An improved MDS-MaP localization algorithm based on weighted clustering and heuristic merging for anisotropic wireless networks with energy holes

Abstract

The MDS-MAP (multidimensional scaling-MAP) localization algorithm utilize almost merely connectivity information, and therefore it is easy to implement in practice of wireless sensor networks (WSNs). Anisotropic networks with energy hole, however, has blind communication spots that cause loss of information in the merging phase of MDS-MAP. To enhance the positioning accuracy, the authors propose an MDS-MAP (CH) algorithm which can improve the clustering and merging strategy. In order to balance the effect of energy consumption and the network topology stabilization, we present a weighted clustering scheme, which considers the residual energy, the degree of connectivity nodes and node density. As the original MAD-MAP method poses a limitation of merging condition, the authors relax the merging requirement and present a heuristic estimation method for lost connectivity over energy holes. Simulation results show that the improved MDS-MAP (CH) localization algorithm has achieved higher localization accuracy, better-balanced energy consumption and stronger network robustness.