The dynamic environment due to traffic mobility and wireless communication from/to vehicles make identity authentication and trust management for privacy preservation based on vehicular edge computing (VEC) an increasingly important problem in vehicular networks. However, existing authentication schemes mainly focus on communication between a single trusted edge computing node and multiple vehicles. This framework may suffer the bottleneck problem due to the single edge computing node, and the performance depends heavily on its resources. In this paper, a blockchain empowered group-authentication scheme is proposed for vehicles with decentralized identification based on secret sharing and dynamic proxy mechanism. Sub-authentication results are aggregated for trust management based blockchain to implement collaborative authentication. The edge computing node with a higher-reputation stored in the tamper-proof blockchain can upload the final aggregated authentication result to the central server to achieve the decentralized authentication. This work analyzes typical attacks for this scheme and shows that the proposed scheme achieves cooperative privacy preservation for vehicles while also reducing communication overhead and computation cost.

With high penetration of large scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this paper, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL based real time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with DPG and DDPG, our proposed method has lower time complexity.