Abstract
The rapid advancement in Wireless Sensor Network (WSN) technology has enabled smart environments to provide ubiquitous real-time applications in various fields such as industry, smart city, transport, health and Internet of Things (IoT). Energy is the most significant resource in WSNs as it has a direct effect on their lifetime. The efficient use of energy is required for the lifetime extension of WSNs. One of the well-known methods for achieving high scalability and efficient resource allocation in WSN is a clustering of sensor nodes. In this paper, the Chicken Swarm Optimization based Clustering Algorithm (CSOCA) is proposed to improve energy efficiency in WSNs. The chicken swarm optimization is discretized by applying a sigmoid function to individuals. Moreover, we proposed CSOCA with Genetic Algorithm (CSOCA-GA) which is an improvement to CSOCA by employing the Genetic Algorithm's processes in CSOCA. CSOCA-GA utilizes crossover and mutation processes for individuals with low fitness value to extend the population diversity. CSOCA and CSOCA-GA are tested and compared with other similar algorithms to confirm their effectiveness in terms of extending WSN lifetime and reducing energy consumption.
Highlights
The growing importance of Internet of Things (IoT) applications in everyday life has revolutionized the lifestyle choices of people
In Cluster Heads Election phase, Chicken Swarm Optimization (CSO) is used for election decision problem by selecting the best nodes that work as cluster head (CH) considering minimizing the consumed energy per round( i.e., minimize the value in equation Eq.12)
Chicken Swarm Optimization based Clustering Algorithm (CSOCA)-genetic algorithms (GA) improves the network lifetime in terms of the first node dies compared with EODC, GCDC, LEACHMS, LEACHPSO, and Shuffled Frog-leaping and Firefly Algorithms (SFFA) up to 25%, 77%, 78%, 80 and 22%, respectively and improves the network lifetime in terms of half nodes die compared with EODC, GCDC, LEACHMS, LEACHPSO, and SFFA up to 35%, 9%, 7%, 10%, and 8%, respectively
Summary
The growing importance of Internet of Things (IoT) applications in everyday life has revolutionized the lifestyle choices of people Many such IoT based applications require node position and node location for efficient communication of data between nodes [1]. The combination of WSN and the Internet of Vehicles (IoV) [4] can get a better understanding of the surrounding environment to prevent any hazardous situations This makes WSNs have many potential applications. The problem of CH selection in sensor network is a NP problem since the energy balanced optimal data aggregation cannot take place in a polynomial time [10]. Modified CSO is utilized to optimize the CHs selection in WSNs and to minimize consumed energy. The rest of the paper is organized as follows: Section II reviews related work in brief.
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