ADAPTIVE DISTRIBUTED DETECTION USING SOFT DECISION REPORT IN MOBILE ACCESS WIRELESS SENSOR NETWORKS UNDER BYZANTINE ATTACKS

One of the overall emerging visions for future applications is to deploy an outsized number of self-sustained wireless sensors to perform, e.g., environmental monitoring, battle field surveillance and health care maintenance. In this paper, two soft-decision fusion rules, which are respectively named the maximum a priori (MAP) and therefore the suboptimal Minimum Euclidean Distance (MED) fusion rules, are designed supported a given employed sensor code and associated local classification. Their performance comparison with the Distributed Classification fusion using Soft-decision Decoding (DCSD) proposed in an earlier work is additionally performed. Simulations show that when the amount of faulty sensors is little, the MAP fusion rule remains the simplest at either low sensor Observation Signal-to-Noise Ratios (OSNRs) or low communication Channel Signal-to-Noise Ratios (CSNRs), and yet, the DCSD fusion rule gives the simplest performance at middle to high OSNRs and high CSNRs. However, when the amount of faulty sensor nodes grows large, the smallest amount complex MED fusion rule outperforms the MAP fusion rule at high OSNRs and high CSNRs