[Paper] Interactive Visualization of Proof-of-Work Consensus Protocol on Raspberry Pi

Source: arXiv - 2511.20391v1

Overview

A team of researchers built a fully functional Ethereum Proof‑of‑Work (PoW) network that runs on a handful of Raspberry Pi devices. The prototype is self‑contained—powered by a local Wi‑Fi router—and each Pi drives an LCD screen that visualizes the blockchain’s state in real time. By turning abstract consensus mechanics into a tangible, interactive display, the system serves as a powerful teaching aid and a sandbox for experimenting with network‑level parameters.

Key Contributions

• End‑to‑end PoW Ethereum cluster on low‑cost hardware – the first publicly documented setup that runs a complete Ethereum PoW chain on multiple Raspberry Pi units.
• Live on‑device visualisation – each Pi drives an LCD that shows block height, hash rate, pending transactions, and consensus status in real time.
• Web‑based control panel – a central UI lets users tweak network topology, latency, mining difficulty, and peer connectivity on the fly.
• Educational toolkit – the system is packaged with step‑by‑step scripts and documentation, lowering the barrier for classrooms, workshops, and hackathons.
• Empirical insights into consensus degradation – the prototype demonstrates how latency, node placement, and topology affect PoW convergence, providing a hands‑on platform for research and debugging.

Methodology

1. Hardware stack – 4–6 Raspberry Pi 4 boards (4 GB RAM) each equipped with a 3.5‑inch LCD, powered via a common USB hub and connected to a standard Wi‑Fi router.
2. Software stack – each Pi runs a lightweight Ubuntu Server image, Docker Engine, and a custom Docker image containing the official Go‑Ethereum client (geth) configured for a private PoW network.
3. Network orchestration – a central web server (hosted on one Pi) exposes a REST API and a React‑based dashboard. The API injects configuration changes (e.g., peer list, artificial latency via tc traffic‑control, mining difficulty) into the running geth instances.
4. Visualization pipeline – a lightweight Python script on each Pi polls the local geth JSON‑RPC endpoint, extracts key metrics, and renders them on the LCD using the pygame library. The display updates every second, showing block number, hash, miner address, and a “consensus health” indicator (green = synced, red = forked).
5. Evaluation – the authors ran a series of experiments varying latency (0‑200 ms), node degree, and mining difficulty, recording how quickly the network reached consensus and how often temporary forks appeared.

Results & Findings

The visual dashboards made these dynamics instantly observable: the LCDs would flash red during a fork and return to green once the chain re‑synchronised. The prototype proved that even modest hardware can faithfully reproduce PoW behaviour, including its failure modes.

Practical Implications

• Education & Training – Instructors can now demonstrate PoW concepts (hash puzzles, block propagation, forks) without needing cloud resources or expensive mining rigs.
• Rapid Prototyping – Developers building layer‑2 solutions, consensus‑altering patches, or custom Ethereum clients can test their code on a real network with controllable latency and topology.
• IoT & Edge Research – Shows that a full blockchain can run on edge‑class devices, opening avenues for decentralized applications in constrained environments.
• Debugging & Visualization Tools – The LCD‑based UI serves as a template for building on‑device monitoring dashboards for any distributed system.
• Community Outreach – Hackathon organizers can set up a “blockchain wall” where participants watch the chain grow in real time, making the technology more approachable.

Limitations & Future Work

• Scalability – The prototype is limited to a handful of nodes; scaling to dozens would require more sophisticated routing and may expose memory constraints on the Pi.
• Performance Gap – While functional, the Pi’s hash rate is orders of magnitude lower than production miners, so the system cannot emulate high‑throughput networks.
• Security Scope – The private network runs without real economic incentives; attacks like selfish mining are not fully represented.
• Future directions suggested by the authors include: integrating other consensus algorithms (e.g., PoS, PBFT) for comparative studies, adding hardware‑accelerated mining modules (ASIC hats) to explore performance trade‑offs, and extending the visualization layer to support remote dashboards and VR/AR interfaces.

Authors

• Anton Ivashkevich
• Matija Piškorec
• Claudio J. Tessone

Paper Information

• arXiv ID: 2511.20391v1
• Categories: cs.DC
• Published: November 25, 2025
• PDF: Download PDF