Optimizing communication and computational costs based on the edge participation probability and the evolved variational message passing algorithm in wireless sensor networks Abstract   Cooperative localization has become an important issue for several applications. One of the most efficient techniques for cooperative localization is the Evolved Variational Message Passing (EVMP) algorithm. This algorithm offers accurate position estimates with less computational complexity compared to other cooperative localization algorithms. What is more, communication cost is a challenging issue facing any localization technique which increases in loopy networks. Consequently, we propose in this paper a reevaluated EVMP version which can meet this challenge. We propound a specific Edge Participation Probability technique, namely EPP, for each edge in the considered network. This technique facilitates the distributed implementation without graph transformation which is highly adequate in loopy networks. Experiments and realistic simulations show that the proposed technique offers considerable localization accuracy for very low processing complexity and cost.

Agent Localization for WSN based on Variable StepSize ILMS and DLMS In this paper, a new localization approach is proposed based on the concept of Diffusion LMS (DLMS) to mitigate node and link failures in Incremental LMS (ILMS). It takes advantage of the known locations of powerful anchor nodes to estimate the location of tremendous unknown agentsnodes in Wireless Sensor Network (WSN). In addition, new techniques are developed according to the idea of Variable StepSize (VSS) for ILMS and DLMS algorithms to extremely enhance the performance and convergence rate. Furthermore, the theoretical performance in terms of Mean Square Deviation (MSD) is derived and evaluated for DLMS and VSSDLMS Algorithms. Simulation results show that the proposed VSSILMS and VSSDLMS algorithms outperform the existing approach. From the Mean Square Error (MSE) point of view, they also have advantage of faster convergence in compare to the traditional ILMS and DLMS algorithms.