Component-Based Localization for Wireless Sensor Networks Combining Angle and Distance Information

Location information of sensor nodes is critical to many wireless sensor network (WSN) applications and protocols. Many localization algorithms have been proposed in the past several years. However, they cannot successfully localize all the localizable nodes in sparse networks in which the average node degree is usually less than 10. In this paper, we propose two component based localization algorithms that can localize many more nodes than state-of-the-art solutions in sparse networks. We first develop BCLA that uses both common nodes and measured distances between nodes of adjacent components to merge components. BCLA outperforms CALL in which only distance measurements are used to merge components. In order to further improve the performance of BCLA, we take into account the angular information among nodes when merging components and propose the CLAD algorithm. We prove the merging conditions for both BCLA and CLAD. Simulation results show that CLAD can locate more than 90 percent of nodes in sparse networks with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.