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Toward Belief Function-Based Cooperative Sensing for Interference Resistant Industrial Wireless Sensor Networks

In harsh and heterogeneous wireless environments, the communication reliability and latency of industrial wireless sensor networks (IWSNs) seriously suffer from both intra- and interinterference. This paper presents an interference resistant approach for IWSNs by utilizing cognitive radio techniques. To improve the interference detection performance while uploading data as little as possible, we present a new computationally efficient and effective belief function (BF) theory-based reliability-probability decision fusion rule for cooperative sensing. A factor called reliability degree is introduced to characterize the imprecision of sensor observations, and the basic belief assignments are constructed by combining this reliability degree and local detection performance. Unlike the inefficient existing BF-based fusion schemes, the proposed rule has an explicit form and it is equivalent to the well-known Chair-Vashney (CV) rule in high signal-to-noise ratio conditions. We applied the proposed rule in interference resistant IWSNs to detect and avoid interference. Both numerical results and tests results demonstrate that the proposed rule has significant improvement in detection performance, diversity gains, and throughput compared with existing BF fusion schemes and CV rule.