High-performance Byzantine Fault Tolerant (BFT) blockchains are designed to achieve high throughput and low latency, yet their observed block time distributions often reveal complex behaviors arising from networking, pipelining, and deployment heterogeneity. In this paper, we diagnose HotStuff-based high-performance BFT consensus by modeling block times through a quorum-based multicast framework that links each block interval to quorum formation latency. We capture multimodal block time distributions using mixture models, where each component represents a distinct network condition characterized by effective transfer rate of block information. The proposed model is fitted to the bulk of mainnet block time data, while tail decay is analyzed separately to assess asymptotic behavior. Applying this methodology to Hyperliquid and Aptos mainnets, we find that Hyperliquid is well explained by a unimodal distribution, consistent with a relatively homogeneous validator deployment. In contrast, Aptos exhibits persistent multimodal structure and a pronounced shift following a consensus upgrade, reflecting heterogeneous deployments and diverse communication paths. These results demonstrate that mixture modeling of block time provides a practical and informative diagnostic tool for analyzing and monitoring high-performance BFT consensus.

With the advancement of blockchain technology, there is an increasing demand for blockchain interoperability solutions that enable fast and secure data exchange between blockchains developed through diverse processes. However, such solutions are often tailored to specific use cases, underscoring the need for more versatile and standardized protocols. Efficient inter-blockchain communication is critical for the scalability and usability of blockchain ecosystems, enabling seamless integration across diverse networks. In response to this demand, initiatives such as the Cosmos ecosystem have emerged, aiming to achieve enhanced security and faster interoperability. The Cosmos ecosystem utilizes the inter-Blockchain Communication (IBC) protocol to enable seamless interaction between heterogeneous blockchains. This study examines the IBC protocol, the core interoperability technology within the Cosmos ecosystem, and evaluates the transaction transfer times of IBC. The evaluation utilized real blockchain data to measure the distribution of transfer times, using approximately 50,000 transactions originating from Cosmos Hub and Osmosis. The findings reveal the statistical characteristics of IBC token transfer times within the Cosmos ecosystem. Specifically, in the IBC transfers from Cosmos Hub to Osmosis, the IBC transfer time distribution exhibits a peak at approximately 15 s, while the Gumbel or Fréchet distribution yields the lowest Akaike Information Criterion value, depending on the IBC section. Moreover, the results of this study confirm that IBC transfer performance has improved compared to the experiments conducted in June 2022. These findings provide insights for optimizing the performance of IBC and guiding future enhancements to improve cross-chain communication within blockchain networks.

Since the initial appearance of Bitcoin, a wide variety of blockchain projects have been proposed. With an increased demand for exchanging cryptocurrencies between blockchains, it has become important for blockchains to achieve crosschain interoperability, which is the property of storing and fetching information from each other on multiple blockchains. However, public blockchains have a scalability problem of an extremely slow transaction processing capacity. Based on this background, we have considered solving the scalability problem by enhancing the crosschain interoperability. In this paper, we propose a chainless multi-layer consensus, which is a more decentralized method compared to existing blockchain projects when taking into consideration the interoperability, including Polkadot and Cosmos. We conducted some experiments using our implemented node program to evaluate the latency times required to achieve a consensus. As a result of the experiments using a large-scale network constructed with more than 100 parachains, we confirmed that the latency times tend to increase superlinearly when the number of parachains increases to parallel transaction processing. By applying this result, we can estimate the blockchain settings for safely executing a chainless multi-layer consensus while achieving a practical transaction processing capacity.

The recent expanded use of mobile phone dataset for research purpose enables studies not only on human mobility patterns, but also on human contact patterns including proximal contact, face-to-face meeting, and serendipitous encounter. In our previous work, we analyze our dataset of long-term experiment with periodic scanning of proximal devices emitting close-range radio waves for wireless communication to investigate the frequency of human serendipitous encounters. As a result, we have found that the frequency of human serendipitous encounter has a scale-free property where the number of encounters is highly biased and its average means nothing. Our dataset is not Big data (the number of participants in our experiment is a few dozen), but it is Long data, but the property is universally observed. It is important to check from a multilateral perspective whether mobile phone data, which is one of well-used Big datasets, also support this fundamental property of human serendipitous encounter. In this paper, we investigate the scale-free property of human contact frequency using mobile phone data of D4D Challenge Senegal which contains hundreds thousands of human mobility traces. Because the data is not provided with high resolution enough to detect exact human encounter at proximity, we roughly assumed that humans who are considered to be placed around the same cell tower encounter with each other. Despite this rough assumption, we successfully reproduce the scale-free property.

In recent years, mobility models have been reconsidered based on findings by analyzing some big datasets collected by GPS sensors, cell phone call records, and Geotagging. To understand the fundamental statistical properties of the frequency of serendipitous human encounters, we conducted experiments to collect long-term data on human contact using short-range wireless communication devices which many people frequently carry in daily life. By analyzing the data we showed that the majority of human encounters occur once-in-an-experimental-period: they are Ichi-go Ichi-e. We also found that the remaining more frequent encounters obey a power-law distribution: they are scale-free. To theoretically find the origin of these properties, we introduced as a minimal human mobility model, Homesick Lévy walk, where the walker stochastically selects moving long distances as well as Lévy walk or returning back home. Using numerical simulations and a simple mean-field theory, we offer a theoretical explanation for the properties to validate the mobility model. The proposed model is helpful for evaluating long-term performance of routing protocols in delay tolerant networks and mobile opportunistic networks better since some utility-based protocols select nodes with frequent encounters for message transfer.