Skip to main content

Advertisement

SpringerLink
Log in

Battery Recovery Based Lifetime Enhancement (BRLE) Algorithm for Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Increasing the lifetime of the network and utilizing the resources to its maximum limit is the major issue in Wireless Sensor Network (WSN). The wireless sensor nodes in sensor network are powered using rechargeable batteries. However, providing energy to nodes in the remote environment is a major issue in WSN. Hence WSN needs a new energy efficient algorithm to enhance the network lifetime. In a sensor node, the transceiving module consumes more energy when compared to other modules. In this paper, a Battery Recovery based Lifetime Enhancement (BRLE) algorithm is discussed, which considers battery voltage curve for scheduling the transceiving module of the sensor nodes. The Markov model helps in determining the state of the sensor node as CH and CM based on battery recovery process. By scheduling the transceiving module based on the battery terminal voltage, recovery factor and distance between the nodes, the lifetime of the network is enhanced. Experimental results show that the algorithm outperforms the others by 1.38 times increased lifetime and 1.574 times increased throughput. The BRLE decreases the HOT SPOT and energy hole problem, avoiding loss in connectivity with the sink.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Tang, Q., Yang, L., Giannakis, G. B., & Qin, T. (2007). Battery power efficiency of PPM and FSK in wireless sensor networks. IEEE Transactions on Wireless Communications, 6(4), 1308–1319.

    Article  Google Scholar 

  2. Kanagachidambaresan, G. R., & Chitra, A. (2015). Fail safe fault tolerant mechanism for wireless body sensor network. Wireless Personal Communication, 78(1), 247–260.

    Article  Google Scholar 

  3. Kanagachidambaresan, G. R., & Chitra, A. (2016). Thermal aware-fail safe fault tolerant mechanism for wireless body sensor network. Wireless Personal Communication, 90(4), 1935–1950.

    Article  Google Scholar 

  4. Li, H., Yi, C., & Li, Y. (2013). Battery friendly packet transmission algorithms for wireless sensor networks. IEEE Sensors Journal, 13(10), 3548–3557.

    Article  Google Scholar 

  5. Chau, C. K., Qin, F., Sayed, S., & Wahab, M. H. (2010). Harnessing battery recovery effect in wireless sensor networks : Experiments and analysis. IEEE Journal on Selected Area in Communication, 28(7), 1222–1232. doi:10.1109/JSAC.2010.100926.

    Article  Google Scholar 

  6. Leu, S. J., Chiang, T. H., Yu, M.-C., & Su, K. W. (2015). Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. IEEE Communication Letters, 19(2), 259–262.

    Article  Google Scholar 

  7. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic based clustering algorithm for WSN to extend the network lifetime. IEEE Sensor Journal, 16(1), 137–144.

    Article  Google Scholar 

  8. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  9. Rukpakavong, W., Guan, L., & Phillips, I. (2014). Dynamic node lifetime estimation for wireless sensor networks. IEEE Sensors Journal. doi:10.1109/JSEN.2013.2295303.

    Google Scholar 

  10. Karakus, C., Gurbuz, A., & Tavli, B. (2013). Analysis of energy efficiency of compressive sensing in wireless sensor networks. IEEE Sensors Journal, 13(5), 1999–2008.

    Article  Google Scholar 

  11. Li, Y., Bakkaloglu, B., & Chakrabarti, C. (2007). A system level energy model and energy-quality evaluation for integrated transceiver front-end. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 15(1), 90–103.

    Article  Google Scholar 

  12. Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Survey and Tutorials, 15(2), 551–591.

    Article  Google Scholar 

  13. Lajara, R. J., Perez solano, J. J., & Pelegri-Sebastia, J. (2015). A method for modeling the battery state of charge in wireless sensor networks. IEEE Sensors Journal, 15(2), 1186–1197. doi:10.1109/JSEN.2014.2361151.

    Article  Google Scholar 

  14. Lee, J. S., & Cheng, W. L. (2012). Fuzzy logic based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal, 12(9), 2891–2896.

    Article  Google Scholar 

  15. Kim, J. Lee, S. & Cho, B. (2009). Discrimination of battery characteristics using discharging/charging voltage pattern recognition, In Proceedings IEEE conference on energy conversion congress and exposition (pp. 1799–1805).

  16. Cloth, L. Haverkort, B. R. & Jongerden, M. R. (2007). Computing battery lifetime distributions, In Proceedings 37th annual IEEE international conference on dependable system network (pp. 780–789).

  17. Rakhmatov, D., Vrudhula, S., & Wallach, D. (2003). A model for battery lifetime analysis for organizing applications on a pocket computer. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 11(6), 1019–1030.

    Article  Google Scholar 

  18. Jongerden M. R. & Haverkort, B. R. (2008). Battery modeling, Department of Electrical Engineering and Mathematical Computer Science, Design Analysis Communication System, Technical Report TR-CTIT-08-01, University of Twente, Enschede.

  19. Ma, C., & Yang, Y. (2006). Battery-aware routing for streaming data transmissions in wireless sensor networks. Mobile Networks and Applications, 11, 757–767.

    Article  Google Scholar 

  20. Li, Y., Li, H., Zhang, Y., & Qiao, D. (2010). Packet transmission policies for battery operated wireless sensor networks. Journal of Frontiers Computer Science in China, 4(3), 365–375.

    Article  Google Scholar 

  21. Abouzar, P., Michelson, D. G., & Hamdi, M. (2016). RSSI-based distributed self-localization for wireless sensor networks used in precision agriculture. IEEE Transactions on Wireless Communications, 15(10), 6638–6650.

    Article  Google Scholar 

  22. Luo, Q., Peng, Y., Li, J., & Peng, X. (2016). RSSI-based localization through uncertain data mapping for wireless sensor networks. IEEE Sensors Journal, 16(9), 3155–3162.

    Article  Google Scholar 

  23. Yaghoubi, Forough, Abbasfar, Ali-Azam, & Maham, Behrouz. (2014). Energy-efficient RSSI-based localization for wireless sensor networks. IEEE Communications Letters, 18(6), 973–976.

    Article  Google Scholar 

  24. Nuggehalli, P., Srinivasan, V., & Rao, R. R. (2006). Energy efficient transmission scheduling for delay constrained wireless networks. IEEE Transactions on Wireless Communications, 5(3), 531–539.

    Article  Google Scholar 

  25. Chiasserini, C. F., & Rao, R. R. (2001). Improving battery performance by using traffic shaping techniques. IEEE JSAC Wireless Series, 19(7), 1385–1394.

    Google Scholar 

  26. Ma, C. & Yang, Y. (2005). Battery aware routing in wireless ad hoc networks. Part II. Battery-aware routing. In Proceeding of 19th international tele traffic congress (ITC-19) (pp. 303–312).

  27. Rakhmatov, D., & Vrudhula, S. (2003). Energy management for battery-powered embedded systems. ACM transactions embedded. Computing Systems, 2(3), 277–324.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electronics and Communication Engineering, Sri Krishna College of Technology, Kovaipudur, Coimbatore, 641042, TAMIL NADU, India

    V. Mahima

  2. Department of Computer Applications, PSG College of Technology, Coimbatore, India

    A. Chitra

Authors
  1. V. Mahima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Chitra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Mahima.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahima, V., Chitra, A. Battery Recovery Based Lifetime Enhancement (BRLE) Algorithm for Wireless Sensor Network. Wireless Pers Commun 97, 6541–6557 (2017). https://doi.org/10.1007/s11277-017-4854-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4854-3

Keywords

Search

Navigation