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Game theory-based optimization for efficient IoT task offloading in 6G network base stations

Authors :
Ismail Keshta
Mukesh Soni
Nabamita Deb
Shweta singh
K. Saravanan
Dr Ihtiram Raza Khan
Source :
Measurement: Sensors, Vol 33, Iss , Pp 101184- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization.

Details

Language :
English
ISSN :
26659174
Volume :
33
Issue :
101184-
Database :
Directory of Open Access Journals
Journal :
Measurement: Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.bd557c088c4694b0e05d58fbd582e9
Document Type :
article
Full Text :
https://doi.org/10.1016/j.measen.2024.101184