Scheduling and Optimization of Wireless Sensor Networks Lifetime using Soft Computing

The paper presents a novel approach to address the problem of lifetime maximization in Wireless Sensor Networks (WSNs) with limited initial energy constraints. The authors introduce a scheduling approach called Non-disjoint Set Cover (NDSC) and propose a method based on the Particle Swarm Optimization (PSO) algorithm to optimize the lifetime of randomly deployed WSNs. The approach involves dividing the problem into two sub-problems. The first sub-problem focuses on finding the maximum number of NDSCs that achieve full coverage of the targets in the area. PSO, using a heuristic method, is employed to address this sub-problem. The second sub-problem deals with finding an optimal scheduling approach to maximize the WSN lifetime while considering the limited initial energy reserve in each sensor. Again, PSO, based on a heuristic method, is used to tackle this sub-problem. The NDSCs obtained from the first sub-problem serve as inputs for the scheduling in the second sub-problem. The solutions obtained by the proposed NDSC-PSO method are compared with those obtained by the NDSC-GA (Genetic Algorithm) method from the literature. Additionally, the quality of the NDSC-PSO solutions is evaluated by comparing them with exact solutions obtained using an Integer Linear Programming (ILP) method from the literature. The experimental results demonstrate that the NDSC-PSO method outperforms the NDSC-GA method in terms of finding optimal solutions for the same instances. The proposed method effectively maximizes the lifetime of WSNs while considering limited initial energy constraints. The paper contributes to the existing literature by presenting a novel approach that utilizes the PSO algorithm for finding NDSCs and scheduling them in WSNs.