Anchor Nodes Positioning for Self-localization in Wireless Sensor Networks using Belief Propagation and Evolutionary Algorithms
This work addresses cost reduction and performance improvement for wireless sensor network deployment in hard environments, but it appears incremental as it builds on existing belief propagation and evolutionary methods.
The paper tackles the problem of reducing the number of expensive anchor nodes in wireless sensor networks for self-localization by introducing a multi-objective optimization algorithm that minimizes estimated location error and anchor count, achieving lower energy consumption and error compared to similar algorithms in simulations.
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.