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

Abstract

Mobile edge computing (MEC) improves network performance by minimizing latency and assigning computing tasks to edge servers. Nonetheless, delegating computations in environments with high device density poses considerable difficulties. Ensuring fairness 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 device battery life and to ensure that MEC systems, which have limited resources, are more sustainable. The results show that FACOO greatly improves throughput and fairness while using significantly less energy, especially in settings with numerous nodes dispersed across large areas. Comprehensive simulations demonstrate that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.