Skip to main content

Advertisement

Springer Nature Link
Log in

An energy-aware routing protocol for wireless sensor network based on genetic algorithm

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Energy saving and effective utilization are an essential issue for wireless sensor network. Most previous cluster based routing protocols only care the relationship of cluster heads and sensor nodes but ignore the huge difference costs between them. In this paper, we present a routing protocol based on genetic algorithm for a middle layer oriented network in which the network consists of several stations that are responsible for receiving data and forwarding the data to the sink. The amount of stations should be not too many and not too few. Both cases will cause either too much construction cost or extra transmission energy consumption. We implement five methods to compare the performance and test the stability of our presented methods. Experimental results demonstrate that our proposed scheme reduces the amount of stations by 36.8 and 20% compared with FF and HL in 100-node network. Furthermore, three methods are introduced to improve our proposed scheme for effective cope with the expansion of network scale problem.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Yan, R., Sun, H., & Qian, Y. (2013). Energy-aware sensor node design with its application in wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 62(5), 1183–1191. doi:10.1109/TIM.2013.2245181.

    Article  Google Scholar 

  2. Yu, J., Qi, Y., Wang, G., & Gu, X. (2012). A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEU-International Journal of Electronics and Communications, 66(1), 54–61.

    Article  Google Scholar 

  3. C, Y.-H., Chen, C.-M., Lin, Y.-H., & Sun, H.-M. (2013). Sashimi: Secure aggregation via successively hierarchical inspecting of message integrity on WSN. Journal of Information Hiding and Multimedia Signal Processing, 4(1), 57–72.

    Article  Google Scholar 

  4. Chang, F.-C., & Huang, H.-C. (2016). A survey on intelligent sensor network and its applications. J. Netw. Intell, 1(1), 1–15.

    Google Scholar 

  5. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749. doi:10.1016/j.asoc.2012.12.029.

    Article  Google Scholar 

  6. Tuna, G., Gungor, V. C., Gulez, K., Hancke, G., & Gungor, V. (2013). Energy harvesting techniques for industrial wireless sensor networks. In G. P. Hancke & V. C. Gungor (Eds.), Industrial wireless sensor networks: Applications, protocols, standards, and products (pp. 119–136). New York: CRC Press.

    Google Scholar 

  7. Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 15(2), 551–591. doi:10.1109/SURV.2012.062612.00084.

    Article  Google Scholar 

  8. Nguyen, T.-T., Dao, T.-K., Horng, M.-F., & Shieh, C.-S. (2016). An energy-based cluster head selection algorithm to support long-lifetime in wireless sensor networks. J. Netw. Intell, 1(1), 23–37.

    Google Scholar 

  9. Goyal, D., & Tripathy, M. R. (2012). Routing protocols in wireless sensor networks: A survey. In 2012 second international conference on advanced computing and communication technologies (pp. 474–480). IEEE.

  10. Zhang, D., Li, G., Zheng, K., Ming, X., & Pan, Z.-H. (2014). An energy-balanced routing method based on forward-aware factor for wireless sensor networks. IEEE Transactions on Industrial Informatics, 10(1), 766–773. doi:10.1109/TII.2013.2250910.

    Article  Google Scholar 

  11. Vazirani, V. V. (2013). Approximation algorithms. Berlin: Springer.

    Google Scholar 

  12. Du, H., Xu, Y., & Zhu, B. (2015). An incremental version of the k-center problem on boundary of a convex polygon. Journal of Combinatorial Optimization, 30(4), 1219–1227. doi:10.1007/s10878-015-9933-3.

    Article  Google Scholar 

  13. Liang, D., Mei, L., Willson, J., & Wang, W. (2016). A simple greedy approximation algorithm for the minimum connected k-center problem. Journal of Combinatorial Optimization, 31(4), 1417–1429. doi:10.1007/s10878-015-9831-8.

    Article  Google Scholar 

  14. Akkaya, K., & Younis, M. (2003). An energy-aware QoS routing protocol for wireless sensor networks. In Distributed computing systems workshops, 2003. Proceedings 23rd international conference on IEEE (pp. 710–715). http://csdl.computer.org/comp/proceedings/icdcsw/2003/1921/00/19210710abs.htm.

  15. Anker, T., Bickson, D., Dolev, D., & Hod, B. (2008). Efficient clustering for improving network performance in wireless sensor networks. In R. Verdone (Ed.), Wireless sensor networks, Vol. 4913 of Lecture Notes in Computer Science (pp. 221–236). Springer. doi:10.1007/978-3-540-77690-1_14.

  16. Banerjee, S., & Khuller, S. (2001). A clustering scheme for hierarchical control in multi-hop wireless networks. In INFOCOM (pp. 1028–1037). www.ieee-infocom.org/2001/paper/676.ps.

  17. W, H., Ni, M.-M., & Zhong, Z.-D. (2010). A novel energy efficient clustering algorithm for dynamic wireless sensor network. Journal of Internet Technology, 11(1), 103–107.

    Google Scholar 

  18. Forero, P. A., Cano, A., & Giannakis, G. B. (2011). Distributed clustering using wireless sensor networks. IEEE Journal of Selected Topics in Signal Processing, 5(4), 707–724. doi:10.1109/JSTSP.2011.2114324.

    Article  Google Scholar 

  19. Ge, R., Ester, M., Gao, B. J., Hu, Z., Bhattacharya, B., & Ben-Moshe, B. (2008). Joint cluster analysis of attribute data and relationship data: The connected k-center problem, algorithms and applications. ACM Transactions on Knowledge Discovery from Data (TKDD), 2(2), 7. doi:10.1145/1376815.1376816.

    Article  Google Scholar 

  20. Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7), 847–860. doi:10.1007/s11276-012-0438-z.

    Article  Google Scholar 

  21. Yu, Y., Govindan, R., & Estrin, D. (2001). Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks.

  22. Ramesh, K., & Somasundaram, D. K. A comparative study of clusterhead selection algorithms in wireless sensor networks. ArXiv preprint arXiv:1205.1673.

  23. Feldmann, A. E. (2015). Fixed parameter approximations for k-center problems in low highway dimension graphs. In International colloquium on automata, languages, and programming, Vol. 9135 of Lecture Notes in Computer Science, Springer (pp. 588–600). Springer. doi:10.1007/978-3-662-47666-6_47.

  24. Chechik, S., & Peleg, D. (2015). The fault-tolerant capacitated k-center problem. Theoretical Computer Science, 566, 12–25. doi:10.1016/j.tcs.2014.11.017.

    Article  Google Scholar 

  25. Elloumi, S., Labbé, M., & Pochet, Y. (2004). A new formulation and resolution method for the p-center problem. INFORMS Journal on Computing, 16(1), 84–94. doi:10.1287/ijoc.1030.0028.

    Article  Google Scholar 

  26. Gonzalez, T. F. (1985). Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, 38, 293–306.

    Article  Google Scholar 

  27. Harel, D., & Koren, Y. (2002). Graph drawing by high-dimensional embedding. In S. G. Kobourov, & M. T. Goodrich (Eds.), International symposium on graph drawing, Vol. 2528 of Lecture Notes in Computer Science, Springer (pp. 207–219). Springer. http://link.springer.de/link/service/series/0558/bibs/2528/25280207.htm.

  28. Robič, B., & Mihelič, J. (2005). Solving the k-center problem efficiently with a dominating set algorithm. CIT. Journal of computing and information technology, 13(3), 225–234.

    Article  Google Scholar 

  29. Marta, M., & Cardei, M. (2009). Improved sensor network lifetime with multiple mobile sinks. Pervasive and Mobile Computing, 5(5), 542–555. doi:10.1016/j.pmcj.2009.01.001.

    Article  Google Scholar 

  30. Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2), 65–85.

    Article  Google Scholar 

  31. Back, T. (1993). Optimal mutation rates in genetic search. In S. Forrest (Ed.), Proceedings of the 5th international conference on genetic algorithms (pp. 2–8). Morgan Kaufmann.

  32. Beasley, J. E., & Chu, P. C. (1996). A genetic algorithm for the set covering problem. European Journal of Operational Research, 94(2), 392–404.

    Article  Google Scholar 

  33. Thi-Kien Dao, T.-S. P., & Nguyen, T.-T. (2015). A compact articial bee colony optimization for topology control scheme in wireless sensor networks. Journal of Information Hiding and Multimedia Signal Processing, 6(2), 297–310.

    Google Scholar 

  34. T, Y.-C., & Huang, C.-F. (2005). A survey of solutions for the coverage problems in wireless sensor networks. Journal of Internet Technology, 6(1), 1–8.

    Google Scholar 

  35. Levy, P. S., & Lemeshow, S. (2013). Sampling of populations: Methods and applications. New York: Wiley.

    Google Scholar 

  36. Csikszentmihalyi, M., & Larson, R. (2014). Validity and reliability of the experience-sampling method. In Flow and the foundations of positive psychology (pp. 35–54). Springer.

Download references

Acknowledgements

This work is partially supported by the Fujian Provincial Natural Science Foundation, China, under Grant No. 2017J01730; and partially supported by the Key Project of Fujian Education Department Funds (JA15323), Shenzhen Innovation and Entrepreneurship Project with the Project Number: GRCK20160826105935160.

Author information

Authors and Affiliations

  1. Innovative Information Industry Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Lingping Kong & Jeng-Shyang Pan

  2. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan & Tien-Wen Sung

  3. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Václav Snášel

  4. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia

    Pei-Wei Tsai

Authors
  1. Lingping Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeng-Shyang Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Václav Snášel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pei-Wei Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tien-Wen Sung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeng-Shyang Pan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kong, L., Pan, JS., Snášel, V. et al. An energy-aware routing protocol for wireless sensor network based on genetic algorithm. Telecommun Syst 67, 451–463 (2018). https://doi.org/10.1007/s11235-017-0348-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-017-0348-6

Keywords

Mathematics Subject Classification

Search

Navigation