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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. A good solution of fine grained localization is the Distributed Least Squares (DLS) algorithm, which splits the costly localization process in a complex precalculation and a simple postcalculation. The latter is performed on constrained sensor nodes, finalizing the localization by adding locale knowledge. This approach lacks for large WSNs, because cost of communication and computation theoretically increases with 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 WSN for the first time. Although, sDLS has lower cost of computation than DLS, for large networks, this cost, caused by matrix updates, is pretty high. In this work an adaptation of sDLS is presented, which dramatically reduces cost of computation by circumventing matrix updates as often as possible.