## 3 Trust-Optimized 6G-IoT Framework
### Trust Management System
Energy consumption is measured based on the power consumption models of the IoT devices, and security protocols are evaluated using detection rates and false positive rates. The proposed TEO-IoT frame work is compared with several state-of-the-art models, including IDSoft [17], CECB [18], RAT6G [19], HM-IoT [20], SREITS [21], DREE [24], and RREN [22]. ### Datasets

In this study, we utilized three publicly available datasets to evaluate the performance and robustness of the proposed trust-optimized 6G-IoT framework. Mainly, these datasets provide various IoT-related data in ensuring that the tests ranging from edge computing to anomaly detection and security are comprehensively implemented. The datasets used for evaluation--Edge-IIoTset, N-BaIoT, and IoT-23--were selected for their representativeness of diverse and real-world IoT scenarios. Edge-IIoTset focuses on industrial IoT applications, N-BaIoT simulates security threats such as botnet attacks, and IoT-23 captures heterogeneous traffic patterns, including both normal and malicious activities. Together, these datasets comprehensively cover industrial, security-critical, and general IoT use cases, ensuring that the proposed method is evaluated under realistic and diverse conditions, which supports its generalizability. * Edge-IIoTset: This dataset is designed for edge computing and industrial IoT applications, providing data on real-time performance metrics, resource utilization, and task offloading scenarios. It contains labeled data that captures various operational states in industrial edge environments. * N-BaIoT: This was designed for the capture of network-based anomaly detection. It has captured data from a wide range of IoT devices under normal and attack conditions. It includes a wide range of attack types such as DoS, distributed attacks, and port scans. Therefore, this dataset is suitable for the evaluation of security aspects in IoT networks. * IoT-23: This dataset contains the extracted traffic captures of various IoT devices, every bit performed with a purpose to understand network performance and security challenges. IoT-23 contains both benign and malicious traffic that can be used effectively for testing anomaly detection mechanisms and policies for network management. The pre-processing includes cleaning, normalizing, and feature extracting. For time-series data, sliding windows were employed to segment the sequences, while missing values were imputed using linear interpolation. Anomaly detection datasets were labeled appropriately, and features were selected based on their relevance to network security and performance. For each dataset, the data were split into training, validation, and test sets, ensuring that the model evaluation process remained unbiased. ### Resource Management Efficiency

The efficiency of resource management in the TEO-IoT framework was evaluated by measuring the percentage of optimal resource usage compared to baseline methods over a 90-minute simulation period. The datasets used for this evaluation are Edge-IIoTset, N-BaIoT, and IoT-23, and the performance outcome is illustrated in \[\mathbf{I}_{i}(t)=\int_{0}^{t}\rho(\mathbf{y}_{i}(\tau),\mathbf{y}_{i}(\tau- \Delta t))e^{-\beta(t-\tau)}d\tau, \tag{15}\]2

For the Edge-IIoTset dataset, the TEO-IoT framework achieved an average optimal resource usage of 92.5%, outperforming IDSoft (86.7%), CECB (84.3%), RAT6G (88.2%), HM-IoT (89.7%), SREITS (87.9%), DREE (90.1%), and RREN (89.3%). The results highlight the framework's superior ability to dynamically allocate resources in industrial IoT environments, optimizing task offloading and power usage. In the N-BaIoT dataset, which focuses on network-based anomaly detection, the TEO-IoT framework maintained an optimal resource usage rate of 91.3%. In comparison, IDSoft achieved 85.5%, CECB 83.8%, RAT6G 87.4%, HM-IoT 88.5%, SREITS 86.3%, DREE 89.8%, and RREN 88.7%. The improvement can be attributed to the framework's adaptive mechanisms that account for fluctuating traffic conditions and security demands.