Skip to main content

Advertisement

SpringerLink
Log in

EETC: Energy Efficient Tree-Clustering in Delay Constrained Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Joint employment of multi-hop data forwarding and mobile data-collector is a popular technique for efficient data collection in energy constraint and delay sensitive wireless sensor networks (WSNs). Existing tree-based data forwarding methods take the joint advantages of clustering and multi-hop data forwarding. However, the performances of all these approaches hardly meet the desired level of efficiency and thus, finding an efficient tree-clustering method to save network energy and extending the network lifetime is still a relevant issue in WSN. In this work, we study the problem of multi-hop data forwarding and propose a novel tree-clustering scheme named energy efficient tree clustering (EETC) which minimizes network energy consumption and extend the network lifetime while maintaining a pre-bound tour delay of the mobile sink. EETC uses a heuristic clustering algorithm named Optimal Generation of Clusters (OGENCL) in the clustering phase. In the proposed method, the number of relay hops between a cluster member node and the CH has been restricted to balance the network load. For further balancing the network load, we use an upper bound on the cluster size. The OGENCL problem is formulated as a Mixed Integer Linear Programming (MILP) Problem. Extensive simulations have been performed and compared with existing works to show the effectiveness of the proposed scheme for network load balance, energy consumption and network lifetime.

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
Fig. 10

Similar content being viewed by others

References

  1. Al-Ma’aqbeh, F., Banimelhem, O., Taqieddin, E., Awad, F., & Mowafi, M. (2012). Fuzzy logic based energy efficient adaptive clustering protocol. In Proceedings of the 3rd international conference on information and communication systems, ICICS ’12 (pp. 21:1–21:5). New York, NY: ACM.

  2. Almi’ani, K., Viglas, A., & Libman, L. (2010). Energy-efficient data gathering with tour length-constrained mobile elements in wireless sensor networks. In IEEE local computer network conference (pp. 582–589).

  3. Applegate, D. L., Bixby, R. E., Chvatal, V., & Cook, W. J. (2007). The traveling salesman problem: A computational study (Princeton series in applied mathematics). Princeton, NJ: Princeton University Press.

    Google Scholar 

  4. Atoui, I., Ahmad, A., Medlej, M., Makhoul, A., Tawbe, S., & Hijazi, A. (2016). Tree-based data aggregation approach in wireless sensor network using fitting functions. In 2016 Sixth international conference on digital information processing and communications (ICDIPC) (pp. 146–150).

  5. Chang, J.-Y., & Ju, P.-H. (2012). An efficient cluster-based power saving scheme for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2012(1), 1–10.

    Article  MathSciNet  Google Scholar 

  6. Chang, J. Y., & Shen, T. H. (2016). An efficient tree-based power saving scheme for wireless sensor networks with mobile sink. IEEE Sensors Journal, 16(20), 7545–7557.

    Article  Google Scholar 

  7. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2013). Introduction to algorithms (3rd ed.). Cambridge: MIT Press.

    MATH  Google Scholar 

  8. Cplex. https://www.ibm.com/in-en/marketplace/ibm-ilog-cplex. Accessed 10 Nov 2017.

  9. Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications, 14(2), 70–87.

    Article  Google Scholar 

  10. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  11. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000. (10–pp.).

  12. Jamalabdollahi, M., & Zekavat, S. A. R. (2015). Joint neighbor discovery and time of arrival estimation in wireless sensor networks via OFDMA. IEEE Sensors Journal, 15(10), 5821–5833.

    Article  Google Scholar 

  13. Jia, D., Zhu, H., Zou, S., & Hu, P. (2016). Dynamic cluster head selection method for wireless sensor network. IEEE Sensors Journal, 16(8), 2746–2754.

    Article  Google Scholar 

  14. Khan, A. W., Abdullah, A. H., Razzaque, M. A., & Bangash, J. I. (2015). VGDRA: A virtual grid-based dynamic routes adjustment scheme for mobile sink-based wireless sensor networks. IEEE Sensors Journal, 15(1), 526–534.

    Article  Google Scholar 

  15. Khodashahi, M. H., Tashtarian, F., Moghaddam, M. H. Yaghmaee, & Honary, M. T. (2010). Optimal location for mobile sink in wireless sensor networks. In 2010 IEEE wireless communication and networking conference (pp. 1–6).

  16. Lin, Y. C., & Zhong, J. H. (2012). Hilbert-chain topology for energy conservation in large-scale wireless sensor networks. In 2012 9th International conference on ubiquitous intelligence and computing and 9th International conference on autonomic and trusted computing (pp. 225–232).

  17. Lindsey, S., & Raghavendra, C. S. (2002). Pegasis: Power-efficient gathering in sensor information systems. In Aerospace conference proceedings, 2002 (Vol. 3, pp. 3-1125–3-1130 ). IEEE.

  18. Ma, M., & Yang, Y. (2007). Sencar: An energy-efficient data gathering mechanism for large-scale multihop sensor networks. IEEE Transactions on Parallel and Distributed Systems, 18(10), 1476–1488.

    Article  Google Scholar 

  19. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.

    Article  Google Scholar 

  20. Salarian, H., Chin, K. W., & Naghdy, F. (2014). An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 63(5), 2407–2419.

    Article  Google Scholar 

  21. Shuai, T.-P., & Hu, X.-D. (2006). Connected Set Cover Problem and Its Applications (pp. 243–254). Berlin: Springer.

    MATH  Google Scholar 

  22. Tan, H. Ö., & Körpeoǧlu, I. (2003). Power efficient data gathering and aggregation in wireless sensor networks. SIGMOD Record, 32(4), 66–71.

    Article  Google Scholar 

  23. Tong, M., & Tang, M. (2010). Leach-b: An improved leach protocol for wireless sensor network. In 2010 6th International conference on wireless communications networking and mobile computing (WiCOM) (pp. 1–4).

  24. Velmani, R., & Kaarthick, B. (2015). An efficient cluster-tree based data collection scheme for large mobile wireless sensor networks. IEEE Sensors Journal, 15(4), 2377–2390.

    Article  Google Scholar 

  25. Wang, J., Cao, Y., Li, B., Kim, H., & Lee, S. (2017). Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Generation Computer Systems, 76, 452–457. (Supplement C).

    Article  Google Scholar 

  26. Wang, W., Du, F., & Xu, Q. (2009). An improvement of leach routing protocol based on trust for wireless sensor networks. In 2009 5th International conference on wireless communications, networking and mobile computing (pp. 1–4).

  27. Xing, G., Wang, T., Xie, Z., & Jia, W. (2008). Rendezvous planning in wireless sensor networks with mobile elements. IEEE Transactions on Mobile Computing, 7(12), 1430–1443.

    Article  Google Scholar 

  28. Xing, G., Wang, T., Jia, W., & Li, M. (2008). Rendezvous design algorithms for wireless sensor networks with a mobile base station. In Proceedings of the 9th ACM international symposium on mobile ad hoc networking and computing, MobiHoc ’08 (pp. 231–240). New York, NY: ACM.

  29. Yang, J., Shi, X., Marchese, M., & Liang, Y. (2008). An ant colony optimization method for generalized TSP problem. Progress in Natural Science, 18(11), 1417–1422.

    Article  MathSciNet  Google Scholar 

  30. Yuan, F., Zhan, Y., & Wang, Y. (2014). Data density correlation degree clustering method for data aggregation in WSN. IEEE Sensors Journal, 14(4), 1089–1098.

    Article  Google Scholar 

  31. Zhang, Z., Gao, X., & Weili, W. (2009). Algorithms for connected set cover problem and fault-tolerant connected set cover problem. Theoretical Computer Science, 410(8), 812–817.

    Article  MathSciNet  Google Scholar 

  32. Zhao, F., Zhao, C., Wang, Y., Sun, X., & Jiang, T. (2010). An energy-saving cluster routing for wireless sensor networks with mobile sink. In 2010 international conference on Advanced Intelligence and Awarenss Internet (AIAI 2010) (pp. 113–117).

  33. Zhu, C., Wu, S., Han, G., Shu, L., & Wu, H. (2015). A tree-cluster-based data-gathering algorithm for industrial WSNs with a mobile sink. IEEE Access, 3, 381–396.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India

    Srijit Chowdhury & Chandan Giri

Authors
  1. Srijit Chowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandan Giri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srijit Chowdhury.

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

Chowdhury, S., Giri, C. EETC: Energy Efficient Tree-Clustering in Delay Constrained Wireless Sensor Network. Wireless Pers Commun 109, 189–210 (2019). https://doi.org/10.1007/s11277-019-06559-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06559-9

Keywords

Search

Navigation