KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

An Intelligent MAC Protocol Selection Method based on Machine Learning in Wireless Sensor Networks

  • Qiao, Mu (College of Electronic Science, National University of Defense Technology) ;
  • Zhao, Haitao (College of Electronic Science, National University of Defense Technology) ;
  • Huang, Shengchun (College of Electronic Science, National University of Defense Technology) ;
  • Zhou, Li (College of Electronic Science, National University of Defense Technology) ;
  • Wang, Shan (College of Electronic Science, National University of Defense Technology)
  • Received : 2017.09.26
  • Accepted : 2018.05.21
  • Published : 2018.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.

Keywords

References

  1. N.C.Luong, D.T.Hoang, P.Wang, D.Niyato, D.I.Kim and Z.Han. "Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey," IEEE Communications Surveys & Tutorials, vol.18, no.4, pp. 2546-2590, 2016. https://doi.org/10.1109/COMST.2016.2582841
  2. A.De.Domenico, E.C.Strinati, and M-G.Di.Benedetto, "A Survey on MAC Strategies for Cognitive Radio Networks," IEEE Communications Surveys & Tutorials, vol.14, no.1, pp.21-44, 2015.
  3. M. Jo, L. Han, N. Tan, and H. Peter "A Survey: Energy Exhausting Attacks in MAC Protocols in WBANs," Telecommunication Systems, vol.58, no.2, pp.153-164, 2015. https://doi.org/10.1007/s11235-014-9897-0
  4. I.Rhee, A.Warrier, M.Aia, and J.Min, "Z-MAC: A hybrid MAC for wireless sensor networks," IEEE/ACM Transactions on Networking, vol.16, no.3, pp.511-524, 2008. https://doi.org/10.1109/TNET.2007.900704
  5. M.H.S.Gilani, I.Sarrafi, and M.Abbaspour, "An adaptive CSMA TDMA hybrid MAC for energy and throughput improvement of wireless sensor networks," Ad Hoc Networks, vol.11, no.4, pp.1297-1304, June.2013. https://doi.org/10.1016/j.adhoc.2011.01.005
  6. Z,Yang, Y.D.Yao, S.Chen, H.B.He, and D,Zheng, "MAC protocol classification in a cognitive radio network," in Proc. of 19th Annual Wireless and Optical Communications Conference, pp.1-5, 2010.
  7. S.Q.Hu, Y.D.Yao, and Z,Yang, "MAC protocol identification using support vector machines for cognitive radio networks," IEEE Wireless Communications, vol.21, no.1, pp.52-60, February.2014. https://doi.org/10.1109/MWC.2014.6757897
  8. G.Naddafzadeh-Shirazi, P.-Y.Kong, and C.K.Tham, "Distributed Reinforcement Learning Frameworks for Cooperative Retransmission in Wireless Networks," IEEE Transactions on Vehicular Technology, vol.59, no.8, pp.4157-4162, 2010. https://doi.org/10.1109/TVT.2010.2059055
  9. K.-H.Phung, B.Lemmensand, M.Goossens A.Nowe, L.Tran, and K.Steenhaut, "Schedule-based multi-channel communication in wireless sensor networks A complete design and performance evaluation," Ad Hoc Networks, vol.26, no.C, pp. 88-102, 2014.
  10. H.Jang, SY.Yun, J.Shin, and Y.Yi, "Distributed Learning for Utility Maximization over CSMA-based Wireless Multihop Networks," in Proc. of the IEEE INFOCOM, 2014, pp.280-288.
  11. G.Stamatakis, E.Z. Tragos, and A.Traganitis, "A Two-Stage Spectrum Assignment Scheme for Power and QoS Constrained Cognitive CSMA/CA Networks," in Proc. of the IEEE Globecom Workshops, 2015, pp.1-6.
  12. S.Pandit, and G.Singh, "Backoff Algorithm in Cognitive Radio MAC Protocol for Throughput Enhancement," IEEE Transactions on Vehicular Technology, vol.64, no.5, pp. 1991-2000, 2015. https://doi.org/10.1109/TVT.2014.2334605
  13. Y.Liu, V.O.K.Li, and K-C.Leung and L.Zhang, "Distributed multi-channel topology-transparent broadcast scheduling in ad hoc networks," in Proc. of the 2014 IEEE Wireless Communications and Networking Conference, 2014, pp. 1556-1561.
  14. M.Sami, N.K.Noordin, and M.Khabazian, "A TDMA-Based Cooperative MAC Protocol for Cognitive Networks With Opportunistic Energy Harvesting," IEEE Communications Letters, vol.20, no.4, pp. 808-811, 2016. https://doi.org/10.1109/LCOMM.2016.2525784
  15. S.Zhou, Y.Song, Z.Wang, and Z.Wang, "Queue-MAC: A queue-length aware hybrid CSMA/TDMA MAC protocol for providing dynamic adaptation to traffic and duty-cycle variation in wireless sensor networks," in Proc. of the IEEE International Workshop on Factory Communication Systems, 2012, pp.105-114.
  16. L.Sitanayah, Cj.Sreenan, and KN.Brown "A hybrid MAC protocol for emergency response wireless sensor networks," Ad Hoc Networks, vol.303, no.20. pp.77-95, 2014.
  17. Q.Ye, W.Zhuang, L.Li, and P.Vigneron, "Traffic Load Adaptive Medium Access Control for Fully-Connected Mobile Ad Hoc Networks," IEEE Transactions on Vehicular Technology, vol.20, no.4, pp.808-811, 2016.
  18. M.Mihaylov, Y.A.Le Borgne, K.Tuyls, and A. Nowe, "Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks," International Journal of Communication Networks & Distributed Systems, vol.9, no.3/4, pp. 207-224, 2012. https://doi.org/10.1504/IJCNDS.2012.048871
  19. K.M.Thilina, E.Hossain, and D.I.Kim I.Kakalou, "DCCC-MAC: A Dynamic Common Control Channel-Based MAC Protocol for Cellular Cognitive Radio Networks," IEEE Transactions on Vehicular Technology, vol.65, no.5, pp.3597-3613, 2016. https://doi.org/10.1109/TVT.2015.2438058
  20. G.Bianchi, L Fratta and M Oliveti, "Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs," in Proc. of the IEEE PIMRC, Taipei, 1996, pp.392-396.
  21. H.Wu, S.Peng and Y.Peng, "IEEE 802.11 distributed coordination function(DCF): analysis and enhancement," in Proc. of the IEEE ICC, New York, 2002, pp.605-609.
  22. Y.Yi, G.de Veciana, and S.Shakkottai, "MAC Scheduling With Low Overheads by Learning Neighborhood Contention Patterns," IEEE/ACM Transactions on Networking, vol.18, no.5, pp.1637-1650, 2010. https://doi.org/10.1109/TNET.2010.2050903
  23. Y.Wei, H.John, and E.Deborah, "Medium access control with coordinated adaptive sleeping for wireless sensor networks," IEEE/ACM Transactions on Networking, vol.12, no.3, pp.493-506, June 2004. https://doi.org/10.1109/TNET.2004.828953
  24. H.A.B.Salameh, M.M.Krunz, and O.Younis, "MAC Protocol for Opportunistic Cognitive Radio Networks with Soft Guarantees," IEEE Transactions on Mobile Computing., vol.8, no.10, pp.1339-1352, October 2009. https://doi.org/10.1109/TMC.2009.19
  25. C.X.Zhu, and M.S.Corson, "A Five-Phase Reservation Protocol for mobile Ad Hoc Networks," Wireless Networks, vol.7, no.4, pp.371-384, 2001. https://doi.org/10.1023/A:1016683928786
  26. T.Chen, H.Zhang, G.M.Maggio, and I.Chlamtac, "Cogmesh: A cluster-based cognitive radio network," in Proc. of the Symposium on Dynamic Spectrum Access Networks, Dublin, Ireland, November 2007, pp. 168-178.
  27. G.Lu, B.Krishnamachad, and CS.Raghavendra, "Adaptive energy-efficient and low-latency MAC for tree-based data gathering in sensor networks," Wireless Communications & Mobile Computing, vol.7, no.7, pp.863-875, 2007. https://doi.org/10.1002/wcm.503
  28. A.Zadeh, "Fuzzy sets", Information and Control, vol.8, no.1, pp.338-353, 1965. https://doi.org/10.1016/S0019-9958(65)90241-X
  29. G.Y.Wang, and Q.H.Zhang, "Granular computing based cognitive computing," in Proc. of the 8th IEEE International Conference on Cognitive Information Conference, pp.155-161, 2009.
  30. Ross Quinlan, C4.5:Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, 1993.
  31. George H.John, and Pat Langley, "Estimating Continuous Distributions in Bayesian Classifiers," in Proc. of the 11th Uncertainty in Artificial Intelligence Conference, pp.338-345, 1995.
  32. JC.Platt, "Sequential Minimal Optimization A Fast Algorithm for Training Support Vector Mechines," Advances in Kernel Methods-support Vector Learning, vol.208, no.1, pp.212-223, 1998.
  33. Leo Breiman, "Random Forests," Machine Learning, vol.45, no.1, pp.5-32, 2001. https://doi.org/10.1023/A:1010933404324