Skip to main content
SpringerLink
Log in

SATC: A Simulated Annealing Based Tree Construction and Scheduling Algorithm for Minimizing Aggregation Time in Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Data collection is one of the most important task in wireless sensor networks where a set of sensor nodes measure properties of a phenomenon of interest and send their data over a routing tree to the sink. In this paper, we propose a new simulated annealing based tree construction algorithm (SATC) to aggregate data with a collision-free schedule for node transmissions. SATC minimizes the time duration of delivering aggregated data to the sink. In the proposed algorithm, the average time latency is considered as the fitness function for the SA algorithm and it is evaluated based on Routing aware MAC scheduling methods. The efficiency of the proposed algorithm is studied through simulation and compared with existing state-of-the-art approaches in terms of average latency and average normalized latency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Salim, A., Osamy, W., & Khedr, A. M. (2014). IBLEACH: Effective LEACH protocol for wireless sensor networks. Wireless Networks, 20, 1515–1525.

    Article  Google Scholar 

  2. Osamy, W., Salim, A., & Khedr, A. M. (2018) An information entropy based-clustering algorithm in heterogeneous wireless sensor networks. Wireless Networks. https://doi.org/10.1007/s11276-018-1877-y.

  3. Aziz, A., Singh, K., Osamy, W., & Khedr, A. M. (2019). Effective algorithm for optimizing compressive sensing in IoT and periodic monitoring applications. Journal of Network and Computer Applications, 126, 12–28.

    Article  Google Scholar 

  4. Salim, A., & Osamy, W. (2015). Distributed multi chain compressive sensing based routing algorithm for wireless sensor networks. Wireless Networks, 21, 1379–1390.

    Article  Google Scholar 

  5. Natarajan, M. (2012). A comprehensive review and performance analysis of data gathering algorithms for wireless sensor networks. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 4(2), 1–29.

    Article  Google Scholar 

  6. Osamy, W., Khedr, A. M., Aziz, A., & El-Sawy, A. (2018). Cluster-tree routing scheme for data gathering in periodic monitoring applications. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2882639.

    Google Scholar 

  7. Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications Journal, 14(2), 7087.

    Google Scholar 

  8. Pham, D., & Karaboga, D. (2012). Intelligent optimisation techniques: Genetic algorithms, tabu search, simulated annealing and neural networks. Berlin: Springer.

    MATH  Google Scholar 

  9. Louail, L., & Felea, V. (2016). Routing-aware TDMA scheduling for wireless sensor networks. In 2016 12th annual conference on Wireless On-demand Network Systems and Services (WONS), Cortina d’Ampezzo, pp. 1–8.

  10. Louail, L., & Felea, V. (2016). Routing-aware time slot allocation heuristics in contention-free sensor networks. In L. Mamatas, I. Matta, P. Papadimitriou & Y. Koucheryavy (Eds.), Wired/Wireless Internet Communications. WWIC.

  11. Xujin, C., Hu, X., & Zhu, J. (2005). Minimum data aggregation time problem in wireless sensor networks. In International conference on mobile ad-hoc and sensor netsworks. Berlin, Heidelberg: Springer.

  12. Xujin, C., Hu, X., & Zhu, J. (2009). Data gathering schedule for minimal aggregation time in wireless sensor networks. International Journal of Distributed Sensor Networks, 5(4), 321–337.

    Article  Google Scholar 

  13. Bertrand Fotue Fotso, D. (2013). Efficient data aggregation and routing in wireless sensor networks. Networking and Internet Architecture [cs.NI], Tlcom ParisTech, chapter 3.

  14. Yu, B., & Li, J.-Z. (2011). Minimum-time aggregation scheduling in duty-cycled wireless sensor networks. Journal of Computer Science and Technology, 26(6), 962–970.

    Article  MATH  Google Scholar 

  15. Min Kyung, A., Lam, N. X., Huynh, D. T., & Nguyen, T. N. (2011). Minimum data aggregation schedule in wireless sensor networks. IJ Computers Applications, 18(4), 254–262.

    Google Scholar 

  16. Natarajan, M. (2015). A benchmarking algorithm to determine minimum aggregation delay for data gathering trees and an analysis of the diameter-aggregation delay tradeoff. Algorithms, 8(3), 435–458.

    Article  MathSciNet  MATH  Google Scholar 

  17. Xu, X., Song, M., & Alani, M. (2014). Duty-cycle-aware minimum latency multiflow scheduling in multi-hop wireless networks. In Global Communications Conference (GLOBECOM). IEEE.

  18. Nguyen, N., et al. (2018). An efficient minimum-latency collision-free scheduling algorithm for data aggregation in wireless sensor networks. IEEE Systems Journal, 12(3), 2214–2225.

    Article  Google Scholar 

  19. Elham, M., & Ghaffari, A. (2016). Data aggregation tree structure in wireless sensor networks using cuckoo optimization algorithm. Information Systems and Telecommunication, 4, 182–190.

    Google Scholar 

  20. Chou, C., & Chuang, K. (2005) CoLaNet : A cross-layer design of energy-efficient wireless sensor networks. In ICW/ICHSN/ICMCS/SENET, pp. 364–369.

  21. Pham, D., & Karaboga, D. (2012). Intelligent optimisation techniques: Genetic algorithms, tabu search, simulated annealing and neural networks. Berlin: Springer.

    MATH  Google Scholar 

  22. Triantaphyllou, E. (2000). Multi-criteria decision making methods. Berlin: Springer.

    Book  MATH  Google Scholar 

  23. Onat, F. A., & Stojmenovic, I. (2007). Generating random graphs for wireless actuator networks. In 2007 IEEE international symposium on a world of wireless, mobile and multimedia networks, Espoo, Finland, pp. 1–12.

  24. Shao-Shan, J., Huang, C.-H., & Guangqiong, Z. (2007). A minimum hop routing protocol for wireless sensor networks. Hua University, 24 2007.12 [China 96.12], pp. 45–57.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Computers and Informatics, Benha University, Benha, Egypt

    Walid Osamy & Ahmed A. El-sawy

  2. Qassim University, Buraydah, Kingdom of Saudi Arabia

    Walid Osamy

  3. Computer Science Department, University of Sharjah, Sharjah, 27272, UAE

    Ahmed M. Khedr

  4. Mathematics Department, Zagazig University, Zagazig, Egypt

    Ahmed M. Khedr

Authors
  1. Walid Osamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed A. El-sawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed M. Khedr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Walid Osamy.

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

Osamy, W., El-sawy, A.A. & Khedr, A.M. SATC: A Simulated Annealing Based Tree Construction and Scheduling Algorithm for Minimizing Aggregation Time in Wireless Sensor Networks. Wireless Pers Commun 108, 921–938 (2019). https://doi.org/10.1007/s11277-019-06440-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06440-9

Keywords

Search

Navigation