Devices and sensors connected to the IoT continues to rapidly expand
IoT, and machine learning are becoming pervasive – even inevitable - in many industries. Synergistically, they feed on each other. Each technology adds value to – and drives the need for - the other. The Internet of Things is being driven by the emergence of low cost sensors, communication, and processing. Mobile phones were one of the first devices participating in the Internet of Things – often, I suspect, without some owners realizing how big a part their phones played…
Here are two example apps: weather forecasting and navigation. One popular weather app has been downloaded by tens of millions of smartphone users. Many of those users – perhaps unwittingly - have given permission for the app author to use their phones to sample barometric pressure and send that data back to the cloud for aggregation and analysis. Essentially, their phones are part of a massive, IoT-based, weather station. Each phone provides precise localized data that together can be used to improve the accuracy and granularity of weather forecasts overall.
Likewise, smartphone-based road navigation apps work in a similar way. During operation, apps typically provide frequent updates, suggesting better routes to users in real-time as traffic conditions change. But, how does the app know where congestion is building up, and where traffic is flowing freely? Because the phones of people using the app provide data on exactly where they, and how fast they are moving using the built-in GPS sensor. This doesn’t work well if only a few people use the app. But, it is estimated that at least 10% of the population of Los Angeles use one popular app1. Navigation apps are, from one perspective at least, IoT applications. The apps use data from millions of devices to help alleviate some of the misery that commuters face by providing faster routes as they open up.
With both examples above, ample data is generated to feed the algorithms that drive the app. That data is also going to be used for analytics. For example, navigation apps may be free to use, but supported by advertising. Drive along a certain highway, at a particular time of day, and an ad might pop-up for a nearby restaurant. For that advertising model to work, potential advertisers need to gain insight into exactly how the application is used: How many people are travelling where, at what time, on what days. That insight comes from analytics – skilled analysts who pour over the data to gain insights that can give them a competitive edge. Here’s an example, showing how this data was used to discern the dining habits of particular cities over Thanksgiving2.