Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges

Highlights

What are the main findings?

• Provides a layered overview of IoV architecture and applications, emphasizing its impact on smart urban mobility and sustainability.
• Analyzes and classifies evolving inter-vehicle communication models in IoT-driven smart cities and autonomous transport systems.

What is the implication of the main finding?

• This paper presents a comprehensive analysis of the Internet of Vehicles (IoV) within smart and sustainable cities, focusing on its layered architecture, communication models, global market trends and levels of vehicle autonomy (Level 0-5).
• It also explores current developments, security issues and research challenges to guide future advancements in IoV.

Abstract

Intelligent transport systems are essential for urban residents in large cities, facilitating not only vehicular mobility but also the movement of residents. Urban mobility is a significant concern, particularly in the context of the Internet of Things, where vehicles evolve into intelligent nodes within sensor networks. This convergence of the mobile Internet and the Internet of Vehicles (IoV) redefines urban mobility. In the context of smart cities, it examines the evolving IoV and communication models, unveiling both current and emerging trends. This research paper offers insights into global market trends and conducts bibliographic data analysis to illuminate the present and future potential of the IoV. It highlights IoV applications, the layered architecture, and connected and autonomous vehicle levels (Level 0 to Level 5). The communication model is explained, along with addressing research challenges and future directions. The conclusion summarizes the key findings and emphasizes the main points addressed in the study.

1. Introduction

The changes in society have led to a transformative period for transportation systems. As urban areas expand rapidly alongside a growing population, demands for enhanced efficiency have grown for better public experience [1]. Within the framework of a smart city, advances in urban design through the use of digital infrastructure and smart applications improve life quality and ensure essential needs even in the dark [2,3]. In the context of a smart city, numerous “smart” objects interact with each other, with the Internet of Things (IoT) technology, a network of interconnected and interoperable objects that contribute to a safe and intelligent environment [4]. The Internet of Vehicles (IoV) represents a customized aspect of IoT, and it essentially serves as a convergence of the IoT and mobile Internet. This technology involves dynamic mobile communication systems or models that facilitate interactions between vehicles and other objects, enabling various communication modes. Figure 1 illustrates a smart city infrastructure enabled by the IoT, showing interconnected vehicles, buildings (e.g., hospitals, banks, schools), and services via cloud, satellite, and wireless communication. It highlights real-time data exchange among traffic systems, public services, and smart devices to enhance urban efficiency, safety, and automation. The arrows represent the data communication and connectivity between various components of a smart city, including, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-network (V2N), etc. By implementing IoV, smart cities can utilize this cutting-edge technology to exchange information related to vehicles, road conditions, infrastructure, buildings, and their immediate surroundings. Intelligent transportation applications gain services from the IoV ecosystem in addition to its support for multimedia and mobile Internet. Advanced transportation networks have the ability to change city movement and confront serious issues, including traffic delays and public safety. The IoV offers a cost-effective solution for smart cities, providing a scalable and efficient framework for data collection, transportation, and processing from smart objects within the city environment. It enables cities to make decisions, improve urban services, and enhance the overall quality of life for residents. The IoV can indeed be used in smart city projects to promote sustainability.

1.1. Motivation

Smart urban areas thrive in encouraging reliable economic growth and delivering a top-standard quality of life. The use of intelligent transport systems (ITSs) motivates urban areas to improve resource use while reducing pollution and effectively serving their community [5]. Employing smart tools like industrial control and urban traffic management is a way to ensure sustainable city growth today [6]. In a smart city, business leaders optimize technology for the benefit of residents’ lives at work and on their commutes while making information readily available [7]. New vehicle models introduce innovative sensing and interactive capabilities along with improved social functionality. Vehicles that provide wireless sensing and communication play a key role in the development of smart cities. Different approaches and methods were studied to ease traffic congestion and improve traffic control for linked vehicles, offering motivation for evolving smart transport systems [8]. A plan for cooperative edge computing in the Social Internet of Vehicles (SIoV) was employed to reduce traffic congestion while exploring new ideas in smart transportation systems [9]. With deep learning (DL) identification capabilities, the IoV achieves effective protection against possible threats [10]. The technology increases the safety and dependability of the public while protecting the user from accidents [11]. Smarter cities are significantly shaped by the traffic congestion control system that utilizes machine learning (ML) [12]. Multiple decentralized methods for managing traffic in vehicles serve to create a more stable network and decrease data packet loss while facilitating effective and trustworthy data transfer in a changing mobility environment [13]. The Internet of Autonomous Things (IoAT) represents a web of independent devices that function on their own and improve automation and data collection across different fields [14]. By integrating vehicles, transportation systems, and various smart objects within the city, the IoV contributes to creating a more sustainable urban environment in several ways [15]. In [16], the concept of utilizing vehicles as mobile sensors for road condition monitoring is introduced. Furthermore, an exhaustive review of authentication methods in smart transportation systems outlines a defined categorization and discusses key issues facing the field [17]. In [18], a new systematic literature review (SLR) methodology was presented for the examination of data-focused methods in the IoV. In [19], a taxonomy categorizes data dissemination techniques for the IoV into distinct classes: intelligent networking and traditional methods establish a clear organizing system for these methods in relation to IoV.

A number of experts have introduced layered systems for the IoV aimed at helping to define and execute IoV systems. These designs allow for simple integration of diverse components and functions to facilitate efficient coordination and communication in the IoV system [20,21,22,23,24,25,26]. A three-layer model was laid out by both [20,21] for the IoV that serves as a guide for its design and execution. In addition to [20], the studies in [21,22] proposed a three-layered IoV architecture that differ slightly from the earlier work. Moreover, an architecture with three layers was introduced [23] for facilitating device-to-device (D2D) connections. In contrast to previous frameworks [24], CISCO introduced a four-layer IoV model focuses on services, infrastructure, operations and user-interface [25]. In addition, a unique variation of IoV architecture [26] and a four-layer model were introduced. The focus is directed towards the emerging era of the IoV, wherein vehicles themselves are equipped with a diverse range of sensors, effectively transforming into sensor nodes [27]. In [28], a comprehensive seven-layered architecture incorporates a D2D interaction model that facilitates various functionalities, interactions, representations, and information exchanges within the IoV ecosystem. Similarly, [29] examines the domain of D2D communications, which serves as a direct communication model connecting two or more devices without the need for an intermediary application server. However, as acknowledged in [30], D2D-assisted cellular networks bring forth numerous advantages such as high spectral efficiency, improved cellular coverage, reduced power consumption, and enhanced user throughput. In [31], two application areas of V2V communications are identified. The first area focuses on improving vehicle tracking by utilizing global positioning system (GPS) information shared through V2V communication, along with a vision system for accurate positioning. The second area introduces a simulated framework that combines embedded data, vision data, and V2V simulations to prototype an anti-collision application. In [32], the software-defined Internet of Vehicles (SD-IoV), a framework that integrates software-defined networking (SDN) and the IoV, is introduced. It aims to improve resource utilization, enhance quality of service (QoS), and support multiple requests. In [33], the authors emphasize the importance of safety in V2I communications for roadway infrastructure. In [34], they propose a cyber–physical architecture called the SIoV, which builds upon existing V2I communication technologies. The study [35] investigates the use of proportional fairness as a basis for resource allocation in a multi-rate multi-lane V2I network specifically designed for drive-thru Internet applications. The studies [36,37,38] discuss various network technologies and their applicability in the field of vehicle communications. Experts investigate various network technologies and their role in vehicle communications across different channels. They investigate how V2V and V2I communications impact functionality and difficulties while seeking ways to enhance safety and efficiency. The transition from ITS to IoV in smart cities marks a fundamental change in urban mobility [39]. The establishment of ITS formed a basis for more efficient traffic operation and upgraded transportation infrastructure. Through IoV advancements, vehicles link with one another effortlessly for the exchange of real-time data that supports enhanced safety and more efficient traffic. This change enables cities to enhance their level of integration as vehicles communicate with both infrastructure and one another. A safer and more efficient urban transportation environment emerges from the IoV’s data-driven for effective decision-making that harmonizes with the idea of a smart city [40].

The study delivers a broad exploration of how the IoV impacts smart cities. This analysis examines the IoV influences the development of smart cities by presenting its features and challenges. In smart cities and intelligent objects such as vehicles or transportation systems, IoV facilitates smart choices that boost service delivery and improve the quality of community life. It highlights various fields where IoV implementation occurs like real-time monitoring of air pollution and road infrastructure. With the help of advanced communication technologies and data-driven urban mobility has been revolutionized by IoV. It allows urban areas to establish transportation frameworks that promote safety and efficiency.

Table 1. Comparative overview of related surveys.

Survey	Scope	Key Focus	Main Outcome	Application Areas
Yi et al. [16]	Mobile sensor vehicles for road condition monitoring	Road condition monitoring	Integration of vehicles as mobile sensors for efficient road condition monitoring and maintenance.	Road condition monitoring
Bagga et al. [17]	Authentication protocols in IoV	Authentication protocols	Structured classification and analysis of authentication protocols, addressing emerging challenges.	Security in IoV
Partovi et al. [18]	SLR method for data-centric approaches	Analysis of data-centric approaches	Systematic literature review method to analyze strengths, weaknesses, and key findings in IoV data approaches.	IoV data analysis
Azzaoui et al. [19]	Taxonomy of data dissemination techniques in IoV	Categorization of data dissemination techniques	Categorization of data dissemination methods into networking-based, intelligent-based, traditional-based, and hybrid-based classes.	Data dissemination in IoV
Contreras- Castillo et al., Golestan et al., Wan et al., Gandotra et al., Bonomi et al., Madani et al., Yang et al. [20,21,22,23,24,25,26]	Layered architectures for IoV	Design and implementation of IoV systems	Proposal of various layered architectures for IoV systems, facilitating efficient communication and coordination.	Communication frameworks
Xie et al. [27]	Requisite data acquisition system	Data acquisition system design	Introduction of a system for gathering vehicle data via controller area network (CAN) through the on-board diagnostics II (OBD2) interface.	Data collection in IoV
Contreras-Castillo et al., Rose et al. [28,29]	D2D communications in IoV	Direct D2D communication	Development of a layered architecture model to facilitate various functionalities within the IoV ecosystem.	Communication efficiency in IoV
Lin et al. [30]	Challenges of D2D-assisted cellular networks	Issues and solutions in D2D-assisted networks	Exploration of direct D2D communication models and challenges, particularly in healthcare applications.	Network interference in IoV
Salameh et al. [31]	Applications of V2V communications	Improving vehicle tracking and anti-collision systems	Identification of V2V communication applications for vehicle tracking and anti-collision systems.	Vehicle tracking and safety
Chen et al. [32]	SD-IoV framework integration	Integration of SDN and IoV	Integration of SDN with IoV to improve resource utilization and quality of service.	Resource utilization and QoS in IoV
Bajaj et al. [33]	Safety considerations in V2I communications	Safety considerations in vehicle-to-infrastructure communications	Emphasis on safety and efficiency goals in V2I communications for roadway infrastructure.	Safety measures in IoV
Alam et al. [34]	Cyber–physical architecture for SIoV	Integration of social IoT with IoV	Proposal of a cyber–physical architecture for the SIoV to enhance communication.	Integration of social aspects into IoV
Harigovindan et al. [35]	Resource allocation in V2I networks	Resource allocation strategies in V2I networks	Investigation of proportional fairness for resource allocation in multi-rate multi-lane V2I networks.	Resource optimization in IoV
Santa et al. [36]	Network technologies for vehicle communication	Analysis of network technologies in IoV	Exploration of various network technologies such as Bluetooth, Wi-Fi, and LTE for V2V and V2I communications.	Network technologies for IoV communications
Dey et al. [37]	Performance evaluation of heterogeneous wireless networks	Evaluation of network performance	Assessment of network resource allocation in heterogeneous wireless networks to improve connectivity.	Network performance optimization in IoV
Ubiergo et al. [38]	Traffic signal optimization for safety	Analysis of traffic signals’ role and impact	Highlighting the importance of traffic signals for safe operations at intersections, with consideration for travel delays.	Traffic management in urban settings
This paper	Provides comprehensive knowledge and deep insights into IoV and its role in smart city development	Examines IoV applications, architectural design, and challenges involved in implementing IoV	Highlighting the significant benefits IoV brings to smart cities and emphasizing the need to address deployment challenges for optimal effectiveness.	Smart transportation, traffic management, autonomous vehicle

includes a review of relevant surveys and highlights the surveyed areas along with their objectives.

The analysis demonstrates the diverse application of IoV techniques across multiple urban areas such as healthcare, transportation, and energy management. It highlights how various algorithms, including CNNs, DL, and hybrid models, have been adapted to address specific challenges, with a focus on accuracy, efficiency, and scalability. It also reflects varying data sources and evaluation metrics, emphasizing both strengths and drawbacks in real-world implementation. However, it reveals a lack of uniformity in dataset use and a limited focus on explainability and standardization, suggesting opportunities for future research. This comparison helps in identifying underexplored areas and gaps in practical deployment. The reviewed studies demonstrate significant advantages, such as improved accuracy, real-time decision-making, and adaptability through advanced AI techniques like DL and reinforcement learning. These approaches enhance performance in diverse urban applications. The drawbacks include limited model interpretability, inconsistent use of datasets, and a lack of standardization, which restrict transparency and cross-comparison of results. Despite the growing use of IoV in smart city applications, there is a lack of standardized datasets, consistent evaluation metrics, and focus on explainability across the studies. This highlights a critical gap in developing transparent, comparable, and generalizable solutions for real-world urban challenges. In this study, the analysis investigates the IoV and its significance for sustainable smart urban environments. It analyzes important factors, including the global marketplace trends and applications, and how vehicles relate to city infrastructure. The study emphasizes various IoV communication models, findings in current research, and developments in inter-vehicle communication. Additionally, it addresses open research challenges and security issues, and future directions in IoV research with a summary of key findings, highlighting the significance of IoV in shaping the future of urban mobility and sustainability. With the aim of developing a comprehensive understanding, this study has devised and investigated four specific research questions (R), outlined as follows.

• R1: How do global market trends influence the development and adoption of IoV technologies, considering layered architectures, applications, and the levels of connected and autonomous vehicles, in the context of sustainable smart cities?
• R2: What are the current advancements in IoV communication models, including wireless technologies and emerging technologies, and how do they contribute to the evolution of inter-vehicle communication research?
• R3: What are the current limitations, challenges, and areas for further research in the implementation of IoV within smart city contexts, and how can these challenges be addressed to realize the full potential of interconnected transportation systems?

1.2. Contribution

The authors’ contributions in this research paper are outlined in the following points:

• A comprehensive overview is provided to elucidate the concept of IoV, with a specific focus on sustainable smart cities and its key components, including the global market trends, the layered architecture, and applications, and levels of the connected and autonomous (from Level 0 to Level 5).
• Emphasized on various IoV communication models, which provide an overview of current inter-vehicle communication research and the current developments in this field.
• Finally, addressed the open research challenges and security issues to discuss future research directions and conclude with a summary of key findings.

1.3. Outline

The paper is structured as follows: In addressing R1, Section 2 investigates the global market/bibliographic data analysis and standards. In Section 3, use cases of Internet-connected vehicles are the main focus. Section 4 and Section 5 are dedicated to the discussion of connected and autonomous systems, ranging from Level 0 to Level 5, and the layered architecture of the IoV and emerging technologies, respectively. In response to R2, Section 6 is dedicated to the models of communication in IoV, and wireless technologies in IoV are discussed in Section 7. In response to R3, it appropriately defines in Section 8, which thoroughly examines the open research challenges and future research directions concerning the IoV within smart cities. Finally, in Section 9, the paper concludes with a summarizing conclusion.

2. Global Market/Bibliographic Data Analysis and Standards

The global IoV market is expected to experience significant growth over the forecast period, from USD 145.24 billion in 2023 to USD 678.94 billion in 2030, at a compound annual growth rate (CAGR) of 24.65%. In 2020, the market size was USD 85.12 billion, but it witnessed a negative demand shock across all regions due to the global impact of COVID-19. This resulted in a substantial decline of 10.72% compared to the average year-on-year growth during 2017–2019 [41]. The IoV industry is anticipated to benefit from advancements in technology, connectivity, and the integration of vehicles with the Internet, leading to improved safety, efficiency, and convenience for consumers. It is important to note that the figures mentioned are based on projections and should be interpreted as estimates for the future growth of the global autonomous vehicle market [42] as depicted in Figure 2. Market conditions and various factors can influence the actual growth trajectory, so it is essential to monitor industry trends and developments for a more accurate assessment. Furthermore, the bibliometric analysis centered on IoV in the context of smart cities, utilizing data sourced from the Scopus database. This study investigates the trends, contributions, and impact of research on IoV in smart cities from 2012 to 2025. This approach ensures that researchers are presented with the most current research developments in the field enabling them to stay informed on emerging trends and advancements. Ultimately, 1514 articles were exported with the search query “(“smart”) AND (“city”) AND (“Internet”) AND (“of”) AND (“Things”)” in English language and saved in CSV format before being included and examined in the bibliometric study. Articles were the primary type of publications analyzed. Visualization analysis was carried out using VOS viewer software 1.6.20, helping to focus on research areas that require further exploration in the IoV and smart cities domain. This bibliometric analysis offers valuable insights for researchers aiming to research unexplored areas within the domain of IoV and smart cities. In this data analysis, the yearly distribution of publications is examined, highlighting the substantial growth observed in this field. Significantly, the year 2021 stands out with the highest number of publications, reaching 98. Articles dominate the document types, constituting a significant 71.7% of the publications.

When considering the distribution of documents by source, IEEE Access emerges as the leading source, contributing over 15 publications. In terms of subject domains, the field of engineering leads with the highest number of publications at 33.6% of the total, closely followed by computer science, which holds the second-largest share at 26.9%. Figure 3 displays the clustering of keywords frequently appearing together within research papers. It visually represents the connections between co-occurring keywords. Significantly, “Internet of Things”, “Internet of Vehicles”, “electric vehicles”, and “mobile communication” are the keywords that tend to co-occur most frequently in the research papers.

2.1. Standards

The IoV is an evolving concept that involves the integration of vehicles with communication technologies and the Internet to enhance safety, efficiency, and transportation experience. To ensure interoperability, security, and efficiency within the IoV ecosystem, several standards have been developed. Here are some key standards relevant to the IoV with technologies used, description, merits, and demerits, as depicted in

Table 2. The key standards used in Internet of Vehicles.

Standards	Technology Used	Description	Merits	Demerits
IEEE 802.11p	WAVE	This standard defines the use of the WAVE protocol suite. It operates in the 5.9 GHz DSRC band and supports communication between vehicles and between vehicles and infrastructure.	Dedicated to vehicular communication, enabling low-latency, and high-speed data exchange.	Limited range and susceptibility to interference in dense urban environments.
ISO 21217	ITS—CALM	This ISO standard specifies the architecture and communication protocols for ITS. It provides guidelines for communication between vehicles and infrastructure, supporting various applications such as traffic management, emergency services, and infotainment.	Provides a standardized framework for interoperable communication between vehicles and infrastructure in ITS.	Challenges in implementation due to varying regulatory environments and interoperability issues between different regions.
ISO 15118	Road Vehicles—Vehicle-to-Grid Communication Interface	This standard focuses on the communication interface between electric vehicles (EVs) and the power grid. It defines the communication protocol for bi-directional communication between EVs and charging infrastructure.	Standardizes communication protocols for EV charging, promoting interoperability and efficient energy management.	Complexity in implementation and potential security vulnerabilities in the communication process.
ETSI ITS-G5	ITS—Vehicular Communications	The ETSI developed ITS-G5 as a set of standards for vehicular communication. It is widely used in Europe for cooperative ITS applications, covering aspects such as message formats, security, and networking protocols.	Low-latency and high-reliability communication for vehicular networks.	Limited deployment and interoperability challenges due to regional variations in spectrum allocation and standards adoption.
SAE J2735	DSRC Message Set Dictionary	Developed by the SAE, J2735 standardizes the messages and data frames used in DSRC for V2V and V2I communication. It establishes a common set of message elements, promoting consistency and interoperability among different manufacturers’ implementations.	Enhancing interoperability and safety applications in ITS.	Limited adoption and potential compatibility issues due to varying implementations by different manufacturers.
ISO 20078	ITS—Vehicular Communications—GeoNetworking	This standard defines GeoNetworking protocols, enabling location-based communication within vehicular networks. It plays a crucial role in supporting applications like traffic management, route planning, and emergency services that rely on geographic information.	Secure and interoperable payment transactions in ITS.	Complexity and potential security vulnerabilities
ISO 15628	ITS—Enhanced Crash Data for Automotive Vehicles	This standard specifies the data structure and format for the exchange of enhanced crash data among vehicles and emergency responders. It aims to improve the accuracy and speed of post-crash assessment and emergency response.	Standardizes protocols for secure and efficient communication between EVs and charging infrastructure, promoting interoperability.	Complexities in implementation and compatibility issues with existing infrastructure.

.

2.1.1. IEEE 802.11p (Wireless Access for the Vehicular Environment (WAVE))

IEEE 802.11p is crucial for enabling cooperative systems and improving road safety. Beyond its role in road safety, IEEE 802.11p plays a fundamental role in the advancement of ITS by promoting vehicle-to-everything (V2X) communication. These standards form the backbone for emerging technologies like connected and autonomous vehicles, facilitating seamless data exchange not only between vehicles and infrastructure but also with pedestrians and other road users. Its deployment is essential in creating a responsive transportation network, contributing to the realization of safer, more efficient, and interconnected smart city mobility solutions [43].

2.1.2. ISO 21217 (ITS—Communications Access for Land Mobiles (CALM))

The standard ISO 21217 sets up a framework that improves connectivity and interoperability in the transportation field by framing its architecture and communication standards. Through its comprehensive framework for vehicle-infrastructure communication, this standard boosts the effectiveness of traffic control and emergency support and enables the incorporation of new technologies. Adopting this standard develops a well-connected network where ITS applications function seamlessly and cooperate to benefit the safety of transportation infrastructures [44].

2.1.3. International Organization for Standardization (ISO) 15118 (Road Vehicles—Vehicle-to-Grid Communication Interface)

The development of smart charging solutions requires ISO 15118 for integrating EVs into the energy grid. ISO 15118 adds value beyond typical charging methods; it is necessary for building a reliable and regular framework for two-way exchanges. This standard equips EVs to take energy from the grid while allowing them to the energy effectively. ISO 15118 is crucial for allowing smooth interaction between vehicles and the energy network. This situation improves energy consumption while providing routes for enhanced stability and effective demand management [45].

2.1.4. European Telecommunications Standards Institute (ETSI) ITS-G5 (ITS—Vehicular Communications)

In Europe’s pursuit of advancing ITS through established vehicular communication systems, the ETSI ITS-G5 plays a crucial role. In addition to its function in developing cooperative ITS applications, it deals with significant issues related to secure message formats and strong networking protocols. The broad use of ITS-G5 guarantees consistent methods for vehicle communication and promotes improvements in traffic safety and management along with connecting car technologies within the smart transportation system in Europe [46].

2.1.5. Society of Automotive Engineers (SAE) J2735 (Dedicated Short-Range Communications Message Set Dictionary)

By producing SAE J2735 the SAE has standardized key messages crucial for DSRC and supported the generation of a seamless connection among vehicles. This standard creates a uniform collection of message components that guarantees both uniformity and better compatibility in different manufacturers’ systems. This standard gain significant support for building a foundation for V2V and V2I exchanges that ensures increased safety and reliability in transportation systems [47].

2.1.6. ISO 20078 (ITS—Vehicular Communications (GeoNetworking))

ISO 20078 acts as essential support for ITS by establishing protocols for GeoNetworking that enable accurate location-based exchanges in-vehicle networks. This standard aids essential applications including traffic guidance and safety service and advances the emergence of inventive ITS solutions centered on geographic information. By establishing a standardized approach to GeoNetworking, ISO 20078 ensures interoperability and advances the evolution of smarter and more responsive transportation ecosystems [48].

2.1.7. ISO 15628 (ITS—Enhanced Crash Data for Automotive Vehicles)

ISO 15628, as a standard for enhanced crash data exchange, not only defines the data structure and format but also represents a significant stride towards expediting post-crash assessment and emergency response. By providing a standardized framework for the exchange of detailed crash data between vehicles and emergency responders, this standard enhances the accuracy and speed of critical information dissemination. ISO 15628 contributes to more effective and timely emergency interventions, ultimately improving overall road safety and the outcomes of post-crash scenarios within the domain of ITS [49].

Compliance with these established standards not only facilitates communication and interoperability within the IoV but also promotes an ecosystem that is crucial for the widespread adoption of connected and autonomous vehicles. As the IoV evolves, ongoing collaboration and the emergence of new standards will play a pivotal role in addressing emerging challenges, ensuring the resilience, security, and continual enhancement of ITS to support the seamless integration of vehicles into the broader smart city infrastructure.

3. Use Cases for the Internet-Connected Vehicle Ecosystem

This section examines IoV use cases, exploring them from two distinct viewpoints outlined in Figure 4; IoV use cases in the transport sector and IoV use cases in the smart city context. The justification for considering both perspectives stems from the observation that numerous surveys have predominantly viewed the IoV as a platform primarily serving ITS applications such as traffic efficiency, safety, and infotainment. There has been no extensive examination or investigation into how IoV can be employed for the sensing, collection, processing, and storage of vast amounts of data. While traditional ITS-based IoV applications focus on individual vehicle entities and network functionality, vehicles within smart cities have the capability to engage in data collection, information transmission, processing, and storage on a network of connected objects.

3.1. IoV Use Cases in the Transport Sector

The transportation-related IoV applications offer several advantages that enhance various aspects of transportation. Some of the key applications are V2V, V2I, vehicle-to-pedestrian (V2P), ITS, connected navigation systems, fleet management, autonomous vehicle communication, parking management, EV integration, and ride-sharing and carpooling. Transportation systems involve a variety of technologies and applications aimed at enhancing travel safety, mobility, productivity, and the mitigation of traffic-related issues. The origins of the ITS concept can be traced back to US-based researchers in the twentieth century [50]. In the present day, both academia and industry are closely focused on ITS. The reason for this is that such systems not only improve vehicle traffic conditions, but also improve the safety and sustainability of the transportation sector. ITS integrates information and communication technologies (ICTs) into the transport sector to addresstraffic congestion and mitigate the impact of climate change on transportation [51]. ITS reduces the time spent in traffic jams, therefore reducing traffic jams and fuel costs, monetary losses in cities, and CO emissions. For transportation, numerous applications in vehicular ad hoc network (VANET) including traffic management, safety alerts, and efficient intra- and inter-vehicle communication are possible, resulting in better transportation efficiency and safety [52]. The study [53] developed a protocol called VANET-to-Internet to enable Internet access through vehicular networks while preserving the quality of service. In [54], a proposal for video streaming and uploading in vehicular is introduced, focusing on cooperative forwarding of video streams from moving vehicles to a fixed network using both V2I and V2V communications. Additionally, [55] discusses how network coding can be employed to support vehicular streaming over the network. It is a technique of recording received packets whereby nodes along the transmission path recover the lost packets. Furthermore, an anonymous authenticated key exchange scheme was adapted for the integration of blockchain technology into the IoV among the smart transportation systems [56]. This scheme provides, the benefits of improved security, privacy and trust, and blockchain-enabled IoV applications, which lead to safer and more reliable smart transportation systems. Advancement of these could change the transportation industry outcomes by making it more efficient, sustainable, and convenient.

3.2. IoV Use Cases in the Smart City

Issues addressing traffic control, surveillance, natural disasters, and the monitoring of the environment are addressed by the concept of smart cities. In order to implement these solutions, urban data need to be gathered and shared over communication infrastructures. As a consequence, these smart city ecosystems call for more integrated, diverse, and intelligent wireless communication. In these environments, the IoT has a key role as it allows for the cost-effective collection, transmission, and processing of huge amounts of data from smart devices [57]. IoV-based applications for smart cities offer many advantages for urban environments by utilizing connectivity, data exchange, and intelligent systems [58]. Examples of the applications of smart city-related IoV are traffic management, parking management, public transportation management, emergency service, pollution monitoring, energy management, waste management, intelligent street lighting, pedestrian safety, and environmental monitoring.

There are four functions performed by a vehicle in a smart city system:

• It enables the moving of the data from one node to another and speeds up the data exchange between several nodes in the network. It helps keep network security and reliability by preserving connections between nodes. Additionally, it plays a crucial role in upholding network security and reliability by preserving connections between nodes.
• Vehicle objects function as clients consuming IoV and Internet services. Mutual authentication and data validation are essential roles for the nodes that exist within this network. These functions are very important for ensuring data transmission integrity and generally for data transmission security and trustworthiness in order to achieve the security and reliability of the IoV services network.
• It represents a vehicle object that will collect data from smart devices and send it to data centers inside a smart city. Data are encrypted and a digital signature is added to the vehicle object before transmitting it to the network. This process enables the data center to verify the authenticity and validity of the data.
• Vehicle objects augment the limited information processing capabilities of smart devices by playing the role of providing distributed computing resources. It offers more complicated data processing and analysis with the addition of extra computational capacity to the network.

IoV applications for smart cities include efficient traffic management, integrated public transportation, optimized parking, improved safety and security, energy efficiency, data-driven urban planning, and better quality of life. In the future, these applications lead to even more connected, sustainable, and livable cities.

4. From Basic Connectivity to Full Autonomy (Levels 0 to 5)

By 2025, scientists expect that some 8 million autonomous or semi-autonomous vehicles will be on the road. Substantial investments in autonomous vehicle technology research and development from companies such as Google, BMW, and Tesla are driving this projection [59]. On top of that, many governments have started taking incentives offered like tax breaks ahead to encourage their purchase. When moving to subsequent levels of the automated driving model, self-driving cars are driven by software, utilizing advanced mathematical computation and data feedback to steer and manage the car on roads, and avoid incidents or hazards on the road. These ranges, defined from 0 (fully manual) to 5 (fully autonomous), are according to the SAE [60] as illustrated in Figure 5. It also serves as a framework to guide manufacturers in determining the extent to which they will continuously innovate to develop and refine the required sensors, algorithms, and other technologies for autonomous vehicles. It also guarantees that these vehicles are safe to use before deciding to make them available for everyday use.

4.1. Level 0: Absence of Driving Automation Technology

Level 0, or “No Driving Automation”, is the lowest level of autonomous technology. There are no automated features or systems in this level of vehicles that assist with driving tasks. All other vehicle aspects are the responsibility of the driver, including steering, acceleration, and braking on the vehicle [61]. Advanced driver assistance systems (ADASs) [62] are now so widely integrated into modern vehicles that Level 0 vehicles without such systems appear significantly less advanced. Level 0 does not do any kind of autonomous functionality, and has some basic safety features like seatbelts and airbags. At this level, Level 0 vehicles require the driver to remain fully engaged and “vigilant at all times”, including every such activity relevant to the task of driving.

4.2. Level 1: Assisted Driving Systems

The second lowest degree of automation within a vehicle is Level 1 automation. At this level, only one driver assistance automated system will be available, usually dedicated to tasks such as acceleration or steering [63]. However, the human driver is responsible for monitoring other critical aspects of driving, such as steering and braking. It is important to note that at Level 1, the driver must remain prepared to take control of the vehicle at any time. Adaptive cruise control is a common Level 1 driver assistance technology that helps drivers maintain a safe distance without requiring their intervention.

4.3. Level 2: Partial Vehicle Automation

Level 2 driving automation signifies a cutting-edge driver assistance system capable of handling steering, acceleration, and braking functions. However, it requires the driver to maintain continuous vigilance and actively oversee the technology’s operation [63]. In such systems, the driver remains responsible for steering, accelerating, and braking while holding the steering wheel, even though technology aids in these tasks. While using BlueCruise, drivers briefly take their hands off the wheel, but they must stay alert and ready to resume control at any moment [64]. Even with the introduction of over-the-air software updates, such as Autosteer for city streets, Level 2 technology still requires human supervision.

4.4. Level 3: Advanced Driver Assistance Systems

Conditional driving automation is the third level of automation that allows a car to take control over steering, acceleration, and braking under particular circumstances [65]. It is this level that depends on a few different driver assistance systems and artificial intelligence (AI) that will decide what to do based on changing driving conditions [66]. Cameras and radar produce data over sensors that detect objects around the vehicle which are used to make decisions to avoid collisions and hazardous situations. An autonomous driving system that allows occupants to do other things in the vehicle without being constantly watching. However, a human driver is always required to be present and ready to take back control if a need arises, such as an emergency or system failure.

4.5. Level 4: High-Level Driving Automation

Highly autonomous vehicles are those capable of operating with no human intervention provided that the system fails elsewhere in its system. Because they never need a human driver, they lack a steering wheel and pedals [67]. Level 4 vehicles are so comfortable that passengers have even been known to take a nap while going. Geofencing is often used in driverless taxis and public transportation services, but the use of this technology limits their operative area to certain geographical areas [68]. Even so, issues concerning Level 4 vehicles are possible under severe weather conditions.

4.6. Level 5: Full-Scale Driving Automation

As Level 5 vehicles are totally autonomous, they do not require human attention while driving [69]. They have on board the front-end sensor array and the cameras that allow them to navigate independently in any situation or environment. Thus, this eliminates the need for a human driver, and makes the concept of a “dynamic driving task” obsolete. But so far, the general public has never taken a ride in a fully autonomous car, even with extensive global testing.

5. The Layered Architecture of the IoV and Emerging Technologies in Smart Cities

The IoV’s layered architecture introduces a structured data processing and decision-making model from the perception layer to the business layer. Not only does it support efficiency, scalability, and adaptability, which are essential for IoV’s integration into the smart city environments and into the higher transportation industry, but it also provides a toolkit for addressing problems existing within the IoV. Additionally, emerging technologies including 5G/6G, D2D communication, SDN, and cyber–physical architectures supplement the IoV. Indeed, even though these technologies play a critical role in enriching the abilities of the IoV in smart city infrastructure, 5G and 6G technologies have better connectivity and data processing features. The IoV architecture consists of multiple layers, including perception, coordination, AI, application, and business layers, each serving distinct functions within the system [70]. This section extends the foundational model by relating it to smart city environments and ITSs. Recent advancements, such as AI-driven coordination, are incorporated along with an exploration of how these layers interact with various levels of connected and autonomous vehicles. The architecture is explained in the context of smart cities highlighting its role in enhancing urban mobility, connectivity, and intelligent transportation services.

5.1. Perception Layer

This layer is very important in the data acquisition process. At the bottom level of IoV architecture, this layer collects real-time data from sensors, cameras, and other embedded devices present in the vehicle. Data from vehicle speed, location, environmental conditions, and more are gathered by these sensors. The data are then converted to digital formats and further processed in the upper layers of the IoV architecture.

5.2. Coordination Layer

This layer is a very important layer that functions as the middle layer between the lower layer and the upper layer of AI. The coordination layer at its core is actually a virtual universal network coordination module, which is the centerpiece of the integration of different networks like Wi-Fi, 4G LTE (long-term evolution), or WAVE. Such integration is critical to furnish a complete data stream that makes communication data between multiple communicative sources of the IoV ecosystem flow. Supporting IoV application and effective communication over the entire network requires this layer to be able to harmonize data from different sources.

5.3. AI Layer

This layer is a key enabler of the intelligent decision-making process that constitutes the applications of IoV. This allows the AI Layer to reveal meaningful information and recommendations for the efficient and viable operation of the IoV network, further accelerating the operation of connected vehicles and devices. AI and big data technologies combined with 6G provide opportunities for smart cities equipped with the new generation of intelligent applications [71]. Research is being carried out on the fusion of ML, DL, and swarm intelligence techniques in conjunction with the 5G/6G-enabled IoV networks to enrich intelligent vehicles within a smart city architecture [72,73].

5.4. Application Layer

This layer is devoted to the deployment of smart applications, which make transportation better in terms of traffic safety and web-based utilities. By utilizing information processed and analyzed in the AI layer, it uses data to offer advanced services at the end-user level. The applications are meant to make transportation more efficient, secure, and user-friendly. The application layer helps achieve the overarching goal of building a more connected smart city, and helps promote a more connected and responsive vehicular ecosystem by generating predictive understanding achieved by utilizing the power of real-time data and intelligent algorithms.

5.5. Business Layer

This layer uses a variety of visualization tools, including graphs, flow charts, tables, and comparisons through statistical analysis. It consists of the architecture model, in which the concept of a heterogeneous network is used to capture the diverse and interrelated nature of IoV components. The role of this layer is to have a strategic and business-oriented perspective whereby the layouts in the IoV ecosystem align with organizational objectives, and innovation, and provide service to the smart city and its inhabitants appropriately.

6. Models of Communication in the Internet of Vehicles

The IoV communication model focuses on communication among vehicles and with the surrounding environment. The systems of vehicular communication are interrelated and the data exchange takes place through various means and different forms of models. Each model plays a vital role in the interconnected ecosystem of vehicular communication, enabling various forms of data exchange and interactions. The IoV communication model is the communication between vehicles and their surrounding environment. The diverse range of communication models in the IoV includes vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian, vehicle-to-home, vehicle-to-roadside, vehicle-to-everything, and vehicle-to-grid models as shown in Figure 6.

6.1. Vehicle-to-Vehicle (V2V)

Modern transportation systems depend on V2V communication, which enables vehicles to communicate directly with one another. V2V communication is an important factor in the growth and development of smart and interconnected transportation networks that allow vehicles to communicate. With fast mobility and short communication windows being the unique characteristics of such networks, designing a robust communication protocol is crucial to the maintenance of efficient data dissemination and network performance. It is a technology that is the key to the development of safer and more efficient transportation systems and is a fundamental part of intelligent and interconnected vehicles. In [74], a novel framework was proposed using V2V for vehicular traffic information monitoring and aggregation. The wireless radio relationships between vehicles and roadside units are based on DSRC and are appropriate to vehicular environments [75]. Automated systems like intersection collision warning systems send DSRC warnings to other vehicles and pedestrians if a vehicle is approaching a red light at high speed. The considerations for designing new V2V communication protocols are crucial for congestion control to improve the efficiency and effectiveness of traffic management strategies in a connected vehicle environment. These protocols help to reduce the congestion of traffic and enhance the performance of the entire network of roads [76]. Likewise, ref. [77] demonstrates that the validating and optimizing the performance of V2V systems for congestion control leads to safer and more efficient transportation management solutions.

6.2. Vehicle-to-Roadside (V2R)

Communication refers to the capability of vehicles to interact with road infrastructure, whether it is traffic signs, signals, or ITS equipment. The role of V2R communication in improving road safety and traffic management is very high. These systems are critical to our connected and autonomous vehicle future and toward more efficient, safer, and smarter cities. V2R communication systems can be deployed along roads to mitigate passenger vehicle crashes [78]. A random linear network coding-based message dissemination algorithm at the intersection of the roads was presented for disseminating a novel basic safety message [79]. A data access scheme for V2R communication [80], where the importance of maintaining data integrity is essential to running V2R systems, especially in safety-critical applications. In [81], the data streaming optimization on V2R communication links is carried out through a hierarchical optimization framework considering some important factors such as application-specific requirements, transit service provider, cost considerations, and wireless network service operator profitability. This system-wide approach results in efficient and balanced resource usage in V2R communication systems.

6.3. Vehicle-to-Infrastructure (V2I)

Wireless or cellular interaction between vehicles and infrastructure is a V2I communication, necessary for enabling Internet-based services. The goals of V2I communication include optimization of road safety and efficiency of traffic flow, and a detailed analysis of the inter-vehicular communication reveals the work of vehicular network controllers [82]. Proportional fairness resource allocation for a multi-rate, multiple-lane V2I network used within a drive-thru Internet application is motivated in order to ensure equitable and optimal data throughput of vehicles. By this approach, available resources can be best used, and the efficiency of V2I communication can be maximized in various systems. In [83], the authors examine the coverage and capacity requirements of V2I communications as well. The communication modalities considered range from digital broadcasting to cellular networks to DSRC. Multi-hop connectivity between isolated vehicles and far-away roadside units is achieved via cooperative vehicles that provide intermediary relays [84]. On one hand, this approach improves the overall network reach and reliability of vehicular communication networks by utilizing vehicles themselves to reach the communication range, and providing robust connectivity for many traffic systems.

6.4. Vehicle-to-Home (V2H)

With the advent of vehicle-to-home communications, the integration and control of many smart home systems from a car is seamless and comfortable. Tasks like remotely controlling home climate control, security, lighting, and even recharging EVs can be accomplished with V2H connectivity. V2H can also function as a backup electricity source for homes by using the vehicle’s energy storage system to supply power during an outage [85]. On the way to smarter and more interconnected living environments, V2H brings greater convenience and energy efficiency to home and EV owners.

6.5. Vehicle-to-Everything (V2X)

Vehicle-to-everything communication forms a key part of the IoV, which covers the V2V and V2I interactions. ITS is a step forward in technology development to support road safety, optimally manage traffic, and provide a better driving experience. V2V communication in V2X allows direct interaction between vehicles on the road and facilitates making collaborative decisions as well as several safety applications. This information is critical to avoiding accidents, improving traffic flow, and detailing the speed, position, and planned schedule of the moving vehicles, unlike V2I communication, which allows vehicles to intercommunicate with roadside infrastructure like traffic lights, road signs, smart intersections, etc. In LTE-based V2X services, vehicles involve cellular networks to transmit data to other V2I [86]. This integration expands the reach and reliability of V2X communication, allowing for seamless information sharing even when traditional V2V or V2I communication may not be available.

6.6. Vehicle-to-Grid (V2G)

Vehicle-to-grid technology systems enable bi-directional communication and energy exchange between EVs and the electrical grid, essentially turning EVs into mobile energy storage units. In a V2G setup, EVs can not only draw power from the grid for charging their batteries, but can also discharge excess electricity back into the grid when needed. V2G systems, in conjunction with plug-in electric vehicles (PEVs), have the capacity to reduce energy costs for both consumers and contribute to more efficient energy management [87,88]. V2G smart charging coordination systems enable intelligent and optimized charging of EVs, allowing them to respond to grid demands and fluctuating electricity prices while reducing the overall cost of charging [89]. The use of AI technology transforms EVs from simple transport devices to valuable assets in energy management systems [90]. This approach will also help to significantly accelerate the spread of EVs and build a greener and more reactive energy ecosystem. V2G is a promising solution for smart grids and renewable energy generation as it helps to define the future clean and reliable energy distribution.

6.7. Vehicle-to-Pedestrian (V2P)

The IoV relies on vehicle-to-pedestrian communication as a vital aspect of improving pedestrian safety on roads. This technology provides direct V2P communication for real-time information sharing that can prevent accidents. To support direct V2P communication in urban environments, wireless local area networks (WLANs) are used for V2P [91]. This technology is used to help vehicles transmit essential data to pedestrians like warnings, alerts, and notifications, to help improve safety. Additionally, vehicles with light-emitting diode (LED) projection modules are able to project warning signals and traffic information onto the road or sidewalks for pedestrians to prevent accidents [92]. This cooperative V2P system promotes better communication and understanding of the vulnerabilities of road users by vehicles [93]. The exchange of information enables both drivers and pedestrians to make more decisions, which thereby reduces the possibility of accidents. It is a key enabling feature of smart transportation systems, driving safer and more efficient mobility in smart cities and beyond.

7. Wireless Technologies in IoV

In the field of IoV, wireless technologies are important tools for intervehicle, vehicular infrastructure, and interrelated elements of the transportation ecosystem communication. The key wireless technologies that are integral to IoV applications, specifically focusing on vehicular communications, cellular mobile communications, and short-range static communications, as depicted in Figure 7.

7.1. Vehicular Communications

7.1.1. Dedicated Short Range Communications

Dedicated Short Range Communications is a wireless communication technology designed to operate within the 5.9 GHz frequency band to facilitate short-range communication between vehicles and infrastructure [94]. V2X communication enables a dynamic and interconnected environment among different ITS applications through the support of DSRC [95]. However, this technology is dominant for safety applications, as it enables communication between vehicles, such as location, speed, and trajectory. DSRC’s short-range capabilities are critical in traffic management and allow vehicles to share data with other vehicles and the roadside infrastructure to optimize traffic flow. DSRC’s versatility is the reason for building the foundation for the connected future of transportation.

7.1.2. IEEE 802.11p (WAVE—Wireless Access in Vehicular Environments)

IEEE 802.11p, an IEEE 802.11 substandard focused on vehicular communications, provides special attention to the communications needs within vehicular environments [96]. This wireless technology operates in the dedicated 5.9 GHz frequency band designed for the challenges of real-time communication between vehicles and infrastructure. The primary purpose of IEEE 802.11p is to facilitate V2X communications between vehicles and infrastructure, sharing critical information [97]. The technology has been found to be valuable in applications such as collision avoidance, where immediate sharing of position, speed, and other crucial data elements improves road safety. Furthermore, 802.11p enables V2X communication for traffic information and the development of ITS in optimizing traffic flow.

7.1.3. Cellular Vehicle-to-Everything (C-V2X)

The extensions of cellular networks, cellular vehicle-to-everything (C-V2X), enable direct and V2V as well as V2X communication [98]. C-V2X breaks through the barriers of traditional communication by utilizing cellular networks as vehicles can communicate directly to other vehicles, to roadside infrastructure, and to other connected entities. This advanced connectivity framework has multiple applications, including safety in ITS by real-time sharing of information. In supporting safety (collision warning and emergency break) applications, C-V2X plays a substantial role in accident prevention. C-V2X is more than just about safety, it also helps in the optimization of traffic efficiency by making coordinated traffic management strategies possible. Furthermore, it contributes toward enhancing overall connectivity in the mobility system, with a deeper impact on a more responsive and more connected smart transportation ecosystem. C-V2X adoption in vehicular communication is expected to bring users to safer, more efficient, and technologically advanced transportation networks.

7.2. Cellular Mobile Communications

7.2.1. The 5G and Beyond

The 5G and its subsequent evolutionary stages constitute this transformative age of cellular mobile communications in the IoV, which rely on high-speed, low-latency connectivity to realize real-time communication of data and dynamic IoV applications [99]. The 5G communications lay a robust foundation for V2X communication goes beyond traditional cellular networks. It includes enabling the free exchange of data between vehicles and their various elements in the transportation environment. IoV offers numerous applications of 5G, including significant services like traffic management enabled with real-time data for devising adaptive solutions to congestion.

7.2.2. Long-Term Evolution

Long-term evolution (LTE) is a major step in cellular mobile communications, giving a high speed and low latency base, with a major positive impact on the evolution of the IoV [100]. As an LTE standard, it provides robust data transmission and seamless connectivity for IoV applications. More specifically, its high-speed capabilities are critical in supporting data services in connected vehicles. The IoV benefits from LTE as a backbone of various applications, in particular for connected car services, such as navigation systems that make full use of (real-time, accurate) data for best routing. The low-latency communication of LTE enables real-time traffic updates that help to create more efficient route planning and avoid congestion. Fundamentally, LTE serves as a cornerstone to IoV by setting up a connectivity infrastructure to run vehicles within an environment that brings together advanced communication technologies to enhance the on-road driving experience, safety, and efficiency.

7.3. Short-Range Static Communications

7.3.1. Bluetooth and Bluetooth Low Energy (BLE)

The BLE technologies are precisely suited for short-range communication and are well-suited to connect the devices within the confines of a vehicle. Bluetooth and BLE are important means of creating in-car connectivity, enabling the systems on board to communicate with each other [101]. Modern systems enable data transmission to allow users to wirelessly stream audio, share data, and access entertainment features. In addition, Bluetooth and BLE facilitate the simple combination of smartphones with automobiles, making it possible for hands-free calling, playing music, and syncing data. These technologies are extremely adaptable and necessary for enabling a connected and interactive in-vehicle environment where devices communicate efficiently to create a more convenient, entertaining, and better user experience.

7.3.2. Wi-Fi (802.11a/b/g/n/ac)

Standards such as 802.11a/b/g/n/ac aid in fast local wireless communication within a limited area of a car or other nearby devices. Since Wi-Fi standards have great versatility, they have become indispensable for IoV applications. Wi-Fi opens up the possibility of building networks to support vehicles to communicate and make in-vehicle connectivity really intelligent vehicles [102]. Furthermore, Wi-Fi makes it possible to stream data, audio, and video with ease to enjoy a more enriched in-car entertainment experience. In addition to providing an entertainment platform for users, these Wi-Fi standards allow for wireless D2D communication, allowing users to easily connect and securely share information between their devices within the vehicle. The use of Wi-Fi technologies for the IoV framework indicates their great role in the creation of a vehicle with a connected, collaborative, and technologically advanced environment.

7.3.3. Near Field Communication (NFC)

A key technology of the IoV is NFC to perform short-distance communication in a secure and easy way between devices [103]. Applications of this technology extend to nearly all aspects of vehicular connectivity, with the use cases including contactless payments, where the drivers and the passengers are enabled with a safe, cashless, and seamless transaction. In terms of vehicles, NFC makes it possible for easy device pairing for smartphones, tablets, or other compatible devices with the car system. NFC’s simplicity, security, and versatility have led to its popularity for driving a connected, user-friendly vehicle experience in today’s modern vehicles.

These wireless technologies act as the communication backbone in IoV applications, enabling an interconnected transport ecosystem. Finally, these technologies are integrated to support safety, efficiency, and innovative services that are helping to shape smart and connected vehicles in the context of larger ITS.

8. Open Research Challenges, Concerns, and Research Directions in Internet of Vehicles in Smart Cities

One of the most challenging problems within the IoV ecosystem is the integration between all components and the ability to communicate between objects. In order to meet this demand, many researchers have proposed IoV architectures with structural, layered designs [104,105,106,107,108,109,110,111]. The following are a few important open research challenges as depicted in Figure 8.

8.1. Security and Privacy Enhancement

One of the foremost research challenges for IoV in smart cities comes in strengthening security and privacy measures. Robust encryption techniques and authentication need to be developed to protect V2V and V2I communications from cyber threats. In addition, one of the challenges is to devise privacy-preserving mechanisms that allow for smart city applications to share data while maintaining individuals’ private information safe. To solution for this challenge is a secure data exchange framework that secures the integrity of data exchange while preventing unauthorized access/manipulation and at the same time upholding users’ privacy. Advancement in these areas can enable IoV to prosper as the trusted and secure part of urban mobility.

8.2. Cyber–Physical System Resilience

Cyberphysical system resilience is a critical concern in the evolution of the IoV in smart cities. Research attempts in this area include the study of ways to maintain the stability and functionality of IoV systems against cyberattacks and technical or communication breakdowns. Such efforts require the development of advanced mechanisms that can detect and respond to cyber–physical threats in real time. Cities can bring the benefits of connected mobility without worrying about potential disruptions if the interoperability of IoV systems is enhanced in terms of their resiliency.

8.3. Interoperability and Standardization

Within the IoV in a smart city, effective interoperability and standardization are essential. It involves developing common communication protocols as well as common data formats to allow for frictionless interaction between vehicles and all components of the smart city infrastructure. This venture makes possible a common language of data exchange to enable seamless collaboration and sharing of information. This aims to achieve its goal by standardizing components for integration, thereby reducing integration complexities, ensuring system interoperability the realization of IoVs efficiently in urban mobility.

8.4. Dynamic Traffic Management

The creation of dynamic traffic management solutions is a key research domain in the convergence of IoV and smart cities. Formulating such adaptive algorithms that take advantage of the real-time data influx from networked vehicles. These algorithms process the data dynamically, optimizing traffic flow, limiting congestion, and amplifying the efficiency of urban mobility systems. Moreover, the research needs to invent new forms of traffic in urban areas, parameters that characterize a traffic system more responsive to the constant changes of modern cities.

8.5. Human-Centric Design and Usability

One important research direction on the merging of the IoV and smart cities lies in human-centric design and usability. This includes a deep investigation of user interfaces and interaction models that link drivers to IoV systems. The goal is to create interfaces that are both intuitive and informative, that allow drivers to comprehend the complexities of IoV technologies, creating a feeling of control and confidence. Not only does this improve safety, but it also increases the trust in the operation of autonomous vehicles by improving the user experience.

8.6. Sustainability and Environmental Impact

Environmental implications from IoV-related technologies are the focus of critical research in the ecosystem of the IoV and the smart cities. This involves finding ways to both consume less energy [112] and emit feweremissions, requiring innovative transportation solutions that effectively address these challenges. This research adds by analyzing IoV’s environmental track to provide this contribution to the vision of smart cities by orienting the development of technology towards a greener eco-friendly direction for the environment in current urban design.

8.7. Computational Complexity

Communication protocols must be strong and should be able to support high-speed data transmission. Additionally, secure and private data processing of IoV systems between computation tasks requires encryption methods, and more privacy-preserving techniques. It raises some other facts like strategies for 6G-IoT, edge computing, distributed computing architecture, and adaptive resource allocation algorithms to improve the performance of computational resources towards improving the overall performance of the systems in the smart city [113]. Additionally, the challenges faced by multicarrier technologies like 802.11p under high Doppler conditions such as degraded channel estimation and equalization performance. Moreover, emerging physical layer alternatives like orthogonal time frequency space (OTFS) modulation offer improved resilience in high-mobility environments, thereby contributing to more reliable and efficient V2X communications [114]. The potential of emerging technologies like blockchain and quantum computing in solving computational complexity issues brings up new possibilities in the field of IoV applications in smart cities.

To address these challenges, the future directions focus on key research priorities in the domain of IoV in smart cities. Key focus areas include enhancing security and privacy, ensuring interoperability and standardization, managing dynamic traffic flows, and addressing computational complexity. This research identifies the need for robust encryption, resilient cyber–physical frameworks, and scalable communication protocols. The emphasis should be on the integration of AI and ML for adaptive systems, the adoption of green and sustainable technologies, and the application of emerging technologies such as edge computing, blockchain, and quantum computing. Additionally, the formulation of ethical policy frameworks and unified infrastructure standards is essential for realizing a secure, intelligent, and user-centric urban mobility environment.

9. Conclusions

The Internet of Vehicles represents a new phase in interaction among vehicles, drivers, and passengers. As a specialized area within the IoT, IoV is enabling communication between vehicles, people, and roadside infrastructure. With its development, IoT is expected to become a key part of daily life, addressing issues such as traffic congestion, road safety, and other transport-related challenges. The development of IoV involves not only enhancing vehicle security and efficiency but also advancing the commercialization of vehicular networks. This study supports researchers and engineers in understanding the transition toward a global, interconnected vehicular communication system. Furthermore, it is important to understand IoV’s layered architecture, network models, and challenges. With this, developers can create technology-based applications aimed at improving transportation services.