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Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller
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
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0003-4208-6757
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. p. 24-32
Series
Procedia Computer Science, E-ISSN 1877-0509 ; 184
Keywords [en]
Internet of Things (IoT), edge computing, reasoning, fuzzy controller, abductive reasoning
National Category
Computer Engineering
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-200470DOI: 10.1016/j.procs.2021.03.014OAI: oai:DiVA.org:su-200470DiVA, id: diva2:1625132
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
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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