The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Minimal and maximal exposure path algorithms for wireless embedded sensor networks
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SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Integrated coverage and connectivity configuration for energy conservation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
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Proceedings of the 11th annual international conference on Mobile computing and networking
Probabilistic Coverage in Wireless Sensor Networks
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Coverage by randomly deployed wireless sensor networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Proceedings of the 6th international conference on Information processing in sensor networks
Bounds on coverage and target detection capabilities for models of networks of mobile sensors
ACM Transactions on Sensor Networks (TOSN)
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Energy Efficient Target-Oriented Scheduling in Directional Sensor Networks
IEEE Transactions on Computers
Selection and orientation of directional sensors for coverage maximization
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Scalable target coverage in smart camera networks
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
On coverage issues in directional sensor networks: A survey
Ad Hoc Networks
IEEE Communications Magazine
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Coverage estimation is one of the fundamental problems in sensor networks. Coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D (omnidirectional) scalar sensor networks (SSNs) because of the directional sensing nature of cameras and the existence of visual occlusion in crowded environments. This article represents a first attempt toward a closed-form solution for the visual coverage estimation problem in the presence of occlusions. We investigate a new target detection model, referred to as the certainty-based target detection (as compared to the traditional uncertainty-based target detection) to facilitate the formulation of the visual coverage problem. We then derive the closed-form solution for the estimation of the visual coverage probability based on this new target detection model that takes visual occlusions into account. According to the coverage estimation model, we further propose an estimate of the minimum sensor density that suffices to ensure a visual K-coverage in a crowded sensing field. Simulation is conducted which shows extreme consistency with results from theoretical formulation, especially when the boundary effect is considered. Thus, the closed-form solution for visual coverage estimation is effective when applied to real scenarios, such as efficient sensor deployment and optimal sleep scheduling.