Routing and Scheduling Issues in Vehicular Ad-hoc Networks

Kamble, Shridevi Jeevan; Kounte, Manjunath R

doi:10.35940/ijrte.c5168.098319

Cited by 8 publications

(7 citation statements)

References 47 publications

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“…This enhances the early prediction of the road conditions from every other predicted environment. In our approach to the final content prediction for all emergency conditions, the messages or instructions are composed of three main parts; the ID of the vehicle or V2I infrastructure that generated the message, the ZoR specification, and finally, the actual messages created for the final zone-based prediction of messages [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…Geocast depends on location-based addresses and enhances the functioning of zone-based communication. The central manager maintains geographic location information through roadside units (RSUs) to sustain Geocasting information [ 35 ]. The target areas where Geocasting is potential in this research are proclaimed as zones, i.e., (zone of reference) ZoR, and zone of forwarding (RoF).…”

Section: Methodsmentioning

confidence: 99%

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Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Nazar

Saeed²,

Ali³

et al. 2022

Sensors

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In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content’s early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Nazar

Saeed²,

Ali³

et al. 2022

Sensors

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“…Communication Layer provides: QoS by selecting the best network as the IoV Platform uses multi wireless networking technologies to create heterogeneous communication Platform. Each network selected for communication must be combined in such a manner that the platform contributes to the proper connectivity and must provide smooth link of connected service along with excellent QoS to the users, which is built on parameters such as device location, available access technologies and service requirement (Kamble et al, 2019). Control and Management layer: Manages the exchange of data among the different network service providers of IoV platform.…”

Section: Comparison Of Iov Architecture Modelsmentioning

confidence: 99%

Trends and Open Research Issues in Intelligent Internet of Vehicles

Kamble

Kounte

2023

Transport and Telecommunication Journal

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The evolution of vehicles has always been continuous with respect to growth in technology.The concept of the Internet of Vehicles (IoV) is the process of allowing vehicles to interact with each other to provide real-time information. This paper introduces the various aspects of IoV and their components. Despite the fact that there are more and more vehicles connected to the IoV, there are still many unknown issues and potentials that needs to be identified to carry out research. In order to identify and classify the current difficulties in implementing and using IoV in urban cities, various research publications on the topic were analysed in this paper. The limitations of the Internet of Vehicular technology are also described. Additionally, a number of current and potential remedies that address the highlighted problems were briefly covered. The background information and reasons for evolving heterogeneous vehicular networks are thoroughly reviewed in this research. Also highlights the key technologies of IoV, network architecture and comparison of IoV architecture models with focus on different communication models The most modern IoV enabling technologies are also highlighted, along with environmental scope of intelligent internet of vehicles. Finally, the paper has reviewed the open research issues of Intelligent IoV such as Poor Connectivity of on road vehicles and Stability, Hard delay constraints, High reliability requirements, high scalability, Security and privacy, etc. and related solutions.

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“…A pattern matching-based highway travel time prediction algorithm has been proposed in (Zhao et al, 2015;Shridevi et al, 2019). Initially, it establishes a pattern library by selecting the traffic volume and speed information.…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Kamble

Kounte

2022

Transport and Telecommunication Journal

Self Cite

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Predicting the most favorable traveling routes for Vehicles plays an influential role in Intelligent Transportation Systems (ITS). Shortest Traveling Routes with high congestion grievously affect the driving comfort level of VANET users in populated cities. As a result, increase in journey time and traveling cost. Predicting the most favorable traveling routes with less congestion is imperative to minimize the driving inconveniences. A major downside of existing traveling route prediction models is to continuously learn the real-time road congestion data with static benchmarking datasets. However, learning the new information with already learned data is a cumbersome task. The main idea of this paper is to utilize incremental learning on the Hybrid Learning-based traffic Congestion and Timing Prediction (HL-CTP) to select realistic, congestion-free, and shortest traveling routes for the vehicles. The proposed HL-CTP model is decomposed into three steps: dataset construction, incremental and hybrid prediction model, and route selection. Firstly, the HL-CTP constructs a novel Traffic and Timing Dataset (TTD) using historical traffic congestion information. The incremental learning method updates the novel real-time data continuously with the TDD during prediction to optimize the performance efficiency of the hybrid prediction model closer to real-time. Secondly, the hybrid prediction model with various deep learning models performs better by taking the route prediction decision based on the best sub-predictor results. Finally, the HL-CTP selects the most favorable vehicle routes selected using traffic congestion, timing, and uncertain environmental information and enhances the comfort level of VANET users. In the simulation, the proposed HL-CTP demonstrates superior performance in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

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Routing and Scheduling Issues in Vehicular Ad-hoc Networks

Cited by 8 publications

References 47 publications

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Trends and Open Research Issues in Intelligent Internet of Vehicles

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

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