research-article

Clustering and Compressive Data Gathering in Wireless Sensor Network

Authors:

Utkarsha S. Pacharaney,

Rajiv Kumar GuptaAuthors Info & Claims

Volume 109, Issue 2

Pages 1311 - 1331

https://doi.org/10.1007/s11277-019-06614-5

Published: 01 November 2019 Publication History

Abstract

In wireless sensor network (WSN) redundant data gathering and transmission occurs due to dense deployment. Recently compressive sensing (CS) has attracted considerable attention for efficient data gathering in WSN. CS can reduce data transmission but the total number of transmissions for data collection is high. To alleviate this, hybrid of CS and raw data collection is proposed and integrated with clustering. Clusters used in this integration reduce the number of CS transmissions, but do not reduce the number of transmissions. In a cluster amount of transmission depends on the number of transmitting nodes and their location in data gathering, hence the way in which nodes are clustered together can significantly effect on the number of transmissions in cluster and overall transmissions in network. When density of sensor nodes in a network is high, we can take advantage of their inherent spatial correlation to reduce the number of transmissions. Motivated by this, we propose a novel base station (BS) assisted cluster, spatially correlated, to reduce the number of transmission in a CS-based clustered WSN. Different from other spatially correlated clusters, in this cluster only CH senses, gathers data in the correlated region, and then transmits compressively sensed measurements to BS without incurring any intra-cluster communication cost. In addition, the clusters so formed, convert a randomly deployed network into a structured one i.e. when several clusters are grouped together they form a hexagonal topology (proved to have a high success rate in cellular network). The proposed system makes WSN transmission efficient by reducing number of transmissions in the network and number of data transmission at the CH using clustering and CS. Also energy consumption is reduced and network lifetime is prolonged.

References

[1]

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Communications of the ACM, 38(4), 393–422.

[2]

Dabirmoghaddam, A., Ghaderi, M., & Williamson, C. (2010). Cluster-based correlated data gathering in wireless sensor networks. In 2010 IEEE international symposium on modeling, analysis and simulation of computer and telecommunication systems (MASCOTS). IEEE.

[3]

Rooshenas, A., Rabiee, H. R., Movaghar, A., & Naderi, M. Y. (2010, December). Reducing the data transmission in wireless sensor networks using the principal component analysis. In 2010 Sixth international conference on intelligent sensors, sensor networks and information processing (ISSNIP) (pp. 133–138). IEEE.

[4]

Gastpar, M., Dragotti, P. L., & Vetterli, M. (2006). The distributed karhunen–loeve Transform. IEEE Transactions on Information Theory, 52(12), 5177–5196.

Digital Library

[5]

Candès, E. J., & Wakin, M. B. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 25, 21.

[6]

Lin, H., Wang, L., & Kong, R. (2015). Energy efficient clustering protocol for large-scale sensor networks. IEEE Sensors Journal, 15(12), 7150–7160.

[7]

Xie, R., & Jia, X. (2014). Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Transactions on Parallel and Distributed Systems, 25(3), 806–815.

Digital Library

[8]

Duarte, M. F., Shen, G., Ortega, A., & Baraniuk, R. G. (2012). Signal compression in wireless sensor networks. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 370(1958), 118–135.

[9]

Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.

Digital Library

[10]

Luo, C., Wu, F., Sun, J., & Chen, C. W. (2009, September). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking (pp. 145–156). ACM.

[11]

Luo, J., Xiang, L. & Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks? In 2010 IEEE international conference on communications (ICC). IEEE.

[12]

L. Xiang, J. Luo, & Vasilakos, A. (2011, June). Compressed data aggregation for energy efficient wireless sensor networks. In Proceedings of the IEEE sensor, mesh and ad hoc communications and networks (SECON’11) (pp. 46–54).

[13]

Nguyen, M. T., & Teague, K. A. (2014). Compressive sensing based data gathering in clustered wireless sensor networks. In 2014 IEEE international conference on distributed computing in sensor systems (DCOSS). IEEE.

Digital Library

[14]

Liu, D., Zhou, Q., Zhang, Z., & Liu, B. (2016). Cluster-based energy-efficient transmission using a new hybrid compressed sensing in WSN. In 2016 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 372–376).

[15]

Lan, K.-C., & Wei, M.-Z. (2017). A compressibility-based clustering algorithm for hierarchical compressive data gathering. IEEE Sensors Journal, 17(8), 2550–2562.

[16]

Lee, S., Pattem, S., Sathiamoorthy, M., Krishnamachari, B., & Ortega, A. (2009, July). Spatially-localized compressed sensing and routing in multi-hop sensor networks. In International conference on geosensor networks (pp. 11–20). Springer, Berlin.

Digital Library

[17]

Xiaoronga, C., Mingxuan, L., & Suc, L. (2012). Study on clustering of wireless sensor network in distribution network monitoring system. Physics Procedia, 25, 1689–1695.

[18]

Vuran, M. C., & Akyildiz, I. F. (2006). Spatial correlation-based collaborative medium access control in wireless sensor networks. IEEE/ACM Transactions on Networking, 14(2), 316–329.

[19]

Deng, X., Zhong, W., Ren, J., Zeng, D., & Zhang, H. (2016). An imbalanced data classification method based on automatic clustering under-sampling. In 2016 IEEE 35th international performance computing and communications conference (IPCCC) (pp. 1–8). IEEE.

[20]

Yuan, J., & Chen, H. (2009, Sep). The optimized clustering technique based on spatial-correlation in wireless sensor networks. In Proceedings of the IEEE youth conference Information, telecommunication and computing YC-ICT (pp. 411–414).

[21]

Liu, C., Wu, K., & Pei, J. (2007). An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Transactions on Parallel and Distributed Systems, 18(7), 1010.

Digital Library

[22]

Shakya, R. K., Singh, Y. N., & Verma, N. K. (2013). Generic correlation model for wireless sensor network applications. IET Wireless Sensor Systems, 3(4), 266–276.

[23]

Liu, Z., Xing, W., Zeng, B., Wang, Y., & Lu, D. (2013, March). Distributed spatial correlation-based clustering for approximate data collection in WSNs. In 2013 IEEE 27th international conference on advanced information networking and applications (AINA) (pp. 56–63). IEEE.

Digital Library

[24]

Ramesh, K., & Somasundaram, K. (2012). A comparative study of clusterhead selection algorithms in wireless sensor networks. arXiv preprint arXiv:1205.1673.

[25]

Vuran, M. C., Akan, Ö. B., & Akyildiz, I. F. (2004). Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks, 45(3), 245–259.

Digital Library

[26]

Zhang, H., & Hou, J. C. (2005). Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc and Sensor Wireless Networks, 1(1-2), 89–124.

[27]

Papastergiou, S., Polemi, D., & Douligeris, C. (2009). SWEB: An advanced mobile residence certificate service. In International conference on e-democracy (pp. 421–430). Springer, Berlin.

[28]

Wang, B. (2010). Coverage control in sensor networks. Computer communications and networks. Berlin: Springer. https://doi.org/10.1007/978-1-84996-059-5_2.

Digital Library

[29]

Liaqat, M., et al. (2014). HEX clustering protocol for routing in wireless sensor network. In 2014 IEEE 28th international conference on advanced information networking and applications (AINA). IEEE.

Digital Library

[30]

Ouni, S., & Ayoub, Z. T. (2013). Predicting communication delay and energy consumption for IEEE 802.15.4/Zigbee wireless sensor networks. International Journal of Computer Networks Communications, 5(1), 141.

[31]

Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. Diss. Massachusetts Institute of Technology.

Digital Library

[32]

Yuan, F., Zhan, Y., & Wang, Y. (2014). Data density correlation degree clustering method for data aggregation in WSN. IEEE Sensors Journal, 14(4), 1089–1098.

[33]

Razzaque, M. A., & Dobson, S. (2014). Energy-efficient sensing in wireless sensor networks using compressed sensing. Sensors, 14(2), 2822–2859.

[34]

Zhu, L., Ci, B., Liu, Y., & Chen, Z. (2015). Data gathering in wireless sensor networks based on reshuffling cluster compressed sensing. International Journal of Distributed Sensor Networks, 11(11), 260913.

Digital Library

[35]

Intel Labs Berkeley Data. http://db.csul.mit.edu/www.select.cs.cmu.edu/data/labapp3/.

[36]

Quer, G., Masiero, R., Pillonetto, G., Rossi, M., & Zorzi, M. (2012). Sensing, compression, and recovery for WSNS: Sparse signal modeling and monitoring framework. IEEE Transactions on Wireless Communications, 11(10), 3447–3461.

Cited By

Mazinani AMazinani SAlyasiri M(2024)EFTVG: An Energy Efficient Fuzzy–Timer Clustering Approach in an Adaptive Virtual Grid Cluster Based WSNWireless Personal Communications: An International Journal10.1007/s11277-024-11453-0137:2(1069-1097)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11277-024-11453-0
Sulthana NDuraipandian M(2024)EELCR: energy efficient lifetime aware cluster based routing technique for wireless sensor networks using optimal clustering and compressionTelecommunications Systems10.1007/s11235-023-01068-485:1(103-124)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11235-023-01068-4
Ghaderi MSheikhan M(2023)An Energy-Aware Model for Wireless Sensor Networks: Hierarchical Compressive Data Gathering for Hierarchical Grid-Based Routing (HCDG-HGR)Wireless Personal Communications: An International Journal10.1007/s11277-023-10200-1129:3(1645-1668)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1007/s11277-023-10200-1
Show More Cited By

Index Terms

Clustering and Compressive Data Gathering in Wireless Sensor Network
1. Hardware
  1. Communication hardware, interfaces and storage
2. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

Index terms have been assigned to the content through auto-classification.

Recommendations

Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing

Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. However, the total number of transmissions for data collection by using pure CS is still large. The hybrid method of using CS was ...
Accurate compressive data gathering in wireless sensor networks using weighted spatio-temporal compressive sensing

The high number of transmissions in sensor nodes having a limited amount of energy leads to a drastic decrease in the lifetime of wireless sensor networks. For dense sensor networks, the provided data potentially have spatial and temporal correlations. ...
Compressive data gathering with low-rank constraints for Wireless Sensor networks

In this paper, a novel compressive data gathering with low-rank constraints is proposed for efficient data gathering and accurate recovery in wireless sensor networks. The proposed method utilizes both the low-rank feature of the data matrix by ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Wireless Personal Communications: An International Journal

Wireless Personal Communications: An International Journal Volume 109, Issue 2

Nov 2019

758 pages

ISSN:0929-6212

Issue’s Table of Contents

Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mazinani AMazinani SAlyasiri M(2024)EFTVG: An Energy Efficient Fuzzy–Timer Clustering Approach in an Adaptive Virtual Grid Cluster Based WSNWireless Personal Communications: An International Journal10.1007/s11277-024-11453-0137:2(1069-1097)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11277-024-11453-0
Sulthana NDuraipandian M(2024)EELCR: energy efficient lifetime aware cluster based routing technique for wireless sensor networks using optimal clustering and compressionTelecommunications Systems10.1007/s11235-023-01068-485:1(103-124)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11235-023-01068-4
Ghaderi MSheikhan M(2023)An Energy-Aware Model for Wireless Sensor Networks: Hierarchical Compressive Data Gathering for Hierarchical Grid-Based Routing (HCDG-HGR)Wireless Personal Communications: An International Journal10.1007/s11277-023-10200-1129:3(1645-1668)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1007/s11277-023-10200-1
Wang XChen H(2022)A Survey of Compressive Data Gathering in WSNs for IoTsWireless Communications & Mobile Computing10.1155/2022/44907902022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4490790
Wang XChen H(2022)A Reinforcement Learning-Based Dynamic Clustering Algorithm for Compressive Data Gathering in Wireless Sensor NetworksMobile Information Systems10.1155/2022/27367342022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2736734
Kulandaivel MNatarajan AVelayutham SSrivastava AGupta SSuresh PGoyal N(2022)Compressive Sensing Node Localization Method Using Autonomous Underwater Vehicle NetworkWireless Personal Communications: An International Journal10.1007/s11277-022-09841-5126:3(2781-2799)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11277-022-09841-5
Bibin Christopher VJasper J(2020)DHGRP: Dynamic Hexagonal Grid Routing Protocol with Mobile Sink for Congestion Control in Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-020-07146-z112:4(2213-2232)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s11277-020-07146-z

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents