Skip to main content
SpringerLink
Log in

An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

In this paper, we propose a cross layer congestion optimization scheme for allocating the resources of wireless sensor networks to achieve maximization of network performance. The congestion control, routing selection, link capacity allocation, and power consumption are all taken account to yield an optimal scheme based on the Lagrangian optimization. The Lagrangian multiplier is adopted to adjust power consumption, congestion rate, routing selection and link capacity allocation, so that the network performance can be satisfied between the trade-off of efficiency and fairness of resource allocation. The proposed algorithm can significantly achieve the maximization of network performance in relieving the network congestion with less power consumption. Excellent simulation results are obtained to demonstrate our innovative idea, and show the efficiency of our proposed algorithm.

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
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. R. Ohayon, Virtual reservation scheme for supporting CBR multimedia services with strict QoS performance over WLAN and wireless mesh, International Journal of Communication Systems, Vol. 25, No. 5, pp. 571–584, 2012.

    Article  Google Scholar 

  2. M. G. Jibukumar, R. Datta and Biswas P. Kumar, Busy tone contention protocol: a new high-throughput and energy-efficient wireless local area network medium access control protocol using busy tone, International Journal of Communication Systems, Vol. 25, No. 8, pp. 991–1014, 2012.

    Article  Google Scholar 

  3. Mohammadreza Balouchestani, Kaamran Raahemifar and Sridhar Krishnan, Wireless body area networks with compressed sensing theory, International Conference on Complex Medical Engineering, Vol. 22–25, p. 2011, 2011.

    Google Scholar 

  4. Carlo Caione, Davide Brunelli and Luca Benini, Distributed compressive sampling for lifetime optimization in dense wireless sensor networks, IEEE Transactions on Industrial Informatics, Vol. 8, No. 1, pp. 30–40, 2012.

    Article  Google Scholar 

  5. Yiran Shen, Hu Wen, Rajib Rana and Chun Tung Chou, Nonuniform compressive sensing forheterogeneous wireless sensor networks, IEEE Sensors Journal, Vol. 13, No. 6, pp. 2120–2128, 2013.

    Article  Google Scholar 

  6. W. Chen and I. J. Wassell, Energy-efficient signal acquisition in wireless sensor networks: a compressive sensing framework, IET Wireless Sensor Syst., Vol. 2, No. 1, pp. 1–8, 2012.

    Article  Google Scholar 

  7. Celalettin Karakus, Ali Cafer Gurbuz and Bulent Tavli, Analysis of energy efficiency of compressive sensing in wireless sensor networks, IEEE Sensors Journal, Vol. 13, No. 5, pp. 1999–2008, 2013.

    Article  Google Scholar 

  8. Longbo Huang and Michael J.1 Neely. (2013)Utility Optimal Scheduling in Energy-Harvesting Networks. IEEE/ACM Transactions on Networking, vol. 21, NO. 4.1117-1130.

  9. Wei Chen,Miguel R. D. Rodrigues,Ian J. Wassell. (2012).A Fr´echet mean approach for compressive sensing date acquisition and reconstruction in wireless sensor networks,” IEEE Transactions on Wireless Communications, 11(10):3598-3606.

  10. Mingwei Li, Yuanwei Jing and Chengtie Li, A Robust and Efficient Cross-layer Optimal Design in Wireless Sensor Networks”, Wireless Personal Communications, Vol. 72, No. 4, pp. 1889–1902, 2013.

    Article  Google Scholar 

  11. Q. Ling and Z. Tian, Decentralized sparse signal recovery for compressive sleeping wireless sensor networks, IEEE Transactions on Signal Processing, Vol. 58, No. 7, pp. 3816–3827, 2010.

    Article  MathSciNet  Google Scholar 

  12. S. Pudlewski, A. Prasanna and T. Melodia, Compressed-sensingenabled video streaming for wireless multimedia sensor networks, IEEE Trans. Mobile Comput., Vol. 11, No. 6, pp. 1060–1072, 2012.

    Article  Google Scholar 

  13. Scott Pudlewski, Tommaso Melodia, Arvind Prasanna. (2010). C-DMRC: Compressive Distortion-MinimizingRate Control for Wireless Multimedia Sensor Networks, IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks:1-9.

  14. Siyuan Xiang and Lin Cai, Transmission control for compressive sensing video over wireless channel, IEEE Transactions on Wireless Communications, Vol. 12, No. 3, pp. 1429–1437, 2013.

    Article  Google Scholar 

  15. Haifeng Zheng, Shilin Xiao, Xinbing Wang, Xiaohua Tian and Mohsen Guizani, Capacity and delay analysis for data gathering with compressive sensing in wireless sensor networks, IEEE Transactions on Wireless Communications, Vol. 12, No. 2, pp. 917–927, 2013.

    Article  Google Scholar 

  16. Wessam Mesbah, Mohammad Shaqfeh and Hussein Alnuweiri, Joint Routing and Resource Allocation for Delay Minimization in Cognitive Radio Based Mesh Networks, IEEE Transactions on Wireless communications, Vol. 13, No. 2, pp. 834–845, 2014.

    Article  Google Scholar 

  17. Davut Incebacaka, Ruken Zilanb, Bulent Tavli, Jose M. Barcelo-Ordinas and Jorge Garcia-Vidal, Optimal data compression for lifetime maximization in wireless sensor networks operating in stealth mode, Ad Hoc Networks, Vol. 24, pp. 134–147, 2015.

    Article  Google Scholar 

  18. Shibo He, Jiming Chen, David K. Y. Yau and Youxian Sun, Cross-layer Optimization of Correlated Data Gathering in Wireless Sensor Networks, IEEE Transactions on Mobile Computing, Vol. 11, No. 11, pp. 1678–1691, 2012.

    Article  Google Scholar 

  19. Jiming Chen, Xu Weiqiang, Shibo He, Youxian Sun and Preetha Thulasiraman, Utility-Based Asynchronous Flow Control Algorithm for Wireless Sensor Networks, IEEE Journal on Selected Areas in Communications, Vol. 28, No. 7, pp. 1116–1126, 2010.

    Article  Google Scholar 

  20. Yongmin Zhang, Shibo He and Jiming Chen, Data Gathering Optimization by Dynamic Sensing and Routing in Rechargeable Sensor Networks, IEEE/ACM Transactions on Networking, Vol. 24, No. 3, pp. 1632–1646, 2016.

    Article  Google Scholar 

  21. Mung Chiang, Balancing Transport and Physical Layers in Wireless Multihop Networks: Jointly Optimal Congestion Control and Power Control, IEEE Journal on Selected Areas in Communications, Vol. 23, No. 1, pp. 104–116, 2005.

    Article  Google Scholar 

  22. S. Boyd and L. Vandenberghe, Convex optimization, Cambridge UnivCambridge, U.K., 2004.

    Book  MATH  Google Scholar 

  23. D. P. Bertsekas, Nonlinear programming, Athena ScientificBelmont, 1999.

    MATH  Google Scholar 

  24. Serge L. Shishkin, Fast and robust compressive sensing method using mixed hadamard sensing matrix, IEEE Journal on Emerging and Seleted Toptics in Circuits and Systems, Vol. 2, No. 3, pp. 353–361, 2012.

    Article  Google Scholar 

  25. T. T. Do, Johns Hopkins, Lu Gan and N. H. Nguyen, Fast and efficient compressive sensing using structurally random matrices, IEEE Transactions on Signal Processing, Vol. 60, No. 1, pp. 139–154, 2012.

    Article  MathSciNet  Google Scholar 

  26. Yalcin Sadi and Sinem Coleri Ergen, Optimal power control, rate adaptation, and scheduling for UWB-based intravehicular wireless sensor networks, IEEE Transactions on Vehicular Technology, Vol. 62, No. 1, pp. 219–234, 2013.

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the National Natural Science Foundation of China under Grant No. 61374097, Fundamental Research Funds for the Central Universities of China No. 142303013, Program of Science and Technology Research of Hebei University No. QN2014326, School Funds Project of Northeastern University at Qinhuangdao No. XNB2015004.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China

    Chengtie Li, Jinkuan Wang & Mingwei Li

  2. School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, Hebei, China

    Chengtie Li & Mingwei Li

Authors
  1. Chengtie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinkuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingwei Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Wang, J. & Li, M. An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks. Int J Wireless Inf Networks 24, 462–469 (2017). https://doi.org/10.1007/s10776-017-0334-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-017-0334-7

Keywords

Search

Navigation