Blockchain, as an innovative distributed ledger technology, has attracted considerable attention in recent years from both academic circles and industry sectors. Its applications span a diverse range of domains, including finance and the Internet of Things (IoT). However, the scalability of blockchain technology is still a critical limitation with the increasing volume of data. To address this limitation, a directed acyclic graph (DAG) data structure has been proposed to improve scalability by supporting asynchronous process of transactions. IOTA is a well-known DAG-based blockchain that theoretically offers faster confirmation speeds with an increasing number of transactions. However, in practice, IOTA still faces the challenge of balancing scalability and security. In this article, we propose a scalable and secure transaction attachment algorithm for the DAG-based blockchain IOTA. We determine two critical parameters through our experimental analysis: one for calculating the selection probability and the other for setting the threshold for abnormal transactions. First, we calculate the selection probability of unconfirmed transactions. Then, we select abnormal transactions whose selection probability falls below the predefined threshold to maintain the security. Finally, new transactions attach randomly to former transactions with a time computational complexity$O(n)$, ensuring the scalability. Through experiments comparing the proposed algorithm to the current transaction attaching algorithm, we demonstrate the scalability and security of our proposed algorithm.

In the era of big data and artificial intelligence, distributed machine learning has emerged as a promising solution to address privacy and security concerns while fostering collaboration between multiple parties. However, with the data increased in terms of volume, velocity, veracity and variety, ensuring effective data management and responsible data sharing in these systems remains a challenge. In this paper, we explore the potential solutions and propose a system architecture that incorporates FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to promote effective and secure collaboration in federated learning. A minimum set of metadata schemes tailored for distributed machine learning and a decentralized authentication and authorization mechanism based on self-sovereign identity and policy-based access control architecture are proposed. To demonstrate the effectiveness of the proposed system, we conduct a FAIRness assessment and evaluate the model performance with a federated learning use case. Our work contributes to the development of an efficient, secure, and collaborative data ecosystem, fostering innovation in artificial intelligence and machine learning.

IOTA is a recent distributed ledger technology that relies on Directed Acyclic Graph (DAG) for its ledger organization. To improve IOTA mechanisms, the state of the art methodology employs graph analysis and, for that, heavily relies on synthetic graph generation. Herein, the most popular generation method simulates IOTA protocol execution. Although this method produces realistic IOTA ledgers, it requires too much memory and time due to repeated random walks on the DAG. In this paper, we propose an alternative Graph Generation and Refinement (GraGR) algorithm designed to generate realistic IOTA ledgers while strongly relaxing memory and timing constraints. The evaluations show that, compared to the state of the art, GraGR can generate a ledger with the same properties with only half of memory and up to 10 times faster.

IOTA blockchain system is lightweight without heavy proof-of-work mining phases, which is considered a promising service platform of Internet of Things applications. IOTA organizes ledger data in a directed acyclic graph (DAG), called Tangle, rather a chain structure as in traditional blockchains. With arriving messages, IOTA tangle grows in a special way, as multiple messages can be attached to the tangle at different locations in parallel. Hence, the network dynamics of an operational IOTA system would justify a thorough study, which is currently unexplored in the literature. In this article, we present the first theoretical modeling for the evolving IOTA tangle based on stochastic analysis. After analyzing snapshots of the real-world IOTA ledger data, our key finding suggests that IOTA tangle follows a rather atypical double Pareto Lognormal (dPLN) degree distribution. In contrast, typical power-law and exponential distributions do not accurately reflect the fact. For model parameter estimation, we further realize that using generic optimization solvers cannot yield quality fitting results. Thus, we design an alternative algorithm based on expectation-maximization (EM) framework. We evaluate the proposed model and fitting algorithm with official data provided by the IOTA Foundation. Quantitative comparisons confirm the fitting quality of our proposed model and algorithm. The whole analysis reveals a deeper understanding of the internal mechanism of the IOTA network.

IOTA blockchain is a new type of distributed ledger systems that is lightweight without mining and feeless-of-using. Rather than using a chain structure as in traditional blockchains, IOTA organizes ledger records with a directed acyclic graph (DAG), called Tangle. When message entries are committed into the ledger, the ledger tangle grows in a special way where multiple messages could be attached by different processing nodes in parallel. Such a unique evolution process motivates us to study the ledger tangle dynamics, which is unexplored so far. In this paper, we present the first generative modeling for IOTA tangle based on stochastic analysis. A key finding is that IOTA tangle renders a double Pareto Lognormal (dPLN) distribution, rather not typical network models (e.g., Power-Law and Exponential distributions). Quantitative comparisons show that the fitting quality of our model outperforms existing popular models on official real world datasets published by IOTA Foundation. Estimated model parameters are provided, which is immediately instrumental for a more realistic IOTA network generator design. The proposed generative model also provides a deeper understanding of the internal mechanics of IOTA network.

With the rapid evolution of blockchain technology, a clear trend is that new blockchain systems (e.g., IOTA) tend to use a Directed Acyclic Graph (DAG) rather a chain structure to organize ledger records. Such a DAG-based blockchain system shows higher scalability as multiple locations are available in the ledger for new message attachment. To decide an attachment location, a popular type of tip selection algorithms follow an approach using weighted random walks on the DAG ledger. In a burst message arrival scenario, however, a processing node deployed at edge using such a method may become a bottleneck because sequentially repeating random walks significantly increases processing delay. In this paper, we propose a new tip selection algorithm for the burst message arrival scenario on an edge node. Our solution abandons the weighted random walk approach, instead, with similar efforts we transfer to calculate in advance the tip selection probability distribution of the DAG ledger. Such a new scheme reduces tip selection to a probability distribution sampling task, which can be done extremely fast. We implement our solution and demonstrate the benefits of our approach by comparing with the random walk approach. We believe our attempt can effectively mitigate the congestion at the edge node and inspire tip selection algorithm design with a new vision for DAG-based blockchain systems.

IOTA organizes transactions in the ledger as a Directed Acyclic Graph (DAG) called Tangle, instead of a hash chain of transaction blocks used by most of traditional blockchains. IOTA is considered a promising platform to support Internet-of-Things (IoT) applications with its key features such as micropayment support and absence of transaction fees. While prior art shows extensive analysis based on synthetic data generated through simulations, an analysis based on empirical data from a deployed IOTA network is still missing. In this paper, we provide the first comprehensive analysis by using real transaction data officially published by IOTA Foundation. Our key finding is that neither the tangle's topological features nor the actual observed performance is consistent with the main conclusions from the literature. In particular, most of transactions take roughly 10 minutes to be officially confirmed, which is not exactly instant as commonly assumed; yet, what is arguably worse is that there is a certain amount (5%) of transactions experiencing exceptionally long confirmation time. This shows that IOTA still has gaps to meet the stringent requirements of IoT applications that are delay sensitive.