Advertisement

Telecommunication Systems

, Volume 67, Issue 3, pp 451–463 | Cite as

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

  • Lingping Kong
  • Jeng-Shyang Pan
  • Václav Snášel
  • Pei-Wei Tsai
  • Tien-Wen Sung
Article

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.

Keywords

Wireless sensor network Genetic algorithm Energy-aware routing protocol 

Mathematics Subject Classification

00-01 99-00 

Notes

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.

References

  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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. 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.CrossRefGoogle Scholar
  6. 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. 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.CrossRefGoogle Scholar
  8. 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. 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.Google Scholar
  10. 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.CrossRefGoogle Scholar
  11. 11.
    Vazirani, V. V. (2013). Approximation algorithms. Berlin: Springer.Google Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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. 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. 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. 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. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 21.
    Yu, Y., Govindan, R., & Estrin, D. (2001). Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks.Google Scholar
  22. 22.
    Ramesh, K., & Somasundaram, D. K. A comparative study of clusterhead selection algorithms in wireless sensor networks. ArXiv preprint arXiv:1205.1673.
  23. 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. 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.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 26.
    Gonzalez, T. F. (1985). Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, 38, 293–306.CrossRefGoogle Scholar
  27. 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. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 30.
    Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2), 65–85.CrossRefGoogle Scholar
  31. 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.Google Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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. 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. 35.
    Levy, P. S., & Lemeshow, S. (2013). Sampling of populations: Methods and applications. New York: Wiley.Google Scholar
  36. 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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Lingping Kong
    • 1
  • Jeng-Shyang Pan
    • 1
    • 2
  • Václav Snášel
    • 3
  • Pei-Wei Tsai
    • 4
  • Tien-Wen Sung
    • 2
  1. 1.Innovative Information Industry Research Center, Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina
  2. 2.Fujian Provincial Key Lab of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina
  3. 3.Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic
  4. 4.Department of Computer Science and Software EngineeringSwinburne University of TechnologyMelbourneAustralia

Personalised recommendations