5 Tech Advances Propel IoT

In the internet of things, it’s not uncommon for development projects to stall or fail because of technical challenges. Today, improvements in areas such as security and network performance could help companies better manage their investments.

Enterprises are investing big money in the internet of things (IoT), but the results so far have been mixed. In a 2017 survey, more than 1,800 IT and business decision-makers in the United States, United Kingdom, and India reported that close to three-fourths of their IoT projects were failing. The reasons are various, but many boil down to technical challenges in such areas as security, complexity, cost, and a lack of the necessary skills. Some enterprises are finding it difficult to manage, analyse, and derive benefit from IoT-generated data; many struggle to process it in real time to gain actionable insights.

Fortunately, progress in IoT technology is helping to overcome these obstacles. Not all of the latest advances are applicable to every industry or application type, but collectively they will likely help drive adoption of IoT solutions and create new possibilities for business and technology leaders.

Tailored Security

Earlier generations of IoT devices often lacked the necessary computing and battery power to run traditional cybersecurity applications and protocols, leaving them vulnerable to attack. Recently, however, many microprocessor manufacturers have introduced low-power hardware products that embed security features—such as a means for providing trusted identities to certify devices on networks—directly into IoT devices.

Another reason securing IoT networks has been a challenge is that existing security tools designed for corporate IT networks were often poorly suited for recognizing threats in networks of IoT devices. Today, cybersecurity solutions tailored for IoT networks are becoming widely available. Some use machine learning to recognize IoT devices’ unique network activity and behaviours and to spot anomalies and potentially compromised devices. The city of Las Vegas, for instance, is using threat detection based on machine learning to monitor its smart city infrastructure for possible intrusions.

Pre-integrated Platforms

It’s also getting easier to develop and deploy IoT solu- tions because providers of IoT platforms—software intended to integrate IoT hardware, networks, and applications—are increasingly pre-integrating third-party technologies through vendor partnerships. Over the past two years, leading IoT platform providers have launched and expanded their partner ecosystems and now boast dozens of major partner vendors each. In a recent survey, 57 percent of IT decision-makers said their companies already use IoT platforms; another 35 percent said they plan to.

In addition to horizontal IoT platforms—which can make it easier to build a wide variety of applications— vertical IoT solutions continue to arrive on the market. These offerings pre-integrate sensors, devices, analytics, and other components to create complete solutions. Many companies in the manufacturing industry have already taken advantage of such turnkey IoT solutions, and other sectors will likely follow suit. One luxury clothing retailer, for instance, deployed a platform with pre-integrated software, sensors, in-store analytics, and RFID tags from different vendors to gain insights into shopper behaviours and real-time inventory visibility.

Low-Cost, Power-Efficient Networks

A major class of IoT application relies on battery-powered sensors and spans large geographical areas. For such cases, the proliferation of low-power, wide-area networks (LPWANs) is providing a crucial advance by delivering connectivity at low cost and with low power requirements. Major telecom players have launched more than 40 LPWANs, and many smaller LPWAN specialists are expanding their proprietary networks globally; networks based on the LoRaWAN standard now cover 100 countries around the world.

Batteries powering LPWAN sensors can last for years, enabling the networks to provide connectivity for IoT devices for as little as $3 per year. (In comparison, cellular connectivity for IoT devices can cost at least a few dollars per month.) Rapidly falling LPWAN module prices can also help reduce implementation costs: Some are already less expensive than traditional cellular modules. According to one prediction, LPWANs will connect more than 4 billion IoT devices by 2025, making them the fastest-growing IoT connectivity option. One industrial equipment maker is using LPWAN technology to remotely monitor connected boilers—a feat that could have been too costly with other connectivity options.

Artificial Intelligence

AI technologies such as machine learning and computer vision are increasingly being used to analyse IoTgenerated data and automate operational decision-making. Nearly every major IoT platform vendor has now augmented its offerings with AI capabilities.

The rich insights and self-learning that AI can help provide enhance the value and utility of IoT in applications such as process optimization, predictive maintenance, dynamic routing and scheduling, and security. For instance, machine learning can reveal hidden patterns in airplane engine performance to make predictive maintenance feasible. The ability to see new patterns in changing data can be crucial in applications that monitor and respond to changing conditions such as weather in agricultural settings, vital signs in health care, and operating parameters in industrial settings.

Analytics on the Edge

Finally, analysis of data generated by IoT devices is increasingly occurring not in the cloud but at the network “edge,” physically close to where the data originates— on local servers, micro data centres, or even on the originating device itself. New hardware and software product launches related to edge computing and IoT have increased by more than 30 percent so far in 2018 compared with all of 2017.

Analysing data at the edge can help organizations sidestep the latency associated with transmitting it between the sensors that generate it and the cloud-based applications that analyse it. Lower latency can make it possible to generate real-time alerts and insights that can improve operational safety and performance in industrial, enterprise, and smart city settings, among others. Analysing data at the edge can also help reduce data transmission and storage costs. With such savings in mind, for example, a major European rail operator preprocesses data on smart sensors before sending it to the cloud for predictive railway maintenance.

AI technology is increasingly making its way onto edge devices as well. Many major cloud providers have been tailoring their AI solutions for on-device deployments, while numerous manufacturers are embedding AI capabilities directly into smaller, low-power chips designed specifically for smart sensors, cameras, and other IoT devices. Deploying AI at the edge may help companies avoid running afoul of data privacy regulations and reduce dependence on unreliable network connectivity in remote areas. ***

These advances can increase IoT technology’s value in many industries and make it easier for companies to build a sound business case for investing in an IoT solution. As barriers to adoption of IoT technology in the enterprise continue to fall, business and technology leaders would do well to keep an eye on these five areas of progress.