## 3 Trust-Optimized 6G-IoT Framework
### Trust Management System
The outcome is illustrated in \[\mathbf{I}_{i}(t)=\int_{0}^{t}\rho(\mathbf{y}_{i}(\tau),\mathbf{y}_{i}(\tau- \Delta t))e^{-\beta(t-\tau)}d\tau, \tag{15}\]6

#### 4.4.1 Latency Analysis

The latency performance of TEO-IoT was evaluated across different network scenarios and compared with the baseline models over 90 min. For the Edge-IIoTset dataset, TEO-IoT exhibited an average latency of 35 ms, significantly lower than IDSoft (48 ms), CECG (52 ms), RAT6G (45 ms), HM-IoT (42 ms), SREITS (47 ms), DREE (39 ms), and RREN (41 ms). TEO-IoT's reduced latency is attributed to its efficient resource management and adaptive routing mechanisms that optimize task distribution across the network. In the N-BaIoT dataset, TEO-IoT achieved an average latency of 33 ms, outperforming IDSoft (46 ms), CECG (51 ms), RAT6G (44 ms), HM-IoT (41 ms), SREITS (46 ms), DREE (38 ms), and RREN (40 ms). Furthermore, the potential of dynamically changing routing paths and giving priority to vital data streams minimized the delay of the framework concerning scenarios of anomaly detection. The proposed framework kept the latency in the IoT-23 dataset at 30 ms, while state-of-the-art methods performed as follows: IDSoft, 44 ms; CECB, 50 ms; RAT6G, 42 ms; HM-IoT, 40 ms; SREITS, 45 ms; DREE, 37 ms; and RREN, 39 ms. Low latency for the IoT-23 dataset features the framework in dealing with computationally high-security workloads and reducing interaction delays and processing overhead. #### 4.4.2 Throughput Analysis

It focused on analyzing the throughput, essentially the rate of data transfer over various network conditions. For the Edge-IIoTset dataset, TEO-IoT recorded an average throughput of 120 Mbps, surpassing IDSoft (108 Mbps), CECB (105 Mbps), RAT6G (113 Mbps), HM-IoT (115 Mbps), SREITS (109 Mbps), DREE (118 Mbps), and RREN (116 Mbps). The adaptive resource management strategies in the TEO-IoT allowed for optimal bandwidth utilization while characteristically guaranteeing higher throughput under variable workloads. In the N-BaIoT dataset, TEO-IoT achieved 118 Mbps, outperforming IDSoft (106 Mbps), CECB (103 Mbps), RAT6G (111 Mbps), HM-IoT (113 Mbps), SREITS (107 Mbps), DREE(117 Mbps), and RREN (114 Mbps). This superior performance is indicative of how the framework has coped with a high volume of anomaly detection data by sustaining consistent throughput across the network. For the IoT-23 dataset, TEO-IoT reached 123 Mbps, while the baseline models demonstrated lower throughput rates: IDSoft (110 Mbps), CECB (108 Mbps), RAT6G (116 Mbps), HM-IoT (118 Mbps), SREITS (112 Mbps), DREE (121 Mbps), and RREN (119 Mbps). Advanced resource allocation techniques implemented in TEO-IoT contributed to higher data transfer rates even in intensive security protocol enforcements. \[\mathbf{I}_{i}(t)=\int_{0}^{t}\rho(\mathbf{y}_{i}(\tau),\mathbf{y}_{i}(\tau- \Delta t))e^{-\beta(t-\tau)}d\tau, \tag{15}\]7 Performance comparison of TEO-IoT against state-of-the-art frameworks (IDSoft, CECB, RAT6G, HM-IoT, SREITS, DREE, RREN) across three datasets (Edge-IloTset, N-BaloT, IoT-23) for latency, throughput, packet delivery ratio (PDR), and workload efficiency over a 90-minute simulation period. #### 4.4.3 Packet Delivery Ratio (PDR) Analysis

Packet delivery ratio was assessed to measure the reliability of data transmission across the network. In the Edge-ItoTset dataset, TEO-IoT achieved a PDR of 98.5%, outperforming IDSoft (96.1%), CECB (94.8%), RAT6G (97.2%), HM-IoT (97.6%), SREITS (95.9%), DREE (98.1%), and RREN (97.4%). It provides resiliency and fault tolerance mechanisms in the framework to provide high PDR even in highly complex industrial IoT environments. For the N-BaloT dataset, TEO-IoT maintained a PDR of 98.2%, surpassing IDSoft (95.9%), CECB (94.4%), RAT6G (96.9%), HM-IoT (97.5%), SREITS (95.5%), DREE (97.9%), and RREN (97.3%). Thus, with TEO-IoT, the data routing can be automatically handled without much packet loss, hence continuing to ensure higher reliability in anomaly detection applications.