A Cross-Layer Approach to Contour Nodes Inference with Data Fusion in Wireless Sensor Networks
A cross-layer approach to contour nodes inference of monitored physical phenomena with data fusion in wireless sensor networks is investigated in this work. The authors first analyze three sources of signal distortion: sensing noise, data quantization error and data communication noise. The sensing noise is often negligible. We need to choose the proper data quantization levels to balance the communication cost and the desired signal quality. Besides, data fusion at a local fusion center can be used to mitigate the communication noise due to the poor wireless channel. Instead of making a hard binary decision, the probability for a sensor node to be a contour node is calculated at the local fusion center. An adaptive data fusion scheme is proposed to avoid excessive packet retransmissions. Simulation results are given to show the effects of different system parameters on the overall system performance.