Recent advances of Internet and microelectronics technologies have led to the concept of smart grid which has been a widespread concern for industry, governments, and academia. The openness of communications in the smart grid environment makes the system vulnerable to different types of attacks. The implementation of secure communication and the protection of consumers’ privacy have become challenging issues. The data aggregation scheme is an important technique for preserving consumers’ privacy because it can stop the leakage of a specific consumer’s data. To satisfy the security requirements of practical applications, a lot of data aggregation schemes were presented over the last several years. However, most of them suffer from security weaknesses or have poor performances. To reduce computation cost and achieve better security, we construct a lightweight data aggregation scheme against internal attackers in the smart grid environment using Elliptic Curve Cryptography (ECC). Security analysis of our proposed approach shows that it is provably secure and can provide confidentiality, authentication, and integrity. Performance analysis of the proposed scheme demonstrates that both computation and communication costs of the proposed scheme are much lower than the three previous schemes. As a result of these aforementioned benefits, the proposed lightweight data aggregation scheme is more practical for deployment in the smart grid environment.

As one of the most significant components of Intelligent Transportation Systems (ITS), traffic prediction has gained much popularity given its enormous application value in vehicular communications, traffic management, and traffic control. As such, many traffic prediction models have been proposed. The current methods commonly adopt graph convolution networks (GCNs) to capture spatial correlations. GCN-based approaches mainly focus on pair-wise interactions between road vertices (i.e. dyadic relations). However, the interactions between road vertices are not necessarily dyadic, but also can be high-order (i.e., multivariate relations). Further, few existing works focus on how to mine correlations between different types of traffic data. To this end, we develop STGHCN, a spatial-temporal gated hypergraph convolution network, that not only captures pair-wise and high-order spatial patterns between vertices but also characterizes the type-aware traffic data influences. Specifically, we adopt a spatial gated graph convolution and a hypergraph convolution to explore pair-wise and high-order interactions. More importantly, we design a spatial-temporal aware channel attention mechanism to mine hidden patterns between cross-type traffic features. Extensive experiments conducted on four real-world traffic datasets validate the effectiveness of STGHCN, with STGHCN's MAE, RMSE, and MAPE at between 6.91%-10.00%, 5.42%-8.90%, and 2.54%-5.03% lower than the state-of-the-art methods.

With the number of smart vehicle drivers increasing rapidly, privacy-preserving identity management methods in Vehicular Ad-hoc Networks (VANETs) become more imperative and receive much attention from researchers. Since identity leakage or a single point of failure may result in serious consequences in the VANET, the decentralized anonymous credential (DAC) could be a potential approach to construct a robust network. However, the supervision under decentrilized environment could be a necessary and troublesome in the VANETs. In this paper, we present an anonymous traceable and revocable credential system using blockchain, called ATRC, built over a generalized group signature. The underlying group signature not only meets the anonymity requirement but also makes the users master their identities. What's more, to tackle the privacy leakage in the revocation process, we employ the Merkle tree to construct a whitelist, which trades off the efficiency and the privacy. Finally, we gives the comparison and experiment performance to show our scheme holds lightweight on the user side and has fewer computation costs in the show and revocation phase.

Black-burst based multi-hop broadcast protocols are effective means to disseminate Emergency messages (EMs) in Vehicular ad hoc networks (VANETs). However, Clear to broadcast (CTB) collision will happen and reduce propagation speed. Aiming at this problem, we propose a Black-burst and multi-channel based multihop broadcast (BMMB) protocol. Vehicles with multiple antennas can transmit and sense black-burst at different channels simultaneously based on multi-channel technology. Compared with existing black-burst based methods, BMMB shortens the iterative procedure to find out the optimal segment. Only one relay vehicle can be rapidly selected within the optimal segment without CTB collision. BMMB enables alternative broadcast methods, i.e., unidirectional broadcast and multi-directional broadcast for straight roads and intersections respectively. Theoretical analysis is done for performance evaluation of BMMB, and simulation results demonstrate that BMMB performs better in terms of average one-hop delay and propagation speed.