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5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0001-5924-5457
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 1, article id 133Article in journal (Refereed) Published
Abstract [en]

Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.

Place, publisher, year, edition, pages
2023. Vol. 23, no 1, article id 133
Keywords [en]
IoT, distributed intelligence, federated learning, reinforcement learning, fifth-generation mobile network (5G), O-RAN
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-214054DOI: 10.3390/s23010133ISI: 000908529800001PubMedID: 36616731Scopus ID: 2-s2.0-85145551048OAI: oai:DiVA.org:su-214054DiVA, id: diva2:1729720
Available from: 2023-01-22 Created: 2023-01-22 Last updated: 2023-08-30Bibliographically approved
In thesis
1. Distributed Intelligence for IoT Systems Using Edge Computing
Open this publication in new window or tab >>Distributed Intelligence for IoT Systems Using Edge Computing
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the past decade, the Internet of Things (IoT) has undergone a paradigm shift away from centralized cloud computing to edge computing. Hundreds of billions of things are estimated to be deployed in the rapidly advancing IoT paradigm, resulting in an enormous amount of data. Sending all the data to the cloud has recently proven to be a performance bottleneck, as it causes many network issues, including high latency, high power consumption, security issues, privacy issues, etc. However, the existing paradigms do not use edge devices for decision-making. Distributed intelligence could strengthen the IoT in several ways by distributing decision-making tasks among edge devices within the network instead of sending all data to a central server. All computational tasks and data are shared among edge devices. Edge computing offers many advantages, including distributed processing, low latency, fault tolerance, better scalability, better security, and data protection. These advantages are helpful for critical applications that require higher reliability, real-time processing, mobility support, and context awareness. This thesis investigated the application of different types of intelligence (e.g., rule-based, machine learning, etc.) to implementing distributed intelligence at the edge of the network and the network challenges that arise. The first part of this thesis presents a novel and generalizable distributed intelligence architecture that leverages edge computing to enable the intelligence of things by utilizing information closer to IoT devices. The architecture is comprised of two tiers, which address the heterogeneity and constraints of IoT devices. Additionally, the first part of this thesis identifies a suitable reasoner for two-level distributed intelligence and an efficient way of applying it in the architecture via an IoT gateway. To mitigate communication challenges in edge computing, the second part of the thesis proposes two-level mechanisms by leveraging the benefits of software-defined networking (SDN) and 5G networks based on open radio access network (O-RAN) as part of a communication overlay for the distributed intelligence architecture. The third part of this thesis investigates integrating the two-tier architecture and the communication mechanisms in order to provide distributed intelligence in IoT systems in an optimal manner.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2023. p. 70
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 23-008
Keywords
Internet of Things (IoT), Edge Computing, Distributed Intelligence, Software Defined Networking (SDN), Federated Learning, 5G, O-RAN, Network Slicing, Reinforcement Learning
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-220549 (URN)978-91-8014-476-6 (ISBN)978-91-8014-477-3 (ISBN)
Public defence
2023-10-13, Lilla hörsalen, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2023-09-20 Created: 2023-08-30 Last updated: 2023-09-12Bibliographically approved

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