Optimizing Residual Energy and Delay in WSN Routing using Particle Swarm Optimization

For reliable use of wireless sensor networks, energy, and delay optimization are equally crucial.Packets must therefore be routed via the path with the least amount of delay and energy consumption possible.For both clustering and non-clustering WSN scenarios, this problem remains an exploratory challenge.The optimization problem is presented here as a multi-objective problem in the clustering and non-clustering WSN contexts.A new energy model is presented for Wireless Sensor Networks (WSN) that has two more components: switching between transmission and reception modes and using the CSMA/CA protocol for packet transfer.This optimization problem is solved in two working environments: clustering and non-clustering, using a stochastic optimization technique particle swarm optimization (PSO) that uses particles to explore the search space.The proposed PSO-based approach increases WSN lifetime by 45% over ACO and twice as much as GA when compared to Genetic Algorithm (GA) and Ant Colony Optimization (ACO).The result additionally demonstrates the WSN's delay-tolerant routing in two operational scenarios.The proposed routing framework offers the potential for prolonging the lifetime of WSNs in many real-time applications, including area monitoring, healthcare monitoring, habitat monitoring, and industrial monitoring.