# Edge and Cloud Computing in Smart Cities
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Description** & **Focused Points** & **Limitations** \\ \hline \multirow{8}{*}{[8]} & Examines the development and implementation of smart cities, analyzing intelligent computing algorithms and their applications in urban environments. It provides insights into smart-city frameworks and various optimization techniques. & Covers smart-city frameworks and various optimization techniques. & Lacks deep insights into resource allocation strategies and AI-driven orchestration. \\ \cline{2-3}  & An overview of edge computing’s role in smart cities, covering applications, classifications, and challenges. The paper also presents a taxonomy of edge computing applications for latency-sensitive smart-city services. & Focuses on latency-sensitive system-city services. & Does not address integration challenges between edge and cloud computing. \\ \hline \multirow{8}{*}{[9]} & Notes how cloud, mobile, and edge computing enhance smart cities by improving urban systems like health, energy, and planning. It highlights their role in addressing urban heat island effects and future integration challenges. & Explores the role of computing in urban planning, health, and energy management. & Limited discussion on real-time processing and AI-driven automation. \\ \hline \multirow{8}{*}{[10]} & Discusses the advantages of edge computing in healthcare, the Internet of Things (IoT), and smart-city applications. Highlights edge computing’s ability to enhance data security, reduce latency, and improve computational efficiency in real-time environments. & Emphasizes security, latency reduction, and computational efficiency. & Does not extensively discuss cloud–edge synergy. \\ \hline \multirow{8}{*}{[12]} & Surveys the role of 5G-enabled multi-access edge computing (MEC) in smart cities. It highlights the potential of MEC to enhance smart-city infrastructure through reduced latency and distributed computing resources. & Focuses on how MEC improves smart-city infrastructure. & Does not compare MEC with other edge–cloud models. \\ \hline \multirow{8}{*}{[13]} & Analyzes cloud computing security within smart-city networks, addressing threats, vulnerabilities, and countermeasures. The survey also discusses privacy concerns and the role of edge computing in mitigating security risks. & Focuses on security risks and privacy issues. & Limited focus on performance trade-offs and resource allocation. \\ \cline{2-3}  & Provides a comprehensive analysis of edge–cloud computing in smart cities, including architectures, resource allocation, AI integration, and security strategies. & - Offers a multi-tier architectural perspective. & \multirow{8}{*}{[13]} \\ \cline{2-3}  & & - Analyzes AI-driven resource allocation. & No major limitations compared to existing surveys, but future work may explore more real-world deployments and experimental results. \\ \cline{2-3}  & & - Containes future research directions (6G, quantum, sustainable computing). & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of surveys on edge and cloud computing in smart cities. More specifically, the remainder of the paper is structured as follows. Section 2 focuses on architectural models for edge and cloud computing in smart cities. Section 3 explores enabling technologies. Moreover, Section 4 notes applications domains. Section 5 provides challenges, open issues, and future research directions. Finally, Section 6 summarizes the findings of this survey.