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Dynamic Learning of the Environment for Eco-Citizen Behavior

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Dynamic Learning of the Environment for Eco-Citizen Behavior THTHESEESE`` En vue de l’obtention du DOCTORAT DE L’UNIVERSITE´ DE TOULOUSE D´elivr´epar : l’Universit´eToulouse III - Paul Sabatier (UT3 Paul Sabatier) Cotutelle internationale Universit`adegli Studi di Catania Pr´esent´eeet soutenue le 14/12/2020 par : Davide Andrea GUASTELLA Dynamic Learning of the Environment for Eco-Citizen Behavior JURY MARIE-PIERRE GLEIZES Professeur d’Universite´ Co-Directrice MASSIMO COSSENTINO Directeur de recherche Co-Directeur VALERIE´ CAMPS Maˆıtre de Conference Co-Encadrante CESARE FABIO VALENTI Maˆıtre de Conference Co-Encadrant JEAN-PAUL JAMONT Professeur d’Universite´ Rapporteur ANDREA OMICINI Professeur d’Universite´ Rapporteur LAURENT VERCOUTER Professeur d’Universite´ Examinateur GIANCARLO FORTINO Professeur d’Universite´ Examinateur Ecole´ doctorale et sp´ecialit´e: MITT : Domaine Math´ematiques: Math´ematiquesappliqu´ees Unit´ede Recherche : Institut de Recherche en Informatique de Toulouse (IRIT) Directeur(s)/Encadrant(s) de Th`ese: Val´erieCamps, Massimo Cossentino, Marie-Pierre Gleizes et Cesare Fabio Valenti Rapporteurs : Jean-Paul Jamont et Andrea Omicini II Davide Andrea Guastella DYNAMIC AND REAL-TIME LEARNING OF THE ENVIRONMENT FOR ECO-CITIZEN BEHAVIOR IN SMART CITIES Thesis Supervisors Marie Pierre Gleizes, Full Professor, Universite´ Toulouse III Paul Sabatier Val´erieCamps, Associate Professor, Universite´ Toulouse III Paul Sabatier Cesare Valenti, Associate Professor, Universita` degli Studi di Palermo Massimo Cossentino, Senior Research Scientist, Consiglio Nazionale delle Ricerche (CNR) HE development of sustainable smart cities requires the deployment of Information and Com- T munication Technology (ICT) to ensure better services and available information at any time and everywhere. As IoT devices become more powerful and low-cost, the implementation of an extensive sensor network for an urban context can be expensive. This thesis proposes a technique for estimating missing environmental information in large scale environments. Our technique enables providing information whereas devices are not available for an area of the environment not covered by sensing devices [1]. The contribution of our proposal is summarized in the following points: • limiting the number of sensing devices to be deployed in an urban environment; • the exploitation of heterogeneous data acquired from intermittent devices; • real-time processing of information; • self-calibration of the system. Our proposal uses the Adaptive Multi-Agent System (AMAS) [53] approach to solve the problem of information unavailability. In this approach, an exception is considered as a Non- Cooperative Situation (NCS) that has to be solved locally and cooperatively. HybridIoT exploits both homogeneous (information of the same type) and heterogeneous information (information of different types or units) acquired from some available sensing device to provide accurate estimates in the point of the environment where a sensing device is not available. The proposed technique enables estimating accurate environmental information under conditions of uncertainty arising from the urban application context in which the project is situated, and which have not been explored by the state of the art solutions [2]: • openness: sensors can enter or leave the system at any time without the need for any recon- figuration; • large scale: the system can be deployed in a large, urban context and ensure correct operation with a significative number of devices; • heterogeneity: the system handles different types of information without any a priori configu- ration. Our proposal does not require any input parameters or reconfiguration. The system can operate in open, dynamic environments such as cities, where a large number of sensing devices can appear or disappear at any time and without any prior notification. We carried out different experiments to compare the obtained results to various standard techniques to assess the validity of our proposal. We also developed a pipeline of standard techniques to produce baseline results that will be compared to those obtained by our multi-agent proposal [4]. REMERCIEMENTS Cette aventure qu’est la these` s’approche finalement a` sa conclusion : une epreuve´ d’effort, de sacrifice, de devouement´ qui est resum´ ee´ dans ces pages. Comme je ne suis pas une personne tres` bavarde, je me limiterai a` remercier dans ces lignes toutes les personnes qui m’ont permis d’arriver a` ce resultat.´ Tout d’abord, je remercie Andrea Omicini et Jean-Paul Jamont d’avoir accepte´ d’evaluer´ cette these,` ainsi que les membres du jury, Giancarlo Fortino et Laurent Vercouter. Je tiens a` remercier mes chers compagnons d’aventure (AKA les SMACkers) : Maxime, Guilhem, Kristell, Augustin, Bruno, Walid, Nicolas, Valerian,´ et tous les autres, permanents et non, vieux et moins vieux. Merci a` Valerie,´ pour ta rigueur et l’immense travail de relecture que t’as fait pour les articles ainsi que pour cette these` et pour m’avoir soutenu et accompagne´ (avec beaucoup de patiente de ta part) vers ce resultat.´ Merci a` Pierre et Marie-Pierre, plus que des simples encadrants, votre sourire, votre disponibilite´ et votre cordialite´ m’ont rassure´ et ont tout a` fait rendu le travail de la these` bien agreable.´ Je remercie Massimo et Cesare, veritables´ guides spirituels depuis longtemps desormais.´ Je ne pourrai jamais vous remercier suffisamment pour tout ce que vous avez fait pour moi, pour m’avoir soutenu (et supporte),´ pour vos precieux´ conseils. Je ne serais pas ici sans votre soutien. Un grand remerciement va a` ma famille pour son enorme´ soutien pendant ces annees´ passees´ loin de chez eux. Pour finir, merci a` Giulia : merci de m’avoir toujours soutenu pendant ces annees´ vecues´ loin de toi, de m’avoir soutenu dans les moments les plus sombres de ce chemin, de m’avoir toujours montre´ le bon cotˆ e´ des choses, de rendre harmonieux ce monde chaotique. Je ne serai jamais capable d’exprimer tout mon amour pour toi, pour cela je dois m’appuyer sur les mots de quelqu’un autre : I feel wonderful, Because I see the love light in your eyes, And the wonder of it all, Is that you just don’t realize how much I love you E. Clapton Contents I Context Domain and State of The Art 1 Introduction 1 1.1 Contribution . .2 1.2 Manuscript Organization . .3 2 Introduction to Smart cities 5 2.1 Smart City Application Domains . .7 2.1.1 Business-Related Domain . .8 2.1.2 Citizens-related Domain . .8 2.1.3 Environment domain . .9 2.1.4 Government domain . .9 2.1.5 Discussion . 10 2.2 Defining the Smart City . 10 2.3 Smart City Value . 12 2.4 Challenges in Smart cities . 14 2.5 Introduction to Artificial Intelligence . 16 2.6 Artificial Intelligence for the Smart City . 17 2.6.1 Discussion . 20 2.7 Conclusion . 21 3 State of the Art 22 3.1 Estimating Missing Information in Smart Cities . 23 3.1.1 Discussion . 24 3.2 Bibliographic Study . 25 3.3 Existing Solutions for Estimating Missing Information . 26 3.3.1 Regression Techniques . 26 3.3.2 Neural Network Techniques . 31 CONTENTS 3.3.3 Gradient Boost Technique . 36 3.3.4 Combined Techniques . 37 3.4 Discussion . 40 3.5 Conclusion . 41 4 Multi-Agent Systems 42 4.1 Multi-Agent Systems . 43 4.1.1 Agents . 44 4.1.2 Environment . 46 4.1.3 Self-Organization . 47 4.2 Cooperation . 48 4.3 Adaptive Multi-Agent Systems . 49 4.3.1 Non-Cooperative Situations . 51 4.3.2 Criticality . 52 4.4 Designing an AMAS with ADELFE . 53 4.5 Discussion . 54 4.6 Conclusion . 54 II Contribution 56 5 The HybridIoT Approach 57 5.1 Problem Statement . 57 5.1.1 Discussion . 59 5.2 General Functioning of HybridIoT by Example . 60 5.2.1 First case: no other sensor available . 60 5.2.2 Second case: some sensors are present in the proximity of S1 .......... 61 5.2.3 Third case: some sensors perceiving information of different types are present 62 5.2.4 Discussion . 63 5.3 System Objective . 64 5.4 System Requirements . 65 5.5 System Analysis . 66 5.5.1 Global Level Analysis . 69 5.5.2 Local Level Analysis . 70 5.5.3 Agents’ Characterization . 72 5.6 Environment . 76 5.7 Nominal Behavior . 77 5.7.1 Agents Nominal Behavior . 78 5.7.2 Discussion . 79 5.8 Cooperative Behavior . 80 CONTENTS 5.8.1 Non-Cooperative Situation . 80 5.9 Estimation Procedure . 81 5.9.1 Endogenous Estimation by Historical Data . 83 5.9.2 Endogenous Estimation by Confidence Zone . 89 5.9.3 Exogenous Estimation . 99 5.10 Dynamic Ambient Context Window Evaluation . 107 5.11 Conclusion . 108 III Evaluation 110 6 Experimental Results 111 6.1 Dataset . 112 6.2 Comparison Solution . 114 6.2.1 Agglomerative Hierarchical Clustering . 115 6.2.2 Normalized Convolution . 116 6.2.3 Analysis of the Results . 118 6.3 Endogenous Estimation . 121 6.3.1 Analysis of the Results . 121 6.3.2 Comparison to the State of the Art . 123 6.4 Endogenous Estimation by Confidence Zone . 126 6.4.1 Analysis of the Results . ..
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