## 3 The Methodology
### Simulation Study
#### 4.1.5 Impact of Network Lifetime  
The effectiveness of the suggested method may be measured, in part, by looking at how long a network lasts. If the network's energy usage is low, this measure is high. \[NL=\frac{E_{0}-E[UU]}{P+\delta E[Rep]} \tag{24}\]  
where \(E_{0}\) is the starting power used by all sensing nodes; E[UU] is the power that is anticipated to be wasted; and E[Rep] is the power that is expected to be used for reporting, and it is the typical reporting frequency of sensors. The longevity of a network may be quantified in terms of either time or rounds. The comparison of network lifetime is shown in Figure 11. Based on these findings, it seems that the suggested interoperable AI-IoMT strategy extends the lifespan of networks, as their sizes grow. In an area of 50 nodes, the network lifespan with the proposed work is 6000 s, and it is much longer than the previous efforts. As a result, the suggested approach avoids the premature death of nodes and uses less power. While the computational burden does impact the network lifespan,  
Figure 10: Number of nodes vs. packet delivery ratio. Figure 9: Packets per second vs. delay. previous techniques such as EIR-CIoT and BDCS-IoMT demonstrate large variations in network lifetime. The false-positive rate of the proposed method refers to the rate at which the incorrect data are mistakenly identified and removed from the network, and it serves as a measure of its effectiveness in ensuring data accuracy. A comparison of the effectiveness of the planned and the existing works is shown in Table 4. Throughput, energy consumption, latency, packet delivery ratio, and network longevity are only a few of the metrics that are dominated by the suggested interoperable AI-IoMT solution. The proposed AI-powered method is used for interoperable and secure data collection and routing in the multivariate Industrial IoT. In this process, the obtained throughput is 35% greater than that of EIR-CIoT and 23% greater than that of the BDCS-IoMT approach. The proposed lightweight AI algorithms are suitable for environment-based data collection and transmission. The presented time-dependent consensus (TDC) model can replace PoW and PoS in blockchain technology. Table 4 provides a comparison between the suggested method and the existing approaches in terms of performance metrics for fault data prediction and incorrect information transfer scenarios in the Internet of Medical Things (IoMT) context. The suggested method (BDCS-IoMT) offers improved throughput, lower energy consumption, reduced delay, higher packet delivery ratio, and extended network lifetime compared with the existing method (EIR-CIoT). Ensuring the privacy of patient data in an Internet of Medical Things (IoMT) environment is crucial, as flaws in traditional AI systems can expose sensitive information. The comparison of nodes versus security is shown in Figure 12.This research tackles the challenges of energy consumption, latency, throughput, packet delivery ratio, and network longevity in the IoMT setting, and it aims to improve the overall system efficiency and performance. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Performance** & **EIR-CIoT** & **BDCS-IoMT** & **Scenario-1** & **Scenario-2** \\ \hline Throughput (Kbps) & \(307\pm 5.0\) & \(346.4\pm 3.0\) & \(464\pm 1.0\) & \(530\pm 1.0\) \\ \hline Energy consumption (J) & Number of nodes & \(0.384\pm 0.05\) & \(0.356\pm 0.03\) & \(0.322\pm 0.01\) & \(0.162\pm 0.01\) \\ \hline Delay (s) & \(3.6\pm 0.5\) & \(3.41\pm 0.3\) & \(2.61\pm 0.1\) & \(1.8\pm 0.01\) \\ \hline Packet delivery ratio (\%) & \(59.8\pm 1.5\) & \(61.6\pm 1.0\) & \(78\pm 0.5\) & \(89.4\pm 0.5\) \\ \hline Network lifetime (s) & \(2224\pm 5.0\) & \(2561.8\pm 3.0\) & \(4620\pm 1.0\) & \(5700\pm 1.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis. Figure 11: Number of nodes vs. network lifetime.