Reliability-Based Adaptive Distributed Classification in Wireless Sensor Networks

Abstract

In many wireless sensor networks, local sensors adopt binary decisions because they can be transmitted to a fusion center using very low power. However, if a binary decision is wrong, the probability of the fusion center making a wrong final decision is dramatically increased. This study proposes a reliability-based adaptive method to resolve this problem with little extra computation. Before a sensor makes a binary local decision, its observation must be evaluated. Unreliable ranges are set for this evaluation. If the sensor's observation result does not fall within the unreliable range, the sensor makes a local decision. Otherwise, the sensor must make another observation. The optimal unreliable ranges are then derived. This study applies the proposed method to an existing distributed classification scheme using the binary decision. Performance analysis shows that this approach efficiently reduces the misclassification probability at the fusion center. Simulation results show that the transmission power is reduced by 7.5 dB to achieve a misclassification probability of 0.1 under some practical conditions.