sDLSne - Improved scalable Distributed Least Squares localization with minimized communication

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 local 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. This restriction have been overcome by scalable DLS (sDLS), which enabled to use the idea of DLS in large WSNs for the first time. Although, sDLS outperforms DLS for large networks, cost of communication and computation is initially higher for small networks, caused by data updates. The approach, presented in this work, dramatically reduces cost of communication of sDLS. Additionally, a new approach of distance estimation is applied to original DLS. In contrast to earlier simulations, this leads to improved localization, which is used for fairer comparison.