NDP: Network Division Positioning for Irregular Multi-Hop Networks

Accurate geographical information of nodes is crucial for network applications. However, many existing positioning algorithms face challenges in achieving efficient, accurate, and robust performance when applied to irregular networks with holes or obstacles. Therefore, we introduce a new algorithm, named Network Division Positioning (NDP), to tackle this issue. In NDP, we use a similarity function to derive the distance between neighboring nodes and explore the routing concurrently, facilitating efficient distance measurement. Next, we analyze measurement errors between landmark nodes to define a threshold that filters out incorrect distances, ensuring measuring and positioning accuracy. We initially identify collinearity issues to enhance positioning robustness by examining the positional relationship between unpositioned nodes and their nearest landmark node. Subsequently, we addressed the poor positioning results and built the subnetwork utilizing the nearest landmark node and its associated measurement distance, seeking the most accurate and robust estimated position within this subnetwork. The simulation results demonstrate that NDP outperforms state-of-the-art algorithms in terms of efficiency, accuracy, and robustness when dealing with various irregular networks. Specifically, NDP enhances positioning accuracy by at least 40.82% in terms of the average median.