Traditional Deep Learning (DL) method is increasingly used in vehicular Intrusion Detection Systems (IDSs). However, there are still some limitations. It combines various models, resulting in a massive model size and numerous parameters, requiring more computational resources. Furthermore, real-time performance is crucial in vehicular IDS. Large models typically require more time for detection, failing to meet the timeliness requirements. Quantum computing offers parallel computing capabilities, prompting our proposal of a Hybrid Quantum Neural Network (HQNN)-based IDS to improve the timeliness of intrusion detection. This IDS employs HQNN to enhance feature extraction and speed up traditional convolution. It retains most of the structure of classical Convolutional Neural Network (CNN), comprising attention layers, batch normalization layers, and quantum circuits. Through quantum superposition and entanglement, certain complex non-linear functions are compressed. Furthermore, residual connections enable seamless gradient flow during back propagation. This accelerates computation and expedites convergence. Comparative experiments on the Car-hacking dataset show improved detection time and convergence speed, with exceptionally high accuracy in practical applications.

Electric Vehicle (EV) is regarded as the optimal alternative to traditional fuel-powered vehicles. However, the exponential surge in EV charging demand poses challenges in charging infrastructure planning and charging behavior management. The efficacy of traditional Grid-to-Vehicle (G2V) charging mode, which obtains power from the grid, is curtailed by the limited number and uneven distribution of charging facilities, inevitably leading to charging congestion. Instead, the concept of Vehicle-to-Vehicle (V2V) charging has emerged as a spatio-temporally flexible charging mode, which expands EV's role from consumer to provider, forming V2V pairs and utilizing urban Parking Lots (PLs) as charging locations. In this paper, we propose a Hybrid Heterogeneous Modes (HHM)-based EV charging optimization scheme in urban settings. Building upon the G2V mode, we introduce synchronous and asynchronous V2V charging modes as optimization strategies, integrating and exploiting the unique advantages of each mode. We also utilize global EV charging scheduling and comprehensively take four dimensions into consideration (energy trading cost, travel cost, waiting time cost and loss cost), thus achieving flexible mode selection, V2V pairing, and designated charging locations. Simulations confirm the effectiveness of the proposed scheme in reducing total charging costs, optimizing user service experiences, and improving charging facility utilization rates.

Pattern matching in Intrusion Detection Systems (IDS) is one of the most critical and time-consuming elements, allowing the system to make decisions based on the real-time threats across the network. A pattern-matching method can be software-based or hardware-based. In this paper, a hardware implementation of bit-split algorithm has been discussed for pattern matching in order to detect unwanted traffic for a maximum number of rulesets. A hardware-based string matching scheme has been preferred here due to its fast speed and quick data parallelism for the high-performance Intrusion Detection System (IDS). The prototype of the detection method is implemented on Spartan SP605 with a small number of rules. For a large number of rules, a multi-scale Field Programmable Gate Array (FPGA)-based hardware architecture has been implemented in which we have used NetFPGA-SUME. This FPGA board has examined incoming packets at a bit rate of 1.25 Gb/sec with an operational frequency of 156.25MHZ. Furthermore, high-level data parallelism has been implemented by instantiating more than one match engine for handling multiple packets to achieve high throughput (Tp).

Detecting objects in remote sensing images (RSIs), characterized by dramatic scale variation and complex backgrounds, has always been a challenging problem. These challenges can be further summarized into three aspects: 1) scale variation among objects; 2) feature fusion misalignment due to the semantic gap between adjacent feature layers and noise from backgrounds; and 3) boundary uncertainty under ambiguous and complex backgrounds. To alleviate these problems, we first utilize a global–local feature enhancement module (GLFEM) to capture local features with multiple receptive fields through cheap pooling operation and obtain global features through nonlocal block, thus alleviating the scale variation issues. Subsequently, attentional feature fusion alignment (AFFA) module is designed to align adjacent feature levels in the feature pyramid from pixel and channel levels. Finally, boundary-uncertainty aware head (BUAH) with distribution focal loss (DFL) is adopted to solve the boundary uncertainty problems. After fusing GLFEM, AFFA, and BUAH modules, we obtain GAB-Net. GAB-Net outperforms state-of-the-art methods on the Dior and NWPU VHR-10 datasets, achieving mAP scores of 73.8% and 89.8%, respectively, without adding high computational costs. The code is available at:https://github.com/Hong-yu-Zhang/GAB-Net.

This article aims to design an intelligent mobile charging control mechanism for electric vehicles (EVs), by promoting charging reservations (including service start time, expected charging time, and charging location). EV mobile charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically mobile plug-in chargers, capable of providing on-site charging services. With intelligent charging management, readily available mobile chargers are predictable and could be efficiently scheduled toward EVs with charging demand, based on updated context collected from across the charging network. The context can include critical information relating to charging sessions and charging demand. Furthermore, with reservations introduced, accurate estimations on charging demand for a future moment are achievable, and correspondingly, optimal mobile chargers selection can be obtained. Therefore, charging demands across the network can be efficiently and effectively satisfied, with the support of intelligent system-level decisions. In order to evaluate critical performance attributes, we further carry out extensive simulation experiments with practical concerns to verify our insights observed from the theoretical analysis. Results show great performance gains by promoting the reservation-based mobile charger selection, especially for mobile chargers equipped with suffice power capacity. Note to Practitioners-The convenience of charging service is one major concern for EVs, especially when an urgent charging is required while none charging points are reachable. Recently, a Chinese EV company (NIO, Inc., Shanghai, China) is promoting its mobile charger (ES8 model) to Tesla. Driven by such market trend, this article proposes an efficient approach toward intelligent scheduling of mobile chargers toward parked EVs. Different from fixed charging stations focusing on the problem of long waiting times, the proposed solution is applicable to charging-on-demand with precharging appointment at mobile chargers. Preliminary experiments show great charging efficiency achieved by concerning the issue of where to reserve, i.e., the consideration of optimal selection on mobile chargers. Such mobile charging services can coexist with the governmental or pilots' initiated charging station deployment. However, future research will need to evaluate the holistic service platform.

Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.

Continuous and effective developments in Autonomous Vehicles (AVs) are happening on daily basis. Industries nowadays, are interested in introducing less costly and highly controllable AVs to public. Current so-called AVP solutions are still limited to a very short range (e.g., even only work at the entrance of car parks). This paper proposes a parking scheduling scheme for long-range AVP (LAVP) case, by considering mobility of Autonomous Vehicles (AVs), fuel consumption and journey time. In LAVP, Car Parks (CPs) are used to accommodate increasing numbers of AVs, and placed outside city center, in order to avoid traffic congestions and ensure road safety in public places. Furthermore, with positioning of reference points to guide user-centric long-term driving and drop-off/pick-up passengers, simulation results under the Helsinki city scenario shows the benefits of LAVP. The advantage of LAVP system is also reflected through both analysis and simulation.

With continually increased attention on electric vehicles (EVs) due to environment impact, public charging stations (CSs) for EVs will become common. However, due to the limited electricity of battery, EV drivers may experience discomfort for long charging waiting time during their journeys. This often happens when a large number of (on-the-move) EVs are planning to charge at the same CS, but it has been heavily overloaded. With this concern, in an EV charging management system, we focus on CS-selection decision making and propose a scheme to manage EVs' charging plans, to minimize drivers' trip duration through intermediate charging at CSs. The proposed scheme jointly considers EVs' anticipated charging reservations (including arrival time and expected charging time) and parking duration at CSs. Furthermore, by tackling mobility uncertainty that EVs may not reach their planned CSs on time (due to traffic jams on the road), a periodical reservation updating mechanism is designed to adjust their charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our proposal, in terms of minimized drivers' trip duration, as well as charging performance at the EV and CS sides.

Industrial Internet of Things (IIoT) is the other name of industrial Internet. It integrates a variety of existing industrial automation technologies with computing, machine learning, and communication technologies. Vehicular ad hoc network, an application of IIoT, is a self‐organized network of vehicles which tends to provide improved road safety, diminished traffic congestion, and ultimate comfort to the travellers. In VANETs, vehicles exchange data with each other directly or through roadside units (RSUs). Data dissemination in VANETs experiences numerous challenging issues including broadcast storm, network partitions, intermittent connectivity between vehicles, and limited bandwidth. In literature, various data dissemination schemes are proposed. However, most of these schemes are designed for either urban or highway VANET scenarios and evaluated under sparse or dense traffic conditions. Moreover, these schemes do not effectively overcome the aforementioned issues simultaneously. In this paper, we present a new data dissemination protocol for VANETs, which disseminates the emergency messages in different scenarios under varying traffic conditions. During dense traffic conditions, DDP4V employs the segmentation of transmission region of a vehicle in order to select the most appropriate next forwarding vehicle (NFV). Accordingly, it divides the transmission region of a vehicle in three distinct segments and selects vehicle(s) inside the highest priority segment to forward the message to all neighbour vehicles, whereas it also uses implicit acknowledgements for guaranteed message delivery during sparse traffic Conditions. Simulation results show that DDP4V protocol outperforms the other existing related protocols in terms of coverage, network overhead, collision, and end‐to‐end delay.

The concept of Delay Tolerant Networks (DTNs) are proposed to facilitate communication in challenged mobile wireless networks using the Store-Carry-Forward (SCF) routing behavior. In this paper, our motivation is to take advantage of geographic routing since it routes message without the knowledge about network topology by using realtime location information, overcoming the challenge of large network topology variation in DTNs. Different from traditional geographic algorithms, our approach only adopts history geographic information due to the difficulty to obtain the realtime location of destination, suffering from sparse network density and high mobility. The key insight of our algorithm is to separate message replication depending on the proximity to the movement range estimated for destination, followed by the proposed scheduling methodology for prioritized transmission between each phase as well as anti-diffusion function for redundancy reduction. Simulation results under the Helsinki city scenario show an improvement comparing with two well known geographic approaches in DTNs, considering delivery ratio, average latency as well as overhead ratio.

The emergence of MANET supported applications and the subsequent widespread acceptance is facilitated by the development and availability of cheap wireless Wi-Fi devices. It is on this premise that Flow-Aware Admission Control (FAAC) protocol is designed to improve the throughput assurance of MANET-based applications. The work presented in this paper is an extension of our designed FAAC protocol to guarantee the provisioning of QoS in many mobile scenarios. The source node is designed to maintain multiple paths to each destination with the reliability of the routes maintained through nodes disjointness. The designed FAAC-Multipath protocol is equipped with the intermediate route repair mechanism to enable the intermediate node to carry out route repair locally without contacting the source node. We have illustrated the effectiveness of FAAC-Multipath with the state of the art admission control protocols.

Privacy, Anonymity, pseudonymity are the debatable terms which on one angle restrict the rights but on other angle open doors for malicious activity.This paper proposes design for a pseudonymised communication which is optimal solution for the privacy issues, where the real identity is hidden from the outside world by using blind signature in a different way of group communication.The protocol is deployed and analysed in extreme environment of Delay Tolerant Networks (DTN), which is overlay on the top of traditional Internet which provides end to end connectivity and characterized with long delay and disruption. A telemedicine application of rural area network is explored to provide source anonymity.The protocol is finally analysed using Dolev Yao model with two different cases and it preserves the original identity of the node.