Vehicular ad-hoc network enhances driving safety and enables various intelligent transportation applications by adopting the revolutionary vehicular wireless communication technology. This has attracted a lot of attentions from both academia and industry in recent years. Given the sophistication of vehicular manufacturing and the heterogeneity of intelligent transport terminals, performing vehicular authentication is of great importance. The existing schemes have largely considered vehicle security and authentication within a single administrative domain, which lacks supervision of the authority and entity in the intelligent transportation system. In this article, we propose a multidomain vehicular authentication architecture by introducing blockchain technique to build distributed trust and share cross-domain information among multiple administrative domains. To guarantee the anonymity and traceability, a pseudonym-based privacy-preserving authentication method is proposed. Specifically, considering the supervision of authority and the resilience to key escrow, we design a two-phase pseudonym distribution mechanism with the assistance of a roadside unit (RSU) proxy. We conduct in-depth security analysis by comparing with existing works and deploy experiments to show the efficiency and feasibility of the proposed scheme in the multidomain scenario.

False data injection (FDI) attacks have recently been introduced as an important class of cyberattacks in modern power systems. By coordinating the injection of false data in selected meters readings, FDI attacks can bypass bad data detection methods in power system state estimation. In this paper, we propose a strategy to enhance detection and identification of FDI that leverages reactance perturbation. We begin by deriving conditions to mitigate attacks in noiseless systems that relates the likelihood of attack detection and identification to the rank of the composite matrix, limited by power system topology {{and the deployment of meters}}. Based on such conditions, we design a secure reactance perturbation algorithm that maximizes the likelihood of FDI attack detection and identification while minimizing {{the effect on the operational cost of power systems, e.g., power losses on transmission lines.}} Simulations on a 6-bus and the IEEE 57-bus system verify the performance of the secure reactance perturbation and {{the effect on power losses in both noiseless and noisy systems

Driven by the rapid development of the Internet of Things (IoT) technology, the issue of trust has become increasingly apparent and received considerable scholarly attention in recent years. With the occurrence of various security incidents, data leakage accidents, and service fraud, which seriously affects the quality of service and system efficiency of IoT, trust is fast becoming a key issue in IoT system. In addition to the security and efficiency, trust further contains the reliability, attack resistance, fairness, flexibility, and incentive, resulting in a wide range of new researches from the trust management framework to the quantification method. In this work, various dimensions of trust, including definition, composition, aggregation, and computation are introduced and analyzed. We focus on comprehensive comparison of the state-of-the-art trust management researches and the related applications. Besides, we explore and discuss the important challenges, including performance bottleneck, bidirectional trust, dynamic changes of context, privacy preserving, and cross-domain issue. Some potential enabling technologies for trust management are further analyzed. The objective of this article is to comprehend the trust issue and the composition of trust management in IoT, and illustrate the difference of existing work, thereby motivating further research interest in this field.

With the increased penetration of renewable energy sources (RESs) and plug-and-play loads, Microgrids (MGs) bring direct challenges in energy management due to the uncertainties in both supply and demand sides. In this paper, we present a coordinated energy dispatch based on Distributed Model Predictive Control (DMPC), where the upper level provides an optimal scheduling for energy exchange between Distribution Network Operator (DNO) and MGs, whereas the lower level guarantees a satisfactory tracking between supply and demand. With the proposed scheme, not only we maintain a supply-demand balance in an economic way, but also improve the renewable energy utilization of distributed MG systems. To describe the dynamic process of energy trading, a novel conditional probability distribution model is introduced, which can characterize randomness of charging/discharging and uncertainties of energy dispatch. Moreover, we formulate a two-layer optimization problem and the corresponding algorithm is given. Finally, simulation results show the effectiveness of the proposed method.

Widely recognized as an excellent solution of global warming and oil crisis, electric vehicles (EVs), however, suffer remarkable weakness, such as the limited cruise range, which can be partly addressed by introducing en-route charging navigation systems. Different from traditional navigation, which solves a shortest path problem, the en-route charging navigation resorts to a joint charging and routing optimization. In this brief, we formulate the en-route charging navigation in a dynamic programming setting in both a deterministic and a stochastic traffic network. Specifically, to relieve computational complexity in navigation systems, a simplified charge-control (SCC) algorithm is presented in the deterministic case, which can simplify the charging control decisions within an SCC set. In the stochastic case, an online state recursion algorithm is designed, which can provide an accurate navigation utilizing online information. Numerical simulation verifies the computing burden and accuracy of the proposed algorithms in a deterministic and a stochastic network.