5G-based V2V broadcast communications: A security perspective

Muhammad, Mujahid; Safdar, Ghazanfar Ali

doi:10.1016/j.array.2021.100084

Cited by 25 publications

(12 citation statements)

References 43 publications

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“…This has been achieved by assuming all the vehicles have no initial spacing error, velocity error and acceleration error. Overall, to begin with all vehicles operate as normal in the TEV lane and the reference lead vehicle transmits signals as described in the piece-wise function shown in the piece-wise function (37). In this simulation, the sample time is 0.01 s therefore the abscissa unit is 0.01 s.…”

Section: Conventional Smc Control With Lead Vehicle Informationmentioning

confidence: 99%

An improved sliding mode control (SMC) approach for enhancement of communication delay in vehicle platoon system

Gulati

et al. 2022

IET Intelligent Trans Sys

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Vehicle platoon systems are widely recognized as key enablers to address mass-transport. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) are two technologies that drive platooning. The inter-vehicle spacing and collaboration velocity in the platoon are important parameters that must be controlled. A new mass-transport system called the Tracked Electric Vehicles (TEV) has been proposed which has reduced the inter-vehicular spacing to only a quarter of the regular car length. This enables mass transport at uniform speed for cars with speed of 200km/h. However, conventional radar based adaptive cruise control (ACC) system fail to control each vehicle in these scenarios. Lately, sliding mode control (SMC) has been applied to control platoons with communication technology but with low speed and without delay. This paper proposes a novel SMC design for TEV using global dynamic information with the communication delay. Also, graph theory has been employed to investigate different V2V communication topology structures. To address issues of node vehicle stability and string stability, Lyapunov candidate function is chosen and developed. Additionally, this paper uses first-order vehicle models with different acceleration/deceleration parameters for simulation validations under communication delay. The results show that this SMC has a significant tolerance ability and meets the design requirements of TEV.

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Section: Conventional Smc Control With Lead Vehicle Informationmentioning

confidence: 99%

An improved sliding mode control (SMC) approach for enhancement of communication delay in vehicle platoon system

Gulati

et al. 2022

IET Intelligent Trans Sys

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“…Known areas of research on the analysis of systems with different mixed types of traffic in a single technological and communication environment of generation 5G focus either on the hardware-technological approach to the description of the control mechanism [ 9 – 13 ] or on the software concept of implementation of the latter [ 7 – 14 ]. It should be noted that the second approach is much more popular.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Naturally, the volume of this resource is a random variable with some distribution law. However, in [ 14 – 18 ], a multilinear queuing system with a similar structure is considered, but these studies ignore the fact that different levels of reliability characterize general-purpose resources and personalized resources shared to support the received request. This fact must be reflected in the state space of the queuing system [ 19 – 23 ], which was not done in the above investigations.…”

Section: State-of-the-artmentioning

confidence: 99%

“…The results of such studies are summarized in the form of analytically determined metrics of qualitative indicators, calculating which can assess the effectiveness of the profile application of specific instances of communication systems. Suppose we focus on works where the study of this process takes place in the paradigm of the mathematical apparatus of queuing systems [ 14 – 22 ]. In that case, it can be argued that the authors either focus on different laws of distribution and service of incoming information requests or focus on formalizing the calculation algorithm’s characteristic parameters of the obtained stochastic models.…”

Section: State-of-the-artmentioning

confidence: 99%

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Modeling a session of subject-system interaction in a wireless communication infrastructure with a mixed resource

2022

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The article examines the subject-system interaction session, where the system is understood as a base station, and the subject is understood as a mobile communication device. The peculiarity of the study is taking into account the phenomenon relevant to modern communication infrastructures, which is that the base station supports the division of information traffic into a subspace of guaranteed personalized traffic and a subspace of general-purpose traffic. The study considers a highly critical empirical emergency when the general-purpose traffic subspace may cease to be available at any time. The presented mathematical apparatus describes the impact of such an emergency on the active communication sessions supported by the system in receiving new incoming requests of increasing intensity. To characterize this emergency situation, expressions adapted for practical application are presented to calculate such qualitative parameters as the probability of stability, the probability of failure, and unavailability.

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“…The ITS and smart cities protocols are grounded in IEEE 802.11p technology, which offers the framework for vehicle ad hoc network communications. The 3rd Generation Partnership Project (3GPP) has recently been working on integrating V2X services into LTE and future 5G cellular networks [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Hemavathi¹,

Akhila²,

Alotaibi

et al. 2022

Energies

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One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

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5G-based V2V broadcast communications: A security perspective

Cited by 25 publications

References 43 publications

An improved sliding mode control (SMC) approach for enhancement of communication delay in vehicle platoon system

An improved sliding mode control (SMC) approach for enhancement of communication delay in vehicle platoon system

Modeling a session of subject-system interaction in a wireless communication infrastructure with a mixed resource

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

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