Localization Algorithms for Wireless Sensor Retrieval
In wireless sensor networks (WSNs), localization has many important applications, among which wireless sensor retrieval bears special importance for cost saving, data analysis and security purposes. Localization for sensor retrieval is especially challenging due to the fact that the number and locations of these sensors are both unknown. In this paper, we propose two probabilistic localization algorithms that iteratively identify the locations of multiple wireless sensors in WSNs, one of which calculates location information offline, and the other online. In both algorithms, we implement a two-step localization process - the first step is called Grid-LEGMM (grid location estimation based on the Gaussian mixture model), a coarse-grain location search using grids by choosing the proper number and locations of the wireless sensors that maximize a likelihood estimation, and the second step is called EM-LEGMM (expectation maximization based on the Gaussian mixture model), which uses the EM-method to refine the results of Grid-LEGMM. An additional step in the online localization algorithm is a credit-based filtering mechanism that removes spurious sensor locations. The performance of both offline and online localization algorithms are analyzed using the Cramer-Rao lower bound (CRLB), and evaluated using simulations and real testbed experiments.
localization gaussian mixture model expectation maximization path loss model