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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.

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.

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.

In the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.

A trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.

When compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.

Additionally, it reduces the amount of routing overhead and the average end-to-end delay.

Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.