## 5 Performance Evaluation  
Simulations are conducted in this section to evaluate the performance of the proposed FLC-QM scheme. Consider a simple yet illustrative WSAN as shown in Figure 6, where \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{4}\) are source sensor nodes, \(s_{5}\) is an interfering source node, \(s_{6}\) is an intermediate node, \(a_{I}\) and \(a_{2}\) are actuator nodes. These nodes reside in one collision area, that is, they have to compete for the use of the same wireless channel for data transmission. It is noteworthy that the sampling period of \(s_{5}\) cannot be adjusted at runtime. The utilized communication protocol is ZigBee with a data rate of 250 kbps. All data packets transmitted over the network are 45 bytes in size, which may correspond to a payload of 32 bytes and an overhead of 13 bytes. The default sampling period for each source node is 10 ms, \(DMR_{R}=10\%\), and \(T_{FLC}=1\)s. The deadline of a data packet is assumed to be equal to current sampling period of the relevant source node.  
The simulation runs as follows. At the beginning, all nodes except \(s_{3}\), \(s_{4}\), and \(s_{5}\) are active; \(s_{5}\) is switched on at time t = 20s and off at time t = 40s; \(s_{3}\) and \(s_{4}\) remains off until t = 60s. The simulation ends at time t = 80s. The simulation tool used here is Matlab along with TrueTime [26].  
Figure 7 shows the deadline miss ratios corresponding to the four source nodes. With the classical design scheme, all of the deadline miss ratios are relatively high throughout the simulation. The deadline miss ratios change dramatically as the traffic over the network changes. When the interfering traffic is introduced, i.e. from t = 20s to 40s, both of the deadline miss ratios associated with \(s_{I}\) and \(s_{2}\) increase; particularly, the deadline miss ratio for \(s_{I}\) reaches nearly 100% during this term. When \(s_{3}\) and \(s_{4}\) become active (after t = 60s), almost all messages sent by the four source nodes miss their deadlines. Further, it is found that under the same network condition the transmission from \(s_{I}\) to \(a_{I}\) may encounter severer deadline miss than that from \(s_{2}\) to \(a_{I}\). For instance, the average deadline miss ratios for \(s_{I}\) and \(s_{2}\) in time interval [0, 20]s are 66.5% and 37.8%, respectively. The reason behind is that the former  
Figure 6: Simulated WSAN system.  
experiences more hops than the latter. The average deadline miss ratio throughout the simulation is 81.1%, 58.4%, 100%, and 98.5%, respectively, for each source node.  
Figure 8: Sampling periods with FLC-QM.  
Figure 7: Deadline miss ratios.  
When the proposed FLC-QM scheme is employed, the deadline miss ratios for all data transmissions are maintained around the desired level 10% and are much lower than those resulting from the classical scheme almost all the time (except for during the limited transient processes). The average deadline miss ratios for the four source sensors are 14.2%, 10.5%, 24.2%, and 14.2%, respectively, which are significantly lower than those associated with the classical scheme. In this case, the sampling periods of the four source sensors are adjusted dynamically at runtime, as shown in Figure 8. This is in contrast to the fixed sampling periods that used in the classical scheme and also explains why the examined two schemes perform differently in managing deadline misses.  
To summarize the above simulation results, the FLC-QM scheme is effective in supporting QoS in WSANs in dynamic and unpredictable environments. It can significantly enhance the flexibility and adaptability of the systems through maintaining the desired level of QoS in terms of deadline miss ratio, and consequently delay and packet loss rate, while maximizing the network utilization as much as possible when traffic load change unpredictably.