# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 5 Conclusions, Limitations, and Future Directions  
Our research concluded that weather forecasting information is necessary for safety and driving efficiency. The secure data distribution among vehicles inside the VANET environment ensures the reliable delivery of the network services and packet delivery. Moreover, routing such information is very important, because a lower hop count and less efficient routing make better weather prediction and are useful for human lives. We present a novel blockchain-based secure weather forecasting and routing framework that efficiently routes information securely through blockchain technology. The routing algorithm efficiently routes the requested data from source to destination with minimum hop counts. We simulate the proposed framework using a blockchain-based environment in NS-2 based on the hop count, network latency, packet delivery ratio, and network overhead. Routing the correct weather forecasting information towards the correct destination is a challenging task achieved through routing in the proposed architecture. We set up a weather forecasting server which stores the information. We added the blockchain-based Hyperledger Sawtooth framework to provide security for our weather data. After adopting the security,  
Figure 10: Network Hop Count.  
Figure 9: Packet Delivery Ratio of Proposed Technique with Distance.  
the public key is distributed to the network and the private key is only exchanged with the destination node. Finally, we adopt the routing through an efficient routing algorithm to adopt and provide effective and logical routing schemes. The results are taken through an OMNeT++ simulator with the crypto secure library to secure the content. This outperformed the evaluations for hop count, latency, packet delivery ratio, and network overhead. The practical results show that the proposed BBSF technique efficiently contributed to the routing field when routing the secure routing in the VANET environment. The BBSF technique showed fewer hop counts, a lower network latency, efficient packet delivery ratio, and a shorter network overhead time for packets distributed to the correct destination vehicle.  
The challenges/limitations of the proposed model/method are illustrated below:  
* The proposed method is only limited to weather forecasting information routing.
* The routing is only based on near-vehicle concepts, and we did not apply any machine learning technique to the route-request response mechanism.
* The challenge of implementing 5G technology was faced during the simulation work of the proposed model, but we successfully implemented the proposed system.  
In the future, a machine learning-based routing technique can be adopted to improve the minimization of hop count, network latency, and throughput values. The machine learning-based framework effectively evolved the performance of the system. Moreover, secure weather forecasting information can be adopted in railway warning systems using the Wi-Fi transmission sensor network.  
Conceptualization, H.S., A.R., M.u.H. and K.R.; data curation, M.A.E., A.R. and A.A. ([PERSON]); formal analysis, H.J., A.S. and A.A. ([PERSON]); funding acquisition, M.A.E., K.R. and A.S.; investigation, M.A.E., A.A. ([PERSON]) and A.S.; methodology, H.S., K.R. and A.S.; project administration, A.A. ([PERSON]), A.S. and K.R.; resources, A.R., M.A.E. and M.u.H.; software, H.J., H.S. and M.u.H.; visualization, A.A. ([PERSON]) and A.S.; writing--original draft, A.A. ([PERSON]), A.R., M.u.H. and A.A. ([PERSON]); validation, H.J., A.R. and A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work, under the Research Groups Funding program grant code (NU/RG/SERC/12/35).  
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The authors declare no conflict of interest.