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Mobile World Congress 2016, Big Event, Big Data


on 29 February 2016

This article was last updated 8 years ago.

Philip McAleese talks sensor data and IoT bike lights

Apparently, over 100,000 people attended Mobile World Congress 2016 this past week. I was one of them. One of 100,000 people that had to be registered, tracked, and admitted daily to the event. As small as I feel in that 100,000, that total is equal to decimal points of a percentage of the amount big data many of the companies and solutions presenting at MWC 2016 are generating.

Two are top MWC startups, See.Sense and Aframe. See.Sense has developed an IoT-based bike light with a range of features and a forward-looking big data aggregation and analysis project. Aframe provides a powerful, video cloud platform that allows companies to work on huge amounts of highly sensitive video. The CEO of Aframe, David Peto, gave us a sneak peak into their upcoming Excession platform.

For a road cyclist, the See.Sense ICON lights offer a bunch of cool, helpful features. They use sensitive, internal sensors that can react to the rider’s environment. For example, when slowing, the lights can flash brighter or change patterns to catch the attention of cars following. Depending on movement detected by the sensors, the light adapts to things like changes in the bike’s direction or approaching headlights.

There’s also a free app for the See.Sense ICON lights that allows you to control the light actions, check battery levels, even set an emergency contact that the light will notify using your lower-power Bluetooth-attached smartphone in the event it detects you’ve been a crash. There are even theft alerts to notify you if there’s foul play whilst you might have popped into a shop.

See.Sense ICON bike lights at MWC keynote presentation

What Philip McAleese, the CEO of See.Sense revealed at MWC is a plan to take the inbound data from the lights’ sensors and do roads and conditions analyses. He explained that certain variations in road conditions are indicative of a degraded surface and the potential for a pothole to open.

By aggregating all of the inbound sensor data, and normalizing for bikes that ride the same route regularly, it could be possible for See.Sense to proactively notify municipal authorities of road conditions and required maintenance before obstructions have even developed. Further safety benefits are the lights providing data of intersections with regular near-miss accidents, potential traffic light integration, and more.

Aframe’s Excession platform reminded me of the TV show Person of Interest, without the artificial intelligence. They’ve leveraged their expertise in working with sensitive video from the likes of companies like HBO and Netflix, and built out a platform for aggregating and analysing the tsunami of consumer and mobile video that is recorded and shared.

Excession stemmed from terrorist attacks and Aframe’s desire to help coordinate and analyse video input to make it searchable and meaningful in real time. Instead of reviewing video hours or days later, it can be reviewed, indexed, and searched as soon as it reaches the cloud.

Real time video analysis dashboard of Excession

The projects that both See.Sense and Aframe revealed accomplish very different things. But they both seek to accomplish them in a similar manner. Huge amounts of data are collected from sensors or cameras potentially located anywhere in the world. That data is aggregated, analysed, and put to use.

The ICON lights and Excession platform are two real world examples of what we mean when we talk about big data. Every organization has the ability to develop and leverage big data in a similar manner. Canonical has tools designed to help build, deploy, and manage big data infrastructure in the same real time manner that the data is generated.

If you want to learn more, we’ve written an eBook that explains further about what big data is, what you might do with it, and how you can use advanced tools to manage it, both for long term analysis and in real time. Download Big Data Explained, Analysed, Solved here.

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