# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 6 Performance Analysis and Comparison
### Performance Metrics and Results  
The performance of the proposed technique was validated with the help of key factors such as packet delivery ratio (PDR), average end to end delay, and average energy consumption. The experiments were conducted for two sets, based on the selected simulation time and the packet interval time. The proposed DTS mechanism was compared with the basic IEEE 802.15.4 standard and the TMP protocol [24]. In the first set of experiment, the simulation time was varied as 50, 75, 100, 125, 150, 175, and 200 s. Figures 7-9 show the comparative results of PDR, average end to end delay, and average energy consumption of IEEE 802.15.4, TMP and the proposed DTS protocols. Figure 7 depicts the PDR from a source node to the root node. It was measured as the percentage of the total number of successfully received packets at the root node to the number of packets transmitted from the source node. The figure shows that PDR was highest for DTS method. This is due to the link reliability function used in the best next hop selection algorithm. It ensured the best path between the source node and the root node by reducing the packet loss. On the other hand, the TMP protocol mainly concentrated on the time slot allocation to minimize the slot wastage. In IEEE 802.15.4 standard the data transmission was based on the CAP and CFP transmission. In comparison, DTS outperformed the TMP and IEEE 802.15.4 standard by 12% and 15% respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Number of WBANs & 15 \\ Number of sensors per WBAN & 5 \\ Frequency & 2.4 GHz \\ Data rate & 20–250 kbps \\ Simulation time & 200 s \\ Data packet size & 50–150 bytes \\ Control packet size & 15 bytes \\ Superframe length & 100 ms \\ Initial energy of sensor nodes & 100 J \\ Energy consumption: Transmission & 16.7 nJ \\ Energy consumption: Reception & 36.1 nJ \\ Energy consumption: Amplifier & 1.97 nJ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 7: Packet delivery ratio under different simulation times. In Figure 8, the average end-to-end delay is shown with varied simulation time. It is the average time taken by the packet to reach the root node from the source node. DTS scheme dynamically allocated the time slot based on the available energy, buffer memory, and packet arrival rate. It allocated the slot dynamically. Hence the slot wastage gets reduced where unnecessary waiting time is minimized for those nodes in the queue. In TMP, computational methods were utilized for MAC parameter tuning and duty cycle adjustment which contributes lesser than the DTS method. There was a 47% and 59% reduction in average end-to-end delay when compared to TMP and IEEE 802.15.4 MAC Standard. The average energy consumption is depicted in Figure 9, where the DTS scheme has the lowest energy consumption. The MCPS algorithm proposed for next hop selection is based on the available energy in each node. The dynamic time slot allocation based on fuzzy rules used the energy ratio to utilize the available energy resources effectively. In TMP, the energy ratio consideration is less when compared to DTS. The percentage of reduction in average energy consumption of DTS is 22% and 31% respectively. The second set of experiments was based on different packet interval time such as 2-7 s. Figures 10-12 show the comparative results of PDR, average end to end delay, and average energy consumption of IEEE 802.15.4, TMP and the proposed DTS protocols. Figure 10 shows the PDR for different packet interval. As packet interval decreases the traffic load increases hence there is a decrease in PDR. This is due to the less number of packets generated during low traffic conditions. As traffic load increased, more packets were injected into the networks and there was an increase in the PDR. As packet interval increased there was a decrease in the PDR. This is due to the congestion and  
Figure 8: Average End-to-End Delay under different Simulation Time.