ClusterLoc: Exploiting Short Range Wireless Technologies for Energy Efficient Localization Author Mohammed Tarek Abdellatif
Faculty Advisor Khaled Harras, Ph.D.
Category Postgraduate
Abstract With the ubiquity of WiFi-enabled smartphones, and large-scale access point deployment, WiFi-based localization is one of the most promising indoor localization systems. Existing WiFi localization solutions, however, exhibit high power demand due to the periodic updates required, which raises the barrier for deployment since battery life is a crucial resource. In this poster, we propose a cluster-based localization algorithm, integrate it with our GreenLoc architecture, and evaluate its performance via simulation and prototype implementation. We design and implement a proximity-based clustering algorithm (CLoc) as a representative strategy that we integrate with GreenLoc. CLoc uses low energy wireless technologies, such as Bluetooth, to detect and cluster individuals moving together. It then assigns a group representative to act as a designated cluster-head (CH) that would be constantly tracked. The location of other group members is then inferred so long as they remain within proximity of the corresponding cluster-head. CLoc dynamically handles the merger or splitting of clusters as a result of mobility. We implement a prototype of GreenLoc and test its operation over different Android devices. We also evaluate the impact of our architecture by studying the performance of our proposed CLoc strategy in our prototype as well as via the QualNet simulator to obtain more scalable results. Our initial results show that we can achieve up to 60% energy reduction with a relatively small degradation in localization accuracy averaging 2 meters. This accuracy reduction is non-impactful given the typical applications expected to leverage our system.
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