Glowworm Swarm Optimization (GSO) based energy efficient clustered target coverage routing in Wireless Sensor Networks (WSNs)

Abstract

The Wireless Sensor Networks is a wireless system comprising uniformly distributed, autonomous smart sensors for physical or environmental surveillance. Being extremely resource-restricted, the major concern over the network is efficient energy consumption wherein network sustainability is reliant on the transmittance, processing rate, and the acquisition and dissemination of sensed data. Energy conservation entails reducing transmission overheads and can be achieved by incorporating energy-efficient routing and clustering techniques. Accomplishing the desired objective of minimizing energy dissipation thereby enhancing the network’s lifespan can be perceived as an optimization problem. In the current era, nature-inspired meta-heuristic algorithms are being widely used to solve various optimization problems. In this context, this paper aims to achieve the desired objective by implementing an optimum clustered routing protocol is presented inspired by glowworm's luminescence behavior. The prime purpose of the Glowworm swarm optimization with an efficient routing algorithm is to enhance coverage and connectivity across the network to ensure seamless transmission of messages. To formulate the Objective function, it considers residual energy, compactness (intra-cluster distance), and separation (inter-cluster distance) to provide the complete routing solution for multi-hope communication between the Cluster Head and Sink. The proposed technique’s viability in terms of solution efficiency is contrasted to alternative techniques such as Particle Swarm Optimization, Firefly Algorithm, Grey Wolf Optimizer, Genetic Algorithm, and Bat algorithm and the findings indicate that our technique outperformed others by as glowworm optimization’s convergence speed is highly likely to provide a globally optimized solution for multi-objective optimization problems.