# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 5 Conclusions and Future Directions  
The research concluded that zone construction and selection, content dissemination, and machine learning-based content pre-caching resolve the problem of pre-caching for early decision-making and results from prediction. In this research, we encounter the limitations faced by the VANET. A simulation-based model is introduced for the proposed work's preliminary introduction, enhancing the correlation for the said objectives. We work on a vehicular ad hoc network to propose a zone-based content pre-caching policy. First, we zone with selected parameters described in Section 3 for selecting the zone and its zonal head for content pre-caching. Zonal coverage and their access scope are set for selecting the zone. Second, after selecting the zones and zonal heads, we encounter the content dissemination approach to special condition safety and special conditions messages  
Figure 16: Confidence interval for accident severity is used in the scenario.  
Figure 17: Confidence interval for the speed limit used in the scenario.  
towards every zonal head node. Finally, we adopt the machine learning model to train for content pre-caching based on the zones, zonal heads, high availability, maximum utilization, and others to place and predict the content for the VANET. The results are evaluated for the proposed technique through NS-3 with efficient and reliable content placements and content delivery ratio. The reliability and adaptability of the network are evaluated through the proposed simulation-based technique. The outperform are evaluations for cache hit ratio, hop count, cache prediction accuracy, and high adaptability of content in each zone.  
In the future, further improvements are focused on the following. The pre-caching approach is extended to adopt the deep learning-based prediction of pre-caching content to improve the results further. A geographical-based machine learning model can be introduced to pre-cache the content based on location privacy. Moreover, security aspects can also be implanted after including the results recommendations. Hijacking the caching node can bring a serious security breach inside the network. Through this, the entire network can also be compromised.  
Conceptualization, F.J.; data curation, K.N., A.D.A., N.F.S., A.A.A. and M.S.A.M.; formal analysis, K.N., Y.S., A.A., A.A.A. and F.J.; funding acquisition, A.D.A., N.F.S., A.A.A., M.S.A.M. and F.J.; investigation, M.S.A.M.; methodology, Y.S., A.D.A., A.A.A. and M.S.A.M.; project administration, A.D.A., N.F.S. and F.J.; resources, A.A., A.D.A. and N.F.S.; software, Y.S. and N.F.S.; visualization, A.A. and A.A.A.; writing--original draft, K.N., A.A. and F.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Norwegian University of Science and Technology and Princess [PERSON] Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge the support of the Norwegian University of Science and Technology for paying the Article Processing Charges (APC) of this publication. Special acknowledgment to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.