Comparative Study of TDMA-Based MAC Protocols in VANET: A Mirror Review

Batth, Ranbir Singh; Gupta, Monisha; Mann, Kulwinder Singh; Verma, Sahil; Malhotra, Atul

doi:10.1007/978-981-15-0324-5_10

Cited by 16 publications

(3 citation statements)

References 24 publications

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“…Video codecs methods can be used to gain additional advantages.R. S. Batth et al [32] addressed the current topologies in Time Division Multiple Access TDMA-based VANETs that are accountable for maintaining secure transmission of broadcast of safety messages between automobiles in ad hoc vehicle network.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

2023

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Fusion of internet of things (IoT) and artificial intelligence for smart cities has allowed structured policy making.In particular, traffic jam is one of the serious concern in metropolitan cities. In advanced countries, specific devices are installed to assemble flow of traffic. The state of traffic is streamed to commuters through network. This dependency on internet connection results in inappropriate solution in absence of stable physical infrastructure. The developing countries lack availability of internet on roadway and urban zones face even worst condition. This study proposes a smart framework to enable optimal path selection by communicating with neighbouring devices. The android smart phones serve the purpose of embedded sensors. A temporary network is created for smart transportation system. Data processing and cleaning were conducted on different routes of two urban cities. The proposed system is evaluated on conventional and proposed optimal path selection techniques. Computed results depict that travel times have been decreased to around 23.3% in comparison to conventional methods using MDSP algorithm.

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Section: Related Workmentioning

confidence: 99%

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

2023

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“…R.S. Batth et.al [22] discussed about the available MAC protocols in TDMA based VANETS which are responsible for reliable communication in a temporary vehicular network for safety message dissemination among vehicles. [21] In this system, if congestion based on VCI which is vehicle congestion index is found, then information is shared with other vehicles and RSU.…”

Section: Related Workmentioning

confidence: 99%

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Shahi

Batth

Egerton

2022

Int. j. commun. netw. inf. secur.

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Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism(MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.

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“…Daily, weekly, and seasonal changes in the meteorological factors along with different sources lead to great variations in the temporal trends in atmospheric pollutant concentrations. Various machine learning algorithms have been used in the air pollution domain for the prediction of air pollution [20,21], source apportionment [22,23], air pollution monitoring [24][25][26], etc.…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning in Air Pollution Research: A Bibliographic Perspective

et al. 2022

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This research is an attempt to examine the recent status and development of scientific studies on the use of machine learning algorithms to model air pollution challenges. This study uses the Web of Science database as a primary search engine and covers over 900 highly peer-reviewed articles in the period 1990–2022. Papers published on these topics were evaluated using the VOSViewer and biblioshiny software to identify and visualize significant authors, key trends, nations, research publications, and journals working on these issues. The findings show that research grew exponentially after 2012. Based on the survey, “particulate matter” is the highly occurring keyword, followed by “prediction”. Papers published by Chinese researchers have garnered the most citations (2421), followed by papers published in the United States of America (2256), and England (722). This study assists scholars, professionals, and global policymakers in understanding the current status of the research contribution on “air pollution and machine learning” as well as identifying the relevant areas for future research.

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Comparative Study of TDMA-Based MAC Protocols in VANET: A Mirror Review

Cited by 16 publications

References 24 publications

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Use of Machine Learning in Air Pollution Research: A Bibliographic Perspective

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