The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. Vital to the success of wireless microsensor networks is the ability of microsensors to "collectively perform sensing and computation". In this paper, we study one of the fundamental challenges in sensor networks, node localization. The collaborative multilateration presented here, enables ad-hoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global non-linear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large non-linear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows fine-grained localization to run on a low cost sensor node we have developed.
Savvides REF used least squares estimation with Kalman filter to locate the positions of sensor nodes to reduce error accumulation in the same algorithm.
The bits and flops of the n-hop multilateration primitive for node localization problems
WSNA '02
Computer Science