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

Abstract

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.

Detecting and Preventing False Nodes and Messages in Vehicular Ad-Hoc Networking (VANET)

“…Comparison of the existing models helps us to differentiate our work from others. The model proposed in [16] supports the node authentication process. It helps to detect fake node detection and eliminate it.…”

“…The public key is known to everyone, and the private key is known only to the owner and the PKG. Thus, the private keys of all users are stored in the PKG [16]. Consequently, with users, the PKG private key can impersonate another user.…”

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

“…VANET is always vulnerable to attacks due to its open wireless access. The wireless network is susceptible to attacks [23]. VANET is also vulnerable due to wireless technology.…”

“…Sensors 2023, 23,2594 3 of 36 and driving habits. They can also send false messages, which can cause confusion, accidents, and even gridlock on the roads.…”

“…23, 2594 24 of 36 repository and named the malicious nodes dataset. The characteristics of the dataset include message ID, message type, time, type, message ID, receiver name, receiver signal strength, no received requests, number of generated requests, source name, longitude, latitude, destination, IPv4 addresses, channel, slope, stopped, route ID, connection ID, lane index, blink left, blink right, break left, break right, and number of decisions.…”