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Creating Collision-Free Communication in IoT with 6G Using Multiple Machine Access Learning Collision Avoidance Protocol

Abstract

Cloud computing is an important technology to offer consumer appliances a wide pool of elastic resources. The heterogeneous network faces collision while making communication, which reduces the entire network performance. The future cloud-edge networks will deal with a vast amount of clients and servers, such as the Internet of Things (IoT) and the 6G networks, which require flexible solutions. From these points, Multiple Machine Access Learning with Collision Carrier Avoidance (MMALCCA) protocol is proposed in the environment of 6G Internet of Things for creating an effective communication process. This protocol employs the Media Access Control (MAC) protocol for the sync of high-speed wireless communication networks in the Terahertz (THz) band. MMALCCA performs multiple machine access and collision control for improving the resource utilization and latency-less services of the users. The decisions of the protocol are made using the output of the classification and regression learning method for improving the efficiency of MAC sync. The performance of the proposed protocol is verified using the metrics latency, collision probability, service failure, and resource utilization by varying channels and user equipment density.

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References

  1. 1.

    Zhang J-H, Tang P, Yu L, Jiang T, Tian L (2020) Channel measurements and models for 6G: current status and future outlook. Front Info Technol Electron Eng 21(1):39–61

    Article  Google Scholar 

  2. 2.

    Long Q, Chen Y, Zhang H, Lei X (2019) Software defined 5G and 6G networks: a survey. Mob Netw Appl 1–21. https://doi.org/10.1007/s11036-019-01397-2

  3. 3.

    Sheron PF, Sridhar KP, Baskar S, Shakeel PM (2019) A decentralized scalable security framework for end-to-end authentication of future IoT communication. Trans Emerg Telecom Technol: e3815. https://doi.org/10.1002/ett.3815

  4. 4.

    Duan B-Y (2020) Evolution and innovation of antenna systems for beyond 5G and 6G. Front Info Technol Electron Eng 21(1):1–3

    Article  Google Scholar 

  5. 5.

    Gillani K, Lee J-H (2020) Comparison of Linux virtual machines and containers for a service migration in 5G multi-access edge computing. ICT Express 6(1):1–2

    Article  Google Scholar 

  6. 6.

    Li C, Sun H, Tang H, Luo Y (2019) Adaptive resource allocation based on the billing granularity in edge-cloud architecture. Comput Commun 145:29–42

    Article  Google Scholar 

  7. 7.

    Tang H, Li C, Bai J, Tang J, Luo Y (2019) Dynamic resource allocation strategy for latency-critical and computation-intensive applications in cloud–edge environment. Comput Commun 134:70–82

    Article  Google Scholar 

  8. 8.

    Li Y, Xu G, Ge J, Fu X, Liu P (2019) Communication and computation cooperation in wireless network for Mobile edge computing. IEEE Access 7:106260–106274

    Article  Google Scholar 

  9. 9.

    Hu S, Yu B, Qian C, Xiao Y, Xiong Q, Sun C, Gao Y (2018) Nonorthogonal interleave-grid multiple access scheme for industrial internet of things in 5G network. IEEE Trans Industr Inform 14(12):5436–5446

    Article  Google Scholar 

  10. 10.

    Vuojala H, Mustonen M, Chen X, Kujanpää K, Ruuska P, Höyhtyä M, Matinmikko-Blue M, Kalliovaara J, Talmola P, Nyström A-G (2019) Spectrum access options for vertical network service providers in 5G. Telecom Pol: 101903

  11. 11.

    Baskar S, Periyanayagi S, Shakeel PM, Dhulipala VS (2019) An energy persistent range-dependent regulated transmission communication model for vehicular network applications. Comput Netw 152:144–153

    Article  Google Scholar 

  12. 12.

    Nguyen NT, Liu BH, Pham VT, Liou TY (2018) An efficient minimum-latency collision-free scheduling algorithm for data aggregation in wireless sensor networks. IEEE Syst J 12(3):2214–2225

    Article  Google Scholar 

  13. 13.

    Xu D, Li Q (2019) Cooperative resource allocation in cognitive wireless powered communication networks with energy accumulation and deadline requirements. SCIENCE CHINA Inf Sci 62(8)

  14. 14.

    Xia Q, Hossain Z, Medley MJ, Jornet JM (2019) A link-layer synchronization and medium access control protocol for terahertz-band communication networks. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2019.2940441

  15. 15.

    Moltafet M, Mokari N, Javan MR, Saeedi H, Pishro-Nik H (2018) A new multiple access technique for 5G: power domain sparse code multiple access (PSMA). IEEE Access 6:747–759

    Article  Google Scholar 

  16. 16.

    Liu Q, Lu Y, Hu G, Lv S, Wang X, Zhou X (2018) Cooperative control feedback: on backoff misbehavior of CSMA/CA MAC in channel-hopping cognitive radio networks. J Commun Netw 20(6):523–535

    Article  Google Scholar 

  17. 17.

    Kazmi SMA, Tran NH, Ho TM, Manzoor A, Niyato D, Hong CS (2018) Coordinated device-to-device communication with non-orthogonal multiple access in future wireless cellular networks. IEEE Access 6:39860–39875

    Article  Google Scholar 

  18. 18.

    Yan C, Zhang N, Kang G (2018) Downlink multiple input multiple output mixed sparse code multiple access for 5G system. IEEE Access 6:20837–20847

    Article  Google Scholar 

  19. 19.

    Zhong Z, Qin J, Zhong Z, Li Z (2019) Fog radio access networks with hierarchical content delivery. IEEE Access 7:20950–20960

    Article  Google Scholar 

  20. 20.

    Zhu L, Xiao Z, Xia X-G, Wu DO (2019) Millimeter-wave communications with non-orthogonal multiple access for B5G/6G. IEEE Access 7:116123–116132

    Article  Google Scholar 

  21. 21.

    Li A, Han G (2018) A fairness-based MAC protocol for 5G cognitive radio ad hoc networks. J Netw Comput Appl 111:28–34

    Article  Google Scholar 

  22. 22.

    Stephan T, Al-Turjman F, Joseph KS, Balusamy B, Srivastava S (2020) Artificial intelligence inspired energy and spectrum aware cluster based routing protocol for cognitive radio sensor networks. J Parallel Distrib Comp 142:90–105

    Article  Google Scholar 

  23. 23.

    Yang M, Li B, Bai Z, Yan Z (2018) SGMA: semi-granted multiple access for non-orthogonal multiple access (NOMA) in 5G networking. J Netw Comput Appl 112:115–125

    Article  Google Scholar 

  24. 24.

    Zhang J, Xu L, Tsai P-W, Lin Z (2018) Minimization of delay and collision with cross cube spanning tree in wireless sensor networks. Wirel Netw 25(4):1875–1893

    Article  Google Scholar 

  25. 25.

    Minet P, Muhlethaler P, Khoufi I (2018) Collision avoidance in shared slots in wireless devices of the internet of things: models and simulations. Ann Telecommun 74(5–6):335–349

    Google Scholar 

  26. 26.

    Mennes R, Claeys M, Figueiredo FAPD, Jabandzic I, Moerman I, Latre S (2019) Deep learning-based Spectrum prediction collision avoidance for hybrid wireless environments. IEEE Access 7:45818–45830

    Article  Google Scholar 

  27. 27.

    Aboelwafa MM, Abd-Elmagid MA, Biason A, Seddik KG, Elbatt T, Zorzi M (2019) Towards optimal resource allocation in wireless powered communication networks with non-orthogonal multiple access. Ad Hoc Netw 85:1–10

    Article  Google Scholar 

  28. 28.

    https://wireless.engineering.nyu.edu/nyusim-5g-and-6g/. Accessed 02.04.2020

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Acknowledgments

This work is funded by Researchers Supporting Project number (RSP-2020/117), King Saud University, Riyadh, Saudi Arabia

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Correspondence to Gunasekaran Manogaran.

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Findings

The proposed MMALCCA achieves 20.09% less latency, 6.77% less collision probability, 13.32% fewer service failures, and improves resource utilization by 8.34% for the varying UEs. For the varying channels, the proposed protocol achieves 7.49% high resource utilization and reduces latency, collision probability, and service failure by 25.21%, 8.57%, and 13.7%, respectively.

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Cite this article

Shakeel, P.M., Baskar, S., Fouad, H. et al. Creating Collision-Free Communication in IoT with 6G Using Multiple Machine Access Learning Collision Avoidance Protocol. Mobile Netw Appl 26, 969–980 (2021). https://doi.org/10.1007/s11036-020-01670-9

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Keywords

  • Multiple machine access learning with collision carrier avoidance
  • Edge cloud computing Higher resource utilization