The Internet of Things (IoT) is a network of interconnected devices and objects that have various functions,such as sensing, identifying, computing, providing services and communicating. It is estimated that by the year 2030, there will be approximately 29.42 billion IoT devices globally, facilitating extensive data exchange among them. In response to this rapid growth of IoT, Artificial Intelligence (AI) has become a pivotal technology in automating key aspects of IoT systems, including decision-making, predictive data analysis among others. The widespread use of AI across various industries has brought about significant transformations in business ecosystems. Despite its immense potential, IoT systems still face several challenges. These challenges encompass concerns related to privacy and security, data management, standardization issues, trust among others. Looking at these challenges, AI emerges as an essential enabler, enhancing the intelligence and sophistication of IoT systems. Its diverse applications offer effective solutions to address the inherent challenges within IoT systems. This, in turn, leads to the optimization of processes and the development of more intelligent and smart IoT systems.This thesis presents a semi-systematic literature review (SSLR) that aims to explore the role of AI in IoT systems. A systematic search was performed on three (3) databases (Scopus, IEEE-Xplore and the ACM digital library), 29 scientific and peer reviewed studies published between 2018-2022 were selected and examined to provide answers to the research questions. This study also encompasses an additional study within the context of AI and trustworthiness in IoT systems, user acceptance within IoT systems and AIoT's impact on sustainable economic growth across industries. This thesis also presents the DIMACERI Framework which encompasses eight dimensions of IoT challenges and concludes with recommendations for stakeholders in AIoT systems. AI is strategically integrated across the DIMACERI dimensions to create reliable, secure and efficient IoT systems.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-63080 |
| Date | January 2023 |
| Creators | Anyonyi, Yvonne Ivakale, Katambi, Joan |
| Publisher | Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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