scholarly journals A Modified RR-ALOHA Protocol for Safety Message Broadcast in VANETs

2020 ◽  
Vol 1650 ◽  
pp. 032005
Author(s):  
Zhong Huang ◽  
Rui Xu ◽  
Chu Chu ◽  
Guangjun Wen
Keyword(s):  
Safety Message ◽  
Aloha Protocol

A Novel Approach for Data Communication and Safety Concern in VANET Using Cognitive Radio

2017 ◽  
Vol 7 (11) ◽  
pp. 174 ◽  
Author(s):  
Ishu Bansal ◽  
Rajnish Kansal
Keyword(s):  
Routing Protocols ◽  
Intelligent Transportation System ◽  
Safety Concern ◽  
Data Communication ◽  
Intelligent Transportation ◽  
Safety Message ◽  
Novel Approach ◽  
Transmission Of Information ◽  
Minimum Delay ◽  
Road Side Units

VANET is branch of networking that is used for communication in intelligent transportation system. In this process of VANET various nodes are interconnected to each other and road side units. R2R, V2V and V2R communication has been done in VANET. Due to variouscommunications under VANET routing protocols have overhead for computation of shortest path and transmission of information with minimum delay. Delay in the network cause minimum safety. In this paper an approach has been proposed that can be used for transmission of safety message over the network with minimum delay. On the basis of proposed approach safety message can be transmitted in shortest interval of time so that safety can be achieved in the network.

A novel safety message dissemination framework in LTE‐V2X system

2021 ◽  
Author(s):  
Eshita Rastogi ◽  
Mukesh Kumar Maheshwari ◽  
Abhishek Roy ◽  
Navrati Saxena ◽  
Dong Ryeol Shin
Keyword(s):  
Safety Message ◽  
Message Dissemination

scholarly journals Safety message generation rate adaptation in LTE-based vehicular networks

2017 ◽  
Vol 128 ◽  
pp. 186-196 ◽  
Author(s):  
Hossein Soleimani ◽  
Thomas Begin ◽  
Azzedine Boukerche
Keyword(s):  
Vehicular Networks ◽  
Rate Adaptation ◽  
Generation Rate ◽  
Safety Message

Does Displaying Safety Messages on Dynamic Message Signs have Measurable Impacts on Crash Risk?

2021 ◽  
pp. 036119812110444
Author(s):  
Megat-Usamah Megat-Johari ◽  
Nusayba Megat-Johari ◽  
Peter T. Savolainen ◽  
Timothy J. Gates ◽  
Eva Kassens-Noor
Keyword(s):  
Negative Binomial ◽  
Driver Behavior ◽  
Field Research ◽  
Crash Risk ◽  
Traffic Crashes ◽  
Crash Data ◽  
Safety Issues ◽  
Dynamic Message Signs ◽  
Safety Message ◽  
Message Signs

Transportation agencies have increasingly been using dynamic message signs (DMS) to communicate safety messages in an effort to both increase awareness of important safety issues and to influence driver behavior. Despite their widespread use, evaluations as to potential impacts on driver behavior, and the resultant impacts on traffic crashes, have been very limited. This study addresses this gap in the extant literature and assesses the relationship between traffic crashes and the frequency with which various types of safety messages are displayed. Safety message data were collected from a total of 202 DMS on freeways across the state of Michigan between 2014 and 2018. These data were integrated with traffic volume, roadway geometry, and crash data for segments that were located downstream of each DMS. A series of random parameters negative binomial models were estimated to examine total, speeding-related, and nighttime crashes based on historical messaging data while controlling for other site-specific factors. The results did not show any significant differences with respect to total crashes. Marginal declines in nighttime crashes were observed at locations with more frequent messages related to impaired driving, though these differences were also not statistically significant. Finally, speeding-related crashes were significantly less frequent near DMS that showed higher numbers of messages related to speeding or tailgating. Important issues are highlighted with respect to methodological concerns that arise in the analysis of such data. Field research is warranted to investigate potential impacts on driving behavior at the level of individual drivers.

An improved car-following model considering the impact of safety messages

2018 ◽  
Vol 32 (32) ◽  
pp. 1850398 ◽  
Author(s):  
Tenglong Li ◽  
Fei Hui ◽  
Xiangmo Zhao
Keyword(s):  
Traffic Congestion ◽  
Dominant Factor ◽  
Velocity Difference ◽  
Car Following ◽  
Safety Message ◽  
Warning Messages ◽  
Gray Correlation ◽  
Car Following Model ◽  
The Stability ◽  
The Impact

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

scholarly journals Blockchain-Based Trust Management Using Multi-Criteria Decision-Making Model for VANETs

2020 ◽  
Author(s):  
Cong Pu
Keyword(s):  
Decision Making ◽  
Road Safety ◽  
Trust Management ◽  
Detection Rate ◽  
Smart Devices ◽  
Multi Criteria Decision Making ◽  
Trust Value ◽  
Safety Message ◽  
Embedded Sensing ◽  
Decision Making Model

<p>Recent advancements in embedded sensing system, wireless communication technologies, big data, and artificial intelligence have fueled the development of Internet of Vehicles (IoV), where vehicles, road side unit (RSUs), and smart devices seamlessly interact with each other to enable the gathering and sharing of information on vehicles, roads, and their surrounds. As a fundamental component of IoV, vehicular networks (VANETs) are playing a critical role in processing, computing, and sharing travel-related information, which can help vehicles timely be aware of traffic situation and finally improve road safety and travel experience. However, due to the unique characteristics of vehicles, such as high mobility and sparse deployment making neighbor vehicles unacquainted and unknown to each other, VANETs are facing the challenge of evaluating the credibility of road safety messages. In this paper, we propose a blockchain-based trust management system using multi-criteria decision-making model, also referred to as Trust<sup>Block</sup><sub>MCDM</sub>, in VANETs. In the Trust<sup>Block</sup><sub>MCDM</sub>, each vehicle evaluates the credibility of received road safety message and generates the trust value of message originator. Due to the limited storage capacity, each vehicle periodically uploads the trust value to a nearby RSU. After receiving various trust values from vehicles, the RSU calculates the reputation value of message originator of road safety message using multi-criteria decision-making model, packs the reputation value into a block, and competes to add the block into blockchain. We evaluate the proposed Trust<sup>Block</sup><sub>MCDM</sub> approach through simulation experiments using OMNeT++ and compare its performance with prior blockchain-based decentralized trust management approach. The simulation results indicate that the proposed Trust<sup>Block</sup><sub>MCDM</sub> approach can not only improve fictitious message detection rate and malicious vehicle detection rate, but also can increase the number of dropped fictitious messages.<br></p>

Adaptive Framework for Collisions in RFID Tag Identification

2008 ◽  
Vol 07 (01) ◽  
pp. 9-14 ◽  
Author(s):  
Selwyn Piramuthu
Keyword(s):  
Supply Chain ◽  
Radio Frequency Identification ◽  
Rfid Tags ◽  
Tag Identification ◽  
Seamless Integration ◽  
Essential Information ◽  
Rfid Tag ◽  
Frequency Identification ◽  
Pass Through ◽  
Aloha Protocol

Radio Frequency Identification (RFID) is promising, as a technique, to enable tracking of essential information about objects as they pass through supply chains. Information thus tracked can be utilised to efficiently operate the supply chain. Effective management of the supply chain translates to huge competitive advantage for the firms involved. Among several issues that impede seamless integration of RFID tags in a supply chain, one of the problems encountered while reading RFID tags is that of collision, which occurs when multiple tags transmit data to the same receiver slot. Data loss due to collision necessitates re-transmission of lost data. We consider this problem when Framed Slotted ALOHA protocol is used. Using machine learning, we adaptively configure the number of slots per frame to reduce the number of collisions while improving throughput.

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