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.

This paper presents a new bearing fault detection algorithm based on analyzing singular points of vibration signals using the Haar wavelet. The proposed Haar Fault Detection (HFD) algorithm is compared with a previously-developed algorithm associated with the Morlet wavelet. We also substitute the Haar wavelet with Daubechies wavelets with larger compact supports and evaluate the results. Simulations carried on real data demonstrate that the HFD algorithm achieves a comparable accuracy while having a lower computational cost. This makes the HFD algorithm an appropriate candidate for fast processing of bearing faults.