Coverage Holes Recovery in Hybrid Sensor Networks Based on Key Perceptual Intersection

Wireless sensor networks are widely used in various applications such as military surveillance, wildlife monitoring, industrial process monitoring and so on. Detecting and recovering from coverage holes is a major issue in wireless sensor networks. The gradual energy depletion of sensor nodes with limited battery energy leads to dysfunction of some of the nodes, thus creating holes in the monitored area. In this paper, propose a method to detect and recover coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit, called an agent. Then, the percentage of each cell covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation indicates show that the algorithm reduces the network energy consumption, reduces the distance travelled by the mobile nodes and increases the percentage of network holes recovery as compared to other algorithms.