| Titre : | Study of Clustering techniques in Wireless Sensor Networks(WSNs) (LEACH and K-means) |
| Auteurs : | Aicha Djehiche, Auteur ; Imene Aloui, Directeur de thèse |
| Type de document : | Monographie imprimée |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2023 |
| Format : | 1vol.(66p.) / ill.couv.ill.encoul / 30 cm |
| Langues: | Anglais |
| Mots-clés: | wireless sensor network, Clustering, LEACH, Energy consumption |
| Résumé : |
The wireless sensor network (WSN) faces a significant challenge due to the limited energy resources of its sensor nodes. Addressing this issue, the clustering technique in WSNs helps in achieving energy efficiency, this study focuses on modifying one of the most widely used Clustering algorithms for data communication in WSNs: LEACH (Low Energy Adaptive Clustering Hierarchy ). The revised version, named "K-means-LEACH," incorporates an intermediate cluster head for efficient data transmission, thereby extending the network’s lifetime and enabling the transmission of more data compared to the original protocol. To evaluate the effectiveness of the proposed algorithm in improving the network’s lifetime, MATLAB conducted simulations. The simulation results confirmed that the modified system outperformed the LEACH protocol, enhancing network lifetime |
| Sommaire : |
List of Figures i General introduction 1 1 Overview Of Wireless Sensor Networks 3 1.1 Introduction . . . . . . . . . . . . 3 1.2 Sensor . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Definition . . . . . . . . . . . . . . . . . . . . . 3 1.2.2 Architecture of Sensor . . . . . . . . . . . . . . 3 1.3 Wireless Sensor Networks . . . . . . . . . . . . . . . 5 1.3.1 Definition . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.2 Architecture of WSN . . . . . . . . . 5 1.3.3 Wireless sensor networks topologies . . . . . . . . . 6 1.3.4 WSN Characteristics . . . . . . . . . . . . . . . . 7 1.3.5 Routing protocols of WSN . . . . . . . . . . . . . 9 1.3.6 Definition of The lifetime of a sensor network . . . 11 1.3.7 Energy consumption in Sensor node . . . . . . . . . . .. 12 1.3.8 Factors involved in energy consumption . . . . . .. . 13 1.3.9 Applications of Wireless sensor networks . . . . . . . 14 1.3.10 Advantages and Disadvantages of WSN . . . . . . . . 18 1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2 Clustering in Wireless Sensor Networks (WSN) 19 2.1 Introduction . . . . . . . . . . . . . 19 2.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.1 Clustering . . . . . . . . . . . . . . . . . . . .. . 20 2.2.2 Cluster . . . . . . . . . . . . . . . . . . . . . . 20 2.2.3 Cluster Head (CH) . . . . . . . . . . . . . . . . .. 20 2.2.4 Base Station (BS) . . . . . . . . . . . . . . . . . . 20 2.2.5 Member Node . . . . . . . . . . . . . . . . . . . . 20 2.3 Clustering attribute taxonomies . . . . . . . .. . . 21 2.3.1 Cluster properties . . . . . . . . . . . . . . . 22 2.3.2 CH properties . . . . . . . . . . . . . . . . .. . 22 2.3.3 Clustering process properties . .. . 23 2.4 Clustering characteristic . . . . . . . . . . . . . . . . . 23 2.4.1 Type of Clustering algorithm . . . . . . . . . . . . 23 2.4.2 The election of the Cluster Heads . . . . . . .. 24 2.4.3 Intra-cluster communication . . . . . . . . . . . . .24 2.4.4 Inter-cluster communication . . . . . . . . . . . . . 24 2.4.5 The level of data aggregation . . . . . . . . . . 24 2.4.6 types of clusters . . . . . . . . . . . . . . . . . . 25 2.5 Benefits of Clustering at WSN . . . . . . . . . . . . . . . . 25 2.5.1 More scalability . . . . . . . . . . . 25 2.5.2 Fault tolerance . . . . . . . . . . . . . . . . . . . 25 2.5.3 Maximizing network life . . . . . . . . . . . . 25 2.5.4 Quality of service . . . . . . . . . . . . 26 2.6 Clustering Techniques . . . . . . . . . . . . . . . . 26 2.6.1 Hierarchical-based . . . . . . . . . . . . . . . . 26 2.6.2 Partitioning-based . . . . . . . . . . . . . . .. . 27 2.6.3 Density-based . . . . . . . . . . . . . . . . . . . 27 2.6.4 Model-based . . . . . . . . . . 2.7 Related works . . . . . . . . . . . . 29 2.7.1 Low-Energy Adaptive Clustering Hierarchy(LEACH) . . . . . . . . 29 2.7.2 Hybrid Energy-Efficient Distributed Clustering(HEED) . . . . . . . 31 2.7.3 Power-Efficient Gathering in Sensor Information Systems (PEGASIS) 33 2.7.4 Chain-cluster based mixed routing (CCM) . . . . . . . . . . 34 2.7.5 Hybrid Clustering Tree-based Energy(HCTE) . . . . . . . . . 35 2.7.6 Cluster Head Election mechanism using Fuzzy Logic (CHEF) . . . . 37 2.7.7 Weight-Based Clustering Protocols (WCR) . . . . . . .. 38 2.7.8 Unequal Clustering Size (UCS) . . . . . . . . . . . . . 38 2.7.9 Energy Efficient Unequal Clustering (EEUC) . . . . . . . . . . .. 39 2.7.10 Threshold-sensitive energy efficient sensor network(TEEN) . . . . . 40 2.8 Protocols Comparison Table . . . . . . . . . . . 42 2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . 43 3 Conception 44 3.1 Introduction . . . . . . . . . 44 3.2 Description of the techniques studied . . . . . . . . . . . 45 3.2.1 K-MEANS algorithm . . . . . . . . . . . . . .45 3.2.2 LEACH Clustering Standard . . . . . . . . . . . . . 3.3 Description of the proposed approach kmeans-LEACH . . . . . . 50 3.3.1 General description . . . . . . . . . . . . . . . . . 50 3.3.2 Detailed description . . . . . . . . . . . . . . . . . 50 3.3.3 Package Format . . . . . . . . . . . . . . . . . 53 3.3.4 Sequence Diagram . . . . . . . . . . . . . . 53 3.4 Conclusion . . . . . . . . . . . . . . . . . 54 4 Implantation 55 4.1 Introduction . . . . . . . . . . . . . . . . . . . .. . 55 4.2 Simulation Tools . . . . . . . . 55 4.2.1 Software Tools . . . . . . . . . . . . . . . . 55 4.2.2 Hardware tools . . . . . . . . . . . . . 56 4.3 Energy model . . . . . . . . . . . . . . . . . . . . 56 4.4 Network model . . . . . . . . . . . . . . . . . 57 4.5 Program Structure . . . . . . . . . . . . . . . . . .. . . 58 4.5.1 Program interface . . . . . . . . . . . . . . 58 4.5.2 Deployment Nodes . . . . . . 4.5.3 K-means Clustering . . . . . . . . . . . . . . . 60 4.5.4 LEACH Clustering . . . . . . . . . . . . .. 61 4.5.5 K-means-LEACH Approach . . . . . . . . 62 4.6 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 63 4.6.1 Simulation parameters . . . . . . . . . . . . . 63 4.6.2 Node parameters . . . . . . . . . . . . . . . . . 63 4.6.3 Energy parameters . . . . . . . . . . . . .. . 63 4.7 Results and discussion . . . . . . . . . . . . . . . . 64 4.7.1 Energy consumption . . . . . . . . . . . . . 64 4.7.2 processing time . . . . . . . . . . . . . . . 65 4.8 Conclusion . . . . . . . . . . . . . . 66 General conclusion 67 Bibliography 68 |
| Type de document : | Mémoire master |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
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| Minf/812 | Mémoire master | bibliothèque sciences exactes | Consultable |
