A novel approach to reliable sensor selection and target tracking in sensor networks
IEEE Transactions on Industrial Informatics
Multiobjective optimization (MO), sensor selection, target tracking, unscented Kalman filter (UKF), wireless sensor network (WSN)
This paper addresses the problem of sensor selection in a sensor network for tracking a moving target. By considering network uncertainties and unpredictable movements of the target, reliable sensor selection approaches such as sigma points probability and target trajectory are proposed. An updated unscented Kalman filter is proposed to achieve effective tracking of the target through the sensor selection. A multialgorithm genetically adaptive multiobjective is utilized to have a selection strategy without knowing the number of sensors to be selected. Extensive experiments are conducted to evaluate the effectiveness of the proposed approach both in simulation and practical experimentation. The proposed algorithm is also tested in the industrial setting where providing safety is of great importance for a human worker who walks in a potentially dangerous workplace. The results confirm the effectiveness and utility of the proposed scheme.
Anvaripour, Mohammad; Saif, Mehrdad; and Ahmadi, Majid. (2020). A novel approach to reliable sensor selection and target tracking in sensor networks. IEEE Transactions on Industrial Informatics, 16 (1), 171-182.