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Fog computing-based beam allocation and co-operative task distribution model for green 5G mobile network

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Vydáno v:Innovations in Systems and Software Engineering vol. 20, no. 4 (Dec 2024), p. 869
Hlavní autor: Deb, Priti
Další autoři: De, Debashis
Vydáno:
Springer Nature B.V.
Témata:
Scheduling
Linear programming
Internet of Things
Wireless communications
Beamforming
Edge computing
Integer programming
Delay
Communication
5G mobile communication
Quality of service architectures
Optimization
Computation offloading
Power management
Quality of service
Spectrum allocation
Mixed integer
Cloud computing
Power consumption
Efficiency
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Abstrakt:The communication and computation world is being gone through a lack of proper power consumption models in multi-tier small cell-based heterogeneous networks. Efficient spectrum utilization with proper task distribution is an emerging criterion for 5G-based wireless communication. Delay incurred by task offloading degrades the quality of services (QoS). To reduce power consumption, delay, and increase spectral efficiency, fog-based power-efficient beam allocation and task distribution model for IoT network are proposed. Femtolet acts as a base station and performs also computational assignments like a cloudlet. Hence, femtolet can act as a fog device. Femtolet allocates beam to each IoT device based on maximum spectral efficiency. Secondly, the task distribution is performed. If the femtolet itself can perform tasks, it processes them and sends back to the IoT device. The global femtolet concept appears when two or more femtolets are needed to perform tasks. The connection between IoT devices (IoTD) and femtolet is done by 5G beamforming. Mixed integer linear programming is formulated. Power consumption, delay, and spectral efficiency are the QoS factors of the IoT network. The simulation result demonstrates that proposed architecture decreases power consumption and delay by 23–47% and 15–25%, respectively, than cloud-sensitive approaches. The proposed approach enhances spectral efficiency and SINR by 12–15% and 15–25%, respectively. The comparative analysis with existing approaches shows that this model is novel, green, and fast.
ISSN:1614-5046
1614-5054
DOI:10.1007/s11334-022-00487-x
Zdroj:Advanced Technologies & Aerospace Database