The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)

• Published in: Information systems (Oxford) 2022-07, Vol.107, p.101840, Article 101840
• Main Authors: Firouzi, Farshad, Farahani, Bahar, Marinšek, Alexander
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Big Data; Case Studies; Cloud Computing; Cloud IoT; Cloudlet; Collaboration; Computer architecture; Convergence; Data processing; Edge Computing; Edge-Fog-Cloud; Electronic devices; Fog Computing; Healthcare IoT; Information systems; Internet of Things; Internet of Things (IoT); Knowledge acquisition; Machine learning; Mobile Computing; Network latency; Offloading; Performance evaluation; Privacy-Preserving Machine Learning; Provisioning; Resource allocation; Resource Management; Security and Privacy; Service Placement; Storage
• ISSN: 0306-4379; 1873-6076
• DOI: 10.1016/j.is.2021.101840

The Internet of Things (IoT) tsunami, public embracement, and the ubiquitous adoption of smart devices are affecting virtually every industry, directly or indirectly. The success of the current and future landscape of IoT and connected devices requires service provision characterized by scalability, ubiquity, reliability, and high-performance, among others. In order to achieve this attribution, the integration of IoT and Cloud Computing (CC), known as cloud IoT, has emerged as a new paradigm providing advanced services specific to aggregating, storing, and processing data generated by IoT. While the convergence of IoT and Cloud brings opportunities, it suffers from specific limitations such as bandwidth, latency, and connectivity. The increasing need for supporting interaction between cloud and IoT led to Edge and Fog Computing (FC) in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. The hierarchical and collaborative edge–fog–cloud architecture brings tremendous benefits as it enables us to distribute the intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints e.g., delay-energy tradeoff. Due to the hierarchical, cross-layer, and distributed nature of this model, achieving an osmotic and effective convergence of IoT, edge, fog, and cloud computing requires overcoming many challenges with respect to design and implementation, as well as deployment and evaluation. This paper provides a comprehensive insight into the edge-fog-cloud computing paradigm by providing a blend of discussions on all important aspects of the underlying technologies to offer opportunities for more holistic studies and to accelerate knowledge acquisition. To gain a deep understanding of edge–fog–cloud, we will begin this paper by providing an in-depth tutorial and presenting the main requirements, state-of-the-art reference architectures, building blocks, components, protocols, applications, and other similar computing paradigms, including their similarities and differences. Following this, a holistic reference architecture for edge–fog–cloud IoT is presented and the major corresponding design and deployment considerations (e.g., service models, infrastructure design, provisioning, resource allocation, offloading, service migration, performance evaluation, and security concerns) are di