AI is becoming an essential part of IoT deployments, creating opportunities for players across the mobile ecosystem, finds Kate O’Flaherty.
The vast number of connections and devices comprising the IoT continues to surge. At the end of 2018, there were an estimated 22 billion IoT devices in use around the world, according to Statistica. By 2030, it is expected this number will reach around 50 billion.
To unlock IoT’s true value, though, the huge amounts of data produced by these connected devices needs to be analysed and acted upon. AI, and in particular the most common variety of it currently in use, machine learning, is key to that unlocking: it can increase efficiency and cut costs across myriad IoT applications, from industrial IoT (IIoT) to healthcare and autonomous vehicles.
AI is already deployed in many IoT ecosystems, but it will become ever more widely used, especially as 5G and edge computing start to emerge. Dr Richard Benjamins, Chief AI and Data Strategist at Telefónica, explains that some of this value comes from predictive analytics, to which AI can be applied. Examples include predictive maintenance to reduce unscheduled downtime and improve safety.
Benedetto Pietrabella, VP of the Americas Network Equipment Providers Business at Altran, a Capgemini company, agrees. He points out that in the past humans could monitor situations, identify problems and react, but this task is now increasingly being passed to a machine learning algorithm. Among the benefits of this approach are, he says, that “It can identify issues early and even predict the month when a piece of equipment will fail.”
Martin Garner, an IoT Analyst at CCS Insight, notes, “There’s a lot that can be done without AI, but IoT systems are becoming complex and the volumes of data are growing at a rapid rate. We are moving into an era where we need AI to make the most of the data and to cope with the sheer volume of it.”
AI use cases in IoT
The use cases for AI in IoT span many sectors. Telefónica, for example, says Dr Benjamins, uses AI to perform predictive maintenance for its industrial clients. The company is also using AI to perform predictive maintenance for connected cars, as well as to detect anomalies in consumption in energy IoT.