Internet of vehicles in big data era

Xu, Wenchao; Zhou, Haibo; Cheng, Nan; Lyu, Feng; Shi, Weisen; Chen, Jiayin; Shen, Xuemin

doi:10.1109/jas.2017.7510736

Cited by 485 publications

(181 citation statements)

References 114 publications

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“…3 Consequently, it became much more complex to deploy a consistent set of policies, to maintain the network correctly while minimizing the cost in traditional network. Each network node is responsible for the path configuration and routing table programming, which is used for data flows transmission.…”

Section: Traditional Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Nowadays, big data, cloud computing, and Internet of Things (IoT) have encouraged the generalization of VANET to the Internet of Vehicles (IoV), 3 where vehicles evolve from passive actors of transportation to smart entities with new perception and communication capabilities. 3 The transmission strategies and routing protocols, representing the core of the communication layer, 6 are the focus of this paper. The IoV paradigm is defined as a dynamic mobile communication system between vehicles and public networks using vehicle-to-vehicle (V2V), vehicle-to-road (V2R), vehicle-to-infrastructure (V2I), vehicle-to-human (V2H), vehicle-to-sensor (V2S), vehicle-to-object (V2O), and vehicle-to-cloud (V2C) communications.…”

Section: Introductionmentioning

confidence: 99%

“…In the upper layer, data are stored in clouds, explored, and used to guide vehicles and to provide them with an abundance of multimedia services and mobile applications. 3 The transmission strategies and routing protocols, representing the core of the communication layer, 6 are the focus of this paper. A consequent number of surveys have been proposed in prior work covering different aspects of routing in vehicular networks.…”

mentioning

confidence: 99%

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Survey on IoV routing protocols: Security and network architecture

Alouache

Nguyen²,

Aliouat

et al. 2018

Int J Communication

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As today, vehicles are equipped with wireless sensors and on-board computers capable of collecting and processing a large amount of data; they can communicate to each other via different communication types and through different relay nodes. Internet of Vehicles (IoV) routing protocols are deployed to monitor these communications with various strategies to achieve a high availability of communication. In this paper, we propose to extend an existing taxonomy representing the necessary criteria to build IoV routing algorithms, by adding two new important criteria: security aspect and network architecture. Enhanced vehicular routing protocols with different security mechanisms have been studied, compared, and classified with respect to the authentication, the integrity, the confidentiality, the nonrepudiation, and the availability of data and communications. Routing protocols using the software-defined networking (SDN) paradigm have also been reviewed in order to compare with those with traditional network architectures. Three types of SDN routing protocols, namely, centralized, decentralized, and hybrid control planes, have been analyzed. This survey will be useful for the choice of IoV routing protocols that take into account the flexibility, the scalability, and the intelligence of vehicular networks, as well as the security mechanisms against cyberattacks while being cost aware.

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Section: Traditional Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Survey on IoV routing protocols: Security and network architecture

Alouache

Nguyen²,

Aliouat

et al. 2018

Int J Communication

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“…According to many studies, the IEEE802.11p standard proposed as the principal access media for ITS has revealed its limitations by providing short communication setup delays and low latency in ITS, particularly for delay‐sensitive services such as traffic efficiency and road safety. Moreover, the lack of strict quality of service (QoS) provisioning and the central management in the 802.11p technology challenges the satisfaction of vehicle‐to‐vehicle (V2V) users' requirements . Device‐to‐device (D2D) communications based on Long Term Evolution (LTE) networks can be considered as one of the best solutions to resolve the problem where devices collaborate and communicate with each other in an autonomous way, without centralized control.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization‐based radio resource allocation and sharing algorithm for D2D‐based V2V communication

Feki

Belghith

Zarai

2019

Trans Emerging Tel Tech

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Vehicular communication systems have recently attracted a lot of attention because of their potential to improve road efficiency, comfort, and safety. In this paper, we investigate the resource management problem for device‐to‐device–based vehicle‐to‐vehicle communication. A multiobjective radio resource allocation and sharing algorithm is proposed based on a swarm intelligence algorithm called ant colony optimization (ACO). First, for the resource allocation process, three objective functions are formulated to maximize the users' transmission capacity, reduce the mobile transmission power, and minimize the intracell interference. Second, for the resource sharing process, we define two objective functions to optimize the resource utilization and reduce the intercell interference. Finally, a weighted sum method is used to join these objective functions and define one multiobjective optimization problem. The ACO algorithm is used to determine the Pareto optimal solution. Simulation results show that the proposed algorithm performs well and achieves promising performance in terms of network sum rate, packet drop rate, average queuing delay, resource utilization, and mobile's transmission power.

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Big Data/Machine Learning andD2D

Althamary

Huang

2020

Wiley 5G Ref

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Recently, big data, machine learning, and D2D communication have gained more attention to support the growth of data traffic and novel applications. The objective of this article is to present critical technology elements and show how new researches move forward to justify user requirements. In particular, D2D communication and vehicular networks can take advantage of the knowledge extracted using machine learning tools from collected data for better understanding and decision. Furthermore, emerging multi‐agent reinforcement learning is highlighted to be a suitable tool with high potential. Finally, several research opportunities and challenges are addressed.

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Internet of vehicles in big data era

Cited by 485 publications

References 114 publications

Survey on IoV routing protocols: Security and network architecture

Survey on IoV routing protocols: Security and network architecture

Multiobjective optimization‐based radio resource allocation and sharing algorithm for D2D‐based V2V communication

Big Data/Machine Learning andD2D

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