# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Cybersecurity and Threat Detection
\begin{table}
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**Application Domain** & **Primary Objective** & **Computational Challenges** & **Key Performance Metrics** & **Edge-Cloud Dependency** & **Critical Constraints** \\ \hline Smart transportation [106; 107; 108; 109; 110; 111; 112; 113] & Real-time traffic management, autonomous mobility, and safety enhancement. & High-speed vehicular data processing, low-latency V2X communication. & Route optimization time, accident avoidance rate, minimumization. & Edge for real-time vehicle control and cloud for long-term traffic analytics. & Stringent safety requirements, dynamic network conditions. \\ \hline Smart healthcare [114; 115; 116; 117; 118; 119; 120; 121] & Remote patient monitoring, medical diagnostics, and emergency response. & AI-based anomaly detection, real-time emergency & Detection accuracy, emergency response time, and medical resource availability. & Edge for immediate health data processing, cloud for historical medical trends. & Regulatory compliance, data security, reliability of edge health models. \\ \hline Industrial automation automation [122; 123; 124; 125; 126; 127; 128] & Predictive maintenance, robotic automation, and process optimization. & Machine status monitoring, robotic coordination, AI-driven analytics. & Fault prediction accuracy, production efficiency, robotic synchronization. & Edge for real-time factory automation, cloud for predictive maintenance. & Synchronization issues in automated systems, cybersecurity risks. \\ \hline Smart cities [129; 130; 131; 132; 133; 134; 135] & Environmental monitoring, traffic regulation, and automated governance. & Distributed sensor fusion, IoT-based analytics, energy optimization. & Data processing efficiency, service availability, energy consumption control. & Edge for localized city services, cloud for policy planning and large-scale infrastructure costs. \\ \hline Smart energy [136; 137; 138; 139; 140; 141; 142] & Energy grid optimization, decentralized trading, and renewable integration. & Smart contract-based balancing, fault-tolerant forecasting. & Grid stability, fault tolerance, power efficiency. & Edge for dynamic balancing, cloud for predictive analytics. & Renewable energy fluctuations, cybersecurity in decentralized trading. \\ \hline \multirow{4}{*}{AR/VR [143; 144; 145; 146; 147; 148; 149; 150]} & Immersive real-time experiences, interactive collaboration. & Low-latency rendering, AI-assisted network congestion management. & Frame rate, response delay, and quality of service (QoS) in interactive sessions. & Edge for real-time processing and cloud for complex graphics & Network latency, power constraints of mobile devices, user experience consistency. \\ \hline \multirow{4}{*}{Disaster management [151; 152; 153; 154; 155; 156; 157]} & Early warning, emergency coordination, and rescue optimization. & AI-based threat detection, UAV-assisted search, large-scale event aggregation. & Mission response time, victim detection rate, disaster resilience. & Edge for reconnaissance, cloud for large-scale stability in crisis environments. \\ \hline \multirow{4}{*}{Cybersecurity [158; 159; 160; 161; 162; 163; 164; 165]} & Real-time threat detection, secure authentication, and data integrity. & FL for intrusion detection and blockchain-based identity verification. & Detection rate, false alarm reduction, access trustworthiness. & Edge for decentralized threat detection, cloud for federated intelligence. & Computational cost of security measures, attack resilience, real-time response response. \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of application domains in edge–cloud computing.