Using Micro-Genetic Algorithms to Improve Localization in Wireless Sensor Networks

Abstract

Abstract-Wireless sensor networks are widely adopted in many location-sensitive applications including disaster management, environmental monitoring, military applications where the precise estimation of each node position is inevitably important when the absolute positions of a relatively small portion as anchor nodes of the underlying network were predetermined. Intrinsically, localization is an unconstrained optimization problem based on various distance/path measures. Most of the existing localization methods focus on using different heuristic-based or mathematical techniques to increase the precision in position estimation. However, there were recent studies showing that nature-inspired algorithms like the ant-based or genetic algorithms can effectively solve many complex optimization problems. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm, as a post-optimizer into some existing localization methods such as the Ad-hoc Positioning System (APS) to further improve the accuracy of their position estimation. Obviously, our proposed MGA is highly adaptable and easily integrated into other localization methods. Furthermore, the remarkable improvements attained by our proposed MGA on both isotropic and anisotropic topologies of our simulation tests prompt for several interesting directions for further investigation.

A Survey of Localization in Wireless Sensor Network

“…Tam et al [82] employed the nearest link as reference to review the information. When there are massive link in dense network and positioning mainly depends on the geometry of the neighbor node topology information, the nearest neighbors may not correspond to the best link.…”

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

“…Hence, a big challenge is how to select and reject information. In the [9] proposed to use the links which are the closest from the agent in consideration. However, the closest neighbors may not correspond to the best links as positioning also depends on the geometric configuration of the agent and its neighbors.…”

MONSOON: A Coevolutionary Multiobjective Adaptation Framework for Dynamic Wireless Sensor Networks

“…As a result, BiS-NET/e requires much less configuration cost for application designers. Also, [14,15,17,[19][20][21] do not consider dynamics in the network, but assumes the network is static.…”

“…Several research efforts have applied genetic algorithms to WSNs, for example, to cluster-based routing [14][15][16][17], data processing [18], localization [19] and node placement [20,21]. Every work uses a fitness function that combines multiple objective values as a weighted sum, and uses the function to rank agents/genes in elite selection.…”