###### Abstract  
The continuous evolvement of IoT networks has introduced significant optimization challenges, particularly in resource management, energy efficiency, and performance enhancement. Most state-of-the-art solutions lack adequate adaptability and runtime cost-efficiency in dynamic 6G-enabled IoT environments. Accordingly, this paper proposes the Trust-centric Economically Optimized 6G-IoT (TEO-IoT) framework, which incorporates an adaptive trust management system based on historical behavior, data integrity, and compliance with security protocols. Additionally, dynamic pricing models, incentive mechanisms, and adaptive routing protocols are integrated into the framework to optimize resource usage in diverse IoT scenarios. TEO-IoT presents an end-to-end solution for security management and network traffic optimization, utilizing advanced algorithms for trust score estimation and anomaly detection. The proposed solution is emulated using the NS-3 network simulator across three datasets: Edge-ItoTset, N-BaloT, and IoT-23. Results demonstrate that TEO-IoT achieves an optimal resource usage of 92.5% in Edge-ItoTset and reduces power consumption by 15.2% in IoT-23, outperforming state-of-the-art models like IISOFT and RAT6G. Internet of Things; resource management; 6 th-generation network; trust management; economic optimization; delay-tolerant routing; trustworthiness +
Footnote †: journal: Computer Phys. Commun. 0000-0001-5000-5000-5000-5000-5000-5000-5000-5000-5000-5000-5000-5000-5000-5000  
However, several research gaps and limitations still need to be addressed in the context of 6G and IoT technologies. A critical challenge lies in ensuring secure and trustworthy communication within IoT networks, especially as the number of connected devices continues to grow at an unprecedented rate [14]. Current resource management strategies often fail to incorporate trustworthiness as a central component in decision-making processes, which can lead to vulnerabilities and inefficiencies. Furthermore, most reviews of the literature on economic optimization in specific contexts, dynamic pricing models, and incentive mechanisms are developed rather partly for 6G-enabled IoT networks [15]. These shortcomings further point to the need for a comprehensive framework that addresses security and trust, and also integrates economic consideration to optimize resource allocation and improve network performance. By identifying these research gaps, there is a need that a trust management approach coupled with an economic optimization and an adaptive routing strategy is needed in the first place to overcome the shortcomings of current systems toward making future 6G-enabled IoT networks resilient and efficient. The motivation of this study is to investigate the timely needs for security, trust, and resource optimization issues in 6G-enabled IoT networks. The trustworthiness of the communications with the proliferation being observed with IoT devices will become pivotal toward the integrity and efficiency of such networks [16]. This work presents the framework of trust-optimized 6G-IoT, integrating dynamic trust management, economic optimization, and delay-tolerant routing into resilience, security, and cost-effectiveness for 6G-enabled IoT systems. Based on the above-discussed gaps in the literature, the main question that drives this research is \"how can trustworthiness and economic optimization be effectively integrated into resource management for operational security, resilience, and efficiency in 6G-enabled IoT networks?\" The proposed framework addresses this question with a novel solution that will enhance security and reliability for IoT networks, while economically optimizing resource allocation. The proposed trust-economically optimized 6G-IoT (TEO-IoT) system is intended to improve performance, security, and economic efficiency for a 6G-enabled IoT network.