## 2 Related Work  
Recent developments in resource allocation and optimization for 6G-enabled IoT systems reveal varied approaches, each targeting specific network challenges. The comparison between the existing approaches and the proposed trust-optimized 6G-IoT framework is summarized in Table 1, highlighting the comprehensive nature of the solution in addressing multiple challenges across 6G-enabled IoT ecosystems. One study examines cloud-edge collaborative systems for blockchain-enabled IoT to enhance resource distribution across devices [18]. This approach contrasts with another that focuses on task offloading and resource allocation in smart edge environments, particularly addressing the demands of 6G [19]. Both tackle the complex nature of IoT, but differ in their primary focus--blockchain integration versus edge offloading. Security plays a critical role in managing massive IoT networks, exemplified by a federated intrusion detection system developed to mitigate threats in 6G networks [17]. This secure environment contrasts with another study centered on federated learning models for secure IoMT resource recommendation, where propagation analysis improves decision-making efficiency [28]. While both studies use federated learning, they target different domains: intrusion detection and resource recommendation. In contrast, other works emphasize network efficiency and interference management. One introduces interference-aware resource control in expanded \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Existing Approaches** & **Proposed Approach** \\ \hline Security & Focus on federated learning or blockchain-based solutions [17; 18] & Trust-based model integrated with security protocols \\ \hline Resource Allocation & Static or hybrid models [19; 20]; Cloud-edge collaborative systems for blockchain-enabled IoT [18]; Resource allocation in smart edge environments [19] & Dynamic, real-time pricing strategies to optimize resources \\ \hline Adaptability to Network Changes & Limited adaptability to real-time conditions [21]; Robust access point selection for adaptable infrastructure [22]; Knowledge collaboration through graph attention models [23] & Adaptive routing, trust management, and economic optimization \\ \hline Energy Efficiency & Energy-aware offloading [24]; Resource optimization for sustainability [24] & Energy-efficient routing combined with delay tolerance \\ \hline Scalability & Restricted to specific domains (e.g., IoT or edge) [22]; Limited to blockchain-integrated systems [18] & Scalable across diverse 6G-enabled IoT ecosystems \\ \hline Interference Management & Interference-aware resource control in expanded IoT networks [25] & Combined with adaptive routing and trust-based optimization \\ \hline Optimization Methods & Multi-objective optimization in hybrid models [20]; Deep-Q learning for secure spectrum allocation [26] & Holistic optimization incorporating trust and economic factors \\ \hline Intelligent Resource Management & Cyber-twin-driven approaches [27]; Softwarized resource allocation for autonomous transportation systems [21] & Real-time dynamic pricing and adaptive resource allocation \\ \hline Federated Learning Applications & Federated intrusion detection [17]; Federated learning for secure IoMT resource recommendation [28] & Trust-based security model integrating real-time analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of existing approaches and the proposed framework. IoT networks [25], while a hybrid optimization model for 6G-enabled IoT uses a multi-objective framework to optimize diverse network demands [20]. Both contribute to efficient IoT network management but differ in focus, with one targeting interference, and the other addressing multi-objective optimization. Softwarized approaches for resource allocation are also noteworthy, particularly in intelligent transportation systems [21], which differ from cyber-twin-driven approaches in edge environments [27].