# Energy-Efficient Wireless Hopping Sensor Relocation Based on Prediction of Terrain Conditions
## 4 Simulation Results and Analysis
However, it is very difficult to implement in reality; therefore, we propose a distributed-based relocation protocol, in order to overcome the drawbacks of the previous central-based relocation protocols. The implementation of the proposed protocol and simulation for performance evaluation are based on OMNeT++, which takes all communication system layers into account and also reflects both distributed computing of all sensors and actual environments. As far as we know, our implementation of hopping movement model considering obstacles is the first attempt in this field of study. As previously explained, a sensing event occurs only in the middle cluster zone. After a three-day-long simulation, the result is shown in Figure 8. The dead member sensor node is colored yellow and the path of each hopping sensor is plotted by a solid line. Also, the movement of the sensor is shown as a zigzag due to environmental factors such as wind. It can also be confirmed that sensors captured in obstacles can no longer move and become faulted. We have simulated the relocation protocols using a sensor network topology generated randomly, and the results are described in Figures 9 and 10. First, Figure 9 shows the sensor retention rate in the middle cluster. Since the minimum number of members for each cluster to properly gather data is 10, the sensor retention rate is 1 if there are more than 10 member sensor nodes. For example, if there are five sensor nodes, the sensor retention rate is 0.5 (= 5/10), and if there is only one sensor, the sensor retention rate is 0.1. Since the previous hopping sensor relocation protocol [19] ignores the existence of obstacles, the sensor retention rate of it tends to be much lower than that of the proposed protocol, as shown in Figure 9. Figure 9 shows the change of each sensor retention rate during about three days. Since the sensing hole of the middle cluster occurs continuously, the neighbors' cluster headers might provide redundant sensors to recover the middle cluster's sensing hole until around 1000 min. However, neighbors' nodes are also depleted at that moment, thus the sensor retention rates in both schemes start to drop as shown in the green circle of Figure 9a. The blue circle of Figure 9a indicates that the proposed method considering obstacles shows better retention rate than previous scheme. Figure 9b illustrates the difference between the sensor retention rates of the proposed and the previous protocols. It is easy to see that the number of top marks is more than that of below marks in Figure 9b. Since the sensor retention rates are only considered in the middle cluster zone, the denominator is ten, which is the minimum number of sensors to recover a sensing hole. In other words, if the difference of the rates is the positive 0.5, the proposed method accommodates five more sensors than the previous method at the same instance. A higher retention rate would result in requesting a smaller number of sensors compared with another method and generating fewer messages for movements to neighboring cluster zones. Hence, the proposed scheme would consume smaller energy to generate messages compared to the previous scheme and can play a major role in extending the lifetime of the network. Figure 8: Simulation snapshots finished for each obstacle.