On Improving the Precision of Localization with Minimum Resource Allocation

Autonomous localization of nodes in wireless sensor networks is essential to minimize the complex self organization task and consequently enhancing the overall network lifetime. Recently, precise localization algorithms are impeded by multi path propagation of signals originated by reflections at walls or other objects in the environment. However, the localization error can be reduced by applying statistical methods that consider the mass of input information provided in large WSNs. In this paper we present the "Iterative DLS"-algorithm (iDLS), a new localization method that reduces the error of the initial position estimate by more than 46% and finally allows a precision of 3 m (fieldsize: 100 mtimes100 m) at noisy input. This is achieved by extending the known "distributed least squares"-algorithm by a refinement-phase, where sensor nodes provide their initial position to neighbors. This algorithm places an absolute minimum of computational requirement on the resource constrained sensor nodes.