A Fish Swarm Inspired Holes Recovery Algorithm for Wireless Sensor Networks

Abstract

Wireless sensors networks (WSNs) have be applied to a number of fields such as environment monitoring, military surveillance, data collection and etc. However, the coverage holes problem are usually caused by some undesirable reasons, such as random deployment of sensors, energy consumption imbalance, unethical attack and hardware failures. The holes affect capabilities of WSNs greatly, so its recovery is one of the pivotal problems in WSNs. In order to enhance the performance of holes recovery, a robust approach based on an improved artificial fish swarm algorithm is presented in this paper. The movement of mobile nodes is analogized to the motion of artificial fish with the network coverage rate as objective function. Besides the classic artificial fish motion such as prey, follow and swarm, two novel fish motions called as leap and rebirth are also presented to enhance the convergence of this algorithm. An approach of self-adaptive visual range and step length for fish motion are adopted when updating the status of artificial fish. Simulation experiments show the effectiveness and robustness of the algorithm. The holes can be recovered efficiently without location information and holes detection using the least amount of mobile nodes. The network coverage is improved significantly with this proposed algorithm.