# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 3 Blockchain and Hashgraph  
Blockchain is a widely adopted distributed ledger technology that ensures data integrity through cryptographic hashing and decentralized consensus mechanisms. Figure 1a illustrates its operational mechanism. Each block in a blockchain contains a list of transactions that are cryptographically linked to the previous block using a hash function [12]. This structure guarantees immutability, as modifying a single transaction would require recalculating all subsequent hashes, making tampering computationally infeasible. Nonetheless, blockchain systems differ widely in how well they support high-performance and low-latency applications. Blockchain networks employ various consensus mechanisms. Proof of Work (PoW)--one of the most common approaches--requires nodes to solve cryptographic puzzles in order to validate transactions. While this ensures security, it results in high latency and energy consumption [13]. Moreover, PoW-based blockchains process transactions sequentially, resulting in network congestion and increased transaction fees, especially during periods of high demand. While consensus protocols such as Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) have been introduced as alternatives, they continue to face limitations in scalability and responsiveness within large-scale or real-time networks. These limitations render traditional blockchains unsuitable for real-time applications such as autonomous driving and large-scale IoT networks [23]. To overcome the limitations of traditional blockchains, hashgraph introduces an innovative consensus mechanism based on parallel event-driven processing as opposed to a conventional sequential block structure [16]. It leverages a directed acyclic graph (DAG) topology, where each transaction is represented as a vertex that references preceding events. This approach facilitates simultaneous transaction recording and significantly improves scalability. Hashgraph achieves consensus through a distinctive integration of \"gossip about gossip\" and \"virtual voting\" [24]. In this process, each node randomly communicates \begin{table}
\begin{tabular}{l c c c} \hline
**Study** & **Core Features** & **Validation** & **Multi-Platform** \\ \hline [PERSON] et al. [17] & Hash Only & X & X \\ [PERSON] et al. [20] & Hash + Contract & X & X \\ [PERSON] et al. [22] & None & O & X \\ Our Work & Hash + Contract & O & O \\ \hline \end{tabular}
\end{table} 
Table 1: Differences between this study and prior research. Figure 1: Operational flow of the system: (**a**) Blockchain (**b**) Hashgraph. with others, exchanging event histories that include both the origin and timestamp of each message. These metadata enable every node to reconstruct a consistent global perspective of the event's chronology [21]. The gossip protocol ensures that information spreads exponentially across the network, realizing complete dissemination in logarithmic time relative to the number of participants. Through virtual voting, the nodes establish consensus without the need for additional message exchanges, thereby minimizing communication overhead while maintaining consistency and fairness [16]. In addition to its performance advantages, hashgraph provides asynchronous Byzantine Fault Tolerance (aBFT), allowing the network to maintain reliable operations even in adversarial scenarios [25]. Unlike partially synchronous protocols, aBFT guarantees both safety and liveness without imposing constraints on message delivery times, making it highly suitable for unstable or malicious network conditions. Moreover, it ensures equitable transaction ordering, effectively reducing the likelihood of front-running and order manipulation--well-documented vulnerabilities in traditional blockchain systems. For instance, in autonomous vehicle coordination, hashgraph enables real-time consensus with respect to traffic data among vehicles without relying on centralized infrastructure.