Point	star, a star of	star, tree (if	star	star	star
			and Star	stars, mesh	there are
					subcontrollers)
	Scalability	1000000	unlimited	>10000	-	>10000	≈ 55000	up to 2 million
	(devices)	by km 2	number of					nodes for a base
			devices					station
			present in the
			visibility zone
	Mobility	yes	yes	yes	yes (node	yes	yes (Complex)	-
	Support				mobility up to a
					range of 2km)
	Direct Internet	yes	no	yes	yes	yes	yes	yes
	Access
	Common Use	Smart	Smart cities,	Applications that do not require high data rate but require a large range and are not time
	Cases	health	Healthcare			sensitive.
			applications,	Examples: Industrial IoT, smart city, smart building, smart agriculture
			Underwater
			applications

Table 6

 6 IoT Application Domains: target environments, target customers and the most important objectives The Internet of Battlefield Things (IoBT): It consists in applying the IoT in the military domain. In other words, devices used in the world of defence and military battles become intelligent as they can communicate with each other. As a result, they become more efficient as they improve the operational efficiency of military applications. The Green Internet of Things (GIoT): It is a version of the IoT that involves energy efficient features and environmental conservation activities. It ensures greener and healthier environment. First, the GIoT aims at reducing energy waste and minimizing energy consumption (Rafeeq

	Application	Target Environments	Target Customers	Important objectives
	Smart City	Urban areas	Citizens	Improves the quality of the citizens' life
		City and local government		Offers good-quality and more comfortable services
		agencies, public and		Maintains the sustainability of services
		private local utilities and quasi government/private sector consortiums		Reduces the operating expenses Helps to deal with congestion and energy waste
	Smart Home	Residences	Homeowners	Satisfies the individual's preferences and needs
			Inhabitants	Provides convenience in daily activities
				Space-saving, security and energy efficiency
				Reduces human involvement
	Smart Building	Buildings, hospitals,	Residents	Satisfies the occupants and guarantee their safety
		hotels, schools, etc.		Reduces the operating and maintenance costs
				Minimizes the environmental impact of the buildings in terms of
				energy and water consumption and waste production.
	Educational	Real and virtual	Learners	Makes educational environments intelligent, more productive and
	Internet of Things	educational environments	Teachers	more efficient
	(EIoT)			Provides collaborative and personalized self-learning and
				learning opportunities
	Agricultural	Agricultural environments	Customers and	Improves production quality and quantity
	Internet of things		owners of	Modernizes and improves the various application fields of the
	(AIoT)		agricultural	agricultural sector.
			environments	Minimizes the human intervention and workforce efforts
	Industrial	Industrial environments	Workers and	Achieves a high operational efficiency and increases productivity
	Internet of Things		owners and of	Ensures better management and control of the equipment and the
	(IIoT)		industrial	industrial processes
			environments	Makes the industrial operations fully autonomous
	Medical Internet	Healthcare environments	Patients	Provides personalized and targeted medicine
	of things (MIoT)		Workers in	Improves the quality of medical treatment
			healthcare	Reduces healthcare costs
			environments	Creates more profitable systems
	Energy Internet	Energy distribution	Customers and	Automates the energy resources and uses them in a smart and
	of Things (EIoT)	networks	providers of	efficient way
			energy	Optimizes energy consumption
			distribution networks	Incorporates renewable energies into distribution networks
	Internet of	Military environments	Soldiers	Saves soldiers' lives and provides autonomous war environment
	Battlefield Things			Implements human monitoring of the war environment
	(IoBT)			Improves the operational efficiency of military applications
	Commercial	Real and virtual	Customers,	Extends e-commerce and mobile commerce
	Internet of Things	Commercial environments	businesses, sellers	Makes the purchasing process automatically driven by algorithms
	(CIoT)		and manufacturers	and minimises human intervention
				Recognizes the customer's needs, improves his/her proposals and
				tracks his/her consumption/usage
	Social Internet of	Any IoT environment	All users of IoT	Uses object relationships to discover objects, the information
	things (SIoT)		networks	they provide and their services
				Improves the performance and functionality of the IoT objects
	Green Internet of	Any IoT environment	All users of IoT	Reduces energy consumption and minimises the emissions of
	Things (GIoT)		networks	toxic pollutants
				Involves environmental conservation activities




Barbara Gouble
email: barbara.gouble@inrae.fr

Patrice Reling

Adrien Servent

Pierre Brat

Véronique Vidal

Sandrine Laurent

Jean-Marc Audergon

-Sandrine Laurent

Apricot

Pell

Puree

Apricot processing suitability linked to its macronutrient composition: a part of DeshyFruit project

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

• 4 to 6.5 bars (130 -165 °C) • Rotary speed: 4 to 6 rpm

Drying conditions

Process faisability 9 cultivars were selected for their various typology and harvested close to maturity To limit losses and wastes in the Fruit & Vegetable sector, the DeshyFruit project aims to study the feasibility of rotary drying applied to fruit purees to obtain powder or flakes, with four main focus :

-influence of the variety and of fruit ripening stage -optimization of the drying parameters (contact time, addition of texturing agent (malto-dextrin, starch…) -evolution of sensorial and nutritional qualities (fruit, powder, reconstituted puree) -elaboration of a predictive processing model by spectral analysis (PIR, MIR) Initial results obtained on apricot are presented.

The DUPRAT dryer allows continuous flash-drying by heating a puree on the surface of a cylinder heated by pressurized steam with a short contact time (fresh product / heated cylinder).

The drastic reduction in water activity allows long storage at room temperature of the dried flakes before rehydration, with a good microbiological stability.

At the end of the first year of the DeshyFruit project, the preliminary results conducted on nine apricot cultivars highlighted:

-the clear interest of the process to obtain fruit powder -the technological and organoleptic quality of the end-products -the huge influence of the variety and maturity stage on the drying parameters settings. Next step is a further analysis of the relationship between fruit biochemical composition and the more or less suitability for drying. According apricot cultivars, refining yields varied from 90% (Medflo) to 50% (A4034) and drying process leads to obtaining a nice continuous film or a sticky paste (Fig1). The drying conditions also influence the quality of the film. A minimum pressure of 5 bars seems necessary (equivalent to 150 °C) and better results were obtained with a 4 rpm rotation than 6 rpm.

Adding malto-dextrin or starch to apricot puree before drying increases Tg and can provide technical assistance to obtain powders with good properties (less hygroscopic). This is not always necessary for apricot, making the film more difficult to peel off the roll, but must be adapted according cultivars and maturity stages.

  Fig 1: Drying process for 2 apricot cultivars: A4049 (left) and A4034 (right).

3rd

  Fruit & Vegetable Processing Symposium -Web conference on November 24-25, 2020Glass-transition temperature TgTg characterizes the range of temperature over which the glasstransition occurs. It is analyzes by Differential Scanning Calorimetry-ThermoGravimetric Analysis (DCS-ATG).

Table 1 :

 1 Glass-transition temperature (Tg) of 3 apricot cultivars powders obtained from puree with drying aids. Process conditions: 160 °C / 4 rpm

	Cultivar	Drying aids	Tg (°C)	Visual quality
	Medflo	-	5.9	sticky
		10% starch	48.4	powdery
		20% starch	65.4	powdery
	Jengat	-	17.3	powdery
		5% malto dextrose	48.8	powdery
	Colomer	-	3.3	powdery
		20% arabic gum	48.9	powdery


Christian Ghiaus

Naveed Ahmad

Thermal circuits assembling and state-space extraction for modelling heat transfer in buildings

Keywords: model assembling, state-space extraction, thermal circuits, building information modelling (BIM), dynamic thermal simulation, control systems

Thermal circuits assembling and state-space extraction for modelling heat transfer in buildings

Christian Ghiaus, Naveed

Introduction

Thermal networks are graphic representations of systems of differential algebraic equations (DAE) which model heat transfer by conduction (described by the weak form of the heat equation [START_REF] Naveros | Frequency response limitation of heat flux meters[END_REF]), convection (described by Newton law) and long wave radiative exchange (described by using radiosity [START_REF] Bergman | Fundamentals of Heat and Mass Transfer[END_REF]). The resistor-capacitor (RC) models have physical meaning that allows the evaluation of the modelling hypothesis considered for buildings and their urban environment [START_REF] Bueno | A resistance-capacitance network model for the analysis of the interactions between the energy performance of buildings and the urban climate[END_REF].

Thermal networks are used for defining the models of elementary components (e.g. walls, floors, doors, windows, etc.) in energy balance method [START_REF] Ghiaus | Causality issue in the heat balance method for calculating the design heating and cooling load[END_REF][START_REF] Wang | Advances in building simulation and computational techniques: A review between 1987 and 2014[END_REF]. Energy balance method is the recommended method of ASHRAE [START_REF] Bruning | Nonresidential cooling and heating load calculations[END_REF] and the basis of the CEN standard for calculation of the design heat load [START_REF] Cen | Heating systems in buildings -Method for calculation of the design heat load EN 12831[END_REF] as well as of other CEN standards related to thermal performance of buildings [START_REF] Cen | Thermal performance of buildings -Calculation of energy use for space heating and cooling -General criteria and validation procedures[END_REF][START_REF] Cen | Thermal performance of buildings -Sensible room cooling load calculation -General criteria and validation procedures EN 15255[END_REF]. Other procedures are seen as variants or simplifications of the heat balance method. In this method, the set of equations is integrated numerically, generally by using existing solvers. It is the case of many commercial simulation software [START_REF]Building Energy Software Tools[END_REF] such as TRNSYS [START_REF]TRNSYS 16[END_REF], EnergyPlus [12], IDA ICE [START_REF] Sahlin | Wholebuilding simulation with symbolic DAE equations and general purpose solvers[END_REF], of research oriented tools, like ESP-r [START_REF] Clarke | Energy Simulation in Building Design[END_REF], CODYRUN [START_REF] Fakra | Development of a new model to predict indoor daylighting: Integration in CODYRUN software and validation[END_REF] or of tools developed "in house" [START_REF] Skeiker | Advanced software tool for the dynamic analysis of heat transfer in buildings: applications to Syria[END_REF][START_REF] Chung | Development of a software package for community energy system assessment -Part I: Building a load estimator[END_REF][START_REF] Buonomano | Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies[END_REF][START_REF] Perera | Multi-floor building heating models in MATLAB and Modelica environments[END_REF][START_REF] Michalak | The development and validation of the linear time varying Simulink-based model for the dynamic simulation of the thermal performance of buildings[END_REF]. Another approach is to use equationbased modeling. In this case, the computational causality (i.e. the input -output relation) is defined after the model was constructed and can be changed. Then a simulation engine performs the calculations [START_REF] Masy | Lessons Learned from Heat Balance Analysis for Holzkirchen Twin Houses Experiment[END_REF][START_REF] Wetter | Modelica-based modelling and simulation to support research and development in building energy and control systems[END_REF][START_REF] Perera | Multi-floor building heating models in MATLAB and Modelica environments[END_REF]. This has the advantage of using the same model for different sets of inputs and outputs but can generate ill-posed problems [START_REF] Ghiaus | Causality issue in the heat balance method for calculating the design heating and cooling load[END_REF].

State-space is the most used input-output representation of linear models, as shown by recent reviews on modelling of building energy systems [START_REF] Harish | A review on modeling and simulation of building energy systems[END_REF][START_REF] Clarke | Energy Simulation in Building Design[END_REF] and on strategies for building energy management [START_REF] Wang | A state of art review on methodologies for control strategies in low energy buildings in the period from 2006 to 2016[END_REF][START_REF] Afroz | Modeling techniques used in building HVAC control systems: A review[END_REF]. State-space representation is widely used for model identification and calibration. In model identification, the structure of the model is proposed and then the parameters are identified by minimizing the error between the output of the model and the measured data. An essential issue is the structure of the model on which the experimental data is projected. This is done mostly empirically by using models with a variable number of states [START_REF] Bacher | Identifying suitable models for the heat dynamics of buildings[END_REF][START_REF] Hasan | A simplified building thermal model for the optimization of energy consumption: Use of a random number generator[END_REF][START_REF] Harish | Reduced order modeling and parameter identification of a building energy system model through an optimization routine[END_REF][START_REF] Alzetto | A perturbation method to estimate building thermal performance[END_REF][START_REF] Brastein | Parameter estimation for grey-box models of building thermal behaviour[END_REF]. The procedure of model calibration is very similar to parameter identification: use optimisation techniques to fit the model to data by changing the values of the parameters. The main difference is that the parameters obtained by calibration of physical models have physical significance [START_REF] Andrade-Cabrera | Ensemble Calibration of lumped parameter retrofit building models using Particle Swarm Optimization[END_REF][START_REF] Rouchier | Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data[END_REF][START_REF] Rouchier | Calibration of simplified building energy models for parameter estimation and forecasting: Stochastic versus deterministic modelling[END_REF][START_REF] Zekar | Development and assessment of simplified building representations under the context of an urban energy model: Application to arid climate environment[END_REF].

State-space representation is widely used for model order reduction, which can be done numerically, when the state-space model is known, as is the case for walls [START_REF] Kim | Urban energy simulation: Simplification and reduction of building envelope models[END_REF] or by projecting the results obtained by simulation on a given structure [START_REF] Robillart | Model reduction and model predictive control of energy-efficient buildings for electrical heating load shifting[END_REF]. A key point in model order reduction is the model order selection [START_REF] Mejri | Energy performance assessment of occupied buildings using model identification techniques[END_REF][START_REF] Naveros | Order selection of thermal models by frequency analysis of measurements for building energy efficiency estimation[END_REF].

State-space model is the most used representation in modern control theory. One approach to obtain the state-space representation is to use a thermal network for the model of the building [START_REF] Hazyuk | Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I e Building modeling[END_REF] and to identify the parameters of the state-space representation from inputoutput data [START_REF] Siroky | Experimental analysis of model predictive control for an energy efficient building heating system[END_REF][START_REF] O'dwyer | Prioritised objectives for model predictive control of building heating systems[END_REF][START_REF] Tanaskovic | Robust Adaptive Model Predictive RobuBsut iAlddinagptCivleimMatoedeCloPnrtreodlictive Building Climate Control Building Climate Control Building Climate Control[END_REF][START_REF] Hazyuk | Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part II e Control algorithm[END_REF]. Usually, the model used for controller synthesis has one state variable: the indoor temperature [START_REF] Figueiredo | A SCADA system for energy management in intelligent buildings[END_REF][START_REF] Vána | Model-based energy efficient control applied to an office building[END_REF][START_REF] Singh | Modeling for reactive building energy management[END_REF][START_REF] Viot | Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part I-Modeling and measurements[END_REF][START_REF] Yang | A statespace thermal model incorporating humidity and thermal comfort for model predictive control in buildings[END_REF][START_REF] Luzi | A tuning methodology of Model Predictive Control design for energy efficient building thermal control[END_REF][START_REF] Ryzhov | Model predictive control of indoor microclimate: Existing building stock comfort improvement[END_REF], although state-space models were obtained from the thermal network of a room for 4 states [START_REF] Kirubakaran | Energy efficient model based algorithm for control of building HVAC systems[END_REF], 6 states [START_REF] Maasoumy | Handling model uncertainty in model predictive control for energy efficient buildings[END_REF] or for 17 states [START_REF] Hu | A state-space modeling approach and multi-level optimization algorithm for predictive control of multi-zone buildings with mixed-mode cooling[END_REF].

Since thermal networks are widely used for modelling heat transfer and state-space is the most used representation in control theory, state-space extraction from thermal networks is of the highest interest. A solution to obtain systematically the state-space representation is by using nodal/mesh analysis to reduce the number of undesired variables [START_REF] Martinez-Marin | State-Space Formulation for Circuit Analysis[END_REF]. Another state-space extraction method uses the concepts of tree (a sub-graph of the original graph containing no loops) and co-tree (a sub-graph of the original graph containing the edges removed to form the tree) but it requires symbolic manipulation [START_REF] Uren | State space model extraction of thermohydraulic systems e Part I: A linear graph approach[END_REF]. Commercial implementations of the state-space extraction are not documented [56]. This paper introduces a method for obtaining state-space models for complex heat transfer in buildings by extracting them from thermal circuits. The thermal circuits are obtained by assembling elementary models which represent building elements (e.g. walls, windows, ventilation, etc.). 2 Obtaining the system of differential-algebraic of equations (DAE) from the thermal networks A thermal circuit is a weighted directed graph in which the branches represent heat flow rates crossing thermal resistances and thermal sources while the nodes represent temperatures to which thermal capacities and heat flow rate sources are connected [START_REF] Naveros | Thermal networks considering graph theory and thermodynamics[END_REF][START_REF] Strang | Computational science and engineering[END_REF].

Algebraic description of the thermal circuits

A thermal circuit is described by three matrices:

1) oriented incidence matrix 𝐀 with the number of lines equal to the number of branches and the number of columns equal to the number of nodes:

𝑎 )* = , 0 if
2) diagonal matrix 𝐆 of conductances of dimension equal to the number of rows of 𝐀:

𝑔 )* = B 𝑅 ) DE for 𝑖 = 𝑗 0 for 𝑖 ≠ 𝑗 (2)
3) diagonal matrix 𝐂 of capacitances of dimension equal to the number of columns of 𝐀:

𝑐 )* = B 𝐶 ) for 𝑖 = 𝑗 0 for 𝑖 ≠ 𝑗 (3)
and three vectors with elements 0 and 1: 1) vector 𝐛 of temperature sources of dimension equal to the number of rows of 𝐀, with the elements 𝑏 ) = L 1 for temperature source on branch 𝑖 0 otherwise (4)

2) vector 𝐟 of flow sources of dimension equal to the number of columns of 𝐀, with the elements 𝑓 ) = L 1 for flow source in node 𝑖 0 otherwise (5)

3) vector 𝐲 of temperature outputs of dimension equal to the number of columns of 𝐀, pointing to the temperature nodes that are used as outputs:

𝑦 ) = L 1 for temperature as output variable 0 otherwise

Then, each thermal circuit (TC) is described by the set of arrays: 𝑇𝐶 = {𝐀, 𝐆, 𝐛, 𝐂, 𝐟, 𝐲}.

System of differential-algebraic equations (DAE)

By writing the differences of temperature,

𝐞 = -𝐀 𝛉 + 𝐛 (8)
the balance of heat rates in nodes,

𝐂 𝛉 ̇= 𝐀 ] 𝐪 + 𝐟 (9)
and the constitutive laws for heat transfer,

𝐪 = 𝐆 𝐞 ( 10
)
where 𝐪 is the vector of heat flow rates, and by substituting 𝐞 from equation (8) into equation [START_REF]Building Energy Software Tools[END_REF], we obtain Karush-Kuhn-Tucker (KKT) equations [START_REF] Strang | Computational Science and Engineering[END_REF]:

_ 𝐆 DE 𝐪 + 𝐀𝛉 = 𝐛 -𝐀 ] 𝐪 + 𝐂𝛉 ̇= 𝐟 (11)
By eliminating 𝐪 from (11), we obtain the set of differential-algebraic algebraic equations describing the thermal circuit:

𝐂 𝛉 ̇= -𝐀 ] 𝐆 𝐀 𝛉 + 𝐀 ] 𝐆 𝐛 + 𝐟 (12)
3 Assembling the thermal circuits

It is easy and convenient to obtain thermal circuits for different elements of the building (walls, floors, windows, doors, etc.). Then, the model of a whole building may be obtained by assembling the elements. Assembling is different from coupling. In coupling, the models of the elements form a set of equations which is solved numerically; in assembling, the model of the whole building is obtained first and then the system of equations is solved. The advantage of assembling is that the model can be analysed: the eigenvalues and the eigenvectors of the whole system can be obtained.

The problem of circuit assembling is to obtain the thermal circuit 𝑇𝐶 by knowing that some nodes of the elementary circuits 𝑇𝐶 E , 𝑇𝐶 `, … , 𝑇𝐶 b are common to several circuits. Since a thermal circuit is described by the set of arrays, 𝑇𝐶 = {𝐀, 𝐆, 𝐛, 𝐂, 𝐟, 𝐲}, the aim of assembling is to form the global KKT system:

c 𝐆 DE 𝐀 -𝐀 ] 𝐂𝑠 e c 𝐪 𝛉 e = c 𝐛 𝐟 e (13)
or, by using the notations:

𝐊 ≡ c 𝐆 DE 𝐀 -𝐀 ] 𝐂𝑠 e ; 𝐮 ≡ c 𝐪 𝛉 e ; 𝐚 ≡ c 𝐛 𝐟 e (14)
to form the equation:

𝐊𝐮 = 𝐚 (15)
from the KKT models of the elementary systems (walls, floors, doors, windows, etc.):

k 𝐆 𝒊 D𝟏 𝐀 ) -𝐀 𝒊 𝑻 𝐂 ) 𝑠 o c 𝐪 ) 𝛉 ) e = p 𝐛 ) 𝐟 ) q (16)
We can write equation [START_REF] Skeiker | Advanced software tool for the dynamic analysis of heat transfer in buildings: applications to Syria[END_REF] as

𝐊 ) 𝐮 ) = 𝐚 ) (17)
where:

𝐊 ) ≡ k 𝐆 𝒊 D𝟏 𝐀 ) -𝐀 𝒊 𝑻 𝐂 ) 𝑠 o ; 𝐮 ) ≡ c 𝐪 ) 𝛉 ) e ; 𝐚 ) ≡ p 𝐛 ) 𝐟 ) q (18)
Let's note the dissembled block matrix 𝐊 r and the disassembled block vectors 𝐮 r , 𝐚 r , the matrix and the vectors obtained by simply placing in order the matrices and the vectors of the elementary models described by equation [START_REF] Chung | Development of a software package for community energy system assessment -Part I: Building a load estimator[END_REF]:

𝐊 r ≡ s 𝐊 E … 𝟎 ⋮ ⋱ ⋮ 𝟎 … 𝐊 b w 𝐮 r ≡ s 𝐮 E ⋮ 𝐮 b w ; 𝐚 r ≡ s 𝐚 E ⋮ 𝐚 b w (19)
There is a disassembling matrix 𝐀 r which transforms the assembled vectors 𝐚 and 𝐮 into the dissembled vectors 𝐚 r and 𝐮 r :

𝐚 r = 𝐀 r 𝐚; 𝐮 r = 𝐀 r 𝐮; [START_REF] Michalak | The development and validation of the linear time varying Simulink-based model for the dynamic simulation of the thermal performance of buildings[END_REF] The relations between the global and the elementary matrices and vectors are:

𝐊 = 𝐀 r ] 𝐊 r 𝐀 r (21)
𝐮 = 𝐀 r ] 𝐮 r (22)
𝐚 = 𝐀 r ] 𝐚 r ( 23
)
The elements of the assembled circuit, 𝑇𝐶 = {{𝐀, 𝐆, 𝐛, 𝐂, 𝐟, 𝐲}, are then obtained from the partition of the arrays:

𝐊 = p 𝐆 D𝟏 𝐀 -𝐀 𝐓 𝐂𝑠 q ; 𝐮 = c 𝐪 𝛉 e ; 𝐚 = c 𝐛 𝐟 e ( 24
)
Figure 1 Simple example for the assembling of thermal circuits: a) three disassembled circuits with local indexing of nodes and branches (the dashed lines show the nodes which are in common); b) the assembled circuit with local and global indexing of nodes and branches; c) the matrices and the vectors characterising the disassembled thermal circuits; d) the disassembling matrix and the transformation of the assembled vector into disassembled vectors; e) the matrices and the vectors characterising the assembled thermal circuit.

(") = 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1

$ % = 1 0 -1 1 + % = diag 0 11 012 3 % = 1 0 4 % = diag 511 0 6 7 % = 1 0 6 $8 = 1 1 +8 = diag 09 0 : 38 = 1 0 4 8 = diag 5/2 6 78 = 1 6 (=) > ̇/2 1 1 2 -+ @A 2 1 1 2 Φ C 1 -+ @ A 1 + - @ D,FG 2 > ̇/2 -+ @ A 2 1 1 -+ @ A 1 + - @ D,FG 2 1 3 1 3 1 2 2 3 Φ C
O 1 O 2 O 8 O Q O R P 1 P 2 P 8 5/2 5/2 > ̇/2 > ̇/2 5/2 5/2 S T = $ T S (K) $ = 1 0 0 -1 1 0 0 -1 1 0 0 1 0 0 1 ; + = diag

S T $ T S (V)

An illustrative example is given in Figure 1. There are three circuits:

-TC1 with two branches and two nodes, -TC2 with one branch and two nodes, and -TC3 with two branches and one node. The second node of TC1 is put in common with the first node of TC2 and the second node of TC2 is put in common with the first node of TC3 (Figure 1a). The local indexes of the dissembled circuits correspond to global indexes in the assembled circuit (Figure 1b). Each disassembled circuit is characterised by the set of arrays 𝑇𝐶 ) = {𝐀 ) , 𝐆 ) , 𝐛 ) , 𝐂 ) , 𝐟 ) , 𝐲 ) }, where 𝑖 = 1, 2, 3, with values given by equations (1)…(5) (Figure 1c). The disassembling matrix, 𝐀 r , transforms the assembled variables, 𝐮, into disassembled variables, 𝐮 r (Figure 1d). Finally, the assembled circuit is characterised by a set of arrays, 𝑇𝐶 = {𝐀, 𝐆, 𝐛, 𝐂, 𝐟, 𝐲} (Figure 1e). [START_REF] Ghiaus | Causality issue in the heat balance method for calculating the design heating and cooling load[END_REF] Extract state-space model from thermal circuits If the thermal circuit contains nodes without heat capacity, the matrix 𝐂 is singular. In order to obtain the state-space model, the equations corresponding to the nodes without heat capacity need to be eliminated from the system of equations ( 12) [START_REF] Ghiaus | Causality issue in the heat balance method for calculating the design heating and cooling load[END_REF]. By partitioning the matrix 𝐂,

𝐂 = p 𝟎 𝟎 𝟎 𝐂 z q (25)
where 𝐂 z corresponds to the nodes having capacities, the set of equations ( 12) may be written as:

p
𝛉 ̇z = 𝐀 ~𝛉z + 𝐁 ~𝐮 ( 27
)
where the state matrix is

𝐀 ~= 𝐂 z DE (-𝐊 `E𝐊 EE DE 𝐊 E`+ 𝐊 ``) (28)
and the input matrix is

𝐁 ~= 𝐂 z DE [-𝐊 `E𝐊 EE DE 𝐊 |E + 𝐊 |`-𝐊 `E𝐊 EE DE 𝐈] (29)
If the outputs are temperatures of nodes with capacities, the observation matrix 𝐂 ~ extracts their values from the state vector and the feed-through matrix, 𝐃 ~= 𝟎. If the outputs are temperatures from nodes without capacities, the observation equation can be obtained from the first row of equation ( 26) [START_REF] Ghiaus | Causality issue in the heat balance method for calculating the design heating and cooling load[END_REF]:

𝛉 { = -𝐊 𝟏𝟏 D𝟏 (𝐊 E`𝛉z + 𝐊 |E 𝐛 + 𝐈 EE 𝐟 { ) = -𝐊 𝟏𝟏 D𝟏 …𝐊 E`𝛉z + [𝐊 |E 𝐈 EE 𝟎] s 𝐛 𝐟 { 𝐟 z w † (30)
Then, the output equation is:

𝐂 ~= -𝐊 EE DE 𝐊 E` (31)
and the feed through matrix is:

𝐃 ~= -𝐊 EE DE [-𝐊 |E 𝐈 EE 𝟎] (32)
5 Experimental validation

House description

Empirical validation of the detailed thermal simulation of buildings was undertaken in the framework of the IEA-EBC Annex 58 (Strachan et al. 2014). Measurements were performed on two typical detached houses operated by Fraunhofer Institute IBP, Holzkirchen, Germany (Figure 2), named N2 and O5. External walls of both houses are insulated, the windows are double glazed and equipped with external roller blinds. During the experiment, the roller blinds in all rooms were up, except the ones on the south façade. In case of N2 house, the South façade roller blinds were up for the entire duration of experiment, whereas in case of O5 house the South facade blinds were down during the initialisation period, i.e. during the first 7 days when the indoor temperature was constant, and up during all other periods. The ground floor of N2 house, consisting of 7 rooms, was modelled, with the cellar and the attic considered as known boundary condition. Doors between kitchen and living room, doorway 9/22 and living room, bedroom and corridor were closed and sealed with tape. Doors connecting the corridor with the living room, bathroom and bedroom 2 were fully open. Infiltration rates were measured between the rooms and between indoor and outdoor. Mechanical ventilation provided an air flow rate of 120 m 3 /h in the living room, which was extracted equally in the bathroom and the bedroom 2.

Experimental protocol

The experiment was conducted for 41 days in summer 2013 starting with an initialisation period of 7 days followed by a period of 7 days with a heating set point of 30°C in all rooms. The constant temperature period was followed by a Randomly Ordered Logarithmic Binary Sequence of heat inputs (ROLBS) for 15 days. A 500 W heating power was supplied only in the living room during the ROLBS period. The time period for heat pulses ranged from 1 hour to 90 hours. The ROLBS period was followed by a re-initialisation period for 7 days with a heating set point of 25°C in all rooms. For the final 11 days of the experiment, no heat input was supplied and the temperature was allowed to float freely (Strachan et al. 2014).

Simulation results

The experimental house consists of seven thermal zones: kitchen, living room, doorway, bedroom 1, bathroom, corridor and bedroom 2. Internal temperature is simulated for living

-+ !" 1 2 1 1 1 ,- 2
1 2 #/6 -+ !" 1 1
!" 1 1 1 #/1 1 1 #/2 2" 2" 1 2 #/2 2" 2" ! " 1 1 2 #/1 ! " 1 1 #/5
12/22 room (single zone), kitchen & living room (two zones), and entire twin house (seven zones), respectively (Figure 2).

A thermal circuit for each building element (wall, roof, window, door, etc.) is obtained by using techniques discussed in section 2 (Figure 3). The spatial discretisation of the building elements can be changed. The wall thermal capacity is located in the middle of each slice.

All thermal properties are considered constant in time (Tables 1 and2). In simulation, the solar radiation on walls and windows is taken from measurements and from calculations; both approaches give very similar results. Solar radiations entering the twin house through windows are calculated as a function of varying solar transmittance that changes with angle of incidence of solar radiation. According to the manufacturer, the heat from heaters is split in thirty percent radiation and seventy percent convection (Strachan et al. 2014). The same split of heat input from the heater is considered in simulation.

The thermal circuits generated for each zone are assembled as discussed in section 3. The state space model for the assembled circuits is generated as discussed in section 4.

The measured data has a time step of 10 minutes and the same time step of 10 minutes is used in simulation.

One-zone (living room)

The model of the living room consists of 13 thermal circuits (building elements). The numbering of each circuit and the position of each input to thermal circuit is shown in Figure 4. Thermal circuit number 13 represents the air node, which receives heat transferred from 12 thermal circuits that model the building elements.

The measured temperatures of all the zones external to the living room (including the walls of the kitchen, doorway, corridor and bedroom and the doors) form the boundary conditions. Figure 5: Simulation results for living room; green line represents the measured internal temperature, 𝑇 ) , the red line is the simulated temperature, 𝑇 ‡)ˆ, and the blue line presents the variation of the outdoor temperature, 𝑇 ‰ , during the period of 41 days The simulated internal temperature follows the measured temperature closely (Figure 5). The histogram of errors (Figure 6) shows that simulation errors lie well within ±1℃. The mean error is 𝜇 = 0.48 °C and the standard deviation is 𝜎 = 0.52 °C.

! " 1 1 $ 2/6 1 2 ! " 1 1 $ !" 1 1 $ 9/1 ! " 1 1 $ 1/3 ! " 1 1 $ 3/3 ! " 1 1 $ 4/6 ! " 1 $ !" 1 1 $ !" 1 1 $ !" 1 1 $ 13

Two-zone model (living room and kitchen)

The kitchen and the living room were modelled together to validate the assembling and the state-space methodologies discussed in sections 3 and 4. The kitchen and the living room share an internal wall and a door. Although the shared door is sealed, there is infiltration of air between the two rooms, equivalent to 1/3 rd of the infiltration between North and South zones. The ventilation supply duct passing through the kitchen is uninsulated and is responsible for loss of heat from the kitchen. The heat losses to ventilation duct are provided in the experimental data and are incorporated in the simulation.

The total number of thermal circuits for two zones is nineteen (Figure 7). All zones external to the kitchen and the living room are considered boundary conditions for the model. The number of state variables in the assembled model is 57.

Simulation of the indoor air temperature in the kitchen and the living room shows that the simulated air temperature lies within +1.5 and -0.5 °C with few outliers for kitchen and within ±0.5℃ for the living room, with a few outliers (Figure 8).

Seven thermal zones (whole house)

The model for the whole house consists of seven thermal zones that are modelled by assembling 56 thermal circuits (Figure 9). The number of states in the final state-space model is 109. The simulation error increases with the number of zones, which is explained by the errors induced in the values of boundary conditions (Figure 10). The simulation results show that for every zone three quantiles of simulated temperature lie within ±1℃. State-space representation can be effectively obtained from thermal circuits, even for very large models. This is specifically suited to detailed thermal models of buildings. The state-space representation, although linear, can also be used for non-linear models if the linearity is considered for a time step. State-space models are completely equivalent to thermal circuits from which they were obtained. The assembling of elementary thermal circuits allows us to obtain only one thermal circuit for the whole building; therefore, the state-space model can model in detail a whole building. Since assembling can be used to create large models from individual elements, it can find applications in the emerging technologies of Building Information Modelling (BIM). Obtaining state-space representation from very large thermal networks can have applications in model order reduction and the synthesis of control algorithms for complex buildings.

7 Bibliography

Figure 2

 2 Figure 2 Plan of the house.

Type 3 :

 3 Outdoor

Figure 4 :

 4 Figure 4: Layout and thermal circuits for single zone (living room): 13 elementary models, 49 states

Figure 6 :

 6 Figure 6: Error histogram for living room

Figure 7 :

 7 Figure 7: Layout and thermal circuits for kitchen and living room: 2 thermal zones, 19 elementary model types, 57 states.

Figure 8 :

 8 Figure 8: Temperature simulation results and error histograms for kitchen and living room air temperature

Figure 9

 9 Figure 9 Assembled model of a house: 7 thermal zones, 56 elementary model types, 109 states.

Figure 10 :

 10 Figure 10: Indoor air temperatures and error histograms for whole twin house (seven zones)

  𝛉 { and 𝐟 { correspond to the nodes without thermal capacity; 𝛉 z and 𝐟 z correspond to the nodes with thermal capacity; 𝐂 z is the bloc of the partitioned matrix 𝐂 for which the elements on the diagonal are non-zero; 𝐊 EE , 𝐊 E`, 𝐊 `E, and 𝐊 `` are blocs of the partitioned matrix 𝐊 obtained according to the partioning of the matrix 𝐂; 𝐊 |E and 𝐊 |` are blocs of the partitioned matrix 𝐊 | obtained according to the partitioning of the matrix 𝐂; 𝐈 EE and 𝐈 `` are identity matrices.

	𝟎 𝟎 𝟎 𝐂 z	q k 𝛉 𝛉 ̇{ ̇zo = p	𝐊 EE 𝐊 EK `E 𝐊 ``q p 𝛉 { 𝛉 z	q + p	𝐊 |E 𝐊 |E	q 𝐛 + p	𝐈 EE 𝟎 𝟎 𝐈 ``q p	𝐟 { 𝐟 z	q	(26)
	where:
	The state equation of the state-space model is:

Table 1

 1 Dimensions of windows

	Window	Overall dimensions	Glass dimensions
	type	with roller blinds (m 2 )	(m 2 )
	W1	1.74 x 1.23	1.30 x 0.99
	W2	2.57 x 1.11	2.13 x 0.865
	W3	1.74 x 3.34	3 x 1.385 x 0.99 (3 panes)

Table 2

 2 Walls and doors

		Wall type	Layer	Thickness	Conductivity	Dens.	Sp. heat	Absorp.	Emiss.
				(m)	(W/mK)	(kg/m 3 )	(J/kg K)	SW	LW
	1	External wall	Ext. plaster	0.01	0.80	1200	1000	0.23	0.90
		U = 0.2	Insulation	0.12	0.035	80	840
			Plaster	0.01	1.00	1200	1000
			Brick	0.20	0.22	800	1000
			Int. plaster	0.01	1.00	1200	1000	0.17	0.90
	2	Internal wall Plaster	0.01	0.35	1200	1000	0.17	0.90
			Brick	0.25	0.33	1000	1000
			Plaster	0.01	0.35	1200	1000	0.17	0.90
	3	Internal wall Plaster	0.01	0.35	1200	1000	0.17	0.90
			Brick	0.13	0.33	1000	1000
			Plaster	0.01	0.35	1200	1000	0.17	0.90
	4	Ceiling	Screed	0.04	1.40	2000	2000	0.60	0.90
		U = 0.25	Insulation	0.04	0.04	80	840
			Concrete	0.22	2.00	2400	1000
			Plaster	0.01	1.00	1200	1000
			Insulation	0.10	0.035	80	840	0.17	0.90
	5	Ground	Concrete	0.22	2.10	2400	1000	0.60	0.90
		U = 0.32	Fill	0.03	0.06	80	840
			Insulation	0.03	0.025	80	840
			Panel	0.03	0.023	80	840
			Screed	0.06	1.40	2000	1000	0.60	0.90
	6	External	Wood	0.04	0.13	600	1000	0.60	0.90
		door
	6a	Internal door Wood with	0.04	0.13	600	1000	0.60	0.90
			glass
	7	Infiltration	1.62 ACH
	8	Ventilation	60 m 3 /hr
	9 *	Pillar	Concrete		0.5	2400	1000	0.60	0.90
	*9 is numbered as 26