## 5 Performance Analysis  
Performance analysis in this work focused on WSN in urban areas is vital for guiding the design and optimization of various applications, such as transportation, disaster, power quality monitoring, and healthcare. By evaluating key metrics like Packet Delivery Ratio \(PDR\)[62], Efficiency \(E\)[63], Network Lifetime \(NL\)[63], Energy Consumption \(EC\)[62], Error Rate \(ER\)[64], Execution Time \(ET\)[65], Cost \(Cost\)[66], Accuracy \(Accuracy\)[64], and Throughput \(T\)[62], researchers can inform decision-making, identify trade-offs, optimize resource utilization, benchmark solutions, and establish design guidelines. These metrics relate differently to various architectures--centralized, decentralized, and distributed--as well as to topologies like star and mesh. For example, centralized architectures may offer lower average delays but at the cost of higher energy consumption, while decentralized architectures can enhance network lifetime and reliability. Similarly, star topologies provide high throughput but can strain central nodes, whereas mesh topologies improve redundancy and reduce error rates at the expense of increased execution time. Overall, performance analysis is essential for understanding the operational characteristics of WSNs, ensuring that they meet the specific needs of urban applications. The different performance metrics are measured as given below. * Impact on Packet Delivery Ratio:The Packet Delivery Ratio is the number of data packets properly received from the number of data packets sent. The formula for calculating the data security level is given below. \[PDR=\left(\frac{\text{Number of data packets correctly received}}{\text{Total number of data packets}}\right)\times 100\] (1) From (1), \(PDR\) represents the Packet Delivery Ratio. It is measured in terms of milliseconds (%). * Impact on Efficiency: Efficiency is defined as the quality of being able to perform data transmission tasks successfully without wasting time or energy. Efficiency is described \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**ReNo.** & **Method Name** & **Objective** & **Sensor Type** & **Network Type** & **Network Topology** & **EC** & **NL** & **E** & **Delay** & **RO** & **T** & **Cost** & **CO** & **Limitations** \\ \hline \multirow{3}{*}{[59]} & New asset tracking & Track assets within & Hybrid RFID & & & & & & & & & Tracking was not carried out in \\  & architecture & construction sites & thermal sensors & & & & & & & & & & accurate manner \\ \hline \multirow{3}{*}{[61]} & Optimization & Efficient medical assets & & & & & & & & & & & The cost of asset \\  & framework & & tracking in hospital & & & & & & & & & & & tracking was not \\  & environments & & & & & & & & & & & & & & reformed in hospital \\ \hline \multirow{3}{*}{[60]} & Optimization & Reduce energy consumption & & & & & & & & & & & & The complexity \\  & decomposition & of sensor nodes with high & & & & & & & & & & & & level during \\  & framework & estimation quality & & & & & & & & & & & quality estimation \\ \hline \end{tabular}
\end{table} 
Table 11: Summary of performance analysis of commercial asset tracking. as the ratio of the number of data packets received to the number of data packets sent. It is measured in terms of percentage. It is formulated as \[E=\left(\frac{\text{Number of data packets received}}{\text{Number of data packets sent}}\right)\times 100\] (2) From (2), the Efficiency \(E\) level of every method is computed. When the efficiency is higher, the method is said to be more efficient. * Impact on Energy Consumption: It is quantified as the amount of energy utilized by sensor nodes during operations such as sensing and data forwarding. This measurement is expressed in joules. \[EC=\sum_{i=1}^{n}Sn_{i}\times EC(\text{SSn})\] (3) From (3), \(EC\) indicates the Energy Consumption, and \(Sn_{i}\) refers to each individual sensor node \(i\) in the network. \(n\) indicates the number of sensor nodes \(Sn_{i}\).