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Building upon Internet of Things (IoT), Internet of Vehicles (IoV) and Social Internet of Things (SIoT), Social Internet of Vehicles (SIoV) is the latest development in the field. SIoV allows vehicles to socialize among themselves and share information of common interests. The increasing popularity of the SIoV concept demands more research to explore its great potential. However, very few studies have systemically investigated this concept to provide a comprehensive view for readers. The paper aims to discuss this issue.

This paper discusses the perspectives of the utilizations and limitations of SIoV, setting forward a systematic and epistemological framework.

The authors summarize the benefits of SIoV from four information-management perspectives: safety management, traffic control and convenience, productivity improvement and commercialization and exploring the factors inhibiting the development of SIoV from the following seven aspects: standardization, adaptability, scalability, infrastructure, lack of application, privacy and security.

The paper lays a solid foundation for researchers to find possible solutions to address the challenges to SIoV and provides valuable insights for practitioners who are interested in adopting SIoV initiatives.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

This paper is a review of a number routing protocols in the internet of vehicles (IoV). IoV emphasizes information interaction among humans, vehicles and a roadside unit (RSU), within which routing is one of the most important steps in IoV network.

In this paper, the authors have summarized different research data on routing protocols in the IoV. Several routing protocols for IoV have been proposed in the literature. Their classification is made according to some criteria such as topology-based, position-based, transmission strategy and network structure. This paper focuses on the transmission strategy criteria. There exist three types of protocols that are based on this strategy: unicast protocol, broadcast protocols and multicast protocols. This later type is classified into two subclasses: geocast and cluster-based protocols. The taxonomy of the transmission strategy is presented in this study. We discuss the advantages and disadvantages of each type with a general comparison between the five types.

The authors can deduce that many challenges are encountered when designing routing protocols for IoV.

A simple and well-explained presentation of the functioning of the IoV is provided with a comparison among each categories of protocols is well presented along with the advantages and disadvantages of each type. The authors examined the main problems encountered during the design of IoV routing protocol, such as the quick change of topology, the frequent disconnection, the big volume of data to be processed and stored in the IoV, and the problem of network fragmentation. This work explores, compares existing routing protocols in IoV and provides a critical analysis. For that, the authors extract the challenges and propose future perspectives for each categories of protocols.

The internet of vehicles (IoV) communication has recently become a popular research topic in the automotive industry. The growth in the automotive sector has resulted in significant standards and guidelines that have engaged various researchers and companies. In IoV, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in IoV and inconsistent network coverage in different areas make routing challenging. This paper aims to provide a lane-based advanced forwarding protocol for internet of vehicles (LAFP-IoV) for efficient data distribution in IoV. The proposed protocol’s main feature is that it can identify the destination zone by using position coordinates and broadcasting the packets toward the direction of destination. The novel suppression technique is used in the broadcast method to reduce the network routing overhead.

The proposed protocol considers the interferences between different road segments, and a novel lane-based forwarding model is presented. The greedy forwarding notion, the broadcasting mechanism, and the suppression approach are used in this protocol to reduce the overhead generated by standard beacon forwarding procedures. The SUMO tool and NS-2 simulator are used for the vehicle's movement pattern and to simulate LAFP-IoV.

The simulation results show that the proposed LAFP-IoV protocol performs better than its peer protocols. It uses a greedy method for forwarding data packets and a carry-and-forward strategy to recover from the local maximum stage. This protocol's low latency and good PDR make it ideal for congested networks.

The proposed paper provides a unique lane-based forwarding for IoV. The proposed work achieves a higher delivery ratio than its peer protocols. The proposed protocol considers the lanes while forwarding the data packets applicable to the highly dense scenarios.

The communication link in the engineering of Internet of Vehicle (IOV) is more frequent than the communication link in the Mobile ad hoc Network (MANET). Therefore, the highly dynamic network routing reliability problem is a research hotspot to be solved.

The graph theory is used to model the MANET communication diagram on the highway and propose a new reliable routing method for internet of vehicles based on graph theory.

The expanded graph theory can help capture the evolution characteristics of the network topology and predetermine the reliable route to promote quality of service (QoS) in the routing process. The program can find the most reliable route from source to the destination from the MANET graph theory.

The good performance of the proposed method is verified and compared with the related algorithms of the literature.

Advances in technology enable companies to collect and analyse data, which were previously not accessible, to either enhance existing business processes or create new ones. The purpose of this paper is to document the role and impact of Big Data Analytics (BDA), and the Internet of Things (IoT), in supporting a large logistics firm’s strategy to improve driver safety, lower operating costs, and reduce the environmental impact of their vehicles.

A single case with embedded units intrinsic case study method was adopted for this research and data were collected from a “real-life” situation, to create new knowledge about this emerging phenomenon.

Truck telematics were utilised in order to better understand, and improve, driving behaviours. Remote control centres monitor live sensor data from the company’s fleet of vehicles, capturing the likes of speed, location, braking, and engine data, to inform future training programs. A combination of truck telematics and geo-information are being used to enable proactive alerts to be sent to drivers regarding possible upcoming hazards. Camera-based technologies have been adopted to improve driver safety, and fatigue management, capturing evidence of important driving events and storing data directly to the cloud, and BDA is also being used to improve truck routing, recommend optimal fuel purchasing times/locations, and to forecast predictive and proactive maintenance schedules.

The type of data collected by Company A, and similar logistics companies, has the potential to greatly inform researchers investigating autonomous vehicles, smart cities, and the physical internet.

Eco-driving, a practice informed/improved by BDA at Company A, has been linked to reductions in fuel consumption and CO₂ emissions, which bring both economic and environmental benefits. Technologies similar to Truckcam are growing in popularity in some parts of the world, to the point where it is now common practice to use dashcam assess of accidents to establish liability. This has implications for logistics firms, in other parts of the world, where such practices might not yet be so commonplace, and for drivers and society more broadly.

Improvements in utilisation and routing have the potential to reduce traffic congestion, which is responsible for losses in productivity, increases in fuel consumption, air pollution and noise, and can incite stress, aggression, anger and unsafe behaviours in drivers. Predictive analytics, which generate refuelling and maintenance schedules, have the potential to be adopted by all vehicle manufacturers, and could generate reductions in customer fuel costs, whilst improving the performance, efficiency, and life expectancy of future motor all vehicles. The high probability of occupations in the logistics industry being replaced by computer automation in the near future is also discussed.

The findings from this research serve as a valuable case example of a real-world deployment of BDA and IoT technologies in the logistics industry, and present implications for practitioners, researchers, and society more widely.

Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT).

The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study.

This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively.

The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.

This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.

Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.

The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

With various challenges in the digital era, stakeholders are expressing growing interests in understanding the impact of emerging and disruptive technologies on freight transportation. This paper provides a systematic literature review of the current state of affairs as well as future trends and aims to support stakeholders' decision-making in logistics management in the era of disruptive technologies.

Several recent and representative articles from academic, industrial and governmental perspectives were investigated to set the scene for this research and to serve as a baseline for electing nine emerging technologies, which were then used to conduct a systematic literature review covering the literature within the area during the past twelve years.

3D printing, artificial intelligence, automated robots, autonomous vehicles, big data analytics, blockchain, drones, electric vehicles and the Internet of Things were identified as the emerging technologies. The current state of existing research and potential future opportunities were analyzed.

Since the potential literature body is almost impossible to fully cover, a tradeoff between the number of emerging technologies and the related literature reviewed has been performed. However, the paper provides a novel approach to select the emerging and disruptive technologies and a systematic literature review to fill the identified research gap in the related literature.

The research support various stakeholders to better capture the current status of and the future opportunities in freight transportation and gain a clearer understanding of the disruptive technologies as well as to guide them in how to deploy these initiatives in future decision-making.

By providing a systematic literature review on the trends, themes and research opportunities in the era of disruptive technologies, the papers bring about broad and comprehensive review on the impact of disruptive technologies on logistics and transportation as well as opportunities to support management decision support in the logistics industry.

To avoid this situation, the authors proposed an optimal artificial bee colony algorithm-based Unmanned Aerial Vehicle (UAV) routing algorithm for efficient data communication between doctors and patients. This proposed method worked in three stages.

In recent decades, wireless body area networks have played an important role in health care applications. It facilitates the transmission of the patients' health data analysis report to the appropriate doctors.

In the first phase, biological sensors are connected to the human body via a controller node and collected data is transmitted via Bluetooth to the Personal Device Assistant (PDA). In the second phase, collected data will be transmitted via the Internet of things using an artificial bee colony algorithm. The second aids in determining the best route. In the third phase, unmanned aerial vehicles will use the best path to send collected data to doctors, caregivers, ambulances and cloud storage servers.

The simulation results show that the network's performance is superior when compared to existing approaches. The proposed algorithm achieves a high throughput, a lower delay, a higher link rate and a higher delivery rate.