Cost-Aware Activity Scheduling for Compressive Sleeping Wireless Sensor Networks

被引:24
作者
Chen, Wei [1 ,2 ]
Wassell, Ian J. [2 ]
机构
[1] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
[2] Univ Cambridge, Comp Lab, Cambridge CB3 0FD, England
基金
英国工程与自然科学研究理事会;
关键词
Compressive sensing (CS); wireless sensor network (WSN); activity scheduling; SPARSE; RECOVERY; REPRESENTATIONS; RECONSTRUCTION; PERFORMANCE;
D O I
10.1109/TSP.2016.2521608
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we consider a compressive sleeping wireless sensor network (WSN) for monitoring parameters in the sensor field, where only a fraction of sensor nodes (SNs) are activated to perform the sensing task and their data are gathered at a fusion center (FC) to estimate all the other SNs' data using the compressive sensing (CS) principle. Typically, research published concerning CS implicitly assume the sampling costs for all samples are equal and suggest random sampling as an appropriate approach to achieve good reconstruction accuracy. However, this assumption does not hold for compressive sleeping WSNs, which have significant variability in sampling cost owing to the different physical conditions at particular SNs. To exploit this sampling cost nonuniformity, we propose a cost-aware activity scheduling approach that minimizes the sampling cost with constraints on the regularized mutual coherence of the equivalent sensing matrix. In addition, for the case with prior information about the signal support, we extend the proposed approach to incorporate the prior information by considering an additional constraint on the mean square error (MSE) of the oracle estimator for sparse recovery. Our numerical experiments demonstrate that, in comparison with other designs in the literature, the proposed activity scheduling approaches lead to improved tradeoffs between reconstruction accuracy and sampling cost for compressive sleeping WSNs.
引用
收藏
页码:2314 / 2323
页数:10
相关论文
共 35 条
[1]   SCIP: solving constraint integer programs [J].
Achterberg, Tobias .
MATHEMATICAL PROGRAMMING COMPUTATION, 2009, 1 (01) :1-41
[2]  
Bajwa W., 2006, Information Processing in Sensor Networks, P134
[3]   A Simple Proof of the Restricted Isometry Property for Random Matrices [J].
Baraniuk, Richard ;
Davenport, Mark ;
DeVore, Ronald ;
Wakin, Michael .
CONSTRUCTIVE APPROXIMATION, 2008, 28 (03) :253-263
[4]   Model-Based Compressive Sensing [J].
Baraniuk, Richard G. ;
Cevher, Volkan ;
Duarte, Marco F. ;
Hegde, Chinmay .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2010, 56 (04) :1982-2001
[5]   Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise [J].
Ben-Haim, Zvika ;
Eldar, Yonina C. ;
Elad, Michael .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (10) :5030-5043
[6]   The Cramr-Rao Bound for Estimating a Sparse Parameter Vector [J].
Ben-Haim, Zvika ;
Eldar, Yonina C. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (06) :3384-3389
[7]  
Boyd S., 2004, Convex optimization, DOI [10.1017/cbo97805118044 41, 10.1017/CBO9780511804441]
[8]   Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information [J].
Candès, EJ ;
Romberg, J ;
Tao, T .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) :489-509
[9]  
Chen W, 2014, IEEE GLOB COMM CONF, P7, DOI [10.1109/PSCC.2014.7038332, 10.1109/GLOCOM.2014.7036776]
[10]   Optimized Node Selection for Compressive Sleeping Wireless Sensor Networks [J].
Chen, Wei ;
Wassell, Ian J. .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, 65 (02) :827-836