Review of Potential Methods for Handover Decision in V2V VANET

Alam, Sahirul; Sulistyo, Selo; Mustika, I Wayan; Adrian, Ronald

doi:10.1109/icomitee.2019.8921117

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Recent attacks targeting VANET (Vehicular ad hoc network) with autonomous Levels 1 to 4, which are not entirely autonomous, have been documented. Denial of service attack [ 26 ], sybil attack [ 27 ], timing attack [ 28 ], illusion attack [ 29 ], message tampering [ 30 ], and node impersonation [ 29 ] are examples of these types of attacks.…”

Section: Related Workmentioning

confidence: 99%

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Chy

Masum

Sayeed

et al. 2021

Sensors

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The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.

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Section: Related Workmentioning

confidence: 99%

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Chy

Masum

Sayeed

et al. 2021

Sensors

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show abstract

“…There are some recent attacks reported on VANET comprising of vehicles with autonomous Levels 1 to 4, which are not fully autonomous. These attacks include sybil attack [79], denial of service attack [80], timing attack [81], message tampering [82], illusion attack [20], and node impersonation [20]. As these attacks do not involve fully autonomous vehicles, i.e., the vehicles with Level 5 [83], therefore these are not the main focus of this survey.…”

Section: Recent Attacks On Self-driving Carsmentioning

confidence: 99%

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

et al. 2020

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Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicleto-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

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“…The pervasiveness of vehicles in the cities was one of the causes that led to the increasing of studies in VANETs. Several solutions in the literature use VANETs to support the daily life of cities when dealing with specific urban aspects, targeting safety and non-safety goals, such as accident prevention and traffic efficiency, respectively [20]. Some solutions for smart cities use clustering to support the tracking of target objects and/or the dissemination of information.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, due to their pervasiveness in urban centers [13], vehicles seem to be an obvious option for the collection of multimedia data of events [14,15,16] and to support cooperation and coordination in monitoring processes [17], since they do not suffer from the limited resources that other ad hoc networks have, such as low processing power, low storage capacity and battery dependency [18]. However, VANETs impose their own challenges, such as high mobility [19], frequent handover [20], variation of traffic density, high scalability and network topological variation [21]. In this context, recent studies have demonstrated a variety of strategies to manage urban events.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Monitoring and Dissemination of Urban Events Supported by Dynamic Clustering of Vehicles

Andrade,

Veloso,

Vasconcelos

et al. 2020

Preprint

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Critical urban events take places at a random way and they need to be dealt with by public authorities quickly to maintain the proper operation of cities. The main challenges for an efficient handling of an event fall precisely in its random nature, and in the speed and accuracy of the notification of its manifestation to the authority. The pervasiveness of vehicles in urban environments, and their communication and monitoring capabilities, allow to employ VANETs to support the dissemination and handling of such events. Thus, this work proposes MINUET, a dynamic system to support the monitoring and dissemination of urban events, which operates in an ad hoc vehicular network. It uses a cooperative-based strategy where vehicles are able to dynamically coordinate the monitoring and dissemination of events in real time by the means of clusters. Results obtained by NS3 show the performance of MINUET regarding the real-time monitoring and dissemination of events detected in urban areas.

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Review of Potential Methods for Handover Decision in V2V VANET

Cited by 7 publications

References 44 publications

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

Cooperative Monitoring and Dissemination of Urban Events Supported by Dynamic Clustering of Vehicles

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