Advertisement

Wireless Personal Communications

, Volume 97, Issue 4, pp 6189–6220 | Cite as

Multi-objective Node Placement Considering Non-uniform Event Pattern

  • Hossein Mohtashami
  • Ali Movaghar
  • Mohammad Teshnehlab
Article
  • 76 Downloads

Abstract

Ease of use, high flexibility and variety of applications have made wireless sensor networks very popular. Node placement in a sensor network is very critical since it affects important network attributes such as coverage, lifetime, and reliability. Therefore, controlled node placement is necessary for achieving specific network features with minimum number of nodes. Since node placement is an NP-hard problem, many placement algorithms have been proposed based on heuristic and meta-heuristic methods. Most of those algorithms assume a uniform event pattern (UEP) throughout the area under investigation. However, in practice some networks deal with non-uniform event pattern (NEP). Optimization of one attribute of the network usually results in degradation of other attributes. That is why in recent studies multi-objective optimization methods have been used which usually provide a set of best answers while the final answer is selected by a decision maker applying a trade-off between all attributes. In this paper a method for controlled node placement is proposed based on multi-objective optimization (MOO) algorithms considering a non-uniform event pattern for the network (NPNEP). In that sense this method is an extension of the previous methods that use uniform event pattern. Because of the good performance of multi-objective optimization evolutionary algorithm based on decomposition (MOEA/D), it is utilized as the optimization tool in node placement. In order to use MOEA/D, a few objective functions are defined which can optimize important attributes like coverage, power consumption, delay, reliability and lifetime. In order to achieve optimal node utilization, load balance, and increased lifetime, a cost function for routing is proposed as well as a data gathering and reporting method which both help increase the lifetime of a network. The proposed method can be used for wireless sensor networks with heterogeneous nodes. This algorithm can be used in initial deployment phase as well as operation phase or when problems like fragmentation or loss of coverage occurs. The result of NPNEP algorithm is the initial position of nodes or position of the new nodes which provide the best answers in MOO algorithm. Performance of the proposed algorithm and the effect of NEP as opposed to UEP have been verified by simulations.

Keywords

Wireless sensor network (WSN) Multi-objective optimization (MOO) Multi-objective optimization evolutionary algorithm based on decomposition Controlled node placement Node deployment Non-uniform event distribution 

References

  1. 1.
    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52, 2292–2330.CrossRefGoogle Scholar
  2. 2.
    Pottie, G. J., & Kaiser, W. J. (2000). Wireless integrated network sensors. Communications of the ACM, 43, 51–58.CrossRefGoogle Scholar
  3. 3.
    Younis, M., & Akkaya, K. (2008). Strategies and techniques for node placement in wireless sensor networks: A survey. Ad Hoc Networks, 6, 621–655.CrossRefGoogle Scholar
  4. 4.
    Yang, L., Liang, J., & Liu, W. (2013). Graphical deployment strategies in radar sensor networks (RSN) for target detection. EURASIP Journal on Wireless Communications and Networking, 2013, 1–9.CrossRefGoogle Scholar
  5. 5.
    Halder, S., Ghosal, A., & Bit, S. D. (2011). A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network. Computer Communications, 34, 1294–1306.CrossRefGoogle Scholar
  6. 6.
    Misra, S., Seung Don, H., Guoliang, X., & Jian, T. (2010). Constrained relay node placement in wireless sensor networks: Formulation and approximations. IEEE/ACM Transactions on Networking, 18, 434–447.CrossRefGoogle Scholar
  7. 7.
    Zhipeng, G., Kan, C., Weijing, C., Yuwen, H., & Xiaoxue, L. (2014). K-extended constrain independent relay node placement with base stations in two-tiered wireless sensor network. In 2014 tenth international conference on intelligent information hiding and multimedia signal processing (IIH-MSP) (pp. 823–826).Google Scholar
  8. 8.
    Chakrabarty, K., Iyengar, S. S., Hairong, Q., & Eungchun, C. (2002). Grid coverage for surveillance and target location in distributed sensor networks. IEEE Transactions on Computers, 51, 1448–1453.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Akkaya, K., Younis, M., & Bangad, M. (2005). Sink repositioning for enhanced performance in wireless sensor networks. Computer Networks, 49, 512–534.CrossRefGoogle Scholar
  10. 10.
    Yuan, X.-X., & Zhang, R.-H. (2011). An energy-efficient mobile sink routing algorithm for wireless sensor networks. In 2011 7th international conference on wireless communications, networking and mobile computing (WiCOM) (pp. 1–4).Google Scholar
  11. 11.
    Flathagen, J., Kure, Ø., & Engelstad, P. E. (2011). Constrained-based multiple sink placement for wireless sensor networks. In 2011 IEEE 8th international conference on mobile adhoc and sensor systems (MASS) (pp. 783–788).Google Scholar
  12. 12.
    Konstantinidis, A., Yang, K., Zhang, Q., & Zeinalipour-Yazti, D. (2010). A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Computer Networks, 54, 960–976.CrossRefzbMATHGoogle Scholar
  13. 13.
    Konstantinidis, A., & Yang, K. (2011). Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Applied Soft Computing, 11, 4117–4134.CrossRefGoogle Scholar
  14. 14.
    Sengupta, S., Das, S., Nasir, M. D., & Panigrahi, B. K. (2013). Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Engineering Applications of Artificial Intelligence, 26, 405–416.CrossRefGoogle Scholar
  15. 15.
    Banimelhem, O., Mowafi, M., & Aljoby, W. (2013). Genetic algorithm based node deployment in hybrid wireless sensor networks. Communications and Network, 05, 273–279.CrossRefGoogle Scholar
  16. 16.
    Ghosh, A., & Das, S. K. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4, 303–334.CrossRefGoogle Scholar
  17. 17.
    Özdemir, S., Attea, B. A. A., & Khalil, Ö. A. (2012). Multi-objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks. Wireless Personal Communications, 71, 195–215.CrossRefGoogle Scholar
  18. 18.
    Li, H., & Zhang, Q. (2009). Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13, 284–302.CrossRefGoogle Scholar
  19. 19.
    Stojmenovic, I., Nayak, A., & Kuruvila, J. (2005). Design guidelines for routing protocols in ad hoc and sensor networks with a realistic physical layer. IEEE Communications Magazine, 43, 101–106.CrossRefGoogle Scholar
  20. 20.
    Bettstetter, C., & Hartmann, C. (2005). Connectivity of wireless multihop networks in a shadow fading environment. Wireless Networks, 11, 571–579.CrossRefGoogle Scholar
  21. 21.
    Damaso, A., Rosa, N., & Maciel, P. (2014). Reliability of wireless sensor networks. Sensors (Basel), 14, 15760–15785.CrossRefGoogle Scholar
  22. 22.
    Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1, 660–670.CrossRefGoogle Scholar
  23. 23.
    Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1, 32–49.CrossRefGoogle Scholar
  24. 24.
    Qingfu, Z., Wudong, L., & Hui, L. (2009). The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In IEEE congress on evolutionary computation, 2009. CEC ‘09 (pp. 203–208).Google Scholar
  25. 25.
    Storn, R., & Price, K. (1997). Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Kim, Y. H., Case, K. E., & Ghare, P. M. (1972). A method for computing complex system reliability. IEEE Transactions on Reliability, 21, 215–219.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Hossein Mohtashami
    • 1
  • Ali Movaghar
    • 2
  • Mohammad Teshnehlab
    • 3
  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer EngineeringSharif University of TechnologyTehranIran
  3. 3.Department of Electronic EngineeringK.N. Toosi University of TechnologyTehranIran

Personalised recommendations