Proceedings of the 5th international conference on Information processing in sensor networks
Sensing the channel: sensor networks with shared sensing and communications
Proceedings of the 5th international conference on Information processing in sensor networks
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Active wireless sensing: a versatile framework for information retrieval in sensor networks
IEEE Transactions on Signal Processing
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
A generic model for optimizing single-hop transmission policy of replenishable sensors
IEEE Transactions on Wireless Communications
Amplify and forward for correlated data gathering over hierarchical sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Distributed detection in UWB wireless sensor networks
IEEE Transactions on Signal Processing
Spatial correlation-based mobile agent routing algorithm in wireless sensor networks
APCC'09 Proceedings of the 15th Asia-Pacific conference on Communications
International Journal of Sensor Networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A decision theoretic approach to Gaussian sensor networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
A little feedback can simplify sensor network cooperation
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
On the optimal performance in asymmetric gaussian wireless sensor networks with fading
IEEE Transactions on Signal Processing
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For a class of sensor networks, the task is to monitor an underlying physical phenomenon over space and time through an imperfect observation process. The sensors can communicate back to a central data collector over a noisy channel. The key parameters in such a setting are the fidelity (or distortion) at which the underlying physical phenomenon can be estimated by the data collector, and the cost of operating the sensor network. This is a network joint source-channel communication problem, involving both compression and communication. It is well known that these two tasks may not be addressed separately without sacrificing optimality, and the optimal performance is generally unknown. This paper presents a lower bound on the best achievable end-to-end distortion as a function of the number of sensors, their total transmit power, the number of degrees of freedom of the underlying source process, and the spatio-temporal communication bandwidth. Particular coding schemes are studied, and it is shown that in some cases, the lower bound is tight in a scaling-law sense. By contrast, it is shown that the standard practice of separating source from channel coding may incur an exponential penalty in terms of communication resources, as a function of the number of sensors. Hence, such code designs effectively prevent scalability. Finally, it is outlined how the results extend to cases involving missing synchronization and channel fading.