Reducing the Calculation for Precise Localization in Wireless Sensor Networks

Large numbers of cheap and easily deployable wireless sensors enable area-wide monitoring of both urban environments and inhospitable terrain. Due to the random deployment of these sensor nodes, one of the key issues is their position determination. Noisy distance measurements and the highly limited resources of every sensor node, due to tiny hardware and small battery capacity, demand the development of robust, energy aware, and precise localization algorithms. This paper presents a new fine-grained localization method RAL (Resource Aware Localization Algorithm). This algorithm uses a least squares adjustment approach which significantly reduces the computational and energy requirements of the sensor nodes while simultaneously giving a considerable increase in accuracy. Simulations show that very low localization errors of less than 1% can be achieved. The presented method has been analyzed with three commonly used numerical techniques - Normal Equations (NE), QR-Factorization (QR), and Singular-Value Decomposition (SVD). Numerical simulation results showed that the complexity on every node can be reduced by more than 79% for NE and more than 99% for QRF and SVD. In addition, the proposed algorithm is robust with respect to high input errors and has comparably low communication requirements.