Telefónica's 'Internet para Todos' project uses modern tools to find and connect Latin Americans

Telefónica is connecting 100 million customers in remote locations across Latin America. (Pixabay)

Telefónica's "Internet para Todos" (Internet for All) program is using big data, machine learning and artificial intelligence to find and connect 100 million inhabitants of Latin America who lack reliable services.

The three steps in the process are to localize and identify the unconnected, optimize transport networks and optimize network operations.

"I think that the type of data driven approach to complex problem solving demonstrated in this project is the key to network operators' sustainability in the future," wrote Patrick Lopez, Telefonica's vice president of network operations, in a LinkedIn post. "It is not only a rural problem, it is necessary to increase efficiency and optimize deployment and operations to keep decreasing the costs."

The project suggests that the most sophisticated modern tool can be an important enabler of providing previously isolated populations with basic services. The strategy for connecting these people—who are disconnected for geographic, population density and/or socioeconomic reasons—is through systematic cost reductions, investment optimization and targeted deployments, according to the article.

The first step is simply to determine the size of this population. Data was old and scarce and the population was mobile. The program uses high-definition satellite imagery at a countrywide scale and neural network models. Visual machine learning algorithms trained by census data enables the system to literally count each house and settlement. That data is combined with regulatory data, geolocalized data sessions and deployment maps from Telefónica.

The results were impressive: 95% of the target population was identified with less than 3% false positive and less than 240 meters of deviation in antenna locations. In other words, an impressive array of tools and methods provided a very precise picture of the population distribution.

The next step was the crucial and expensive job of amassing transport networks data. This process added road and infrastructure data from public sources to the model. Graphs were generated that showed settlement clusters. Graph analysis identified density-optimized transport routes.

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Operational efficiencies and minimizing downtime are key, especially when a trip to fix something could take days. This makes the ability to detect and perform preventive maintenance vital. It also makes it important to create determine routes that are as efficient as possible. The model included historical failure analysis and network metrics. The output was a model that automates supervision of network health. Possible failures were predicted and maintenance routes optimized.

Different service providers, carriers and other organizations are deeply involved in the drive to find and serve these remote populations in countries such as Peru. A SlideShare posting of slides from the project is here.

One idea being floated—literally and figuratively—is Project Loon. The Alphabet subsidiary uses balloons high in the stratosphere to provide wide coverage to the earth below. Project Loon, which gained its name because even Google thought the idea was a bit odd, was used to provide connectivity to Puerto Rico after Hurricane Maria. Telefónica partnered with Project Loon to provide coverage in the wake of flooding in May 2017.