English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 The optimal scheduling decisions of the scenario are provided,and the optimality loss on system utility under outdated NSI is analyzed.Simulations verify our theoretical claims and demonstrate the gains of ourproposed ERS algorithm over alternative benchmark schemes.

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