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


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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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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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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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).

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