A Novel Low-Complexity Numerical Localization Method for Dynamic Wireless Sensor Networks

The localization of distributed and wirelessly connected sensor system is of decisive importance for many applications. This paper focuses on the range-based localization and tracking problem for very large dynamic, i.e., moving sensor networks. We study the explorability of underwater systems with massive swarms of tiny sensors with reduced and restricted capabilities. In this context, we propose a least-squares based localization algorithm which shows superior performance and lower computational complexity than other methods, based, e.g., on unscented Kalman-filtering or semidefinite programming. The new and existing algorithms are evaluated using two different system models, derived from real fluid dynamics. We also compare our algorithm to other least-squares based methods, determine the computational overhead of the new method and investigate its performance gain in terms of estimation accuracy.