Distributed Artificial Intelligence Based on Edge Computing

Abstract: The future Internet is expected to be driven by the prevalence of the Internet of Things (IoT), where it is envisioned that anything can be connected. In the last decade, there has been a paradigm shift in IoT from centralized cloud computing to so-called edge computing in order to compute tasks closer to the source of data generation. However, IoT still faces some major challenges when it comes to computational, storage and network. Therefore, this systematic literature review aims to investigate how edge computing can assist in accomplishing distributed intelligence in IoT systems and the known challenges and barriers. Using the PRISMA guidelines, a systematic database search and selection process was carried out to find relevant research on the topic. The data analysis method chosen for this study is content analysis, which aids in structuring and categorizing the data, allowing for the application of a coding process. By using content analysis and following the selection criteria, 15 out of 53 papers were chosen to be reviewed, published between 2017 and part of 2023. One of the main challenges mentioned by all published papers was the resource constraint of IoT devices together with the growing amounts of data that have become a bottleneck in the system. Limited processing capacity makes it difficult for the devices to independently complete complex data processing and AI analysis. The distributed nature of edge computing relies on heavy information exchange between edge devices, thus creating a huge communication load that limits its efficiency. However, edge computing opens up a more natural way of processing data at the edge of the network which aims to bring low latency, high reliability, distributed intelligence and network bandwidth for applications requiring real-time analysis.