Robust Routing Design with Consideration of Lifetime Maximization for Wireless Sensor Networks in a Framework of Anti-risk Strategy with the Improved Constrained Particle Swarm Optimization Approach | Wireless Personal Communications Skip to main content
Springer Nature Link
Log in

Robust Routing Design with Consideration of Lifetime Maximization for Wireless Sensor Networks in a Framework of Anti-risk Strategy with the Improved Constrained Particle Swarm Optimization Approach

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Since the energy constraint is a fundamental issue for wireless sensor networks, the expectation of network lifetimes becomes a critical performance index. In actual applications, the data traffic on an available routing path could fluctuate from time to time, so there is a compelling need for a robust routing strategy to make the links robust enough when the link outage happens. In the paper, we jointly integrate the maximization of network lifetime and the designing of robust routing strategy into consideration, by which both two subproblems are modeled in a framework of cross-layer nonlinear optimization. By using the popular anti-risk methods popularly used in financial mathematics, our proposed cross-layer optimization problem can be transformed to a convex expression, while its uncertain probabilistic constraints are further changed to a specific solvable expression. Inspired by the particle swarm intelligence in recent development of evolutionary computation, a penalty function based method is introduced and applied in the optimal solution searching process. The effectiveness of our robust routing design is validated by the extensive simulation results, and the quantitative interactions among different neighboring links are also analysed in a typical network topology.

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
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Hou, Y. T., & Shi, Y. (2007). Variable bit rate flow routing in wireless sensor networks. IEEE Transactions on Wireless Communications, 6(6), 2140–2148.

    Article  Google Scholar 

  2. Yang, B., Feng, G., Long, C., & Guan, X. (2006). Maximum lifetime rate control and random access in multi-hop wireless networks. Computer Communications, 29(18), 3844–3855.

    Article  Google Scholar 

  3. Boulfekhar, S., & Benmohammed, M. (2013). A novel energy efficient and lifetime maximization routing protocol in wireless sensor networks. Wireless Personal Communications, 72(2), 1333–1349.

    Article  Google Scholar 

  4. Shi, Y., Hou, Y. T., & Shereli, H. D. (2008). Cross-layer optimization for MIMO-based wireless ad hoc networks: Routing, power allocation, and bandwidth allocation. IEEE Journal on Selected Areas in Communications, 26(6), 913–926.

    Article  Google Scholar 

  5. Ding, L., Melodia, T., Batalama, S. N., Matyjas, J. D., & Medley, M. J. (2010). Cross-layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Transactions on Vehicular Technology, 59(4), 1969–1979.

    Article  Google Scholar 

  6. Li, M., Jing, Y., & Li, C. (2013). A robust and efficient cross-layer optimal design in wireless sensor networks. Wireless Personal Communications, 72(4), 1889–1902.

    Article  Google Scholar 

  7. Huang, X., Zhai, H., & Fang, Y. (2008). Robust cooperative routing protocol in mobile wireless sensor networks. IEEE Transactions on Wireless Communications, 7(12), 5278–5285.

    Article  Google Scholar 

  8. Pandana, C., Arraycomm, S., & Liu, K. J. R. (2008). Robust connectivity-aware energy-efficient routing for wireless sensor networks. IEEE Transactions on Wireless Communications, 7(10), 3904–3916.

    Article  Google Scholar 

  9. Yang, M., Wang, J., Gao, Z., Jiang, Y., & Yoohwan, K. (2005). Coordinated robust routing by dual cluster heads in layered wireless sensor networks. In Proceedings of the 8th international symposium on parallel architectures, algorithms and networks (ISPAN'05) (pp. 454–461). Las Vegas, Nevada.

  10. Lima, C., & de Abreu, G. T. F. (2008). Clusterization for robust geographic routing in wireless sensor networks. In Proceedings of IEEE wireless communications and networking conference (IEEE WCNC’2008) (pp. 2385–2390). Las Vegas, Nevada.

  11. Song, Y., Zhang, C., & Fang, Y. G. (2009). Harnessing traffic uncertainties in wireless mesh networks—A stochastic optimization approach. ACM Mobile Networks and Applications (MONET), 14(2), 124–133.

    Article  Google Scholar 

  12. Applegate, D., & Cohen, E. (2003). Making intra-domain routing robust to changing and uncertain traffic demands: Understanding fundamental tradeoffs. In Proceedings of ACM conference of the special interest group on data communication’03 (SIGCOMM'03) (pp. 313–324). Karlsruhe, Germany.

  13. Li, Y. X., Harms, J., & Holte, R. (2006). Optimal traffic-oblivious energy-aware routing for multihop wireless networks. In Proceedings of 25th IEEE international conference on computer communications (INFOCOM'06) (pp. 1–12). Barcelona, Spain.

  14. Wang, H., Agoulmine, N., Ma, M., & Jin, Y. (2010). Network lifetime optimization in wireless sensor networks. IEEE Journal on Selected Areas in Communications, 28(7), 1127–1137.

    Article  Google Scholar 

  15. Chen, L., Low, S. H., Chiang, M., & Doyle, J. C. (2006). Cross-layer congestion control, routing and scheduling design in ad hoc wireless networks. In Proceedings of 25th IEEE international conference on computer communications (INFOCOM'06) (pp. 1–13). Barcelona, Spain.

  16. Kodialam, M., & Nandagopal, T. (2005). Characterizing achievable rates in multi-hop wireless mesh networks with orthogonal channels. IEEE/ACM Transactions on Networking, 13(4), 868–880.

    Article  Google Scholar 

  17. Xue, L., Yang, B., Zhao, J., Liu, Z., & Guan, X. (2013). Joint resource reconfiguration and robust routing for cognitive radio networks: A robust optimization approach. Wireless Communications and Mobile Computing, 13(18), 2165–2184.

    Google Scholar 

  18. Sohrabi, K., Gao, J., Ailawadhi, V., & Pottie, G. (2000). Protocols for selforganizing of a wireless sensor network. IEEE Personal Communications, 7, 16–27.

    Article  Google Scholar 

  19. Yang, B., Shen, Y., Johansson, M., Chen, C., & Guan, X. (2013). Opportunistic multichannal access with decentralized channel state information. Wireless Communications and Mobile Computing. doi:10.1002/wcm.2348.

    Google Scholar 

  20. Madan, R., & Lall, S. (2006). Distributed algorithms for maximum lifetime routing in wireless sensor networks. IEEE Transactions on Wireless Communications, 5(8), 2185–2193.

    Article  Google Scholar 

  21. Jiang, C., Shi, Y., Hou, Y. T., Lou, W., & Sherali, H. D. (2013). Throughput maximization for multi-hop wireless networks with network-wide energy constraint. IEEE Transactions on Wireless Communications, 12(3), 1255–1267.

    Article  Google Scholar 

  22. Yu, Y. L., Li, Y., Schuurmans, D., & Szepesvari, C. (2009). A general projection property for distribution families. In Proceedings of 23rd annual conference on neural information processing systems (NIPS'09) (pp. 2232–2240). Vancouver, Canada.

  23. Zhu, S., & Fukushima, M. (2009). Worst-case conditional value-at-risk with application to robust portfolio management. Operations Research, 57(5), 1155–1168.

    Article  MathSciNet  MATH  Google Scholar 

  24. Song, Y., Zhang, C., & Fang, Y. (2009). Harnessing traffic uncertainties in wireless mesh networks—A stochastic optimization approach. ACM Mobile Networks and Applications, 14(2), 124–133.

    Article  Google Scholar 

  25. Fan, R., Li, Y., & Jiang, H. (2012). Power-efficient robust routing and resource allocation in wireless mesh networks. In Proceedings of IEEE international conference on communications (ICC'2012) (pp. 450–454). Ottawa, Canada.

  26. Daneshyari, M., & Yen, G. G. (2012). Constrained multiple-swarm particle swarm optimization within a cultural framework. IEEE Transactions on System, Man, and Cybernetics-Part A: Systems and Humans, 42(2), 475–490.

    Article  Google Scholar 

  27. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural networks (INN'1995) (pp. 1924–1948). Perth, Australia.

  28. Qua, Z., & Yang, Q. (2013). Improved particle swarm optimization for constrained optimization. In Proceedings of 2013 international conference on information technology and applications (ITA'2013) (pp. 244–247). Chengdu, China.

  29. Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J. C., & Harley, R. G. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12, 171–195.

    Article  Google Scholar 

  30. Sun, X., Li, Z., Zhao, C., & Zhou, Z. (2012). Cognitive UWB pulse waveform design based on particle swarm optimization. Ad Hoc & Sensor Wireless Networks, 16(1–3), 215–228.

    Google Scholar 

  31. Elsayed, S. M., Sarker, R. A., & Mezura-Montes, E. (2013). Particle swarm optimizer for constrained optimization. In Proceedings of 2013 IEEE congress on evolutionary computation (CEC'2013) (pp. 2703–2711). Cancun, Mexico.

  32. Perez, R. E., & Behdinan, K. (2007). Particle swarm approach for structural design optimization. Computers and Structures, 85, 1579–1588.

    Article  Google Scholar 

  33. http://www.stanford.edu/boyd/cvx/.

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (61304131, 61402147), the Grant of China Scholarship Council (201608130174), the Natural Science Foundation of Hebei Province (F2016402054, F2014402075), the Scientific Research Plan Projects of Hebei Education Department (BJ2014019, ZD2015087, QN2015046), the Research Program of Talent Cultivation Project in Hebei Province (A2016002023).

Author information

Authors and Affiliations

  1. School of Information and Electrical Engineering, Hebei University of Engineering, Handan, 056038, China

    Liang Xue, Yanlong Wang, Zhihua Li & Jijun Zhao

  2. Handan Key Laboratory of Optical Fiber Communication Broadband Access Technologies, Handan, 056038, China

    Liang Xue, Yanlong Wang, Zhihua Li & Jijun Zhao

  3. System Control and Information Processing Key Laboratory of Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xinping Guan

  4. Trustworthy Distributed Computing and Service Key Laboratory of Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yanlong Wang

Authors
  1. Liang Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanlong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jijun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinping Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Xue.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, L., Wang, Y., Li, Z. et al. Robust Routing Design with Consideration of Lifetime Maximization for Wireless Sensor Networks in a Framework of Anti-risk Strategy with the Improved Constrained Particle Swarm Optimization Approach. Wireless Pers Commun 94, 527–558 (2017). https://doi.org/10.1007/s11277-016-3453-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3453-z

Keywords

Search

Navigation