Socialization between Quarantine Vehicle and Road Side Unit for Handling COVID-19: A Concept

2021 ◽  
Author(s):  
Zaheeruddin ◽  
Hina Gupta ◽  
Deepti Mehrotra
Keyword(s):  
Road Side Unit

scholarly journals RCVC: RSU-Aided Cluster-Based Vehicular Clouds Architecture for Urban Areas

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 193
Author(s):  
Mohamed Ben bezziane ◽  
Ahmed Korichi ◽  
Chaker Abdelaziz Kerrache ◽  
Mohamed el Amine Fekair
Keyword(s):  
Urban Areas ◽  
High Speed ◽  
Service Discovery ◽  
Cluster Head ◽  
Experimental Comparison ◽  
The Road ◽  
Road Side Unit ◽  
End To End Delay ◽  
End To End ◽  
On The Road

As a promising topic of research, Vehicular Cloud (VC) incorporates cloud computing and ad-hoc vehicular network (VANET). In VC, supplier vehicles provide their services to consumer vehicles in real-time. These services have a significant impact on the applications of internet access, storage and data. Due to the high-speed mobility of vehicles, users in consumer vehicles need a mechanism to discover services in their vicinity. Besides this, quality of service varies from one supplier vehicle to another; thus, consumer vehicles attempt to pick out the most appropriate services. In this paper, we propose a novel protocol named RSU-aided Cluster-based Vehicular Clouds protocol (RCVC), which constructs the VC using the Road Side Unit (RSU) directory and Cluster Head (CH) directory to make the resources of supplier vehicles more visible. While clusters of vehicles that move on the same road form a mobile cloud, the remaining vehicles form a different cloud on the road side unit. Furthermore, the consumption operation is achieved via the service selection method, which is managed by the CHs and RSUs based on a mathematical model to select the best services. Simulation results prove the effectiveness of our protocol in terms of service discovery and end-to-end delay, where we achieved service discovery and end-to-end delay of 3 × 10−3 s and 13 × 10−2 s, respectively. Moreover, we carried out an experimental comparison, revealing that the proposed method outperformed several states of the art protocols.

scholarly journals Malicious node identification routing and protection mechanism for VANET against various attack

2020 ◽  
Vol 20 ◽  
pp. 110-122
Author(s):  
Er. Ritika Saini ◽  
Harish Kundra
Keyword(s):  
Key Management ◽  
Traffic Information ◽  
Delivery Ratio ◽  
Malicious Node ◽  
Protection Mechanism ◽  
The Road ◽  
Emergency Situations ◽  
Road Side Unit ◽  
Private Data ◽  
Identification Mechanism

With the help of road side unit vehicles communicate among themselves. This technique termed as VANET. This network helps us to improve the safety and efficiency of the occupants during travelling in vehicles. The basic idea of this technique is to send information about the traffic information to the road side unit or other vehicles. These vehicles get safe from attacks and misuse of their private data. The objective of this paper to secure the communication among the vehicles and the road side unit. In this technique the communication mainly dependant on the safety of the road such as vehicles tracking, emergency situations and message monitoring. There are various attacks like Sybil and Gray hole attack are vulnerable to VANET. To protect from these attacks our technique provide malicious node identification mechanism that help us to provide better facility to send data to vehicles safely. To avoid these types of attacks, our propose technique include feature like key management system to prevent the communication among the vehicles. Our proposed system mostly focus on Bandwidth, packet loss and packet delivery ratio [12].

scholarly journals The Lightweight Autonomous Vehicle Self-Diagnosis (LAVS) Using Machine Learning Based on Sensors and Multi-Protocol IoT Gateway

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2534 ◽  
Author(s):  
YiNa Jeong ◽  
SuRak Son ◽  
ByungKwan Lee
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Autonomous Vehicle ◽  
Sensor Data ◽  
The Internet ◽  
Message Queue ◽  
Road Side Unit ◽  
The Neural Network ◽  
Total Condition ◽  
Vehicle Sensors

This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messages as it passes through protocol buses. The changed messages are divided into header information, sensor messages, and payloads and they are stored in an address table, a message queue, and a data collection table separately. In sequence, the sensor messages are converted to the message type of the other protocol and the payloads are transferred to an in-vehicle diagnosis module (In-VDM). The LAVS informs the diagnosis result of Cloud or road side unit(RSU) by the internet of vehicles (IoV) and of drivers by Bluetooth. To design the LAVS, the following two modules are needed. First, a multi-protocol integrated gateway module (MIGM) converts sensor messages for communication between two different protocols, transfers the extracted payloads to the In-VDM, and performs IoV to transfer the diagnosis result and payloads to the Cloud through wireless access in vehicular environment(WAVE). Second, the In-VDM uses random forest to diagnose parts of the vehicle, and delivers the results of the random forest as an input to the neural network to diagnose the total condition of the vehicle. Since the In-VDM uses them for self-diagnosis, it can diagnose a vehicle with efficiency. In addition, because the LAVS converts payloads to a WAVE message and uses IoV to transfer the WAVE messages to RSU or the Cloud, it prevents accidents in advance by informing the vehicle condition of drivers rapidly.

scholarly journals New Off-Board Solution for Predicting Vehicles' Intentions in the Highway On-Ramp Using Probabilistic Classifiers (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13841-13842
Author(s):  
Zine El Abidine Kherroubi ◽  
Samir Aknine ◽  
Rebiha Bacha
Keyword(s):  
New Approach ◽  
Road Side Unit

This paper proposes a new approach for predicting drivers' intentions in a Highway on-ramp merge situation using a central road side unit (RSU) with probabilistic classifiers.

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