112 research outputs found
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Compressive sleeping wireless sensor networks with active node selection
In this paper, we propose an active node selection framework for compressive sleeping wireless sensor networks (WSNs) in order to improve the signal acquisition performance and network lifetime. The node selection can be seen as a specialized sensing matrix design problem where the sensing matrix consists of selected rows of an identity matrix. By capitalizing on a genie-aided reconstruction procedure, we formulate the active node selection problem into an optimization problem, which is then approximated by a constrained convex relaxation plus a rounding scheme. The proposed approach also exploits the partially known signal support, which can be obtained from the previous signal reconstruction. Simulation results show that our proposed active node selection approach leads to an improved reconstruction performance and network lifetime in comparison to various node selection schemes for compressive sleeping WSNs.This work is supported by EPSRC Research Grant EP/K033700/1; the Natural Science Foundation of China (61401018, U1334202); the Fundamental Research Funds for the Central Universities (2014JBM149); the State Key Laboratory of Rail Traffic Control and Safety (RCS2012ZT014) of Beijing Jiaotong University; the Key Grant Project of Chinese Ministry of Education (313006); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/GLOCOM.2014.703677
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Optimized Node Selection for Compressive Sleeping Wireless Sensor Networks
In this paper, we propose an active node selection framework for compressive sleeping wireless sensor networks (WSNs) in order to improve signal acquisition performance, network lifetime and the use of spectrum resources. While conventional compressive sleeping WSNs only exploit the spatial correlation of SNs, the proposed approach further exploits the temporal correlation by selecting active nodes using the support of the data reconstructed in the previous time instant. The node selection problem is framed as the design of a specialized sensing matrix, where the sensing matrix consists of selected rows of an identity matrix. By capitalizing on a genie-aided reconstruction procedure, we formulate the active node selection problem into an optimization problem, which is then approximated by a constrained convex relaxation plus a rounding scheme. Simulation results show that our proposed active node selection approach leads to an improved reconstruction performance, network lifetime and spectrum usage in comparison to various node selection schemes for compressive sleeping WSNs.This is the accepted manuscript. The final published version is available from IEEE at http://dx.doi.org/10.1109/TVT.2015.2400635
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