## 4 Evaluation
### Transaction Measurement  
This subsection evaluates in detail the performance of the distributed ledger applied to the proposed localization framework through two experiments. For these experiments, we implemented a simple Proof-of-Concept (PoC) system and evaluated our proposal through a simulation in an environment where transactions were scheduled to be generated similar to real situations. The experiment was largely divided into two parts.  
First, we measured the change in transaction throughput according to the change in the confirmation threshold and analyzed the results. According to the proposed consensus algorithm, each transaction recorded in the DAG had a score accumulated each time it was approved by the _prover_, and when this score was greater than the threshold, the transaction was judged to be in the confirmation state. For this reason, how the threshold is set can greatly affect the transaction processing performance of the proposed localization framework. We conducted a simulation by changing the threshold from 10 to 50 in 10 increments in an environment with 30 tags and 20 fixed nodes composed of a single server and multiple anchors, and the result appears as Figure 6. The x-axis of the graph represents the size of the threshold, and the y-axis represents the confirmed transaction rate. According to the results, it can be seen that the smaller the threshold, the faster transaction confirmations reached saturation. Analyzing the results, as the threshold became smaller, the proposed localization framework could process transactions faster, but the security level was lowered, making it vulnerable in protecting the system from malicious transactions. Conversely, as the threshold increased, our system provided stronger security performance, but the delays occurring during transaction processing increased, which, in turn, lowered the quality of the positioning service. When the threshold was high, a single transaction must be approved by more _provers_ to increase the cumulative score above the threshold, and in this process, factors that cause performance degradation, such as processing delay, which occurs while the _prover_ approves the transaction, and network delay, which occurs in the process of broadcasting a newly created transaction or a transaction indicating that the _prover_ has approved the transaction, their influence increased. In other words, safety and positioning performance are a trade-off, and it is important to set the optimal value that can properly maintain both.  
Second, we measured the change in system performance as we varied the network scale of the localization framework. In an environment such as a vehicular network where the topology of participating nodes changes in real-time and the network scale increases or decreases irregularly, it is a very important consideration whether the system maintains constant performance and stably provides services. We measured the average transaction processing time of the proposed localization framework, which changes, while simultaneously increasing the number of _miners_ and _provers_ at a constant rate, and the results appear as Figure 7. According to the results, the average processing time was between 0.09 and 0.11 s, and the difference between the maximum and minimum average times was only 0.02 s. For reference, as the network scale increased, the processing speed tended to become slightly faster. This is because the speed of approving transactions increased as the number of _provers_ increased, so it had nothing to do with what we wanted to suggest through this experiment.