## 3 Related Works
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Ref.** & **Methodology** & **Objectives** & **Applications** & **Metis** & **Demers** & **Monitoring Scheme** & **Attributes** & **Parameters** \\ \hline
[9] & Real-time smart garbage bin apparatus & To reduce traffic jams & Solid waste monitoring & Cost was lower by & Energy consumption & Solid waste monitoring & Special waste tens year & Latency, Delay \\ \hline
[50] & Pruit system & To provide real-time solid waste collection & Solid waste monitoring & Energy efficiency was improved by 88\% & Waste management was not performed & Solid waste monitoring & Population number of people, Solid waste & Network Lifetime, Delay \\ \hline
[51] & Solid waste management architecture & To ensure an efficient waste management process & Accuracy was measured by 95.3125 & Data trading was not performed & Solid waste monitoring & Waste collection overage, total percent of geographic area & Delay, Cost \\ \hline
[52] & ICT-based SWCSCS & To address water-gathering issues & Solid waste monitoring & Network lifetime was higher by 6.8 & Waste gathering issues were not addressed & Solid waste monitoring & Waste treatment \\ \hline
[53] & SWC route optimization model & To improve collection efficiency & Solid waste monitoring & Efficiency was improved by 26.00\% & Cost was not lower & Solid waste monitoring & Waste collection overage urban & Efficiency, Cost \\ \hline
[54] & Comprehensive Framework & To maintain a high-quality service & Water Pipeline & Throughput was & Failed to perform water pipeline & Water Pipeline & Throughput, Time & Growth, Year & Throughput, Time \\ \hline
[55] & Leakage Detection Method & To enhance the precision & Water Pipeline & Accuracy rate was improved by 92\% & Recall was not considered & Water Pipeline & Time, Month, Year & Energy Consumption, Latency \\ \hline
[56] & Heterogeneous [50] & To minimize power consumption & Water Pipeline & Cost was minimal by 39 MB & Water Pipeline & Water Pipeline & Energy Consumption, Machine time was & Network Latency, Delay \\ \hline
[57] & Wirectly threshold & To reduce power consumption & Water Pipeline & Computational cost & Energy consumption & Water Pipeline & Time, Month, Year & Efficiency, Throughput \\ \hline
[58] & Zanaraki & To measure water & Water Pipeline & Energy efficiency was & Energy consumption & Water Pipeline & Time, Month, Year & Efficiency, Cost \\ \hline
[59] & New asset tracking & To maximize smart & Commercial Asset Tracking Application & Time was lower by 26 ms & Tracking was not performed & Tracking Monitoring & Sr State, Sr Date, Sr Date, Sr Date, Sr Date & Energy Consumption, Network Latency \\ \hline Optimization framework & To minimize energy consumption & Commercial Asset Tracking & Application cost & Cost tracking was not improved by 7 MB & Control & Energy Consumption, Smart Indexing & Energy Consumption, Network Latency, Delay \\ \hline
[50] & Optimisation framework & To measure critical levels of the hospital locations & Commercial Asset Tracking Application & Overhead was lower by 33 ms & Cost was increased & Tracking Monitoring & Dust, Sr Date, Sr Date & Energy Consumption, Network Latency, Delay \\ \hline \end{tabular}
\end{table} 
Table 2: Cost. The existing research has focused on solving problems in Wireless Sensor Networks (WSNs) for various applications, including healthcare, disaster management, and transportation. Many conventional methods have been explored for these areas, often using Machine Learning (ML) techniques in urban environments. However, these existing methods have several limitations, such as higher delays, lower accuracy, increased costs, shorter network lifetimes, lower efficiency, and higher energy consumption. To overcome these challenges, this study investigates WSN applications specifically for transport monitoring, traffic optimization, healthcare, disaster response, and security in urban areas.