Anchor Nodes Positioning for Self-localization in Wireless Sensor Networks using Belief Propagation and Evolutionary Algorithms

TL;DR

This paper presents a multi-objective optimization approach combining belief propagation and evolutionary algorithms to efficiently position anchor nodes for accurate self-localization in wireless sensor networks, reducing costs and improving performance.

Contribution

It introduces a novel algorithm that minimizes both localization error and the number of anchor nodes using belief propagation and evolutionary algorithms.

Findings

Reduced localization error compared to existing methods

Lower number of anchor nodes needed for effective localization

Decreased energy consumption in sensor networks

Abstract

Locating each node in a wireless sensor network is essential for starting the monitoring job and sending information about the area. One method that has been used in hard and inaccessible environments is randomly scattering each node in the area. In order to reduce the cost of using GPS at each node, some nodes should be equipped with GPS (anchors), Then using the belief propagation algorithm, locate other nodes. The number of anchor nodes must be reduced since they are expensive. Furthermore, the location of these nodes affects the algorithm's performance. Using multi-objective optimization, an algorithm is introduced in this paper that minimizes the estimated location error and the number of anchor nodes. According to simulation results, This algorithm proposes a set of solutions with less energy consumption and less error than similar algorithms.