# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Edge Performance Metrics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Key Features** & **Importance** & **Considerations** & **Common Metrics** \\ \hline Data privacy and security compliance [104] & Measure of data privacy level and compliance to security standards & Ensures sensitive AI data are processed locally instead of being exposed to security risks in the cloud. & Minimize data transmission to cloud, implementing local encryption and access control. & Compliance with GDPR, HIPAA, or ISO 27001 security standards. [104] \\ \hline Model robustness [105] & Resilience of AI model against noise and outliers & AI models should handle adversarial attacks or noisy inputs effectively. & A robust model can handle unexpected situations on the edge without significantly compromising its performance. & Error rate under adversarial conditions (\%). \\ \hline System uptime and failure rate [106] & The availability of system & Measures the reliability and fault tolerance of the edge AI system. & How long it remains active and processing data without experiencing downtime & Mean Time Between Failures (MPBF) in hours. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Security and reliability metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Key Features** & **Importance** & **Considerations** & **Common Metrics** \\ \hline Bandwidth usage [101] & Measures the amount of data transferred between edge devices and the cloud. & Ensures efficient data processing to reduce network congestion. & Low bandwidth usage is crucial for remote IoT applications. & Data transfer in megabits per second (Mbps). \\ \hline Edge–cloud offloading ratio [102] & The ratio of computing that occurs in edge or cloud. & Ensures efficient resource allocation. & Higher edge computation = lower latency and better privacy. & Percentage of inference performed on-device vs. cloud. \\ \hline Connectivity latency [103] & Data transfer delay from edge to cloud. & Is critical for real-time applications. & It is influenced by data transfer size and network condition & Round-trip time (ms) for cloud interactions. \\ \hline Success transmission rate [99] & Measures success rate of packet delivery. & Ensures successful transmission of data packet. & It is influenced by network condition, QoS level, and data packet size. & Success rate (\%). \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Communication and network metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Key Features** & **Importance** & **Considerations** & **Common Metrics** \\ \hline Data privacy and security compliance [104] & Measure of data privacy level and compliance to security standards & Ensures sensitive AI data are processed locally instead of being exposed to security risks in the cloud. & Minimize data transmission to cloud, implementing local encryption and access control. & Compliance with GDPR, HIPAA, or ISO 27001 security standards. [104] \\ \hline Model robustness [105] & Resilience of AI model against noise and outliers & AI models should handle adversarial attacks or noisy inputs effectively. & A robust model can handle unexpected situations on the edge without significantly compromising its performance. & Error rate under adversarial conditions (\%). \\ \hline System uptime and failure rate [106] & The availability of system & Measures the reliability and fault tolerance of the edge AI system. & How long it remains active and processing data without experiencing downtime & Mean Time Between Failures (MPBF) in hours. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Security and reliability metrics. Table 11 summarizes existing related research and the performance metrics used in the evaluation [23; 44; 45; 107; 108; 109; 110; 111]. Listed research in Table 11 is conducted on different hardware platforms, i.e., Jetson platform [23; 107; 109; 110; 111], Raspberry Pi [108], and FPGA [44]. mAP and F1-score are the most common accuracy parameters used.