Researchers at Texas Tech University, Sayed Erfan Arefin and Abdul Serwadda, recently used our (IPinfo.io) data in their paper “The Road to Web 3.0: Empirical Insights into Full Node Workload in the Bitcoin Blockchain.” Our friends at Dewey Data made the data accessible to them.
Research Review
The researchers wanted to understand what it takes to run a Bitcoin full node. They compared two types of setups: a powerful desktop computer and a low-power Single-Board Computer (like an Orange Pi). They examined how each setup affected power use, internet bandwidth, and data flow.
They also studied how the nodes performed during and after the initial setup and checked the locations of nodes worldwide by examining their IP addresses. Their goal was to uncover the real-world challenges of running a Bitcoin node, especially during market volatility, and to provide insights into the blockchain’s behavior.
How they used IPinfo in the research
| Particulars | Details |
|---|---|
| Data type | IP to Geolocation data |
| Data service | API |
| Usage | Mapping IP address locations |
The researchers used IPinfo’s IP to Geolocation API to determine the locations of IP addresses associated with Bitcoin nodes. This API provided details like latitude, longitude, city, state, and country for each IP address.
They then added this location data to their analysis and used it to create heat maps with the Folium library. These heat maps showed where nodes were located and how their activity changed, especially after the initial synchronization of the node. The heat maps, which focused on incoming “ping” messages to the desktop node, revealed increased node activity after the synchronization process.
Suggestions from IPinfo
If you are trying to simply map a bunch of IP addresses, you can use our free online tool: Map IP Addresss Data with IPinfo API Tools - IPinfo.io.
We also provide a free summarizing tool to summarize IP information and generate IP geolocation maps: IP Summarization & Data Visualization - IPinfo.io.
These tools support up to 500,000 IP address inputs.
If on the other hand you want more operational flexibility, you can generate your own map using Python and the Folium Library. Here is an example project I did: IPinfo map IPs using folium and add info on hover · GitHub.
If you are handling IP addresses on a million-level scale, you can experiment with Google Looker Studio. Using our data and Google Looker Studio, we created heatmaps that mapped 2.2 million IP addresses.
Feel free to explore the data and reach out to us or the Dewey Team if you need assistance in using our data.
We appreciate Dewey Data’s support in hosting our data on their platform. It is great to see how Sayed Erfan Arefin and Abdul Serwadda use our data in their research. If you are interested in using our data for research applications, please contact Dewey Data or submit a request through our research program.