## 3 Result and Discussion
### Latency Characterization
It can be seen from Table 1 that the percentage of delayed/lost packets drops from 33% to 18.50% (33 to 185 data packets), and the percentage of out-of-order sending packets drops from 16.16% to 8.50% with data packets 100 to 1000 when the number of external hosts = 0. The reason behind more delayed/ dropped data packets with packets = 100 than packets = 10 is that initially, ML models learn the hidden pattern from the data. Therefore, after learning the pattern, it takes less processing time with packets = 1000. Similarly, when considering data packets without ML models, the percentage of delayed or dropped packets remains at 0% and there are no out-of-order sending packets across the range of 10 to 1000 data packets. Similarly, the percentage of delayed/lost packets increases with the increase in the number of external hosts = 5, 10, and 15. This is due to the congestion in the communication network during external host communication. In this regard, in the future, we will use ML methods to estimate the network congestion level and select optimal network coding techniques to decrease the percentage of delayed/lost packets. On there other hand, the percentage of out-of-order data packets is high at the number of packets = 10, while it decreases with the higher number of packets. This indicates the system performance is also good with external hosts, having fewer out-of-order packets. A detailed analysis of the results in terms of data packet latency investigation and a histogram analysis are presented in the Supplementary File of this paper. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Latency**} & \multicolumn{3}{c}{**Number of Packets**} & \multirow{2}{*}{**Average**} \\ \cline{3-3} \multicolumn{1}{c}{**External Hosts**} & & **10** & **100** & **1000** & \\ \hline \hline \multicolumn{5}{c}{**Without Prediction Model**} \\ \hline \multirow{3}{*}{**Host-0**} & Max & 8.082 & 8.188 & 7.042 & \multirow{3}{*}{2.30} \\  & Min & 1.880 & 1.633 & 1.553 & \\ \multirow{3}{*}{**Host-5**} & Max & 6.975 & 8.613 & 6.478 & \multirow{3}{*}{1.319} \\  & Min & 1.197 & 1.118 & 1.132 & \\ \multirow{3}{*}{**Host-10**} & Max & 6.691 & 7.503 & 7.927 & \multirow{3}{*}{1.431} \\  & Min & 1.147 & 1.242 & 1.124 & \\ \multirow{3}{*}{**Host-15**} & Max & 9.840 & 14.575 & 8.201 & \\  & Min & 1.240 & 1.237 & 0.181 & \\ \hline \hline \multicolumn{5}{c}{**With Prediction Model**} \\ \hline \multirow{3}{*}{**Host-0**} & Max & 41.973 & 34.636 & 30.421 & \multirow{3}{*}{10.51} \\  & Min & 8.877 & 4.841 & 4.587 & \\ \multirow{3}{*}{**Host-5**} & Max & 49.353 & 42.123 & 127.847 & \multirow{3}{*}{14.294} \\  & Min & 4.550 & 5.019 & 3.970 & \\ \multirow{3}{*}{**Host-10**} & Max & 37.162 & 40.913 & 124.115 & \\ \multirow{3}{*}{**Host-15**} & Min & 7.764 & 4.904 & 3.726 & \multirow{3}{*}{14.295} \\  & Max & 34.838 & 44.826 & 127.106 & \\ \multirow{3}{*}{**Host-15**} & Min & 12.823 & 6.840 & 3.888 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the results with complex network scenarios under different parametric settings. In this study, we developed an ECI-TeleCaring system to tackle critical issues such as reducing latency, providing personalized care, and ensuring system stability. Compared to existing telecare systems, our approach offers several key improvements. For example, the work in [29] focused on data privacy and scalability using blockchain and MEC, while our system emphasizes real-time interaction and adapts to the specific needs of each patient. Similarly, work in [30] designed a system that reduces latency and manages bandwidth efficiently but mainly supports video consultations and preliminary diagnoses. Our proposed system integrates AI at the edge to provide dynamic, personalized care that enhances both the quality and experience of elder care services. Other existing systems also have limitations that our ECI-based system overcomes.