# Detection of Intelligent Intruders in Wireless Sensor Networks
## 4 Demonstration, Simulation and Discussion
### Impact of Sensing Range \(r_{s}\)  
In this section, we examine the impact of sensing range \(r_{s}\) on the WSN win probability (\(P_{wsn}\)) and the intruder escape probability (\(P_{intru}\)). Again, we fix the network parameters as \(N=25\), \(r_{c}=100\) m, \(L=500\) m, \(A=500^{2}\) square meters and vary the the sensing range \(r_{s}\) from 25 to 75 m. Figures 9 and 10 illustrate the WSN win probability (\(P_{wsn}\)) and the escape probability (\(P_{intru}\)) of an intruder that adopts the linear, pinball and flood-fill algorithms in random WSNs with varying sensing range \(r_{s}\). All results plotted here are the average of 500 simulation runs under the same network settings.  
Figure 8: The variances of the intruder’s escape probability (\(P_{intru}\)) of an intruder that adopts the linear, pinball and flood-fill algorithms in a WSN with varying node densities \(\lambda\).  
It is observed in Figures 9 and 10 that, as the sensing range \(r_{s}\) increases, the WSN win probability (\(P_{n\equiv n}\)) increases, and the intruder's escape probability (\(P_{intru}\)) decreases for all three considered intruders. This is because the network sensing coverage increases as the increase of the sensing range \(r_{s}\), which makes it more difficult for the intruders to find a safe path to escape the WSN and reach their destination. Second, the WSN win probability (\(P_{n\equiv n}\)) in detecting and preventing the pinball intruder stays the highest, as indicated in Figure 9, and the pinball intruder performs the worst in escaping the WSN, as shown in Figure 10. This is because the pinball algorithm only avoids nearby sensors for detection avoidance without trying to make progress toward the destination. In addition, there exists a critical sensing range \(r_{s}=60\) m, below which the WSN is shown to have the worst probability in preventing and detecting the flood-fill intruder. This is due to the best artificial intelligence strategies applied by the flood-fill intruder, including sensor detection, detection avoidance, map building, updating and backtracking, as described in Section 3.3. On the other hand, when the sensing range \(r_{s}\) exceeds 60 m, the escape probability of the flood-fill intruder drops below the linear intruder and converges to the pinball intruder. This is caused by the high sensing coverage of the WSN when the sensing range exceeds 60 m, where the flood-fill intruder has to spend most of the time in avoiding  
Figure 10: The escape probability (\(P_{intru}\)) of an intruder that adopts the linear, pinball and flood-fill algorithms in a WSN with varying sensing range \(r_{s}\).  
Figure 9: The WSN win probability (\(P_{n\equiv n}\)) of an intruder that adopts the linear, pinball and flood-fill algorithms in a WSN with varying sensing range \(r_{s}\).  
nearby sensors instead of making progress toward the destination and, thus, gradually degrades to a pinball intruder.  
The results in this section indicate that intelligent intruders may not always outperform the linear intruders when the network is well covered with a high sensing range. More specifically, there exists a critical sensing range, below which intelligent intruders have a higher probability of successfully traversing the WSN and reaching its destination and above which linear intruders outperform the intelligent intruders in terms of having a higher probability of reaching its destination.