Abstract
This paper presents an auction-based consensus mechanism for cooperative targets tracking using minimum numbers of mobile sensors in order to reduce energy consumption due to sensor mobilization. After targets are detected, they are clustered using hybrid subtractive K-means clustering technique to reduce the number of trackers needed to track these detected targets. The proposed target tracking process is based on an Extended Kohonen neural network. In order to decrease the network sensitivity to initial conditions, a supervised learning technique is used to get the initial weights of unsupervised Extended Kohonen Map instead of random initialization. An auction-based consensus mechanism is used as a cooperation methodology between trackers during tracking. Monitoring sensors either remain stationary or begin following their targets is based on this mechanism. The simulation results confirms that the proposed approach outperforms other approaches in energy saving and achieves better coverage.