Fairness-Aware Computation Offloading for Mobile Edge Computing With Energy Harvesting

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Abstract
Mobile edge computing (MEC) improves network performance by minimizing latency and assigning computing tasks to edge servers. Nonetheless, delegating calculations in environments with elevated device density poses considerable difficulties. Ensuring equity in resource distribution among users is essential for preserving network stability and user satisfaction in these contexts. This research formulates the Fairness-aware Computation Offloading Optimization (FACOO) algorithm. The Lyapunov approach and sequential least squares quadratic programming (SLSQP) are used to ascertain the best offloading ratio, transmission power, and CPU frequency while complying with signal-to-interference-plus-noise ratio (SINR) limitations. Energy harvesting (EH) is built into FACOO to prolong the battery life of the device and to ensure that MEC systems, which are limited in resources, are more sustainable. The results show that FACOO greatly improves throughput and fairness while using much less energy, especially in settings where a lot of nodes are spread out over a large area. Comprehensive simulations show that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.
Funding Information
  • Indonesia Endowment Fund for Education (71/E/KPT/2024)

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