We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU factorizations. We derive an analytic result which expedites the sensor selection in a greedy manner. We also provide the complexity of the proposed algorithm in comparison with previous selection methods. We evaluate the performance through numerical experiments using random measurements under correlated noise and demonstrate a competitive estimation accuracy of the proposed algorithm with a reasonable increase in complexity as compared with the previous selection methods.

This paper addresses the problem of sensor selection in wireless sensor networks (WSN) subject to a distortion constraint. To do so, first, a cost function is derived based on the spatial correlation obtained using the best estimation of the event source. Then, a new adaptive algorithm is proposed in which the number of active sensors is adaptively determined and the best topology of the active set is selected based on the add-one-sensor-node-at-a-time method. Simulations results show that the active sensors selected using the proposed cost function have less event distortion. Also, it is shown that the proposed sensor selection algorithm is near optimum and it has better performance than other algorithms with regard to the computational burden and distortion.