Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
A directionality based location discovery scheme for wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Power conservation and quality of surveillance in target tracking sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
IEEE Transactions on Computers
MSWiM '05 Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Monitoring Civil Structures with a Wireless Sensor Network
IEEE Internet Computing
VigilNet: An integrated sensor network system for energy-efficient surveillance
ACM Transactions on Sensor Networks (TOSN)
Self-Organizing Sensor Networks for Integrated Target Surveillance
IEEE Transactions on Computers
Vineyard Computing: Sensor Networks in Agricultural Production
IEEE Pervasive Computing
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
Communication paradigms for sensor networks
IEEE Communications Magazine
Computer Networks: The International Journal of Computer and Telecommunications Networking
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A wireless sensor network (WSN) consists of many tiny and low-power devices deployed in a sensing field. One of the major tasks of a WSN is to monitor the surrounding environment and to detect events occurring in the sensing field. Given an event appearing in a WSN, the event detection latency is to model the time that it takes for the WSN to be aware of the event. In this work, we analyze the latency using a probabilistic approach under an any-sensor-detection and a k-sensor-detection models, where k1 is an integer. Such an analysis can be used as an index to evaluate a WSN's coverage and thus can help guide the deployment of a WSN. We also develop simulations to verify our analytical results.