A Flexible Framework for Hierarchical Clustering on Wireless Sensor Networks

Abstract: In Wireless Sensor Networks, a clustering algorithm is a good option to reduce redundant data transmission and organize nodes efficiently for long life span. In this thesis, we mainly introduce, implement and evaluate a novel flexible hierarchical clustering algorithm on middleware layer based on Publish/Subscribe paradigm. The goal of our algorithm is to provide the five features, scalability, energy efficiency, fault tolerance, load balancing and multi-level clustering. Our algorithm includes three protocols. Flat Clustering protocol constructs a 2-level clustering network structure. Function Delegation protocol provides the fault tolerance and load balancing features. Multi-Level Clustering protocol builds up a more than 2-level clustering network structure. The thesis also studies several popular simulators and explicates the design and implementation of our algorithm on the ns-2 simulator. Then, we evaluate the algorithm from different angles. Based on our simulation, we optimize key parameters for our algorithm and find that the performance and the energy efficiency of our algorithm are promising, especially in the bridge topologies.