Dozer: ultra-low power data gathering in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
The hitchhiker's guide to successful wireless sensor network deployments
Proceedings of the 6th ACM conference on Embedded network sensor systems
Passive diagnosis for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Comparative performance analysis of the PermaDozer protocol in diverse deployments
LCN '11 Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks
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As the application of WSNs for long-term monitoring purposes becomes real, the issue of WSN system health monitoring grows increasingly important. Manually understanding the root causes of an observed behavior is time-consuming and difficult, often knowledge of prior behavior is necessary for understanding the potential risk on the long-term system performance. The challenges lie in the balance between the amount of system data collected and the level of detail in which state can be inferred from this data. In this paper, we propose a lightweight runtime logging and corresponding network state inference mechanism that enables scalable WSN health monitoring. Concretely, we propose that nodes only report their internal state on the occurrence of important events. Having a very low computational complexity and message overhead within the sensor network, reported events are analyzed at a less constrained network sink.