Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Timing-sync protocol for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ActorNet: an actor platform for wireless sensor networks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Attack vs. failure detection in event-driven wireless visual sensor networks
Proceedings of the 9th workshop on Multimedia & security
A novel distributed privacy paradigm for visual sensor networks based on sharing dynamical systems
EURASIP Journal on Applied Signal Processing
Basic Video-Surveillance with Low Computational and Power Requirements Using Long-Exposure Frames
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Wireless image sensor networks: event acquisition in attack-prone and uncertain environments
Multidimensional Systems and Signal Processing
Context-Aware emergency remedy system based on pervasive computing
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
A high-frequency sampling monitoring system for environmental and structural applications
ACM Transactions on Sensor Networks (TOSN)
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Wireless sensor network (WSN) gives the characteristics of an effective, feasible and fairly reliable monitoring system which shows promise for Structural Health Monitoring (SHM) applications. Monitoring of civil structures generates a large amount of sensor data that is used for structural anomaly detection. Efficiently dealing with this large amount of data in a resource-constrained WSN is a challenge. This paper proposes a, WSN based, novel framework that triggers smart events from sensor data. These events are useful for both intelligent data recording and video camera control. The operation of this framework consists of active & passive sensing modes. In passive mode, selected nodes can intelligently interpret local sensor data to trigger appropriate events. In active mode, most of the sensing nodes perform high frequency sampling and record useful data. Unnecessary data is suppressed which improves the lifespan of the network and simplifies data management.