Enhancing Edge-Cloud Collaboration With Blockchain-Assisted Digital Twin Intelligence Offloading Scheme

Recently, Edge-Cloud Collaborative (ECC) has emerged as an efficient and promising technique to empower various computation-intensive applications in Digital Twin Network (DTN). The integration of ECC and DTN serves to bridge the gap between data analysis and physical states. In ECC, a reliable and optimal task offloading scheme is required to maximize resource utilization and provide satisfying services to End Users (EU). However, existing offloading schemes still face significant challenges, such as the instability and complexity of network topologies, the intricacies of massive data, and the lack of trust among EU. In this paper, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">enhancin<u>G</u> edge-cl<u>O</u>ud collabora<u>T</u>ion wi<u>T</u>h blockchain-assist<u>E</u>d digital twin intelligence offloadi<u>N</u>g</i> scheme (GOTTEN) which transmits large-scale tasks generated by DTs to Edge Station (ES) or Cloud Station (CS) in dynamic DTN scenarios. We first formulate this resource allocation and task offloading problem and provide an appropriate initial solution which guarantees that tasks generated by DTs can be accurately mapped to physical entities, while optimizing block allocation and reducing the decision space of task offloading. Then, we employ the Lagrange Multiplier based Distributed Island model-enhanced Genetic Algorithm (LM-DIGA) to transform our formulated problem into a convex form and achieve an optimal resource allocation under a specific scheme. Additionally, our proposed architecture also leverages blockchain verification mechanisms to enhance system stability, strengthening privacy protection for DT data as well. Finally, extensive simulation results demonstrate that, compared with seven baselines, our proposed scheme achieves a 10 percent the total system delay and privacy overhead with regard to other schemes in ECC.