Titre :	Fuzzy logic & Q_learning based approach using IoT devices Smart environments.
Type de document :	texte imprimé
Auteurs :	Allah Anis Acila Mouti, Auteur ; Islam Aloui ; A Beghrich, Directeur de thèse
Editeur :	Setif:UFA
Année de publication :	2024
Importance :	1 vol (66 f .)
Format :	29 cm
Langues :	Anglais (eng)
Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Internet of Things (IoT) Urban security systems Fuzzy logic Q_learning, Intelligent IoT environments.
Index. décimale :	004 - Informatique
Résumé :	The rapid development of Internet of Things (IoT) technology has significantly impacted various aspects of daily life by enabling smart decision-making, reducing maintenance costs, optimizing functions, and enhancing environmental monitoring. However, the proliferation of connected devices and the use of open communication channels have escalated security concerns within IoT networks. This dissertation addresses the integration of IoT devices into urban security systems, emphasizing the need for real-time decision-making, uncertainty management, and adaptability to dynamic urban environments. Traditional security systems often struggle due to their rigidity and lack of learning capabilities. To overcome these challenges, this study proposes an innovative solution that integrates fuzzy logic to handle data uncertainties and Q_learning to optimize decisionmaking and enhance system adaptability. This approach aims to rectify the limitations of existing methods, thereby strengthening security in intelligent IoT environments. The dissertation is structured to provide a comprehensive understanding of IoT, its applications, security concerns, and the implementation of fuzzy logic and Q_learning to enhance IoT security. Evaluation of this solution demonstrates its potential to significantly improve the robustness and adaptability of IoT security systems in smart urban environments.
Note de contenu :	Sommaire Problematic and hypothesis 14 Problematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1 Internet Of Things Overview 16 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.1 Definition of the Internet of Things . . . . . . . . . . . . . . . 16 1.2 IoT architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.2.1 Object layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.2.2 Object abstraction layer . . . . . . . . . . . . . . . . . . 18 1.2.3 ServiceManagement Layer . . . . . . . . . . . . . . . . 18 1.2.4 Application layer . . . . . . . . . . . . . . . . . . . . . . . 19 1.2.5 Business layer . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3 Cloud Computing and the IoT . . . . . . . . . . . . . . . . . . 19 1.3.1 Definition of Cloud Computing . . . . . . . . . . . . . 19 1.3.2 Cloud Computing’s Significance in IoT . . . . . . . 20 1.3.3 the integration of IoT and cloud computing . . . . 21 1.3.4 Cloud Computing’s Advantages for IoT . . . . . . . 21 1.4 The IoT and Smart Environments . . . . . . . . . . . . . . . . 22 1.4.1 Smart grids . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4.2 Smart homes . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.4.3 Smart healthcare . . . . . . . . . . . . . . . . . . . . . . . 24 1.4.4 Smart traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.4.5 Smart agriculture . . . . . . . . . . . . . . . . . . . . . . . 25 1.5 IoT Technologies for Developing Smart City Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.5.1 Definition of Smart City . . . . . . . . . . . . . . . . . . 26 1.5.2 IoTApplication in theDevelopment of Smart Cities 27 1.5.3 Essential Technologies for Smart City . . . . . . . . 28 1.5.4 Challenges to the Development of Smart Cities . 29 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Security of IoT-based smart environments 31 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.1 Definition of IoT Security . . . . . . . . . . . . . . . . . . . . . . 31 2.2 security-related risks and assaults at every IoT layer . . . 31 2.2.1 Security threats at the object layer . . . . . . . . . . . 31 2.2.2 Security threats at the object abstraction layer . . 33 2.2.3 Security threats at the service management layer 34 2.2.4 Security threats at the application layer . . . . . . . 34 2.2.5 Security threats at the business layer . . . . . . . . . 35 2.3 IoT security requirements . . . . . . . . . . . . . . . . . . . . . . 35 2.3.1 Data security . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.3.2 Communication security . . . . . . . . . . . . . . . . . 36 2.3.3 Device security . . . . . . . . . . . . . . . . . . . . . . . . 41 2.4 IoT security mechanisms . . . . . . . . . . . . . . . . . . . . . . 41 2.4.1 cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.4.2 Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.4.3 Firewall and Antivirus . . . . . . . . . . . . . . . . . . . . 43 2.4.4 Intrusion Detection System . . . . . . . . . . . . . . . 43 2.5 Future Trends and Challenges in IoT Security . . . . . . . . 45 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3 Fuzzy Logic and Q_learning in IoT 48 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1 Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.2 Fuzzy and Classical Logics . . . . . . . . . . . . . . . . 49 3.1.3 Domains of Application . . . . . . . . . . . . . . . . . . 49 3.1.4 Principle of Fuzzy Logic . . . . . . . . . . . . . . . . . . 49 3.1.5 Fuzzy Logic System’s General Structure . . . . . . . 50 3.1.6 Advantages of fuzzy logic . . . . . . . . . . . . . . . . . 51 3.1.7 Disadvantages of fuzzy logic . . . . . . . . . . . . . . . 51 3.2 Q_Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.2.1 Characteristics of Q_learning . . . . . . . . . . . . . . 52 3.2.2 Operation of the Q_Learning process . . . . . . . . 54 3.3 Fuzzy Logic in IoT Systems . . . . . . . . . . . . . . . . . . . . . 54 3.3.1 Fuzzy Logic Functioning in the IoT . . . . . . . . . . 55 3.3.2 Applications of Fuzzy Logic in the IoT . . . . . . . . 55 3.3.3 Implementing Fuzzy Logic in IoT Devices and Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.3.4 IoT Challenges and Drawbacks with Fuzzy Logic 57 3.4 Q_Learning in IoT Systems . . . . . . . . . . . . . . . . . . . . . 58 3.4.1 Q_Learning Functioning in the IoT . . . . . . . . . . 58 3.4.2 Applications of Q_Learning in IoT: . . . . . . . . . . 58 3.4.3 IoTDifficulties andConsequences with Q_Learning 59 3.5 Integration of Fuzzy Logic and Q_learning in IoT . . . . . 59 3.5.1 Complementarity between fuzzy logic and Q_learning 59 3.5.2 Approaches to Integrating Fuzzy Logic and Q_learning 60 3.5.3 Applications of Fuzzy Logic & Q_Learning Integration in IoT . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.5.4 Challenges and Implementation Considerations 61 3.5.5 Future Perspectives and Emerging Research Areas 61 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4 Proposed Solution and Implementation 63 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.1 Problem Statement and Objectives . . . . . . . . . . . . . . . 63 4.1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . 63 4.1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2.1 System Architecture . . . . . . . . . . . . . . . . . . . . . 64 4.2.2 Fuzzy LogicModule . . . . . . . . . . . . . . . . . . . . . 64 4.2.3 Q_learningModule . . . . . . . . . . . . . . . . . . . . . 64 4.2.4 Integration of Fuzzy Logic and Q_learning . . . . . 65 4.3 Implementation: Tools and Libraries . . . . . . . . . . . . . . 65 4.3.1 Equipment Used . . . . . . . . . . . . . . . . . . . . . . . 65 4.3.2 Libraries andModules . . . . . . . . . . . . . . . . . . . 65 4.3.3 Design and Development . . . . . . . . . . . . . . . . . 66 4.4 Application: Smart City Security . . . . . . . . . . . . . . . . . 66 4.4.1 System Setup: . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.4.2 Fuzzy Logic Implementation: . . . . . . . . . . . . . . 67 4.4.3 Custom Q-learning Implementation: . . . . . . . . 68 4.4.4 Simulation Results: . . . . . . . . . . . . . . . . . . . . . . 68 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Côte titre :	MAI/0834

Fuzzy logic & Q_learning based approach using IoT devices Smart environments. [texte imprimé] / Allah Anis Acila Mouti, Auteur ; Islam Aloui ; A Beghrich, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (66 f .) ; 29 cm.
Langues : Anglais (eng)

Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Internet of Things (IoT) Urban security systems Fuzzy logic Q_learning, Intelligent IoT environments.
Index. décimale :	004 - Informatique
Résumé :	The rapid development of Internet of Things (IoT) technology has significantly impacted various aspects of daily life by enabling smart decision-making, reducing maintenance costs, optimizing functions, and enhancing environmental monitoring. However, the proliferation of connected devices and the use of open communication channels have escalated security concerns within IoT networks. This dissertation addresses the integration of IoT devices into urban security systems, emphasizing the need for real-time decision-making, uncertainty management, and adaptability to dynamic urban environments. Traditional security systems often struggle due to their rigidity and lack of learning capabilities. To overcome these challenges, this study proposes an innovative solution that integrates fuzzy logic to handle data uncertainties and Q_learning to optimize decisionmaking and enhance system adaptability. This approach aims to rectify the limitations of existing methods, thereby strengthening security in intelligent IoT environments. The dissertation is structured to provide a comprehensive understanding of IoT, its applications, security concerns, and the implementation of fuzzy logic and Q_learning to enhance IoT security. Evaluation of this solution demonstrates its potential to significantly improve the robustness and adaptability of IoT security systems in smart urban environments.
Note de contenu :	Sommaire Problematic and hypothesis 14 Problematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1 Internet Of Things Overview 16 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.1 Definition of the Internet of Things . . . . . . . . . . . . . . . 16 1.2 IoT architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.2.1 Object layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.2.2 Object abstraction layer . . . . . . . . . . . . . . . . . . 18 1.2.3 ServiceManagement Layer . . . . . . . . . . . . . . . . 18 1.2.4 Application layer . . . . . . . . . . . . . . . . . . . . . . . 19 1.2.5 Business layer . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3 Cloud Computing and the IoT . . . . . . . . . . . . . . . . . . 19 1.3.1 Definition of Cloud Computing . . . . . . . . . . . . . 19 1.3.2 Cloud Computing’s Significance in IoT . . . . . . . 20 1.3.3 the integration of IoT and cloud computing . . . . 21 1.3.4 Cloud Computing’s Advantages for IoT . . . . . . . 21 1.4 The IoT and Smart Environments . . . . . . . . . . . . . . . . 22 1.4.1 Smart grids . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4.2 Smart homes . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.4.3 Smart healthcare . . . . . . . . . . . . . . . . . . . . . . . 24 1.4.4 Smart traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.4.5 Smart agriculture . . . . . . . . . . . . . . . . . . . . . . . 25 1.5 IoT Technologies for Developing Smart City Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.5.1 Definition of Smart City . . . . . . . . . . . . . . . . . . 26 1.5.2 IoTApplication in theDevelopment of Smart Cities 27 1.5.3 Essential Technologies for Smart City . . . . . . . . 28 1.5.4 Challenges to the Development of Smart Cities . 29 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Security of IoT-based smart environments 31 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.1 Definition of IoT Security . . . . . . . . . . . . . . . . . . . . . . 31 2.2 security-related risks and assaults at every IoT layer . . . 31 2.2.1 Security threats at the object layer . . . . . . . . . . . 31 2.2.2 Security threats at the object abstraction layer . . 33 2.2.3 Security threats at the service management layer 34 2.2.4 Security threats at the application layer . . . . . . . 34 2.2.5 Security threats at the business layer . . . . . . . . . 35 2.3 IoT security requirements . . . . . . . . . . . . . . . . . . . . . . 35 2.3.1 Data security . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.3.2 Communication security . . . . . . . . . . . . . . . . . 36 2.3.3 Device security . . . . . . . . . . . . . . . . . . . . . . . . 41 2.4 IoT security mechanisms . . . . . . . . . . . . . . . . . . . . . . 41 2.4.1 cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.4.2 Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.4.3 Firewall and Antivirus . . . . . . . . . . . . . . . . . . . . 43 2.4.4 Intrusion Detection System . . . . . . . . . . . . . . . 43 2.5 Future Trends and Challenges in IoT Security . . . . . . . . 45 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3 Fuzzy Logic and Q_learning in IoT 48 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1 Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.2 Fuzzy and Classical Logics . . . . . . . . . . . . . . . . 49 3.1.3 Domains of Application . . . . . . . . . . . . . . . . . . 49 3.1.4 Principle of Fuzzy Logic . . . . . . . . . . . . . . . . . . 49 3.1.5 Fuzzy Logic System’s General Structure . . . . . . . 50 3.1.6 Advantages of fuzzy logic . . . . . . . . . . . . . . . . . 51 3.1.7 Disadvantages of fuzzy logic . . . . . . . . . . . . . . . 51 3.2 Q_Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.2.1 Characteristics of Q_learning . . . . . . . . . . . . . . 52 3.2.2 Operation of the Q_Learning process . . . . . . . . 54 3.3 Fuzzy Logic in IoT Systems . . . . . . . . . . . . . . . . . . . . . 54 3.3.1 Fuzzy Logic Functioning in the IoT . . . . . . . . . . 55 3.3.2 Applications of Fuzzy Logic in the IoT . . . . . . . . 55 3.3.3 Implementing Fuzzy Logic in IoT Devices and Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.3.4 IoT Challenges and Drawbacks with Fuzzy Logic 57 3.4 Q_Learning in IoT Systems . . . . . . . . . . . . . . . . . . . . . 58 3.4.1 Q_Learning Functioning in the IoT . . . . . . . . . . 58 3.4.2 Applications of Q_Learning in IoT: . . . . . . . . . . 58 3.4.3 IoTDifficulties andConsequences with Q_Learning 59 3.5 Integration of Fuzzy Logic and Q_learning in IoT . . . . . 59 3.5.1 Complementarity between fuzzy logic and Q_learning 59 3.5.2 Approaches to Integrating Fuzzy Logic and Q_learning 60 3.5.3 Applications of Fuzzy Logic & Q_Learning Integration in IoT . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.5.4 Challenges and Implementation Considerations 61 3.5.5 Future Perspectives and Emerging Research Areas 61 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4 Proposed Solution and Implementation 63 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.1 Problem Statement and Objectives . . . . . . . . . . . . . . . 63 4.1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . 63 4.1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2.1 System Architecture . . . . . . . . . . . . . . . . . . . . . 64 4.2.2 Fuzzy LogicModule . . . . . . . . . . . . . . . . . . . . . 64 4.2.3 Q_learningModule . . . . . . . . . . . . . . . . . . . . . 64 4.2.4 Integration of Fuzzy Logic and Q_learning . . . . . 65 4.3 Implementation: Tools and Libraries . . . . . . . . . . . . . . 65 4.3.1 Equipment Used . . . . . . . . . . . . . . . . . . . . . . . 65 4.3.2 Libraries andModules . . . . . . . . . . . . . . . . . . . 65 4.3.3 Design and Development . . . . . . . . . . . . . . . . . 66 4.4 Application: Smart City Security . . . . . . . . . . . . . . . . . 66 4.4.1 System Setup: . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.4.2 Fuzzy Logic Implementation: . . . . . . . . . . . . . . 67 4.4.3 Custom Q-learning Implementation: . . . . . . . . 68 4.4.4 Simulation Results: . . . . . . . . . . . . . . . . . . . . . . 68 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Côte titre :	MAI/0834