|
| Titre : |
Quality of Service-Driven Services Composition Approaches in Uncertain Internet of Things Environments |
| Type de document : |
document électronique |
| Auteurs : |
Mohamed Siddig Eniass Yagoub ; Fateh Seghir, Directeur de thèse |
| Editeur : |
Setif:UFA |
| Année de publication : |
2025 |
| Importance : |
1 vol (56 f .) |
| Format : |
29 cm |
| Langues : |
Anglais (eng) |
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Internet of Things (IoT) services
Quality of Service (QoS) uncertainty
Fuzzy numbers
Multi-objective optimization
Teaching-Learning-Based-Optimization (TLBO) algorithm |
| Index. décimale : |
004 Informatique |
| Résumé : |
The Quality of Service (QoS)-aware Internet of Things (IoT) Service Composition (QIoTSC)
problem, subject to global QoS-user constraints, is recognized as one of the NP-hard, challenging,
and constrained combinatorial multi-objective optimization issues. In uncertain IoT environments,
the QoS parameters of elementary IoT services are frequently non-deterministic,
exhibiting vague and ambiguous values due to various environmental factors such as changes
in network architectures, communication congestion, and economic policies. Consequently,
QoS ambiguity is considered when formulating the QIoTSC problem. Given that fuzzy numbers
are robust, versatile, and general models for expressing uncertain values, the QIoTSC
problem is formulated as a fuzzy constrained multi-objective optimization (FMOQIoTSC)
one. To address the FMOQIoTSC, a novel Fuzzy Multi-Objective Teaching Learning-Based
Optimization (FMOTLBO) algorithm is developed. Indeed, the non-dominated ranking
method and crowding distance computation from the well-known NSGA-II algorithm are
integrated into FMOTLBO, while the deterministic dominance relation and crisp crowding
distance formula of NSGA-II are adapted to handle the fuzzy ambiguity of QoS values. Additionally,
FMOTLBO utilizes an external archive to store its Pareto-optimal non-dominated
solutions. Moreover, rather than employing the continuous equations from the teaching and
learning processes of the conventional TLBO algorithm to generate new solution positions,
FMOTLBO introduces and applies new discrete methods for positioning learners. Furthermore,
a discarded learner substitution process is incorporated into FMOTLBO to enhance
diversity and prevent the algorithm from becoming trapped in local optima. Comparative
results between FMOTLBO and a recent bio-inspired multi-objective QIoTSC approach,
using real and simulated QoS datasets of varying sizes, demonstrate the superior performance
of FMOTLBO over the compared approach. |
| Note de contenu : |
Sommaire
List of Figures xi
List of Tables xii
General Introduction 1
1 IoT services : Standards and Technologies 4
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 From a traditional thing to a smart thing . . . . . . . . . . . . . . . . . . . 5
4 The architecture of IoT system . . . . . . . . . . . . . . . . . . . . . . . . 6
5 The advantages of Internet of Things . . . . . . . . . . . . . . . . . . . . . 7
6 The disadvantages of Internet of Things . . . . . . . . . . . . . . . . . . . 8
7 The challenges of Internet of Things . . . . . . . . . . . . . . . . . . . . . 8
8 The application fields of Internet of Things . . . . . . . . . . . . . . . . . . 9
9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Preliminaries on Optimization and Fuzzy Numbers 10
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Background on Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1 Single-Objective Optimization . . . . . . . . . . . . . . . . . . . . 11
2.2 Multi-Objective Optimization . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Dominance and Pareto Optimally . . . . . . . . . . . . . 12
2.2.2 Weighted Sum Method . . . . . . . . . . . . . . . . . . 13
2.3 Continuous and Discrete Optimization Problems . . . . . . . . . . 13
3 Preliminaries on Fuzzy Numbers . . . . . . . . . . . . . . . . . . . . . . . 14
4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Problem Formulation and its Solution Approach 20
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2 Mathematical Formulation of the QIoTSC under Fuzzy QoS Properties . . . 20
3 Conventional TLBO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1 Teaching Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 Learning Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4 Proposed FMOTLBO for addressing the FMOQIoTSC problem . . . . . . 27
4.1 Learner Encoding and Population Initialization . . . . . . . . . . . 28
4.2 Fuzzy Non-Dominated Ranking Method . . . . . . . . . . . . . . . 30
4.2.1 Global Fuzzy Constraint Violation . . . . . . . . . . . . 31
4.3 Fuzzy Crowding Distance Computation . . . . . . . . . . . . . . . 32
4.4 External Archive Initialization . . . . . . . . . . . . . . . . . . . . 34
4.5 Discrete Teaching Process . . . . . . . . . . . . . . . . . . . . . . 36
4.5.1 Discrete Subtraction and Addition Selection Operators . . 37
4.6 Discrete Learning Process . . . . . . . . . . . . . . . . . . . . . . 39
4.7 Discarded Learner Substitution Process . . . . . . . . . . . . . . . 40
4.8 External Archive Updating . . . . . . . . . . . . . . . . . . . . . . 40
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4 Experimental Results and Discussions 43
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2 Fuzzy Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3 Fuzzy Dataset Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4 Analysis and Discussion of Comparison Results . . . . . . . . . . . . . . . 47
4.1 Comparative Analysis Based on FCR and FIGD Using Fuzzy Real-
World Dataset Instances QW˜ Smns . . . . . . . . . . . . . . . . . . . 47
4.2 Comparative Analysis Based on FCR and FIGD Using Very Large-
Scale Fuzzy Random Dataset Instances RQËœWSmn
s . . . . . . . . . . 48
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
General Conclusion and Prospects 51
Bibliography 52
|
| Côte titre : |
MAI/1013 |
Quality of Service-Driven Services Composition Approaches in Uncertain Internet of Things Environments [document électronique] / Mohamed Siddig Eniass Yagoub ; Fateh Seghir, Directeur de thèse . - [S.l.] : Setif:UFA, 2025 . - 1 vol (56 f .) ; 29 cm. Langues : Anglais ( eng)
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Internet of Things (IoT) services
Quality of Service (QoS) uncertainty
Fuzzy numbers
Multi-objective optimization
Teaching-Learning-Based-Optimization (TLBO) algorithm |
| Index. décimale : |
004 Informatique |
| Résumé : |
The Quality of Service (QoS)-aware Internet of Things (IoT) Service Composition (QIoTSC)
problem, subject to global QoS-user constraints, is recognized as one of the NP-hard, challenging,
and constrained combinatorial multi-objective optimization issues. In uncertain IoT environments,
the QoS parameters of elementary IoT services are frequently non-deterministic,
exhibiting vague and ambiguous values due to various environmental factors such as changes
in network architectures, communication congestion, and economic policies. Consequently,
QoS ambiguity is considered when formulating the QIoTSC problem. Given that fuzzy numbers
are robust, versatile, and general models for expressing uncertain values, the QIoTSC
problem is formulated as a fuzzy constrained multi-objective optimization (FMOQIoTSC)
one. To address the FMOQIoTSC, a novel Fuzzy Multi-Objective Teaching Learning-Based
Optimization (FMOTLBO) algorithm is developed. Indeed, the non-dominated ranking
method and crowding distance computation from the well-known NSGA-II algorithm are
integrated into FMOTLBO, while the deterministic dominance relation and crisp crowding
distance formula of NSGA-II are adapted to handle the fuzzy ambiguity of QoS values. Additionally,
FMOTLBO utilizes an external archive to store its Pareto-optimal non-dominated
solutions. Moreover, rather than employing the continuous equations from the teaching and
learning processes of the conventional TLBO algorithm to generate new solution positions,
FMOTLBO introduces and applies new discrete methods for positioning learners. Furthermore,
a discarded learner substitution process is incorporated into FMOTLBO to enhance
diversity and prevent the algorithm from becoming trapped in local optima. Comparative
results between FMOTLBO and a recent bio-inspired multi-objective QIoTSC approach,
using real and simulated QoS datasets of varying sizes, demonstrate the superior performance
of FMOTLBO over the compared approach. |
| Note de contenu : |
Sommaire
List of Figures xi
List of Tables xii
General Introduction 1
1 IoT services : Standards and Technologies 4
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 From a traditional thing to a smart thing . . . . . . . . . . . . . . . . . . . 5
4 The architecture of IoT system . . . . . . . . . . . . . . . . . . . . . . . . 6
5 The advantages of Internet of Things . . . . . . . . . . . . . . . . . . . . . 7
6 The disadvantages of Internet of Things . . . . . . . . . . . . . . . . . . . 8
7 The challenges of Internet of Things . . . . . . . . . . . . . . . . . . . . . 8
8 The application fields of Internet of Things . . . . . . . . . . . . . . . . . . 9
9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Preliminaries on Optimization and Fuzzy Numbers 10
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Background on Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1 Single-Objective Optimization . . . . . . . . . . . . . . . . . . . . 11
2.2 Multi-Objective Optimization . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Dominance and Pareto Optimally . . . . . . . . . . . . . 12
2.2.2 Weighted Sum Method . . . . . . . . . . . . . . . . . . 13
2.3 Continuous and Discrete Optimization Problems . . . . . . . . . . 13
3 Preliminaries on Fuzzy Numbers . . . . . . . . . . . . . . . . . . . . . . . 14
4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Problem Formulation and its Solution Approach 20
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2 Mathematical Formulation of the QIoTSC under Fuzzy QoS Properties . . . 20
3 Conventional TLBO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1 Teaching Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 Learning Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4 Proposed FMOTLBO for addressing the FMOQIoTSC problem . . . . . . 27
4.1 Learner Encoding and Population Initialization . . . . . . . . . . . 28
4.2 Fuzzy Non-Dominated Ranking Method . . . . . . . . . . . . . . . 30
4.2.1 Global Fuzzy Constraint Violation . . . . . . . . . . . . 31
4.3 Fuzzy Crowding Distance Computation . . . . . . . . . . . . . . . 32
4.4 External Archive Initialization . . . . . . . . . . . . . . . . . . . . 34
4.5 Discrete Teaching Process . . . . . . . . . . . . . . . . . . . . . . 36
4.5.1 Discrete Subtraction and Addition Selection Operators . . 37
4.6 Discrete Learning Process . . . . . . . . . . . . . . . . . . . . . . 39
4.7 Discarded Learner Substitution Process . . . . . . . . . . . . . . . 40
4.8 External Archive Updating . . . . . . . . . . . . . . . . . . . . . . 40
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4 Experimental Results and Discussions 43
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2 Fuzzy Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3 Fuzzy Dataset Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4 Analysis and Discussion of Comparison Results . . . . . . . . . . . . . . . 47
4.1 Comparative Analysis Based on FCR and FIGD Using Fuzzy Real-
World Dataset Instances QW˜ Smns . . . . . . . . . . . . . . . . . . . 47
4.2 Comparative Analysis Based on FCR and FIGD Using Very Large-
Scale Fuzzy Random Dataset Instances RQËœWSmn
s . . . . . . . . . . 48
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
General Conclusion and Prospects 51
Bibliography 52
|
| Côte titre : |
MAI/1013 |
|
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