DCP-AHS: A High-Performance Distributed Cooperative Positioning Model for Concave Networks

Node positioning is an essential function of wireless networks and serves as the foundation for many applications. In the existing works, the cooperative positioning approaches have been extensively studied and are shown to be effective for scenarios with energy and cost constraints. However, these approaches may not perform well in concave networks with holes or obstacles. To address this issue, this paper proposes a
distributed cooperative positioning model with adaptive hop-range selection
(DCP-AHS for short) for concave networks. DCP-AHS first uses a low-complexity and fast convergent distance estimation method based on the local neighbor nodes. It then uses an adaptive hop-range selection method based on the residual analysis between pairs of anchors. Within the hop range, an unknown node uses multi-lateration with the optimal weight function to determine its estimated position. Finally, a weighted Bounding-Box method with the virtual anchor is employed to avoid significant position estimation errors caused by the collinearity issues. Simulation results demonstrated that the proposed DCP-AHS significantly outperformed the existing algorithms regarding efficiency, accuracy, and stability in various concave networks. Specifically, our proposed model achieved a median improvement of 16.62% to 81.65% in positioning accuracy compared to the comparison algorithms.