Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Fault Tolerance in Collaborative Sensor Networks for Target Detection
IEEE Transactions on Computers
Energy-efficient surveillance system using wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Distributed detection and fusion in a large wireless sensor network of random size
EURASIP Journal on Wireless Communications and Networking
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
Fidelity and yield in a volcano monitoring sensor network
OSDI '06 Proceedings of the 7th symposium on Operating systems design and implementation
Air-dropped sensor network for real-time high-fidelity volcano monitoring
Proceedings of the 7th international conference on Mobile systems, applications, and services
Data fusion improves the coverage of wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Impact of Data Fusion on Real-Time Detection in Sensor Networks
RTSS '09 Proceedings of the 2009 30th IEEE Real-Time Systems Symposium
Exploiting Reactive Mobility for Collaborative Target Detection in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
IEEE Transactions on Signal Processing
Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks
IEEE Transactions on Signal Processing
On the Impact of Node Failures and Unreliable Communications in Dense Sensor Networks
IEEE Transactions on Signal Processing
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Volcano monitoring is of great interest to public safety and scientific explorations. However, traditional volcanic instrumentation such as broadband seismometers are expensive, power hungry, bulky, and difficult to install. Wireless sensor networks (WSNs) offer the potential to monitor volcanoes on unprecedented spatial and temporal scales. However, current volcanic WSN systems often yield poor monitoring quality due to the limited sensing capability of low-cost sensors and unpredictable dynamics of volcanic activities. In this article, we propose a novel quality-driven approach to achieving real-time, distributed, and long-lived volcanic earthquake detection and timing. By employing novel in-network collaborative signal processing algorithms, our approach can meet stringent requirements on sensing quality (i.e., low false alarm/missing rate, short detection delay, and precise earthquake onset time) at low power consumption. We have implemented our algorithms in TinyOS and conducted extensive evaluation on a testbed of 24 TelosB motes as well as simulations based on real data traces collected during 5.5 months on an active volcano. We show that our approach yields near-zero false alarm/missing rate, less than one second of detection delay, and millisecond precision earthquake onset time while achieving up to six-fold energy reduction over the current data collection approach.