## 2 Literature Review
After stating many challenges and opportunities, the authors inferred that the edge computing paradigm is still in its infancy and has a lot of potential ahead to pave the way for many advancements in distributed computing. They also presented five research challenges: general purpose computing on edge nodes, discovering edge nodes, partitioning, and offloading tasks, uncompromising quality-of-service and experience, and using edge nodes publicly and securely. Furthermore, they introduced five major existing opportunities: standards, benchmarks and marketplace, framework and languages, lightweight libraries and algorithms, micro-operating systems and virtualization, and industry-academic collaborations. Edge computing has also been used a lot in AI and introduces what is called edge AI. The paper in [10] presents an on-demand accelerating deep neural network interface through the edge computing. The research in the paper is driven by the limitation of mobile devices' resources, which makes it hard to run deep neural networks. The authors propose a framework that they named Edgent which is a framework for edge computing for DNN. They implemented their prototype using a Raspberry Pi and a desktop computer. Edge computing on SBCs was tackled in [11]. Since IoT devices are expanding, centralized cloud computing data centers are no longer suitable for IoT applications as they now present many challenges. These challenges are mainly related to the latency, high throughput, and bandwidth. This paper introduces edge computing as a solution to latency sensitive applications. Furthermore, the authors present an architecture that is based on low-cost single-board computers placed near data sources to deliver real-time responses to IoT applications while keeping scalability. Edge devices are being interfaced with FPGAs to deliver low latency for IoT applications. In [12], the authors proposed the prototyping of an FPGA-based edge device designed for IoT. It mainly takes care of the connectivity space problem. The variety of IoT devices and the diversity of their communication mechanisms gave birth to a set of different communication standards and technologies. These technologies had to co-exist to provide interoperability among devices. That is why, the IoT-ARM specifies a full communication stack. Green computing is also one of the main pillars in our work. It is also a trendy topic in the literature. The authors in [13] present green computing as a practice that uses computing resources in an eco-friendly way without impacting the overall performance of the system. The world is witnessing a huge need to adopt green computing practices due to many factors: rapid growth of the internet, increasing equipment power density, increasing cooling requirements, increasing energy costs, restrictions on energy supply and access, low server utilization rates, and the growing awareness of IT impact on the environment. The use of SBCs can also be looked at as a measure to provide green computing via their use in green data centers. Many clusters have been conceived and developed based on Raspberry Pis. The authors in [14] present an HPC platform that is based on Raspberry Pi which makes it a cost effective and an energy efficient approach. The cluster studied in this work was part of a research project about smart grids. The nano/green data center consists of 5 Raspberry Pis connected in a cluster. The authors presented an analysis involving the performance of the cluster and the energy consumption. For this purpose, a comparison between the Raspberry Pi cluster and a single-node commodity hardware cluster was conducted. The authors used Hadoop as a big data analytics platform for both clusters and used the famous Terasort benchmark. The comparison results have shown that even though the single-node cluster outperforms the Raspberry Pi cluster, it consumes more energy. Thus, if energy efficiency and cost effectiveness are a matter but the performance is not, then the Raspberry Pi is the way to go.