Optimized Node Deployment in Wireless Sensor Network for Smart Grid Application

  • M. Vergin Raja SarobinEmail author


At present the low cost, low power and collaborative feature of Wireless Sensor Network (WSN) is becoming a popular communication technology in smart grid including power generation, transmission and distribution. Among these, the health monitoring of wind power generation system has emerged as one of the many possible applications of WSNs. However the harsh environmental condition of wind farm application brings node deployment as a major design issue in WSN which is well associated with coverage and connectivity issues. Hence the research objective here is twofold. Firstly the sensor nodes are placed optimally in the key components of the wind turbines to improve target coverage. The adjacent turbine span varies with several hundred meters apart which results in independent wireless sensor sub-networks. Connectivity among these sub-networks is a second vital issue, which is guaranteed by joining all the independent sub-networks with the base station by placing minimum number of relay nodes. Hence the connectivity problem is considered as Relay Node Deployment Problem. Connectivity is obtained in this work by bio-inspired Ant Colony Optimization (ACO) algorithm. ACO is further enhanced as ACO-Intelligent Movement, by introducing intelligent movement mechanism. The goal of this approach is to optimize number of relay nodes, decrease deployment cost and to bring up network connectivity. The performance of our novel deployment approach is validated through extensive simulation results.


Wireless sensor network Node deployment Coverage Connectivity Relay node Ant colony optimization (ACO) ACO-intelligent movement 



  1. 1.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks,38(4), 393–422.CrossRefGoogle Scholar
  2. 2.
    Erol-Kantarci, M., & Mouftah, H. T. (2011). Wireless sensor networks for cost-efficient residential energy management in the smart grid. Transactions on Smart Grid,2(2), 314–325.CrossRefGoogle Scholar
  3. 3.
    Wang, P., Yan, Y., Tian, G. Y., Bouzid, O., & Ding, Z. (2012). Investigation of wireless sensor networks for structural health monitoring. Journal of Sensors,2012, 1–7.CrossRefGoogle Scholar
  4. 4.
    Younis, M., & Akkaya, K. (2008). Strategies and techniques for node placement in wireless sensor networks: A survey. Journal of Ad-Hoc Networks,6(4), 621–655.CrossRefGoogle Scholar
  5. 5.
    Wang, B. (2011). Coverage problems in sensor networks: A survey. ACM Computing Surveys,43(4), 1–53.CrossRefGoogle Scholar
  6. 6.
    Zhao, Q., & Gurusamy, M. (2008). Lifetime maximization for connected target coverage in wireless sensor networks. IEEE Transactions on Networks,16(6), 1378–1391.CrossRefGoogle Scholar
  7. 7.
    Katiyar, V., Chand, N., & Soni, S. (2011). A survey on clustering algorithms for heterogeneous wireless sensor networks. International Journal on Advanced Networking and Applications,2(4), 745–754.Google Scholar
  8. 8.
    Cheng, M. X., Ling, Y., & Sadler, B. M. (2017). Network connectivity assessment and improvement through relay node deployment. Theoretical Computer Science,660(2017), 86–101.MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Lloyd, E., & Xue, G. (2007). Relay node placement in wireless sensor networks. IEEE Transactions on Computers,56(1), 134–138.MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Bai, X., Yun, Z., Xuan, D., Lai, T., & Jia, W. (2010). Optimal patterns for four-connectivity and full coverage in wireless sensor networks. IEEE Transactions on Mobile Computing,9(3), 435–448.CrossRefGoogle Scholar
  11. 11.
    Misra, S., Hong, S., Xue, G., & Tang, J. (2010). Constrained relay node placement in wireless sensor networks: Formulation and approximations. IEEE/ACM Transactions on Networking,18(2), 434–447.CrossRefGoogle Scholar
  12. 12.
    Yun, Z., Bai, X., Xuan, D., Lai, T., & Jia, W. (2010). Optimal deployment patterns for full coverage and k-connectivity (k ≤ 6) wireless sensor networks. IEEE/ACM Transactions on Networking,18(3), 934–947.CrossRefGoogle Scholar
  13. 13.
    Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computing,1(1), 53–66.CrossRefGoogle Scholar
  14. 14.
    Deif, D. S., & Gadallah, Y. (2014). Classification of wireless sensor networks deployment techniques. IEEE Communications Surveys and Tutorials,16(2), 834–855.CrossRefGoogle Scholar
  15. 15.
    Park, Y.K., Lee, M.G., Jung, K.K., Yoo, J.J., & Lee, S.H. (2011). Optimum sensor nodes deployment using fuzzy c-means algorithm. In International symposium on computer science and society (pp. 389–392). IEEE.Google Scholar
  16. 16.
    Li, D., Liu, W., & Cui, L. (2010). EasiDesign: An improved ant colony algorithm for sensor deployment in real sensor network system. In Proceedings of the IEEE conference on global telecommunications (pp. 1–5).Google Scholar
  17. 17.
    Liu, X. (2012). Sensor deployment of wireless sensor networks based on ant colony optimization with three classes of ant transitions. IEEE Communications Letters,16(10), 1604–1607.CrossRefGoogle Scholar
  18. 18.
    Yoon, Y., & Kim, Y. H. (2013). An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Transactions on Cybernetics,43(5), 1473–1483.CrossRefGoogle Scholar
  19. 19.
    Shiu, L. C., Lee, C. Y., & Yang, C. S. (2011). The divide-and-conquer deployment algorithm based on triangles for wireless sensor networks. IEEE Sensors Journal,11(3), 781–790.CrossRefGoogle Scholar
  20. 20.
    Swartz, R. A., Lynch, J. P., Zerbst, S., Sweetman, B., & Rolfes, R. (2010). Structural monitoring of wind turbines using wireless sensor networks. Smart Structures and Systems,6(3), 183–196.CrossRefGoogle Scholar
  21. 21.
    Zhixin, F. U., & Yue, Y. (2012). Condition health monitoring of offshore wind turbine based on wireless sensor network. In Proceedings of the IEEE international conference on power and energy (pp. 649–654).Google Scholar
  22. 22.
    Li, F., Luo, J., Wang, W., & He, Y. (2015). Autonomous deployment for load balancing k-surface coverage in sensor networks. IEEE Transactions on Wireless Communications,14(1), 279–293.CrossRefGoogle Scholar
  23. 23.
    Zorlu, O., & Sahingoz, O. K. (2016). Increasing the coverage of homogeneous wireless sensor network by genetic algorithm based deployment. In Sixth IEEE international conference on digital information and communication technology and its applications (pp. 109–114).Google Scholar
  24. 24.
    Ozturk, C., Karaboga, D., & Gorkemli, B. (2012). Artificial bee colony algorithm for dynamic deployment of wireless sensor networks. Turkish Journal of Electrical Engineering & Computer Sciences,20(2), 255–262.Google Scholar
  25. 25.
    Mini, S., Udgata, S. K., & Sabat, S. L. (2014). Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sensors Journal,14(3), 636–644.CrossRefGoogle Scholar
  26. 26.
    Chen, C., Yan, J., Lu, N., Wang, Y., Yang, X., & Guan, X. (2015). Ubiquitous monitoring for industrial cyber-physical systems over relay-assisted wireless sensor networks. IEEE Transactions on Emerging Topics in Computing,3(3), 352–362.CrossRefGoogle Scholar
  27. 27.
    Zhu, S., Chen, C., Ma, X., Yang, B., & Guan, X. (2015). Consensus based estimation over relay assisted sensor networks for situation monitoring. IEEE Journal of Selected Topics in Signal Processing,9(2), 278–291.CrossRefGoogle Scholar
  28. 28.
    Yang, D., Misra, S., Fang, X., Xue, G., & Zhang, J. (2012). Two-tiered constrained relay node placement in wireless sensor networks: Computational complexity and efficient approximations. IEEE Transactions on Mobile Computing,11(8), 1399–1411.CrossRefGoogle Scholar
  29. 29.
    Misra, S., Majd, N. E., & Huang, H. (2014). Approximation algorithms for constrained relay node placement in energy harvesting wireless sensor networks. IEEE Transactions on Computers,63(12), 2933–2947.MathSciNetzbMATHCrossRefGoogle Scholar
  30. 30.
    Han, X., Cao, X., Lloyd, E. L., & Shen, C. C. (2010). Fault-tolerant relay node placement in heterogeneous wireless sensor networks. IEEE Transactions on Mobile Computing,9(5), 643–656.CrossRefGoogle Scholar
  31. 31.
    Lee, S., & Younis, M. (2012). Optimized relay node placement for connecting disjoint wireless sensor networks. Computer Networks,56(12), 2788–2804.CrossRefGoogle Scholar
  32. 32.
    Nigam, A., & Agarwal, Y. K. (2014). Optimal relay node placement in delay constrained wireless sensor network design. European Journal of Operational Research,233(1), 220–233.MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Efrat, A., Fekete, S. P., Mitchell, J. S., Polishchuk, V., & Suomela, J. (2016). Improved approximation algorithms for relay placement. ACM Transactions on Algorithms,12(2), 20.MathSciNetzbMATHGoogle Scholar
  34. 34.
    MATLAB and Statistics Toolbox Release. (2015). The MathWorks Inc, Natick.Google Scholar

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

Authors and Affiliations

  1. 1.School of Computing Science and EngineeringVIT ChennaiChennaiIndia

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