# Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems
## 3 Methodology and System Model
### Simulation Setup
The MPR model has influenced the QLRR network in a way that led to increasing the ENL by a huge factor. This is observed from the green boxplots in Figure 8, where the original mean ENL of the QLRR algorithm was 5.6 years and then increased to 11 years as the MPR method was applied in the network. The MPR model outperforms the original network topology by more than two times the ENL median. In this context, it can be said that the proposed MPR model has significantly contributed to enhancing the network performance where the power consumption, in this

Figure 7: ANL with and without packet drop. case, is lessened, resulting in extending the nodes lifetime and hence increasing the factor of expected network lifetime. The bottom line is that the proposed MPR method has enhanced the expected network lifetime of three algorithms. The overall improvement of the network lifetime, including the three selected algorithms, can be observed by looking at the MPR based boxplots in Figure 8. It can be clearly noted that the proposed MPR model has produced the highest ENL when it is implemented with the QLRR algorithm. The QLRR method outperforms other methods by having a mean ENL of 11 years when implemented over the proposed MPR method. To wrap up, network life is an essential measure in wireless networks. This study, in turn, proposed an MPR model that makes the state-of-the-art algorithms more effective and better performance. Figure 9 depicts the boxplot of the ENL when packet drops occur at different links of the MPR model. It can be observed that the proposed model is robust where ENL in all of the utilized algorithms was not very affected by the packet drop. When the response is compared with the situation of no packet drop, the average and the mean of the data is nearly the same. The MRPQLRR was the algorithm least affected by the packet drop where the mean ENL remains the same. Despite the small effect due to the packet drop, the ENL of the WSAN over the proposed MPR model still outperforms the original algorithms shown in Figure 8 as discussed in Section 4.2. Figure 8: Expected network lifetime (ENL) performance comparison. ### Average Energy Consumption

MPR has been proven to be efficient for minimizing the power consumption by choosing the location of the routers and reducing the transmission pathways between source and sink nodes. Having many unplanned nodes in the network decreases the efficiency of the network in terms of power consumption rate. The average energy consumption decreases when the packets are transmitted via multipath routes in which sensor nodes on alternative paths are not scheduled to consume energy for complying with the requirement of coverage and connectivity. Therefore, the developed MPR model has achieved better results in terms of energy consumption as compared to the other algorithms as the transmission is performed through the shortest path, which helps the other sensor nodes in the network preserve their energy. Figure 10 shows the energy consumption of the MPR as compared to the algorithm associated with MPR itself. MPR consumes a lower energy amount as compared to the other algorithms used in the state of the art. Due to the low consumption rate of MPR, it significantly prolongs the IWSN lifetime. Figure 10: Energy consumption performance comparison. Figure 9: ENL with and without packet drop. ### Packet Delivery Ratio (PDR)

One more critical factor to consider in this paper is the packet delivery ratio (PDR). The data collected from the simulation of the original protocols and the proposed MPR method were projected into boxplots and discussed accordingly. Several repetitions of the simulation were performed using the Han [25] algorithm with original topology and MPR-based topology to get statistics of PDR. Those statistics are depicted in blue boxplots in Figure 6.0 The proposed MPR method has shown a high rate of reliability when simulated with the Han algorithm.