Koopman based trajectory model and computation offloading for high mobility paradigm in ISAC enabled IoT system
Minh-Tuan Tran
TL;DR
This work tackles energy-efficient computation offloading in MEC-enabled ISAC IoT under high mobility. It introduces a greedy offloading strategy that iteratively reallocates the offload fractions $l_{n,k}$ to minimize the aggregate energy $\sum_{n,k} E_{n,k}$, considering both local compute time $T_{local}$ and offload time $T_{off}$ with velocity-dependent spectral efficiency $calSE(v,f_c)$. A Koopman-based trajectory model is employed to predict velocity and feed into the uplink rate $R_n = W_n \cdot calSE(v_n,f_c)$, influencing energy and delay trade-offs. Evaluations on the VED dataset show meaningful energy reductions (e.g., ~33% per 1000 iterations) but also reveal convergence limitations and the need for improved velocity prediction and convergence guarantees, highlighting both potential and current gaps for practical MEC-ISAC IoT deployments.
Abstract
User experience on mobile devices is constrained by limited battery capacity and processing power, but 6G technology advancements are diving rapidly into mobile technical evolution. Mobile edge computing (MEC) offers a solution, offloading computationally intensive tasks to edge cloud servers, reducing battery drain compared to local processing. The upcoming integrated sensing and communication in mobile communication may improve the trajectory prediction and processing delays. This study proposes a greedy resource allocation optimization strategy for multi-user networks to minimize aggregate energy usage. Numerical results show potential improvement at 33\% for every 1000 iteration. Addressing prediction model division and velocity accuracy issues is crucial for better results. A plan for further improvement and achieving objectives is outlined for the upcoming work phase.