# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 5 Simulation Results
### Simulation Results  
The results are averaged over 100 independently runs by NS2 (Network Simulator version 2, NS2). To evaluate the performance of EEBD, the selected benchmark algorithms are DBD [16] and LFEBD [18]. We also suppose that nodes are uniformly deployed in the monitoring region and the locations of all one hop neighbour sensors are available to a sensor node. Since this is one of the most prevalent and simple mobility models, sensor node motion is simulated using the random waypoint motion model [21]. Without loss of generality, we report the results with boundary shapes of a circle or a straight line. Figure 9a shows a visualized result of EEBD with network node density \(\rho\)= 20, Figure 9b presents a visualized result of EEBD with network node density \(\rho\)= 10. The small dots represent nodes that do not belong to the boundary sensor nodes while the bold ones represent the detected boundary sensor nodes. This shows that the detected event boundary by EEBD gives a good approximation of the event boundary as introduced in definition 3. Figure 9: _Cont._Figures 10 and 11 show comparative studies of the DF and FR, as mentioned above. From Figure 10, one can observe that the proposed algorithm has a better degree of fitting than the DBD and LFEBD algorithms when the node density is under 40. This demonstrates that the node density is proportional to the DR in both boundary shapes. When the density is increased, the number of sensors in \(N(S_{i})\) will increase accordingly. Thus, more sensors are close to the boundary, that is, more sensors are in the \(B(S_{i},r)\). So the geometric probability based on the area is more accurate. From the Figure 10, with the increase in node density, especially when it is greater than 30, both the degree of fitting increase slightly but our algorithm converges earlier with a density of about 20. Figure 11 shows a comparative study of FR. The EEBD algorithm false detection rate gradually decreased with the increase of node density but the FR of the proposed algorithm is slightly higher than the LFEBD and DBD algorithm, especially in the case of low node density but still in a very small  
Figure 10: Degree of fitting vs. network density. Figure 9: Visualized simulation result. (**a**) \(\rho\)= 20 (**b**) \(\rho\)= 10.  
range. This is owing to the small number of neighbour sensors in the case of low density, which causes the entropy of the node which is close to critical nodes to fluctuate beyond the entropy threshold but this error decreases with the increase of the node density. Thus, even if the node being misjudged is still near the boundary, this has little effect on fitting the event boundary. For practical applications, there is a certain error within the acceptable range. Figure 12 shows that setting the different boundary width has little effect on the accuracy of the detection under the constraint conditions. This permits us to try to choose few boundary nodes, both to fit the event boundary but also save data fusion and communication costs. WSNs can be deployed on a large scale in target monitoring sea areas and are often utilized to monitor ocean information such as light, ocean currents, wind direction, hydrology and water pollution. A second series of simulations has been made in an ocean monitoring experiment using WSNs. In the marine environment, the position of the node and its relative position to the other nodes are time-varying. We randomly select 50-80% of nodes as mobile nodes and the remaining nodes are anchor nodes. The simulation set the mobile node speed from 6 to 10 m/s to demonstrate more different performances of EEBD. We use the random waypoint model to characterize the movement of the sensor nodes. A marine sensor node renews its own one-hop neighbouring node data at time intervals 5-20 s. The relationship between the detection rates and node speed are displayed in Figure 13. As the density  
Figure 11: False detection rate vs. network density. Figure 12: Degree of fitting vs. boundary width.