Smart consumer electronics, particularly electric vehicles (EVs), have become integral to modern life as smart cities evolve. Intelligent speed advisory (ISA) systems utilize advanced vehicle network technologies to provide real-time speed recommendations and route planning for EVs. While these technologies offer convenience to drivers, they also introduce security threats and potential privacy risks. In this article, we analyze the leading security and privacy threats that EV users face in ISA systems, introduce existing protection solutions, and highlight the critical challenges that need to be addressed. By emphasizing how these smart consumer electronics integrate into our daily lives and enhance the intelligence of urban transportation, this work is significant for the technical community.

Energy demand management, especially energy consumption analysis, is crucial for rational energy allocation and monitoring consumption behaviors. Data privacy and regulatory issues limit the sharing of smart meter data among power companies, challenging the acquisition of sufficient training data. Generating synthetic data through generative adversarial networks (GANs) offers an effective alternative to sharing and using real data. However, the limited quantity and diversity of data samples hinder power companies from independently training well-performing GAN models. To solve the above problems, this paper proposes DPP-GAN, a distributed and privacy-preserving GAN system for collaborative smart meter data generation. Specifically, DPP-GAN aggregates dispersed resources through federated learning (FL) to enrich the global GAN model, generating usable data without actual data transmission. Meanwhile, considering the security risks FL faces, such as single point of failure and poisoning attacks, blockchain is employed to store and share local training models in a decentralized manner. It also performs validity verification and aggregation operations through the consensus algorithm to ensure secure joint learning. In addition, a new adaptive weighted model aggregation method and an incentive mechanism are presented to aggregate and reward with reference to local model contributions, enhancing the performance of the global generative model. Simulation results on real-world datasets demonstrate that DPP-GAN maintains high model generation performance while ensuring data privacy and overall security. The generated smart meter data effectively captures the temporal and periodic characteristics of real data, providing essential data support for research and applications in efficient energy management of smart grids.

Edge computing migrates tasks to the edge of the network for execution, which can provide users with lower latency and better quality of service (QoS). However, due to the complexity and dynamism of edge environments, many task scheduling algorithms in edge computing face challenges in achieving real-time scheduling, while there are also trust issues in heterogeneous and dynamic edge environments. To tackle these issues, we introduce a novel framework for trusted task scheduling in edge computing, leveraging blockchain and deep reinforcement learning (DRL) technologies, named BD-TTS. Specifically, we design a blockchain-based trust management scheme tailored for task scheduling in edge computing. The scheme uses blockchain to store, propagate, and update trust information in a decentralized manner to evaluate the trustworthiness of edge servers. In addition, to assign tasks to edge servers with higher trust values for execution, we introduce a DRL-driven task scheduling algorithm. The algorithm dynamically schedules tasks in real-time based on fluctuations in the trust values of edge servers. The experimental results show that compared to other baseline approaches, BD-TTS effectively reduces the number of tasks assigned to malicious edge servers by over 64.4%, reduces the average task response time by at least 13.9%, and improves the success rate by more than 14.7%.

In modern urban areas, inefficiency traffic management is one of the main causes of road congestion, leading to reduced fuel efficiency and increased traffic safety hazards. Traditional researches typically focus only on enhancing the throughput of intersections by optimizing traffic signals or individual vehicle trajectories. However, these methods often overlook the dynamic nature of the traffic system and the potential benefits of vehicle platooning, limiting their effectiveness in complex traffic environments. Addressing this challenge, this article presents MARP, a Cooperative Multiagent deep reinforcement learning (DRL) System for connected autonomous vehicle (CAV) Platooning. Utilizing vehicle to vehicle (V2I) and vehicles to infrastructure (V2V) technologies, MARP integrates sensing, computing, and communication to collect and process real-time data on traffic conditions, thereby achieving dynamic synchronization between traffic signal controllers and CAV platoons. By constructing platoons that collaborates with the infrastructure through a multiagent DRL collaboration model, MARP adapts to real-time traffic flow changes, significantly optimizing the fluidity and efficiency of the entire traffic network. Detailed experiments show that MARP effectively reduces traffic congestion, shortens intersection travel times, and cuts fuel consumption and emissions, surpassing the state-of-the-art approach.

2022年12月19日,中共中央、国务院对外发布了《中共中央国务院关于构建数据基础制度更好发挥数据要素作用的意见》,从数据产权、流通交易、收益分配、安全治理四个方面初步搭建我国数据基础制度体系,提出了20条政策举措.这一文件的发布,对加快我国数据基础制度建设,推动我国数字经济高质量纵深发展具有划时代的里程碑意义.

In data-sharing scenarios in the energy sector, it is essential to establish a sound and reliable access control mechanism to protect energy resources and make informed decisions. However, traditional access control has flaws such as centralization, low transparency, and low flexibility, making it challenging to meet the current security needs of energy data sharing. In addition, due to the distributed, dynamic, and sensitive nature of energy data, it is necessary to ensure its security and auditability. To solve the above problems, this paper proposes an auditable access control model for energy data based on blockchain. The model adopts the attribute-based access control (ABAC) authorization model, which uses attributes as the adjudication element to build access control policies while adding data and user hierarchy attributes to improve adjudication efficiency and meet the need for fine-grained authorization. Based on this authorization model, blockchain technology ensures the data-sharing process is decentralized and transparent. The access control process is automated through four types of smart contracts, and the traceability of blockchain is used to store the access control operation records to monitor the illegal behavior of data requesters. The experimental results show that the model can control the access rights of data requesters with different attributes and ensure the security and controllability of data.

The escalating prevalence of security incidents in the blockchain sphere is posing sig- nificant challenges to its future development. The integration of knowledge graphs into blockchain security is being investigated as a potential solution to offer a com- prehensive view of the blockchain security landscape. Despite the promise, the di- versity and subpar quality of existing blockchain threat intelligence data complicate the use of knowledge graphs for representing this information. The paper proposes the use of knowledge graph fusion, particularly focusing on entity alignment and en- tity linking, as an innovative approach to reconcile knowledge graphs of blockchain threat intelligence from disparate sources. Additionally, it utilizes GCN to model the structural information and an improved TransE to model the attribute information. By combining both representations, the accuracy of blockchain threat intelligence knowledge graph alignment is significantly improved.

The rapid development of blockchain technology and the rise of Ethereum as its representative platform has triggered a wide range of research and applications. However, this development is also accompanied by new security challenges, among which the Eclipse attack is one of the significant security threats currently facing Ethereum networks. In response to these challenges, we propose an Ethereum Eclipse attack detection method based on a multi-head attention mechanism with Bi-LSTM. This approach utilizes the Bi-LSTM model and multi-head attention mechanism to process time-series data, capturing and focusing on the features most relevant to the Eclipse attack for accurate identification. Additionally, we employ PCA and UMAP dimensionality reduction techniques in data preprocessing to enhance processing efficiency. Experimental results demonstrate that this method distinguishes regular traffic from attack traffic more accurately. Compared to the existing random forest method, our detection approach based on a multi-head attention mechanism with Bi-LSTM achieves a higher detection rate and lower false alarm rate, highlighting its effectiveness in addressing Ethereum network security.

The ultimate goal of Internet of Things (IoT) technology is to evolve into the Internet of Everything. Two key elements of IoT are artificial intelligence (AI) for smart devices and the Internet for communication. Privacy protection has posed as a critical challenge for the next intelligent IoT technology revolution as the rapid development of communication technology and big data. Federated learning (FL) combines the privacy protection with machine data analytic and it balances the needs of huge volume data for AI and privacy protection, which also makes it as a leading position in the field of machine learning. However, the way of communication that adopted in federated learning resulted in several critical challenges, such as limited bandwidth, data security, and inconsistent internet speed. In this article, we introduce a super-wireless-over-the-air federated learning framework based on 6G technology to address these issues. By training private data in wireless communication with interference-resistant solid radio waves, future security, and ultra-high-performance AI technology can be realized, which could drive the development of IoT to be smarter, wider, and faster.

Future worldwide 6G research will drive the evolution of emerging intelligent control technologies, such as intelligent speed advisory systems (ISA), to a more advanced generation. As a special type of ISA, consensus-based speed advisory systems (CSAS) can be widely used to recommend a consensus speed for a vehicle platoon, enabling minimizing energy consumption or emissions over a planned route. Recently, speed recommendation services that protect data privacy (i.e., how to obtain an optimal speed in a privacy-preserving way) have drawn tremendous attention. However, current approaches could still encounter service trust issues with central servers and the malicious behavior of vehicles. Furthermore, existing research lacks considering road safety constraints (i.e., safe distance between adjacent vehicles and road speed limits) that are essential for the practical deployment of CSAS. To address the above issues, this paper proposes Eco-CSAS, a safe and eco-friendly consensus speed advisory system using blockchain. We formulate an optimization problem subject to the minimum following distance and maximum road speed limit to minimize the energy consumption of the automatic vehicle platoon. In addition, we introduce a consortium blockchain and cryptographic primitives to ensure service trust and data privacy. We implement the system on the Hyperledger platform, and experimental results show that the system can achieve speed recommendations in a trustworthy and privacy-preserving manner while ensuring a secure platoon.

Autonomous vehicle platooning benefits significantly from the Consensus Speed Advisory System (CSAS), an emerging technology that recommends a consensus speed to reduce energy consumption. However, managing trust to ensure system security and identify malicious nodes poses a considerable challenge in these autonomous environments. Furthermore, while CSAS optimizes the total energy consumption of the platoon, it may inadvertently result in increased energy use for specific vehicles, discouraging their continued participation and hindering an efficient formation of a platoon. This paper proposes a trust-aware and decentralized speed advisory system (TD-SAS) to address these challenges. TD-SAS employs a consortium blockchain for managing trust nodes, providing non-repudiation and tamper resistance for reputation data. Capitalizing on this platform, we use a multi-weight subjective logic model for precise reputation value calculation. Additionally, we present a trust-aware consensus speed recommendation scheme capable of adapting its recommendations to vehicular reputation variations. To mitigate the potential disincentives for vehicles experiencing increased energy consumption, we incorporate an incentive mechanism to encourage their re-engagement with TD-SAS. Comprehensive security analysis and extensive simulation experiments confirm the robustness and effectiveness of TD-SAS, emphasizing its potential for enhancing the energy efficiency and security of autonomous vehicle platoons.

Consensus-based Speed Advisory System (CSAS) is used to recommend a consensus speed to a group of vehicles for specific application purposes, such as minimizing emissions or energy consumption. To remedy data privacy concerns for speed advisory services, the latest works have investigated how to get an optimal speed in a privacy-preserving manner. However, almost all the designs are based on a centralized architecture, which could still meet service trust issues, such as that a random speed could be recommended when the central server meets cyber incursion attacks. To address the problem, in this paper we propose BSAS, a trustworthy and privacy-preserving CSAS over the blockchain. Specifically, BSAS follows a fully decentralized architecture with cryptographic primitives to guarantee service trust and data privacy. Moreover, to encourage vehicles to participate in the service computing process, a value-driven incentive mechanism is also employed. We present the detailed design and implementation of BSAS, and our emulation results show that the proposed BSAS can achieve promising system performance in terms of real-time speed recommendation in a trustworthy and privacy-preserving way.