Combining intelligent computing and sensor-based technology in industrial applications, the Internet of Things (IOT) and Artificial Intelligence (AI) are delivering a wide range of economic benefits and quality improvements to the manufacturing, security and agricultural industries.
IOT is a technology that uses measurement and communications-enabled devices to create logs, perform tests, make calculations and identify and report on data from the environment.
Enhancing the way that this sensor data is interpreted and used, AI is demonstrating its utility in IOT implementations by giving the devices the ability to make decisions with the information that they learn from their implementations.
Control loops, primitive “if this then, then do that (ITTT)” control sequences, are the primary drivers of IOT applications. As a model, ITTT’s use dates back to the 1800’s in on/off decision-making chains of relays. As an example of ITTT in a modern example: if a sensor measures the temperature of a piece of equipment above a certain threshold, then begin a sequence of events that results in the operation(s) that decrease the machine’s motor speed and reports the situation to the service contract provider.
One type of AI gaining widespread application, machine learning, by adding dynamic decision control, enables software systems, to improve their performance and respond to changes in their environment, through making adjustments based upon trial and error-based learning, increasing the power of ITTT in field applications
Already, IOT and machine learning are changing the standard practices in several major industries.
Modern image recognition in intelligent computer systems succeed in matching and classification pictures and patterns with accuracy percentages in the high 90’s. Security systems are taking advantage of this in software-enhanced camera devices that can distinguish between persons who are and are not authorized access to buildings and structures. These intelligent image capturing devices are increasing safety and simultaneously decreasing costs in implementations ranging from home protection to screenings at secure facilities and airports.
In manufacturing, IOT and machine learning are paired to provide system usage monitoring, to report system failures and to recommend environmental changes that can extend the useful life of industrial equipment. Since the technology adapts to and learns from the data it collects and interprets from its environment, the intelligent sensor model allows a uniform device and software implementation in completely dissimilar settings. IOT/machine learning can even learn different definitions of what normal use is in very different environments.
Providing farmers with powerful tools that assist them with planning their harvests, conserving water by improving irrigation and recognizing issues with their crops early in the season are some of the key benefits that IOT has brought to agriculture.
One highly promising agricultural application of IOT has been in crop inspection. Historically, this has been a challenging, time-consuming and expensive operation. However, the use of intelligent sensors coupled with image recognition software trained to identify flooded, under-watered and diseased crops from aerial drone photo compilations and satellite images can protect food supplies, reduce costs and increase profits – before the problem devastates the entire season’s harvest.
The promise of intelligent IOT applications across industries is unfathomable.
With estimates by General Electric of $19 trillion+ in profits over the next decade, the economic impact of IOT is anticipated to be huge.