Vehicular Internet Of Things Research Articles

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues.

Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. FL attracts tremendous interests from a large number of industries due to growing privacy concerns. Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the communication, computing, and storage resources must be efficiently utilized. FL could be a promising approach to solve these existing challenges. In this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT. Then we discuss the significance and technical challenges of applying FL in vehicular IoT, and point out future research directions.

Blockchain-Based Secure and Trustworthy Internet of Things in SDN-Enabled 5G-VANETs

The modern intelligent transportation system brings not only new opportunities for vehicular Internet of Things (IoT) services but also new challenges for vehicular ad-hoc networks (VANETs). Apart from enhanced network performance, a practical and reliable security scheme is needed to handle the trust management while preserving user privacy at the same time. The emerging 5G mobile communication system is viewed as a prominent technology for ultra-reliable, low-latency wireless communication services. Furthermore, incorporating software-defined network (SDN) architecture into the 5G-VANET enables global information gathering and network control. Hence, real-time IoT services on transportation monitoring and reporting can be well supported. Both pave the way for an innovative vehicular security scheme. This paper investigates the security and privacy issue in the transportation system and the vehicular IoT environment in SDN-enabled 5G-VANET. Due to the decentralized and immutable characteristics of blockchain, a blockchain-based security framework is designed to support the vehicular IoT services, i.e., real-time cloud-based video report and trust management on vehicular messages. This paper explicitly illustrates the SDN-enabled 5G-VANET model and the scheduling procedures of the blockchain-based framework. The numerical simulation results also show that malicious vehicular nodes or messages can be well detected while the overhead and impact on the network performance are acceptable for large-scale scenarios. Through case studies and theoretical analysis, we demonstrate our design substantially guarantees a secure and trustworthy vehicular IoT environment with user privacy preserved.

Decentralized Trust Evaluation in Vehicular Internet of Things

Trust management in a decentralized vehicular network, such as vehicular ad hoc network, is particularly challenging due to the lack of centralized communication infrastructure and a fast varying feature of the vehicular environment. In this paper, we propose a decentralized trust management scheme for vehicular networks. The proposed scheme uses a fuzzy logic-based trust calculation approach to evaluate the direct trust where trustee nodes are located within the transmission range of a trustor node. A reinforcement learning-based approach is also employed to estimate the indirect trust where the behaviors of trustee cannot be observed directly. The extensive simulations are conducted to show the advantage of the proposed scheme over other baseline approaches.

Towards video streaming in IoT Environments: Vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues.

Phantom Cell Architecture for LTE and its Application in Vehicular IoT Environments

Proliferation of Internet of Things (IoT) devices (smart wearables/vehicles, etc.) in the near future, would raise the capacity and bandwidth demands from the cellular infrastructure manifold. Deploying small cells is an effective solution to cope with the problem. This work focuses on a special kind of small eNBs, termed as Phantom eNBs. Phantom eNB acts as a supplement to the current radio access network (RAN) in the LTE infrastructure. It handles the data plane while a Macro eNB holds the control plane. Definitions of control and data plane, along with the modifications needed in the protocol stack are explained i n this paper. Communication mechanisms are developed for Phantom and Macro eNB to communicate over the X2 interface between them. NS-3 simulations are performed for handover scenarios of Vehicular IoT environment, consolidating the architecture and network topology designed for Phantom based Heterogeneous Networks (HetNets). Network throughput improvements of 80% and 14% are observed in comparison to the Macro-only RAN and existing small cell solutions (Femto cells), respectively.

Research of Intelligent Vehicle Internet of Things Based on Anti-Worm Model

ntelligent transport system that based on Internet of Vehicles is regarded as effective measure to guarantee the safety of highway transport. Anti-worm model in vehicular IOT is constructed based on divide-and-conquer with velocity and the drive velocity of vehicle node as the conversion condition between active and passive anti-worms in hybrid anti-worms. Implement this model on the design of Internet of Vehicles terminal, the simulation results show that this model can make the performance of network improved in highway environment regardless of complex road conditions domain and provides a theoretical basis for programming real-time detection strategy and preventing worm destructive epidemics in vehicular Internet of things.