Internet Of Intelligent Vehicles Research Articles

An intelligent FL-based vehicle route optimization protocol for green and sustainable IoT connected IoV

The intelligent Internet of Vehicles (IoV) provides superior results in effectively addressing complex transportation challenges. Predicting vehicle traffic, crashes, demand, location, communication, and travel safety are all critical issues in today’s transportation systems. The proposed paper optimizes vehicle traffic by incorporating reroute recommendations, increasing the use of public transportation, and providing onboard vehicle drivers with intelligent health assistance using Federated Learning (FL). This research also focuses on resolving complex transportation issues such as a vehicle’s current location, exact vehicle count information on each route, and onboard vehicle vacant seat information. Furthermore, vehicle communication contributes to the proposed system’s efficiency by avoiding communication delays or information loss to registered users and the cloud server. An intelligent FL-based scheme for vehicle route optimization has been proposed as part of this research to prevent vehicle traffic in a real-time Internet of Things (IoT) connected IoV transportation system. The vehicle detection approach determines the number of vehicles traveling on each route to recommend the best route to registered users. The effective implementation of cluster-based vehicle communication and location estimation models enhances the efficiency of the proposed system.

A vehicular network based intelligent transport system for smart cities using machine learning algorithms

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%.

Decentralized Parallel SGD Based on Weight-Balancing for Intelligent IoV

Training machine learning models in a decentralized way has attracted tremendous attention on intelligent Internet of Vehicles (IIoV). However, it is highly dynamic and asymmetric for the connections between vehicles in IIoV due to the mobility of vehicles and the complex communication environment, which poses great challenges on designing efficient distributed learning algorithms. To address this problem, we focus on the basic stochastic gradient descent (SGD) algorithm and propose a decentralized parallel SGD algorithm (DPSGD-WB) for the complex IIoV. The algorithm is based on weight-balancing to overcome the difficulty caused by the dynamic and asymmetric connectivity in IIoV. With rigorous analysis, we show that DPSGD-WB converges on the optimal rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(1/\sqrt{Kn})$</tex-math> </inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> is the number of vehicle terminals and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula> is the number of iterations. To the best of our knowledge, our proposed algorithm is the first known decentralized parallel SGD algorithm that can be implemented in asymmetric and dynamic intelligent IoV systems. Finally, extensive experiments demonstrate the efficacy of our algorithm.

Trends and Open Research Issues in Intelligent Internet of Vehicles

Abstract The evolution of vehicles has always been continuous with respect to growth in technology.The concept of the Internet of Vehicles (IoV) is the process of allowing vehicles to interact with each other to provide real-time information. This paper introduces the various aspects of IoV and their components. Despite the fact that there are more and more vehicles connected to the IoV, there are still many unknown issues and potentials that needs to be identified to carry out research. In order to identify and classify the current difficulties in implementing and using IoV in urban cities, various research publications on the topic were analysed in this paper. The limitations of the Internet of Vehicular technology are also described. Additionally, a number of current and potential remedies that address the highlighted problems were briefly covered. The background information and reasons for evolving heterogeneous vehicular networks are thoroughly reviewed in this research. Also highlights the key technologies of IoV, network architecture and comparison of IoV architecture models with focus on different communication models The most modern IoV enabling technologies are also highlighted, along with environmental scope of intelligent internet of vehicles. Finally, the paper has reviewed the open research issues of Intelligent IoV such as Poor Connectivity of on road vehicles and Stability, Hard delay constraints, High reliability requirements, high scalability, Security and privacy, etc. and related solutions.

Intelligent transportation system for internet of vehicles based vehicular networks for smart cities

A popular research area is internet traffic analysis as it has many applications, mainly for classifying internet traffic. Innovative technologies have been developed for predicting and identifying traffic congestion in the intelligent Internet of vehicles (IOVs). In this paper, an intelligent transport system for the IOVs-based vehicular network traffic for smart city scenario is proposed based on tree-based Decision Tree (DT), Random Forest (RF), and Extra Tree (ET), and XGBoost machine learning (ML) models. Simulation results indicate that the proposed system can provide high detection accuracy and low computational costs thanks to ensemble learning and averaging important feature selection. The tree-based ML techniques with feature selection performed better than those without feature selection for IOV-based vehicular network traffic. The Stacking model shows higher classification accuracy, 99.05%, compared to the lowest KNN accuracy, 96.6%, and SVM accuracy, 98.01%.

A Fuzzy Logic-Based Smart Traffic Management Systems

Traffic congestion is a serious problem in many developing countrieslike Egypt. In the presence of thetraditional traffic light system, it creates health risks, wastes time andfuel, and pollutes the air. Therefore, there is a big need for a smart trafficmanagement system. This study contributes in solving this problem byintroducing an artificial intelligence-based smart traffic light system usingfuzzy logic to ensure the smooth flow of traffic in cities. Research resultsindicate that smart traffic light management is very crucial in smart cities toreduce their carbon footprint to save the world and save energy. Other possibleapplication could be implemented with minor system upgrades such as Detectionand Management of traffic Congestion, Automatic Billing of Toll Charges,Automatic detection of speed limit Violation, Route planning, IntelligentInternet of Vehicles, and Prevention of Road Accidents. Four traffic lightapproaches (fixed-time, smart-time, fixed-time-fuzzy logic, andsmart-time-fuzzy-logic) are developed with different eight traffic scenarios.The four approaches are applied with two tools, the first tool is a realMarquette which consists of 4 roads with only one or two vehicles. The secondis a simulation software which consists of 4 roads with more than 100 vehiclesthrough 10 min. Many sensors such as (IR sensors, rain sensors, and LEDs tocontrol traffic light) are used to collect data and then the fuzzy algorithm isused to ensure the smooth flow of traffic in cities. The traffic data acquiredfrom the vehicles is fed into the proposed model to maximize the duration ofthe green light based on the road state. According to experimental results, theproposed smart fuzzy technique simulation software reduced the average waitingtime of the vehicles from 769 to 289 sec in heavy rain condition. It also reducedthe total time for all vehicles from 2490 to 1154 sec.

Security in Edge-Centric Intelligent Internet of Vehicles: Issues and Remedies

Propelled by the growth of automotive industry, and the ubiquity of smart sensors, intelligent transport systems such as the Internet of Vehicles (IoV) have seen significant research interest in recent times. The emerging distributed IoV networks support real-time vehicular applications through on-device computing, communication-efficient data processing, edge computing, and cloud aggregation. While enriching the user experience by minimizing the end-to-end latency through efficient energy management, IoV deployments face the fundamental challenge of security attacks. In this article, we discuss various security attack modes in an edge-centric intelligent IoV framework, consisting of distributed smart vehicles, and remote processing units. We highlight various attack detection and mitigation mechanism for the proposed IoV framework, to address the security challenges. Finally, we shed light on several future research directions to ensure security of sensory data in edge-centric IoV systems.

Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network.

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

Optimization of ACF-DSR-based joint Doppler shift and SNR estimator for Internet of Vehicle system

Accurate vehicle speed estimation is required for intelligent internet of vehicles, and it can be realized by Doppler shift estimation in mobile communication. In this paper, the ACF-DSR (autocorrelation function-double sampling rate) method for joint Doppler shift and SNR estimation is further investigated. Based on the analysis of ACF, an improved algorithm model taking account of estimation deviation is formulated. Then, the effects of sampling intervals in DSR are figured out by mean square error analysis of estimation, and two better choices are obtained. By Monte Carlo simulations, it is demonstrated that the optimized ACF-DSR method with better choice of sampling intervals can achieve better estimation and outperform previous methods.

A Vision of IoV in 5G HetNets: Architecture, Key Technologies, Applications, Challenges, and Trends

With increasing traffic congestion and accidents, the Internet of Vehicles (IoV) has become a focus of fifth generation (5G) heterogeneous networks (HetNets) and intelligent transportation systems (ITS). IoV has been studied extensively to improve vehicles, road safety, and efficiency. However, the limited spectrum efficiency and interference caused in the 5G HetNets may require robust technology and architecture. The enabling requirements of IoV in 5G HetNets are elaborated and conceptualized in the article. The deep learning algorithms proposed and used in IoV in 5G HetNets can be achieved to reduce delay and improve transmission reliability. The research progress, advantages, and challenges of deep learning in 5G HetNets IoV are illustrated and explored in detail. Moreover, the overall framework of IoV in 5G HetNets is outlined and compared with the related research, which can become the Foundation for future development of intelligent IoV.

Wideband Gain Enhancement of a Dual-Polarized MIMO Vehicular Antenna

A compact dual-polarized multiple-input-multiple- output (MIMO) Vivaldi antenna with a wide bandwidth, high gain, and high isolation is proposed for wireless communications in intelligent Internet of Vehicles (IoV). It consists of four identical orthogonally-crossed Vivaldi radiators, crossed-fishbone-shaped slot, metasurface lens, parasitic stubs, electromagnetic (EM) wave reflector, and three-dimensional (3D) printed radome with dielectric resonator (DR) array. These functional modules jointly contribute to optimized antenna performance. Four identical antenna elements are rotationally symmetrical. To reduce the inter-element mutual coupling, the crossed-fishbone-shaped slot is etched to reduce the surface current flowing in adjacent radiators. To raise the gain, the metasurface lens, parasitic stubs, and DR array are loaded to guide the EM wave in the frontward radiation. An EM wave reflector located underneath the Vivaldi radiators could increase the boresight gain and reduce the influence of the vehicle body on the antenna performance. Diversity performance analysis indicates the proposed antenna could offer better MIMO capability. Besides, the impact of weather conditions on the antenna is analyzed. As a proof of concept, an expanded 8-element antenna array prototype is designed, fabricated, and tested. A good agreement is observed between measured and simulated results. It achieves an -10-dB impedance bandwidth of 7.55-22.85 GHz (100.66% FBW) with port isolation of higher than 18.05 dB. The realized gain is 6.98-11.54 dBi, and the radiation efficiency is 85.05-90.27%. The proposed antenna could be used in wireless communications between vehicles and the satellite/infrastructure.

Test Platform for Intelligent Internet of Vehicles Video Exchange System

Intelligent Internet of Vehicles is the hot spot of current traffic industry, Video information as a very important part of multidimensional perception, is widely used in vehicle-vehicle interconnection, vehicle-road interconnection and vehicle-management platform interconnection. Because there are obvious differences in the efficiency level between different methods and devices in specific operation, relevant video exchange testing and verification become necessary. This paper presents a test platform and method for video information exchange in Intelligent Internet of Vehicles.Through this platform, a combination of subjective evaluation of video streams and objective testing of transmission network is used to test the availability of the test platform for video exchange on the Internet of Vehicles.

Hybrid Entangled States With Multi-Degree of Freedom and High Purity for Internet of Vehicles

The development of intelligent Internet of vehicles (IoV) is based on communication technology, especially the quantum communication of hybrid entangled states based on orbital angular momentum (OAM). The quantum key distribution (QKD) method is demonstrated using hybrid entangled states with multi-degree of freedom and high purity based on OAM. First, the polarization entangled photon pairs are produced by two type I BBO crystals by means of spontaneous parametric down-conversion (SPDC). The polarization-OAM hybrid entangled states are created by q-plate (QP) to realize QKD. It is found that the polarization entangled photon pairs have the characteristics of OAM. A single mode fiber (SMF) is proposed to filter the topological charge non-zero photons and purify the polarization entangled photon pairs. Then the purification setup with M-Z interferometer and beam rotator (BR) is presented which can purify the polarization-OAM hybrid entangled states. The high purity polarization-OAM hybrid entangled states are implemented, and the QP conversion rate of 3 secondary feedbacks is 99.84% theoretically. Analysis indicates that precise purity polarization-OAM hybrid entangled state can increase the fidelity of information and improve the anti-interference ability in this program without mutually unbiased bases (MUBs). The photon utilization rate, energy-efficient and low-latency communication can be greatly improved by using the OAM carrying information.

Machine Learning Approach on Traffic Congestion Monitoring System in Internet of Vehicles

Abstract Traffic congestion is a major issue in urban cities leading to aggregated traffic. With the advancement in intelligent internet of vehicles, new technologies and protocols have been developed to predict the traffic congestion and utilize this traffic-related knowledge for congestion prediction and identification. This paper highlight the ML approach to identify traffic congestion based on multiple parameters such as hard delay constraints, the speed available through GSP vehicle trajectory. Here, we have used the Gaussian process in ML for prediction of traffic speed which uses 3 datasets i.e. training set, prediction set, and road sector data frame. ML can provide live traffic prediction in real-time, future traffic prediction and short-term traffic prediction on recent observation and historical data. In this paper using the data set, we have identified three different time slots for vehicle traffic congestion monitoring and evaluated the average speed of vehicles on the road sector during respective time slots.

Deep Reinforcement Learning for Intelligent Internet of Vehicles: An Energy-Efficient Computational Offloading Scheme

The emerging vehicular services call for updated communication and computing platforms. Fog computing, whose infrastructure is deployed in close proximity to terminals, extends the facilities of cloud computing. However, due to the limitation of vehicular fog nodes, it is challenging to satisfy the quality of experiences of users, calling for intelligent networks with updated computing abilities. This paper constructs a three-layer offloading framework in intelligent Internet of Vehicles (IoV) to minimize the overall energy consumption while satisfying the delay constraint of users. Due to its high computational complexity, the formulated problem is decomposed into two parts: 1) flow redirection and 2) offloading decision. After that, a deep reinforcement learning-based scheme is put forward to solve the optimization problem. Performance evaluations based on real-world traces of taxis in Shanghai, China, demonstrate the effectiveness of our methods, where average energy consumption can be decreased by around 60% compared with the baseline algorithm.

Mobile Edge Intelligence and Computing for the Internet of Vehicles

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

An Overview of the Application on Big Data Technology in Intelligent IOV

The traditional automotive industry is currently moving towards intelligent and informational technology. IOV (Internet of Vehicles) technology is the latest technology which can adapt to the future. At present, IOV technology faces many unresolved technical problems, and one of the core technologies is big data technology. This paper introduces the application of big data technology in intelligent IOV, and introduces various aspects of big data technology in IOV.

Guest Editorial Special Issue on IoT on the Move: Enabling Technologies and Driving Applications for Internet of Intelligent Vehicles (IoIV)

The new era of the Internet of Things (IoT) is prompting the evolution of conventional vehicle ad-hoc networks (VANETs) into the Internet of Intelligent Vehicles (IoIV). Different from VANETs, where a vehicle is essentially considered as a node disseminating messages, the emerging IoIV paradigm is expected to regard each vehicle as a smart object equipped with a powerful multisensor platform, unprecedented communication capability, computing units, and Internet protocol (IP)-based connectivity. As such, the vehicles in IoIV are highly efficient in a broad array of vehicular and transportation applications. As a unique subset of general purpose IoT, IoIV can benefit from the existing research on VANET, which lays the foundation toward a more pervasive and ubiquitous communications and networking core that is essential for IoIV. Nevertheless, research in many aspects of IoIV, especially those that are application-driven and data-oriented ones, is still at its infancy.

Optimal Resonant Beam Charging for Electronic Vehicles in Internet of Intelligent Vehicles

To enable electric vehicles (EVs) to access to the Internet of Intelligent Vehicles (IoIV), charging EVs wirelessly anytime and anywhere becomes an urgent need. The resonant beam charging (RBC) technology can provide high-power and long-range wireless energy for EVs. However, the RBC system is unefficient. To improve the RBC power transmission efficiency, the adaptive RBC (ARBC) technology was introduced. In this paper, after analyzing the modular model of the ARBC system, we obtain the closed-form formula of the end-to-end power transmission efficiency. Then, we prove that the optimal power transmission efficiency uniquely exists. Moreover, we analyze the relationships among the optimal power transmission efficiency, the source power, the output power, and the beam transmission efficiency, which provide the guidelines for the optimal ARBC system design and implementation. Hence, perpetual energy can be supplied to EVs in IoIV virtually.