Positioning is a key aspect for many applications in wireless sensor networks. In order to design practical positioning algorithms, employment of efficient algorithms that maximize the battery lifetime while achieving a high degree of accuracy is crucial. The number of participating anchor nodes and their transmit power have an important impact on the energy consumption of positoning a node. This paper proposes a game theoretical algorithm to optimize resource usage in obtaining location information in a wireless sensor network. The proposed method provides positioning and tracking of nodes using RSS measurements. We use the Geometric Dilution of Precision as an optimization metric for our algorithm, with the aim of minimizing the number and power of anchor nodes that collaborate in positioning, thus saving energy. The algorithm is shown to be a potential game, therefore convergence is guaranteed. A distributed low complexity solution for the implementation is presented. The game is applied to WSN and results show the trade-off between power saving and positioning error.

This paper deals with the effect of mesh and cluster-tree topologies (that are defined in 802.15.4/Zigbee or 802.15.4a standards) in the performance of cooperative and range-based localization algorithms. In this type of localization at least three reference or anchor nodes within range are needed for location estimation (two dimensions). Therefore the successful localization depends on the connectivity between nodes. Mesh topology allows complex networks because the connectivity between nodes is high, thus this is an advantage for range-based localization. But the mesh structure is energy-consuming. On the other hand, cluster-tree topology simplifies routing and allows energy saving. But in cluster-tree topology the connectivity between nodes is reduced to parent-children relationships. It affects to the range-based localization because of nodes can not do ranging with all nodes within range. In order to reach a trade-off between energy saving and high connectivity, in this paper we propose two new solutions for the localization in a cluster-tree Wireless Sensor Network.