# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 4 Implementation and Performance Evaluation
### Analysis on Experimental Results  
#### 4.3.1 Performance Comparison of Scheduling Algorithms  
In a queue, while packets are waiting to be transmitted on the link, the time cost of waiting for other packets that arrive first to complete their transmission is queuing delay. The queuing delay of a packet will depend on the number of packets that have arrived first or are waiting for transmission to the link. In this experiment, we use three physiological signals as inputs, ECG, EEG, and EMG, and the link bandwidth is 250 Kbps. To assess the proposed algorithms, Figure 19 shows the comparison of the Low Latency Queueing (LLQ) algorithm [21] and the proposed schemes, which describes the average time in the queueing process at the CH for different scheduling algorithms. In a PQ scheme (i.e., LLQ with PQ [21]), if higher-priority packets are inserted frequently, lower-priority packets are probably not allocated with resources, resulting in long queueing delays. For WPQ_RR and WPQ_RRE scheduling schemes, which are related to LLQ with CBWFQ [21], the transmission bandwidth is assigned according to the weight, so that each queue has a fair chance to transmit. However, the lower-priority packets still have to wait before the higher priority queue completes the transmission, so the waiting time is slightly longer. For the proposed WPQ_TE, WPQ_TPE, and WPQ_TP scheduling schemes, in addition to the priority ranking, time stamp is considered to allow the packets to be transmitted even if they are of lower priority but enter the queue first, which results in a shorter waiting time than those with the priority ranking only. As shown in Figure 19, compared to the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Physiological Signals** & **CR** & **PRD (\%)** \\ \hline ECG & 14.76 & 15.25 \\ EEG & 18.97 & 21.9 \\ EMG & 21.65 & 17.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The result of compressed physiological signals. \begin{table}
\begin{tabular}{c c} \hline \hline
**Physiological Signals** & **SNR (dB)** \\ \hline ECG & 9.75 \\ EEG & 14.71 \\ EMG & 17.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The result of denoised physiological signals. LLQ with PQ and LLQ with CBWFQ algorithms [21], the performances of the proposed WFQ_TE, WFQ_TPE, and WFQ_TP scheduling schemes are superior or competitive in term of queueing delay. In order to evaluate the effectiveness of each algorithm in relieving transmission jitter, the average standard deviation (ASD) of the service delay is used as an evaluation index [25]:  
\[A_{SD}=\frac{1}{N_{d}}\sum_{i=1}^{K}\sqrt{\frac{1}{N}\sum_{j=1}^{N}\left(t_{ij} -\overline{t_{i}}\right)^{2}} \tag{6}\]  
where \(N_{d}\) is the total number of packet levels, which is 3, K is the packet level, \(t_{ij}\) is the transmission delay of the \(j\)th data packet of the \(i\)th priority level, and \(\overline{t_{i}}\) is the average transmission delay of the \(i\)th level packet. In purpose of obtaining accurate service delay performance, the monitoring time is set to 1 s \(\sim\)15 s [25]. Table 5 shows the average standard deviation of service delay with varying monitoring times. Comparing to the standard deviations in the service delay of WFQ-based algorithms, the PQ algorithm has the largest ASD. As the monitoring time further increases, the service traffic also increases and the ASD of each algorithm shows an increasing trend, but the ASD of the WFQ algorithm is growing at the slowest rate. Observe that the WFQ_TP algorithm has better results than other algorithms in reducing the transmission jitter. Figure 19: Average delay comparison of physiological signals scheduling. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Monitoring** \\ **Time (s)** \\ \end{tabular}