Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

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

View through CrossRef
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.
Title: Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
Description:
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.

Related Results

TACA: Trust Aware Clustering using ACO for Secure and Reliable Vehicular Ah hoc Network Routing
TACA: Trust Aware Clustering using ACO for Secure and Reliable Vehicular Ah hoc Network Routing
Abstract In the modern era, the Vehicular Ad-hoc Network (VANET) received significant attention for information sharing among the societies. The emerging Internet of Things...
Optimal Video Caching at The Edge of Network by Using Machine Learning
Optimal Video Caching at The Edge of Network by Using Machine Learning
Abstract Efficiently managing network resources in the dynamic field of video-on-demand (VoD) services is a significant challenge. This requires creative methods to optimiz...
Joint caching and sleeping optimisation for D2D‐aided ultra‐dense network
Joint caching and sleeping optimisation for D2D‐aided ultra‐dense network
Device‐to‐device (D2D) communication provides the communication of the users in the vicinity and thereby decreases end‐to‐end delay and power consumption. More importantly, D2D com...
RMBCC: A Replica Migration-Based Cooperative Caching Scheme for Information-Centric Networks
RMBCC: A Replica Migration-Based Cooperative Caching Scheme for Information-Centric Networks
How to maximize the advantages of in-network caching under limited cache space has always been a key issue in information-centric networking (ICN). Replica placement strategies aim...
Congestion Control in CoAP Observe Group Communication
Congestion Control in CoAP Observe Group Communication
The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small mem...
Clustering Algorithms and Comparisons in Vehicular Ad Hoc Networks
Clustering Algorithms and Comparisons in Vehicular Ad Hoc Networks
Vehicular Ad hoc Network (VANET) is a new era in the transmission of dynamic information across communities. Intelligent Transportation Systems is only one of the many applications...
A Path Load-Aware Based Caching Strategy for Information-Centric Networking
A Path Load-Aware Based Caching Strategy for Information-Centric Networking
Ubiquitous in-network caching plays an important role in improving the efficiency of content access and distribution in Information-Centric Networks (ICN). Content placement strate...
Security in Vehicular Ad hoc Network (VANET) using Trusted Platform Module (TPM): A Survey
Security in Vehicular Ad hoc Network (VANET) using Trusted Platform Module (TPM): A Survey
Vehicular Ad hoc Networks (VANETs) are gaining more attention from automobile industries due to user safety on highway. However, security and safety critical issues need to be reso...

Back to Top