## 3 Trust-Optimized 6G-IoT Framework
### Trust Management System
The TEO-IoT framework sustained an optimal resource usage of 93.1% in the IoT-23 dataset, used for the evaluation of the network performance and security. This is far larger compared to IDSoft at 87.2%, CECB at 85.6%, RAT6G at 89.1%, HM-IoT at 90.3%, SREITS at 88.4%, DREE at 91.5%, and RREN at 90.2%. The performance of the TEO-IoT framework within this dataset assures its efficiency in handling the network traffic and the security protocols while energy and bandwidth are optimally allocated. ### Power Efficiency

The power efficiency of the TEO-IoT framework was assessed by evaluating the reduction in power consumption during both data transmission and processing over a 90-minute simulation. The framework's performance was compared against state-of-the-art methods, including IDSoft [17], CECB [18], RAT6G [19], HM-IoT [20], SREITS [21], DREE [24], and RREN [22], across three datasets: Edge-IIoTset, N-BaloT, and IoT-23. The outcome is illustrated in \[\mathbf{I}_{i}(t)=\int_{0}^{t}\rho(\mathbf{y}_{i}(\tau),\mathbf{y}_{i}(\tau- \Delta t))e^{-\beta(t-\tau)}d\tau, \tag{15}\]3

For the Edge-IIoTset dataset, TEO-IoT achieved a 14.8% reduction in power consumption during data transmission and 12.3% during processing, outperforming IDSoft (10.4% and 9.7%, respectively), CECB (9.2% and 8.5%), RAT6G (11.5% and 10.2%), HM-IoT (12.1% and 11.4%), SREITS (10.7% and 9.9%), DREE (13.2% and 12.1%), and RREN (12.5% and 11.8%). Optimizing the TEO-IoT tasks and the mechanism of adaptive routing enhance power efficiency due to the minimization of energy consumption in the IoT environment. TEO-IoT exhibited a transmission power reduction of 13.6% along with a processing power of 11.9%. IDSoft in turn, exhibited transmission and processing power reductions of 9.8% and 8.6%, respectively; CECB showed 8.7 and 7.9%, respectively; RA6 TG showed 10.9 and 9.5%, respectively; HM-IoT showed 11.6 and 10.9%, respectively; SREITC showed

\[\mathbf{I}_{i}(t)=\int_{0}^{t}\rho(\mathbf{y}_{i}(\tau),\mathbf{y}_{i}(\tau- \Delta t))e^{-\beta(t-\tau)}d\tau, \tag{15}\]4 Performance comparison of resource management efficiency across different IoT datasets, evaluated over 90 min. \[\mathbf{I}_{i}(t)=\int_{0}^{t}\rho(\mathbf{y}_{i}(\tau),\mathbf{y}_{i}(\tau- \Delta t))e^{-\beta(t-\tau)}d\tau, \tag{15}\]5 Performance comparison of power efficiency across different IoT datasets, evaluated over 90 min. 9.5 and 8.7%, respectively, while DRRE and RRN showed 12.8% and 11.2%, and 11.9 and 10.5%, respectively. All these are on the N-BaIoT dataset, which focuses on anomaly detection in IoT networks. The conditions of fluctuating networks and the ability of TEO-IoT to adapt to them optimize the paths of transmission and, therefore, considerably reduce power consumption in high-traffic instances. In the IoT-23 dataset, which was used for the assessment of network performance and security, the transmission power reduced to 15.2% with the proposed TEOIoT framework, while a reduction of 13.1% in processing power was realized. The developed framework outperformed IDSoft by 11.1%, 9.5%; CECG by 9.6%, 8.4%, respectively; RAT6G by 12.3%, 10.8%; HM-IoT by 13, 11.7%; SREITS by 10.9, 9.8%; DREE by 14.1, 12.3%; and RREN by 12.7, 11.4%, respectively. These results demonstrate TEO-IoT's advanced power-saving strategies, which are critical for IoT networks with high energy demands and intensive security protocols. ### Network Performance

The performance evaluation of the proposed TEO-IoT framework was conducted to measure latency, throughput, and packet delivery ratio (PDR) under varying network conditions and workloads. These metrics were compared against state-of-the-art frameworks, including IDSoft [17], CECG [18], RAT6G [19], HM-IoT [20], SREITS [21], DREE [24], and RREN [22]. The comparison was based on three primary datasets: Edge-IIoTset, N-BaIoT, and IoT-23. Below is a detailed discussion of the performance analysis for each dataset.