TUNICATE SWARM BASED CLUSTERING AND ROUTING ALGORITHM FOR INTERNET OF THINGS

Document Type : Original Article

Authors

1 Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

2 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University

3 Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University

Abstract

Wireless Sensor Networks (WSNs) are an essential part of the Internet of Things (IoT). Indeed, the usage of efficient routing algorithms makes IoT applications work better. Since sensors are connected with limited sources of energy, some sensor nodes lose energy in a short time. This can affect the network lifetime. This paper proposes a routing algorithm that works on extending the network lifetime. The proposed algorithm uses Tunicate Swarm Algorithm (TSA), which is a new bio-inspired algorithm. TSA-based clustering is used to select the best cluster heads. Many parameters are considered while selecting the optimal cluster heads such as distance and energy parameters. TSA-based routing is used to create efficient paths from the cluster head to the base station. The path length and the number of hops in the path are considered during creating the paths. The proposed algorithm is compared with three of the most used metaheuristic-based routing algorithms like Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO). The comparison evaluates the performance of the TSA-based routing algorithm. TSA-based clustering is used with all the algorithms that are compared. The comparison proves that the proposed algorithm extends the lifetime of the network more than the other algorithms. The time before half of the nodes were dead was extended to be 3.17% more than PSO and GWO, and 1.36% more than ACO.

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