Wireless Personal Communications

, Volume 96, Issue 4, pp 4947–4972 | Cite as

Network Coding Based Converge-Cast Scheme in Wireless Sensor Networks

  • Li Xu
  • Jing ZhangEmail author
  • Yang Xiang
  • Xinyi Huang


The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.


Converge-cast Network coding Rate prediction Connected dominating set Wireless sensor networks 



The authors wish to thank National Natural Science Foundation of China (Grant Nos.: 61072080, 61572010). National Natural Science Foundation of China (Nos. 61072080, U1405255). The Education Department of Fujian Province science and technology project (JAT160328, JA14217, JA15329), the scientific research project in Fujian University of Technology (GY-Z160066) Fujian Normal University Innovative Research Team (No. IRTL1207), Fujian provincial key project of science and technology (2015H0009, 2014H0008, 2014J01218).


  1. 1.
    Ahlswede, R., Cai, N., Li, S., & Yeung, R. (2000). Network information flow. IEEE Transactions on Information Theory, 46(4), 1204–1216.MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Chen, J., He, K., Du, R., Zheng, M., Xing, Y., & Yuan, Q. (2015). Dominating set and network coding-based routing in wireless mesh networks. IEEE Transactions on Parallel and Distributed Systems, 26(2), 423–433.CrossRefGoogle Scholar
  3. 3.
    Chen, J., Li, T., & Du, R. (2011). Efficient reliable opportunistic network coding based on hybrid flow in wireless network. China Communications, 8(4), 125–131.Google Scholar
  4. 4.
    Ostovari, P., Wu, J., & Khreishah, A. (2014). Network coding techniques for wireless and sensor networks, the art of wireless sensor networks. Berlin: Springer.Google Scholar
  5. 5.
    Ye, X., Li, J., & Xu, L. (2014). Distributed separate coding for continuous data collection in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 11(1), 17.CrossRefGoogle Scholar
  6. 6.
    Le, J., Lui, J. C., & Chiu, D. M. (2008). How many packets can we encode?—An analysis of practical wireless network coding. In Proceedings of IEEE INFOCOM.Google Scholar
  7. 7.
    Jiang, H. B., Jin, S. D., & Wang, C. G. (2011). Prediction or not? An energy-efficient framework for clustering-based data collection in wirelesssensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(6), 1064–1071.CrossRefGoogle Scholar
  8. 8.
    Liang, Q., Zhang, J., & Zhang, Y. H. (2014). The placement method of resources and applications based on request prediction in cloud data center. Information Sciences, 279, 735–745.CrossRefGoogle Scholar
  9. 9.
    Alippi, C., Camplani, R., Galperti, C., Marullo, A., & Roveri, M. (2013). A high-frequency sampling monitoring system for environmental and structural applications. ACM Transactions on Sensor Networks (TOSN), 9(4), 41.CrossRefGoogle Scholar
  10. 10.
    Tang, Z. Z., Wang, H. Y., & Hu, Q. (2013). Network coding in convergecast of wireless sensor networks: friend or foe? In Proceedings of IEEE 24th international symposium on personal, pp. 2469–2473.Google Scholar
  11. 11.
    Tang, Z. Z., Wang, H. Y., Hu, Q., & Hai, L. (2012). How network coding benefits converge-case in wireless sensor networks. In Proceedings of IEEE vehicular technology conference (VTC Fall), pp. 1–5.Google Scholar
  12. 12.
    Ding, L., Wu, W., Willson, J., Du, H., Lee, W., & Du, D. Z. (2011). Efficient algorithms for topology control problem with routing cost constraints in wireless networks. IEEE Transactions on Parallel and Distributed Systems, 22(10), 1601–1609.CrossRefGoogle Scholar
  13. 13.
    Hsu, C-H, Slagter, K. D., Chen, S. C., & Chung, Y. C. (2014). Optimizing energy consumption with task consolidation in clouds. Information Sciences, 258(10), 452–462.CrossRefGoogle Scholar
  14. 14.
    Zhao, Y., Wu, J., Li, F., & Lu, S. (2012). On maximizing the lifetime of wireless sensor networks using virtual backbone scheduling. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1528–1535.CrossRefGoogle Scholar
  15. 15.
    Kui, X., Sheng, Y., Du, H., & Liang, J. (2013). Constructing a CDS-based network backbone for data collection in wireless sensor networks. International Journal of Distributed Sensor Networks, 9(2013), 233–256.Google Scholar
  16. 16.
    Li, S. Y. R., Yeung, R. W., & Cai, N. (2003). Linear network coding. IEEE Transactions on Information Theory, 49(2), 371–381.MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Ho, T., Medard, M., Koetter, R., Karger, D., Effros, M., Shi, J., et al. (2006). A random linear network coding approach to multicast. IEEE Transactions on Information Theory, 52(10), 4413–4430.MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Hou, I. H., Tsai, Y. E., Abdelzaher, T. F., & Gupta, I. (2008). Adapcode: Adaptive network coding for code updates in wireless sensor networks. In Proceedings of IEEE INFOCOM.Google Scholar
  19. 19.
    Katti, S., Rahul, H., Hu, W., Katabi, D., Mdard, M., & Crowcroft, J. (2006). XORs in the air: Practical wireless network coding. ACM SIGCOMM Computer Communication Review, 36(4), 243–254.CrossRefGoogle Scholar
  20. 20.
    Le, J., Lui, J. C., & Chiu, D. M. (2010). DCAR: Distributed coding-aware routing in wireless networks. IEEE Transactions on Mobile Computing, 9(4), 596–608.CrossRefGoogle Scholar
  21. 21.
    Guo, B., Li, H., Zhou, C., & Cheng, Y. (2011). Analysis of general network coding conditions and design of a free-ride-oriented routing metric. IEEE Transactions on Vehicular Technology, 60(4), 1714–1727.CrossRefGoogle Scholar
  22. 22.
    Miao, L. S., Karim, D., Anish, K., & Noel, G. (2012). Network coding and competitive approach for gradient based routing in wireless sensor networks. Ad Hoc Networks, 10(6), 990–1008.CrossRefGoogle Scholar
  23. 23.
    Lu, Z., Wu, L., Pardalos, P. M., Maslov, E., Lee, W., & Du, D. Z. (2014). Routing-efficient CDS construction in disk-containment graphs. Optimization Letters, 8(2), 425–434.MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Du, H., Wu, W., Ye, Q., Li, D., Lee, W., & Xu, X. (2013). CDS-based virtual backbone construction with guaranteed routing cost in wireless sensor networks. Optimization Letters, 24(4), 652–661.Google Scholar
  25. 25.
    Du, H., Ye, Q., Wu, W., Lee, W., Li, D., Wu, Q.W., & Du, D. Z. (2011). Constant approximation for virtual backbone construction with guaranteed routing cost in wireless sensor networks. In Proceedings of IEEE INFOCOM, pp. 1737–1744.Google Scholar
  26. 26.
    Aziz, A. A., Sekercioglu, Y. A., & Fitzpatrick, P. (2013). A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks. Optimization Letters, 15(1), 121–144.Google Scholar
  27. 27.
    He, J., Ji, S. L., & Pan, Y. (2014). Greedy construction of load-balanced virtual backbones in wireless sensor networks. Optimization Letters, 14(7), 673–688.Google Scholar
  28. 28.
    Wang, S., Vasilakos, A., Jiang, H. B., Ma, X. Q. (2011). Energy efficient broadcasting using network coding aware protocol in wireless ad hoc network. In Proceedings of IEEE international conference on communications (ICC), pp. 1–5.Google Scholar
  29. 29.
    Wang, X., Fu, L., Tian, X., Bei, Y., Peng, Q., Gan, X., et al. (2012). Converge cast: On the capacity and delay tradeoffs. Optimization Letters, 11(6), 970–982.Google Scholar
  30. 30.
    Ostovari, P., Khreishah, A., & Wu, J. (2012). Deadline-aware Broadcasting in wireless networks with network coding. In Proceedings of IEEE global communications conference (GLOBECOM), pp. 4435–4440.Google Scholar
  31. 31.
    Vazintari, A., Vlachou, C., & Cottis, P. G. (2013). Network coding for overhead reduction in delay tolerant networks. Optimization Letters, 72(4), 2653–2671.Google Scholar
  32. 32.
    Zhang, J., & Xu, L. (2013). Research on topology control system for mesh networks. Optimization Letters, 34(1), 140–144.MathSciNetGoogle Scholar
  33. 33.
    Zhang, J., Xu, L., Zhou, S. M., Wu, W. (2013). Constructing connected dominating set based on crossed cube in WSN. In Proceedings of IEEE 5th intelligent networking and collaborative systems, pp. 443–447.Google Scholar
  34. 34.
    Wang, D. J., Lin, L. W., & Xu, L. (2011). A study of subdividing hexagon-clustered WSN for power saving: Analysis and simulation. Optimization Letters, 9(7), 1302–1311.Google Scholar
  35. 35.
    Chen, B. L. (2011). Optimization theory and algorithms. Beijing: Tsinghua University Press.Google Scholar
  36. 36.
    Kim, D., Wu, Y., Li, Y., Zou, F., & Du, D. Z. (2009). Constructing minimum connected dominating sets with bounded diameters in wireless networks. Optimization Letters, 20(2), 147–157.Google Scholar
  37. 37.
    Zhang, J., Xu, L., & Lin, H. (2014). A CDS-based network coding scheme in wireless sensor converge-cast networks. In Proceedings of IEEE 17th international conference on computational science and engineering, pp. 1599–1604.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Mathematics and Computer ScienceFujian Normal UniversityFuzhouPeople’s Republic of China
  2. 2.School of Information Science and Engineering, Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouPeople’s Republic of China
  3. 3.School of Information TechnologyDeakin UniversityMelbourneAustralia

Personalised recommendations