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Wireless Networks

, Volume 25, Issue 1, pp 13–28 | Cite as

A multi attribute decision routing for load-balancing in crowd sensing network

  • Huahong Ma
  • Guoqiang ZhengEmail author
  • Honghai Wu
  • Baofeng Ji
  • Jishun Li
Article
  • 183 Downloads

Abstract

The emerging crowd sensing network (CSN) can complete the large-scale and complicated sensing tasks by utilizing the collaboration among nodes consciously or unconsciously, which has great significance in practical application. However, the mobility of the nodes leads to intermittent network connectivity, which makes the efficient data delivery become more challenging. Routing design is regarded as an efficient way to deal with this problem, and many schemes have been proposed for such kind of network environments, especially for the complicated sensing tasks in CSN. As for the existing routing schemes, the vast majority of them choose the nodes with higher utility values as relay nodes to forward packets, which can easily cause the load extremely imbalance among nodes. In this paper, we regard the action of relay node selection as a multi attribute decision making problem. Combined with a duplicate optimally stopping strategy, a novel multi attribute decision routing for load-balancing, named MADR-LB, is proposed, which can not only reduce the load of the whole network, but also balance the load of each participating node. Extensive simulations based on four real-life mobility traces and a TVCM model have been done to evaluate the performance of our proposed protocol compared with other existing protocols. The results show that, our proposed protocol can greatly balance the load of nodes and improve the fairness of the nodes while ensuring the overall delivery performance of the network.

Keywords

Crowd sensing network Opportunistic routing Load balancing MADM Duplicate stopping 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61671144), National Key Technology R&D Program of China (2015BAF32B04-3), the Joint Funds of the National Natural Science Foundation of China (U1404615), the Key Science and Research Program in University of Henan Province (16A460018, 17A520005), the Project of Basic and Advanced Technology Research of Henan Province of China (152300410081), the Natural Science Foundation of Henan Province (162300410098), Program for Science and Technology Innovation Talents in the University of Henan Province (Educational Committee) (17HASTIT025), Project for Industry-University Cooperative Education of Education Department, and the Program for Innovative Research Team (in Science and Technology) in University of Henan Province (15IRTSTHN008), Open Funds of State Key Laboratory of Millimeter Waves (Grant No. K201504), China Postdoctoral Science Foundation (Grant No. 2015M571637) and Youth Science Foundation of Henan University of Science and Technology.

References

  1. 1.
    Liu, Y. (2012). Crowd sensing computing. China Communication of the ACM, 8(10), 38–41.Google Scholar
  2. 2.
    Guo, B., Wang, Z., Yu, Z., Wang, Y., & Yen, N. Y. (2015). Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Computing Surveys (CSUR), 48(1), 7.CrossRefGoogle Scholar
  3. 3.
    Koukoumidis, E., Peh, L., & Martonosi., M. (2011). SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory. In Proceedings of the ACM MobiSys.Google Scholar
  4. 4.
    Ma, H., Zhao, D., & Yuan, P. (2014). Opportunities in mobile crowd sensing. IEEE Communications Magazine, 52(8), 29–35.CrossRefGoogle Scholar
  5. 5.
    Fall, K. (2003). A delay-tolerant network architecture for challenged internets. In Proceedings of the ACM SIGCOMM.Google Scholar
  6. 6.
    Xiong, Y., Sun, L., Niu, J., & Liu, Y. (2009). Opportunistic network. Journal of Software, 20(1), 124–137.CrossRefGoogle Scholar
  7. 7.
    Wu, H., & Ma, H. (2014). Opportunistic routing for live video streaming in vehicular ad hoc networks. In World of wireless, mobile and multimedia networks (WoWMoM) (pp. 1–3). IEEE.Google Scholar
  8. 8.
    Zhang, D., Wang, L., Xiong, H., & Guo, B. (2014). 4W1H in mobile crowd sensing. IEEE Communications Magazine, 52(8), 42–48.CrossRefGoogle Scholar
  9. 9.
    Marjanovic, M., Skorin-Kapov, K., Pripuzic, L., Antonic, A., & zarko, I. P. (2015). Energy-aware and quality-driven sensor management for green mobile crowd sensing. Journal of Network and Computer Applications, 59, 95–108.CrossRefGoogle Scholar
  10. 10.
    Zanakis, S., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507–529.CrossRefzbMATHGoogle Scholar
  11. 11.
    Jolliffe, I. (2002). Principal component analysis. Hoboken: Wiley.zbMATHGoogle Scholar
  12. 12.
    Yuan, P., Fan, L., Liu, P., & Tang, S. (2016). Recent progress in routing protocols of mobile opportunistic networks: A clear taxonomy, analysis and evaluation. Journal of Network and Computer Applications, 62, 163–170.CrossRefGoogle Scholar
  13. 13.
    Wu, H., Ma, H., Liu, L., Ma, H., & Yuan, P. (2016). A traffic-camera assisted cache-and-relay routing for live video stream delivery in vehicular ad hoc networks. Wireless Networks, 2016, 1–17.Google Scholar
  14. 14.
    Vahdat, A., & Becker, D. (2000). Epidemic routing for partially-connected ad hoc networks. Technical Report CS-200006.Google Scholar
  15. 15.
    Ma, H., Zheng, G., Wu, H., Ji, B., & Li, J. (2016). EBRP: An energy-efficient and buffer-aware routing protocol for mobile crowdsensing network. International Journal of Distributed Sensor Networks, Hindawi, 2016.Google Scholar
  16. 16.
    Schurgot, M., Comaniciu, C., & Jaffres-Runser, K. (2012). Beyond traditional DTN routing: Social networks for opportunistic communication. IEEE Communications Magazine, 7(50), 155–162.CrossRefGoogle Scholar
  17. 17.
    Lindgren, A., Doria, A., & Schelen, O. (2003). Probabilistic routing in intermittently connected networks. In ACM MobiHoc.Google Scholar
  18. 18.
    Dubois-Ferriere, H., Grossglauser, M., & Vetterli, M. (2003). Age matters: Efficient route discovery in mobile ad hoc networks using encounter ages. In Proceedings of the 4th ACM international symposium on mobile ad hoc networking and computing (pp. 257–266). ACM.Google Scholar
  19. 19.
    Burges, J., Gallagher, B., Jensen, D., & Levine, B. N. (2006). MaxProp: Routing for vehicle-based disruption-tolerant networks. In Proceedings of the IEEE INFOCOM.Google Scholar
  20. 20.
    Balasubramanian, A., Levine, B. N., & Venkataramani, A. (2010). Replication routing in DTNs: A resource allocation approach. IEEE/ACM Transactions on Networking (TON), 18(2), 596–609.CrossRefGoogle Scholar
  21. 21.
    Nguyen, H. A., & Giordano, S. (2012). Context information prediction for social-based routing in opportunistic networks. Ad Hoc Networks, 10(8), 1557–1569.CrossRefGoogle Scholar
  22. 22.
    Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., & Scott, J. (2007). Impact of human mobility on the design of opportunistic forwarding algorithms. IEEE Transactions on Mobile Computing, 6(6), 606–620.CrossRefGoogle Scholar
  23. 23.
    Bulut, E., & Szymanski., B. (2012). Exploiting friendship relations for efficient routing in mobile social networks. IEEE Transactions on Parallel and Distributed Systems, 23(12), 2254–2265.CrossRefGoogle Scholar
  24. 24.
    Mtibaa, A., May, M., Diot, C., & Ammar, M. (2010). PeopleRank: SocialOpportunistic forwarding. In Proceedings of the IEEE INFOCOM.Google Scholar
  25. 25.
    Daly, E., & Haahr, M. (2007). Social network analysis for routing in disconnected delay-tolerant MANETs. In Proceedings of the ACM MobiHoc.Google Scholar
  26. 26.
    Daly, E., & Haahr, M. (2009). Social network analysis for information flow in disconnection delay-tolerant MANETs. IEEE Transactions on Mobile Computing, 8(5), 606–621.CrossRefGoogle Scholar
  27. 27.
    Pan, H., Crowcroft, J., & Yoneki, E. (2011). BUBBLE rap: Social-based forwarding in delay tolerant networks. IEEE Transactions on Mobile Computing, 10(11), 1576–1589.CrossRefGoogle Scholar
  28. 28.
    Yuan, P., Ma, H., & Fu, H. (2014). Hotspot-entropy based data forwarding in opportunistic social networks. Pervasive and Mobile Computing, 16(1), 136–154.Google Scholar
  29. 29.
    Pujol, J. M., Toledo, A. L., & Rodriguez, P. (2009). Fair routing in delay tolerant networks. In Infocom2009 (pp. 837–845). IEEE.Google Scholar
  30. 30.
    Lee, S. J., & Gerla, M. (2001). Dynamic load-aware routing in ad hoc networks. In IEEE international conference. ICC, IEEE, 2001 (Vol. 10, pp. 3206–3210).Google Scholar
  31. 31.
    Kaur, G., Hamsapriya, T., & Lalwani, P. (2014). A new energy efficient queue based multipath load balancing in Ad hoc network. In Computer communication and informatics (ICCCI) proceedings of the ACM mobiHoc (pp. 1–6). IEEE.Google Scholar
  32. 32.
    Li, S., Zhao, S., Wang, X., Zhang, K., & Li, L. (2014). Adaptive and secure load-balancing routing protocol for service-oriented wireless sensor networks. IEEE Systems Journal, 8(3), 858–867.CrossRefGoogle Scholar
  33. 33.
    Fan, X., Victor, O., & Xu, K. (2014). Fairness analysis of routing in opportunistic mobile networks. IEEE Transactions on Vehicular Technology, 63(3), 1282–1295.CrossRefGoogle Scholar
  34. 34.
    Le, T., Kalantarian, H., & Gerla, M. (2016). A novel social contact graph-based routing strategy for workload and throughput fairness in delay tolerant networks. Wireless Communications and Mobile Computing, 16(11), 1352–1362.CrossRefGoogle Scholar
  35. 35.
    Shiryaev, A. N. (2008). Optimal stopping rules. New York: Springer.zbMATHGoogle Scholar
  36. 36.
    Hsu, W.-J., Spyropoulos, T., Psounis, K., & Helm., A. (2007). Modeling time-variant user mobility in wireless mobile networks. In INFOCOM 2007 (pp. 758–766). IEEE.Google Scholar
  37. 37.
  38. 38.
  39. 39.
    Eagle, N., & Pentland, A. (2006). Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255–268.CrossRefGoogle Scholar
  40. 40.
    Hossmann, T., Spyropoulos, T., & Legendre, F. (2010). Social network analysis of human mobility and implications for dtn performance analysis and mobility modeling. In Computer engineering and networks laboratory ETH Zurich, Tech (p. 323).Google Scholar
  41. 41.
    Mahendran, V., Anirudh, S. K., Murthy, C. S. R. (2011). A realistic framework for delay-tolerant network routing in open terrains with continuous churn. In International Conference on Distributed Computing and Networking (pp. 407–417). Berlin: Springer.Google Scholar
  42. 42.
    Anh, N. H. M., & Hu, C. L. (2014). Using stationary relay nodes (thrown boxes) to maximize message forwarding performance in delay-tolerant networks. International Journal of Science and Engineering, 4(4), 33–40.Google Scholar
  43. 43.
    Spyropoulos, T., & Sermpezis, P. (2016). Soft cache hits and the impact of alternative content recommendations on mobile edge caching. In Proceedings of the eleventh ACM workshop on challenged networks (pp. 51–56). ACM.Google Scholar
  44. 44.
    Jindal, A., & Psounis, K. (2006). Performance analysis of epidemic routing under contention. In proceedings of the 2006 international conference on wireless communications and mobile computing (pp. 539–544). ACM.Google Scholar
  45. 45.
    Jain, R., Chiu, D. M., & Hawe, W. R. (1984). A quantitative measure of fairness and discrimination for resource allocation in shared computer system. MA:Eastern Research Laboratory, Digital Equipment Corporation Hudson.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Information EngineeringHenan University of Science and TechnologyLuoYangChina
  2. 2.Henan Key Laboratory for Machinery Design and Transmission SystemHenan University of Science and TechnologyLuoYangChina

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