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Distributed Intelligence for IoT Systems Using Edge Computing
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0002-2689-6760
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 [en]
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: urn:nbn:se:su:diva-220549ISBN: 978-91-8014-476-6 (print)ISBN: 978-91-8014-477-3 (electronic)OAI: oai:DiVA.org:su-220549DiVA, id: diva2:1792930
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
List of papers
1. An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner
Open this publication in new window or tab >>An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner
2020 (English)In: The 10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020): November 2-5, 2020, Madeira, Portugal, 2020, Vol. 177, p. 102-111Conference paper, Published paper (Refereed)
Abstract [en]

With recent advancements in communications and sensor technologies, the Internet of Things (IoT) has been experiencing rapid growth. It is estimated that billions of objects will be connected, which would create a vast amount of data. Cloud computing has been the predominant choice for monitoring connected objects and delivering data-based intelligence, but high response time and network load of cloud-based solutions are limiting factors for IoT deployment. In order to cope with this challenge, this paper proposes a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge

Series
Procedia Computer Science, E-ISSN 1877-0509 ; 177
Keywords
Internet of Things (IoT), context-awareness, edge computing, reasoning, type two fuzzy controller
National Category
Computer Engineering
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-188852 (URN)10.1016/j.procs.2020.10.017 (DOI)
Conference
The 10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN2020), Madeira, Portugal, November 2-5, 2020
Available from: 2021-01-13 Created: 2021-01-13 Last updated: 2023-08-30Bibliographically approved
2. Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller
Open this publication in new window or tab >>Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller
2021 (English)In: Procedia Computer Science: The 12th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 4th International Conference on Emerging Data and Industry 4.0 (EDI40) / Affiliated Workshops / [ed] Elhadi Shakshuki; Ansar Yasar, Elsevier , 2021, p. 24-32Conference paper, Published paper (Refereed)
Abstract [en]

The introduction of distributed-reasoning through ubiquitous instrumentation within the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault-tolerance, traffic, healthcare, so on. Using a ubiquitous controller to interconnect devices in the IoT, however monumental, is still in its embryonic stage, it has the potential to create distributed-intelligent IoT solutions that are more eclient and safer than centric intelligence. It is essential to step in a new direction for designing a distributed intelligent controller for task scheduling as a means to, first, dynamically interact with a smart environment in eclient real-time data processing and, second, react to flexible changes. To cope with these issues, we outline a two-level intelligence schema, using edge computing to enhance distributed IoT. The edge schema pushes the streaming processing capability from cloud to edge devices to better support timely and reliable streaming analytics to improve the performance of smart IoT applications. In this paper, in order to provide better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller to provide low-level intelligence by employing a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.

Place, publisher, year, edition, pages
Elsevier, 2021
Series
Procedia Computer Science, E-ISSN 1877-0509 ; 184
Keywords
Internet of Things (IoT), edge computing, reasoning, fuzzy controller, abductive reasoning
National Category
Computer Engineering
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-200470 (URN)10.1016/j.procs.2021.03.014 (DOI)
Conference
The 12th International Conference on Ambient Systems, Networks, and Technologies (ANT) March 23 - 26, 2021, Warsaw, Poland
Available from: 2022-01-05 Created: 2022-01-05 Last updated: 2023-08-30Bibliographically approved
3. Federated Learning for Distributed Reasoning on Edge Computing
Open this publication in new window or tab >>Federated Learning for Distributed Reasoning on Edge Computing
2021 (English)In: Procedia Computer Science: Proceedings of the XI Latin and American Algorithms, Graphs and Optimization Symposium / [ed] Carlos Eduardo Ferreira; Orlando Lee Flávio; Keidi Miyazawa, Elsevier , 2021, p. 419-427Conference paper, Published paper (Refereed)
Abstract [en]

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site

Place, publisher, year, edition, pages
Elsevier, 2021
Series
Procedia Computer Science, E-ISSN 1877-0509 ; 184
Keywords
Distributed Reasoning, SDNFederated Learning, Edge Computing, Internet of Things, LSTM, Smart Grid
National Category
Computer Engineering
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-200481 (URN)10.1016/j.procs.2021.03.053 (DOI)
Conference
The 12th International Conference on Ambient Systems, Networks and Technologies (ANT), March 23 - 26, 2021, Warsaw, Poland
Available from: 2022-01-05 Created: 2022-01-05 Last updated: 2023-08-30Bibliographically approved
4. A Distributed SDN Controller for Distributed IoT
Open this publication in new window or tab >>A Distributed SDN Controller for Distributed IoT
2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 42873-42882, article id 3168299Article in journal (Refereed) Published
Abstract [en]

Over the past decade, the Internet of Things (IoT) has undergone a paradigm shift away from centralized cloud computing towards edge computing. Hundreds of billions of things are estimated to be deployed in the rapidly advancing IoT paradigm, resulting in enormous amounts of data. Transmitting all these data to the cloud has recently proven to be a performance bottleneck, as it causes many network issues relating to latency, power consumption, security, privacy, etc. However, existing paradigms do not use edge devices for decision making. The use of distributed intelligence could strengthen the IoT in several ways by distributing decision-making tasks to edge devices within the network, rather than sending all data to a central server. In this approach, all computational tasks and data are shared among edge devices. To achieve efficient distribution of IoT intelligence and utilization of network resources, it is necessary to integrate the transport network control with distributed edge and cloud resources to provide dynamic and efficient IoT services. This challenge can be overcome by equipping an edge IoT gateway with the intelligence required to extract information from raw data and to decide whether to actuate itself or to outsource complex tasks to the cloud. Distributed intelligence refers to a collaboration between the cloud and the edge. In this context, we first introduce a distributed SDN-based architecture for IoT that enables IoT gateways to perform IoT processing dynamically at the edge of the network, based on the current state of network resources. Next, we propose an algorithm for selecting clients for the training process of Federated Learning Applications, based on the context information of the network. In the last step, we deploy Federated Learning Applications in our distributed SDN-based architecture, using the gateways to provide distributed intelligence at the edge of the network, and conduct a comprehensive and detailed evaluation of the system from several perspectives.

Keywords
Distributed intelligence, SDN, federated learning, edge computing, Internet of Things
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:su:diva-204759 (URN)10.1109/ACCESS.2022.3168299 (DOI)000788905100001 ()
Available from: 2022-05-19 Created: 2022-05-19 Last updated: 2023-08-30Bibliographically approved
5. 5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems
Open this publication in new window or tab >>5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems
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.

Keywords
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:nbn:se:su:diva-214054 (URN)10.3390/s23010133 (DOI)000908529800001 ()36616731 (PubMedID)2-s2.0-85145551048 (Scopus ID)
Available from: 2023-01-22 Created: 2023-01-22 Last updated: 2023-08-30Bibliographically approved
6. Delay-Sensitive Resource Allocation for IoT Systems in 5G O-RAN Networks
Open this publication in new window or tab >>Delay-Sensitive Resource Allocation for IoT Systems in 5G O-RAN Networks
2024 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 26, article id 101131Article in journal (Refereed) Published
Abstract [en]

The rapid advancement in sensors and communications has led to the expansion of the Internet of Things (IoT) services, where many devices need access to the transport network using fixed or wireless access technologies and mobile Radio Access Networks (RAN). However, supporting IoT in RAN is challenging as IoT services may produce many short and variable sessions, impacting the performance of mobile users sharing the same RAN. To address this issue, network slicing is a promising solution to support heterogeneous service segments sharing the same RAN, which is a crucial requirement of the upcoming fifth-generation (5G) mobile network. This paper proposes a two-level network slicing mechanism for enhanced mobile broadband (eMBB) and Ultra-Reliable and Low Latency communications (URLLC) in order to provide end-to-end slicing at the core and edge of the network with the aim of reducing latency for IoT services and mobile users sharing the same core and RAN using the O-RAN architecture. The problem is modeled at both levels as a Markov decision process (MDP) and solved using hierarchical reinforcement learning. At a high level, an SDN controller using an agent that has been trained by a Double Deep Q-network (DDQN) allocates radio resources to gNodeBs (next-generation NodeB, a 5G base station) based on the requirements of eMBB and URLLC services. At a low level, each gNodeB using an agent that has been trained by a DDQN allocates its pre-allocated resources to its end-users. The proposed approach has been demonstrated and validated through a real testbed. Notably, it surpasses the prevalent approaches in terms of end-to-end latency.

Keywords
IoT, Network slicing, O-ran, Reinforcement learning, Resource allocation
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-220547 (URN)10.1016/j.iot.2024.101131 (DOI)001202089500001 ()2-s2.0-85187017196 (Scopus ID)
Available from: 2023-08-30 Created: 2023-08-30 Last updated: 2024-04-29Bibliographically approved

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