As a core component of the maritime Internet of Things (IoT), the Automatic Identification System (AIS) continuously collects dynamic vessel navigation data, providing a solid foundation for addressing complex maritime traffic prediction tasks that support intelligent Vessel Traffic Services (VTS), such as vessel trajectory prediction and vessel arrival time (VAT) estimation. However, existing methods typically focus on single prediction objectives, falling short of meeting practical multi-task requirements. To address this gap, this study proposes a geospatial grid-constrained deep learning framework based on AIS data to simultaneously handle three key prediction tasks: vessel trajectory prediction, whether the vessel arrives within the specified time, and VAT. The framework incorporates a dynamic patch construction method and a Graph Soft Evolution (GSE) module to capture temporal correlations among observations under spatial grid constraints. An encoder-decoder architecture is introduced, where the encoder employs a Squeeze-and-Excitation (SE) block to adaptively select feature channels, and the decoder models dependencies across both variable and temporal dimensions. In a case study of New York Harbor, the model achieved an R² of 0.8386 and RMSE of 0.0329 for latitude increment prediction, and an R² of 0.8432 with RMSE of 0.0322 for longitude increment prediction. It also attained 99.83% accuracy in arrival status prediction and an R² of 0.9350 with RMSE of 0.0701 for VAT prediction. The framework demonstrated effectiveness in port scheduling and robust generalizability in cross-validation experiments at the Port of Los Angeles, thereby demonstrating its substantial potential to enhance the operational efficiency of VTS within maritime IoT systems.

Reverse engineering embedded firmware for Real-Time Operating Systems (RTOS) is a formidable challenge in Internet of Things security. The common practice of stripping symbolic information from firmware to optimize performance and storage makes identifying standard library functions exceptionally difficult, creating a significant bottleneck for functional analysis and vulnerability discovery. This paper introduces a novel, automated method for identifying these functions in RTOS firmware by leveraging Large Language Models. Our approach operates directly on raw binary code, requiring no symbolic or debugging information, and demonstrates broad generalizability across diverse hardware architectures and compilers. At its core, the method combines Retrieval-Augmented Generation (RAG) to enhance identification accuracy with a newly designed Adaptive Iterative Screening Algorithm (AISA), which optimizes analysis efficiency by prioritizing candidate functions based on a weighted score of call frequency, call depth, and address proximity to reduce token costs. We validated our method through rigorous experimentation on Zephyr RTOS firmware spanning nine architectures (e.g., ARM, MIPS, RISC-V). The results are compelling: our approach achieves a 90.59% accuracy rate in identifying standard library function names. Moreover, its application to commercial IoT firmware confirms its high efficiency, identifying functions significantly faster and more economically than traditional heuristic techniques. This work contributes a powerful, general-purpose, and cost-effective solution for automated firmware analysis.

Vehicle electrification presents challenges and opportunities across multiple sectors, including the automotive, energy and infrastructure domains. Battery charging and swapping are the two primary technologies for refuelling electric vehicles (EVs). However, the involvement of multiple participants and various factors makes EV refuelling a complex and multi-domain issue. Since conventional conductive charging requires vehicles to remain stationary for a period of time, parking naturally provides opportunities for EV charging. Therefore, parking and EV charging are intrinsically connected in how they are organised and planned. This paper presents a comprehensive literature review on the features of EV refuelling demand and its relation to parking patterns. The review focuses on key study issues related to the interaction between EVs and the power grid, namely forecasting, planning, and scheduling. These issues are examined at three different scales: the individual, station, and regional levels. Based on the findings from the literature, an integrated framework is provided to capture the features and linkages between refuelling demand and parking patterns across the different study issues and scales. Finally, the paper proposes several open issues that could be explored in future studies from the perspective of integrating parking and refuelling analysis.

This paper proposes a non-isolated converter topology capable of achieving a wide output voltage range (200 V to 1000 V). This design addresses the limitations of traditional two-stage isolated and single-stage chargers, where the use of high-frequency resonant converters often restricts the power density and narrows the output voltage range. The proposed topology integrates a buck-stage rectifier circuit with a three-level DC/DC boost stage. To ensure high-efficiency operation and prevent redundant high-frequency switching, a coordinated modulation strategy is developed. When the system operates in buck mode, the high-frequency switches in the boost stage are clamped; when in boost mode, the buck-stage switches are clamped; and during transition mode, both stages coordinate their switching behavior based on the required output voltage. This modulation strategy ensures that the minimum number of high-frequency switches are active at any given time, thereby reducing switching losses caused by hard switching and enabling a smooth transition across the entire output voltage range. Simulation results validate the feasibility and effectiveness of the proposed topology and control method.

In this paper, a multiple-channel wireless power transfer system with anti-interference capability is proposed to provide simultaneous wireless power and data transfer (SWPDT). Two rectangular coils and two DD coils are used for double-channel power transmission, while two other DD coils are used for single-channel data transmission. The crosstalk interference between power and data transfer channels can almost be neglected due to the decoupled design. The design principle of the decoupled coils is illustrated and verified by the finite element method. The circuit topology analysis of the power transfer channel and the data transfer channel based on amplitude shift keying (ASK) is also presented. The effectiveness and anti-interference performance of the system is verified using Simulink. It achieves a data rate of 100kb/s and a power of 2.6kW.

The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV toward large scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this article focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data are abstracted from text form into a graph-structured data form. At the same time, graph differential privacy (DP) together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph-structure transformation, an accelerated nodes and edges combined graph DP (ACGDP) algorithm is proposed. Based on the simulations of real-world data that combine electric and nonelectric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph DP processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers’ income. Drivers’ satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.

Personal driving behavior affects vehicle energy consumption as well as driving safety; therefore, driving behavior is key information for electric vehicle (EV) energy management and advanced driver assistance systems. Eco-driving is an efficient way to reduce energy consumption and air pollution. As the basis of driving behavior, limited types of driving event information, as used in several other studies, cannot be used to meet eco-driving evaluation study needs. Complex and inconsistent human-defined rules are not conducive to the establishment of driving events. Hence, it is necessary to establish a driving event classification system with more categories of drive-topics that can present a better linkage between driving behavior and energy consumption. This paper proposes a driving event recognition method. Dynamic Local Minimum Entropy is proposed, and the Latent Dirichlet Allocation algorithm is used to classify different driving events. Drive-topics are proposed which describe driving events more accurately. The data from fifty battery-electric taxis are used to train the algorithm with data collected by the Service and Management Center for EVs, Beijing, in 2018. The relationship between drive-topic and energy consumption is analyzed to demonstrate that driving behavior can be established using drive-topics to support the evaluation of eco-driving for battery-electric vehicles.

The development of car-sharing system using electric vehicles (EVs) is a promising solution to mitigate traffic pressure and reduce carbon emissions. As the market scale of car-sharing expands gradually, the electricity refueling of EVs in car-sharing system becomes quite vital. We propose the concept of car-sharing battery swapping station (CSBSS), which has both the functions of a car-sharing station and a battery swapping station. An individual CSBSS is modeled as a coupled queuing network. Deep Q-Network (DQN) is implemented in this paper to control the charging operation of replaced batteries (RBs). The proposed charging control strategy can fetch more profit than the baseline scheme. Moreover, the simulation results show that more profits can be obtained by adjusting the state of charge (SOC) threshold or the total number of batteries. Our work provides a practical perspective and guidance for the problem of refueling car-sharing EVs.

The development of plug-in electric vehicles (PEVs) brings lucrative opportunities for charging station operators (CSOs). To attract more CSOs to the PEV market, provision of reasonable pricing policy is of great importance. However, dynamic environments and uncertain behavior of competitors make the pricing problem of CSOs challenging. In this paper, we focus on the dynamic pricing policy for maximizing the long-term profits of CSOs. Firstly, we propose a hierarchical framework to describe the economic association of PEV market, which is composed of smart grid, CSOs and charging stations (CSs) serving PEVs from top to bottom. Next, we leverage the Markov game to model the layer of CSOs as a competitive market. Finally, we design a dynamic pricing policy algorithm (DPPA) based on multi-agent reinforcement learning to achieve higher long-term profits of CSOs. Based on the real data of PEVs in Beijing, the experiment results show that DPPA has a significant improvement in long-term profit of CSOs, and the improvement gains increase over time. Moreover, DPPA can reduce the profit loss of CSOs effectively while involving more competitors.

In recent years, there has been an increasing interest in electric vehicles. However they are still not a major choice for the consumer. This is probably due to many reasons including price and driving range, which is largely due the limitations of current battery technology and the speed with which they can be charged. Wireless charging systems have shown potential for electric vehicle charging. They are convenient, safe when compared to traditional plug-in charging systems. Wireless charging can help reducing electric vehicle pricing if it is done during travel because this will minimize the required battery storage on board. However wireless charging is still limited by the wireless coupler - they currently have low transfer efficiency. This paper presents a state-of-the-art literature review on the recent advancements in charging coupler design.