6533b85ffe1ef96bd12c1a56

RESEARCH PRODUCT

Energy-Efficient Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

Haijun LiaoZhenyu ZhouZheng Chang

subject

Computer scienceServerReliability (computer networking)Distributed computingResource allocationContext (language use)Lyapunov optimizationEnhanced Data Rates for GSM EvolutionEdge computingEfficient energy use

description

Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this chapter, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory.

yearjournalcountryeditionlanguage
2020-11-06
https://doi.org/10.1007/978-3-030-64054-5_6