The current status of IoT
About this time two years ago, this column in Daily Trust featured a series of articles on the technology paradigm known as the Internet of Things (IoT) and Predictive Manufacturing, delving into the technology, the promises and opportunities, and giving examples of deployments. It’s time to revisit the state of affairs with IoT/ Predictive Manufacturing. In short, where do things stand as of today? A few governments - such as South Korea and the United Kingdom - had allocations in their budgets to fund IoT initiatives. With a few years on, what kinds of results are we seeing, and what remain the challenges?
The framework within which the previous articles in this column discussed IoT is Predictive Manufacturing (PM). In PM, you embed small gadgets (sensors, actuators, cameras, etc.) inside products that you produce and/or market for the purpose of collecting real-time data on the state of the products wherever they may be located in the universe. The collected data is communicated via the Internet to you - most likely via some cloud interactions - for business intelligence. Processing the collected data allows you to assess the state of the hardware (product), communicate with the hardware, and remotely fix impending problems with it; that is, even before the problems occur. By this means, you prevent a downtime at the customer’s site, resulting in a happy customer and presumably better business for you. Thus, in this model, you don’t just sell your customer a piece of hardware and disappear; you continuously and remotely service the product based on the predictive power you acquire from the data you collect; hence, the phrases “Predictive Manufacturing” or “Product as a Service.” General Electric Aviation is one of the companies that have deployed PM - in its aircraft engines. The elements of PM include: a) the technology to collect data, b) the Internet capability and bandwidth to transfer the data to some cloud site, c) the storage capacity of the cloud servers, d) the Big Data analytics wherewithal to process petabytes of data, and e) the feedback to the hardware - again via the Internet. The embedded devices for collecting data may include those for sensing; such as GPS trackers, switches, temperature probes, cameras; and those for actuating, such as valves, bulbs, and locks - most of which communicate using lowpower radios, Wi-Fi, and cellular means.
In the rental-car business, as well as in some insurance companies, location sensors are installed in cars to obtain data on how the car is being driven and where it is driven to. The information is used to customize the insurance premium, in the case of insurance, or to base pricing on more realistic risks associated with your driving, in the case of car renting.
As stated in one of the previous articles in this column, IoT does not just apply to manufacturing but to numerous other occupations as well. An example involves a caplet-sized camera that is placed in the human digestive tract to collect data from our body and send back hundreds or even thousands of images (via the Internet), which could be analyzed to predict the possibility of diseases.
So, what is the state of IoT? Certainly, it has not yet exploded in deployment, even as we look into the technology to boost the efficiency of products and services, create new revenue streams and reduce operational costs. Simply put, the market is still immature, with all components of deployment - architectures, technologies, standards and vendors - being moving targets, which implies a risky business, at least for now. In many cases, the state of IoT is that of experimentation and pilot plants.
There are a few hurdles to overcome before the technology can skyrocket to adoption. The security of the data that is being transferred across the Internet is of great concern to prospective technology adopters. It is also a fact that many industrial machines are not yet connected to the Internet. Furthermore, a large proportion of highly disruptive companies is not into manufacturing of noncomputer-based consumer goods that are potential candidates for IoT.
There is the recruiting challenge as well. There is a huge difference between IoT and the regular Internet. Everyone knows how to work the Internet or log in hours per day. That will not pass as a qualification for an IoT job. Recent computer graduates want to work for the likes of Apple, Microsoft, Amazon, Facebook, or Salesforce. Thus, IoT initiatives face a hard time in recruiting talents. Security professionals are also in short supply, as are digital marketers.
Regulation and standardization, which are taking a back seat now because IoT hasn’t really achieved a critical threshold for recognition, will become more critical and attract government interests when the situation changes. The foregoing include some of the issues that will need to be resolved before IoT/Predictive Manufacturing/Predictive Maintenance takes off for real.