Modern Intelligent Transportation Systems Research Articles

A security-enhanced authentication scheme for quantum-key-distribution (QKD) enabled Internet of vehicles in multi-cloud environment

The Internet of vehicles (IoV) is an essential part of modern intelligent transportation systems (ITS). In the ITS, intelligent connected vehicle can access a variety of latency-sensitive cloud services through the vulnerable wireless communication channel, which could lead to security and privacy issues. To prevent access by malicious nodes, a large number of authentication schemes have been proposed. However, with the diversification of cloud services and the rapid development of quantum computing, there are many drawbacks remain, including timeliness of authentication and resisting quantum computing. In light of this, we propose a lattice-based secure and efficient multi-cloud authentication and key agreement scheme for quantum key distribution (QKD) enabled IoV. Its features are as follows: i) Security-enhanced and Efficient Authentication: We combine the lattice-based lightweight signatures and quantum authentication keys to guarantee security-enhanced authentication. Meanwhile, we propose the quantum security service cloud (QSC) to manage the authentication of all vehicles and cloud server providers (CSPs) to reduce the authentication rounds and improve efficiency. ii) Extended Quantum Key Distribution (eQKD): In wireless networks, quantum key agreement is achieved through the pre-filled quantum keys. In wired networks, quantum key is accomplished by QKD with Bennett-Brassard 1984 (BB84) protocol. Furthermore, formal and informal security demonstrates that the scheme could resist potential security attacks. The performance comparison illustrates that our scheme could decrease the computational overhead by 27.23%-81.78% and authentication rounds by 81.34%-93.10%.

A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection

The Game Theory Application in the Transport Flows Control Algorithm Development in the Intelligent Transport System

Traffic management in modern intelligent transport systems includes monitoring the actual traffic situation in real time (volumes, speeds, incidents, etc.) and management or controlling transport traffic using this information. At the same time, cars as intelligent transport systems components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and with road infrastructure (V2I). All this is connected with the presence of special equipment connected to the on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of “smart” regulation of intersections is quite difficult to solve, it combines the efforts of scientists from different fields of knowledge. However, the rapid development of ICT technologies and their rapid application to transport tasks makes it possible to develop some approaches to optimize the current situation at the intersection.

Enhancing Security in Vehicle-to-Vehicle Communication: A Comprehensive Review of Protocols and Techniques

Vehicle-to-vehicle (V2V) communication has played a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research focused on investigating the security protocols that have been employed in vehicle-to-vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers was conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompassed key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delved into the challenges and limitations associated with implementing these protocols. The research aimed to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper proposes improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research contributes to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

Learning traffic as videos: A spatio-temporal VAE approach to periodic traffic raster data imputation

For modern Intelligent Transportation System (ITS), data missing during traffic raster acquisition can be inevitable because of the loop detector malfunction or signal interference. Nevertheless, missing data imputation is meaningful due to the periodic spatio-temporal characteristics and individual randomness of traffic raster data. In this paper, traffic raster data collected from all spatial regions at each time interval are considered as a multiple channel image. Accordingly, the traffic raster data over a period of time can be regarded as video, on which an unsupervised generative neural network called MSST-VAE (Multiple Streams Spatial Temporal-VAE) is proposed for traffic raster data imputation, and this model can even robustly performs at varied missing rates while many other approaches fail to conduct. Two major innovations can be summarized in MSSTVAE: Firstly, it uses multiple periodic streams of Variational Auto-Encoders (VAEs) with Sylvester Normalizing Flows (SNFs), which shows strong generalization ability. Secondly, after the traffic raster data are transferred into videos, an ECB (Extraction-and-Calibration Block) consisting of dilated P3D gated convolution and multi-horizon attention mechanism is employed to learn global-local-granularity spatial features and long-short-term temporal features. Extensive experiments on three real traffic flow datasets validate that MSST-VAE outperforms other classical traffic imputation models with the least imputation error.

Algorithm for Control of Traffic Flow in an Intelligent Transport System

Traffic management in modern Intelligent Transport Systems (ITS) includes monitoring the actual traffic situation in real time and management transport traffic using this information. At the same time, cars as ITS components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and road infrastructure (V2I). Such approach is connected with usage of special equipment connected to on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of "smart" regulation of intersections is quite difficult to solve a problem, nevertheless it is possible due to rapid development of ICT technologies. Keywords: ITS, transport crossroads, game theory

GRAPHITE — Generative Reasoning and Analysis for Predictive Handling in Traffic Efficiency

Traffic forecasting is a crucial aspect of modern Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), playing a vital role in improving the safety and efficiency of daily transportation activities. Despite the valuable contributions of traditional machine learning (ML) models and advanced deep learning (DL) techniques, there persist challenges in capturing the intricate spatial and temporal dependencies inherent in traffic flow. In response to these challenges, we present GRAPHITE, an innovative framework that combines Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to leverage generative reasoning for efficient traffic management. Our model seamlessly integrates historical traffic volume data collected by road sensors with local spatial information encoded through knowledge graphs (KGs) associated with each sensor. These KGs offer a structured representation of relationships between traffic sensors and points of interest (POIs) in their neighborhood, thereby enhancing the comprehension of the urban context and leading to more accurate traffic predictions. Extensive experiments conducted on diverse datasets underscore the efficacy of GRAPHITE. Notably, we achieved a maximum decrease in RMSE of 31.05% compared to GAN-GRU and a maximum increase in R2 of 8.15% compared to GAN-RNN, positioning GRAPHITE as a standout solution among the current state-of-the-art approaches. Our code is available at: https://github.com/MODAL-UNINA/GRAPHITE.

Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making

In the continuously evolving context of urbanization, more people flock to cities for job opportunities and an improved quality of life, resulting in undeniable pressure on transportation networks. This leads to severe daily commuting challenges for residents. To mitigate this urban traffic pressure, most cities have adopted urban dockless bicycle sharing systems (UDBSS) as an effective measure. However, making accurate decisions regarding UDBSS demand in different city locations is crucial, as incorrect choices can worsen transportation problems, causing difficulties in finding bicycles or excessive deployments leading to disorderly accumulation. To address this decision-making challenge, it is essential to consider uncertain factors like daily weather, temperature, and workdays. To tackle this effectively, we construct an adjustable multi-granularity (MG) complex intuitionistic fuzzy (CIF) information system using complex intuitionistic fuzzy sets (CIFSs). This system objectively determines classification thresholds using an evaluation-based three-way decision (TWD) method, creating adjustable MG CIF probability rough sets (PRSs). Additionally, to recognize the irrationality of decision-makers (DMs), we propose a method that combines prospect theory (PT) with regret theory (RT), providing a more comprehensive understanding of the influence of DMs' psychological factors on decision outcomes. Building upon these foundations, we present static rebalancing strategies for UDBSS based on MG PRSs and prospect-regret theory (P-RT) within the CIF information system. Finally, using UDBSS data collected from various sensors, we conduct experimental analysis to verify its feasibility and stability. In summary, this approach considers residents’ daily usage preferences, including bicycles utilization and return, with the aim of minimizing unmet resident demands and predicting usage patterns for the next day. It effectively addresses the issue of UDBSS distribution inefficiencies and holds a significant advantage in prediction, making it suitable for broader applications in transportation systems and contributing to the establishment of more advanced modern intelligent transportation systems (MITSs) in the future.

GT-LSTM: A spatio-temporal ensemble network for traffic flow prediction

Traffic flow prediction plays an instrumental role in modern intelligent transportation systems. Numerous existing studies utilize inter-embedded fusion routes to extract the intrinsic patterns of traffic flow with a single temporal learning approach, which relies heavily on constructing graphs and has low training efficiency. Different from existing studies, this paper proposes a spatio-temporal ensemble network that aims to leverage the strengths of different sequential capturing approaches to obtain the intrinsic dependencies of traffic flow. Specifically, we propose a novel model named graph temporal convolutional long short-term memory network (GT-LSTM), which mainly consists of features splicing and patterns capturing. In features splicing, the spatial dependencies of traffic flow are captured by employing self-adaptive graph convolutional network (GCN), and a non-inter-embedded approach is designed to integrate the spatial and temporal states. Further, the aggregated spatio-temporal states are fed into patterns capturing, which can effectively exploit the advantages of temporal convolutional network (TCN) and bidirectional long short-term memory network (Bi-LSTM) to extract the intrinsic patterns of traffic flow. Extensive experiments conducted on four real-world datasets demonstrate that the proposed network obtains excellent performance in both forecasting accuracy and training efficiency.

Spatial–Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning

With the development of advanced embedded and communication systems, location information has become a crucial factor in supporting context-aware or location-aware intelligent services. Among these services, modern Intelligent Transportation System (ITS) has the strictest requirements for real-time, accurate, and private location data. In this study, a Spatial-Temporal Federated Transfer Learning (ST-FTL) framework is designed and introduced to achieve more precise cooperative positioning with multi-sensor data fusion while protecting the location data privacy in urban ITS. Specifically, a three-layer FTL architecture is constructed to enhance the prediction accuracy on GPS positioning errors for vehicles in different regions especially when facing missing local data in some specific scenarios (e.g., urban canyons), in which Transfer Learning (TL) is incorporated to optimize the initialization of global model with faster convergence but less communication cost in Federated Learning (FL). A multi-attribute based spatial-temporal clustering algorithm is developed to facilitate the finding of more appropriate source domains similar to the target domain in a density-based scheme, while a convolutional-gated unit is newly designed to further filter out the useless features and add new features based on the convolution operations and gating mechanism, resulting in more effective global model initialization and weight aggregation from cross-region selection. A multi-sensor data fusion model is built in local to improve the prediction accuracy on positioning errors, in which an improved time-aware asymmetric attention mechanism is involved to selectively adjust the weight importance of the incorporated GPS and Inertial Measurement Unit (IMU) data in a more targeted fusion process. Additionally, an improved Siamese network structure, which breaks the traditional dual network structure and only adopts one single network structure, is leveraged to realize the lightweight data augmentation based on the better utilization of historical similar data from local vehicles themselves, when suffering from insufficient training data. Experiments and evaluations based on two different public datasets demonstrate the outstanding performance of our proposed model and method in achieving superior prediction accuracy and faster convergence for cooperative positioning compared with other state-of-the-art positioning methods in urban ITS.

A2DJP: A Two Graph-Based Component Fused Learning Framework for Urban Anomaly Distribution and Duration Joint-Prediction

Modern intelligent transportation system (ITS) has greatly benefitted people's daily life. However, the chanciness and suddenness of urban anomalies may greatly restrict the trouble-free operations of ITS. To be aware of future urban anomalies and their possible influences, great efforts have been achieved on these two aspects, but comprehensive predictions of urban anomalies including the predictions of distributions and durations, are still beingless. And the spatiotemporal cascade self/mutual exciting influences among anomalies have never been considered in previous studies. In this paper, we propose a novel Anomaly Distribution and Duration Joint-Prediction (A2DJP) algorithm to simultaneously filtrate urban subregions and estimate the durations of corresponding potential anomalies in the future. To capture the spatiotemporal correlations between urban traffics and anomalies, we use a modified Graph Convolution Network and Long Short-Term Memory integrated network. To learn the cascade correlations among anomalies themselves, we devise a novel Spatiotemporal neural Hawkes Process model, which contains a Hawkes Process (HP) based GCN and HP-based LSTM to extract the anomaly-wise spatiotemporal cascading correlations. By fusing the spatiotemporal correlations between traffics and anomalies, we then simultaneously predict the distributions and durations of future anomalies. Extensive experiments on real-world datasets demonstrate that our proposed method significantly outperforms the state-of-the-art solutions.

An Efficient Vehicle-Assisted Aggregate Authentication Scheme for Infrastructure-Less Vehicular Networks

In recent years, growing research interest from both industry and academia has been aroused to the vehicular networks, which is regarded as the fundamental component of the modern intelligent transportation system (ITS). Lots of remarkable research outputs with respect to secure vehicular data interactions and user privacy preservation has been witnessed. However, the existing schemes all focus on the common vehicular communication scenarios where facilities are deployed, whereas the secure data exchange in the abnormal infrastructure-less vehicular environment has not been properly investigated. To deal with unpredictable abnormal situations caused by artificial or natural disasters such as earthquakes and floods, a distinctive vehicle-assisted aggregate authentication mechanism for infrastructure-less vehicular networks is presented in this paper. With assistance from the neighboring vehicles, the homomorphic signature involving all requesting vehicles is generated and forwarded to the remaining functional RSUs. Meanwhile, vehicular group communication among the validated entities is enabled. Additionally, the fault-tolerant verification method is adopted such that the ineffective entities can be easily distinguished and removed without interfering with other requesting vehicles. The security proofs and discussions regarding vital security properties are presented, while the performance analysis follows. Compared with the state-of-the-art, advantages in terms of security and performance properties can be proved.

An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction

Traffic prediction is an important part of modern intelligent transportation systems (ITS), which helps transportation management and city planning. However, it is a very challenging task for modeling complex spatio-temporal dependencies, since the traffic data belongs to highly periodic multivariate time series which makes it hard to model accurate spatial dependencies only from time series and observed geolocation information of road segments. The existing research mainly focuses on finding ways of capturing dynamic spatial dependencies of road segments while neglecting the importance of periodicity, and few studies have explored a pure embedding-driven method that is robust to corrupted data to model periodicity. In this paper, we propose an embedding-driven multi-hop spatio-temporal attention network for traffic prediction (), which mainly focuses on leveraging the multi-scale periodicity of traffic data. Specifically, the proposed network applies a designed Fourier-series-based embedding, to capture the periodicity, which is more in line with real-world facts. Driven by the designed embedding, both local and global temporal dependencies are modeled properly by combining the attention-based methods and the convolution-based methods. Besides, we implement a trial that can hardly be seen in the existing traffic prediction works to combine the graph self-attention mechanism with a multi-hop diffusion process to explore the large-scale structural information on a designed set of graphs. Experiments on two real-world traffic datasets which contains traffic speed data for months show the effectiveness of our proposed methods. The experiments also suggest the methods can provide stable reasonable and smooth predictions for completely corrupted data.

Удосконалення методу забезпечення паливної економічності вантажних автомобілів засобами інтелектуальних транспортних систем

The purpose of the study is improving the method of ensuring the fuel efficiency of trucks by means of intelligent transport systems in variable operating conditions. The analysis of the processes of ensuring the operational control of the fuel consumption of cargo vehicles shows the perspective of their implementation on the basis of modern intelligent transport systems, the main tool of which is the method of remote monitoring. Remote monitoring of cargo vehicles is an effective tool designed not only for tracking the location and movement of vehicles, but also for a certain number of parameters of their technical condition. A mathematical model of an improved method of ensuring the fuel economy of trucks has been developed, which includes a process and analytical component for estimating limit values, forming optimal characteristics and forecasting technical condition parameters and fuel consumption by the engine and vehicle in the processes of cargo transportation. The mathematical model is built on the basis of differential and algebraic equations developed at KhNADU (Kharkiv National Automobile and Road University), NTU (National Transport University) and personally by the authors, which was adapted with appropriate changes in the part of the initial data and algebraic equations to improve the existing method. The improved method for determining and calculating the fuel consumption of a vehicle in operating conditions is based on the information and software complex (ISK) "Virtual Mechanic "HADI-12"" and the ISK "Service Fuel Eco "NTU-HADI-12"" of Khnadu.
 As a result, the authors formulated as a general approach to the formation and improvement of the method of ensuring the fuel economy of a cargo vehicle of category N3 in the conditions of operation by means of intelligent transport systems, and actually the processes of solving the set tasks are based on the implementation of the system interaction of three interrelated components: process, information and analytical.

A Vision-Based Method for Real-Time Traffic Flow Estimation on Edge Devices

Traffic flow estimation is an essential task in modern intelligent transportation systems. Many types of information, including vehicle type, vehicle totals, and movement direction, are vital for mitigating transportation-related tasks and effective traffic control strategies. With the development of embedded devices, systems can process captured video at the edge instead of transferring data to centralized processing servers. This paper proposes a real-time and edge-based traffic flow estimation system. The proposed system follows a detect-and-track mechanism where lightweight deep learning models perform vehicle detection. A novel scenario-based tracking and counting technique is developed to provide multi-class, multi-movement vehicle counting. The method uses predefined regions to assign the movement for each vehicle initially detected. It then performs spatial-temporal trajectory matching between the vehicle trajectory and the movement path throughout the whole video. Extensive experiments have shown that the proposed method achieves high effectiveness with multiple camera types and viewpoints.

Analysis of the security of on-board information systems in vehicles

The features of the functioning of the on-board information systems of a car are considered. Threats to their security are analyzed, and methods for ensuring information security and functional security of on-board information systems are proposed. The design of road networks in the organization of road traffic is one of the factors in ensuring the functional security of modern intelligent transport systems, that is, compliance with such information security attributes as data confidentiality, integrity, availability, authenticity and novelty of data. The security of on-board vehicle information systems is a critical issue in the modern world, as more and more vehicles are equipped with electronic systems that may be vulnerable to cyber attacks. One of the main challenges of protecting on-board information systems is the wide range of devices and technologies used in modern vehicles. Different systems may have different security requirements and vulnerabilities. They may interact with each other in a complex way. Another challenge is that many of these systems were not designed originally with security in mind. They may lack basic security features such as encryption and authentication and use outdated software and protocols that are vulnerable to known attacks. The main types of attacks and threats to the elements of the transportation system that interact with the VANET were identified to analyze the information security of vehicle in-vehicle systems. Based on the theory of fuzzy sets under conditions of uncertainty and using the Fuzzy Logic Toolbox in the integrated Matlab environment, the level of information security of the OBU-VANET system was modeled. The obtained results allowed us to formulate the degree of information security of vehicle operation elements against unauthorized access to data. The results of the study showed that technical communication systems have the highest security level (> 0.7), and vehicles become the most vulnerable in public places.

Deep Learning-Based Vehicle Type Detection and Classification

Modern intelligent transportation systems heavily rely on vehicle-type classification technology. Deep learning-based vehicle-type categorization technology has sparked growing concern as image processing, pattern recognition, and deep learning have all advanced. Over the past few years, convolutional neural work, in particular You Only Look Once (YOLO), has shown to have considerable advantages in object detection and image classification. This method speeds up detection because it can predict objects in real time. High accuracy: The YOLO prediction method produces accurate results with low background errors. YOLO also understands generalized object representation. This approach, which is among the best at object detection, outperforms R-CNN approaches by a wide margin. In this paper, YOLOv5 is used to demonstrate vehicle type detection; the YOLOv5m model was chosen since it suits mobile deployments, the model was trained with a dataset of 3000 images, where 500 images were allocated for each class with a variety of vehicles. Hyperparameter tuning was applied to optimize the model for better prediction and accuracy. Experimental results for a batch size of 32 trained for 300 epochs show a precision of 98.2%, recall of 94.9%, mAP@.5 of 97.9%, mAP@.5:.95 of 92.8%, and overall accuracy of 95.3% trained and tested on four vehicle classes.

Globally Optimal Robust Radar Calibration in Intelligent Transportation Systems

Radar is among the most popular sensors in modern Intelligent Transportation Systems (ITSs), enabling weather-robust perception. The orientation and position of the traffic radar relative to the ITS coordinate system are necessary for the perception fusion in ITSs. However, due to the unknown target association, sparseness and noisiness of traffic radar measurements, the robust and accurate extrinsic calibration of traffic radar is challenging. In this paper, we propose a targetless traffic radar calibration method based on GPS to overcome the inconvenience during ITS operation, because the installation of a dedicated calibration target on the highway is impractical and dangerous. On the other hand, the high-precision GPS device installed on the moving vehicle can provide traffic radar with accurate positioning information of the detection target. Furthermore, during the optimization process of extrinsic calibration, we propose a globally optimal registration method, which is robust to noise and outliers in radar measurements, and is called Gaussian Mixture Robust Branch and Bound (GMRBnB). Specifically, we first construct the robust objective function by utilizing the Gaussian Mixture Model (GMM). Then, we derive novel relaxation bounds and present the GMRBnB algorithm that overcomes the susceptibility to local minima and the dependence on initialization of traditional optimization methods. Compared with existing methods, extensive experiments in synthetic and real-world data demonstrate that our method is not only globally optimal, but also more accurate and robust.

SecGreen: Secrecy Ensured Power Optimization Scheme for Software-Defined Connected IoV

Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology that is being used in modern intelligent transportation systems (ITS). The ultimate goal of SD-IoV is to provide seamless connectivity to the end-users with low latency and high-speed data transfer. However, due to the increase in the density of the connected IoV using an open channel, i.e., the Internet, the foremost challenges of high power consumption and secure data transfer are inevitable in such an environment. An external eavesdropper may intercept the transmitted message to access the legitimate information over the public channel, i.e., the Internet. Most of the solutions reported in the literature to tackle these issues may not be applicable in the SD-IoV environment due to high computation and communication costs. Motivated from this, in this paper, the problems of high power consumption and secure data transfer in SD-IoV are formulated using mixed-integer non-linear programming (MINLP) with associated constraints. To solve the aforementioned problem, we propose a joint power optimization and secrecy ensured scheme known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> has an efficient energy harvesting algorithm using simultaneous wireless information and power transfer (SWIPT) to maximize the energy efficiency. Moreover, to mitigate various security attacks, a resilient lightweight secrecy association protocol is designed between vehicle and trusted gateway node of SD-IoV so that only trusted vehicles can communicate with each other and with the nearest base stations. The secrecy association protocol uses security primitives such as– physically unclonable function (PUF), one-way hash function, and bitwise exclusive OR (XOR) operations which are suitable for energy-constraint sensors in SD-IoV. The performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> is compared with the existing schemes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stable &amp; Scalable Link Optimization (SSLO), and Secure &amp; Energy-Efficient Blockchain-enabled (SEEB)</i> respectively. The result shows that when the number of packets across the subchannel increases, the energy consumption increases. Also, the result shows that the proposed scheme attains 22.5% and 20.34% better energy efficiency as compared to SSLO and SEEB schemes, respectively. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> scheme achieves 37.48% and 32.15% higher throughput as compared to SSLO and SEEB schemes. The results obtained show the superior performance of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecGreen</i> scheme in comparison to these existing competitive schemes in the literature.

Novel Sparse Feature Regression Method for Traffic Forecasting

Traffic forecasting is an integral part of modern intelligent transportation systems. Although many techniques have been proposed in the literature to address the problem, most of them focus almost exclusively on forecasting accuracy and ignore other important aspects of the problem. In the paper at hand, a new method for both accurate and fast large-scale traffic forecasting, named “sparse feature regression”, is presented. Initially, a set of carefully selected features is extracted from the available traffic data. Then, some of the initial features are sparsified, namely they are transformed into sets of sparse features. Finally, a linear regression model is designed using the sparse feature set, which is trained by solving an optimization problem using a sparse approximate pseudoinverse as a preconditioner. We evaluated the proposed method by conducting experiments on two real-world traffic datasets, and the experimental results showed that the method presents the best balance between accuracy of predictions and time required for achieving them, in comparison with a set of benchmark models.