Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
On the interdependence of routing and data compression in multi-hop sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Information Theory and Reliable Communication
Information Theory and Reliable Communication
Coding Theory Framework for Target Location in Distributed Sensor Networks
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
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
Data-gathering wireless sensor networks: organization and capacity
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Lattice sensor networks: capacity limits, optimal routing and robustness to failures
Proceedings of the 3rd international symposium on Information processing in sensor networks
Complexity constrained sensor networks: achievable rates for two relay networks and generalizations
Proceedings of the 3rd international symposium on Information processing in sensor networks
Power-bandwidth-distortion scaling laws for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
The method of types [information theory]
IEEE Transactions on Information Theory
Asymptotic results for decentralized detection in power constrained wireless sensor networks
IEEE Journal on Selected Areas in Communications
Distributed multitarget classification in wireless sensor networks
IEEE Journal on Selected Areas in Communications
On the interdependence of sensing and estimation complexity in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Energy-driven detection scheme with guaranteed accuracy
Proceedings of the 5th international conference on Information processing in sensor networks
Wireless Personal Communications: An International Journal
Target Counting under Minimal Sensing: Complexity and Approximations
Algorithmic Aspects of Wireless Sensor Networks
The impact of quasi-equally spaced sensor topologies on signal reconstruction
ACM Transactions on Sensor Networks (TOSN)
Increasing sensor measurements to reduce detection complexity in large-scale detection applications
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
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We bound the number of sensors required to achieve a desired level of sensing accuracy in a discrete sensor network application (e.g. distributed detection). We model the state of nature being sensed as a discrete vector, and the sensor network as an encoder. Our model assumes that each sensor observes only a subset of the state of nature, that sensor observations are localized and dependent, and that sensor network output across different states of nature is neither identical nor independently distributed. Using a random coding argument we prove a lower bound on the 'sensing capacity' of a sensor network, which characterizes the ability of a sensor network to distinguish among all states of nature. We compute this lower bound for sensors of varying range, noise models, and sensing functions. We compare this lower bound to the empirical performance of a belief propagation based sensor network decoder for a simple seismic sensor network scenario. The key contribution of this paper is to introduce the idea of a sharp cut-off function in the number of required sensors, to the sensor network community.