## 4 Conclusions  
This study proposed an Edge Computational Intelligence-based remote elderly care system called ECI-TeleCaring to improve the quality and efficiency of elderly care services. The proposed ECI-TeleCaring system utilizes SDN and MEC to tackle the challenges of high latency, inconsistent service quality, and inadequate personalized service in traditional caring services. Experimental evaluation demonstrates significant improvements in stability and responsiveness. The end-to-end communication delay decreases from 8.18 ms to 1.55 ms with the increase in number of data packets. Moreover, after deploying the activity and localization prediction model at the edge, the system maintains QoS and QoE requirements, resulting in 60\(\sim\)95% data packets centered around 11\(\sim\)12 ms for data packets ranging from 10 to 1000, respectively. We observed that the packet latency for most data packets is between 1 and 4 ms without the prediction model and between 9 and 14 ms with the prediction model. Experimental results also indicate a reliability improvement from 33% to 18.50% in delayed or dropped packets with the increase in number of data \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multicolumn{5}{c}{**Packet Performance Metrics**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Dropped/Delayed (\%)**} & \multicolumn{2}{c}{**Out of Order (\%)**} & \multicolumn{2}{c}{**Dropped/Delayed (\%)**} & \multicolumn{2}{c}{**Out of Order (\%)**} \\ \hline \multicolumn{6}{c}{**Data Packet Transmission without ML Model**} & \multicolumn{2}{c}{**Data Packet Transmission with ML Model Overhead**} \\ \hline \multirow{6}{*}{**CIFIF**} & 10 & 0.00 (0 packets) & 0.00 & 10.00 (1 packets) & 0.00 & \multirow{6}{*}{**Out of Order (\%)**} \\  & 100 & 0.00 (0 packets) & 0.00 & 33.00 (33 packets) & 16.16 & \\  & 1000 & 0.00 (0 packets) & 0.00 & 18.50 (185 packets) & 8.50 & \\ \cline{2-6}  & 10 & 0.00 (0 packets) & 0.00 & 60.00 (6 packets) & 33.33 & \multirow{6}{*}{**Out of Order (\%)**} \\  & 100 & 0.00 (0 packets) & 0.00 & 25.00 (25 packets) & 6.06 & \\ \cline{2-6}  & 1000 & 0.00 (0 packets) & 0.00 & 21.60 (216 packets) & 7.90 & \\ \cline{2-6}  & 10 & 0.00 (0 packets) & 0.00 & 30.00 (3 packets) & 11.11 & \\ \cline{2-6}  & 100 & 0.00 (0 packets) & 0.00 & 39.00 (39 packets) & 18.18 & \\ \cline{2-6}  & 1000 & 0.00 (0 packets) & 0.00 & 17.00 (170 packets) & 5.70 & \\ \cline{2-6}  & 10 & 0.00 (0 packets) & 0.00 & 60.00 (6 packets) & 33.33 & \multirow{6}{*}{**Out of Order (\%)**} \\  & 100 & 1.00 (1 packets) & 0.00 & 22.00 (22 packets) & 7.07 & \\ \cline{2-6}  & 1000 & 0.00 (0 packets) & 0.00 & 18.20 (182 packets) & 6.00 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the reliability characterization for haptic, activity, and localization sensors data transmission for real-time remote elder caring service with latency cutoff between 11\(\sim\)15 ms.  
packets from 100 to 1000. In the future, one of our main goals is to optimize the system architecture further. We plan to undertake this by exploring other AI algorithms, such as reinforcement learning, for the SDN controller. This will help the system adapt better by estimating network congestion in real time and taking steps to reduce transmission delays, jitter, and packet loss, which are common issues in communication networks. We also want to improve the accuracy of our predictive models by using recent machine learning techniques and working with larger, more diverse datasets. Another key area of future work is to carry out extensive field experiments in different care settings. These experiments are important for testing how well the system performs and how reliable it is in real-world conditions, especially in elder care. Additionally, we plan to expand the proposed ECI-TeleCaring system's functionality by adding more telecare services, such as better health monitoring features and improved user interfaces.