mart and connected technologies are presently driving the astonishing growth of the Internet of Things (IoT). However, growth in these technologies is, in turn, a result of the tremendous development of various sensors. According to the Boston Consulting Group, by 2020, expect to spend US$ 265 billion for IoT technologies, services, and products. Much of this growth will owe its progress to that of sensors.

One can now find sensors almost everywhere, for instance, in smart retail, smart healthcare, and smart homes. Today, most people start their day with pressing a couple of apps while still in bed, thereby turning on the high-end coffee maker for the first-morning cup, or their night adjusting the climate control zoning system for keeping the bedroom in that ideal sleeping temperature.

As an example, a large health insurer in Australia is placing sensors throughout the house of elderly members for monitoring their health and preventing them from falling. They place sensors within refrigerators, medicine cabinets, bathrooms, and doorways. The sensors monitor movement by tracking the temperature within the home. Any break in routing such as a change in the temperature notifies the family immediately.

Viewers of professional golfing can see information on the heart rates of the players on their TV screens thanks to a special camera and sensors monitoring the faces of the players. This contactless vital sensing technique allows TV viewers to read the stress levels of the athletes as they play.

The past decade has seen a drastic drop in the prices of sensors as a result of the advancement of technology. This reduction has exponentially increased the use of sensors not only in civilian applications, but also in military, aerospace, and in collision avoidance systems in the automotive industry.

Advances in complex micro-electro-mechanical systems (MEMS) and thermopiles are improving uncooled IR sensor technology. This MEMS-based technology offers free-standing thermal isolation structures surrounding a printed thin-film IR absorber. This allows the collection of radiated power to determine the temperature of a remote object. Using semiconductor technology, it is now possible to add hundreds on thermocouples on several square millimeters of a thermopile sensor. Besides being small and reasonably priced, these thermopile array sensors are smart enough to be accurate with faster response time. It makes them ideal for building automation, people counting, security systems, medical instruments, and more.

For instance, the 8×8 thermopile array device is a sensor with 64-pixel IR sensors fitting within a surface mount package that can withstand reflow soldering. Apart from a silicon lens that collects the infrared energy, the package consists of a digital ASIC, a MEMS detector chip, and RF-shielded metal cover, and an I2C interface.

While operating, the thermopile array sensor has a 60-degree field of view for absorbing emitted thermal energy. The 64 sensing elements in the array individually convert the absorbed thermal energy to produce a proportional output signal. After amplification, an ADC converts these analog temperature signals to digital, while also referencing them against the ambient temperature value measured by a thermistor. A microprocessor collects the digital data and proceeds to map the temperature from individual thermopile elements into a thermal representation of the entire field of view.

In the Industry, collection of IoT data, specifically that from manufacturing processes is very important. Apart from the quantity of the data collected, the quality of information from various machines is also equally vital for analysis, and to make decisions.

IIoT puts a lot of stress on the usefulness of predictive analytics based on big data. According to the Forbes magazine, big data offers the volume, speed, and variety of information about important effects that traditional methods of empirical research and the human eye is unable to capture. Therefore, big data becomes the primary step towards generating valuable insights from evidence-based interventions. From a theoretical and practical perspective, big data not only helps to predict outcomes, but it also helps in explaining them, especially in understanding the underlying causes.

Companies usually build plug-n-play adapters for controls, thereby enabling them to capture hundreds of data points directly from PLCs. Although this generates vast quantities of data for analysis, and a large part of it will be helpful as deep data, there will always be some part of the data that will remain useless, as will some results.

By taking the analysis down to a more granular level, deep data can eliminate irrelevant information and focus on the streams for a certain course of investigation. Analyzing deep data offers more accurate overall predictive trends.

Data from a specific sensor on a machine offers a snapshot within a designated timeframe. Sensor data monitors specific situations, such as vibrations that signify to an operator the state of operation of the machine—on versus off. However, all sensor data may or may not be useful during a review or analysis.

On the other hand, PLCs can collect large amounts of data, and when combined with sensor data, allows the operator to gather a full picture of the machine status at any time. This data can help to monitor inputs to and outputs from a machine, and based on programming, can make logical decisions when necessary.

Older machines with legacy controls and those with no controls need additional integration/hardware support for capturing data. While auxiliary hardware can capture digital and analog IO, adding sensors can generate additional data points.

The ability to capture deep PLC data and data from sensors that monitor specific items that the PLC cannot reach forms the basis of high-quality analytics and results—all the more reasons for the necessity of sensor as well as PLC data.

For instance, while a sensor may provide information on the vibration limits of a certain machine or parts thereof, the PLC data from the machine may include parameters signaling an impending fault. Therefore, the PLC data offers the ability to control the operation of or sequence of activity of a nearby a machine. When the sensor data signals one or more parameters are beyond the programmed limits, the operator can respond quickly, and need not wait for analysis.

Therefore, using both sets of data from sensors as well as from PLCs offers more information to the user than either on their own do. This allows the operator greater flexibility for avoiding expensive downtimes and maintenance issues.