# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 4 Results
### Analysis and Verification of Results
#### 4.5.1 Analysis of Simulation Results  
Simulation results are analyzed and discussed in this section. The analysis is divided into two parts, i.e., analysis of different routes in multiple scenarios and analysis of the effect of different motion models on network performance. (1) Analysis of the change rules of routings with the different parameters  
Adjusting transmission ranges or robot rate means changing the probability of node encounters. For all protocols, the message delivery ratio will increase as the nodes meet frequently. However, Prophet V2 performs the worst, as it is impossible to accurately describe the relationship among the network changes in RDTSN. As the large number of copies can clog the network, the growth of Epidemic's delivery rate is also limited. Spray and Wait adopts the mechanism of restricting the number of copies, which determines its better performance in delivery ratio. The proposed DTMA considers the link availability that uses the calculation of the signal strength, which enhances the performance of message delivery. The best delivery performance is achieved in the simulation. The increase in the probability of node encounters also has a significant improvement in delivery time. Figure 31: Results of Scenario 3, Network life time with different rate of robots. (**a**) BPRW movement model; (**b**) PRW movement model. Figure 32: Results of Scenario 3, Energy consumption with different rate of robots. (**a**) BPRW movement model; (**b**) PRW movement model. For all protocols, increasing buffer size can affect the update rate of messaging. In particular, an excessively small buffer size can significantly accelerate the message update rate, resulting in the discard of a large number of delayed messages. This process allows messages to be delivered in a real-time manner, as shown in Figure 18, where the values of message delay and delivery rate are both maintained at very low levels. When buffer size is large enough, such as over 500 M in Scenario 2, as shown in Figures 18-20, since all message updates are not limited by buffer size, all policy performances tend to be stable. Under this situation, the performance of each algorithm is more dependent on the change in the probability of node encounters. The change in the number of robots not only changes the probability of node encounters but also changes the total amount of messages. With the increase in the number of robots, the node interaction capability is strengthened, and the congestion of messages is increased. This results in a positive correlation between the delivery rate and the number of robots for all algorithms, and it also forms a stable delay. In addition, this phenomenon of stable delay can also be proved by the comparison of Figures 21-26 and Figures 27-32. In the simulation, Epidemic and DTMA try to deliver more messages through more hops. Epidemic does not discriminate between the delivery of copies, resulting in the worst performance in energy consumption, and the increase in hops, copies and overhead. DTMA has good energy consumption performance in different scenarios. This is because it filters transmission paths by calculating energy consumption. However, due to the consideration of energy consumption, DTMA always tries to choose a short path with lower energy consumption, which also leads to an increase in the number of copies, hop count and overheads. Analysis of the effect of different motion models on network performance  
Three motion models based on RDTSN are proposed. These models have different performances in dynamic scenarios. Firstly, PRW is a basic random motion model. Although PRW model is simple and has low cost of control, the network performance is not satisfying, especially in high-speed scenarios, where the probability of motion interference and uneven distribution is increased because of the random motion of the robot on a single ground line, and the performance of the PRW-based network is not satisfying. Secondly, BPRW model introduces a two-way traffic rule, which reduces the motion interference.