Predictive Filtering for Adjacency-Based Localization in MANET

Abstract

The Network layer adjacency information in ad-hoc networks can be used for a coarse estimation of the location of mobile sensor nodes in such networks. This method may be particularly useful for collecting approximate location information for a situational awareness system without taking too much bandwidth. However, past research has shown that the accuracy of this method is limited. In this paper, we explore the use of predictive filtering methods for improving the accuracy of adjacency-based coarse localization in MANETs. Using the fact that a mobile node would have a continuous path with smooth physical transitions, we treat abrupt turns and irregular jumps in estimated nodal speeds as noise, and explore the possibility of minimizing this noise by applying predictive filtering techniques. We examine and compare moving average, Kalman filtering and finally a hybrid method, and use simulations to show that a hybrid predictive filtering method could improve the accuracy of coarse localizations significantly.