Reconstruction of coverage hole model and cooperative repair optimization algorithm in heterogeneous wireless sensor networks

Abstract

Coverage hole repair not only determines whether the heterogeneous wireless sensor networks (HWSNs) can work normally and effectively, but also determines the network coverage, performance and time to live. The sensor nodes may be covered by random deployment or network failure during the network operation. Therefore, coverage hole repair is a very critical and challenging problem in HWSNs. In this paper, we reconstruct the hole model and proposes a multi-factor collaborative hole repair optimization algorithm (FCH-ROA) between HWSNs nodes. The algorithm considers the distance between the virtual repair node and the mobile node, the energy consumption during the mobile repair process and the confidence of the node to be repaired. A Pearson-like fuzzy matching relationship between the virtual repair node and the mobile node is established to cover the hole repair. Firstly, the hole model is reconstructed, and the Voronoi polygons are randomly divided into the static nodes of the HWSNs in the two-dimensional square monitoring area. Secondly, the key areas of the static nodes are connected in the counterclockwise direction to determine the hole areas. According to the different concave shapes, the convexity is degenerate into a convex hull, and the convex hull center is used as the base point. The Delaunary triangulation is combined with the bulge vertices to calculate the position of the virtual repair node. Finally, based on the relative distance of the nodes, residual energy, and the node of criticality, the multi-factor synergy matching decision table between the virtual repair node and the mobile node, according to the decision table, the mobile node performs the finite distance movement to realize the repair optimization of the coverage hole. Simulation experiments show that compared with the existing coverage hole algorithm, FCH-ROA algorithm improves network coverage and extends network life cycle.