Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks

Network coverage plays an indispensable role in determining the Heterogeneous Wireless Sensor Networks (HWSNs) potentiality towards the process of monitoring the physical world with maximized service quality. This HWSNs possesses the limitations of complex deployment environments, poor node reliability and restricted energy which directly influences the transmission and data collection process of sensor nodes and minimizes the network performance. An efficient network coverage controlling mechanism need to be devised and implemented for improving the network service quality, lifetime, reducing energy consumption, and achieve rational utilization of limited resources. In this paper, a Hybrid Sand Cat Swarm Optimization Algorithm-based Reliable Coverage Optimization Strategy (HSCOARCS) is proposed for preventing the issue of coverage redundancy and coverage blind areas, and maximally optimize the sensor node deployment location to achieve reliable sensing and monitoring of target area. This proposed HSCOARCS is implemented over a HWSN coverage mathematical model which represents a problem of combinatorial optimization. The hybridization of Sand Cat Swarm Optimization Algorithm (SCSOA) is achieved for enhancing the speed of the global convergence with the initial population achieved using the method of Gaussian distribution. It targets on the optimization objectives that aids in minimizing the network costs and improve its coverage. The simulation results of the proposed HSSCSOA confirmed better network reliability of 21.38%, network coverage of 19.76%, and minimized energy consumption of 17.92% with different number of sensor nodes on par with the benchmarked schemes used for comparison.