Reinforcement Algorithm Of Directional Sensor Network Coverage Based On Virtual Potential Field

The wireless sensor network (WSN) is composed of a large number of low-cost, low-power, self-organized networks of micro-sensor nodes. In recent years, with its diversity and breadth, WSN is widely used in military and civilian fields such as battlefield surveillance, environmental monitoring, rescue and relief, bio-medical, agricultural and industrial controls. Coverage control is a fundamental problem in wireless sensor networks. It is used to describe quality of monitoring service. It has important implications on the rational allocation of network resources, betterment of environment perception, access to information tasks, enhancement of network viability. Unlike omnidirectional sensor, the sensing region to the directional sensor is a fan-shaped area. But the fan-shaped area can be rotated around the node location to switch to a different directions. Therefore, the past coverage enhancement algorithms for omnidirectional sensor network do not apply to the directional sensor network directly. People need to find the new solution, the main contents of this paper are as follows.This paper analyzes the research situation of the coverage enhancement algorithms in the directional sensor network. Based on existing algorithms, it introduces a new virtual potential field based coverage algorithm for directional sensor networks (PCAFD) involving the concepts of "Overlapping Centroid" and "Effective Centroid". Through the interaction between overlapping centroid and effective centroid, the algorithm utilizes the areas and positions of the overlapping regions to determine the size of the virtual opposing forces and directions on the nodes. Increased network coverage is quickly achieved by gradually eliminating coverage overlapping regions and blind regions.In addition, in the late stage of the network coverage optimization, it causes frequent calculation and repeated adjustments due to the node reciprocating phenomenon generally appears; secondly, there is no specific treatment to the boundary conditions, and this indirectly lowers the performance of the algorithm. In this paper, the algorithm is improved for above problems. The effectiveness of the algorithm (PCAFD) has been verified by a series of matlab emulations. And it is better than the rest algorithms with better effect at present.