# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
###### Abstract
In this evolving landscape, edge solutions are increasingly empowering industrial plants by enhancing computational capacity at the edge, often through devices equipped with CPUs and sometimes GPUs. This enables existing machinery to utilize machine learning and artificial intelligence, transforming them into smart machines. A pivotal application of this is seen in Deep Neural Networks (DNNs), which, as [13] points out, surpass traditional machine learning techniques in tasks like image classification and speech recognition. [PERSON] further observes that edge applications are integrating AI for tasks such as augmented reality and face recognition. The study by [PERSON] et al. reinforces these points by highlighting the transformative potential of edge AI across sectors such as manufacturing, transportation, and healthcare, by enabling real-time data processing, thus reducing latency and enhancing privacy and security. This underscores the synergy between edge computing and AI in driving forward industrial innovation and efficiency [14]. Yet, the necessity of edge computing alongside cloud computing is debated.Ref. [15] argues that many IoT applications demand real-time data analysis and decision-making, which are requirements often unmet by cloud computing solutions in latency-sensitive \begin{table}
\begin{tabular}{l l l} \hline \hline
**Origin** & **Destination** & **Time** \\ \hline Cloud & Edge Solution & \(<\)50 ms \\ Edge Solution & Cloud & \(<\)50 ms \\ Edge Solution & Edge Device & \(<\)5 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recorded communication time by location. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Device** & **Storage** & **Time** \\ \hline Raspberry Pi B+\textbackslash{} & LiteDB & 00:07:35 \\ NVIDIA Jetson Nano\textbackslash{} & LiteDB & 00:08:15 \\ NVIDIA Jetson TX2\textbackslash{} & LiteDB & 00:07:55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recorded communication time by device. contexts. They suggest that AI, in conjunction with edge devices, has the potential to enhance the capabilities of such applications significantly. [PERSON] et al. further elaborate on this point by discussing how edge computing, particularly through technologies like Mobile Edge Computing (MEC) and serverless computing, can mitigate the limitations of cloud computing by bringing computational resources closer to the data source. This architectural shift is essential for applications requiring real-time processing, showcasing the complementary roles of edge and cloud computing in modern IT infrastructure. The integration of AI at the edge further amplifies this capability, making a compelling case for the combined use of edge computing and AI in meeting the demands of next-generation applications [14]. The synergy between edge and cloud computing forms a stratified approach to data processing in the IoT era, each offering unique benefits in terms of efficiency, latency, and storage capacity. Understanding their strengths and limitations is essential for their effective deployment within an IoT ecosystem. The introduction of distributed deep learning (DL) techniques, particularly those leveraging Mobile Edge Computing (MEC), presents a promising solution to overcome the computational challenges and enhance security across IoT networks. By offloading computing operations to the edge, these methods support low latency and high accuracy in data processing and analysis, which are crucial for the efficiency of IoT devices with limited computational capabilities. Edge computing applications are diverse, notably in sectors like smart factories and agriculture. These applications embed software solutions in computing-powered devices at the network's edge to perform various tasks, including sensing, decision-making, and learning. The integration of artificial intelligence across networks of interconnected devices in systems like smart homes, factories, and cities is increasingly addressing complex challenges, from supply chain management to climate change.