Wireless network systems operating on a blockchain technology provide application programming interface (API) utilities for fetching useful information about data traffic on the network. The same holds for the Helium Network, whose Blockchain API contains data about transactions, users, hotspots, and other features of the network. In this research, Blockchain API was accessed in Python language, and the retrieved data was organized using various libraries (Numpy, Matplotlib, etc.) This information was used for plotting graphs that depict economic trends of the network throughout the previous month. Part of the attention was focused on HNT (Helium virtual currency) rewards for hotspots, which also sheds light on network demand. The results are mostly in the form of list rankings and linear graphs. They show among other things that Helium network is not as much data transfer-oriented as it is centered around coverage validation and transaction witnessing.
Abstract
Introduction
SOFTWARES AND TOOLS
HARDWARE
Programs with HTTP requests used to acquire data from the Blockchain were ran on a computer with [PC specifics]
JUPYTER NOTEBOOK
A web-based open source interactive computing environment developed across many programming languages. In this research, it was used for file organization and running Python code.
PYTHON
High level, scripting and interpreted programming language suitable for scientific data analysis.
GITHUB
Internet software development platform for storing of and team working on coding projects.
LIBRARIES AND MODULES (IN PYTHON)
ALGORITHM
DATA COLLECTION AND REPRESENTATION PROCESS:
Four programs in Python were written to obtain all the data required for network demand analysis. The library used for communicating with the Blockchain API was Requests library. Original fetched results were in dictionary format. JSON syntax was used for making the request output more readable. Since the desired data was very large in memory size, the Blockchain would return a cursor code, which had to be used iteratively to get further batches of data for each HTTP request. Once full data outputs were obtained, programs searched for specific information within data dictionaries and store it in proper data structures (lists, dictionaries, etc.) Upon getting all the necessary numerical and/or textual data, algorithms would use Matplotlib library to plot the results over specified time intervals for each hotspot address. In each program execution, geographical latitude and longitude information had to be provided, to specify the area being analyzed; along with the perimeter distance around the chosen geographical point.
Methods
Discussion
References
Acknowledgments
Basic Concepts
Objectives
School of Cybersecurity, Old Dominion University
Kristijan Hornung
Economics of the Helium Network: User Demand and Network Traffic
Results
General Theory
Figure 5: Cumulative HNT rewards for all hotspots in 20 mile radius from the center of Denver, CO over 9 weeks (from 5/25 to 7/27, 2022)
Figure 1: Hotspot providing coverage
to various IoT devices in its range
Figure 2: Staleness factor δ used for determining miner score
in the scoring function (Haleem et al. 2018)
Figure 3: Miners scoring function (sigmoid curve)
(Haleem et al. 2018)
Figure 4: Helium network system overview (Haleem et al. 2018)
a consensus group
In addition, desired time intervals for time series graphing were selected in each program run. The visual data had to be represented more succinctly, so the Pickle module was employed to plot the data about all the hotspots in a given perimeter in the same graph.
Figure 6: Cumulative HNT rewards for all hotspots in 20 mile radius from the center of Miami, FL over 9 weeks (from 5/25 to 7/27, 2022)
Denver, CO
Miami, FL
Figure 7: Cumulative HNT rewards for all hotspots in 20 mile radius from the center of Turlock, CA over 9 weeks (from 5/25 to 7/27, 2022)
Turlock, CA
Figure 8: Data from Hotspot ranking list by total HNT earnings (6/23-7/23, 2022)
Note: Cumulative HNT rewards for Challenger role (creating PoC challenges), Consensus (for Validators in a Consensus group), and Securities (for investors who hold security tokens) was 0 (flat line graph) for all locations studied.
Similarity between Witnessing and Total HNT rewards (in line shape and y-axis values) suggests that majority of hotspots derive most of their earnings from witnessing transactions as part of PoC challenges. Data Transfer accounts for low earnings across all studied areas (judging by the y-axis values). Very few miners provide Data Transfer service compared to performing Challengee or Witnessing roles (based on the density of linear graphs for each). Most hotspots perform Challengee roles, while only a small fraction transfers data across the network. This means that most hotspots make HNT from solely providing coverage and/or witnessing other hotspots providing coverage (as part of PoC protocol). From this we can assume low network traffic, and therefore user demand. Around 70-80% of hotspots perform mining in all locations (except for Rome, GA), other hotspots are seemingly inactive or have only recently been added to the network.
All the code used to obtain the data is available in a public repository on GitHub (https://github.com/kristijanH1998/REU-Summer-Internship). Further research can be conducted based off of this work (for instance to look for HNT fluctuation and data traffic over longer time periods for deeper economic analysis). It can be useful for other students and scholars interested in studying decentralized IoT networks and virtual currencies. With the Blockchain being an open public ledger (meaning that all the data about the network is available through the Blockchain API), there exists a revolutionary opportunity for researchers (data scientists, cybersecurity specialists, etc.) to study Internet-based economics.
Seattle
Turlock
Denver
Topeka
Rome
Miami
Figure 9: Map of the Helium hotspots providing coverage in the continental US; locations studied are highlighted with pink dots
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