Car Mesh Networks.
Connected Cars change the usage of Big Data.
Digitisation - driving force of the present.
Big data is already transforming diverse areas of society and business. But a critical juncture of the data revolution will be driverless cars. Instead of human input, these cars will be guided by a vast mesh network of technical and environmental data.
In terms of global data volume, the arrival of automated cars will be like a comet landing in a flooded basin. Only ten years ago, this kind of smart data held very low priority. Now in 2017, even a single connected car will upload 25 gigabytes - or 12 HD movies worth of data to a wireless cloud every hour. Managing this flood of data will hatch important questions about the future of the automobile and telecommunication industry, and how they intersect.
Seams of Information.
Big data is the term given to very large or complex sets of data which are difficult to analyse. With six billion smartphones on the planet and half of the world’s population online, a colossal amount of this data is produced every second. For businesses, much of this information is valuable because it provides honest insight into customers’ browsing or buying habits.
Whether the consumer knows it or not, big data is also the magic behind many new mobility services. Late last year, Brian Krzanich, CEO of Intel stated that ‘data is the new oil’. And this was not total hyperbole. Driverless cars, rigged with a regiment of sensors and microprocessors, will be roaming powerplants of data. Indeed, Intel estimates the amount of data created in 1.5 hours of driving (the average amount of time a person spends in their car daily) to be four terabytes.
Perhaps some arithmetic can put this into perspective. In 2016, worldwide internet traffic totalled 1.2 million terabytes. In the same year, 88.1 million cars were sold. So, if driverless cars constituted 1% of all cars sold, this would be 88,100 units in 2016. With each of these generating four terabytes every day, this amounts to 1.28 million terabytes of data a year - more than the entire web traffic of 2016. As such, there is an urgent demand for services to meet this dramatic influx of data.
Big Data, Big Business.
Big data is booming. It has sprouted many companies who traffic in new ways to understand and utilise exploding information flows. For example, Israeli startup Panoply has automated the data process completely - reducing to a single click what once required expensive equipment and a team of data engineers. This shift towards low-cost and simple solutions could persuade more businesses to embrace big data technology.
‘Panoply.io believes in ease of use, simplifying your experience and making it as intuitive as possible.’
From the same country, Endor pioneers what they call predictive software technology, based on ‘social physics’. Whereas other systems can evaluate data to predict, for example, the most likely time for a vehicle to break down, social physics uses data to predict human behaviour. For example, a car manufacturer can directly ask this software questions like ‘where should we open a new garage?’ or ‘is this new range likely to sell?’ - and receive a high-quality answer in minutes. The potential of this technology is so significant that the company was named as one of 30 technology pioneers by the World Economic Forum in 2017.
Data - the New Oil.
Cars have always utilised data. From the start of the 1990’s, vehicles with integrated GPS systems would store user statistics like destinations and speed in their internal memory. And a decade ago, telematic methods used early wireless technology to record external data like location, as well as internal diagnostics.
During this time, there was little focus on smart data. In 2007 - the same year the first iPhone was released - the startup car2go was informed by its telecommunication partners that their services would prioritise conventional features like in-car telephones, instead of data promoting interconnectivity.
This has since changed enormously. Ten years later in 2017, data has become an increasingly valuable commodity to be harnessed. Brian Krzanich expands: “Each car driving on the road will generate about as much data as about 3,000 people,” So exactly what kind of data will be flowing in a driverless car? This information can be split into three types. The first is technical data. Just as a human uses its senses to gather information, this is the data that sensors will register inside and surrounding the car. It empowers the system’s intelligence to recognise traffic lights and differentiate between, say, street debris that can be run over and a traffic cone to be avoided.
The second is crowdsourced, or interconnective data, which concerns the wider world outside the vehicle. Like how Google Maps can register the number of users in an area to determine traffic, the car will receive continuous information from other vehicles and devices in the network. This will influence the car’s routing from A to B.
Finally, there is personal data. This monitors information like the number of occupants in a car, radio preferences and frequently visited destinations. Similar to browsing history, it is likely that this data will be commercialised for the purpose of targeted advertisements.
Go Digital or Go Home.
As the arc of progress bends towards a data-flooded world, automobile producers have begun to emphasise digital services powered by big data. This will come through mesh networking - a huge grid of connected cars and devices, all of which are constantly receiving and transmitting data. Each travelling car acts as a node in the network, creating a pulsing web of energy and intelligence.
Drivers will benefit from a suite of smart assistants enabled by this interconnectivity. For example, if the airbag inside the car is activated, the system can automatically alert nearby emergency services. Other manufacturers are building similar frameworks. It is clear that the automobile and telecommunication industries are converging fiercely around the topic of data.
With human lives at risk, a car must be able to receive and act on cautionary data immediately. This poses yet another challenge for telecommunication companies, who are competing to develop the digital infrastructure needed to accommodate both the volume and speed of this new data.
The top candidate for this infrastructure may be Intel, who have been creating 5G modem chips. These can transfer data with only one millisecond latency. Due to this exceptional speed, the company claims that 5G wireless networking will be the “oxygen” for driverless cars. Although 4G has supported great advances in smartphones for the past decade, it simply was not designed to carry the immense data load that is expected with autonomous cars. Indeed, the big data revolution is even changing how we define progress. Whereas automobile progress was once measured by metrics like top speed or horsepower, data speed will become a deciding factor for the consumer. It is entirely plausible that car advertisements of the future will advertise data functionality as a primary selling point.
The Future is Automation.
The importance of mobile communication is growing rapidly. Connected technology will soon bear more data weight than cable infrastructure ever did. The focus of data developments has long since moved away from merely providing smartphone users with faster mobile internet. In the USA, a tipping point has already been reached where most newly-issued SIM cards are installed in cars, not mobile phones.
In addition, leading telecommunication companies expect that the mobile networks of the future will be built by new players. T-Mobile CEO John Legere is convinced that Facebook and Google will present serious competition here. Indeed, Facebook already generates 84% of its advertising revenue through mobile users.
It’s unlikely that social media giants are overly concerned about maintaining their data streams. But in the case of driverless cars carrying human passengers, the stakes are much higher. Who will be the companies that ensure these vehicles are supplied a gushing and uninterrupted flow of data?
Authors: Christian Geiss and Neelesh Vasistha