Clustering and Routing Protocols for Wireless Sensor Networks: Design and Performance Evaluation

Abstract

In this thesis, we propose a suite of Evolutionary Algorithms (EA)-based protocols to solve the problems of clustering and routing in Wireless Sensor Networks (WSNs). At the beginning, the problem of the Cluster Heads (CHs) selection in WSNs is formulated as a single-objective optimization problem. A centralized weighted-sum multi-objective optimization protocol is proposed to find the optimal set of CHs. The proposed protocol finds a predetermined number of CHs in such way that they form one-hop clusters. The goal of the proposed protocol is to enhance the network’s energy efficiency, data delivery reliability and the protocol’s scalability. The formulated problem has been solved using three evolutionary approaches: Genetic Algorithms (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) and we assessed each of their performance. Then, a PSO-based hierarchical clustering protocol that forms two-hop clusters is proposed to investigate the effect of the number of CHs on network’s energy efficiency. This protocol enhances the WSN’s energy efficiency by setting an upper bound on the number of CHs and trying to minimize the number of CHs compared to that upper bound. It also maximizes the protocol’s scalability by using two-hop communication between the sensor nodes and their respective CHs. Then, a centralized weighted-sum PSO-based protocol is proposed for finding the optimal inter-cluster routing tree that connects the CHs to the Base Station (BS). This protocol is appropriate when the CHs are predetermined in advance. The proposed protocol uses a particle encoding scheme and defines an objective function to find the optimal routing tree. The objective function is used to build the trade-off between the energy-efficiency and data delivery reliability of the constructed tree. Finally, a centralized multi-objective Pareto-optimization approach is adapted to find the optimal network configuration that includes both the optimal set of CHs and the optimal routing tree. A new individual encoding scheme that represents a joint solution for both the clustering and routing problems in WSNs is proposed. The proposed protocol uses a variable number of CHs, and its objective is to assign each network node to its respective CH and each CH to its respective next hop. The joint problem of clustering and routing in WSNs is formulated as a multi-objective minimization problem with a variable number of CHs, aiming at determining an energy efficient, reliable ( in terms of data delivery) and scalable clustering and routing scheme. The formulated problem has been solved using two state-of-the-art Multi-Objective Evolutionary Algorithms (MOEA), and their performance has been compared. The proposed protocols were developed under realistic network settings. No assumptions were made about the nodes’ location awareness or transmission range capabilities. The proposed protocols were tested using a realistic energy consumption model that is based on the characteristics of the Chipcon CC2420 radio transceiver data sheet. Extensive simulations on 50 homogeneous and heterogeneous WSN models were evaluated and compared against well-known cluster-based sensor network protocols.