Hybrid PSO and Evolutionary Game Theory Protocol for Clustering and Routing in Wireless Sensor Network

Abstract

Compared with traditional networks, WSNs have more limited resources such as energy, communication, computing, and storage. The problem of how to achieve energy saving, extend network life cycle, and improve network performance under these limited resources has always been an issue of great interest in WSN research. However, existing protocols do not consider that sensor nodes within the BS threshold may not be clustered. These nodes can directly transmit data to the BS. This simplifies the cluster routing process of the entire WSN and saves more energy. This paper introduces an efficient, and energy-efficient, clustering and equalization routing protocol called the PSOLB-EGT protocol. This protocol introduces a new approach by combining improved particle swarm optimization (PSO) and evolutionary game theory (EGT) algorithms to address the problem of maximizing the network lifetime. The operation of the wireless sensor network is divided into an initialization phase and a data transmission phase. In the initialization phase of the wireless sensor network, the improved PSO algorithm is used to establish clusters and select CHs in areas other than the BS threshold. Entering the data transmission phase, we analyze this problem from the perspective of game theory. We use improved noncooperative evolutionary game theory to build models to solve the problem of the energy waste caused by routing congestion. The proposed PSOLB-EGT protocol is intensively experimented with a number of topologies in various network scenarios, and the results are compared with the well-known cluster-based routing protocols that include the swarm intelligence-based protocols. The obtained results prove that the proposed protocol has increased 9%, 8%, and 5% compared with the ABC-SD protocol in terms of network life, network coverage, and amount of data transmitted, respectively.

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