Rough Sets And Hamming Network Integration Of Wireless Sensor Network Node Fault Diagnosis

Recently, the research on wireless sensor networks (WSN) with automatic configurations has become a very dynamic area. It is an interdisciplinary study, integrating radio communication, sensor, micro-electro-mechanism system and distributed information process. Combining the logic information world with the external physical world, it can be applied in various fields, and thus nowadays regarded as one of the most promising technologies.Key issues for wireless sensor networks include prolonging the lifetime and assuring the reliability of information transmission. Accurate and timely diagnosis of node fault is essential in ensuring the reliable transmission of information through WSN, facilitating the efficient routing planning, node management for upper computers or sink nodes, long-distance node service and therefore prolonging the lifetime of WSN.Algorithm fusion is a popular international research mode in recent years. In the dissertation, we proposed an algorithm to achieve the online fault diagnosis of WSN nodes with limited energy under major uncertainty. It is an application of the rough set theory and Hamming neural networks. By applying rough set theory, the fault decision-making table is first reduced and then used to training the Hamming neural networks, which is used to diagnose and locate the faults of WSN nodes. The algorithm utilizes the capability of data reduction by rough set theory, and the advantages of Hamming networks in parallel computation and interference resilience. We proposed an improved attribute reduction algorithm based on attribute importance, which improves the computation efficiency. We also improved the competition layer of the Hamming network to avoid too much iterations in computation.Simulation results show that the algorithm reveals the inherent redundancy of fault characteristics in WSN nodes. The algorithm can resolve online fault diagnosis of WSN nodes accurately and timely. It can also yield reasonable diagnosis result when information is incomplete or partial information is false. The proposed algorithm outperforms the traditional if-then diagnosis rules significantly in terms of the accurate diagnosis rate, especially when the fault characteristic data are not reliable. The advantanges of the proposed method include high diagnosis accuracy, low communication cost and low energy consumption. In conclusion, the algorithm has improved the robustness and practicality of fault diagnosis under the limited energy constraint.