## 6 Simulation and Results  
In our simulation, the WSN covers an area of interest, 600 m by 600 m. The network consists of 25 sensor nodes and the lifetime of the simulation is 100 intervals. They monitor the movement of an asset such as a Panda. The network has only one sink on one side of the network as shown in Figure 2. If a node detects a Panda, it starts communicating with its neighbors to inform the sink about the current location of the Panda. All sensor nodes have a transmission range of 200 m. The network is injected by different transmitting rates of (0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4) That means, if the random number between 0 and 1 is less than the selected transmitting rate, and a real event is received, a real packet will be sent otherwise, a dummy packet will be sent instead. If the random number exceeds the transmission rate, no packet will be sent. For example, if transmission rate is 0.05, then \(\mathbf{rate_{realtraffic}}\) and \(\mathbf{rate_{faketraffic}}\) will be 0.08 and 0.02 respectively for DAD and CAD techniques using Equations (6) and (7). Finally, the maximum number of intervals to wait before mandatory transmission in CAD is selected to be 5 (i.e., the maximum wait before the node is forced to transmit the real packet). Panda can be detected by only one node in the network. One thousand random cases are created for each transmitting rate to evaluate the performance of the WSN. Each case has a random position for the Panda and random starting interval of the event between 0 and 49. A comparison between different rates and how they affect average delay, overhead, and delivery ratio of real events is conducted as shown in Figure 3. All the proposed techniques were developed using C#. As shown in Figure 3a CAD performs much better in terms of average delay than DAD especially in low rates, which is desired by most of the applications. In case of average delay, DUD is the worst as expected, because it does not have any mechanism to improve the delay of real packets as the other two techniques. Also in terms of delivery ratio, as shown in Figure 3b, CAD is the best and it has an advantage over the other two techniques because it forces the packet of a real event to be sent after a specific waiting time, which is 5 intervals in this experiment. Moreover, DAD is still performing better than DUD since real packets have higher probability to be sent than the dummy ones. Meanwhile, the overhead traffic in Figure 3c is almost the same for all three techniques, because the total number of  
Figure 3: (**a**) Average delay; (**b**) delivery ratio; (**c**) overhead. Figure 2: Network topology: blue nodes are regular sensors and the red node is the sink. packets stays the same for all techniques. Thus, it is concluded that CAD and DAD reduce the average delay and increase the delivery ratio without gaining additional overhead. Another comparison is conducted between our techniques and the technique used in [15] in terms of overhead. As shown in Figure 4, our techniques reduce the overhead rapidly compared to the FACP, technique especially in low rates. This is because DUD, DAD, and CAD are probability-based techniques. They create fake traffic based on probability instead of sending dummy packets every single interval like FACP which causes unnecessary overhead. Most of the anonymity applications in WSNs will require low fake rates to reduce overhead to maximize the lifetime of the network. Results show that CAD technique is able to control the delay and improve the delivery ratio since it uses a forcing mechanism to transmit the real event's packet after a specific number of intervals. The forcing mechanism is only applied if the transmitting node fails to transmit the real packet using a probability. Moreover, the proposed techniques perform much better than FACP technique in case of overhead. This reduces the power consumption and increases the lifetime of the network. The location of the sink in the experiment is on one side of the network.