Dennis Trautwein

We present the first longitudinal, cross-network measurement study of 36 public blockchain peer-to-peer (P2P) networks, spanning nine months from late 2024. We deployed 15 active crawlers with hourly connectivity probes, performed Internet-wide scans, and decomposed shared discovery substrates to identify 19 auxiliary networks. We find that active node counts span three orders of magnitude (10,000) and vary substantially in reachability, discovery coverage, and deployment structure. To characterize these differences, we quantify discovery effectiveness, churn dynamics, peer-table coverage, and AS- and geographic concentration (via the Herfindahl--Hirschman Index). Our results reveal pronounced heterogeneity in network structure and highlight fundamental observability limitations in widely deployed peer-discovery mechanisms. This extended abstract accompanies the full paper [10].

We present the first longitudinal, cross-network measurement study of 36 public blockchain peer-to-peer (P2P) networks, spanning nine months from late 2024. We deployed 15 active crawlers with hourly connectivity probes, performed Internet-wide scans, and decomposed shared discovery substrates to identify 19 auxiliary networks. We find that active node counts span three orders of magnitude (10,000) and vary substantially in reachability, discovery coverage, and deployment structure. To characterize these differences, we quantify discovery effectiveness, churn dynamics, peer-table coverage, and AS- and geographic concentration (via the Herfindahl--Hirschman Index). Our results reveal pronounced heterogeneity in network structure and highlight fundamental observability limitations in widely deployed peer-discovery mechanisms. This extended abstract accompanies the full paper [10].

Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months (since late 2024), we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes (e.g., pings and protocol-level handshakes) to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.

Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months, we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.

Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources.