Maria Belesioti
email: mbelesioti@oteresearch.gr

Ioannis P Chochliouros
email: ichochliouros@oteresearch.gr

Stefan Vanya
email: stefan.vanya@bavenir.eu

Viktor Oravec
email: viktor.oravec@bavenir.eu

Natalia Theologou
email: nataliath@iti.gr

Maria Koutli
email: mkoutli@iti.gr

Athanasios Tryferidis

Dimitrios Tzovaras
email: dimitrios.tzovaras@iti.gr

e-Health Services in the Context of IoT: The Case of the VICINITY Project

Keywords: Internet of Things (IoT), eHealth, mobile and portable healthcare devices, interoperability

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Recently, there have been significant advances in the field of Internet of Things (IoT) in conjunction to the continuous and constantly increasing demand for support services in the emerging sector of e-health. Aged people with chronic diseases are in need for care and prevention of accidents at 24/7 basis, thus the need to define new models of healthcare that will advance existing systems is of high importance for modern electronic communications market sector. The VICINITY EU-funded project under Grant Agreement (GA) No.688467 [http://vicinity2020.eu/vicinity/] aims to illustrate that the communication models, protocols and technologies promoted under the IoT concept have a great potential in the implementation of Internet-based healthcare systems. In addition, VICINITY intends to provide the owners of connected IoT infrastructures with a decentralized interoperability. The concept of "decentralism" is expressed by the fact that the corresponding platform includes neither central operator roles nor central databases to store sensitive data about the involved users. Instead of that, it connects different smart objects into a "social network" called as "virtual neighborhood" where infrastructure owners keep under control their shared devices and data, thanks to web-based operator console called as the "VICINITY neighborhood manager (VNM)". When using the VNM, the user can control which of his/her IoT asset is shared with whom, and to which extent [START_REF]Open virtual neighborhood network to connect IoT Infrastructures and smart objects[END_REF]. This VICINITY-related technology aspires to apply in its e-health scenario. E-health comprises a novel combination of Telemedicine and IoT-based monitoring system and it is considered as one of "the most promising domains in the field of IoT", since it can offer effective and direct healthcare services improving the quality of existing healthcare systems by supporting reliable and efficient solutions. In order to offer and support several e-health solutions, VICINITY will build and demonstrate a device and standard agnostic platform for IoT infrastructures that will offer "Interoperability as-a-Service". This platform will rely upon a decentralised and usercentric approach that offer a complete transparency across vertical domains, while retaining full control of the ownership and distribution of data.

In order to realize such a network, a bridging point, the Gateway API (Application Programming Interface) facilitates an exchange of data between a connected IoT infrastructure and Value-added services. This gateway has an overall knowledge and control over both the sensor network and the data to be transmitted. More specifically, The VICINITY Gateway API utilizes a Communication Node with a set of technologies that makes the data transfer possible even in case when an IoT ecosystem resides behind the Network Address Translation -NAT [START_REF]Open virtual neighborhood network to connect IoT Infrastructures and smart objects[END_REF].

VICINITY Concept and Approach

The ongoing digital evolution has very high impact in the healthcare system, by leading to the rapid growth of many healthcare practices supported by electronic processes and communication known as electronic Health (eHealth) applications [START_REF]An Open NCP-based Secure eHealth Data Exchange System[END_REF].

The expected huge number of interconnected devices used in the health sector and the significant amount of data gathered by sensors and smart meters create both technical and business opportunities by introducing new services that will bring tangible benefits to the society [START_REF] Borgia | The Internet of Things vision: Key features, applications and open issues[END_REF]. Mobile devices, such as cellular phones, wearables and personal digital assistants (PDAs), are very popular and various IoT networks are being deployed for sensing, measuring, controlling and business process optimization purposes, while various IoT platforms are emerging on the market to manage these networks. Since these infrastructures are mostly acting as "isolated islands" in the global IoT landscape, their inter-connection might bring significant value added (such as an ecosystem running on close-to-zero energy for example). Taking this into consideration, VICINITY aims to present a new approach by exploring new opportunities and addressing new challenges. For this reason, the project aims to create new ecosystems, composed by integrated platforms and smart objects which will be supported by network technologies and will make use of the evolving ICT advances. Owners of connected IoT infrastructures could be also connected to this ecosystem, which is called "virtual neighborhood" and which represents a social network where each one will be able to keep under control their shared devices and data, thanks to a web-based operator console called as the "VICINITY neighborhood manager" (VNM). When using the VNM, each user can control which of his/her IoT asset is shared with whom and to which extent, just like they do in existing social networks and, furthermore, to manage access rules to his/her discovered smart objects through a catalogue appeared in the user's device when open the "VICINITY auto discovery device", through the "VICINITY gateway API" with sample implementations. Once user's IoT infrastructure is connected to the VICINITY platform, the IoT value chains become unlocked, thus opening the way towards seamless interoperability between IoT islands present in the current IoT landscape and this enables the exploitation of independent value-added services, including various cross-domain IoT applications (as discussed in [START_REF]eHealth Taskforce: Accelerating the Development of the eHealth Market in Europe[END_REF]).

Figure 1, as illustrated below, depicts a generalized concept coming from the wider VICINITY approach. The VICINITY quality features focus mainly upon user experience, trust, privacy, security and scalability. VICINITY functions are designed around the user so as to ensure "as best as possible" user experience during installation, configuration, integration of VICINITY components in its infrastructure and usage of the VICINITY interoperability features. The high availability and performance quality measures do enable VICINITY to scale-up and scale-out to "handle" various communication loads within neighborhoods, as introduced by different applications coming from building, energy, transport and health domain requirements.

The VICINITY Cloud components such as the VICINITY Neighborhood manager (providing user interface to the VICINITY Users), Semantic discovery and dynamic configuration agent platform (providing semantic platform) and the VICINITY Communication Server (providing control of communication between integrated infrastructures) shall be deployed as high available software components being to scale in/out the VICINITY cloud to current needs to the integrated infrastructures and valueadded services. The distributed infrastructure of the VICINITY context is enhanced by an interoperability approach aiming to provide a standard way to both Discover and Access heterogeneous IoT objects, distributed among sparse IoT infrastructures based on the work being done by the W3C Web of Things (WoT) WG. Therefore, VICINITY shall rely on Thing description (TD) introduced by WoT to describe every IoT object (which can represent either physical or abstract Things) that belong to any integrated IoT infrastructure which, in turn, shall be described as an ecosystem of IoT objects.

The IoT Operator should manage privacy throughout the lifecycle, by using consent to process any sort of private data. These consents should be associated with valueadded services and, additionally, confirmed consents should be part of the value-added service's profile. They should be visible only to the IoT Operator and the Service provider. The second one should also be able to create -or remove-a value-added service group and add -or remove-value-added services from this group and to perform group actions on value-added services (such as changing value-added service access rules). Each value-added service group should have a profile including at least name, avatar, description, value-added service group visibility, accessibility rules, optionally terms and conditions and/or consent to process the private data template. Finally, the device owner should be able to confirm consent so that to process private data by a value-added service -or organization-, which has access to the IoT device and to manage all confirmed consents and revoke them, individually. Furthermore, the device owner should be able to create -or remove-an IoT device group and add -or remove-IoT devices from this group. The next figure (Fig. 2) depicts a schematic view of the essential VICINITY neighborhood concept. Integrated IoT infrastructures consist of existing IoT assets. The VICINITY Gateway Adapters translate the end-user devices' specific communication protocol into VICINITY Interoperability Gateway API calls while, at the same time, the Gateway Adapter will offer a control interface towards the end-user devices by establishing bidirectional communication with the end-user devices. In most of IoT-based patient monitoring systems, especially at smart homes or hospitals, there exists a bridging point (i.e., a gateway) between a sensor network and the Internet, which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks [START_REF] Rahmani | Smart e-health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems[END_REF]. The VICINITY solution is expected to be based upon an open interoperability gateway, enabling different IoT networks to be connected into large ecosystems. The connections will be organised in a peer-to-per way, building social networks of smart objects (i.e., virtual neighborhoods). The operators of the particular IoT infrastructures can select which other systems they wish to connect to in a similar way as we are selecting friends in social networks, via a web-based neighborhood manager where they can configure the scope of the cooperation with other systems (e.g. to decide which data are visible and which controls are applicable for a particular partner entity). Facilitating the integration of smart objects built on widely adopted standards the platform will be equipped with semantic discovery and dynamic configuration features automatically integrating such assets to VICINITY. At this point, it should be mentioned that several potential barriers could occur, such as:  Lack of IoT protocol interoperability (systems are often vendor locked by design);  Interconnected smart objects of different owners require data sharing that raises serious privacy issues;  IoT component vendors might be reluctant to share interface specifications (Intellectual Property problem), and;  Large-scale integration imposes rules that are disadvantageous for particular participants. All these issues are expected to be assessed, in a proper way, within the VICINITY framework.

VICINITY e-Health Scenario

Digital technologies such as 4G and the upcoming 5G mobile network, artificial intelligence and IoT-based systems provide new opportunities to transform the way of healthcare services provision. In January 2014, in a research held in the United States, revealed that 85% of the adult population owns a mobile phone, and nearly 46 million smartphone owners used health or fitness applications (apps) [START_REF] Nielsen | Hacking health: how consumers use smartphones and wearable tech to track their health[END_REF]. At the same time, another research, this time on behalf of industry, has reported a 40% increase among health care professionals such as doctors, who use electronic tools for patient communications, and more than 20% of physicians now use mobile technologies for remote patient monitoring [START_REF] Terry | Physicians warm to digital communications with patients[END_REF]. These figures create a potential augmentation in distance care practices and VICINITY has already recognized this. In Europe, eHealth systems are developing despite the existence of many different healthcare systems. According to the European Commission, e-health comprises the following four interrelated categories of applications [START_REF]eHealth Taskforce: Accelerating the Development of the eHealth Market in Europe[END_REF]: (i) Clinical information systems; (ii) telemedicine and home care, personalized health systems and services for remote patient monitoring, teleconsultation, telecare, telemedicine and tele-radiology; (iii) integrated regional/national health information networks, distributed electronic health record systems and associated services such as e-prescriptions or ereferrals, and; (iv) secondary usage of non-clinical systems (such as specialized systems for researchers, or support systems such as billing systems.

E-health still receives a great deal of attention at the EU level, and the Commission has invested in several research programs related to this area [START_REF]European Commission: eHealth portfolio of projects[END_REF]. The VICINITY ehealth scenario aims to transform healthcare delivery for elderly patients with long terms needs (such as people with dementia and obesity) and to provide a scalable and holistic approach in healthcare systems. Moreover, it shall be deployed where the term "virtual neighborhood" is referred to supporting communication of personal health status data to selected participants of the network such as family members, for example. The novelty of VICINITY project in the e-health domain is due to the fact that participants can decide with whom they wish to cooperate and to which extent. Thus, each participant can decide on the conditions for himself in his own maximal favor.

The VICINITY ehealth scenario is divided to two sub-scenarios: The first one is called ehealth & assisted living and its main purpose is to demonstrate, through constant remote monitoring, how sensors, actuators and integrated communication devices installed at home can provide assisted living to elderly people and people with long-terms needs, as previously mentioned. The second scenario is about fitness and preventive medicine with wearable IoT and aims mainly at middle-aged persons, who need to promote their health as a method of diseases preventions. Unique identification of the user is of high importance in this use case. VICINITY platform repeatedly associates the entities within the system with an individual name, code, symbol, or number and offering interaction with the entities, or tracing and controlling their activities. This distinction is the key enabler for interoperability and global services across heterogeneous IoT systems and it is important for entities to be uniquely identifiable so that IoT systems can monitor and communicate with specific entities. Fig. 3 illustrates as schematic view of the VICINITY e-health scenario in Greece, as proposed by the national involved partners. The entire approach is based upon the broader VICINITY context.

The sensors and wearables will be in direct communication with a call center manned with specialized staff and maybe doctors or alternatively with their relatives. The real-time combination of information, provided by the sensors, creates a unique user profile and, therefore, any deviation from this is characterized as "abnormal behavior" thus triggering an alarm. The target of the ehealth scenario is to assist both elderly and middle-aged people to support their everyday life by offering them a secure and independent way of living. This VICINITY service has all the potential to be a new promising vehicle able to enhance the limited abilities of the healthcare system by assisting patients with chronic disease management and monitoring and by tracking their everyday activities so as to promote a healthier lifestyle. Of course, despite these potential benefits, there several issues and barriers and limitations that should overcome and many quality measures that should be addressed ( [START_REF] Clarke | Evaluating the promise of health IT to enhance/expand the reach of mental health services[END_REF], [START_REF] Chang | Bridging the digital divide: reaching vulnerable populations[END_REF], [START_REF] Walji | Efficacy of quality criteria to identify potentially harmful information: a cross-sectional survey of complementary and alternative medicine web sites[END_REF], [START_REF] Sagaram | Inter-observer agreement for quality measures applied to online health information[END_REF]).

Conclusion

The importance of prevention and monitoring in the health sector becomes clear when the combined effects of the limited capacity of the existing healthcare system to support the increasing demands of the aging population, and the continues increase of need for disease prevention and healthier way of living expand. Using information technology to monitor patient's care and if need be to assist them, may have a positive impact especially in developing countries?

The evolution of IoT has created a variety of application in several domains, including healthcare. EHealth could transform healthcare delivery by offering the potential to improve the quality, accountability, and cost-effectiveness of healthcare services 0. VICINITY project through its "virtual neighborhood" and its applications aims to redesign modern healthcare services with promising technological, economic, and social prospects. The proposed services can have positive results like improved everyday life with increased quality.

only the authors' views and the Commission is not liable for any use that may be made of the information contained therein.

Fig. 1 .

 1 Fig. 1. Visualised VICINITY concept.

Fig. 2

 2 Fig. 2 Schematic view of the VICINITY neighborhood concept [10].

Fig. 3 .

 3 Fig.3. Schematic view of the VICINITY e-Health scenario in Greece [11].

Acknowledgments. This work has been performed in the scope of the VICINITY European Research Project and has been supported by the Commission of the European Communities (H2020, Grant Agreement No.688467). This paper reflects


Vaishali Nagpure
email: vnagpure@hawk.iit.edu

Cynthia Hood
email: hood@iit.edu

Stephanie Vaccaro
email: svaccaro@hawk.iit.edu

Semantic Models for Labeling Spectrum Data

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Wireless communication plays an increasingly important role across a variety of domains. With the growing demand for new and expanded wireless services, one of the key challenges is the scarcity of spectrum. Spectrum is a finite resource and in the United States (as in most parts of the world) the spectrum has been fully allocated. This means that there is no "free" spectrum that can be utilized to meet the growing demands, thereby making spectrum sharing necessary. An important step to determine the feasibility of sharing is to learn how the spectrum is currently being used. This involves understanding the occupancy of the spectrum in space, time and frequency and identifying where opportunities exist for sharing. Characterizing the details of how the spectrum is used -e.g., the time-frequency utilization patterns is fundamental to the design of dynamic spectrum access (DSA) systems as it can significantly increase the opportunity for spectrum sharing by leveraging signal periodicity. Obtaining detailed spectrum utilization patterns is challenging, particularly when analyzing multiple bands.

Multiband spectrum measurements are highly complex spatio-temporal data sets that require very specialized domain knowledge to collect, analyze and interpret. [START_REF] Ding | Big Spectrum Data: The New Resource for Cognitive Wireless Networking[END_REF] The entire process is extremely time consuming. To analyze and interpret the measurements, a variety of contextual information ranging from the specific configuration of the spectrum sensor and potential emitters to drivers of spectrum use is needed. Current analysis is largely manual, and typically limited to one or a few bands. Multiband analysis is usually based on visual interpretation of waterfall charts or spectrograms. Although context is sometimes used anecdotally to interpret the dynamics of spectrum usage, it has not been integrated into the characterization. Deciding what is normal or abnormal for each band requires domain expertise. These factors limit the amount of analysis that is done on spectrum measurements and thus limit the understanding of existing spectrum use. This work focuses on the analysis of spectrum measurements in the Land Mobile Radio (LMR) bands that include the spectrum used for public safety in Chicago. Given the nature of public safety communication, it is important to be able to meet communication demands whenever emergencies or other large-scale events occur. Therefore it is critical to understand how the public safety spectrum is used in dynamic situations, especially as these events are unfolding. More specifically, how do the communication demands evolve over time and what triggers the demands. To understand the triggers, it is necessary to understand the context of the demands.

Figure 1 was generated from two weeks of observation of Land Mobile Radio (LMR) band data in 2009 and shows the difference in spectrum occupancy on weekdays and weekends. Figure 2 shows the increase in call rate in the 406 MHz LMR band during the NATO Summit held in Chicago in May 2012. Both of these figures show that there is a relationship between spectrum use and human activity. As illustrated above, context is necessary to fully understand the significance of the patterns and anomalies in the spectrum measurements. More importantly, these patterns and events need to be identified so they can be incorporated into demand models. In an urban environment like Chicago, events drive a significant portion of public safety communication. These events may be planned (i.e. sporting or entertainment events) or unplanned emergency events of different scales and at different locations.

The importance and role of human activity has been recognized in context-aware services and networks. Context enhances understanding of spectral usage through improvement in the process of spectrum data analysis as well as interpretation of the results. One of the challenges with wideband monitoring efforts is identifying events or time intervals of interest to analyze. Context may be utilized to identify potential events of interest for near-term or offline analysis. As a first step toward automation of spectrum analysis, this paper proposes to model configuration, domain knowledge and other potentially relevant information with the use of semantic modeling in a way that it can be fused with measurements for analysis and in particular can provide labels for the spectrum data.

The rest of the paper is organized as follows. Section 2 describes the challenges of measuring the spectrum and provides details on the measurements utilized in this work and typical analysis techniques. Public safety communication and related work is described in Section 3. Our approach and a case study during the Chicago Marathon is described in Section 4. Finally, conclusions and future work is discussed in Section 5.

Spectrum Measurements

The spectrum is organized into bands where a band is a contiguous range of frequencies that are used for the same general purpose. Bands may be further subdivided into channels. The frequency resolution is the sampling interval in the frequency domain whereas the resolution bandwidth (RBW) is the bandwidth of a single spectrum measurement obtained during a specific sampling interval in time at a specific location in the spatial domain. If the RBW is too large, part of the adjacent channel power is measured during the measurement of a certain channel leading to incorrect results. This is an enormously challenging constraint and may lead to the use of multiple detectors, each covering a specific portion of the spectrum to provide a small enough RBW to ensure that all channels are correctly registered and at the same time the revisit time is positioned at a level where no signals are missed. The band plan is also a critical part that is basically a division of the particular range of radio frequencies of interest into bands with common parameter settings such as resolution bandwidth. Typically band plans are relatively static, but if you need to capture details with higher resolution measurements on a particular day for an event, the band plan can be dynamic instead of static. For example, if we are aware of an event such as a sporting event or protest, the band plan can be changed to gather higher resolution measurements in bands that might be impacted (e.g. public safety).

The IIT Spectrum Observatory has been monitoring the 30 -6000 MHz radio spectrum of the city of Chicago since mid-2007 from its location on top of the 22 story Tower on IIT's Main campus on the south side of Chicago. [START_REF] Roger B Bacchus | Long-term, wide-band spectral monitoring in support of dynamic spectrum access networks at the iit spectrum observatory[END_REF] Energy detection sensing is used in our measurement system and the resolution bandwidth is kept at 3 kHz for all bands. This results in around 93 MB of data per day.

In the case of the public safety bands where channel bandwidths are narrow (< 30 kHz), due to the high sweep time, short transmissions cannot be detected. To address these issues, a new measurement system utilizing a Tektronix RSA 306 spectrum analyzer was added. This allows us to obtain higher time and frequency resolution data with a focus on the LMR bands which include the public safety bands in Chicago. After conducting experiments, resolution bandwidth of 1 kHz for the Chicago Police Department (CPD) frequencies is configured as CPD is a case study for this research. This results in around 16 GB of data per day. The measurements are transmitted to a local storage system daily.

Analysis With Spectrograms

The data files produced are transmitted from the data capture PC to a high performance workstation. The data is processed to produce a variety of plots and usage statistics. Spectrograms or waterfall charts are used to present much of the spectrum occupancy data. For these charts, time is represented on the y-axis (days or hours), frequency on the x-axis (MHz) and power (dBm) as color level, with the indicated mapping. Higher power indicates higher occupancy. Several waterfall charts derived shown below in Figures 345. The spectral occupancy was estimated based on the threshold method and different thresholds were selected for each band by estimating the noise floor of the system. The utilization of different bands can be found to range from highly utilized through sporadically used to not used at all. A good example of highly utilized bands is the TV bands shown in Figure 3 with their consistently high power. The obvious reason for the high occupancy is that TV channels in a metropolitan market like Chicago are always broadcasting and are well occupied. The interpretation of Figure 3 is quite straightforward once you know how the band has been allocated. Notice that Figures 4 and5 look different and explaining the visualizations shown in these figures is more challenging. It is clear that one must be familiar with the band allocations, the measurement system location and configuration, as well as usage behavior within the band to accurately interpret the measurements.

Public Safety Background and Related Work

Public safety communication networks are typically designed with enough capacity to handle "worst case" scenarios, with some channels largely unused under typical dayto-day activity [START_REF] Lehr | Public safety radios need to pool spectrum[END_REF]. Studies [START_REF] Bacchus | Spectrum utilization study in support of dynamic spectrum access for public safety[END_REF] have noted this to be the case with occupancy figures generally being fairly low, however during atypical periods of high activity -most notably during disasters and other emergencies -channel capacities may approach or even meet their limits.

There have been many studies of public safety traffic such as [START_REF] Aschenbruck | Modelling Voice Communication in Disaster Area Scenarios[END_REF][START_REF] Aschenbruck | How to Assign Traffic Sources to Nodes in Disaster Area Scenarios[END_REF][START_REF] Sharp | Analysis of Public Safety Traffic on Trunked Land Mobile Radio Systems[END_REF][START_REF] Vujicić | Modeling and Characterization of Traffic in Public Safety Wireless Networks[END_REF] and including [START_REF] Tanim | Empirical modeling of public safety voice traffic in the land mobile radio band[END_REF] utilizing the SDR-based spectrum monitoring system mentioned above to collect measurements. These studies are focused on traffic in the deployed LMR systems. They generally consider typical traffic along with traffic during large-scale disaster or emergency events and generally separate these into two classes. Part of the challenge with having richer models is the lack of events to study. [START_REF] Chen | Modeling of a Public Safety Communication System for Emergency Response[END_REF] Patterns of communication are identified [START_REF] Aschenbruck | How to Assign Traffic Sources to Nodes in Disaster Area Scenarios[END_REF] and models of heterogeneous networks are developed [START_REF] Badarch | Incoming traffic modeling of heterogeneous Public Safety Network[END_REF] and mobility on disaster sites is considered. [START_REF] Aschenbruck | Modeling public safety scenarios to evaluate wireless communication systems[END_REF] Accurate traffic models are necessary for a service provider of emergency communications to properly maintain the capacity planning of the network. To do so, good traffic models need to be developed that can capture characteristics including occurrences of few and large random incidents and accidents. These occurrences can be described as unusual spikes and/or long tails in a probability model term of an actual network load.

Today's Public Safety agencies and organizations have started planning to evolve their networks to LTE based public safety solutions [START_REF]LTE networks for Public Safety Services" Nokia Networks[END_REF]. With LTE, the first responders can access a wide variety of services, starting from high bandwidth to real time communications. This will help the first responders in case of mission critical communications such as natural disasters or terrorist attacks. In [START_REF] Belt | A Prioritized Traffic Embedding Mechanism enabling a Public Safety Virtual Operator[END_REF], a virtual Public Safety (PS) operator is proposed that relies on shared infrastructure of commercial LTE networks to deliver services to its users. Several methods of allocating spectrum resources between virtual operators are compared at peak times and they examine how this influences differing traffic services. They showed that it is possible to provide services to the Public Safety users reliably during both normal and emergency operation. The authors of [START_REF] Aschenbruck | Modeling public safety scenarios to evaluate wireless communication systems[END_REF] focused on the performance evaluation of the communication systems in disaster scenarios.

Approach and Case Study

One of the challenges with wideband spectrum measurements, particularly long-term measurements, is finding data of interest. For example, the IIT Spectrum Observatory collects wideband measurements between 30-6000 MHz. This covers many different frequency bands and channels over a wide variety of uses. There is a wealth of data over many years, so how to choose a frequency band and time period to study? In the context of dynamic spectrum sharing, the focus is on frequency bands that are not fully utilized and we are generally interested in understanding the usage and how the usage changes over various time periods. But it is not enough to understand changes in the energy from a signal processing perspective, it is also important to understand why the usage is changing. This is relatively straightforward for major events like the NATO Summit shown in Fig. 2, because you can identify the days of interest and then visualize the data. This can be quite challenging for many other types of events though given the manual nature of visualization. Patterns can also be identified manually through visualization, but for the same reasons can be challenging to capture without time-consuming visualization. This can be effective for small datasets but does not scale well. To address this issue, this paper proposes to combine different types of information from several different sources using both statistical and semantic modeling techniques.

Case Study

A large city like Chicago has many different types of events occurring almost on a daily basis providing a good opportunity to model spectrum use over a variety of different scenarios. This case study focuses on the channels used by the Chicago Police Department (CPD). The goal of this case study is to step through the analysis of an event that results in noticeable changes to identify the information and steps needed for automation. The event we studied was the Chicago Marathon on October 8, 2017. The Chicago Marathon has a 26.2 mile course that covers 22 neighborhoods in Chicago. Approximately 45,000 runners participate in the marathon and there are many fans along the way. Public safety works to secure the course as well as to do traffic management around the city. In this case study, as in the past, we traversed backwards from events of interest to see if the impact on spectrum could be detected. The first question was what frequencies to analyze. The frequencies used by CPD are shown in Figure 7. CPD has organized the city into zones that cover one or more districts as shown in Figure 6.

[15] The Chicago neighborhood and district map is shown in Figure 7. [15] . As shown in Figure 6, there are both citywide channels for specific types of communication as well as location-specific zone channels. To determine the communication channel and frequency for a given public safety event, it is necessary to determine the type of event and also the location. Each location must be translated into the appropriate district and corresponding zone to determine the channel and corresponding frequency of communication related to the event. The relationship between neighborhoods, districts and zones shown in Figure 7 along with the CPD radio communication information was encoded using SWI-Prolog. Events from various City of Chicago event calendars were also encoded. For each event, the neighborhood was included. The result of a query to the resulting knowledge base for the date "October 8, 2017" is shown in Figure 8. The marathon goes through several neighborhoods so the corresponding zones are listed. In addi-tion, since the marathon is considered an "event" and citywide channels 1,5 and 6 are used for events, they are also listed. We analyzed the bands corresponding to the zone and citywide channels in the query result. Citywide channel 6 showed unusual occupancy. The waterfall charts for citywide channel 6 are shown in Figures 9 and10. Figure 9 shows a sweep time from 11pm on October 7, 2017 to 11 pm on October 8, 2017. Figure 10 shows from 11pm on October 8, 2017 to 11pm on October 9, 2017. These waterfall charts plot the energy detected in the band on each sweep. The x-axis gives the frequency and the yaxis gives the sweep number. There is one sweep approximately every 2 seconds.

The channel beginning at 460.25 MHz is clearly evident from the waterfall chart. The yellow color indicates a higher level of power in the band. It is clear from Figure 9 that there is activity in the band starting very early in the morning of October 8 th and continuing through 11pm. Looking at Figure 10, one can see the activity continuing until the early hours of October 9 th . This appears to be consistent with expected CPD activity during the marathon. It is clear that a significant amount of information is needed to put together this simple analysis. One of the reasons we chose to study the public safety spectrum is that there are many publicly available sources of information that are relevant. This information and the relationships between various pieces of information can be modeled using semantics. These models can be used to reason about potential explanations for increased activity given unusual spectral activity. It can also be used to trigger analysis of specific frequency bands due to a certain type of event.

Conclusion And Future Work

This paper describes the challenges of spectrum measurement analysis and motivates the need for more in-depth analysis and fine-grained modeling for the purpose of spectrum sharing. The necessary analysis is limited by the complexity of the measurement, analysis and interpretation of the data. Analysis requires a substantial amount of domain knowledge along with contextual information. Contextual information can be derived from a variety of different information sources including events. This paper focuses on the public safety spectrum in the LMR band in Chicago. More specifically, we study the CPD spectrum. Semantic models were developed and coded in SWI-Prolog to describe the organization and use of the CPD allocated spectrum along with planned events in Chicago. The models were used to help identify relevant channels used during the Chicago marathon. These models can be used to label periods of unusual activity. Our case study uses visualization of the waterfall charts to determine unusual activity. The contribution of this work is the use of semantic models for labeling. Our long-term goal is to automate the analysis of spectrum data across frequency and time.

Ongoing work aims to create statistical models of typical activity and identify periods of unusual activity automatically. These periods of unusual activity can then be correlated with events or vice versa. Public Safety spectrum behavior is challenging to interpret and prediction of usage is driven by many factors such as planned and unplanned events, weather and human protocols. Further development of the seman-tic models is needed to capture the many different information sources as well as the correlations across time and frequency bands.

Fig 1 .

 1 Fig 1. Two week plot of spectrum occupancy in the 450 MHz LMR Band

Fig 2 .

 2 Fig 2. Call rate in 406 MHz band

Fig 3 .

 3 Fig 3. 600 MHz television bands during 2013

Fig 4 .

 4 Fig 4. 2.4 GHz Band during 2013

Fig 6 .

 6 Fig 6. Frequencies used by the Chicago Police Department [15]

Fig 7 .

 7 Fig 7. Chicago Police Department Radio Zone Map [15]

Fig 8 .

 8 Fig 8. Result of the query to knowledge database.

Fig 9 .Fig 10 .

 910 Fig 9. Waterfall chart for Chicago Police Citywide channel 6 (460.25 MHz)


Argyro Mavrogiorgou

Athanasios Kiourtis
email: kiourtis@unipi.gr

Dimosthenis Kyriazis

A Generic Approach for Capturing Reliability in Medical Cyber-Physical Systems

Keywords: Cyber-physical systems, Medical cyber-physical systems, Reliability

Cyber-physical systems (CPSs) are slowly emerging to dominate our world through their tight integration between the computational and physical components. While the reliability evaluation of physical systems is well-studied, the one referring to CPSs is difficult due to the fact that software systems do not degrade, as they follow a well-defined failure model like in physical systems. Henceforth, a great attention has been given to tackle the challenge of reliability in CPSs, especially in the field of Medical CPSs (MCPSs) that are being considered as a powerful candidate for healthcare applications. This paper proposes a generic approach for effectively measuring reliability in MCPSs, taking into consideration the multiple MCPSs' applications that exist. The proposed approach captures the MCPS's reliability by initially modelling its components, accompanied with the selection of the evaluation environment, which is finally being followed by the failure analysis, and the reliability estimation, which are necessary for deciding whether a MCPS is considered as reliable or not.

Introduction

Cyber-Physical Systems (CPSs) are attracting a lot of attention in recent years and are being considered as an emerging key research area, where according to [START_REF]Cyber-physical Systems Market Globally Expected to Drive Growth through 2027[END_REF], CPS market is globally expected to drive growth through 2027, whilst it is listed as the number one research priority by the US President's Council of Advisors on Science and Technology [START_REF] Wang | A secured health care application architecture for cyber-physical systems[END_REF]. A CPS is able to combine computation and communication capabilities with the physical world, meaning that it can control the physical world as well as the connections between objects. Therefore, a CPS is a concept that seeks to converge with the cyber world composed of various physical systems [START_REF] Seo | Cyber Physical Systems for User Reliability Measurements in a Sharing Economy Environment[END_REF], while using a distributed software that implements smart algorithms in order to control these entities. CPSs are able to add smart mechanisms to fully automate manufacturing processes, manage and enhance the operations and safety of environments and infrastructures, enhance energy consumption in smart buildings, or improve healthcare for patients, among others [START_REF] Mohamed | Middleware to support cyber-physical systems[END_REF] [START_REF] Al-Jaroodi | Software Engineering Issues for Cyber-Physical Systems[END_REF].

However, the development of such complex systems that are composed of many distributed and heterogeneous components interacting in various ways and capabilities, is extremely difficult [START_REF] Mohamed | Middleware to support cyber-physical systems[END_REF]. CPSs, compared to purely computational or purely physical systems, exhibit quite a number of challenges, as the connection between the computational and the physical entities is far from smooth. Due to device proliferation and large-scale connectivity, a variety of functionalities are now feasible in CPSs. Connectivity however, also means that CPSs function in unreliable open environments, where due to the fact that the software gets further coupled with hardware and users, reliability evaluation becomes a significant challenge [START_REF] Lazarova-Molnar | Reliability of cyber physical systems with focus on building management systems[END_REF]. Henceforth, a great attention has been given in particular to tackle this challenge, confronting system reliability as a fundamental requirement of CPSs.

This requirement becomes extremely important to the healthcare domain [START_REF]Fda approves world's smallest pacemaker that attaches directly to heart[END_REF], where CPSs are being considered as a powerful candidate for healthcare applications including in-hospital and in-home patient care [START_REF] Milenkovi´c | Wireless sensor networks for personal health monitoring: issues and an implementation[END_REF]. In fact, a separate class of CPSs, namely Medical Cyber Physical Systems (MCPSs) are recognized in the literature [START_REF] Lee | Medical cyber physical systems[END_REF][10], as interconnected, intelligent systems of medical devices that support a holistic treatment of a patient. For example, in the context of a hospital, the ones that were previously used as stand-alone medical devices are now being designed with embedded software, and integrated with network interfaces [START_REF] King | Assuring the safety of ondemand medical cyber-physical systems[END_REF] [START_REF] Sokolsky | Challenges in the regulatory approval of medical cyber-physical systems[END_REF]. These network interfaces are used to communicate with other devices during patient treatments as well as monitoring, and healthcare systems [START_REF] Grispos | Medical Cyber-Physical Systems Development: A Forensics-Driven Approach[END_REF]. Hence, CPSs constitute a technological chance for new applications in healthcare assuring more advanced care and treatment of patients.

However, the development of a systematic reliability analysis of CPSs, especially in the healthcare domain, has not received an adequate consideration. To address this challenge, in this paper a generic approach is proposed that can be used to effectively measure the reliability in MCPSs. This approach captures a general MCPS's reliability following four (4) sequential steps. Initially, the modelling of the MCPS's components takes place, whilst afterwards, the selection of the evaluation environment occurs, followed by the failure analysis, and the reliability estimation that are necessary for finally deciding whether the MCPS is being considered as reliable or not.

The rest of this paper is organized as follows. Section 2 describes the study of the state of the art regarding MCPSs and their applications, while the challenges of the MCPSs are being analysed, citing a more detailed view of the reliability challenge. Section 3 describes the proposed approach for measuring reliability in MCPSs, while Section 4 is addressing the future challenges, analyzing our conclusions and plans.

2

Related Work

Medical CPS

The term "Cyber-Physical System" was created a few years ago. The concept had existed for several decades, as the Computer Science and Engineering community has been dealing with it by calling CPSs as "real-time computing systems" or "embedded computing systems" [START_REF] Kim | Challenges and future directions of cyber-physical system software[END_REF]. However, the last few years, those terms have been replaced by the official name of "Cyber-Physical Systems", suggesting that these systems provide the people with much more properties and functionalities. In particular, the definition from Cyber-Physical Systems Week [START_REF]Cyber-Physical Systems Week[END_REF] refers to the CPSs as "complex engineering systems that rely on the integration of physical, computation, and communication processes to function". In more details, CPSs refer to the integration of computation with physical processes (i.e. they are about intersection, not the union of the physical and the cyber [START_REF] Lee | Introduction to embedded systems -a cyber-physical systems approach 17[END_REF][17]). In that case, embedded computers and networks monitor and/or control the physical processes based upon local and remote computational models, usually with feedback loops, where physical processes affect computations and vice versa [START_REF] Group | Cyber-physical systems executive summary[END_REF][19] [START_REF] Lee | Computing foundations and practice for cyber-physical systems: A preliminary report[END_REF]. In other words, CPSs are specialized computing systems that interact with control or management objects, integrating computing, communication, and data storage with real world's objects and physical processes, in a real-time, safe, secure, as well as efficient manner [21][22].

CPSs are being applied in many domains [START_REF]Strategic R&D Opportunities for 21st Century Cyber-Physical Systems[END_REF][17] [START_REF] Nannapaneni | Towards reliability-based decision making in cyber-physical systems[END_REF], however those in the healthcare domain in particular, are among the most remarkable ones. More specifically, in this domain a separate class of CPSs exists, namely Medical Cyber Physical Systems (MCPSs) [START_REF] Lee | Challenges and Research Directions in Medical Cyber-Physical Systems[END_REF][17], that are being considered as interconnected, intelligent systems of medical devices that support a holistic treatment of a patient. The inherent feature of MCPSs is a conjunction of embedded software control of networked medical devices with complex safety that always have to match the needs of the patients [START_REF] Pawlak | Dependable Medical Cyber-Physical System for Home Telecare of High-Risk Pregnancy[END_REF]. Hence, MCPSs are context-aware, life-critical systems with patient safety as the main concern, demanding rigorous processes for validation to guarantee user requirement compliance and specification-oriented correctness [START_REF] Silva | A model-based approach to support validation of medical cyber-physical systems[END_REF]. For that reason, medical devices and systems must be dynamically reconfigured, distributed, and interact with patients and caregivers in complex environments. For example, devices such as infusion pumps for sedation, ventilators and oxygen delivery systems for respiration support, as well as a variety of sensors for monitoring patients' conditions are used in many operating rooms. Often, these devices must be assembled into a new system configuration to match specific patient or procedural needs. The challenge is to develop systems and control methodologies for designing and operating these systems that are certifiable, safe, secure, and reliable [27].

Consequently, CPSs' research is revealing numerous opportunities and challenges in the healthcare domain, aiming to transform the delivery of health care by enabling smart medical treatments and services [START_REF]Cyber-Physical Systems: Enabling a Smart and Connective World[END_REF]. Some examples of these include intelligent operating rooms and hospitals, image-guided surgery and therapy, fluid flow control for medicine and biological assays, sensors in home for detecting changing health conditions, new operating systems for making personalized medical devices interoperable, and the development of physical and neural prostheses [START_REF]Cyber-Physical Systems: Enabling a Smart and Connective World[END_REF] [START_REF] Al-Mhiqani | Investigation study of Cyber-Physical Systems: Characteristics, application domains, and security challenges[END_REF]. Other opportunities of utilizing CPSs in healthcare include the introduction of coordinated interoperation of autonomous and adaptive devices, as well as new concepts for managing and operating medical physical systems using computation and control, miniaturized implantable smart devices, body area networks, programmable materials, and new fabrication approaches [START_REF] Haque | Review of cyber-physical system in healthcare[END_REF].

Applications of Medical CPS

The research on CPSs in healthcare is still in its early stages. Although many CPSs' architectures have been proposed in the literature, the number of CPSs' architectures proposed for healthcare applications is very low. However, various research efforts have been conducted on developing CPSs for healthcare applications, based on integrating sensor and cyber infrastructures, and focusing mainly in the areas of the patients' daily living, monitoring, as well as medication intake.

Concerning the patients' daily living applications and their medication intake, the authors in [START_REF] Lu | Robust location-aware activity recognition using wireless sensor network in an attentive home[END_REF] proposed the Ambient-Intelligence Compliant Objects (AICOs) that exist in a virtual layer overlaid by ordinary household objects integrated by various multimodal and unobtrusive wireless sensors, so as to represent one or more activities of a person. In the same concept, the authors in [START_REF] Philipose | Battery-free wireless identification and sensing[END_REF] presented the Wireless Identification and Sensing Platform (WISP) that utilizes the enhanced passive Radio Frequency Identification (RFID) tags with sensors so as to facilitate the data communication from sensor to receiver. The authors in [START_REF] Lopez-Nores | Monitoring medicine intake in the networked home: The iCabiNET solution[END_REF] proposed the iCabiNET, a system that utilizes smart RFID packaging. Being capable of recording the removal of a pill by breaking an electric flow into the RFID circuit, using either residential network at home or smart appliances. In the same notion, the authors in [START_REF] Pang | A pervasive and preventive healthcare solution for medication noncompliance and daily monitoring[END_REF] presented the iPackage, an intelligent packaging prototype that consists of remote medication intake and vital signs monitoring. Moreover, in [START_REF] Sung | Wearable feedback systems for rehabilitation[END_REF] the LiveNet is presented, which is a realtime distributed mobile platform for monitoring the activities of Parkinson's disease and epilepsy patients. What is more, the authors in [START_REF] Wang | Development of a fall detecting system for the elderly residents[END_REF] proposed a system that detects fall by using an accelerometer on the head level and identify the fall via an algorithm. Finally, the authors in [START_REF] Iso-Ketola | HipGuard: A wearable measurement system for patients recovering from a hip operation[END_REF] proposed the HipGuard, which is a posture analysis application used for detecting the posture for the recovery period of eight to twelve weeks after hip replacement surgery, by integrating seven sensors positioned in specific locations near surgery.

Concerning the patients' monitoring, the authors in [START_REF] Konstantas | Continuous monitoring of vital constants for mobile users: the MobiHealth approach[END_REF] proposed the MobiHealth, a system that gathers data from the wearable sensor devices that the patients carry all day, collecting audio and video signals to provide early response in case of accidents. Furthermore, in [START_REF] Dagale | Cyphys+: A reliable and managed cyber-physical system for old-age home healthcare over a 6lowpan using wearable motes[END_REF] the CyPhyS+ system is presented, a comprehensive, low-cost and standards' compliant CPS, based on the concept of Internet of Things (IoT) for remote health monitoring of elderly, while in [START_REF] Pawlak | Dependable Medical Cyber-Physical System for Home Telecare of High-Risk Pregnancy[END_REF] a dependable MCPS for telecare of pregnant women at home has been presented. Moreover, the authors in [START_REF] Kailanto | Mobile ECG measurement and analysis system using mobile phone as the base station[END_REF] presented the Mobile ECG system that uses smart phones as base station for electrocardiography (ECG) measurement and analysis, forwarding the received data to the medical professionals. The authors in [START_REF] Shnayder | Sensor networks for medical care[END_REF] presented the CodeBlue, a platform that consists of biomedical sensors (e.g. pulse oximeter, motion sensor), aiming to manage the communication among these devices. Finally, in the same concept, the authors in [START_REF] Wood | Context-aware wireless sensor networks for assisted living and residential monitoring[END_REF] proposed the AlarmNet, a wireless biosensor network system prototype, consisting of heart rate, pulse rate, oxygen saturation, and ECG system, that is able to monitor all the patients' measurements, and provide a graphical user interface to assist healthcare professionals to monitor the vital signs of their patients.

Reliability in Medical CPS

Due to the importance of MCPSs' applications and the complexity of their development process, huge research efforts have been started on different CPSs' challenges [START_REF] Mohamed | Middleware to support cyber-physical systems[END_REF][30] [START_REF]Key Design Drivers and Quality Attributes[END_REF]. However, a great attention has been given in particular to the reliability challenge, confronting system reliability as a fundamental requirement of MCPS. More particularly, reliability is a measure of the ability that the system operates as expected under predefined conditions for a predefined duration of time. As systems are composed of a number of components, reliability of systems is expressed through the aggregation of the reliability of each of their components [START_REF] Lazarova-Molnar | Reliability of cyber physical systems with focus on building management systems[END_REF]. Reliability may be measured in different ways depending on the particular situation [START_REF] Thomas | Reliability Evaluation Metrics for Internet of Things, Car Tracking System: A Review[END_REF][45], and can be estimated using either a qualitative or a quantitative method. To accurately describe quantitatively the concept of reliability it is essential to define the notions of fault, failure and error, as all of them are highly related to the concept of reliability [START_REF] Lee | Computing foundations and practice for cyber-physical systems: A preliminary report[END_REF]. However, some systems' reliability cannot be estimated quantitatively due to various reasons (e.g. lack of failure data), and therefore qualitative methods may be applicable. Reliability has been recognized as a critical requirement for CPSs. In [17] it is pointed out that CPSs will not be deployed into mission critical applications as traffic control, automotive safety, and health care without improved reliability. An unreliable system may lead to disruption of service, financial cost and even loss of human life [START_REF] Wu | Improving system reliability for cyber-physical systems[END_REF][47] [START_REF] Voas | Reliability of Embedded and Cyber-Physical Systems[END_REF]. For that reason, the demand for reliability in CPSs, and especially in MCPSs, has constantly been increased. If demands for reliability are not addressed effectively, further deployment of MCPSs will be slowed down in applications [17] [START_REF] Group | Cyber-physical systems executive summary[END_REF]. Therefore, the reliability analysis for MCPSs is very challenging, and for that reason a lot of effort has been put into the research area in order to cope with this challenge. More particularly, in [START_REF] Sha | Design of complex cyber physical systems with formalized architectural patterns[END_REF] a hybrid method that uses fault-tolerant structures with formal verification is proposed. The presented architecture supports the design of reliable CPSs. Another example of such efforts is presented in [START_REF] La | A service-based approach to designing cyber physical systems[END_REF] that describes a service-oriented CPS with a service-oriented architecture and a mobile Internet device [START_REF] Lazarova-Molnar | Reliability of cyber physical systems with focus on building management systems[END_REF]. What is more, the authors in [START_REF] Wu | Cyber-physical avionics systems and its reliability evaluation[END_REF] developed a reliability model where Markov models are constructed for each component, in order to estimate the reliability of an Integrated Modular Avionics (IMA) system. In the same concept, in [START_REF] Marashi | Towards comprehensive modeling of reliability for smart grids: Requirements and challenges[END_REF] a Markov Imbedded System (MIS) is used in order to model dependence between components in a smart power grid. Additionally, in [START_REF] Sun | Reliability of cyber physical systems assessment of the aircraft fuel management system[END_REF] a phased-mission system model, which consists of Markov models for individual components and a binary decision diagram is proposed, so as to analyze the reliability of a fuel management system in an aircraft. Furthermore, the authors in [START_REF] Li | Strategy for reliability testing and evaluation of cyber physical systems[END_REF] developed a reliability framework through a weighted reliability metric, using individual components' reliabilities and the performance metric of the CPSs considering their services, cyber security, resilience, elasticity, as well as vulnerability. Moreover, the authors in [START_REF] Wu | FARE: A framework for benchmarking reliability of cyber-physical systems[END_REF] presented the Failure Analysis and Reliability Estimation (FARE), a data-driven approach for reliability evaluation using historical data, accelerated life testing data, and real-world data.

Therefore, it becomes clear that the reliability of CPSs has received great attention in different applications. However, all of the aforementioned researches, do not highlight the complete reliability of the existing systems, which is crucial for any CPS in the healthcare domain. Moreover, most of these approaches are giving specific solutions to problems of a particular domain, arising the need of a holistic approach. Especially in MCPSs a little work has been done, even though this field seems to be of extremely importance, taking into consideration that the MCPSs are expected to be safe and reliable even in changing environments and unforeseen conditions [START_REF] Software | High-confidence medical devices: Cyber-physical systems for 21st century health care[END_REF]. For that reason, in this paper an approach is being presented that constitutes a generic approach for effectively measuring the reliability in MCPSs. More specifically, this approach provides a set of methods and metrics on failure analysis, as well as reliability estimation for capturing MCPSs' reliability. Therefore, the proposed approach includes a more general and accurate representation of MCPSs' reliability, measuring various metrics for estimating systems' reliability, as well as offering a holistic system representation that covers the different MCPSs' applications that exist. It is worth mentioning that the proposed approach is extensible for accommodating new reliability measurement techniques and metrics. It does not only provide a retrospect evaluation and estimation of the MCPSs' system reliability using past data, but also provides a mechanism for continuous monitoring and evaluation of MCPSs' reliability for runtime enhancement.

Proposed Approach

Our approach proposes a generic way for effectively measuring the reliability in MCPSs. More specifically, the proposed approach consists of four (4) different stages: (i) the CPS modelling, (ii) the evaluation environment, (iii) the failure analysis, and (iv) the reliability estimation, as depicted in Fig. 1.

Fig. 1. Proposed approach architecture

CPS Modelling. In this stage of the proposed approach, the modelling of the MCPS's components takes place, using a specific domain modelling language [START_REF] Pradhan | Chariot: A domain specific language for extensible cyber-physical systems[END_REF] to capture the component-component interactions, whilst taking into consideration the different applications that the MCPSs may be implemented. In more details, the specific MCPS whose reliability is going to be examined, may have been used for daily living applications, or for patients' monitoring, or for medication intake of patients. As depicted in Fig. 1, the MCPS always consists of the physical or hardware components (i.e. physical part), the cyber or software components (i.e. cyber part), and the communication between them. However, each one of them is differentiating according to the three (3) aforementioned situations of usage of the MCPS.

Regarding the patients' daily living applications and their medication intake, as for the physical part, in both situations this is comprised of a set of networked diverse medical devices (MDs) including biomedical sensors and actuators. The latter are used either for monitoring different patients' measurements (e.g. a smart watch for measuring patients' daily steps) or for capturing whether a patient has taken her medication or not (e.g. an electronic monitoring device for measuring medication adherence). Regarding the cyber part, this is responsible for the control and the management of these MDs, the processing of the acquired biosignals, as well as the invocation of the smart alarms that go back to the patient herself.

Regarding the patients' monitoring, as for the physical part, this is comprised of a set of MDs including biomedical sensors and actuators. The latter are being used, as in the previous scenario, for monitoring different patients' measurements (e.g. a smart watch for measuring patients' daily steps) as well as for in-home monitoring systems. Concerning the cyber part, this is responsible for the control and the management of these MDs, in combination with the monitoring system, the processing of the acquired biosignals, the invocation of smart alarms, and the communication with the surveillance center, whose outputs are finally sent back to the caregiver.

Henceforth, all of the aforementioned are considered as potential applications of MCPSs, being feasible to be modelled through a model-based analysis framework. In this framework, the MCPS that is being used is being modeled in a domain specific modelling language [START_REF] Pradhan | Chariot: A domain specific language for extensible cyber-physical systems[END_REF] in which each system-level function is associated to the corresponding component(s) through functional decomposition and component association. It should be noted, that in this stage, it is considered that there are used only MCPS of known nature (i.e. their architecture is known).

Evaluation Environment. In this stage, after the MCPS's modelling, the selection of the evaluation environment takes place. As it can be observed in Fig. 1, there exist various different evaluation environments where the failure data (i.e. either MCPS does not provide correct solutions or MCPS provides correct solution, but not within the expected time) will be collected, and the reliability tests will be implemented. This collection is being implemented regardless of the situation that the MCPS has been used for, as all of the possible existing situations are being constituted of the general concept that covers the MCPSs. In more details, the MCPS's model is sent to the lab environment, as medical systems need to be properly tested for reliability prior to their use in the medical operations. To this end, two (2) different types of tests may occur in the lab environment. In the first case, accelerated tests [START_REF] Meeker | Statistical methods for reliability data[END_REF] can be applied based on a life test that simulates the actual running environment. More specifically, these tests include the Highly Accelerated Life Test (HALT) in a normal pace, which is similar to the stress test that creates a situation such that failure is more likely to happen. It is a method based on physics of failure, an approach for reliability assessment based on modelling and simulation that relies on understanding the physical processes contributing to the appearance of the failures [START_REF] Matic | The physics-of-failure approach in reliability engineering[END_REF]. However, in some cases the accelerated tests are not applicable in a lab environment, thus the second type of test can be applied. This type, is being based upon the components' reliability data (i.e. compositional reliability) that can be used to construct the whole MCPS's reliability. Although there are many ways to do the compositional reliability [START_REF] Glaß | Cross-Level compositional reliability analysis for embedded systems[END_REF], it should be noted that these estimates are not often indicative or accurate for representing the whole system reliability, mainly due to the communication failure that is often not easy to be incorporated in these models [START_REF] He | Reliability calculus: A theoretical framework to analyze communication reliability[END_REF].

Failure Analysis. After the selection of the most suitable case for the lab environment in order to perform the evaluation, the failure analysis stage occurs, which includes the failure detection and diagnostics, along with domain knowledge and heuristics. In general, the operation of a CPS can be divided into three (3) possible scenarios: (i) the CPS provides the correct solution, within the expected time, (ii) the CPS does not provide correct solution (i.e. incorrect solution or no solution at all), and (iii) the CPS provides the correct solution, but not within the expected time. The last two cases are considered as system-level failure cases of a CPS and are being taken into consideration in our proposed approach. In more details, regarding the failure detection, this can be used as a proxy to a system's failure (e.g. an out of range measurement), whilst it might be induced by the external environment, a human mistake or an internal system fault [62] [START_REF] Phan-Ba | A literature review of failure detection[END_REF].

As for the failure diagnostics, these are responsible for processing the detected failure data using [START_REF] Croskerry | Diagnostic failure: a cognitive and affective approach[END_REF]: (i) the root cause analysis that is used to classify the failure type, analyze its nature and mechanism, (ii) the corrective action recommendation that is used to correct the current failure and avoid future recurrence of the same type of failure, (iii) the preventive action recommendation that is used to prevent occurrence of a certain potential failure before it happens.

Reliability Estimation. In the final stage of the proposed approach, after ingesting the failure detection and diagnostics data, the estimation of the MCPS's reliability takes place. More particularly, as mentioned in Section 2.3, reliability can be estimated using either a qualitative or a quantitative method. In our approach, we primarily examined and used quantitative methods for MCPSs' reliability estimation, implementing some commonly used reliability metrics [START_REF] Wu | FARE: A framework for benchmarking reliability of cyber-physical systems[END_REF]:

• Failure Rate: It is defined as the total number of failures within an item population, divided by the total time expended by that population, during a particular measurement interval under stated conditions • Mean Time Between Failures (MTBF): It is the mean expected time between system failures, in terms of the predicted elapsed time between inherent failures of a system during operation. • Mean Time To Failure (MTTF): It is sometimes used instead of MTBF in cases where a system is replaced after a failure. • Mean Time To Repair (MTTR): It is the mean time required to repair a failed component or device. • Availability or Mission Capable Rate: It is the proportion of time that a system is in a functioning condition. • Power-on hours (POH): It is the length of time (in hours), during which electrical power is applied to a device.

• Availability at time: It is the probability that the system is able to function on a specific pre-defined time. • Survival Probability: It is the probability that the system does not fail in a time interval (0; t]. Consequently, in order to calculate the reliability of the chosen MCPS in terms of whether it is considered as reliable or not, a pre-defined threshold level is being set for each different reliability metric. Afterwards, the results of each metric are being aggregated, calculating the average value of the pre-defined metrics, and finally deciding whether the MCPS is considered as reliable or not.

Conclusions

In this paper, we have raised the importance of addressing reliability of CPSs, by deeply studying the challenging topic of MCPSs' reliability. We have considered all the possible existing applications of MCPSs whose architecture is known in advance, and proposed a generic approach for effectively capturing the reliability metrics of MCPSs so as to calculate the degree of the MCPSs' reliability. In this approach four (4) sequential steps were implemented, beginning from the modelling of the MCPS's components, followed by the selection of the evaluation environment, the failure analysis, and finally, the reliability estimation. Currently, we are working on the evaluation of the developed approach, by testing it with multiple existing MCPSs in the lab environment. Our future work includes the development of a mechanism that does not require prior knowledge of the used MCPS's architecture. Furthermore, one of our main goals is to extend the existing approach by including more measuring metrics concerning the failure analysis, as well as the reliability estimation. Finally, we are willing to implement a visualization module providing the final results of the approach, enabling the users to observe the reliability results of each MCPS.

Acknowledgements. The authors would like to acknowledge the financial support from the "Hellenic Foundation for Research & Innovations (HFRI)".


Anna Umbert
email: annau@tsc.upc.edu

Oriol Sallent

Jordi Pérez-Romero

Juan Sánchez-González

Diarmuid Collins

Maicon Kist

An Experimental Assessment of Channel Selection in Cognitive Radio Networks

Keywords: Channel Selection, Classification, Cognitive Radio

The management of future networks is expected to fully exploit cognitive capabilities that embrace knowledge and intelligence, increasing the degree of automation, making the network more self-autonomous and enabling a personalized user experience. In this context, this paper presents the use of knowledge-based capabilities through a specific lab experiment focused on the Channel Selection functionality for Cognitive Radio Networks (CRN). The selection is based on a supervised classification that allows estimating the number of interfering sources existing in a given frequency channel. Four different classifiers are considered, namely decision tree, neural network, naive Bayes and Support Vector Machine (SVM). Additionally, a comparison against other channel selection strategies using Q-learning and game theory has also been performed. Results obtained in an illustrative and realistic test scenario have revealed that all the strategies allow identifying an optimum solution. However, the time to converge to this solution can be up to 27 times higher according to the algorithm selected.

Introduction

The increasing traffic demand will lead future wireless networks to face a severe shortage of spectrum, especially when considering the highly dense deployments of small cells envisaged for meeting the demands of future systems. Cognitive Radio Networks (CRN), based on the Cognitive Radio (CR) paradigm [START_REF] Mitola | Cognitive radio: an integrated agent architecture for software defined radio[END_REF], will bring light to this problem. Briefly, CR observes the environment, analyzes these observations, makes decisions to intelligently configure certain radio parameters, and finally executes these decisions. Analysis and decision can be supported by means of learning mechanisms that exploit the knowledge obtained from the execution of prior decisions.

CRN concepts are also expected to play a relevant role in the context of future 5G (5th Generation) networks [START_REF] Hattachi | NGMN 5G White Paper[END_REF], which should include by design unprecedented network flexibility and highly efficient/adaptive network resource usage, including flexi-ble spectrum management. Thus, the introduction of intelligence in the network will be an important requirement. In this direction, the advent of big data analytics [START_REF] Liu | On Big Data Analytics for Greener and Softer RAN[END_REF] will boost the extraction of the meaningful information from the available data, to support the use of cognitive capabilities both in the Radio Access Network (RAN) and in the Core Network.

Using knowledge-based procedures and Artificial Intelligence (AI) as key elements of cognition for supporting the optimization in future networks has been considered in the literature for the last several years. Specific algorithms for learning time domain traffic patterns and mobility patterns, respectively, have been proposed and analyzed [START_REF] Pérez-Romero | On Learning and Exploiting Time Domain Traffic Patterns in Cellular Radio Access Networks[END_REF] [START_REF] Sánchez-González | On Learning Mobility Patterns in Cellular Networks[END_REF]. Similarly, in [START_REF] Sánchez-González | On Extracting User-centric Knowledge for Personalised Quality of Service in 5G Networks[END_REF] a clustering strategy was proposed to identify the user's daily motifs and extract the personalised Quality of Service observed by a user when being connected to a real 3G/4G network. Nevertheless, the authors believe that one important reason for the (relatively) low penetration of AI concepts in this domain so far is due to the difficulty for the research community in general to test (and hopefully prove the validity of) potential solutions in realistic conditions. Clearly, AI-based knowledge discovery models (e.g. classification, prediction, clustering) can hardly be properly assessed in simulated environments, where many of the real-world effects are not retained. Instead, more solid results and conclusions can be derived from implementing such mechanisms in realistic conditions.

In this respect, WiSHFUL is a European project from the European Horizon 2020 Programme that focuses on speeding up the development and testing cycles of wireless solutions and, therefore, it offers a great opportunity to gain access to realistic data and measurements [START_REF] Wishful | [END_REF]. It defines software modules with unified interfaces that permit wireless developers to quickly implement and validate advanced wireless network solutions. The WiSHFUL project offers access to different advanced wireless testbeds, among them the IRIS testbed at Trinity College Dublin [START_REF]IRIS Testbed[END_REF].

In this context, this paper describes a specific experiment using the IRIS testbed. The experiment focuses on the Channel Selection functionality for CRN, so that an access point decides the most appropriate channel to use within a band that is shared among multiple transmitters. This selection is based on a supervised classification that allows estimating the number of interfering sources existing in a given frequency channel. Specifically, four different classifiers have been implemented: decision tree, neural network, naive Bayes and Support Vector Machine (SVM). Additionally, a comparison against other channel selection strategies using Q-learning and game theory has also been performed. In this way, this experiment contributes to expand the capabilities of the existing WiSHFUL Intelligence framework [START_REF]Design of software architecture for intelligent control and showcases[END_REF] that offers an experimentation environment for early implementation and validation of end-to-end 5G solutions that improve resource utilization through advanced reconfigurability of radio and network settings.

The rest of the paper is organised as follows. Section 2 presents the IRIS testbed used for executing experiments. Section 3 discusses the considered approaches for channel selection. The experimental results obtained with these approaches are presented in Section 4, while Section 5 summarizes the main conclusions.

2

The IRIS testbed

The IRIS testbed is the reconfigurable radio testbed at Trinity College Dublin [START_REF]IRIS Testbed[END_REF]. It provides access to radio hardware that supports the experimental investigation of the interplay between radio capabilities and networks.

The testbed employs 18 ceiling or wall mounted Universal Software Radio Peripheral (USRP) N210s equipped with SBX daughterboard, reaching frequencies between 40 MHz and 4.4 GHz, and 4 other radio nodes not available within the WiSHFUL context, as underlying radio resources. All these elements are connected to a private computational cloud, allowing to deploy an array of computational environments. By default, each USRP device of the testbed is associated to a Virtual Machine (VM) that occupies 4 CPU cores and 4 GB of RAM from the computational cloud. Testbed access was supported by jFed Experimenter suite developed by the Fed4FIRE+ EU project.

For setting up and executing the experiments with the IRIS testbed, we modify the code and the configuration files in a remote local machine at Universitat Politècnica de Catalunya (UPC) premises, and then we upload the files to the testbed machines, we execute the test, and we download back the results files to our local machines. To perform these operations, a custom made code implemented with Python programming language that uses the WiSHFUL software framework and the Unified Programming Interface (UPI) functions and runs on the IRIS Testbed has been created.

Two different pieces of python code, namely the wishful_controller and the agent, have been used. The wishful_controller runs on a computer, whereas one agent runs on each radio node. The configuration of a radio node as a transmitter or receiver is made by the wishful_controller when the radio program is activated. The purpose of the agent is to connect to the wishful_controller and wait for instructions (passed through UPI calls). In turn, the wishful_controller executes the logic for controlling the experiment.

A deployment example of the experimentation framework is illustrated in Fig. 1. In this case, a scenario with three nodes acting as transmitters (AP1, AP2, and AP3) and three nodes acting as receivers (STA1, STA2 and STA3) is considered.

Experimenting Channel Selection functionality using the IRIS testbed

The experiment considered here focuses on learning the interference characterisation and using the learnt information for supporting channel selection in CRN. Specifically, the approach consists in analyzing the environment where a given cell (or access point) is operating by performing both radio-frequency and performance measurements and, based on these measurements, to characterise the observed interference in terms of the number of interfering sources. To support this knowledge discovery, the capabilities of the IRIS testbed are extended through the inclusion of the RapidMiner tool [START_REF]RapidMiner[END_REF]. It is a powerful all-in-one tool that features hundreds of pre-defined data preparation and machine learning algorithms to support data science projects.

Le

The The classification model is initially obtained by means of a training stage consisting of a supervised learning process. The training stage uses as input S different tuples X j j=1,...,S composed of measurements performed under interference conditions that are known a priori, meaning that the number of interferers, i.e. the class of each training tuple C(X j ), is known during the measurements. These tuples and their associated classes are used as inputs to the training algorithm that will build the internal structure of the classifier. The specific training algorithm depends on the considered classification tool. The following alternatives are considered in this study [START_REF] Han | Data Mining Concepts and Techniques[END_REF]: decision tree, naive Bayes classifier, SVM and neural network.

2. Classification stage (on-line operation): The classification model obtained in the training stage is used to estimate the number of interferers for any tuple X t ={x t,1 , x t,2 , ...., x t,M } with the measurements obtained at a certain time t.

Fig. 2. Classification process

In the specific experiment on the IRIS testbed, we initially create tuples X t with measurements of the throughput (Th) and Received Signal Strength Indicator (RSSI) at time t, i.e. X t ={Th(t), RSSI(t)} under different interference situations (with 0 interferers, 1 interferer and 2 interferers). During the training stage, each of these tuples and the number of interferers for each one are used to build a classification model. Then, during the classification stage, the model is used each time that the methodology needs to estimate the number of interferers for each new tuple of measurements.

Channel Selection

Channel selection (also denoted as carrier selection) is the mechanism used to decide the operating channel (i.e. center frequency and associated bandwidth) of a transmitter. A smart channel selection mechanism is relevant to facilitate the coexistence between multiple transmitters in wireless scenarios operating in unlicensed spectrum when there is little or no coordination between these transmitters. This could be the case of e.g. Wi-Fi networks or unlicensed LTE (LTE-U).

The design of a proper channel selection functionality can greatly improve the overall efficiency of a wireless system when using unlicensed spectrum, since it will impact on the overall interference experienced by the receivers and thus on the achieved throughput performance.

Under the above considerations, the purpose of the experiment considered here is to use the IRIS testbed to assess a channel selection algorithm (Algorithm 1) that exploits the extracted knowledge from the supervised classification process for char-M measurements at time t

X t ={x t,1 ,..., x t,M } Classification tool C(X t )
Class of tuple X t :

Training set X j , C(X j ) j=1,...,S

Supervised learning

Number of interferers at time t acterizing the interference as explained in Section 3.1. For benchmarking purposes, a channel selection algorithm using Q-learning (Algorithm 2) and another one using game theory (Algorithm 3) have also been tested.

The general scenario assumes a total of T transmitters with their associated receivers and a total of K possible frequency channels. The considered channel selection algorithms are described in the following:

Algorithm 1: Supervised classification-based channel selection algorithm

For the supervised classification-based channel selection algorithm for the i-th transmitter, i=1,...,T, it is assumed that the training stage explained in Section 3.1 has been executed previously to build the classifier. Then, each time step, the receiver measures the values of throughput and RSSI for all the channels. Then, the classifier estimates the number of interferers in each of the channels. The estimated number of interferers is averaged considering a time window of N samples. The selected channel will be the one with minimum number of interferers. The process is subsequently repeated at the next time steps to consider possible changes in the environment (e.g. due to channel selections made by other transmitters) which could lead to new channel changes.

Algorithm 2: Q-learning-based channel selection algorithm

Q-learning is a type of Reinforcement Learning (RL) technique [START_REF] Sutton | Reinforcement Learning: An Introduction[END_REF] where learning is achieved through the interaction with the environment, so that the learner discovers which actions yield the most reward by trying them. In this way, each transmitter progressively learns and selects the channels that provide the best performance based on the previous experience. In the considered algorithm, described in detail in [START_REF] Sallent | Learning-based coexistence for LTE operation in unlicensed bands[END_REF] [START_REF] Perez-Romero | A Robustness Analysis of Learning-Based Coexistence Mechanisms for LTE-U Operation in Non-Stationary Conditions[END_REF], each transmitter i stores a value function Q(i,k) that measures the expected reward (i.e. throughput) that can be achieved by using each channel k according to the past experience. Whenever a channel k has been used by the transmitter i, Q(i,k) is updated following a single state Q-learning approach with null discount rate and learning rate  L . Based on this, the channel selection decision-making follows the softmax policy with temperature .

Algorithm 3: Game theory-based channel selection algorithm

In this algorithm, the channel selection problem is modelled as a game in which each transmitter/receiver pair is a player and the actions made by each player are the selected channels. Specifically, here we consider the Iterative Trial and Error Learning-Best Action (ITEL-BA) algorithm described in [START_REF] Pérez-Romero | On Modeling Channel Selection in LTE-U as a Repeated Game[END_REF]. In ITEL-BA, each transmitter retains a benchmark action a B,i (t) (i.e. a benchmark channel to select) and the corresponding benchmark reward r B,i (t) as a reference to evolve the action selection strategy. The reward is measured as the obtained throughput averaged during a time window of N samples. At a certain time, a channel is chosen depending on the so-called mood of the player, which basically captures the degree of satisfaction of the player with the current benchmark action and benchmark reward. The mood m i (t) of player i at the beginning of time step t can be content, discontent, hopeful or watchful. The general idea is that a content player will be selecting the benchmark action most of the time, and will occasionally experiment with new actions according to a probability  << 1 called exploration rate. Instead, a discontent player will try out new actions frequently, eventually becoming content. The hopeful and watchful moods correspond to transitional situations, triggered by changes in the behavior of other players (or in the environment), and they will facilitate updates in the values of the benchmark action and reward to cope with these changes. The reader is referred to [START_REF] Pérez-Romero | On Modeling Channel Selection in LTE-U as a Repeated Game[END_REF] for a detailed specification of the ITEL-BA algorithm.

Results

The evaluation of the channel selection algorithms is performed using the set-up of the IRIS testbed illustrated in Fig. 1. It is considered that 3 nodes act as APs (AP1, AP2, AP3). Each APs has an associated receiver (STA1, STA2, STA3). There are 3 possible channels to select: Channel #1: 2890 MHz, Channel #2: 2900 MHz and Channel #3: 2910 MHz. Initially, all the APs transmit on Channel #1. Subsequently, each AP can change channel being used according to the different channel selection algorithms explained in Section 3.2.

Algorithm 1: Supervised classification-based channel selection

Different executions are performed for each of the considered classifiers. The algorithm is tested with an averaging window of N=50 samples. The results shown in Fig. 3, Fig. 4, Fig. 5 and Fig. 6 depict the channel number selected by each AP as a function of the number of channel selection decisions for the decision tree, naive Bayes, SVM and neural network classifiers, respectively. It is observed that, although all the APs start with the same Channel #1, in all the cases the APs are able to switch to a channel that is estimated by the classifier to be free of interferers. As a result, the system is able to find an optimum configuration in which each AP uses a different channel and correspondingly there is no interference. It is also worth observing that the naive Bayes and SVM classifiers are able to switch to a channel free of interferers very quickly, in just one channel selection decision. In the decision tree, naive Bayes and SVM classifiers, AP1 switches to Channel #3, AP2 switches to Channel #2 and AP3 remains in the same Channel #1. This solution is kept for the rest of the execution and no further changes are performed. In turn, focusing on the behavior of the decision tree classifier (see Fig. 3), it is observed that, due to the lower accuracy of this classifier, it requires a few more decisions to reach the optimum configuration in which each AP uses a different frequency. For example, it is observed that, at the beginning, AP3 makes a wrong decision by switching temporarily to Channel #3, which is being used by AP1, but then it moves to Channel #1. As for the neural network classifier, which also has lower accuracy, Fig. 6 reflects that, at the beginning, the APs quickly find a solution with different channels (i.e. AP1 using Channel #3, AP2 using Channel #2 and AP3 using Channel #1). However, after some time, AP2 makes a wrong decision and switches to the Channel #1 used by AP3. This situation is solved after 10 further decisions, when AP3 switches to Channel #2.

Algorithm 2: Q-learning-based channel selection

The set-up for this execution is the same as for Algorithm 1, with all the three APs working initially in Channel #1. The Q-learning algorithm is configured with learning rate  L = 0.1, while the temperature parameter  is initially 0.15 and is reduced in each decision following a logarithmic cooling approach as explained in [START_REF] Sallent | Learning-based coexistence for LTE operation in unlicensed bands[END_REF]. Fig. 7 depicts the evolution of the channels selected by each AP with the successive channel selection decisions. It is observed that after some fluctuations associated to the probabilistic behavior of the softmax decision-making criterion finally the experiment converges to a solution where each AP has selected a different channel. Specifically, after convergence AP1 operates with Channel #2, AP2 with Channel #1 and AP3 with Channel #3. The maximum number of decisions taken by an AP before converging in this case is 15.

Fig. 3 .Fig. 4 .Fig. 5 .Fig. 6 .

 3456 Fig. 3. Selected channel with Algorithm 1 and Decision Tree classifier for each AP

Fig. 7 . 3 Algorithm 3 :

 733 Fig. 7. Selected channel numbers with Algorithm 2 (Q-learning) for each AP

Fig. 8 .

 8 Fig. 8. Selected channel numbers with Algorithm 3 (Game theory) for each AP

Acknowledgements. This work has been done using the IRIS testbed available through the WiSHFUL Platform. This work has been partly supported by the Spanish Research Council and FEDER funds under RAMSES and SONAR 5G grants (ref. TEC2013-41698-R and TEC2017-82651-R).