Xiangmo Zhao

A lightweight fault detection method for sensor networks based on anomaly execution statistics

Fault detection of deployed senor networks is difficult due to server constrained resources and limited access to the networks. A lightweight fault detection method is proposed to address the problem of unknown software fault detection in post-deployment phase. By analyzing characteristics of node programs, a statistical model and corresponding test statistic are devised to detect unknown fault at runtime. Evaluation on typical sensor network applications shows that the storage and runtime overhead are acceptable. A case study further demonstrates the effectiveness of the proposed method.

Reliable clock estimation using linear weighted fusion based on pairwise broadcast synchronization

Clock synchronization in wireless sensor networks (WSNs) has been studied extensively in recent years and many protocols are put forward based on the point of statistical signal processing, which is an effective way to optimize accuracy. However, the accuracy derived from the statistical data can be improved mainly by sufficient packets exchange, which will consume the limited power resources greatly. In this paper, a reliable clock estimation using linear weighted fusion based on pairwise broadcast synchronization is proposed to optimize sync accuracy without expending additional sync packets. As a contribution, a linear weighted fusion scheme for multiple clock deviations is constructed with the collaborative sensing of clock timestamp. And the fusion weight is defined by the covariance of sync errors for different clock deviations. Extensive simulation results show that the proposed approach can achieve better performance in terms of sync overhead and sync accuracy.