Advertisement

An Energy-Efficient Objective Optimization Model for Dynamic Management of Reliability and Delay in WSNs

  • Wenwen Liu
  • Rebecca J. Stones
  • Gang Wang
  • Xiaoguang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11335)

Abstract

As application-driven networks, Wireless Sensor Networks generally require short transmission delay and high data reliability when minimizing energy consumption. Although some approaches have been proposed to tackle this issue, there are few studies that draw attention to the effect of transmission delay and data reliability on minimizing energy consumption. In this paper, we have lots of comprehensive theoretical studies and give the computation models of energy consumption, data transmission delay and data transmission success rate based on IEEE 802.15.4 standard. What’s more, we propose an objective optimization model that minimizing energy consumption while having the constraints of data transmission time and accuracy. The optimization model could dynamically achieve the optimal equilibrium solution by setting the parametric values of optimal equation according to the different requirements of data transmission time and data transmission success rate. The simulation results demonstrate that the validity of computation models. And we find the objective optimization model has a better performance than traditional approaches in the case of dynamically balancing data transmission time and data transmission success rate. Specifically, the proposed optimization model can save up to 41.85% energy consumption compared to Flooding routing algorithm and improve the energy efficient of Reed Solomon code by a factor of 52.6% for the best result.

Keywords

Wireless sensor networks Objective optimization model Reliable transmition Real-time transmission Energy consumption 

References

  1. 1.
    Ipopt. https://projects.coin-or.org/Ipopt. Accessed Jan 2018
  2. 2.
    Bolot, J.C.: End-to-end packet delay and loss behavior in the internet. In: Conference Proceedings on Communications Architectures, Protocols and Applications, pp. 289–298 (1993)Google Scholar
  3. 3.
    Buttyán, L., Gessner, D., Hessler, A., Langendoerfer, P.: Application of wireless sensor networks in critical infrastructure protection: challenges and design options. IEEE Wirel. Commun. 17(5), 44–49 (2010)CrossRefGoogle Scholar
  4. 4.
    Dâmaso, A., Rosa, N., Maciel, P.: Integrated evaluation of reliability and power consumption of wireless sensor networks. Sensors 17(11), 2547 (2017)CrossRefGoogle Scholar
  5. 5.
    Khan, M.K., Kumari, S.: An improved user authentication protocol for healthcare services via wireless medical sensor networks. Int. J. Distrib. Sens. Netw., 1–10 (2014)Google Scholar
  6. 6.
    Kashani, Z.H., Shiva, M.: Channel coding in multi-hop wireless sensor networks. In: International Conference on ITS Telecommunications Proceedings, pp. 965–968 (2007)Google Scholar
  7. 7.
    Konstantopoulos, C., Vathis, N., Pantziou, G., Gavalas, D.: Employing mobile elements for delay-constrained data gathering in WSNs. Comput. Netw. 135, 108–131 (2018)CrossRefGoogle Scholar
  8. 8.
    Liu, A., Chen, Z., Xiong, N.N.: An adaptive virtual relaying set scheme for loss-and-delay sensitive WSNs. Inf. Sci. 424, 118–136 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liu, J., Shen, H.: A low-cost multi-failure resilient replication scheme for high data availability in cloud storage. In: IEEE International Conference on High Performance Computing (2017)Google Scholar
  10. 10.
    Liu, Y., Ota, K., Zhang, K., Ma, M., Xiong, N., Liu, A., Long, J.: QTSAC: an energy-efficient MAC protocol for delay minimization in wireless sensor networks. IEEE Access 6(99), 8273–8291 (2018)CrossRefGoogle Scholar
  11. 11.
    Marco, P.D., Park, P., Fischione, C., Johansson, K.H.: Trend: a timely, reliable, energy-efficient and dynamic WSN protocol for control applications. In: IEEE International Conference on Communications, pp. 1–6 (2010)Google Scholar
  12. 12.
    Mittal, N., Singh, U., Sohi, B.S.: A stable energy efficient clustering protocol for wireless sensor networks. Wirel. Netw. 23(6), 1809–1821 (2017)CrossRefGoogle Scholar
  13. 13.
    Mohemed, R.E., Saleh, A.I., Abdelrazzak, M., Samra, A.S.: Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks. Comput. Netw. 114, 51–66 (2016)CrossRefGoogle Scholar
  14. 14.
    Sankarasubramaniam, Y., Akyildiz, I.F., Mclaughlin, S.W.: Energy efficiency based packet size optimization in wireless sensor networks. In: Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, pp. 1–8 (2003)Google Scholar
  15. 15.
    Singh, V.K., Kumar, R., Sahana, S.: To enhance the reliability and energy efficiency of WSN using new clustering approach. In: International Conference on Computing, Communication and Automation, pp. 488–493 (2017)Google Scholar
  16. 16.
    Torres, C., Glösekötter, P.: Reliable and energy optimized WSN design for a train application. J. Syst. Archit. 57(10), 896–904 (2011)CrossRefGoogle Scholar
  17. 17.
    Tse, R.T., Xiao, Y.: A portable wireless sensor network system for real-time environmental monitoring. In: World of Wireless, Mobile and Multimedia Networks, pp. 1–6 (2016)Google Scholar
  18. 18.
    Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Program. 106(1), 25–57 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Wen, H., Lin, C., Ren, F., Yue, Y., Huang, X.: Retransmission or redundancy: transmission reliability in wireless sensor networks. In: IEEE International Conference on Mobile Adhoc and Sensor Systems, pp. 1–7 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Nankai-Baidu Joint Lab, College of Computer ScienceNankai UniversityTianjinChina

Personalised recommendations