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Tigli, JY. Rocher, G. Abstract ne Terms Index bqiosComputing Ubiquitous vriwadCalne fAbetSystems, Ambient of Challenges and Overview Fo lsdadcnrle niomn,neglect- environment, controlled and closed a —From o ad osrciitApoc otheir to Approach Constructivist a Towards PraieCmuig bqiosComputing, Ubiquitous Computing, —Pervasive [ 2 hs onigie a led efound be already can idea founding whose ] I. éadRce,Ja-vsTgi tpaeLvrte n Nh and Lavirotte, Stéphane Tigli, Jean-Yves Rocher, Gérald Introduction hc eterzdi 91i his in 1991 in theorized he which , h otpoon technologies profound most The 1 .Ti paradigm This ]. ance Modelling eUniversity he rthese or systems ligence, .The ”. ctions com- osed of n of n d. lity a s ric by of se e. ts ts is i, t. y g s l l , [ ue.Tkn ptekyieso bqioscmuigas computing interchangeab ubiquitous used are of [ concepts both ideas Weiser, key Mark by the stated up Taking duced. nteery20s h term the 2000s, early the network. o In this concept to that The connected objects etc.). beyond everyday lamp, well “things” watch, object goes of chair, connected etc.) notion (e.g., “objects” garden, the throughout of sense, building, spread broad house, sensors a (e.g., through In environments. Internet our the into world [ meddo vrdyojcst sitpol nterdaily term the their adopted in Things Ashton people Kevin assist 1999, In to activities. objects everyday on embedded of concept the introduced Zelkha n20,mil u oteepoino oiedevices mobile t of objects explosion connected earth more the on are to people there etc.), due tablets, mainly (phones, 2008, In nutiso vnsae hthv nesodteeminentl particular, the In technologies. understood these have of that by dimension promoted states strategic often even emerged, or have industries concepts many then, etc. Since appli- objects, compose connected on to based etc.), cations (MQTT), Messa Protocol web to Telemetry (CoAP), RESTful possible Queue (e.g., Protocol then objects Application is access Constrained It to services, interface model. common OSI a the use of layer application the States pro- United physical the with them in coordinating [ objects of connected cesses objective of that the to systems with embedded of concept the “ of that to similar iltasomtewyte neatwt h hsclworld physical the with CPSs interact other, they each way [ with the interact transform humans will way the transformed the ( about evolution wondering an already such is of Media-lab, consequences the the at researcher a n20,HlnGl Ntoa cec onain introduc Foundation) term Science (National the Gill Helen 2006, In Emerging and (Future FET). Commission Technologies, European the of initiative 4 3 8 6 hc ist rn oehrtecmue resources computer the together bring Eli to work, aims Weiser’s which Mark by ] inspired largely 1998, In ]). 3 2 1 ]. “ of notion the 2002, In ]. http://www.disappearing-computer.org https://www.cisco.com/c/dam/en_us/about/ac79/docs/i https://www.nsf.gov/geo/geo-data-policies/pcast-nit e fThings of Web IT [ (IoT) 8 ye-hsclSystems Cyber-Physical .Sne20,CS aebe ainlpriority national a been have CPSs 2007, Since ]. 5 hc eet h nerto ftephysical the of integration the reflects which ] 3 oiiu unr adtefudtosof foundations the laid Guinard Dominique . hnapae nodrt hrceiethe characterize to order in appeared then amtechnology Calm WT [ (WoT) 2 uta h nenthsprofoundly has Internet the as Just . nL Thanh Le an 9 iapaigComputing Disappearing hc nertsteojcsat objects the integrates which ] evsv Computing Pervasive CS [ (CPS) min Intelligence Ambient ,apae spr fan of part as appeared ”, 7 hc generalizes which ] bet htthink that objects -final.pdf nnov/IoT_IBSG_0411FINAL.pdf nentof Internet a intro- was ” 1 (AmI) very , nthe in han ge ed ly y 1 f , 2 early 2010’s, Cisco™ introduced the concept of the Internet of Direct, etc.). The objective, while not exhaustive, is to identify Everything (IoE) which, beyond Machine-to-Machine (M2M) a trend. communications, encompasses technology assisted People-to- People (P2P) communications and Machine-to-People (M2P) communications (via, for instance, social networks or other digital media). In line with CPS, the concepts of Industrial ActivitiesofDailyLiving HumanActivities Internet of Things (IIoT) [10] and Industry 4.0 [11], by their FaceRecognition highly strategic dimension, are gaining some momentum and, SpeechRecognition GestureRecognition within the European Union, widely developed in Germany. ActivityRecognition This brief historical overview highlights a number of concepts AccidentPrevention HomeAutomation that characterize the fusion of the physical and digital worlds, Robotics prophesied by Mark Weiser as early as 1991. So as to clarify WebServices MultimediaServices the picture, we provide, in the first part of this paper, a Education synthesis of the main concepts (§II). Without claiming to Teaching be exhaustive, this synthesis makes it possible to clarify E-Learning SustainableDevelopment their particularities and overlaps in terms of application and Agriculture scientific research areas. Vehicles Forecasting It results from the synthesis (§II-F) that the information SocialNetworking AugmentedReality processing systems underlying these concepts have in common VirtualReality that their purposes are achieved in the physical environment FactoryAutomation through actions effecting some of its properties. This common Manufacture Industry4.0 denominator brings them together under the term ambient Healthcare systems. This semantics is justified by the general system Monitoring theory [12] which defines an ambient environment by all the ElderlyPeople AmbientAssistedLiving external factors which, by acting on certain properties of a IntelligentBuilding physical system, determine its evolution. SmartSpace SmartHome By interacting with the physical environment, these systems SmartCity SmartGrid inherit its complexity. This complexity is also driven by their EnergyUtilization infrastructure and the rationality of their decisions. These ele- EnergyEfficiency ments are detailed in §III. They justify an observation resulting from the synthesis : since their effects are unpredictable, (1) responsibility for actions is often left to users, (2) otherwise, IOT AmI there are risks to individuals and their environment. Mark CPS Weiser’s vision of disappearing computing is still far from Pervasive being a reality. Ubiquitous This situation calls for an epistemological rupture that we Fig. 1: Application domains associated with the concepts (each propose to concretize through the systemic approach whose row sums to 100%). methodology is presented in §IV. Without claiming to predict, the proposed approach makes it possible to evaluate in vivo The methodology chosen consists in determining all the the effectiveness of these systems. keywords associated with articles published over the period To conclude (§V), we introduce some perspectives that the 1989-2019 relating to the various concepts, and classifying evaluation of the effectiveness can bring to these systems. them according to their frequency of appearance (1989 being the year from which the term Ubiquitous Computing was II. Thematic classification introduced). Scopus® queries are given in the table I. It is worth setting up a methodological framework which, In the sequel, the different concepts are analysed on the basis without claiming for exhaustiveness, makes it possible to of these keywords (Denoted by keyword). The figure 1 depicts study the particularities and the commonalities of the main the main application domains associated with each of the concepts highlighted in terms of application and research concepts. The figure 3 shows a synthesis of the most common areas, namely “Internet of Things”, “Ubiquitous Computing”, keywords associated with the concepts used in the study. This “Pervasive Computing”, “Ambient Intelligence” and “Cyber- figure also depicts the relationships that can exist between the Physical Systems”. Figure 2 shows the evolution of the number different concepts. In the sequel, these results serve as the basis of scientific publications relating to them. The graph was for the analysis of each of the concepts. compiled from Scopus®, a database of citations and abstracts from peer-reviewed scientific publications (journals, books and A. Internet of Things conferences). The results obtained cannot be exhaustive here, In its common sense, the notion of object is associated with as other databases are available (e.g., IEEE, ACM, Science any material entity perceived in our environments and assigned 3

Scopus® queries #publications (KEY("internet of things") OR KEY("internet of thing") OR KEY(iot)) AND (LIMIT-TO(SUBJAREA,"COMP")) AND 42967 (LIMIT-TO(LANGUAGE,"English")) KEY("ubiquitous computing") AND (LIMIT-TO(SUBJAREA,"COMP")) AND (LIMIT-TO(LANGUAGE,"English")) 30047 (KEY("cyber physical system") OR KEY("cyber physical systems") OR KEY("cyber-physical system") OR KEY("cyber-physical systems") OR KEY("cps")) AND (LIMIT-TO(SUBJAREA,"COMP")) AND 9822 (LIMIT-TO(LANGUAGE,"English")) KEY("pervasive computing") AND (LIMIT-TO(SUBJAREA,"COMP")) AND (LIMIT-TO(LANGUAGE,"English")) 4457 KEY("ambient intelligence") AND (LIMIT-TO(SUBJAREA,"COMP")) AND (LIMIT-TO(LANGUAGE,"English")) 3061 TABLE I: Scopus® queries for keywords extraction.

10,000 CPS IoT UbiquitousComputing 8,000 PervasiveComputing AmbientIntelligence

6,000 There are more connected objects than humans on earth 4,000

Things That Pervasive Amount of publications 2,000 Think (TTT) Computing

0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

Ubiquitous Ambient IoT CPSWoT IoE IIoT Computing Intelligence Industry 4.0

Fig. 2: Chronology of appearance of terms relating to ambient systems. to a specific usage (e.g., table, book, chair, etc.). Recent virtual) things based on existing and evolving interoperable advances in electronics (e.g., reduction of power consumption, information and communication technologies (ICT)”. miniaturization of components, wireless communication tech- In this context, new forms of interaction emerge from these nologies, etc.) make it possible to envisage new usages based devices, embodied through remotely accessible software ser- on the digital exploitation of contextualized data resulting from vices, resulting in the fusion of the digital and physical the interactions of these objects with the environment. dimensions of our environments. Thereby, we define herebelow Objects are equipped with resources allowing to process the notions of connected thing and connected object as follows: digital data. This processing makes it possible, on the one hand, to translate this data into different forms of energy Connected thing – A material entity made useful to individu- through actuator/effector chains. It also makes it possible to als by means of organized electronic and computer resources convert different forms of energy into numerical data using that, together with the material entity, form a finalized sys- sensors. The use of these resources leads to a multidimen- tem, technological artifact formally accessible and identifiable sional variability in the ability of the objects to perceive within a computer network. their environment and, through their energy activity, to be The notion of connected thing then generalizes that of con- perceived by both humans and machines. The perceptible nected object by integrating, for example, entities delimited in modification of certain environmental properties (e.g., radio space (building, house, factory, garden, forest, etc.). A thing waves, electromagnetic waves, etc.) then allows objects to does not necessarily have an identified primary function as it communicate. The organized set of these elements with which is for an object. the objects are equipped forms a device. Connected object – An everyday object whose primary On this basis, it becomes possible to connect objects to a net- function is transcended by organized electronic and computer work through OSI4 hardware layers. The Internet of Things has resources that, together with the object, form a finalized sys- been defined by the International Telecommunication Union tem, technological artifact formally accessible and identifiable (ITU) as “a global infrastructure for the information society, within a computer network. enabling advanced services by interconnecting (physical and With these elements in mind, the structural model of a connected thing is described in figure 4. This model is based 4Open Systems Interconnection on those described in [13] and [14]. 4

Optimization

Signal Processing Formal Robots Verification Stochastic Distributed Computer Systems Systems Risk Network Security Robotics Physical Assessment Networked Physical Environments Production Control Systems Systems World Security Systems Home Real Time Accident Intelligent Systems User Automation Prevention Experience Physical Actuators Power Systems Hybrid Cyber Autonomous Systems Transmission Artificial Intelligence Distributed Attacks Agents Industrial Networks Ambient Systems Decision Research Intelligence Smart Support Real Time Intrusion Systems Elderly Network Power Decision Making Systems Systems Detection People Security Grids Vehicles Intelligent Cyber Knowledge Agents Security Based Crime Systems Manufacture Industry AAL CPS Dynamics Costs Intelligent 4.0 Robots Energy Efficiency Activities of Artificial Daily Living Intelligence Ambient Zigbee Sustainable Intelligence Energy Dev. Software Harvesting Blockchain Gesture Agents Quality Recognition Low Power Of Life Big Adaptive Electronics Fog Computing Data Systems Edge Computing Smart 5G Mobile Context Aware Spaces Communication Sustainable WSNs Systems Contextual IoT Systems Development Information Green Computing Mobile Computer Agents Communication Interoperability Privacy Service Internet Gateways technologies Data Discovery Energy Utilization Reduction Mobile Pervasive Data Inteligent Buildings Devices Computing Wi-Fi Security transfer NextGen & Middleware Networks Privacy

Semantics Network Mobile Computing Context Ubiquitous Mgmt. Bluetooth Multimedia Location Sensors Computing WLAN Wearable Computing Mobile Services Ad Hoc Portable Networks Pervasive Systems Trusted Computing Equipments Human Visualization Mobile Virtual Reality Location Forecasting Computer Mobile Security Context based Interaction Devices Services Aware WSNs Information Systems Services Internet Feature Computer Routing PDA Extraction Big Data Networks Signal Protocols Middleware Mobile Encoding Sensors Phones Ubiquitous Ad Hoc Systems Mobile Sensor Nodes Networks Wearable Information Mgmt. Computers Computer Systems Telephone Learning Systems Multimedia Systems Sets Quality of Service Cellular Telephone Systems Data Privacy Sensor Networks Context Aware Computing Wireless Networks Wireless Telecommunication Systems

Keyword Keywords common to all concepts Keywords whose use is exclusive to the concept Keyword Keywords common to adjacent concepts

Links between each of the concepts Most frequently associated Keywords shared (e.g. a link of the color of the concept A keywords (without being exclusively with to a concept B represents the percentage adjacent concepts of publications of the concept A referring to exclusive to the concept) ... the concept B Fig. 3: Mapping of the keywords associated with the selected concepts.

The extraction of the keywords associated with scientific other standardisation actors around the world, is trying publications shows three predominant research areas re- to solve (OneM2M [15]). lated to the Internet of Things: 2) The second research area concerns sensor data. Due to 1) The first research area focuses on technological as- their volume (Big Data), these data require special stor- pects, both from the point of view of the devices that age and processing strategies (Edge, Fog Computing). are highly constrained (Low Power Electronics) and 3) Finally, the research focuses on security aspects, par- wireless communication protocols (WLAN, Zigbee, Wi- ticularly those related to data (Security & Privacy, Mobile Fi, Bluetooth, 5G, etc.) and network (Next Generation Security, Blockchain, Trusted Computing, etc.). Neworks, Gateways, Routing Protocols, etc.). The current From an application point of view, three areas diversity of protocols is at the heart of interoperability emerge: smart-cities/homes/buildings, healthcare and en- issues (Interoperability) that ETSI (European Telecom- ergy, mainly oriented towards monitoring: munications Standards Institute) in Europe, together with 1) In so-called smart cities (Smart City [16]), sensors 5

1..* Environment 1..* properties

Memory interfaces 1..* modifies observes consumes 1 1 1 1 produces 1..* Effector Sensor Data

1..* 1..* 1..* acts on is consumes produces 1 1 1 1 1..* Actuator Command Processor consumes

est consumes storage/treatment * 1 1..* consumes 1 is Software Resource Service component * contains 1 consumes 1 1..* Power Device source * mechanically linked 1 * mechanically linked modifies 1 Material Entity

Fig. 4: Structural model of a connected object – devices, mechanically attached to the everyday objects, carry resources that, by allowing interaction with the physical world (through sensors and actuators) and the digital world (through services and software components), transcend the primary usage of these objects.

provide information allowing to effectively manage re- patient care. Via connected pill boxes, the various actors sources and assets. These range from optimised road in the care pathway are able to know whether or not a traffic management [17], transportation [18], parking patient has taken the prescribed treatment (therapeutic spaces [19], lighting [20], to water supply network [21], compliance) [25]. Even more reliable, the connected waste management [22], etc. absorbable pill (Abilify MyCite from the Otsuka labora- For instance, since 2006 in Paris, RFID chips have been tory), available in the United States, allows therapeutic installed in the city’s 95,000 trees, allowing them to compliance for patients in psychiatric hospitals. Finally, be accurately monitored and maintained. Per Caroline deformable/stretchable electronic patches that adhere to Lohou, head of the parks, gardens and green spaces the skin and track body movements provide doctors department at the Paris city hall, “the logger has one with real-time information on patients’ health status click access to everything he needs to treat the trees: (epidermal electronics [26]). watering, pruning, fertilization, treatments, old address 3) Finally, in the field of energy (Energy Efficiency, Energy if the tree has been transplanted, etc.”. Utilization, Smart Power Grids), IoT makes it possible to 2) In the healthcare area (Healthcare [23]), IoT’s advan- support the energy transition by complying, for instance, tages are also undeniable. In New York, the Mt. Sinai with the 2009 European directive on the smart grids Medical Center, in partnership with GE Healthcare, has deployment [27]. Thus, by 2021, more than 35 million implemented an intelligent bed allocation management Linky electricity consumption meters will be installed that has resulted in a 50% reduction in patient referrals in France. The meter is equipped with sensors and uses time [24]. On their side, Stanley Healthcare5 offers the existing network to receive and transmit data without hospitals a real-time geolocation (Location) solution the physical intervention of a technician. for their patients, staff and medical devices. Thereby, hospitals can thus optimize management and improve Connected things, through sensors, allow the acquisition

5https://www.stanleyhealthcare.com 6

of data relating to humans and their environment. How- within the home (e.g., Smart Lock from the Austrian ever, the valuation of this data is often the responsibility company Nuki is a box that can be installed on a of users. Thus, the treatment of trees in the city of Paris lock and allows it to be opened and closed remotely remains the responsibility of the loggers; the optimization via a dedicated application). Another example is the of patient care in hospitals to that of the nursing staff, refrigerator, a connected object that is emblematic both therapeutic compliance to that of the patients, etc. for the fact that it is one of the first objects to have been connected (LG, 2000) and for the usage scenarios B. Ubiquitous Computing it suggests. Using RFID chips attached to food, the refrigerator reads its contents, offers adapted menus and Ubiquitous computing, by moving computational resources warns users when food is missing or outdated [36][37]. from the personal computer to everyday objects, invites re- Finally, the Luminon of the Ubiant startup is a watch- searchers to rethink the ways in which humans and tower that connects to all connected objects in the computers interact. home and indicates by its colour (green or red) whether This research area (Human Computer Interaction) is predom- household consumption exceeds the predefined target. If inant, as is that concerning mobile devices (Mobile Devices, the consumption target is exceeded, the user receives Telephone Sets, Mobile Phones, Wearable Computers, etc.), a notification on his smartphone to warn him of the vectors of these new interaction modalities through services location of the excess consumption. with high added-value (Location Based Services, Multimedia Services, Visualization, etc.). For instance, connected glasses Ubiquitous computing offers humans, via mobile devices, (Wearable Computers), often associated with Eyewear Com- new ways of interacting with computer. However, it must puting [28] are a representative example of the new modalities be noted that these interactions most often result in a of interaction with the computer [29]. In the field of mainte- set of notifications from applications associated with the nance, the relevance of these new interaction modalities is all devices and are all the more restrictive because they the greater because it allows significant time and efficiency require the permanent availability of users. gains thanks to information elements superimposed on reality (Augmented Reality)[30]. C. Pervasive Computing From an application point of view, two areas emerge from Pervasive computing has strong connections with ubiq- the study of keywords: healthcare and smart environments uitous computing. This is very clear from the figure 3. (intelligent buildings, smart-homes and smart-buildings): However, the predominant research area here is on middleware 1) In the field of healthcare (Healthcare) the most em- (Middleware), an architectural approach to mediation and or- blematic device is the connected watch (Wearable Com- chestration of services [38], some of which being executed on puters) which, based on a certain number of physio- connected things. logical data, makes it possible to prevent cardiovascular The exchange of messages (e.g., Message Queue Telemetrie or cerebral risks, to detect falls [31], biological dangers Transport, MQTT) and the implementation of remote soft- [32], etc. The LVL bracelet from the American company ware services (e.g., Service Oriented Approach, SOA) allow BSX measures the wearer’s cardiac parameters and computer applications to interact, cooperate and exchange hydration level. It signals in real time the amount of information with connected things (System of Systems). The water to be absorbed via a dedicated application on the dynamic nature of the everyday objects and services they carry smartphone. means that they can appear and disappear over time. The 2) In the field of so-called intelligent buildings and implementation of service discovery protocols (Service Dis- houses (Smart Home, kwrdIntelligent Buildings), sen- covery) is an important research area that allows middleware to sors embedded on clothing pave the way for new in- be aware of their execution context (Context Aware Services), a teractions. For instance, biometric sensors sewn into context that is defined by the purposes of the system concerned clothing allow temperature and lighting to be modulated [39]. in a contextual way (Context Aware Computing, Location) From an application point of view, the same domains for well-being and energy saving purposes (Energy effi- as above emerge with a strong predominance for the ciency, Energy Utilization)[33][34]. healthcare domain (Healthcare) The Homni [35] connected lamp, developed by Teraillon, . adjusts its brightness throughout the day. Equipped with There are many examples of middleware implementation [40]. sensors for room temperature, brightness, humidity and Context consideration is characterized by adaptive systems noise, it also makes it possible to offer diagnostics and (Adaptive Systems) that can rely on semantic annotations advice via the dedicated application “Terraillon Wellness (Semantics) to select the most relevant available services Coach” to improve sleep quality. It also includes a available [41] to ensure continuity and quality of service bluetooth speaker allowing to broadcast a suitable sound (Quality of Service, QoS) [42]. atmosphere in the early and late hours of the night. Connected switches (e.g., the SmartPeeble portable D. Ambient Intelligence switch/variator from Awox) and connected locks com- Ambient intelligence, as defined by Eli Zelkha, aims to bring plete the possibilities offered by connected objects together the computer resources embedded in objects to 7 assist people in their daily activities (Activities of Daily Life). E. Cyber-Physical systems Cyber-physical systems (CPSs) are a generalization of the The elderly (Elderly People), often dependent because they are concept of embedded systems to that of connected things restricted in their freedom of movement and are subject to with the objective of making them collaborate for the con- a decline in their cognitive abilities, are the ideal target of trol of physical processes [8]. The purposes of these systems ambient intelligence. The economic and social impacts that are achieved through the physical environment (Physical Sys- ambient intelligence promises are enormous in this context tems, Physical Environments, Physical World) autonomously [43] both on the prospects of reducing the costs of their care (Robots, Robotics, Actuators). by society and their families and on improving their quality Two research areas emerge from the study of keywords of life (Quality Of Life). related to these systems (figure 3) which could be related Two research areas emerge clearly from the study of to “Trustworthy Systems of Systems” [47]): keywords related to ambient intelligence (figure 3): 1) Research that addresses cyber security aspects 1) Research on artificial intelligence to assist people in (Network, Security, Intrusion Detection, their daily activities (Ambient Assisted Living, AAL). Cyber-Attack, Cyber Security, Security Systems), Addressing the cognitive and physical deficiencies of 2) Research related to risks and their management (Risk these people suggests a form of autonomous intelli- Assessment, Accident, Prevention). From this point of gence (Intelligent Systems,Autonomous Agents) that can view, it is interesting to note the efforts regarding the interpret the needs of these people (Activity Recognition, formal verification of these systems (Formal Verification). Gesture Recognition, Speech Recognition) and make From an application point of view, three areas emerge: relevant decisions for them (Decision, Support Systems, energy, industry and robotics: Decision Making, Knowledge Based Systems). 2) Research on the robotization of environments for 1) In the fields of energy and smart power grids (Smart support and physical assistance to people (Intelligent Power Grids)[48], the aim is to adjust electricity produc- Robots, Home Automation.) tion and distribution according to real time demand in a targeted manner. For instance, Linky meters allow to From an application point of view, people assistance determine electricity demand in real time. Depending (Ambient Assisted Living) is logically the main field of on this demand, local production resources favouring application with repercussions in the fields of robotics, renewable energy sources (solar panels, wind power) are smart environments (Intelligent Buildings, Smart Home) used to meet this demand. In the long term, CPS should and healthcare (Healthcare)[44][45]. allow the advent of Zero-Net Buildings (ZNEs) [49] that, There are many examples and usage scenarios. For example, using sensors, solar panels, LED lighting and batteries, through the means of communication available in the home do not use more energy than they produce, (televisions, tablets, lamps, speakers, etc.) an intelligent system 2) In the field of industry (Industry 4.0, Manufacturing) can send messages that are useful to people (e.g., “think about [50], a striking example is that of stock management at taking the medication”) and adapted to their physical condition Amazon, carried out by robots that autonomously walk (e.g., deafness, visual impairment). the warehouse and constitute orders using tags present Another example concerns the use of sensors scattered on each of the products [51], throughout the habitat from which an intelligent system is 3) Finally, the implementation of robots such as au- likely to recognize people’s activity in order to anticipate their tonomous vehicles (Vehicles) promises to improve the needs or prevent rescue in the event of a fall or abnormal fluidity of road traffic. Connected to online services and activity suggesting a physical problem. equipped with sensors, their perception and reactivity, Combined with activity recognition, connected objects such by not being subject to fatigue or inattention, are much as lamps, blinds, loudspeakers, radiators, etc. allow intelligent better than that of humans. These vehicles are therefore systems to learn people’s behavioural habits in order to im- intended to drastically reduce accidents due to fatigue, prove their well-being by reproducing their favourite visual alcohol, etc., (94% of which being of human origin and sound environments (Decision Making) or by suggesting [52]), and costs by increased driving efficiency. relevant actions (Decision Support Systems). A concrete ex- ample is the Nest thermostat, which controls the boiler by CPSs embody a robotization of the world whose interest observing the habits of the occupants of the house in order to lies mainly in the optimization of resources, their means optimize energy consumption [46]. of supply, etc. However, by interacting with their environ- ment, these systems are not without risks, as evidenced Ambient intelligence, by suggesting a form of robotiza- by the current lines of research concerning them. tion of our habitats, concretizes the need for assistance to people, especially those whose cognitive and/or physical abilities are declining. However, the promised assistance F. Synthesis is most often translated into a set of notifications that This brief synthesis highlights the fact that purpose of the require the permanent availability of users or their carers. systems underlying the different concepts discussed, beyond 8 the collection of environmental data from sensors, is mainly The introduction of autonomy in this definition refers to the the implementation of relevant actions that the processing intrinsic capacity of a system to maintain, by itself, its unity of these data suggests in our environments: in order to perpetuate the achievement of its purposes. The 1) In cities, it is a question of controlling resources and action of maintaining suggests feedback loops, a particular assets in order, for example, to fluidify traffic [17], to form of interaction whose study is at the heart of cybernetics: manage energy distribution (water and electricity) [27], the system modifies the environment which, in turn, modifies etc., the system within circular relationships. 2) Within the home, it is a question of controlling electrical As an integral part of these systems, computer systems (or, appliances in order to optimize household consumption more generally, digital systems) can be defined as follows: [46]; modifying certain physical parameters in order to Computer system – A computer system is a set of computer, assist and improve people’s well-being [35], their safety telecommunications equipment and software used to collect, [53], etc., process, store, transmit and present data [60]. 3) At the human level, it is about influencing behaviours to improve physical and cognitive performances [25]. It is a technological artifact whose purposes are achieved through data manipulation. All the IT and telecommunica- This purpose, common denominator of the concepts discussed, tions resources form the coherent unit that refers here to the brings them together under the term ambient systems. This notion of infrastructure, which will have to be maintained in semantics is relevant and reflects well the idea of the fusion line with the purposes of the system. For instance, mechanisms of the physical and digital worlds. It finds roots in the general for periodically refreshing the contents of DRAMs (Dynamic system theory [12] which defines an ambient environment by Random Access Memory) or hardware redundancy ensure the all the external factors which, by acting on certain properties integrity of the data stored in the memory. Cyclic redundancy of a physical system, determine its evolution. (CRC) or Error-Correcting Code (ECC) control mechanisms Nevertheless, responsibility for actions is still left to indi- ensure the integrity of data as it is transmitted over a network. viduals, these systems only providing support for decision- Consequently, the achievement of the purposes of these sys- making (Decision Support Systems). This is evidenced by the tems is deterministic, being conditioned only by the validity mobile applications associated with these systems, most of of the means of data processing implemented, regardless of which being comparable to remote controls. However, the time and space. This determinism allows the implementation multiplication of dedicated applications and the notifications of models and tests that allow the validation and prediction of resulting from their analyses induce a cognitive overload which the results of data processing. has the consequences of reducing users’ performance [54] and An ambient system, fusion of the digital and physical worlds, inducing stress [55] likely to motivate the abandonment of the is a system of systems whose singularity lies in the fact systems and their applications [56][57]. Not to mention the that its purposes are achieved, no longer exclusively through risks to both humans and their environment that the decisions the manipulation of data, but also through the manipulation made by these systems are likely to cause. This situation of some properties of the physical environment. It is no is explained by the complexity of these systems [58]. This longer merely a question of collecting, processing and complexity, by preventing the accurate modelling of these storing data, but of observing, modifying and maintaining systems, also makes it impossible to predict with certainty the properties of the physical environment through transducers effects that these systems are likely to have in the environment. (sensors and actuators), interfaces between the physical and digital worlds at the heart of their infrastructure. The next paragraph is devoted to formalizing this problem and In fact, the realization of the purposes of an ambient system justifying the complex nature of these systems. is part of a set of dimensions (including spatio-temporal dimensions) that characterize the notion of context. For Bazire et Brézillon [39], the context is defined by the purposes of the III. Complex systems, irreducible to a model system concerned: “context acts as a set of constraints that A definition of the notion of system appeared in the 1940s influence the behavior of a system dedicated to a certain task”. as part of the work on the systemic approach, which is at the origin of currents of thought such as structuralism in The set of constraints here is threefold: France or cybernetics in the United States. From the 1970s, – First, it corresponds to the set of external factors inherent under the influence of Ludwig von Bertalanffy [12], this work to the physical environment, complex system in non- was extended to emerging systems and the concept of self- linear interactions with the ambient system and possibly organization. In this context, a system is defined by: in conflict with the effects it produces, System – A system is a set, forming a coherent and au- – It then corresponds to the latent dynamicity of the tonomous unit, of real or conceptual objects (material ele- infrastructure inherent to the connected objects that do ments, individuals, actions, etc.) organized according to a goal not offer any guarantee of interoperability and spatio- (or a set of goals, objectives, purposes, projects, etc.) by means temporal availability, of a set of relationships (mutual interrelationships, dynamic – Finally, it corresponds to the rationality of the decisions interactions, etc.), all immersed in an environment [59]. specific to the decision-making algorithms underlying the 9

achievement of their purposes through transducers em- For instance, a macroscopic state for a set of 52 playing bedded on connected things (in this context, the notions cards may correspond to “all cards are hidden”. Entropy is of substantive and procedural rationalities introduced by minimal, this macroscopic state is indeed the representation of Herbert Simon are particularly relevant). a single organization of the cards. When the macroscopic state corresponds to “all cards are hidden except one” (the position These constraints, which are as much challenges in main- of the visible card being important), entropy increases (the taining the unity of ambient systems and in achieving their macroscopic state is the representation of possibly 522 = 2704 purposes, are justified and detailed below. organizations). Indeed, the macroscopic state of the system is no longer the representation of a single organization of A. The challenge of the physical environment its elements. It is not informative of the underlying real organization. Entropy is maximum when the macroscopic state The purposes of ambient systems are achieved through the corresponds to “all cards are visible” (the macroscopic state is physical environment. In other words: the representation of possibly 52! = 8.06×1067 organizations). – It is no longer a question of storing a value in memory whose persistence is guaranteed by design, but of main- The notion of representation calls for that of modelling which, taining a set of properties of the physical environment in its simplifying logic, is subject to abstractions that cannot around a point of equilibrium (the temperature of a room allow it to be a single representation of the real organization for example), which many physical processes, indepen- of the constituent of a system. On this basis, a definition of dently of the ambient system, are likely to disturb in a the entropy of a system could then be as follows: random manner, Entropy of a system – The entropy of a system characterizes – It is no longer a question of transmitting information by the degree of ignorance, embodied in the impossibility of a bounded or guided channels (optical fibre, coaxial cables, model to predict the real evolution of the elements of the etc.), but by air channels (wireless telecommunications), system. The higher the entropy of a system, the less its model the keystones of ambient systems whose sensitivity to dis- is reliable to predict its future evolution. turbances inherent to the physical environment has been demonstrated. In particular, temperature is a factor in the The degree of ignorance is highlighted in the principle of degradation of data transmission performance [61][62], maximum entropy. When the (imperfect) knowledge of a – It is not a question of connecting the components of phenomenon is represented by a probability law, the principle ambient systems to the wired electrical network, but of of maximum entropy consists in choosing, among the set collecting and storing energy. The technological con- of distributions responding to the constraints specific to the straints associated with batteries and weather variations phenomenon, the one with the greatest entropy, i.e., the one are potential disruptive factors here, with the highest variance and which is therefore the least – Finally, it is no longer merely a question of controlled informative. systems, reducible to a model, for processing and trans- In light of entropy, the definition of a system can be completed forming information but, more broadly, of complex open in different ways. In the context of systems whose purposes systems, irreducible to a model, leading irreversible trans- are achieved through the physical environment, onre may refer formations of physical properties [63]. to so-called open or controlled systems. Before going further, it is necessary to introduce the notions Open system – An open system is in permanent relationship of entropy, negentropy, controlled systems and open systems. with its environment with which it exchanges energy, matter and information. The entropy of the system can then increase, The notion of entropy was introduced by Rudolf Clausius stabilize or decrease as these reorganizing exchanges progress in 1865 and is at the heart of the second principle of (figure 5.a). Entropy is minimal if the evolution of the system, thermodynamics; it has since been adopted in many fields depending on these exchanges, remains predictable (cf. negen- including statistical physics, information theory, mathematics, tropy). philosophy, human sciences, etc. In the sense of thermodynamics, entropy characterizes the Controlled system – A controlled system is a system in which degree of dispersion, the homogenization of the energy of a all the exchanges of energy, matter and information likely to system that no longer allows it to produce organized effects. transform its organization are regulated by feedback loops in Thus, entropy characterizes disorder, the disorganization of active opposition to the increase in its entropy, i.e., constrains the system. In the sense of statistical physics, the entropy the evolution of the system so that it remains predictable (figure of a system characterizes the lack of information relating to 5.b). the organization of its constituents. Entropy is minimal if the Controlled systems are said neguentropic systems [64] macroscopic state of the system is the representation of a single organization of its constituents. It increases with the number of Negentropy – Negentropy, unlike entropy, characterizes the possible organizations that lead to the same macroscopic state. degree of knowledge available to enable a system to overcome In other words, the higher the entropy, the less informative the entropy, i.e., to maintain the predictability of its future evolu- macroscopic state is about the actual internal organization of tions. The entropy of a system is minimal if its neguentropy is the system. maximal. 10

Uncontrolled processes

Negentropy Negentropy Negentropy

feedback feedback

Entropy Entropy Entropy

fig.b Controlled system fig.a Open system fig.c Ambient system (a.k.a. negentropic system) Fig. 5: Open, controlled and ambient systems, entropy and neguentropy.

Ambient systems often use feedback loops to counter entropy. 1) Contextual factors: When the purpose of an ambient In particular, ambient systems auto-adaptive (Adaptive Sys- system is to maintain a property of the physical environment tems) which, through observations (Adaptive Systems), adapt at a certain value, this purpose only makes sense if it is to changes through feedback loops (e. g. Models@runtime, spatially bounded, i.e., concerns a well-defined subspace of MAPE-K (Monitor-Analyze-Plan-Execution over a shared that environment (e.g., a kitchen, a building, etc.). It is Knowledge) as part of Autonomic Computing). therefore clear that the purposes of an ambient system can only Nevertheless, while all the exchanges of energy, matter and be achieved in presence of relevant devices and services, i.e. information that contribute to the preservation of the organi- capable of influencing the physical property in question within zation of a system can, under certain conditions, be known the spatial boundaries of the subspace concerned (figure 6. a)). and controlled (like cyber-physical systems in an industrial However, even if this were the case: (1) the nomadic nature environment), it is illusory to think that they can be completely of the connected objects does not guarantee the availability in known within the physical environment. In other words, time and space of their associated devices and services (figure except in special cases, it is unrealistic to expect to control 6. b); (2) many devices and services, within the subspace, are all the sources of entropy of a system whose objectives likely to be able to also influence the physical property. The are achieved through the physical environment (figure 5.c), choice of the most pertinent devices and services, given the irreducible to a model and therefore complex. purposes of the system, is not a trivial task (figure 6.c). In this context, maintaining the adequacy of the infrastructure Beyond the physical environment in which they operate, the with the purposes of ambient systems lies in their ability at complexity of ambient systems is also related to the dynam- reorganizing themselves (self-organized) as the infrastructure icity and variability of their infrastructure, inherent in a evolves. From this point of view, middleware (Middleware) are set of contextual, technological and environmental factors that intended to organize the services involved and their relation- are detailed in the following paragraph. ships. They ensures the continuity of service. 2) Technological factors: Middleware implementation is B. The challenge of the infrastructure only possible within an ecosystem of interoperable services, Infrastructure is at the heart of the ambient systems organi- both in terms of their accessibility within the network and zation. It refers to the notion of (structural) unity that these in terms of the formal interpretation of the semantics of the systems must maintain in order to perpetuate the achievement interactions they offer with the physical environment. In this of their purposes. It is characterized by a set of technological context, many challenges remain to be addressed. means (such as those discussed in the context of connected The interoperability of the devices embedded on objects is things, i.e., Devices, Resources, Services and Energy), im- primarily limited by the heterogeneity of the many com- mersed in the physical environment and interrelated within munication protocols as no standard has clearly emerged to a communication network (Network). Thereby, the infras- date. Hence, players in the field are grouped into consortiums tructure of ambient systems is subject to dynamics and around communication protocols, the main ones being Wi-Fi, variability that may call into question its adequacy with Cellular (2G, 3G, 4G and 5G), Bluetooth LE, ZigBee, KNX, the purposes of the system. Z-Wave, LoRaWAN, enOcean, etc. The main factors leading infrastructure dynamics and variabil- On top of the communication protocols, application proto- ity are technological, contextual and environmental. cols based on service-oriented architectures (SOAP WS-*), re- 11

Devices Considered subspace of the environment

fig. a : The effects produced by the fig. b : The nomadic nature of the fig. c : Different devices allow to modify device are able of modifying certain devices does not guarantee their a given physical property of the considered properties of the considered availability in time and space subspace of the environment. It remains subspace of the environment to select the most relevant one..

Fig. 6: Contextual factors. sources (REST API, CoAp) or messages (MQTT, AMQP,...) are 3) Environmental factors: The nomadic nature of con- widely used. These application protocols have the advantage nected objects suggests that the vast majority of them can- of providing a generic way to access the data and APIs of not rely on wired power supply. Therefore, energy harvest- the devices through gateways and thus abstract the underlying ing and storage strategies are implemented (self-powered) technological heterogeneity. They also allow these devices to [66][67][68]. In this context, at the heart of the concepts Zero be integrated into the world wide web. Therefore, it is possible Energy and Green Computing, wireless power transfer tech- to use existing web technologies on devices and their data nologies (e.g., RFID tags) and solar energy recovery technolo- (i.e., search engines, indexing robots, semantic annotations and gies are the most widely used. However, these technologies are knowledge bases, security, composite applications, physical largely dependent on the physical environment in which they mashups, etc.). This is referred to as the web of objects (Web are implemented. Thus, the performance of wireless energy of Things, WoT). The formal description of the data and APIs transfer is highly dependent on the obstacles that can be of each of the devices can then be considered on the basis found between the transmitter and receiver. The efficiency of of semantic annotations (Semantic Web of Things, SWoT). solar panels, beyond technological considerations specific to With these annotations, middleware are then able to index the materials used to produce them, is largely dependent on and search for devices and their services in the environment, weather variations and the condition of photovoltaic panels interpret their interactions and compose high added-value which, when exposed to dust, require frequent cleaning [69]. applications that implement multiple devices in a relevant way. In addition, when batteries are used to store energy, they must Originally syntactic, description formalisms such as RSDL be replaced regularly, as their lifespan is limited. Finally, some (RESTful Service Description Language), WSDL (Web Ser- approaches use the piezoelectric effect to allow objects to vice Description Language), etc. have evolved into semantic produce their own energy [70]. This is the case, for example, formalisms such as OWL-S (Semantic Markup for Web Ser- with enOcean’s switches, whose energy is created by the vices), SAWSDL (Semantic Annotations for WSDL), WSMO pressure exerted by the user. In terms of communication, the (Web Service Modeling Ontology), etc. In general, semantic nomadic nature of connected objects also suggests the im- description formalisms are based on a vocabulary (an ontol- plementation of wireless communication technologies, again, ogy). Interoperability is then only possible if all annotations largely dependent on the physical environment in which they are based on a common ontology. Unfortunately, it is not are implemented. For example, temperature is a factor in the possible to describe semantically, a priori, all objects, their degradation of data transmission performance [61][62]. devices, data and APIs within a single and global ontol- 4) Human factors: The vulnerability of ambient systems to ogy (semantic heterogeneity) [65]. Hence, there are now a cyber attacks (Cyber Security, Cyber Attacks, Crime, Network multitude of heterogeneous ontologies covering all fields of Security) could also compromise the integrity of these systems application. Thus, to date, 700 ontologies6, which follow [71]. Whether by accidental or malicious modification of the W3C’s recommendations in terms of quality and whose sensors data (e.g., man-in-the-middle attack), by failure of a sources are available, are listed to date. This list does not take device (e.g. Denial of Service attack, DoS, malfunction, etc.) into account proprietary ontologies, many manufacturers of or by its malicious takeover, the purposes of an ambient system connected objects are likely to develop their own ontologies can be compromised or no longer be achieved, with potential to describe objects, their devices, data and API. risks for individuals and their environment [72]. Before closing the aspects related to the complexity of ambient systems, it is necessary to study the processes at the heart of 6http://lov.okfn.org/dataset/lov/ the genesis of their decisions and which contribute to their 12 complexity. The arguments developed in this paragraph illustrate the in- trinsically complex nature of ambient systems. This complexity C. The challenge of the Simonian rationality is embodied in the impossibility of predicting their behaviour and therefore the achievement of their purposes, with the risks When ambient systems acquire a certain level of autonomy, that this entails for both humans and their environment. At their actions and the resulting effects are likely to be the best, can we hope to get close to the desired behaviour but result of bilateral or unilateral decisions which result from never reach it exactly: “[...] as soon as a system is open there algorithmic reasoning (Decision Support Systems, Decision is no optimum and any equilibrium is in interaction with its Making). Hence, the genesis of the decisions made by ambient environment”[77]. As a result, responsibility for actions is systems and their rationality determine the achievement of the often left to individuals, with ambient systems only providing purposes of these systems. In this context, Herbert Simon’s support for decision-making. work, on the basis of Thomas Kuhn’s work on Philosophy of Science, is particularly relevant. Economist (Nobel Prize IV. Towards an epistemological rupture in Economics in 1978) and sociologist, his work on limited rationality led him to focus on decision-making organizations The situation described in the previous paragraphs calls for an and procedures as well as artificial intelligence (in 1975, with epistemological rupture that some researchers have advocated Allen Newell, he was awarded the Turing Prize for his work in for [78][79][80][81][82]. Indeed, current software engineering this field). It is within the framework of this work that Herbert approaches, which include formal testing and verification Simon introduces the concepts of substantive rationality and methodologies (Formal Verification), find their epistemological procedural rationality [73][74]. foundations in positivism, a doctrine theorized by Auguste 1) Substantive rationality: Substantive (or optimizing) ra- Comte in the continuity of Cartesianism, which claims for tionality refers to the behaviour of a system that makes its predictability. But, as indicated by Jean-Louis Le Moigne, “In decisions while trying to optimize (Optimization) the resources practice, analytical modelling is increasingly proving inade- at its disposal in order to achieve its purposes. It deals with quate, whenever it is agreed that one is not sure that something the adequacy between the means possessed by the agent and cannot be forgotten (the hypothesis of closing the model), that the purposes. [75]. The rationality of decisions here is entirely objective evidence is only evident in a given ideology (...), in determined by the characteristics of the environment in which other words, whenever one has to make the assumption that they take place, i.e., the context. For instance, middleware the modelled phenomenon is not complicated but complex” demonstrate substantive rationality by selecting, from among [63] p.19. all the available devices, those whose services are in adequacy The predictability is assumed in the field of software engineer- with the purposes of the system. The selection process then ing and justified by the technological means implemented to follows an optimising logic, one of the criteria of which is the control computer systems and maintain their organisation in quality of service (QoS, Quality of Service). permanent adequacy with their purposes. This is not the case 2) Procedural rationality: Procedural rationality is a with ambient systems for which the notions of dependability decision-making process in which a system makes decisions [83] and trustworthiness, condition the acceptability of these according to predefined procedures. In this context, the ra- systems. tionality of the decisions taken is entirely determined by Pragmatically, without predictability and being only able to their algorithmic procedures, the improvement of which is get close to the desired behaviour without reaching it exactly, necessarily accompanied by experimentation phases. Thus, the a relevant question to ask is then “to what extent does the system examines, through a succession of trials and errors, a ambient system actually do what it must do ?”, the answer limited number of possible decisions and decides on the one to which can only be given in vivo. In order to address that allows it to reach not the optimum but a minimum level this question, it may be interesting to consider the systemic of satisfaction. Procedural rationality highlights three essential approach . This approach finds its epistemological foundations phenomena that may call into question the achievement of the in constructivism [84]. It is then a question of considering an purposes of ambient systems: ambient system as a whole by focusing on the purposes it 1) Due to their limited computational capabilities and the must produce (teleological point of view) in interaction with complexity of the physical environment, ambient sys- its environment (phenomenological point of view) without a tems cannot claim to be optimal in their decisions, priori knowledge of its constituents. 2) The set of choices facing ambient systems is finite, Systemic modelling consists in “representing a complex phe- and, based on [76], “"all the choices available to the nomenon as and by a system, a system that is general and systems are not exogenous but endogenous to their past stable enough to account for all the types of complexity that activity”. As a result, the processes of learning (Learning can be considered”[63] p.38. Although it is not possible to Systems) and decision-making are mutually constrained reduce complex systems to explanatory finite models, they and difficult to distinguish, still remain intelligible. Unlike analytical modelling, associated 3) Ambient systems are likely to be highly heterogeneous, with disjunctive logic, systemic modelling is associated with not only in terms of the means they use to achieve their conjunctive logic, which makes it possible to account for the goals, but also in terms of their operating environment perceived phenomena in and through their complex conjunc- and the local nature of their learning. tions (homomorphic correspondence between the object and 13

Hold the form (F) Hold the form (F) Hold the form (F)

Transforming/Producing (T) Transformation/Production (T)

Input Output Input Output Input Output

prop#1 prop#1 prop#1 prop#1 prop#1 prop#1 Process Process ... Process ...... (t) (t+1) (t+n) prop#n prop#n prop#n prop#n prop#n prop#n

Link (E) Link (E) Fig. 7: Systemic modelling consists in considering a complex system as the conjunction of three actions: that of transforming/producing (T) over time, that of linking (E) in space and that of maintaining an identifiable form (F) characterized by a vector whose components correspond to certain values of physical properties. its model, and not isomorphic as is the case with analytical presence presence modelling approaches). This translates into considering a set of actions characterized by the general notion of active processes acting on the environment and characterized by inputs and presence outputs. On these basis, the figure 7 depicts the evolution of a complex system in the (T)ransform-(S)pace-(F)orm referential. Lighted Lighted

presence The systemic approach is a modelling method that takes place (a) Qualitative model. in iterative steps [85]. The objective of these different steps is pres < 3.0 (input) pres > 20.0 (input) to obtain a consensual understanding of the complex system considered:

1) The first step consists in identifying the complex system pres > 20.0(input) to be studied ("the observable"), isolate it to define its duration ≤ 5 lum < 5.0 lum > 25.0 contours (systemic breakdown) according to different Lighted Lighted (output) (output) criteria that remain at the discretion of the modeler, duration ≤ 7 2) Using the triangulation method [86] p.64, the com- pres < 3.0(input) plex system thus defined is then approached from the (b) Quantitative model. functional, structural and historical points of view. The functional aspect consists in questioning the purposes of Fig. 8: Systemic modelling of a lighting control system. the system (“What does it do?”). The structural aspect focuses on studying the elements that make up the system by focusing on their interactions rather than on Based on this model and the observation of inputs and outputs, the elements themselves. Finally, the historical aspect it will therefore be necessary to assess the effectiveness of an consists in taking an interest in the temporal evolutions ambient system, i.e. the degree of adequacy between its pur- of the system. The knowledge of the system will then be poses and the effects it produces. The effectiveness assessment represented by a barycentre whose position can evolve is part of the Gibert’s performance model as depicted in figure throughout the process of apprehending the object and 9. Per Jean-Louis Le Moigne, “"effectiveness is assessed by a improving its knowledge, multidimensional vector relating the behaviour of a system to 3) This knowledge, once ordered, makes it possible to build its purposes”[63]. a qualitative model of the complex system (figure 8.a), It is worth noting that middlewares act mainly at the relevance 4) Once the qualitative model has been constructed, it must level of the Gibert model. For example, it is a matter of be formally represented by qualifying the forms of inputs choosing the most relevant services (means) from among those and outputs (non-functional properties) that are involved available on the basis of their semantic annotations. Self- in the evolution of the system in a quantitative manner adaptive systems, on the other hand, generally act on low-level 8.b). This formalization then allows the implementation performance indicators that are part of an optimization prob- of the model through temporal simulations. lem at the efficiency level of Gibert’s model (e.g. self-healing, By successive iterations, the model can be modified and self-configuring, self-optimizing, self-tuning, etc.). The same improved until it reaches a consensus. A simple example is applies to evaluations relating to the concepts of dependability, depicted in figure 8. performance, performability and quality of service (QoS) that focus mainly on low level objective properties, which need to 14

PURPOSES

The relevancy of a system represents EFFECTIVENESS The effectiveness of a system represents the degree of adequacy between the the degree of adequacy between its purposes resources it mobilises and its purposes. and the results (effects) obtained.

RELEVANCY

PERFORMANCE

EFFICIENCY MEANS EFFECTS

The efficiency of a system (understood here as synonymous with yield) is the relationship between the means deployed and the results achieved. A system is said to be efficient when it maximizes its efficiency (i.e., it mobilises the minimum possible means to obtain the maximal result). Fig. 9: Gibert’s performance model [87]. be optimized over time. B. DevOps However, “the efficiency of a vehicle can be measured by the The DevOps approach [92] seeks to unify the practices ratio: 5 litres per 100 km; 5 litres of gasoline, resources of software development (i.e., development, integration and consumed; 100 km travelled, resources produced. But if the testing) and systems administration actors (i.e., deployment, purpose of the vehicle was to go to Paris and it ends up in Bor- operation and maintenance). This unification is justified by deaux, the measurement of its efficiency (a priori very good) the antagonistic objectives of these actors. On the one hand, will not say anything about the quality of its effectiveness.” software development actors are constrained by cost and time [63]. constraints with the negative impacts that this can have on the quality of the software delivered. On the other hand, IT V. Perspectives administration actors are constrained to stability and quality The evaluation of effectiveness, in addition to formal testing objectives even if this has repercussions in terms of costs and verification methods, may offer interesting perspectives. and delays. The effectiveness assessment is part of a set Some of them are described below. of recommendations for the implementation of the DevOps approach. In particular [92]: A. Self-Adaptation 1) Continuous integration including continuous testing. Ambient systems are often self-adaptive, they are able, through The idea of using the evaluation of effectiveness as a feedback loops, to modify themselves in response to changes result of unit tests (which do not sanction execution in their environment or the objectives they must achieve [88] with a PASS/FAIL binary result but with a degree of (e.g. Model-Analyze-Plan-Execute over a shared Knowledge compliance) is fully justified here. They contribute to (MAPE-K) [88], Model Identification MIAC and Model Refer- the dichotomy between positivist test approaches based ence Adaptive Controllers (MRAC) [89]). In practice however, on models of the systems considered and implemented existing self-adaptive ambient systems are mainly designed by at development time (Dev/Tests) and constructivist ap- focusing on low-level performance indicators rather than high- proaches based on observations (Ops, monitoring), level requirements [90][91] (e.g., self-healing, self-configuring, 2) Users’s feedback. The qualitative model as depicted in self-optimizing, self-tuning, etc.). figure 8 may denote users’ preferences. In this context, Other approaches exist, based on reference models whose the evaluation of the effectiveness could be used as an parameters are adjusted over time and on which formal verifi- automatic users’ feedback indicator, cation methodologies are applied (Model Identification Adap- 3) Monitoring of operation and production quality us- tive Control (MIAC) and Model Reference Adaptive Control ing key metrics and indicators. As such, the effective- (MRAC)[89]). These approaches, however, are only relevant if ness assessment could provide such indicators. the models are in line with reality (i.e. are isomorph). Equipped with continuous evaluation of their effectiveness, these systems would be able to focus on a high-level C. Quality of Experience performance indicator that does not assume the illusory The Quality of Experience (QoE) provides an assessment of existence of an isomorphic model. end-users satisfaction with a service or, more generally, a 15 product [93]. It aims to take into account all factors that performance as “the degree to which a system or component contribute to the quality of a system or service as perceived accomplishes its designated functions within given constraints, by the users, beyond purely technological factors. A definition such as speed, accuracy or memory usage.”. The performa- is provided by the International Telecommunication Union bility “quantifies how well the object system performs in the (ITU) : “The degree of delight or annoyance of the user of an presence of faults over a specified period of time”[98]. application or service. It results from the fulfillment of his or When these attributes are not based on non-functional indi- her expectations with respect to the utility and/or enjoyment of cators, they require an implicit assessment of the behaviour the application or service in the light of the user’s personality of the system under consideration. This includes, for instance, and current state.” measuring Mean Down Time (MDT), Mean Time To Failure In order to measure the quality of experience, human evalu- (MTTF), Mean Time Before Failure (MTBF), etc. In this con- ations can be used. In this context, the Mean Opinion Score text, the evaluation of effectiveness can be used as a high- (MOS [94]) is widely used. Évaluations are mainly governed level indicator for both dependability and performance by ITU recommendations which define strict experimental analysis, beyond non-functional indicators. frameworks for multimedia application areas (communication, video, speech, etc.). Besides MOS, some approaches are based VI. Conclusion on Random Neural Networks (RNN) as, for instance, the Pseudo Subjective Quality Assessment (PSQA) [95] which in- Their purposes being realized in the physical environment, volves learning, through a set of subjective tests, how humans ambient systems are complex systems that are embodied in respond to quality. a set of concepts such as Pervasive Computing, Ubiquitous Computing, Cyber-Physical Systems, Ambient Intelligence, MOS is only a limited form of quality of experience assess- etc. This complexity, by resulting in the inability to obtain ment, acquired in controlled environments that cannot reflect a predictive isomorphic model of the effects of their actions, the open nature of environments where users’ experiences are implies that the responsibility for them often lies with users. shaped [96]. RNN-based approaches require a significant num- When this is not the case, the effects produced are not without ber of annotated datasets with the estimation of their respective risk for users and their environment. In this context, we quality. In the context of ambient systems, datasets must be have proposed the systemic modelling approach, which has acquired in different real situations, the number of which is its epistemological roots in constructivism: it is no longer a likely to be prohibitive. In addition, given the subjective nature question of predicting the effects produced by these systems of the QoE assessment, this number should be multiplied by an but, in a more pragmatic way, of evaluating their effectiveness equally large number of users. This approach therefore seems on the basis of a model of their purposes. impracticable when it comes to generalizing it to ambient systems. The proposed modelling approach paves the way to a set Here again, assuming that the qualitative model as depicted of opportunities in the areas of auto-adaptive systems, user in figure 8 may denote users’ preferences, the evaluation experience, dependability, etc. First results are encourag- of the effectiveness could be used as a QoE indicator. ing. For instance, the proposed modelling approach has been implemented in [99] where effectiveness assessment is achieved using probabilistic Input/Output Hidden Markov D. Dependability Model (IOHMM) and extended into the possibilistic frame- The notion of dependability [83] covers all critical aspects work in [100], accounting for temporal constraints. related to the reliability of ambient systems [97]. A definition of dependability, given by the International Federation for Acknowledgment Information Processing (IFIP), is the following:“The trust- This work has been conjointly supported by GFI Informatique, worthiness of a computing system which allows reliance to Innovation group, Saint-Ouen, France and the French National be justifiably placed on the service it delivers, enables these Center for Scientific Research (CNRS), Sophia Antipolis, various concerns to be subsumed within a single conceptual France. framework. Dependability thus includes as special cases such attributes as reliability, availability, safety, security.” References IEEE defines reliability as the “ability of a system or compo- [1] M. Weiser, “The computer for the 21 st century,” Scientific american, nent to perform its required functions under stated conditions vol. 265, no. 3, pp. 94–105, 1991. for a specified period of time” (continuity of correct service). [2] M. Weiser and J. S. Brown, “Designing calm technology,” PowerGrid Availability is concerned with the ability of a system to provide Journal, vol. 1, no. 1, pp. 75–85, 1996. [3] M. Hawley, R. D. Poor, and M. Tuteja, “Things that think,” Personal a service when it is required. Safety is about the ability of a Technologies, vol. 1, no. 1, pp. 13–20, 1997. system to operate normally or abnormally, without adverse [4] E. Zelkha, B. Epstein, S. Birrell, and C. Dodsworth, “From devices effects on humans or the environment. Finally, security is to’ambient intelligence’: The transformation of consumer electronics (conference keynote),” in Digital living room conference, 1998, pp. the ability of a system to prevent unauthorized access or 16–19. manipulation of its state. [5] K. Ashton et al., “That ‘internet of things’ thing,” RFID journal, vol. 22, no. 7, pp. 97–114, 2009. The notion of dependability is often associated with the [6] M. Satyanarayanan et al., “Pervasive computing: Vision and chal- notions of performance and performability. IEEE defines the lenges,” IEEE Personal communications, vol. 8, no. 4, pp. 10–17, 2001. 16

[7] E. A. Lee and S. A. Seshia, Introduction to embedded systems: A cyber- [30] O. Champalle, B. David, R. Chalon, and G. Masserey, “Ordinateur physical systems approach. Mit Press, 2016. porté support de réalité augmentée pour des activités de maintenance [8] R. Rajkumar, I. Lee, L. Sha, and J. Stankovic, “Cyber-physical systems: et de dépannage,” arXiv preprint arXiv:0807.2836, 2008. the next computing revolution,” in Design Automation Conference. [31] D. Paret and P. Crégo, Wearables, textiles et vêtements intelligents. IEEE, 2010, pp. 731–736. ISTE Editions, 2019. [9] D. Guinard and V. Trifa, “Towards the web of things: Web mashups [32] Y. Miaolei and R. M. Ribeiro, “Chemically robust miniature gas for embedded devices.” sensors,” 2019, uS Patent App. 16/041,655. [10] H. Boyes, B. Hallaq, J. Cunningham, and T. Watson, “The industrial in- [33] I. A. Essa, “Ubiquitous sensing for smart and aware environments,” ternet of things (iiot): An analysis framework,” Computers in Industry, IEEE personal communications, vol. 7, no. 5, pp. 47–49, 2000. vol. 101, pp. 1 – 12, 2018. [34] O. Ojuroye, R. Torah, S. Beeby, and A. Wilde, “Smart textiles for smart [11] H. Kagermann, J. Helbig, A. Hellinger, and W. Wahlster, Recom- home control and enriching future wireless sensor network data,” in mendations for implementing the strategic initiative INDUSTRIE 4.0: Sensors for Everyday Life. Springer, 2017, pp. 159–183. Securing the future of German manufacturing industry; final report of [35] Teraillon, “Homni, une solution de sommeil intelligente,” the Industrie 4.0 Working Group. Forschungsunion, 2013. https://www.terraillon.com/fr/homni [Online]. [12] L. Von Bertalanffy, “General system theory,” New York, vol. 41973, [36] S. Luo, H. Xia, Y. Gao, J. S. Jin, and R. Athauda, “Smart fridges with no. 1968, p. 40, 1968. multimedia capability for better nutrition and health,” in 2008 Interna- [13] S. De, T. Elsaleh, P. Barnaghi, and S. Meissner, “An internet of tional Symposium on Ubiquitous Multimedia Computing. IEEE, 2008, things platform for real-world and digital objects,” Scalable Computing: pp. 39–44. Practice and Experience, vol. 13, no. 1, pp. 45–58, 2012. [37] S. Luo, J. Jin, and J. Li, “A smart fridge with an ability to enhance [14] J.-B. Waldner and J. B. Waldner, Nano-informatique et intelligence health and enable better nutrition,” International Journal of Multimedia ambiante: inventer l’ordinateur du XXIe siècle. Hermès science and Ubiquitous Engineering, vol. 4, no. 2, pp. 66–80, 2009. publications, 2007. [38] J. Footen and J. Faust, The service-oriented media enterprise: SOA, [15] J. Swetina, G. Lu, P. Jacobs, F. Ennesser, and J. Song, “Toward a BPM, and web services in professional media systems. Focal Press, standardized common m2m service layer platform: Introduction to 2012. onem2m,” IEEE Wireless Communications, vol. 21, no. 3, pp. 20–26, [39] M. Bazire and P. Brézillon, “Understanding context before using it,” in 2014. International and Interdisciplinary Conference on Modeling and Using [16] D. Minoli and B. Occhiogrosso, “Internet of things applications for Context. Springer, 2005, pp. 29–40. smart cities,” Internet of Things A to Z: Technologies and Applications, [40] C. Becker, C. Julien, P. Lalanda, and F. Zambonelli, “Pervasive pp. 319–358, 2018. computing middleware: current trends and emerging challenges,” CCF [17] P. Masek, J. Masek, P. Frantik, R. Fujdiak, A. Ometov, J. Hosek, Transactions on Pervasive Computing and Interaction, pp. 1–14, 2019. S. Andreev, P. Mlynek, and J. Misurec, “A harmonized perspective [41] S. Muhammad, N. Admodisastro, H. Osman, and N. Ali, “A review on transportation management in smart cities: The novel iot-driven of dynamic adaptation in context aware system using semantic-based environment for road traffic modeling,” Sensors, vol. 16, no. 11, p. approach,” Journal of Fundamental and Applied Sciences, vol. 10, 1872, 2016. no. 5, 2018. [18] P. Santi, G. Resta, and C. Ratti, “Quantifying the benefits of taxi trips [42] X. Masip-Bruin, E. Marin-Tordera, A. Jukan, and G.-J. Ren, “Managing in new york through shareability networks,” Smart Cities, p. 25. resources continuity from the edge to the cloud: Architecture and [19] A. Khanna and R. Anand, “Iot based smart parking system,” in performance,” Future Generation Computer Systems, vol. 79, pp. 777– 2016 International Conference on Internet of Things and Applications 785, 2018. (IOTA). IEEE, 2016, pp. 266–270. [43] H. Sun, V. De Florio, N. Gui, and C. Blondia, “Promises and chal- [20] M. Castro, A. J. Jara, and A. F. Skarmeta, “Smart lighting solutions lenges of ambient assisted living systems,” in 2009 Sixth International for smart cities,” in 2013 27th International Conference on Advanced Conference on Information Technology: New Generations. Ieee, 2009, Information Networking and Applications Workshops. IEEE, 2013, pp. 1201–1207. pp. 1374–1379. [44] P. Rashidi and A. Mihailidis, “A survey on ambient-assisted living tools [21] T. Dickey, “Smart water solutions for smart cities,” in Smart Cities. for older adults,” IEEE journal of biomedical and health informatics, Springer, 2018, pp. 197–207. vol. 17, no. 3, pp. 579–590, 2013. [22] A. Medvedev, P. Fedchenkov, A. Zaslavsky, T. Anagnostopoulos, and [45] M. Pieper, M. Antona, and U. Cortés, “Ambient assisted living,” Ercim S. Khoruzhnikov, “Waste management as an iot-enabled service in News, vol. 87, no. 2011, pp. 18–19, 2011. smart cities,” in Conference on smart spaces. Springer, 2015, pp. [46] G. Hernandez, O. Arias, D. Buentello, and Y. Jin, “Smart nest thermo- 104–115. stat: A smart spy in your home.” [23] S. B. Baker, W. Xiang, and I. Atkinson, “Internet of things for smart [47] W. Steinhogl, “Trustworthy systems of systems-a prerequisite for the healthcare: Technologies, challenges, and opportunities,” IEEE Access, digitalization of industry,” 2015. vol. 5, pp. 26 521–26 544, 2017. [48] A. Keyhani, Design of smart power grid renewable energy systems. [24] B. G. Thomas, S. Bollapragada, K. Akbay, D. Toledano, P. Katlic, John Wiley & Sons, 2016. O. Dulgeroglu, and D. Yang, “Automated bed assignments in a complex [49] M. Krarti and P. Ihm, “Evaluation of net-zero energy residential and dynamic hospital environment,” Interfaces, vol. 43, no. 5, pp. 435– buildings in the mena region,” Sustainable cities and society, vol. 22, 448, 2013. pp. 116–125, 2016. [25] P. Benhamou, “L’observance thérapeutique à l’heure du numérique et [50] V. Alcácer and V. Cruz-Machado, “Scanning the industry 4.0: A liter- des objets connectés,” Médecine des maladies Métaboliques, vol. 12, ature review on technologies for manufacturing systems,” Engineering no. 6, pp. 502–510, 2018. Science and Technology, an International Journal, 2019. [26] T. Tamura and Y. P. Maeda, In Seamless Healthcare Monitoring. [51] N. Correll, K. E. Bekris, D. Berenson, O. Brock, A. Causo, K. Hauser, Springer, 2018. K. Okada, A. Rodriguez, J. M. Romano, and P. R. Wurman, “Analysis [27] J. officiel de l’union européenne, “Directive 2009/72/ce and observations from the first amazon picking challenge,” IEEE du parlement européen et du conseil concernant des Transactions on Automation Science and Engineering, vol. 15, no. 1, règles communes pour le marché intérieur de l’électricité,” pp. 172–188, 2018. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:211:0055:0093:FR:PDF[52] U. D. of Transportation, “Critical reasons for crashes investigated [Online]. in the national motor vehicle crash causation survey.” [28] B. Tag, C. Holz, P. Lukowicz, O. Augereau, Y. Uema, and K. Kunze, https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115 “Eyewear 2018: Second workshop on eyewear computing,” in Pro- [Online]. ceedings of the 2018 ACM International Joint Conference and 2018 [53] A. Viderberg, “Security evaluation of smart door locks,” 2019. International Symposium on Pervasive and Ubiquitous Computing and [54] S. Leroy, “Why is it so hard to do my work? the challenge of attention Wearable Computers. ACM, 2018, pp. 964–967. residue when switching between work tasks,” Organizational Behavior [29] M. Bâce, T. Leppänen, D. G. De Gomez, and A. R. Gomez, “ubigaze: and Human Decision Processes, vol. 109, no. 2, pp. 168–181, 2009. ubiquitous augmented reality messaging using gaze gestures,” in SIG- [55] G. Mark, D. Gudith, and U. Klocke, “The cost of interrupted work: GRAPH ASIA 2016 Mobile Graphics and Interactive Applications. more speed and stress,” in Proceedings of the SIGCHI conference on ACM, 2016, p. 11. Human Factors in Computing Systems. ACM, 2008, pp. 107–110. 17

[56] M. Pielot, B. Cardoso, K. Katevas, J. Serrà, A. Matic, and N. Oliver, [79] Z. B. Celik, P. McDaniel, G. Tan, L. Babun, and A. S. Uluagac, “Beyond interruptibility: Predicting opportune moments to engage “Verifying internet of things safety and security in physical spaces,” mobile phone users,” Proceedings of the ACM on Interactive, Mobile, IEEE Security & Privacy, vol. 17, no. 5, pp. 30–37, 2019. Wearable and Ubiquitous Technologies, vol. 1, no. 3, p. 91, 2017. [80] D. Garlan, “Software engineering in an uncertain world,” in Proceed- [57] P. C. Shih, K. Han, E. S. Poole, M. B. Rosson, and J. M. Carroll, “Use ings of the FSE/SDP workshop on Future of software engineering and adoption challenges of wearable activity trackers,” IConference research. ACM, 2010, pp. 125–128. 2015 Proceedings, 2015. [81] E. A. Lee, “The past, present and future of cyber-physical systems: A [58] J. Coutaz and J. Crowley, “Plan “intelligence ambiante”: Défis et focus on models,” Sensors, vol. 15, no. 3, pp. 4837–4869, 2015. opportunités,” Document de réflexion conjoint du comité d’experts "In- [82] T. Bures, D. Weyns, C. Berger, S. Biffl, M. Daun, T. Gabor, D. Garlan, formatique Ambiante" du département ST2I du CNRS et du Groupe de I. Gerostathopoulos, C. Julien, F. Krikava et al., “Software engineering Travail "Intelligence Ambiante" du Groupe de Concertation Sectoriel for smart cyber-physical systems–towards a research agenda: Report on (GCS3) du Ministère de l’Enseignement Supérieur et de la Recherche, the first international workshop on software engineering for smart cps,” vol. 1, 2008. ACM SIGSOFT Software Engineering Notes, vol. 40, no. 6, pp. 28–32, [59] F. Le Gallou and B. Bouchon-Meunier, “Systémique: Théorie et 2015. applications,” 1992. [83] A. Avizienis, J.-C. Laprie, B. Randell et al., Fundamental concepts [60] J. Lonchamp, Introduction aux systèmes informatiques: Architectures, of dependability. University of Newcastle upon Tyne, Computing composants, mise en oeuvre, ser. InfoSup. Dunod, 2017. [Online]. Science, 2001. Available: https://books.google.fr/books?id=16_uDQAAQBAJ [84] J. L. Le Moigne, Les épistémologies constructivistes. Presses univer- [61] C. A. Boano, N. Tsiftes, T. Voigt, J. Brown, and U. Roedig, “The sitaires de France Paris, 1995. impact of temperature on outdoor industrial sensornet applications,” [85] G. Donnadieu, D. Durand, D. Neel, E. Nunez, and L. Saint-Paul, IEEE Transactions on Industrial Informatics, vol. 6, no. 3, pp. 451– “L’approche systémique: de quoi s’agit-il,” Synthèse des travaux du 459, 2010. Groupe AFSCET" Diffusion de la pensée systémique", Paris, Associ- [62] R. Marfievici, A. L. Murphy, G. P. Picco, F. Ossi, and F. Cagnacci, ation Française de Science des Systèmes Cybernétiques, Cognitifs et “How environmental factors impact outdoor wireless sensor networks: Techniques, p. 8, 2003. a case study,” in 2013 IEEE 10th international conference on mobile [86] J. L. Le Moigne, La théorie du système général: théorie de la ad-hoc and sensor systems. IEEE, 2013, pp. 565–573. modélisation. FeniXX, 1994. [63] J. L. Le Moigne, La modélisation des systèmes complexes. Bordas [87] P. Gibert, Le contrôle de gestion dans les organisations publiques. Paris, 1990. Editions d’organisation, 1980. [64] E. Morin, La méthode: la nature de la nature. Le seuil, 2013. [88] J. O. Kephart and D. M. Chess, “The vision of autonomic computing,” [65] G. Rocher, J.-Y. Tigli, S. Lavirotte, and R. Daikhi, “Run-time knowl- Computer, no. 1, pp. 41–50, 2003. edge model enrichment in swot: A step toward ambient services [89] Y. Brun, G. D. M. Serugendo, C. Gacek, H. Giese, H. Kienle, M. Litoiu, selection relevancy,” in 2015 5th International Conference on the H. Müller, M. Pezzè, and M. Shaw, “Engineering self-adaptive systems Internet of Things (IOT). IEEE, 2015, pp. 62–69. through feedback loops,” in Software engineering for self-adaptive [66] A. P. Murdan and S. Caremben, “An autonomous solar powered systems. Springer, 2009, pp. 48–70. wireless monitoring and surveillance system,” in 2018 13th IEEE [90] X. Peng, B. Chen, Y. Yu, and W. Zhao, “Self-tuning of software Conference on Industrial Electronics and Applications (ICIEA). IEEE, systems through goal-based feedback loop control,” in 2010 18th IEEE 2018, pp. 784–789. International Requirements Engineering Conference. IEEE, 2010, pp. [67] F. Ferrero, H. Le-Quoc et al., “Multi-harvesting solution for au- 104–107. tonomous sensing node based on lora technology,” in 2017 Inter- [91] F. D. Macías-Escrivá, R. Haber, R. Del Toro, and V. Hernandez, “Self- national Conference on Advanced Technologies for Communications adaptive systems: A survey of current approaches, research challenges (ATC). IEEE, 2017, pp. 250–253. and applications,” Expert Systems with Applications, vol. 40, no. 18, [68] X. Yue, M. Kauer, M. Bellanger, O. Beard, M. Brownlow, D. Gibson, pp. 7267–7279, 2013. C. Clark, C. MacGregor, and S. Song, “Development of an indoor [92] S. Sharma and B. Coyne, “Devops for dummies. 3rd limited ibm edn,” photovoltaic energy harvesting module for autonomous sensors in 2017. building air quality applications,” IEEE Internet of Things Journal, [93] N. Crespi, B. Molina, C. Palau et al., “Qoe aware service delivery in vol. 4, no. 6, pp. 2092–2103, 2017. distributed environment,” in 2011 IEEE Workshops of International [69] M. T. Chaichan, K. I. Abass, and H. A. Kazem, “Energy yield loss Conference on Advanced Information Networking and Applications. caused by dust and pollutants deposition on concentrated solar power IEEE, 2011, pp. 837–842. plants in iraq weathers,” International Research Journal of Advanced [94] R. C. Streijl, S. Winkler, and D. S. Hands, “Mean opinion score Engineering and Science, vol. 3, no. 1, pp. 160–169, 2018. (mos) revisited: methods and applications, limitations and alternatives,” [70] M. Babar, A. Rahman, F. Arif, and G. Jeon, “Energy-harvesting Multimedia Systems, vol. 22, no. 2, pp. 213–227, 2016. based on internet of things and big data analytics for smart health [95] G. Rubino, “Quantifying the quality of audio and video transmissions monitoring,” Sustainable Computing: Informatics and Systems, vol. 20, over the internet: The PSQA approach,” in Communication Networks pp. 155–164, 2018. And Computer Systems: A Tribute to Professor Erol Gelenbe. World [71] M. N. Al-Mhiqani, R. Ahmad, W. Yassin, A. Hassan, Z. Z. Abidin, Scientific, 2006, pp. 235–250. N. S. Ali, and K. H. Abdulkareem, “Cyber-security incidents: a review [96] I. Wechsung and K. De Moor, “Quality of experience versus user cases in cyber-physical systems,” International Journal of Advanced experience,” in Quality of experience. Springer, 2014, pp. 35–54. Computer Science and Applications, vol. 9, no. 1, pp. 499–508, 2018. [97] E. Irene, F. Felix, and R. Ralf H, Dependability Metrics: GI-Dagstuhl [72] W. Dudek and W. Szynkiewicz, “Cyber-security for mobile ser- Research Seminar, Dagstuhl Castle, Germany, October 5-November 1, vice robots-challenges for cyber-physical system safety,” Journal of 2005, Advanced Lectures. Springer, 2008. Telecommunications and Information Technology, no. 2, pp. 29–36, [98] J. F. Meyer and W. H. Sanders, “Specification and construction of 2019. performability models,” in Proceedings of the Second International [73] H. A. Simon, “From substantive to procedural rationality,” in 25 years Workshop on Performability Modeling of Computer and Communica- of economic theory. Springer, 1976, pp. 65–86. tion Systems, 1993, pp. 28–30. [74] J. L. Le Moigne, Sur la capacité de la raison à discerner les deux [99] G. Rocher, J.-Y. Tigli, and S. Lavirotte, “Probabilistic models toward formes de rationalité, substantive et procédurale (d’Aristote à HA controlling smart-* environments,” IEEE Access, vol. 5, pp. 12 338– Simon, par Descartes et Vico). Paris, 1993. 12 352, 2017. [75] H. Simon, “Les sciences de l’artificiel (trad. jean-louis le moigne),” [100] G. Rocher, J.-Y. Tigli, S. Lavirotte, and N. Le Thanh, “A possibilistic 2004. i/o hidden semi-markov model for assessing cyber-physical systems ef- [76] M. Amendola and J.-L. Gaffard, La dynamique économique de fectiveness,” in 2018 IEEE International Conference on Fuzzy Systems l’innovation. FeniXX, 1988. (FUZZ-IEEE). IEEE, 2018, pp. 1–9. [77] C. Rochet, “Public management as a moral science,” Habilitation à diriger des recherches, Université de droit, d’économie et des sciences - Aix-Marseille III, Dec. 2007. [78] A. Bennaceur, C. Ghezzi, K. Tei, T. Kehrer, D. Weyns, R. Calinescu, S. Dustdar, Z. Hu, S. Honiden, F. Ishikawa et al., “Modelling and analysing resilient cyber-physical systems,” 2019.