Abstract
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.
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Zhou, Z., Chang, Z., Liao, H. (2021). Energy-Efficient Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT. In: Green Internet of Things (IoT): Energy Efficiency Perspective. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-64054-5_6
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DOI: https://doi.org/10.1007/978-3-030-64054-5_6
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