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
Online learning for industrial IoT : the online convex optimization perspective
|Author:||Chatzieleftheriou, Livia Elena1; Liu, Chen-Feng2; Koutsopoulos, Iordanis3;|
1IMDEA Networks Institute, Madrid, Spain
2Technology Innovation Institute, Abu Dhabi, United Arab Emirates
3Department of Informatics, Athens University of Economics and Business, Athens, Greece
4Centre for Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023021026804
|Publish Date:|| 2023-02-10
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.
|Pages:||7 - 12|
2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)
IEEE International Mediterranean Conference on Communications and Networking
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the CHIST-ERA grant CHIST-ERA-18-SDCDN-004 (grant number T11EPA4-00056) through the General Secretariat for Research and Innovation (GSRI).
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