## 1 Introduction
The reviewed approaches for the specified applications play a significant role in enabling smart urban systems by providing a foundation for reliable, real-time monitoring and data-driven decision-making. In smart cities, where urban infrastructures are highly interconnected and data-intensive, it is essential to identify the adequate architectures and techniques that meet specific application requirements. This leads to responsive public services, improved sustainability, and enhanced quality of life. The improvements in energy consumption, execution time, and computational cost are critical for managing increasing urban population densities, resource limitations, and environmental stress. Furthermore, understanding the strengths and limitations of the existing approaches helps municipal planners and engineers to design more resilient, adaptive, and intelligent systems to meet the evolving needs of smart cities. This work aims to compare the metrics presented in Table 1 for various applications, including power systems, gully pot monitoring, transportation, disasters, healthcare, gas monitoring, solid waste monitoring, water pipeline monitoring, and commercial asset tracking while using specific WSN sensors and network topologies. The contributions of this work are as follows:  
* Functionalities of sensor and sink nodes within WSN deployments are examined to provide network architecture and communication dynamics. * Classifying network types and topologies in WSNs are discussed for design considerations, communication patterns, and scalability in urban settings. The analyses enhance the understanding of how network structures influence application performance and efficiency. * Analyze the characteristics and identify the core challenges within the following WSN applications, which include power system monitoring, transportation optimization, healthcare delivery enhancement, solid waste management optimization, continuous surveillance of gas leaks and water distribution systems, and efficient asset tracking and inventory management in commercial sectors. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Performance Metric** & **Acronym** & **Brief Description** \\ \hline Energy Consumption & \(EC\) & The amount of energy consumed by the network or system during operation. \\ Network Lifetime & \(NL\) & The duration a network can function effectively before the nodes lose their energy. \\ End-to-End Delay & \(Delay\) & The total time a data packet travels from the source to the destination. \\ Efficiency & \(E\) & The effectiveness of a system in utilizing resources to achieve its goals. \\ Routing Overhead & \(RO\) & The additional network resources required for maintaining and managing routing information. \\ Throughput & \(T\) & The rate at which data are successfully transmitted over the network. \\ Computation Cost & \(Cost\) & The total cost associated with computational resources used in processing tasks. \\ Computational Overhead & \(CO\) & The extra computational resources required beyond the basic operation. \\ Reliability & \(R\) & The ability of the system to perform consistently under specified conditions. \\ Loss Rate & \(LR\) & The percentage of data packets lost during transmission. \\ Execution Time & \(ET\) & The total time to complete a specific task or process. \\ Accuracy & \(Accuracy\) & The number of data points delivered to the base station. \\ Error Rate & \(ER\) & The number of data points mistakenly delivered to the total number of data points sent. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics and their descriptions. * Provide a comprehensive study of the different network topologies considered in the literature when applied to different applications, emphasizing the performance comparison and current limitations. * Performance analysis of WSN methodologies in urban applications and different metrics such as energy consumption, accuracy, delay, cost, efficiency, network lifetime, reliability, etc., are conducted.