An Adaptive Fish Swarm-Based Mobile Coverage in WSNs

Abstract

Swarm intelligent algorithms are embedded into sensor networks to achieve perfect coverage with minimal cost. However, these methods are often highly complex and easily fall into the local optimum when balancing coverage and resource consumption. We introduce adaptive improved fish swarm optimization (AIFS) that extricates each node from the local optimum and reduces overlap and overflow coverage. Drawing on the habits of fish, AFIS ensures node mobility with respect to the food concentration at a certain point. Node dispersion shows good compromise under coordination by two presented parameters, namely, food concentration and crowd density. In addition to inheriting properties from traditional fish swarm, the initial random nodes become dispersed without overflow in assisting the proposed jumping and dodging behavior. The resulting network avoids potential local optima and improves the network boundary coverage efficiency. The convergence speed and efficiency of AIFS are verified. Extensive simulation experiments reveal that an improved coverage gain is obtained, and computation cost and overflow waste are reduced.