Minimizing Cost of Scalable Distributed Least Squares Localization

Wireless Sensor Networks (WSNs) have been of high interest during the past couple of years. One of the most important aspects of WSN research is location estimation. As a good solution of fine grained localization Reichenbach et al. introduced the Distributed Least Squares (DLS) algorithm, which splits the costly localization process in a complex precalculation and a simple postcalculation which is performed on constrained sensor nodes to finalize the localization by adding locale knowledge. This approach lacks for large WSNs, because cost of communication and computation theoretically increases with the network size. In practice the approach is even unusable for large WSNs. An important assumption of DLS is that each blind node is able to communicate with each beacon node to receive the precalculation and to determine distances to beacon nodes. This restriction have been overcome by scalable DLS (sDLS), which enabled to use the idea of DLS in large WSNs for the first time. In this work we present an adaptation of sDLS, that reduces the cost of update operations which are an integral part of sDLS.