Proof-of-stake consensus protocols often rely on distributed randomness beacons (DRBs) to generate randomness for leader selection. This work analyses the manipulability of Ethereum's DRB implementation, RANDAO, in its current consensus mechanism. Even with its efficiency, RANDAO remains vulnerable to manipulation through the deliberate omission of blocks from the canonical chain. Previous research has shown that economically rational players can withhold blocks known as a block withholding attack or selfish mixing when the manipulated RANDAO outcome yields greater financial rewards.

After successfully transitioning from proof-of-work to proof-of-stake, the Ethereum blockchain’s developer community has set an ambitious goal of achieving rapid block finalization, ideally completing it before the next block proposal, a concept known as single slot finality. Currently, block finalization on the ETH beacon chain takes ∼ 15 minutes to collect the attestation from the majority of the validators. The current protocol has several drawbacks, including slow finalization, high bandwidth usage and a rigid aggregation structure that is prone to failures. The challenge is collecting cryptographic signatures from close to a million validators distributed worldwide, connected to the network in 12 seconds without requiring high bandwidth internet connections from the peers in the network. This study presents an alternative scheme that has the potential to realize single-slot finality. Ours is a fully decentralized approach in which no node has a specific role, rendering it more robust than the current one. We simulate our heuristics on the Ethereum network topology and demonstrate that it can efficiently collect a million attestations from almost ten thousand physical nodes.

Decentralisation is one of the most important structural principles of blockchains and it should be present in all aspects of these systems. There is a growing scientific effort to develop protocols that enable the exchange of digital assets between two heterogeneous blockchains without a third-party arbitrator. We investigate the scenario where two parties wish to exchange digital tokens and run a swap protocol without the intervention of a third party. This is a complex and cooperative process for which – in a trust-less environment – atomicity and fairness must be ensured. While smart contracts provide a convenient way to achieve this, we want to ensure that the two parties leave no traceable data on the respective chains. The challenge here is that the two digital assets can reside on different chains with different signature schemes. Tackling these issues, we present a protocol description for the exchange of two arbitrary cryptocurrencies. A generic standard for atomic swap implementations in terms of the required cryptographic primitives is given. Additionally, we derive a protocol for the necessary interactions between two arbitrary chains using these primitives. To demonstrate applicability, we illustrate how this protocol can be instantiated with Schnorr and BLS signatures.

Payment channels are auspicious candidates in layer-2 solutions to reduce the number of on-chain transactions on traditional blockchains and increase transaction throughput. To construct payment channels, peers lock funds on 2-of-2 multisig addresses and open channels between one another to transact via instant peer-to-peer transactions. Transactions between peers without a direct channel are made possible by routing the payment over a series of adjacent channels. In certain cases, this can lead to relatively low transaction success rates and high transaction fees. In this work, we introduce pliability to constructing payment channels and graft edges with more than two endpoints into the payment graph. We refer to these constructions as hyperedges. We present hyperedge-based topologies to form hypergraphs and compare them to Bitcoin’s Lightning network and other state-of-the-art solutions. The results demonstrate that hyperedge-based implementations can both increase transaction success rate, in addition to decreasing the network cost by more than 50% compared to that of the Lightning Network.

Estimating Quality of Experience (QoE) and Quality of Service (QoS) metrics is crucial for delay-sensitive use cases, and it relies on the available information in an operator's network. However, for low-latency traffic types that use unreliable and best-effort transport solutions for data transfer, obtaining these metrics only based on transport network probing – and ignoring the radio characteristics – is challenging. While end-to-end QoS metrics can be obtained by correlating per session radio and core data, predicting QoE is more difficult and requires the use of machine learning (ML) models. Nevertheless, training these models demands a vast amount of diverse measured reference metric data, which can be hard to acquire. In this study, we propose a delay-critical service-focused data collection framework that automatizes the measurements of networked services under various synthetic network degradation and jointly monitors radio and transport metrics, e.g., radio quality, throughput, latency, and user plane traffic. Our proposed framework runs on general-purpose laptops, eliminating the need for any specialized or expensive hardware. Furthermore, our tool provides a significant amount of data and meta-labels in an easy and automated way, which can be used to train future ML models to estimate QoS and QoE for the examined service. This demo paper demonstrates our proposed data collection framework, with synthetic transport and radio degradation, on a video conferencing use case and provides insight into how the main disturbance types influence the service streams.