The Disadvantages of Crowdsourcing Network Data

In a recent article, we discussed how our sensor provides visibility into networks at the cell site level — resulting in an accurate, impartial way of determining the size and density of wireless networks. We found all sorts of market-analysis and development implications in the data.

But what about crowdsourced (application-based) data, which captures information from devices operating within the network, such as active channels and device performance?

Are these two types of data collection compatible? Which is better than the other?

Aurora Insight vs. crowdsourced

To explore fusing these data sets, we compared our dataset of cell locations in Cheyenne, Wyoming with data from a crowdsourced application. Here’s what we learned:

• Aurora Insight’s network independent assessment creates a significantly different snapshot of the spectrum landscape.
• Crowdsourced datasets are comprised of individual measurements over several months, while our dataset can be compiled within a few hours.
• In the crowdsourced data, Sprint, Verizon, and Union appear to have no identified cells, or very little. However, we know this to be incorrect.
• The crowdsourced data favors LTE, compared to any other standards because users are most likely to have LTE phones, whereas our method captures all mobile standards with equal accuracy.

 Pictured Above: Number of cells located per network operator