Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Simulated annealing-based reprogramming scheme of wireless sensor nodes

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Code dissemination is a main component of reprogramming which enables over-the-air software update in wireless sensor networks. In this paper, we present a Simulated Annealing-based reprogramming scheme (SA). Unlike well-known wireless reprogramming data dissemination protocols such as Deluge, whose primary goal is to minimize energy consumption, SA’s design criterion is to minimize energy consumption while balancing the energy consumption of the entire sensor network. To achieve these goals, we first establish the NP-hard model of data dissemination routing and its simplified form. Then, the relaying nodes and corresponding communication radius are determined by their remaining energy and the balanced consumption of the covered nodes, respectively. Finally, the iterative optimization strategy with simulated annealing is achieved. Physical experimental results and numerical analysis show that the proposed algorithm can find the near-optimal solutions to reduce and balance the total energy consumption when there is an energy imbalance among different nodes.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Taherkordi, A., Loiret, F., Rouvoy, R., & Eliassen, F. (2013). Optimizing sensor network reprogramming via in situ reconfigurable components. ACM Transactions on Sensor Networks,9(2), 1–37.

    Article  Google Scholar 

  2. Hagedorn, A., Starobinski, D., & Trachtenberg, A. (2008). Rateless Deluge: Over-the-air programming of wireless sensor networks using random linear codes. In Proceedings of the IEEE international conference on information processing in sensor network (IPSN), pp. 457–466.

  3. Jeong, J., Kim, S., & Broad, A. (2003). Network reprogramming, Berkeley, California, USA. http://www.tinyos.net/tinyos-1.x/doc/. Accessed 14 Jan 2013.

  4. Stathopoulos, T., Heidemann, J., & Estrin, D. (2009). A remote code update mechanism for wireless sensor networks. In Proceedings of IEEE military communications conference (MILCOM), pp. 1–7.

  5. Hui, J. W., & Culler, D. (2004). The dynamic behavior of a data dissemination protocol for network programming at scale. In Proceedings of the 2nd international conference on embedded networked sensor systems (SenSys), pp. 81–94.

  6. Kulkarni, S., & Wang, L. (2005). MNP: multihop network reprogramming service for sensor networks. In Proceedings of 25th IEEE international conference on distributed computing systems (ICDCS), pp. 7–16.

  7. Naik, V., Arora, A., Sinha, P., & Zhang, H. (2005). Sprinkler: A reliable and energy efficient data dissemination service for wireless embedded devices. In Proceedings of IEEE international real-time systems symposium (RTSS), pp. 1–15.

  8. Alam, S. M. I., Sultana, S., Hu, Y. C., & Fahmy, S. (2014). SYREN: Synergistic link correlation-aware and network coding-based dissemination in wireless sensor networks. In Proceedings of 2013 IEEE 21st international symposium on modelling, analysis and simulation of computer and telecommunication systems (MASCOTS), pp. 485–494.

  9. Levis, P., & Culler, D. (2002). Mate: A tiny virtual machine for sensor networks. In Proceedings of the 10th international conference on Architectural support for programming languages and operating systems (ASPLOS), pp. 85–95.

  10. Panta, R. K., Bagchi, S., & Midkiff, S. P. (2009). Zephyr: Efficient incremental reprogramming of sensor nodes using function call indirections and difference computation. In Proceedings of the 2009 conference on USENIX An-nual technical conference (USENIX), pp. 411–424.

  11. Rossi, M., Zanca, G., Stabellini, L., Crepaldi, R., Harris, A. F. I., & Zorzi, M. (2010). SYNAPSE: A network reprogramming protocol for wireless sensor networks using fountain codes. In Proceedings of 5th annual IEEE communications society conference on sensor, mesh and AdHoc communications and networks (SAHCN), pp. 188–196.

  12. Reijers, N., & Langendoen, K. (2003). Efficient code distribution in wireless sensor networks. In Proceedings of the 2nd ACM international conference on wireless sensor networks and applications (WSNA), pp. 60–67.

  13. Qiu, J., Li, D., Shi, H., & Cui, L. (2014). EasiLIR: Lightweight incremental reprogramming for sensor networks. International Journal of Distributed Sensor Networks,10(4), 1–15.

    Google Scholar 

  14. Huang, L., & Setia, S. (2008). CORD: Energy-efficient reliable bulk data dissemination in sensor networks. In Proceedings of the 27th conference on computer communications (INFCOM), pp. 1247–1255.

  15. Kim, D., Nam, H., & Kim, D. (2016). Adaptive code dissemination based on link quality in wireless sensor networks. IEEE Internet of Things Journal,4(3), 685–694.

    Article  Google Scholar 

  16. Hosseini, S., Khaled, A., & Vadlamani, S. (2014). Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem. Neural Computing and Application,25(7–8), 1871–1885.

    Article  Google Scholar 

  17. Sharma, M., & Sharma, K. (2012). An energy efficient extended LEACH (EEE LEACH). In Proceedings of international conference on communication systems and network technologies (CSNT), pp. 377–382.

  18. Kim, S., & Eom, D. S. (2012). Dynamic transmission power control for wireless sensor network reprogramming. In Proceedings of 17th Asia-Pacific conference on communications (APCC), pp. 145–150.

  19. Tinyos community forum. http://www.tinyos.net. Accessed 14 Jan 2013.

  20. Dang, H. V., Ngan, L. T. C., Khoa, N. K., & Quan L. T. (2019). Towards an integration of AES cryptography into Deluge dissemination protocol for securing IoTs reconfiguration. In Proceedings of 2019 IEEE-RIVF international conference on computing and communication technologies, pp. 1–6.

  21. Do, D. S., & Kim, Y. (2015). Lightweight reprogramming and energy balancing in wireless sensor networks. International Journal of Distributed Sensor Networks,11(8), 1–8.

    Article  Google Scholar 

  22. Krasniewski, M. D., Panta, R. K., Bagchi, S., Yang, C. L., & Chappell, W. J. (2008). Energy-efficient on-demand reprogramming of large-scale sensor networks. ACM Transactions on Sensor Networks,4(1), 1–38.

    Article  Google Scholar 

  23. Panta, R. K., Khalil, I., & Bagchi, S. (2007). Stream: Low overhead wireless reprogramming for sensor networks. In Proceedings of 26th IEEE international conference on computer communications (INFCOM), pp. 928–936.

  24. Gao, Y., Bu, J., Dong, W., Chen, C., Rao, L., & Liu, X. (2013). Exploiting concurrency for efficient dissemination in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,24(4), 691–700.

    Article  Google Scholar 

  25. Li, J. W., Li, S. N., Zhang, Y., Gu, T., Law, Y. W., Yang, Z., et al. (2017). An analytical model for coding-based reprogramming protocols in lossy wireless sensor networks. IEEE Transactions on Computers,66(1), 24–37.

    Article  MathSciNet  MATH  Google Scholar 

  26. Liu, X., Li, G., Zhang, S., & Liu, A. (2018). Big program code dissemination scheme for emergency software-define wireless sensor networks. Peer-to-Peer Networking and Applications,11(5), 1038–1059.

    Article  Google Scholar 

  27. Mujica, G., & Portilla, J. (2018). Distributed reprogramming on the edge: A new collaborative code dissemination strategy for IoT. Electronics,8(3), 267–286.

    Article  Google Scholar 

  28. Teng, H., Liu, W., Wang, T., Liu, A., Liu, X., & Zhang, S. (2019). A Cost-efficient greedy code dissemination scheme through vehicle to sensing devices (V2SD) communication in smart city. IEEE ACCESS,7, 16675–16694.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, Anhui, People’s Republic of China

    Zhangling Duan, Xing Wei, Jianghong Han, Yang Lu & Lei Shi

Authors
  1. Zhangling Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xing Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianghong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhangling Duan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duan, Z., Wei, X., Han, J. et al. Simulated annealing-based reprogramming scheme of wireless sensor nodes. Wireless Netw 26, 495–505 (2020). https://doi.org/10.1007/s11276-019-02156-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-019-02156-7

Keywords