Comparative Analysis of Chicken Swarm Optimization and IbI Logics Algorithm for Multiobjective Optimization in k-Coverage and m-Connectivity Problem

Internet of Things (IoT)-based Wireless Sensor Network (WSN) plays a crucial role in several applications across industries, various monitoring systems, smart cites, etc. Most of the modern applications require proper coverage and connectivity constantly from the monitoring objective, regardless of sensor node failure. Thus, the optimal deployment of sensor nodes with k-coverage and m-connectivity as constraints will improve the Quality of Service (QoS) in the IoT-based WSN. Multiobjective optimization is an appropriate approach to solve these kind of one or more objective. Hence, an Incomprehensible but Intelligible-in-time (IbI) logic and Chicken Swarm Optimization (CSO) are proposed to achieve better QoS in IoT-based WSN. The proposed algorithms are compared with the Artificial Bee Colony (ABC) algorithm, and a statistical analysis is also performed to illustrate the efficiency of proposed algorithms.