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Wireless Sensor Networks (WSNs) have been of high interest during the past couple of years. One of the most important aspect 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 allows to perform an originally complex calculation with high precision on constrained nodes. Besides this advantage, DLS lacks in two harmful constraints concerning practical appliance. On the one hand the algorithm does not scale, i.e. calculation and communication increases with the number of beacon nodes or with network size, respectively. On the other hand DLS even does not work for large networks. An important assumption of DLS is that each blind node can communicate with each beacon node to receive the precalculation and to determine distances to beacon nodes. In this work we present an adaptation of DLS, concerning major changes, which enables DLS to be used in large WSNs for the first time. At the same time computational and communicational cost of each node becomes independent from network size, while precision is kept on the same high level.

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In large wireless sensor networks, low energy consumption is a major challenge. Hence, deployed nodes have to organize themselves as energy efficient as possible to avoid unnecessary sensor and transceiver operations. The energy conserving operations are limited by the task of the network, usually the network has to guarantee complete functionality during its lifetime.The contribution of this paper completes the functionality-aware and energy-efficient clustering algorithm family MASCLE by two innovative algorithms. As already given by the MASCLEalgorithms, the proposed Hex-MASCLE algorithms combine advantages of temporal and spatial network fragmentation. In contrast to previous approaches, the shapes of the basic cells are given by regular hexagons, similar to honeycombs. In the present work, two possible versions for hexagon-based clustering with self-healing abilities are proposed and evaluated.As result, the applying sensor network achieve a significant improve of network lifetime. Additionally, the algorithms are more fault-tolerant against localization errors.