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An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner
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.
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

Place, publisher, year, edition, pages
2020. Vol. 177, p. 102-111
Series
Procedia Computer Science, E-ISSN 1877-0509 ; 177
Keywords [en]
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: urn:nbn:se:su:diva-188852DOI: 10.1016/j.procs.2020.10.017OAI: oai:DiVA.org:su-188852DiVA, id: diva2:1517276
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
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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