# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 2 Literature Review
Content delivery time (CDT) is considered a novel technique to improve CDT in highly congested areas for traffic congestion and load balancing. In ref. [23], the authors worked on vehicular content-centric networks. User volubility is established to achieve the network considerably. The pre-caching chunks mechanism is adopted to achieve high network distribution and performance accuracy. The author proposes a mechanism for pre-caching chunks of large content objects, such as videos, among RSUs. First, they adopt the hidden Markov model (HMM) to predict a user's moving trajectory. Based on the time gap from the current location to the predicted location of the mobile user, the corresponding RSU can pre-cache the required video chunks and provide them to the user as soon as he arrives at the predicted location. Another approach mentioned in ref. [24] supports the hybrid opinion dynamics comprising averager, copier, and voter agents, which ramble as random walkers on a spatial network. In ref. [25], the authors present a machine learning-based content pre-caching scheme for V2V and V2I communication. The situation is limited to cache enhancement and cache control which shows that cache content is one of the techniques which places the content toward cache enhancement. The joint strategy is implemented using machine learning-based deep reinforcement learning to joint allocation strategy to minimize the weighted average delay of data acquisition. Edge-based content caching is implemented with reinforcement learning to enhance the knowledge related to the proposed caching framework. In ref. [26], the authors present realistic time-varying channels for the VANET approach. The Markov-based channels are common and provide effective, realistic collaboration among different techniques to get presented. A machine learning-based deep reinforcement learning approach is applied, which uses the deep network to simulate and provide effective information learning mechanism coordination. In ref. [27], the authors present joint caching, communication, and computational design problem in one study. The authors worked on the cost efficiency and excellence of the network to promote the caching strategies. In ref. [28], the authors presented the deep reinforcement learning-based content caching framework to present the knowledge and provide the computational capabilities to compute where to place the content based on the existing methodologies. The authors presented a new internet of connected vehicles methodology for the proposed technique to address vehicles' challenges in accessing the appropriate contents. In ref. [29], the authors consider the optimal caching strategy to implement the proposed technique in this study. The personal information enabled the placement and caching enhancement approaches for vehicular ad hoc networks. Deep reinforcement learning is another technique implemented to resolve the current issues and challenges faced during the presented methodology for problem description and enhancement. High dimensional and time-variant features are implemented through the framework to contribute to and provide high-accuracy network resources. The authors integrate DRL and permissioned blockchain into vehicular networks for intelligent and secure content caching. They first propose a blockchain-based caching framework to enhance the content and promote the network features for effective content placement on target devices. The zone-based content caching approach in VANET for congestion control using a machine learning framework has not been suggested to the best of our knowledge. This research proposes a framework for content pre-caching task execution at a very low cost. Furthermore, through the proposed framework, we ensure the content pre-caching for VANET vehicular devices over highway scenarios.