## 3 Result and Discussion
### Latency Characterization
Figure 9: Data packet latency analysis for real-time remote elder caring human–robot communication with edge-enabled activity prediction and localization model; (**a**) data packets = 10, (**b**) data packets = 100, (**c**) data packets = 1000.  
prediction model in the ECI engine with the number of data packets ranging from 10 to 1000, respectively. These results also show that the network communication latency tends to decrease as the number of packets increases from 10 to 1000. The latency value in Figure 9a, where the number of packets is 10, lies between 41.97 and 9 ms. The packet latency approaches 42 ms when the number of data packets is 1, in contrast to Figure 9b,c, where packet delay is less than 35 ms. Figure 10a-c depict the histogram of the packet latencies to demonstrate the in-depth analysis. Figure 10a-c indicate that most of the latency of the packet lies between 9 and 14 ms, and the frequency of the number of packets under 11 ms is more than 60%. Figure 10a shows that the occurrence of the number of the data packets with latency under 12 ms is 95%. Similarly, Figure 10b,c show that the total number of the packets with latency under 12 ms is 100%. In Figure 9a-c, communication latency between master and slave with packets =10,100, and 1000 are 41.9-9, 35-11, and 31-10, respectively, compared to Figure 7a-c, where the value of latency lies between 8 and 2 ms. This investigation shows how the processing time of the prediction model causes E2E delays in data transmission. To investigate the effect of the activity and localization prediction model processing for overall service latency, in-depth latency analysis was performed to capture prediction model processing and is depicted in Figure 11a-c. Similar to Figure 9a-c, the result in Figure 11a-c illustrates the edge-enabled prediction model processing time over the communication network, which is responsible for the extra delay in overall application delay. The result in Figure 11a-c also illustrates that the model processing time reduces for an increase in the number of data packets. It is clear from the histogram results in Figure 12a-c that the latency is more concentrated between 7 and 10 ms. It is noteworthy that as the number of data packets increases, along with a higher sampling rate, the proposed model exhibits lower processing time while meeting the application latency requirement. Moreover, we created complex network scenarios with varying parametric settings to evaluate the system's performance. In these scenarios, different numbers of external hosts, ranging  
Figure 10: Data packet latency analysis in terms of a histogram for real-time remote elder caring human–robot communication with edge-enabled activity and localization prediction model; (**a**) data packets = 10, (**b**) data packets = 100, (**c**) data packets = 1000.  
from 0 to 15, were connected near both the master and slave sides. During telecaring operations, these hosts also communicated with each other, simulating real-time dense network conditions. Detailed results from the experiments involving these dense networks are presented in the Supplementary File of this paper, and latency analysis is presented in Table 2, while a summary of the system's reliability and stability is provided in Table 3.  
Figure 11: Data packet latency analysis for edge-enabled activity and localization prediction model processing time; (**a**) data packets = 10, (**b**) data packets = 100, (**c**) data packets = 1000. Figure 12: Data packet latency analysis in terms of a histogram for edge-enabled activity and localization prediction model processing time; (**a**) data packets = 10, (**b**) data packets = 100, (**c**) data packets = 1000. Table 3 shows a comprehensive reliability analysis for the transmission of haptic, activity, and localization sensor data transmission between the remote robot and the master domain. The reliability of the data traffic is estimated based on its delayed/dropped and out-of-order packets.