Increasing Localization Precision in Sensor Networks with Mobile Beacons – a Genetic Path Planning Approach

This work describes a genetic algorithm based approach to approximate an optimal path for a mobile beacon node in a grid of stationary wireless sensors. As the beacon moves over the field of sensors it broadcasts its location. Sensors that are currently in the proximity of the beacon will receive this communication and can then use several of these messages to compute estimates on their locations. An optimal path is defined as a path that will result in the highest overall precision of location estimates among sensors given a maximum path length for the beacon. We assume that sensors are uniformly deployed in a predefined deployment area. We evaluate location precision calculating the maximum achievable accuracy using Cramer Rao Bound (CRB) for unbiased evaluation. We describe the path of the mobile beacon using strings of 'X' and 'Y' coordinate pairs. As paths are described using strings, they lend themselves to genetic algorithm manipulations. Thus, to improve on the localization precision given by the path of the mobile beacon, a genetic optimization approach is used. Multiple genetic operators including mutation, splicing, selection and cross-over are used to create new paths which are evaluated for precision. Details of the genetic optimization approach to find better and better generations of paths are given. Extensive optimization simulations are performed in order to look for paths resulting in high precision. We describe the best paths found as well as look at the relationship of maximum path length versus precision of overall location estimates.