Adaptive protocols for information dissemination in wireless sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The emergence of networking abstractions and techniques in TinyOS
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Macroprogramming heterogeneous sensor networks using cosmos
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Noiseless coding of correlated information sources
IEEE Transactions on Information Theory
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Dense sensor deployments impose significant constraints on aggregatenetwork data rate and resource utilization. Effective protocols for suchdata transfers rely on spatio-temporal correlations in sensor data for in-network data compression. The message complexity of these schemes is generally lowerbounded by n, for a network with n sensors, since correlation is not collocatedwith sensing. Consequently, as the number of nodes and network density increase,these protocols become increasingly inefficient. We present here a novel protocol,called SNP, for fine-grained data collection, which requires approximatelyO(n-R) messages, where R, a measure of redundancy in sensed data generallyincreases with density. SNP uses spatio-temporal correlations to near-optimally compress data at the source, reducing network traffic and power consumption.We present a comprehensive information theoretic basis for SNP and establishits superior performance in comparison to existing approaches. We support ourresults with a comprehensive experimental evaluation of the performance of SNPin a real-world sensor network testbed.