## 3 Trust-Optimized 6G-IoT Framework
### Trust Management System
In the IoT-23 dataset, the framework exhibited a PDR of 99.1%, outperforming IDSoft (96.8%), CECB (95.2%), RAT6G (97.8%), HM-IoT (98.3%), SREITS (96.7%), DREE (98.9%), and RREN (98.2%). The robust security protocols and adaptive mechanisms in TEO-IoT played a key role in achieving near-perfect data delivery under network congestion and high-traffic conditions. ### Workload Scenarios

To evaluate performance under different workloads, we simulated low, medium, and high network loads across all three datasets. For Edge-ItoTset, TEO-IoT sustained 95.2% efficiency under high workloads, compared to IDSoft (90.3%), CECB (89.7%), RAT6G (91.5%), HM-IoT (93.1%), SREITS (90.9%), DREE (94.1%), and RREN (92.5%). In N-BaloT, TEO-IoT demonstrated 94.8% efficiency under high network loads, outperforming IDSoft (89.9%), CECB (88.5%), RAT6G (90.7%), HM-IoT (92.8%), SREITS (89.8%), DREE (93.6%), and RREN (92.2%). For the IoT-23 dataset, TEO-IoT achieved 96.7% efficiency under high workloads, compared to IDSoft (91.8%), CECB (90.2%), RAT6G (93.3%), HM-IoT (94.5%), SREITS (92.1%), DREE (95.3%), and RREN (94.0%). This consistent performance under high workloads demonstrates TEO-IoT's scalability and robustness in managing varying levels of network stress. ### Cost-Efficiency

The economic optimization of the TEO-IoT framework was analyzed by evaluating the cost savings achieved by dynamic pricing and incentive mechanisms against those achieved by static pricing models used by state-of-the-art frameworks. Figure 7 provides the cost savings achieved by TEO-IoT compared to these baseline methods for different economic scenarios of each dataset. #### 4.6.1 Edge-ItoTset Dataset

The first scenario involved an analysis of resource allocation under dynamic pricing models in industrial IoT settings. TEO-IoT achieved a 22.7% reduction in operational costs, significantly outperforming IDSoft (18.5%), CECB (16.4%), RAT6G (19.8%), HM-IoT (20.5%), SREITS (18.1%), DREE (21.2%), and RREN (20.3%). In the second scenario for the Edge-ItoTset dataset, focused on task offloading, TEO-IoT realized a 20.3% cost savings, compared to IDSoft (16.2%), CECB (14.9%), RAT6G (17.4%), HM-IoT (18.2%), SREITS (15.8%), DREE (19.4%), and RREN (18.7%). TEO-IoT's dynamic incentive mechanisms encouraged optimal resource utilization, allowing for a more effective distribution of tasks. The third scenario examined energy costs in data processing. TEO-IoT reduced energy costs by 23.4%, surpassing IDSoft (19.2%), CECB (17.3%), RAT6G (20.6%), HM-IoT (21.5%), SREITS (19.1%), DREE (22.1%), and RREN (21.3%). #### 4.6.2 N-BaIoT Dataset

For the N-BaIoT dataset, which focuses on network anomaly detection, the first scenario evaluated the overall operational cost. TEO-IoT achieved 21.9% cost savings, while IDSoft recorded 17.8%, CECB 15.6%, RAT6G 19.1%, HM-IoT 19.9%, SREITS 17.4%, DREE 20.7%, and RREN 19.6%. The second scenario involved the cost associated with bandwidth management. TEO-IoT reduced bandwidth costs by 20.6%, compared to IDSoft (16.5%), CECB (14.7%), RAT6G (18.2%), HM-IoT (18.8%), SREITS (16.1%), DREE (19.9%), and RREN (18.4%). In the third scenario, focused on the economic impact of network security measures, TEO-IoT showed a 22.8% reduction in security-related costs, outperforming IDSoft (18.4%), CECB (16.3%), RAT6G (20.1%), HM-IoT (20.9%), SREITS (18.3%), DREE (21.6%), and RREN (20.5%). #### 4.6.3 IoT-23 Dataset

For the IoT-23 dataset, the first scenario assessed overall operational costs in IoT security settings. TEO-IoT achieved a 24.5% reduction in operational costs, compared to

\[\mathbf{I}_{i}(t)=\int_{0}^{t}\rho(\mathbf{y}_{i}(\tau),\mathbf{y}_{i}(\tau- \Delta t))e^{-\beta(t-\tau)}d\tau, \tag{15}\]8 Performance comparison of TEO-IoT against state-of-the-art frameworks (IDSoft, CECB, RAT6G, HM-IoT, SREITS, DREE, RREN) across three datasets (Edge-IIoTset, N-BaIoT, IoT-23) for latency, throughput, packet delivery ratio (PDR), and workload efficiency over a 90-minute simulation period.