Optimizing topologies in wireless sensor networks: A comparative analysis between the Grey Wolves and the Chicken Swarm Optimization algorithms

Abstract

Finding the suitable solution in constrained environments, such as wireless sensor networks (WSNs), is in the application area of meta-heuristic algorithms. The constrained resources of such networks include: low surplus energy, limited computational power, and small communication bandwidth. Despite this fact, not all meta-heuristic algorithms guarantee suitable energy-efficient routing due to their complexity and the need for specific parameters’ tuning. This had motivated our attempts to benchmark the efficacy of topology control protocols through the application of two meta-heuristic algorithms; the Grey Wolves Optimizer (GWO) and the Chicken Swarm Optimization (CSO) algorithms. In addition to the performance analysis that has been proven through the simulations, the assessment had covered a comparative analysis of the behavior of each algorithms’ operators. The performance indicator was finding the smallest number of active nodes for the network’s operation. These nodes should have high residual energy for the purpose of extending the network’s operation time. The proposed solution penalizes any topology that trades-off the coverage characteristic of the network. Through a number of quantitative experiments, the results showed the dominance of CSO based algorithm as active nodes’ reduction had reached up to 12% of the total numbers of network’s nodes.