# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 5 Experimental Results  
To evaluate the proposed system, we conducted experiments in a controlled virtual environment using the CARLA simulator (version 0.9.13). The simulation server was executed via Docker on a Linux-based system with the following hardware specifications:  
* **Operating System:** Ubuntu 20.04 LTS (Canonical Ltd., London, UK);
* **CPU:** Intel Xeon Silver 4216 @ 2.10 GHz (32 cores) (Intel Corporation, Santa Clara, CA, USA);* **Memory:** 256 GB RAM;
* **GPU:** NVIDIA RTX A6000 (NVIDIA Corporation, Santa Clara, CA, USA). The CARLA simulator was containerized and launched using Docker, with all sensor simulations and environment controls managed internally. Sensor data were collected from simulated autonomous driving scenarios using a custom Python 3.7-based acquisition script integrated with CARLA's API. This script emulated real-time sensor inputs (camera and LiDAR) to generate raw datasets. To simulate blockchain-based verification, smart contracts were deployed to local test environments using both Hedera and Ethereum platforms. The smart contract logic followed the procedures described in Section 3, implementing hash storage and validation using Web3 APIs. Client-side operations, including hashing and transaction requests, were performed on a macOS-based system with the following specifications:  
* **CPU:** Apple M1 (Apple Inc., Cupertino, CA, USA);
* **Memory:** 16 GB RAM;
* **Storage:** 1 TB SSD. Our proposed system is designed for deployment in real-world autonomous vehicle environments, where on-device data integrity verification is critical. Due to practical limitations, however, the experiments were conducted using the CARLA simulator, which offers a realistic and controllable testing environment for autonomous driving research. CARLA enables simulations with virtual vehicle sensors, allowing us to evaluate the performance of the system under conditions that approximate real-world deployment. The results obtained from this simulated environment provide theoretical validation and insights that are transferable to actual use cases, thereby supporting the feasibility of blockchain-based data integrity frameworks. All blockchain-related functionalities, including transaction requests and hash comparisons, were implemented using JavaScript with Web3.js, supporting interoperability across Hedera and Ethereum platforms. To assess the performance of Hedera and Ethereum, we measured the time required to upload and verify hash values across datasets of different sizes (10, 100, 300, 500, 700, and 1000 images). Each image was processed using the SHA-256 hashing algorithm before being uploaded to the blockchain, and the corresponding hash values were later retrieved and verified via smart contracts. The experiments were carried out on a local Hedera network and an Ethereum Geth node, with both running under identical hardware conditions. The measured execution times for each dataset size are presented in Figures 4 and 5. Our analysis showed that Hedera significantly outperformed Ethereum in terms of transaction speed, and this is primarily due to its asynchronous Byzantine Fault-Tolerant (aBFT) consensus algorithm. In contrast, Ethereum exhibited considerably higher latency because of its Proof-of-Work (PoW) consensus mechanism. Hedera also demonstrated near-linear scalability, whereas Ethereum's execution time increased exponentially with the dataset's size. Additionally, Ethereum incurred escalating gas fees proportional to the dataset volume, while Hedera maintained consistently low and predictable transaction costs. Ethereum was included in our evaluation as a representative benchmark due to its widespread adoption and foundational role in smart contract development. As one of the most extensively used blockchains for decentralized applications and academic studies, Ethereum provides a meaningful baseline for assessing the practicality of alternative platforms. These findings suggest that Hedera offers a more efficient and scalable solution for high-volume AI training data verification.