Beyond the Chain 2024

The Tangle, the distributed ledger of IOTA, is built on a directed acyclic graph (DAG) where the nodes represent messages, and the edges correspond to reference relations. This feature of the Tangle makes it suitable for analysis using network science methods. While previous research on the Tangle has mainly focused on stability, security, scalability, and the forthcoming IOTA Coordicide update, there is limited empirical research on the evolution of the Tangle, particularly during the Chrysalis stage (IOTA 1.5). To address this gap in the literature, our study aims to develop workflows for collecting IOTA Tangle data and transaction data in the Chrysalis stage, conduct a comprehensive analysis to examine the features of messages and reconstruct the Tangle graph to study its characteristics from a network science viewpoint. Our study is the first to illuminate activities and information transferred on the Tangle, as well as its graph characteristics during the Chrysalis stage, which can enhance our comprehension of its functionality and contribute to the improvement of the Tangle’s performance in the upcoming upgrades.

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Existing Distributed Ledger Technologies (DLTs) based models like blockchain pose scalability and performance challenges for IoT systems due to resource-demanding Proof of Work (PoW), slow transaction confirmation rates, and high costs. Against a need to adopt a viable approach, especially for low-power IoT devices, IOTA emerges as a promising technology, leveraging the Direct Acyclic Graph (DAG) based approach called Tangle for IoT-focused applications. In this paper, we design a system enabling secure data exchange between IoT devices on IOTA Chrysalis, the latest version. We perform extensive experiments on two machines, a Workstation PC and Raspberry Pi, to demonstrate the performance gap between powerful and low-power devices. Our findings show that even low-power devices, such as Raspberry Pi, perform well with small payload sizes on the Chrysalis network but face challenges with larger payloads. We observe that variation in transmission time increases as payload size grows, indicating the impact of PoW complexity, but it still is feasible for Raspberry Pi. We further validated our experimental setup to ensure the validity and accuracy of our approach through discussions with the IOTA Foundation’s technical team.

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We present an agent-based model of the IOTA system to provide an estimate of block finality on the Tangle. Our simulation framework is based on the Gillespie agent-based model. Our approach provides insights into the behavior of the IOTA system and can be used to explore different scenarios and test potential improvements. The results of our simulation show that the finality and confirmation time of blocks on the Tangle are affected by the underlying topology of the peer-to-peer network. Depending on the topology, the blocks of higher Mana nodes may have an advantage in gaining approval weight.

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While Distributed Ledger Technologies offer (DLT) increased security, transparency, and resilience, the decentralized ecosystem is currently fragmented, lacking interoperability between different DLT platforms. Bridging protocols act as a bridge between different DLT networks, enabling the transfer of assets and data across disparate systems. This paper explores the development of bridging protocols in DAG-based DLTs, with a focus on facilitating interaction between IOTA and other networks. This paper offers insights into the characteristics and requirements of effective bridging protocols, which are crucial for realizing the full potential of DLTs and unlocking new use cases for decentralized systems.

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This paper discusses congestion control and inconsistency problems in DAG-based distributed ledgers and proposes an additional filter to mitigate these issues. Unlike traditional blockchains, DAG-based DLTs use a directed acyclic graph structure to organize transactions, allowing higher scalability and efficiency. However, this also introduces challenges in controlling the rate at which blocks are added to the network and preventing the influence of spam attacks. To address these challenges, we propose a filter to limit the tip pool size and to avoid referencing old blocks. Furthermore, we present experimental results to demonstrate the effectiveness of this filter in reducing the negative impacts of various attacks. Our approach offers a lightweight and efficient solution for managing the flow of blocks in DAG-based DLTs, which can enhance the consistency and reliability of these systems.

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In this paper, we present an overview of a wearable smart-mask prototype that is fully described in [1]. The purpose of the mask is to encourage people to comply with social distancing norms, through the use of incentives. The smart mask is designed to monitor Carbon Dioxide and Total Volatile Organic Compounds concentrations. The IOTA Tangle ensures that the data is secure and immutable and here IOTA Tangle acts as a communication backbone for the incentive mechanism. A hardware-in-the-loop simulation based on indoor positioning, paired with Monte-Carlo simulations, is developed to demonstrate the efficacy of the designed prototype.

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