Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

TL;DR

This paper introduces an online energy-aware scheduling algorithm for wireless powered MEC in IIoT, optimizing system utility amid device heterogeneity and feedback delays, with proven asymptotic optimality.

Contribution

It proposes a novel ERS algorithm based on Lyapunov and convex optimization that handles network uncertainties without prior NSI knowledge, extending to delayed feedback scenarios.

Findings

ERS outperforms benchmark schemes in simulations.

The algorithm achieves asymptotic optimality.

Delay in NSI feedback causes quantifiable utility loss.

Abstract

A spurt of progress in wireless power transfer (WPT) and mobile edge computing (MEC) provides a promising approach for Industrial Internet of Things (IIoT) to enhance the quality and productivity of manufacturing. Scheduling in such a scenario is challenging due to congested wireless channels, time-dependent energy constraints, complicated device heterogeneity, and prohibitive signaling overheads. In this paper, we first propose an online algorithm, called energy-aware resource scheduling (ERS), to maximize the system utility comprising throughput and fairness, with consideration on both system sustainability and stability. Based on Lyapunov optimization and convex optimization techniques, the proposed algorithm achieves asymptotic optimality for heterogeneous IIoT systems without prior knowledge of network state information (NSI). Subsequently, we extend the ERS algorithm to a more realistic scenario where the overhead and delay of NSI feedbacks are nonnegligible. The optimal scheduling decisions of the scenario are provided, and the optimality loss on system utility under outdated NSI is analyzed. Simulations verify our theoretical claims and demonstrate the gains of our proposed ERS algorithm over alternative benchmark schemes.