Sampling Sensor Nodes to Extend the Longevity of Wireless Sensor Networks

Author(s): Mohammed Baqer, Luisella Balbis*

Journal Name: International Journal of Sensors, Wireless Communications and Control

Volume 9 , Issue 1 , 2019


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Background & Objective: Wireless Sensor Networks (WSN) are one of the most important elements in the Internet of Things (IoT) paradigm. It is envisaged that WSNs will seamlessly bridge the physical world with the Internet resulting in countless IoT applications in smart cities, wearable devices, smart grids, smart retails amongst others. It is necessary, however, to consider that sensing, processing and communicating large amounts of sensor data is an energy-demanding tasks. Recharging or replacing those battery-powered sensor nodes deployed in inaccessible locations is generally a tedious and time-consuming task. As a result, energy efficient approaches for WSN need to be devised in order to prolong the longevity of the network.

Methods: In this paper, we present an approach that reduces energy consumption by controlling the sampling rate and the number of actively communicating nodes. The proposed approach applies compressive sensing to reduce the sampling rate and a statistical approach to decrease the sample size of sensor nodes.

Results and Conclusion: The proposed approach is expected to significantly increase the lifetime of the network whilst maintaining the event detection accuracy.

Keywords: Compressed sensing, energy consumption, lifetime, sample size, wireless sensors networks, transceiver.

[1]
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. Wireless sensor networks: a survey. Int J Computer 2002; 38: 393-422.
[2]
Yick J, Mukherjee B, Ghosal D. Wireless sensor network survey Comp Netw 2008.
[http://dx.doi.org/org/10.1016/j.comnet.2008.04.002.]
[3]
Libelium Comunicaciones Distribuidas SL. Libelium 2017. Available at: http://www.libelium.com/develop ment/waspmote/documentation/waspmote-datasheet/ (Accessed November 1, 2017).
[4]
Maulik T, Nagaraj U, Ganjewar PD. Data reduction techniques in wireless sensor network: a survey. Intl J Commun Eng 2014; 2: 6621-34.
[5]
Tan LG, Wu M. Data Reduction in wireless sensor networks: a hierarchical lms prediction approach. IEEE Sensors J 2015; 16: 1-18.
[6]
Deshpande A, Guestrin C, Madden SR, Hellerstein JM, Hong W. Model-Driven Data Acquisition in Sensor Networks. 30th VLDB Conference 2004; 31-3. Toronto, Canada.
[7]
Singh M, Singh V. A survey of optimizing energy utilization in wireless sensor network using duty cycle approach. Int J of Adv Res Comp Sci 2016; 7: 202-5.
[8]
Chen X, Zhao Q, Guan X. Energy-efficient sensing coverage and communication for wireless sensor networks. J Syst Sci Compl 2007; 20: 225-34.
[9]
Shrivastava P, Pokle SB. Energy efficient scheduling strategy for data collection in wireless sensor net-works. Proceedings of the International Conference on Electronic Systems, Signal Processing and Com-puting Technologies 2014; 14: 9-11.
[10]
Harb H, Makhoul A. Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks, Peer-to-Peer Network Applications 2018. Available at: link.springer.com/ article/10.1007/s12083-018-0639-z
[11]
Carbunar B, Grama A, Vitek J, Carbunar O. Coverage preserving redundancy elimination in sensor networks. 1st IEEE Int Conf Sensor and Ad Hoc Communications and Networks. Santa Clara, California, USA. 2004; pp. 6: 4-7.
[12]
Carle J, Gallais A, Simplot-Ryl D. Preserving area coverage in wireless sensor networks by using surface coverage relay dominating sets. 10th IEEE Symposium on Computers and Communications 2005; 3: 27-30.
[13]
Zhang H, Hou JC. Maintaining sensing coverage and connectivity in large sensor networks 2005; 1: 89-123.
[14]
Wang Z, Tan R, Hu J, et al. Heterogeneous incentive mechanism for time-sensitive and location-dependent crowdsensing networks with random arrivals. Comp Netw 2018; 131: 96-109.
[15]
Wu K, Gao Y, Li F, Xiao Y. Lightweight deployment-aware scheduling for wireless sensor networks. Mob Netw App 2005; 10: 837-52.
[16]
Tian D, Georganas ND. Location and calculation-free node-scheduling schemes in large wireless sensor networks. Ad Hoc Netw 2004; 2: 65-85.
[17]
Baqer M, Al-Mutawah KA. Random node sampling for energy efficient data collection in wireless sensor networks. Proceedings of the 8th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing 2013; 2-5.
[18]
Alippi C, Anastasi G, Di Francesco M, Roveri M. An adaptive sampling algorithm for effective energy management in wireless sensor networks with energy-hungry sensors. IEEE Trans Instr Measurement 2010; 18: 59.
[19]
Masoum A, Meratnia N, Havinga PJ. An energy-efficient adaptive sampling scheme for wireless sensor networks. Proceedings of the 8th IEEE Int Conf Int Sensor Sensor Netw Inf Processing 2013; 1: 2-5.
[20]
Kho J, Rogers A, Jennings NR. Decentralized control of adaptive sampling in wireless sensor networks. ACM Trans Sensor Netw 2009; 5(3): 322-5.
[21]
Senouci MR, Lehtihet HE. Sampling-based selection-decimation deployment approach for large-scale wireless sensor networks. Ad Hoc Netw 2018; 75: 135-46.
[22]
Middya R, Chakravarty N, Kanti NM. Compressive sensing in wireless sensor networks - a survey. IETE Tech Rev 2016; 13: 642-54.
[23]
Cao G, Yu F, Zhang B. Improving network lifetime for wireless sensor network using compressive sensing. 13th IEEE Int Conf Perform. Comput Commun 2011; 8: 2-4.
[24]
Chen W, Wassell I. Energy efficient signal acquisition via compressive sensing in wireless sensor networks. 6th Int Symp Perv Comput 2011; 13: 23-5.
[25]
Ji S, Tan C, Yang P, Sun YJ, Fu D, Wang J. Compressive sampling and data fusion-based structural damage monitoring in wireless sensor network. J Supercomputing 2018; 74: 1108-31.
[26]
Wu FY, Yang K, Duan R, Tian T. Compressive sampling and reconstruction of acoustic signal in underwater wireless sensor networks. IEEE Sensor J 2018; 18: 5876-84.
[27]
Traub J, Breß S, Rabl T, Katsifodimos A, Markl V. Optimized on-demand data streaming from sensor nodes. Proc Symp Cloud Comput 2017; 1(2): 24-7.
[28]
Pavez E, Ortega A. Active covariance estimation by random sub-sampling of variables. 2018 CoRR, abs/1804.01620 Availabe at: https://arxiv.org/abs/1804.01620v1.
[29]
Lawrance RJ, Chung JJ, Hollinger GA. Fast marching adaptive sampling. IEEE Robotic Auto Lett 2017; p. 1.
[30]
Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Netw 2005; 3: 325-49.
[31]
Candès EJ, Wakin MB. An introduction to compressive sampling. IEEE Trans Process 2008; 25: 21-30.
[32]
Eldar Y, Kutyniok G. Compressed sensing: theory and Applications. Cambridge: Cambridge University 2012.
[33]
Cohen A, Dahmen W, DeVore R. Compressed sensing and best k-term approximation. J Am Math Soc 2009; 22: 211-31.
[34]
Donoho D, Elad M. Optimally sparse representation in general (nonorthogonal) dictionaries via `1 minimi-zation. Nat Acad Sci 2003; 100: 2197-202.
[35]
DeVore R. Deterministic constructions of compressed sensing matrices. J Complexity 2007; 23: 918-25.


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 9
ISSUE: 1
Year: 2019
Published on: 14 July, 2019
Page: [53 - 63]
Pages: 11
DOI: 10.2174/2210327908666180727125534
Price: $25

Article Metrics

PDF: 24
HTML: 3