Online learning for industrial IoT : the online convex optimization perspective
Chatzieleftheriou, Livia Elena; Liu, Chen-Feng; Koutsopoulos, Iordanis; Bennis, Mehdi; Debbah, Mérouane (2022-11-02)
L. E. Chatzieleftheriou, C. -F. Liu, I. Koutsopoulos, M. Bennis and M. Debbah, "Online Learning for Industrial IoT: The Online Convex Optimization Perspective," 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Athens, Greece, 2022, pp. 7-12, doi: 10.1109/MeditCom55741.2022.9928703
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https://urn.fi/URN:NBN:fi-fe2023021026804
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Abstract
Industrial Internet of things (IIoT), one enabler for Industry 4.0 Smart Factories, is a mission-critical and latency-sensitive application of 5G networks. Due to the stringent latency requirements in IIoT, coordinating the simultaneous transmissions of massive entities and knowing the interference they create to each other is not feasible. Additionally, due to the mobility feature of mobile robots and automated guided vehicles, the experienced channel fading may differ from the estimated one. Therefore, some uncertainties exist in IIoT networks while we decide the communication and control mechanisms. Within the context of IIoT, this paper discusses some resource allocation solutions from the perspective of Online Convex Optimization (OCO). OCO is a computationally lightweight and memory-efficient mathematical tool which tackles the optimization problems, given that the network environment is arbitrary and unknown. We first introduce the key performance indicators in IIoT networks and highlight the uncertain factors, which we may encounter while allocating the communication resources in IIoT. Then we provide an overview of main principles of OCO and present the comparison benchmarks and related metrics for performance evaluation. Moreover, we discuss the kind of resource allocation problems in IIoT that can be tackled by OCO. Finally, we summarize the advantages of applying OCO to IIoT networks.
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