Blockchain and RL-Based Secured Task Offloading Framework for Software-Defined 5G Edge Networks
5G (Fifth Generation) technology represents a significant advancement in telecommunications, facilitating industry innovation and advancement for applications across various sectors. It enhances high-speed, low-latency communication, making it ideal for task offloading in resource-constrained mobile devices. By leveraging task offloading, 5G networks maximize the efficiency of both computational and network resources, ensuring faster, more reliable data delivery and enabling high-performance requirements of modern applications. However, dynamic and secure task offloading remains a challenge due to fluctuating network conditions and trust concerns. This paper proposes SAGE (Secured Adaptive Generalized Edge), a reinforcement learning-based task offloading framework that leverages contextual bandits for adaptive decision-making in Multi-Access Edge Computing (MEC) environments. By integrating blockchain smart contracts for security and SDN-based orchestration for dynamic resource management, SAGE ensures robust, low-latency offloading. Experimental evaluations demonstrate that SAGE reduces task offloading delays by 36% and task durations by 30% compared to baseline methods under varying load and energy constraints.
Authors
- Prabhakar Krishnan (ORCID: https://orcid.org/0000-0001-6702-112X)
- Kurunandan Jain (ORCID: https://orcid.org/0000-0003-2038-1114)
- Rajkumar Buyya (ORCID: https://orcid.org/0000-0001-9754-6496)
- Tulika Pandey
- K. Suresh Kumar
- Narayanan Subramanian
Institutions
- Centre for Development of Advanced Computing (IN)
- The University of Melbourne (AU)
- Government of India (IN)
- Ministry of Electronics and Information Technology (IN)
- Amrita Vishwa Vidyapeetham (IN)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3554638
- Citations
- 12
- Primary Topic
- Blockchain Technology Applications and Security
- Type
- article
- Field-Weighted Citation Impact
- 38.95