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5G Data Offloading Using Fuzzification with Grasshopper Optimization Technique

V. R. Balaji1,*, T. Kalavathi2, J. Vellingiri3, N. Rajkumar4, Venkat Prasad Padhy5

1 Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
2 Department of EIE, Kongu Engineering College, Erode, 638060, India
3 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
4 Department of CSE, School of Engineering, Presidency University, Bangalore, 560064, India
5 School of Computing Science and Engineering, VIT Bhopal University, Bhopal, 466114, India

* Corresponding Author: V. R. Balaji. Email: email

Computer Systems Science and Engineering 2022, 42(1), 289-301.


Data offloading at the network with less time and reduced energy consumption are highly important for every technology. Smart applications process the data very quickly with less power consumption. As technology grows towards 5G communication architecture, identifying a solution for QoS in 5G through energy-efficient computing is important. In this proposed model, we perform data offloading at 5G using the fuzzification concept. Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption. Two base stations, small (SB) and macro (MB) stations, are initialized and the first tasks randomly computed. Then, the tasks are processed using a fuzzification algorithm to select SB or MB in the central server. The optimization is performed using a grasshopper algorithm for improving the QoS of the 5G network. The result is compared with existing algorithms and indicates that the proposed system improves the performance of the system with a cost of 44.64 J for computing 250 benchmark tasks.


Cite This Article

V. R. Balaji, T. Kalavathi, J. Vellingiri, N. Rajkumar and V. Prasad Padhy, "5g data offloading using fuzzification with grasshopper optimization technique," Computer Systems Science and Engineering, vol. 42, no.1, pp. 289–301, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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