Optimizing Connectivity for the Internet of Vehicles

Gill, Sumanjit; Wong, Robert Y.; Tayeb, Shahab; Trueblood, Fletcher; Pirouz, Matin

doi:10.1007/978-3-030-63092-8_39

Cited by 2 publications

(5 citation statements)

References 41 publications

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“…Additionally, combining EVs and AVs improves the existing features of the smart city. Finally, social lifestyle will be improved (smart mobility assistance) by combining CVs and EVs for physically challenged and elderly citizens [2,6].…”

Section: Introductionmentioning

confidence: 99%

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Basavaraj

Tayeb

2022

JSAN

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With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models.

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Section: Introductionmentioning

confidence: 99%

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Basavaraj

Tayeb

2022

JSAN

Self Cite

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“…If these attacks were to remain unaddressed, severe consequences (e.g., injury, death) can occur for drivers and pedestrians. Therefore, security mechanisms must be put into place to preserve the goal of safety in the IoV [ 15 ]. In terms of securing the IoV, there are several exiting work that incorporate learning including [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, WAVE utilizes IEEE 1609 which defines security services in IEEE 1609.2 and multi-channel operation in IEEE 1609.4 [ 19 ]. A depiction of the IEEE protocols in layers is shown in Figure 3 taken from [ 15 ]. The left side of the figure summarizes the main network layers separated by their color coding.…”

Section: Introductionmentioning

confidence: 99%

“…Roadside Units (RSUs) are an integral part of communication for autonomous vehicles and facilitating network communications [ 14 ]. Research has been done on multiple aspects of RSUs, such as their placement and how they can support other mechanisms within the IoV [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Another researched feature is the provision of security mechanisms, such as authentication of vehicular communication [ 15 ]. Kovalev et al [ 31 ] proposed a public key infrastructure scheme that employs the following security mechanisms: a handshake between the vehicle and the RSU, the signing of messages, and message authentication.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Traffic Light Implementation on a Roadside Unit over 802.11p Wireless Access in Vehicular Environments

Wong

White

Gill

et al. 2022

Sensors

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Blind intersections have high accident rates due to the poor visibility of oncoming traffic, high traffic speeds, and lack of infrastructure (e.g., stoplights). These intersections are more commonplace in rural areas, where traffic infrastructure is less developed. The Internet of Vehicles (IoV) aims to address such safety concerns through a network of connected and autonomous vehicles (CAVs) that intercommunicate. This paper proposes a Road-Side Unit-based Virtual Intersection Management (RSU-VIM) over 802.11p system consisting of a Field-Programmable Gate Array (FPGA) lightweight RSU that is solar power-based and tailored to rural areas. The RSU utilizes the proposed RSU-VIM algorithm adapted from existing virtual traffic light methodologies to communicate with vehicles over IEEE 802.11p and facilitate intersection traffic, minimizing visibility issues. The implementation of the proposed system has a simulated cloud delay of 0.0841 s and an overall system delay of 0.4067 s with 98.611% reliability.

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Optimizing Connectivity for the Internet of Vehicles

Cited by 2 publications

References 41 publications

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Virtual Traffic Light Implementation on a Roadside Unit over 802.11p Wireless Access in Vehicular Environments

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