Wireless integrated network sensors
Communications of the ACM
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
The impact of spatial correlation on routing with compression in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Energy conservation in wireless sensor networks: A survey
Ad Hoc Networks
EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks
Computer Communications
Compressive data gathering for large-scale wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Transform-based distributed data gathering
IEEE Transactions on Signal Processing
Efficient measurement generation and pervasive sparsity for compressive data gathering
IEEE Transactions on Wireless Communications
Energy-aware data processing techniques for wireless sensor networks: a review
Transactions on large-scale data- and knowledge-centered systems III
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Noiseless coding of correlated information sources
IEEE Transactions on Information Theory
Determination method of optimal number of clusters for clustered wireless sensor networks
Wireless Communications & Mobile Computing
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Compressive sensing based in-network compression is an efficient technique to reduce communication cost and accurately recover sensory data at the sink. Existing compressive sensing based data gathering methods require a large number of sensors to participate in each measurement gathering, and it leads to waste a lot of energy. In this paper, we present an energy efficient clustering routing data gathering scheme for large-scale wireless sensor networks. The main challenges of our scheme are how to obtain the optimal number of clusters and how to keep all cluster heads uniformly distributed. To solve the above problems, we first formulate an energy consumption model to obtain the optimal number of clusters. Second, we design an efficient deterministic dynamic clustering scheme to guarantee all cluster heads uniformly distributed approximately. With extensive simulation, we demonstrate that our scheme not only prolongs nearly 2x network's lifetime compared with the state of the art compressive sensing based data gathering schemes, but also makes the network energy consumption very uniformly.