WSN node faults are usually due to the following causes: the failure of modules (such as communication and sensing module) due to fabrication process problems, environmental factors, enemy attacks and so on; battery power depletion; being out of the communication range of the entire network.The node status in WSNs can be divided into two types [7-8]: normal and faulty. Faulty in turn can be ��permanent�� or ��static��. The so-called ��permanent�� means failed nodes will remain faulty until they are replaced, and the so-called ��static�� means new faults will not generated during fault detection. In [7,9], node faults of WSNs can be divided into two categories: hard and soft. The so-called ��hard fault�� is when a sensor node cannot communicate with other nodes because of the failure of a certain module (e.
g., communication failure due to the failure of the communication module, energy depletion of node, being out of the communication range of entire mobile network because of the nodes’ moving and so on). The so-called ��soft fault�� means the failed nodes can continue to work and communicate with other nodes (hardware and software of communication module are normal), but the data sensed or transmitted is not correct.The remainder of the paper is organized as follows: In Section 2, related works in the area of fault detection in WSNs is reviewed. In Section 3, the DFD node fault detection scheme is introduced and the theory and realization of improved DFD node fault detection scheme is described in detail. The advantages and disadvantages of the two schemes are also analyzed.
Simulation examples compare the fault detection accuracy of the two schemes with different network sizes, average number of neighbor nodes and failure ratios in Section 4. The paper is concluded in Section 5.2.?Related WorkIn this section, we briefly review the related works in the area of fault detection in WSNs. The existence of faulty sensor measurements in WSNS will cause not only a degradation of the network quality AV-951 of service, but also a huge burden on the limited energy. Article [10] investigates using the spatial correlation of sensor measurements to detect faults in WSNs. An approach of weighting the neighbors’ measurement and presents a method to characterize the difference between sensor measurements are introduced.
A weighted median fault detection scheme (WMFDS) is proposed and evaluated for both binary decisions and real number measurements.In [11] the design of a distributed fault-tolerant decision fusion in the presence of sensor faults when the local sensors sequentially send their decisions to a fusion center is addressed. A collaborative sensor fault detection (CSFD) scheme is proposed to eliminate unreliable local decisions when performing distributed decision fusion.