| In Wireless Sensor Networks (WSNs), hierarchical network structures have the advantage of providing scalable and resource efficient solutions. Thus, finding an optimal way to generate clusters is an important topic in WSNs. To achieve this goal, this master's thesis improves the Hierarchical Agglomerative Clustering (HAC) algorithm by proposing a Distributed the HAC (DHAC) algorithm. With simple six-step clustering, DHAC provides a bottom-up clustering approach by grouping similar nodes together before the Cluster Head (CH) is selected. DHAC can accommodate both quantitative and qualitative information types in clustering, while offering flexible combinations using four well-studied the HAC algorithm methods, SLINK, CLINK, UPGMA and WPGMA. With automatic CH rotation and rescheduling, DHAC avoids reclustering and achieves uniform energy dissipation through the whole network. Simulation results in the NS2 platform demonstrate the longer network lifetime of the DHAC than the better-known clustering protocols, LEACH and LEACH-C. |
