Consistent sensor, relay, and link selection in wireless sensor networks

被引:5
作者
Arroyo-Valles, Rocio [1 ,4 ]
Simonetto, Andrea [2 ,5 ]
Leus, Geert [3 ]
机构
[1] Univ Carlos III Madrid, Dept Teoria Senal & Comunicac, Avda Univ 30, Madrid 28911, Spain
[2] Catholic Univ Louvain, Appl Math Dept, Louvain La Neuve, Belgium
[3] Delft Univ Technol, Fac Elect Engn Math & Comp Sci, NL-2628 CD Delft, Netherlands
[4] European Patent Off, NL-2280 HV Rijswijk, Netherlands
[5] IBM Res, Optimizat & Control Grp, Dublin, Ireland
关键词
Sensor selection; Link and relay selection; Convex relaxations; Sparsity; Resources optimization; Wireless sensor networks; SPARSITY; PLACEMENT;
D O I
10.1016/j.sigpro.2017.04.020
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 wireless sensor networks, where energy is scarce, it is inefficient to have all nodes active because they consume a non-negligible amount of battery. In this paper we consider the problem of jointly selecting sensors, relays and links in a wireless sensor network where the active sensors need to communicate their measurements to one or multiple access points. Information messages are routed stochastically in order to capture the inherent reliability of the broadcast links via multiple hops, where the nodes may be acting as sensors or as relays. We aim at finding optimal sparse solutions where both, the consistency between the selected subset of sensors, relays and links, and the graph connectivity in the selected subnetwork are guaranteed. Furthermore, active nodes should ensure a network performance in a parameter estimation scenario. Two problems are studied: sensor and link selection; and sensor, relay and link selection. To solve such problems, we present tractable optimization formulations and propose two algorithms that satisfy the previous network requirements. We also explore an extension scenario: only link selection. Simulation results show the performance of the algorithms and illustrate how they provide a sparse solution, which not only saves energy but also guarantees the network requirements. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:32 / 44
页数:13
相关论文
共 32 条
[1]  
Arampatzis T, 2005, 2005 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL & 13TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1 AND 2, P719
[2]  
Arroyo-Valles R, 2007, PROCEEDINGS OF THE 2007 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING, P1
[3]  
Bhatel'e A., 2014, International Conference on High Performance Computing (HiPC), P1, DOI [10.1109/ECTICon.2014.6839829, DOI 10.1109/ECTICON.2014.6839829]
[4]   Self-organizing sensor networks for integrated target surveillance [J].
Biswas, Pratik K. ;
Phoha, Shashi .
IEEE TRANSACTIONS ON COMPUTERS, 2006, 55 (08) :1033-1047
[5]   Enhancing Sparsity by Reweighted l1 Minimization [J].
Candes, Emmanuel J. ;
Wakin, Michael B. ;
Boyd, Stephen P. .
JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS, 2008, 14 (5-6) :877-905
[6]   Sparsity-Promoting Sensor Selection for Non-Linear Measurement Models [J].
Chepuri, Sundeep Prabhakar ;
Leus, Geert .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (03) :684-698
[7]   Environmental Wireless Sensor Networks [J].
Corke, Peter ;
Wark, Tim ;
Jurdak, Raja ;
Hu, Wen ;
Valencia, Philip ;
Moore, Darren .
PROCEEDINGS OF THE IEEE, 2010, 98 (11) :1903-1917
[8]  
Dai R., 2011, P IEEE C DEC CONTR S
[9]   Cooperative communications with relay-selection: When to cooperate and whom to cooperate with? [J].
Ibrahim, Ahmed S. ;
Sadek, Ahmed K. ;
Su, Weifeng ;
Liu, K. J. Ray .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2008, 7 (07) :2814-2827
[10]   Distributed Sparsity-Aware Sensor Selection [J].
Jamali-Rad, Hadi ;
Simonetto, Andrea ;
Ma, Xiaoli ;
Leus, Geert .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (22) :5951-5964