## 3 Security and Privacy-Preserving Solutions for IoE
### Edge AI and Secure Computing in IoE
#### 3.2.5 AI Performance Metrics of Edge Computing Devices  
The hardware capacity of devices used in edge computing architectures plays a critical role in executing AI operations effectively [15; 106; 108]. In this context, factors such as real-time data processing capabilities, memory capacity, the presence of AI accelerators, and energy efficiency must be considered [100; 104; 105]. Table 11 presents a comparative overview of commonly used edge devices, evaluating them based on AI support, use cases, and performance metrics. For the effective implementation of AI-based security and processing mechanisms, the hardware capacity and processing performance of the devices used in edge and cloud infrastructures are of paramount importance [16; 108; 106]. Due to the requirements of real-time data processing, low latency, and on-device decision-making, Edge AI architectures are particularly sensitive to hardware selection [100; 105; 107]. To technically evaluate the feasibility of the proposed system, Table 11 provides a comparative analysis of the AI-enabled performance metrics of commonly used edge and cloud devices.  
Table 11 presents the key hardware features and performance metrics of various edge and cloud devices used in AI applications. Devices are evaluated based on their AI accelerator types (GPU, TPU, NPU), memory capacities (RAM), AI processing power  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Device Name** & **AI Accelerator (TIPU/TPU/GPU)** & **RAM** & **AI Performance (TOPS)** & **Energy Consumption** & **Use Case** \\ \hline \hline Raspberry Pi 4 & None/External (USB NPU) & 4-8 GB & \textless{}0.1 TOPS & Low (-5 W) & Education, Lightweight IoT Projects \\ \hline Arduino Portenta H7 & None/Microcontroller-based & 0.5 MB & None & Very Low & Sensor Data Collection, Prototyping \\ \hline Google Coral Dev Board & Edge TPU & 1 GB & 4 TOPS & Low (-2.5 W) & Image Processing, Smart Sensors \\ \hline Intel Neural Compute Stick 2 & Intel Movidius Myriad X & External USB & 1 TOPS & Very Low & Prototyping, Video Analysis \\ \hline BeagleBone AI-64 & Embedded DSP + Vision Engine & 4 GB & \textasci{}1 TOPS & Medium & Robotics, Automation \\ \hline Jetson Xavier NX & NVIDIA Volta GPU + 384 CUDA Cores & 8–16 GB & 21 TOPS & Medium (-15 W) & Edge AI, Autonomous Systems \\ \hline Jetson AGX Orin & Ampere GPU + 2048 CUDA + 64 Tensor Cores & 32–64 GB & 275 TOPS & High (-50 W) & Autonomous Vehicles, Drones \\ \hline Huawei Atlas 200 DK & Ascend 310 NPU & 8 GB & 16 TOPS & Medium & Industrial Visual Analytics \\ \hline Cloud (AWS EC2 p4d) & NVIDIA A100 GPU & 1 TB+ & 1000+ TOPS & Very High & Large-Scale Model Training, Analytics \\ \hline \hline \end{tabular}
\end{table}
Table 11: AI processing capacity and performance metrics of edge and cloud devices [111; 112; 113; 114; 115; 116; 117; 118].  
(TOPS), energy consumption levels, and use case scenarios. These metrics are particularly critical in edge architectures, where applicability, energy efficiency, and task compatibility must be carefully assessed. For example, high-performance devices like the NVIDIA Jetson AGX Orin are suitable for advanced visual analytics and autonomous decision systems, while lower-power devices like the Raspberry Pi 4 or Arduino Portenta H7 are more appropriate for education, prototyping, or lightweight IoT scenarios. On the other hand, cloud-based solutions such as AWS EC2 offer very high AI processing capacity, but may present limitations in real-time performance and energy consumption compared to edge solutions.