CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

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. INDEX TERMS Wireless sensor network, clustering, Internet of Things, genetic algorithm, chicken swarm optimization.

Energy‐efficient routing protocol and optimized passive clustering in WSN for SMART grid applications

“…CSO is the latest bio-inspired algorithm for the single-objective optimization (CSO). 75 CSO simulates a chicken swarm's hierarchical order and behavior in the food search, where a chicken receives a potential solution to the optimization dilemma. CSO mainly uses the four laws to idealize the behavior of chickens:…”

Snapshot of Energy Optimization Techniques to Leverage Life of Wireless Sensor Network

“…They are designed on the basis of cognitive behaviour of certain biologically inspired entity e.g., ant, honeybee, firefly, frog, fish, cat, dolphin, etc. The studies that has used swarm intelligence linking with energy efficiency are as follows: Gray-wolf optimization (Arafat et al [91]), Bat algorithm (Cao et al [92]), flocking control scheme using swarm intelligence (Dai et al [93]), firefly mating optimization (Faheem et al [94]), fish algorithm with k-means clustering (Feng et al [95]), multi-swarm optimization (Hasan et al [96]), Harris' Hawk optimization (Houssein et al [97]), particle swarm optimization (Mukherjee et al [98]), Chicken swarm optimization (Osamy et al [99]), reinforcement learning with swarm intelligence (Wei et al [100]). However, different approaches have their own structure of working which is implemented on WSN on different targets of optimization towards energy efficiency.…”

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

“…1) Clustering in IoT Networks: Recently, some research efforts have been devoted to use SI techniques to solve clustering issues in wireless IoT networks. For example, the work in [186] suggests an SI-based clustering framework for wireless IoT sensor networks. The focus is on the energy efficiency improvement and energy usage balance through a fitness function that is optimized by a chicken swarm optimization algorithm.…”

“…As an example, PSO can provide intelligent 3D UAV placement solutions for indoor users in small coverage areas that is promising for wireless convergence in massively crowded events. In terms of clustering and routing solutions for IoT networks, SI has also many applications, ranging from providing IoT sensor network clustering for energy efficiency improvement and energy usage balance [186] to enabling optimized routing models for IoT ecosystems such as vehicular networks [191]. Recently, SI has been used in smart city and smart grid domains, where bio-inspired algorithms such as PSO, ACO help realize intelligent services such as vehicular traffic prediction [197] and cost-efficient power management [200].…”