Multi-hop Localization in Large Scale Deployments
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The development of Wireless Sensor Networks (WSNs) is enabled by the recent advances in wireless communication and sensing technologies. WSN have a wide range of scientific and commercial applications. In many applications the sensed data is useless if the location of the event is not associated with the data. Thus localization plays a substantial role in WSNs. Increased dependence on devices and sensed data presses for more efficient and accurate localization schemes. In many Internet of Things (IoT) deployments the area covered is large making it impossible to localize all devices and Sensor Networks (SNs) using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate devices' positions. In small areas SNs require at least three anchor nodes within their transmission range to estimate their location. Despite numerous existing localization techniques, the fundamental behavior of multi-hop localization is, as yet, not fully examined. Thus, we study the main characteristics of multi-hop localization and propose new solutions to enhance the performance of multi-hop localization techniques. We examine the assumptions in existing simulation models to build a more realistic simulation model, while studying and investigating the behavior of multi-hop localization techniques in large scale deployments before the actual deployment. We find that the introduced error follows the Gaussian distribution, but the estimated distance follows the Rayleigh distribution. We use the new simulation model to characterize the effect of hops on localization in both dense and sparse multi-hop deployments. We show that, contrary to common beliefs, in sparse deployments it is better to use long hops, while in dense deployments it is better to use short hops. Using short hops in dense deployments generates a large amount of traffic. Thus we propose a new solution which decreases and manages the overhead generated during the localization process. The proposed solution decreased the number of messages exchanged by almost 70% for DV-Distance and 55% for DV-Hop. Finally, we utilize mobile anchors instead of fixed anchors and propose a solution for the collinearity problem associated with the mobile anchor and use Kalman Filter (KF) to enhance the overall localization accuracy. Through simulation studies, we show that the scheme using a Kalman Filter decreases the estimation errors than using single direction by 31% and better than using weighted averages by 16% . As well, our new scheme overcomes the collinearity problem that appears from using mobile anchor nodes.