RF-DEGO: A Range Free Localization Algorithm for Non Uniform Node Distributions and Obstacle Environments

Range-free localization algorithms have attracted considerable attention for outdoor wireless sensor network (WSN) positioning because they are less susceptible to environmental factors when estimating inter node distances and require only a few beacon nodes with known locations to rapidly determine all node positions. Among these, the connectivity based DV Hop algorithm has become widely used due to its simplicity and ease of implementation. However, its localization accuracy is limited and it is easily degraded by non uniform node distributions and obstacle environments. To address these shortcomings, this paper proposes a novel range free localization algorithm (RF-DEGO). First, a new distance estimation formula is derived from node connectivity and the probability distribution of distances. Next, the estimated distances are corrected using the local node density along communication paths, and paths identified as detouring around obstacles receive a further correction. Finally, an enhanced hierarchical Grey Wolf Optimization algorithm computes the node positions. Extensive simulation experiments under various network scenarios and parameter settings show that the proposed algorithm outperforms several existing localization methods in both accuracy and computation time, demonstrating superior overall performance and strong competitiveness.