Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3122 2019 2019 Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India K. K. Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India R. Santhosh The ability to facilitate high-performance task offloading while maintaining participant confidence is crucial, but not essential, to Cloud-Edge-Network (CEN) computing due to the geographic distribution and operation by various parties. Additionally, conflicts of interest may arise among the highly dynamic and diverse CEN members who provide resources. This study proposes a collaborative task offloading framework for CEN computing, called Trustable Block Chain and Bandwidth Sensible-based Task Offloading (TBBS-TO) and resource allocation empowered CEN. The E-PEFT consensus algorithm for block chain in task offloading optimizes resource allocation and task execution by dynamically adjusting consensus parameters based on environmental factors and performance feedback. Moreover, in our work for alleviating heterogeneous issues IoT users are mobility aware clustering is performed using Bi-directional Clustering Algorithm based on Local Density (BCALoD). In this work, block chain is essential to BC-CED's core functions, such as task delegation, resource utilization brokerage, and bandwidth sensible resource allocation. By modifying the block chain consensus procedure, TBBS-TO distinguish itself from other solutions by enabling participants to reach a consensus on task offloading. To achieve this, we formulate the offloading problem by considering both network performance and the computational capabilities of potential nodes. Using Multi-agent Double Deep Q-Network (MA-DDQN) based technique, TBBS-TO allow participants to compete for the right to produce a block by evaluating offloading policies and selecting the most effective one for the next period. Additionally, dynamically bandwidth sensible resource allocation is performed by considering significant parameters. Comprehensive testing on a commercial block chain platform has shown that TBBS-TO outperforms existing solutions in task offloading and blockchain maintenance. 2025 2025 133 150 10.54216/JCIM.150111 https://www.americaspg.com/articleinfo/2/show/3122