Trustworthy Multi-Hop Cooperative Task Offloading in Device-Edge-Cloud Computing

Jianhua Liu, Xin Wang, Shui Yu, Guangtao Xue, Minglu Li

Abstract: Multi-hop cooperative task offloading (MCTO) allows resource-constrained edge clouds to collaborate and assist each other in completing computation-intensive tasks, such as training machine learning models through device-edge-cloud (DEC) computing. However, internal fake service attacks can pose a threat to the security and reliability of MCTO in DEC computing. In this paper, we propose a trust model based on a directed acyclic graph (DAG) and Proof-of-Work (PoW) to safeguard tasks against potential attacks. The edge node selection for task offloading involves two key steps: offloading confirmation and trust-based node selection. To mitigate the unreliability caused by internal fake service attacks during cooperative offloading, we propose a multi-hop offloading node selection algorithm based on the soft actor-critic (SAC) coalition. This algorithm helps identify trustworthy nodes for constructing secure offloading paths. Our experimental results demonstrate that the proposed algorithm effectively counters internal fake service attacks and significantly reduces cooperative offloading latency compared to existing leading approaches.

External IDs:dblp:journals/tsc/LiuWYXL25