Distributed problem solving in sensor networks
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Maximum lifetime routing in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards
Computer Communications
Minimizing effective energy consumption in multi-cluster sensor networks for source extraction
IEEE Transactions on Wireless Communications
Optimal flow control for utility-lifetime tradeoff in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Journal of Systems and Software
Toeplitz compressed sensing matrices with applications to sparse channel estimation
IEEE Transactions on Information Theory
Cross-layer design in multihop wireless networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Centralized and Clustered k-Coverage Protocols for Wireless Sensor Networks
IEEE Transactions on Computers
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
A centralized energy-efficient routing protocol for wireless sensor networks
IEEE Communications Magazine
Fast and Efficient Compressive Sensing Using Structurally Random Matrices
IEEE Transactions on Signal Processing
Wireless Personal Communications: An International Journal
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When using wireless sensor networks (WSNs) for data transmission, some critical respects should be considered. These respects are limited computational power, storage capability and energy consumption. To save the energy in WSNs and prolong the network lifetime, we design for the signal control input, routing selection and capacity allocation by the optimization model based on compressed sensing (CS) framework. The reasonable optimization model is decomposed into three subsections for three layers in WSNs: congestion control in transport layer, scheduling in link layer and routing algorithm in network layer, respectively. These three functions interact and are regulated by congestion ratio so as to achieve a global optimality. Congestion control can be robust and stable by CS theory that a relatively small number of the projections for a sparse signal contain most of its salient information. Routing selection is abided by fair resource allocation principle. The resources can be allocated more and more to the channel in the case of not causing more severe congestion, which can avoid conservatively reducing resources allocation for eliminating congestion. Simulation results show the stability of our algorithm, the accurate ratio of CS, the throughput, as well as the necessity of considering congestion in WSNs.