Diagnosis, Based On Artificial Immune Wireless Sensor Network Node Failure

As the Wireless Sensor Networks (WSN for short) are often run in the bad or even dangerous environment that humans can not access to, its node will occur all kinds of faults as a result form exposure. A direct result is the wrong measurement, even the loss of some of the features of WSN and paralysis of the entire network. Therefore, in order to improve the reliability of operation of WSN, timely and accurate fault diagnosis of the node is very necessary.In this paper, two new methods which from the combination of the Artificial Immune System(AIS for short) and other algorithms are used to diagnose the sensor module of the node. The result of diagnosis is type determination of the faults so that we can repair the fault timely to ensure the safe operation of the network. The main contain includes:1. A brief introduction of the artificial immune system for the related algorithm model and network model. Two methods of fault diagnosis of WSN are described simply.2. Design the model of sensor component of nodes in the Matlab platform and do some research on the type of the faults and the manifestations.3. The classical BP neural network algorithm is derived in detail and improved by the artificial immune algorithm. Details of improvement process are given out.4. Obtaining the training samples and testing samples of sensor components of nodes by simulation experiment, including normal, bias, short-circuit and drift faults.5. BP neural network algorithm improved by the artificial immune algorithm is used to do fault diagnosis of the nodes of WSN and the performance of the improved algorithm are compared and analyzed.6. Propose the aiNet-KNN classifier algorithm on the basis of the analysis of the aiNet immune network model and KNN classification algorithm. A specific process of the algorithm is given.7. aiNet-KNN classifier algorithm is used for fault diagnosis of WSN nodes. The impact of parameters on the algorithm is discussed and made some conclusions. In this paper, artificial immune theory will attempt to use for node fault diagnosis of WSN. Though a large number of simulated examples, the satisfying fault diagnosis ability of the two methods is testified. Exploring on theory of the paper will provide a reference for the development of fault diagnosis technology on WSN node.