A Fault Detection Scheme Based on Self-Clustering Nodes Sets for Wireless Sensor Networks
In this paper, we present a fault detection scheme based on self-clustering nodes sets for wireless sensor networks. In WSN, sensors' measurements are spatially related, and fault nodes can be detected by local comparisons. We firstly cluster nodes into several sets by the similarity of nodes' measurements using self-clustering algorithm. Nodes in the several top large-scale sets are mainly credible. Then we set credible nodes' status as healthy, and run the adjacent nodes comparing algorithm. Faulty sensor nodes can be identified by the comparisons between neighbor nodes and dissemination of the decision made at each node. Time redundancy is used to tolerate transient faults in sensing and communication. Simulation results show that the fault detection accuracy is kept high and the false alarm rate is kept low, even when 35% of the sensor nodes are faulty.
We propose a framework for examining trust in the storage stack based on different levels of trustworthiness present across different channels of information flow. We focus on corruption in one of the channels, the data channel and as a case study, we apply type-aware corruption techniques to examine Windows NTFS behavior when on-disk pointers are corrupted. We find that NTFS does not verify on-disk pointers thoroughly before using them and that even established error handling techniques like replication are often used ineffectively. Our study indicates the need to more carefully examine how trust is managed within modern file systems.