Blockchain and Learning-Based Secure and Intelligent Task Offloading for Vehicular Fog Computing
Haijun Liao, Yansong Mu, Zhenyu Zhou, Meng Sun, Zhao Wang, Chao Pan
Abstract
Vehicular fog computing has emerged as a complementary framework for edge computing by leveraging the under-utilized computational resources of vehicles. However, how to reduce task offloading delay, queuing delay, and handover cost with incomplete information while simultaneously ensuring privacy, fairness, and security remains an open issue. In this paper, we develop a secure and intelligent task offloading framework to address these challenges. We exploit blockchain and smart contract to facilitate fair task offloading and mitigate various security attacks. Then, we design a subjective logic-based trustfulness metric to quantify the possibility of task offloading success, and develop a trustfulness assessment mechanism. An online learning-based intelligent task offloading algorithm named QUeuing-delay aware, handOver-cost aware, and Trustfulness Aware Upper Confidence Bound (QUOTA-UCB) is proposed, which can learn the long-term optimal strategy and achieve a well-balanced tradeoff among task offloading delay, queuing delay, and handover cost. Finally, extensive theoretical analysis and simulations are carried out to demonstrate the reliability, feasibility, and efficiency of the proposed secure and intelligent task offloading scheme.