# Secure Cognitive Radio Vehicular Ad Hoc Networks Using Blockchain Technology in Smart Cities
## 4 Performance Evaluation
The destination must pay both the sender and the miner who participated in the process and provided benefits to the recipient because the message provides important information. Figure 2f demonstrates that [PERSON]D\) has paid both [PERSON]\) and the miner. This payment involves a reward for \(V\) who takes the initiative to spread information about the incident before [PERSON]D\) arrives at the location. This assists \(D\) in altering its travel route. This payment also includes a share for the miner in the form of an incentive. Therefore, after [PERSON]D\) pays for the message, both [PERSON]\) and the miner will be rewarded with a higher amount than the original amount deducted from their wallets for providing information to the receiver. Figure 2g indicates a rise in the wallets of both. Finally, \(D\) can read the message as depicted in Figure 2h. To sum up, the scheme allows any vehicle to transmit its message to other vehicles securely and efficiently through unalterable transactions. The destination changes its route without any doubt because all authentic nodes are involved in the process. Any other legitimate vehicle that is moving toward the source can also read this message by paying a fee from its wallet and makes the same decision to change its route as the destination did. In the following, we tested a few more parameters to check the validity of our proposed scheme. Since this is a novel way of implementing cognitive technology using blockchain networks, we chose different references for each parameter to evaluate the performance of our proposed scheme. Our cognitive routing scheme takes into account both V2V and V2R communications. Due to this cognitive nature, the wireless channel is susceptible to both small-scale and large-scale fading. Due to not having a direct line-of-sight path between the primary transmitter and secondary receiver, the received signals are a composite of multiple multipath components. As a result, the sensing performance is affected. To address this issue, we utilized a Nakagami distribution to represent the fading of PU activity on the efficiency of an energy detector-based spectrum sensing framework. This distribution is able to exhibit the statistical properties of both small-scale and large-scale fading. Figures 3 and 4 show the execution time as a function of the number of transactions performed on the network. We compared our reading and sending functions to a reference scheme [18] and found that our scheme outperforms the reference scheme. The trend is common; as the number of transactions increases, the execution time increases. However, due to considering RSU as a central entity to register all nodes before communication starts, our scheme processes messages more quickly than the reference scheme. This trend is representative of the system's robustness, with the ability to process incoming messages swiftly even under high transaction loads, an essential feature for real-time response required in vehicular networks. The cognitive aspect of this scheme ensures that nodes only start communicating when PU provides them with a channel, keeping its own activity safe, which is also a time-saver. This improved performance indicates the system's capacity to handle outbound communications efficiently, a critical factor in maintaining the flow of information across the network. We then compared our proposed scheme to two reference schemes to see how it performed in terms of delays and overheads. We labeled one as a reference blockchain and the other as a reference cognitive scheme. The former [19] considers the security of travelers using a smart contract of blockchain technology to provide a safe route for all customers  
Figure 4: Performance comparison of execution time for send function in terms of number of transactions. Figure 3: Performance comparison of execution time for read function in terms of number of transactions. and drivers.