Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
The Tenet architecture for tiered sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Data compression algorithms for energy-constrained devices in delay tolerant networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Capturing high-frequency phenomena using a bandwidth-limited sensor network
Proceedings of the 4th international conference on Embedded networked sensor systems
Fidelity and yield in a volcano monitoring sensor network
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
A survey on clustering algorithms for wireless sensor networks
Computer Communications
Accurate, fast fall detection using posture and context information
Proceedings of the 6th ACM conference on Embedded network sensor systems
A Holistic Approach to Decentralized Structural Damage Localization Using Wireless Sensor Networks
RTSS '08 Proceedings of the 2008 Real-Time Systems Symposium
Air-dropped sensor network for real-time high-fidelity volcano monitoring
Proceedings of the 7th international conference on Mobile systems, applications, and services
Adaptive Linear Filtering Compression on realtime sensor networks
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Quality-Driven Volcanic Earthquake Detection Using Wireless Sensor Networks
RTSS '10 Proceedings of the 2010 31st IEEE Real-Time Systems Symposium
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Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for active volcano monitoring. This paper studies the problem of picking arrival times of primary waves (i.e., P-phases) received by seismic sensors, one of the most critical tasks in volcano monitoring. Two fundamental challenges must be addressed. First, it is virtually impossible to download the real-time high-frequency seismic data to a central station for P-phase picking due to limited wireless network bandwidth. Second, accurate P-phase picking is inherently computation-intensive, and is thus prohibitive for many low-power sensor platforms. To address these challenges, we propose a new P-phase picking approach for hierarchical volcano monitoring WSNs where a large number of inexpensive sensors are used to collect fine-grained, real-time seismic signals while a small number of powerful coordinator nodes process collected data and pick accurate P-phases. We develop a suite of new in-network signal processing algorithms for accurate P-phase picking, including lightweight signal pre-processing at sensors, sensor selection at coordinators as well as signal compression and reconstruction algorithms. Testbed experiments and extensive simulations based on real data collected from a volcano show that our approach achieves accurate P-phase picking while only 16% of the sensor data are transmitted.