Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A Compressed Sensing Approach to Resolve The Energy Hole Problem in Large Scale WSNs

  • 176 Accesses

  • 6 Citations

Abstract

Sensor nodes in close proximity to the sink experience heavy traffic than the nodes which are far from the sink. Since, the continuous monitoring and reporting of events throughout the network involves the nodes closer to the sink to be part of the relay, the transmission load is hardly even throughout the network. The uneven energy consumption in the network eventually results nodes near the sink to die out quickly, creating energy holes in the network. This work proposes a compressive in-network data processing scheme, to resolve the energy hole problem and to improve the network lifetime. The sensed data is transmitted to the sink through a three-level clustering scheme. A few designated nodes in the first and second level apply compressed sensing and obtain weighted samples of the received data. A lossless compression is applied at the nodes in the third level before it is finally transmitted to the sink. Results show the proposed scheme to be is 43.75% transmission efficient and is able to resolve the energy hole problem efficiently by distributing the load evenly over the network.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Boubiche, D. E., Boubiche, S., & Bilami, A. (2015). A cross-layer watermarking-based mechanism for data aggregation integrity in heterogeneous WSNs. IEEE Communications Letters, 19(5), 823–826.

  2. 2.

    Yang, P., et al. (2011). An efficient privacy preserving data aggregation scheme with constant communication overheads for wireless sensor networks. IEEE Communications Letters, 15(11), 1205–1207.

  3. 3.

    Abdul-Salaam, G., Abdullah, A. H., Anisi, M. H., Gani, A., & Alelaiwi, A. (2016). A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols. Telecommunication Systems, 61(1), 159–179.

  4. 4.

    Luo, C., Wu, F., Sun, J., & Chen, C. W. (2009). 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.

  5. 5.

    Luo, J., Xiang, L., & Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks?. In Communications (ICC), 2010 IEEE international conference on (pp. 1-6). IEEE.

  6. 6.

    Kim, M.-G., Han, Y.-T., & Park, H.-S. (2011). Energy-aware hybrid data aggregation mechanism considering the energy hole problem in asynchronous MAC-based WSNs. Communications Letters, IEEE, 15(11), 1169–1171.

  7. 7.

    Abo-Zahhad, M., Ahmed, S., Sabor, N., & Sasaki, S. (2015). Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sensors Journal, 15(8), 4576–4586.

  8. 8.

    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.

  9. 9.

    Medeiros, H. P., Maciel, M. C.., Demo Souza, R., & Pellenz, M. E. (2014) Lightweight data compression in wireless sensor networks using huffman coding. International Journal of Distributed Sensor Networks.

  10. 10.

    Candes, E., & Justin, R. (2015) l1-magic: Recovery of sparse signals via convex programming. www.acm.caltech.edu/l1magic/downloads/l1magic.pdf4, 14.

  11. 11.

    Li, S., Da Xu, L., & Wang, X. (2013). Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Transactions on Industrial Informatics, 9(4), 2177–2186.

  12. 12.

    Li, C., Wang, J., & Li, M. (2016). Efficient data transmission of wireless sensor networks through compressive sensing and matrix completion. International Journal of Wireless Information Networks, 23(2), 135–140.

  13. 13.

    Caione, C., Brunelli, D., & Benini, L. (2012). Distributed compressive sampling for lifetime optimization in dense wireless sensor networks. IEEE Transactions on Industrial Informatics, 8(1), 30–40.

  14. 14.

    Ebrahimi, D., & Assi, C. (2013). Optimal and efficient algorithms for projection-based compressive data gathering. Communications Letters, IEEE, 17(8), 1572–1575.

  15. 15.

    Apilo, O., Lasanen, M., & Mämmelä, A. (2016). Energy-efficient dynamic point selection and scheduling method for intra-cell CoMP in LTE-A. Wireless Personal Communications, 86(2), 705–726.

  16. 16.

    Jiang, D., Ying, X., Han, Y., & Lv, Z. (2016). Collaborative multi-hop routing in cognitive wireless networks. Wireless Personal Communications, 86(2), 901–923.

Download references

Author information

Correspondence to Vishal Krishna Singh.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Singh, V.K., Kumar, M. A Compressed Sensing Approach to Resolve The Energy Hole Problem in Large Scale WSNs. Wireless Pers Commun 99, 185–201 (2018). https://doi.org/10.1007/s11277-017-5047-9

Download citation

Keywords

  • In-network inference
  • Compressed data gathering
  • Traffic load balancing
  • Uniform energy consumption
  • Wireless sensor networks