## 3 Result and Discussion
### Latency Characterization  
A haptic-driven TeleCaring system is developed to investigate the end-to-end (E2E) communication delay including the master and the slave with and without prediction model processing delay during haptic communication between the master and the slave operators in the remote elder caring application. Figures 7 and 8 illustrate the analysis of the teleoperation system in terms of the E2E communication delay. The latency investigation between the robot (slave domain) and human operator (master domain) without the activity prediction model with the number of data packets (haptic, activity sensors) = 10, 100, and 1000 for the elder caring system is shown in Figure 7a-c, respectively. Figure 5: The acquired training and validation loss along with respective accuracies to evaluate the learning convergence of the model for indoor localization. (**a**) illustrates training/validation loss over 100 epochs, and (**b**) illustrates training/validation accuracy over 100 epochs. Figure 6: Training and validation losses along with respective accuracies of the model for activity recognition. (**a**) illustrates training/validation loss over 100 epochs, and (**b**) illustrates training/validation accuracy over 100 epochs. These results show that there is a trend for E2E latency to reduce from 8.18 to 1.55 ms as the number of data packets increases. When there is only one data packet, as depicted in Figure 7a, the packet delay tends to be close to 8 ms, whereas Figure 7b,c depict a packet delay of less than 7 ms. To investigate this communication delay analysis in depth, Figure 8 explains the findings as a histogram of the packet delay. The latencies shown in Figure 8 show the most frequent occurrence of the data packets, contrasting with Figure 7.  
Similar to Figure 7a-c, the experimental results in Figure 8a-c demonstrate a decrease in E2E packet latencies, with most of the latencies falling between 1 and 4 ms. Figure 8a indicates the frequency of the number of the packet under 2.5 ms is around 40%. Figure 8b,c demonstrate that the latencies of the data (haptic, activity sensors) traffic decrease, which indicates the occurrence of the number of the data packets with latency under 2.5 ms is around 60%. It can be clearly seen from Figure 8a-c that the maximum data packet falls between 1 and 3 ms, which is one of the strict requirements for Internet of Senses (IoS) services. This demonstrates how the proposed framework would be able to offer elder remote caring service while adhering to QoS/QoE requirements (1-50 ms). However, the challenge is when we implement an activity and localization prediction model at the edge server on the master domain side to guide the human operator in the case of an anomaly and wrong activity on the remote side. So, the human operator controls the remote robot to perform the desired task accurately, with a focus on ensuring high QoT. In this regard, Figure 9 illustrates the analysis of the remote elder caring system with a prediction model in terms of the E2E round-trip delay. Figure 7: Data packet latency analysis for real-time remote elder caring human–robot communication without edge-enabled activity and localization prediction model; (**a**) data packets = 10, (**b**) data packets = 100, (**c**) data packets = 1000. Figure 9a-c depicts the latency investigation between the robot (slave domain) and human operator (master domain) with the integration of the activity and localization  
Figure 8: Data packet latency analysis in terms of a histogram for real-time remote elder caring human–robot communication without edge-enabled activity and localization prediction model; (**a**) data packets = 10, (**b**) data packets = 100, (**c**) data packets = 1000.