In order to understand the way in which wireless sensor networks and IoT impact on industries, it is highly important that we first define these concepts. The Internet of Things (IoT) is a network of physical objects that are embedded with electronics, software and sensors and have network connectivity, allowing them to exchange and collect data.
Wireless connectivity products (e.g., lightbulbs or thermostats) are more common in homes today than ever before. According to one report, 79% of U.S. homeowners have at least one connected device.
However, the technology’s roots lie in an industry that existed long before the advent of smart appliances: Industrial Manufacturing.
But how can wireless sensor networks and IoT change the manufacturing industry? The Industrial Internet of Things uses networked sensors, intelligent devices, and places those technologies directly on the manufacturing floor. It collects data for predictive analytics and AI.
In IIoT technology, physical assets possess sensors, and these sensors collect data wirelessly and then use analytics and machine learning to perform some type of action.
IIoT Impact on the Manufacturing Industry
Industrial Internet of Things is easily transforming linear and traditional manufacturing supply chains into dynamic, interconnected networks. IIoT technologies can transform the delivery and manufacturing of products.
These technologies make factories safer and more efficient. In certain cases, they can make factories save millions of dollars.
Predictive Maintenance at Work
The many benefits of IIoT include the ability to improve operational efficiencies. Wireless sensor networks and IoT can help to detect the cause of a machine’s downtime and send an engineer a request for service.
The combination of IIoT with an EAM or CMMS allows engineers to generate these requests from their mobile devices and then immediately go to the asset to repair it or assign it to another engineer nearby.
IIoT is able to predict when a machine will eventually fail or when it will end its useful life cycle. This preventive maintenance approach will save facility owners thousands on unnecessary repairs and replacements.
An Example of Real Life
An establishment paying $16/hour for a worker to manually check sixteen meters around the property once per day will cost $3,840. It would cost $92,160 if this individual checked the meters every hour to decipher differences.
Imagine checking meters every second or every minute. It would be impossible for anyone to do this without IoT and machine learning.
Machine learning can detect small changes in meters at scale. Workers can analyze these changes to begin the next level of predictive maintenance.
IIoT is a way to save time and money as well as to keep workers safe. Based on vibration analysis and the nature of sensors, operators will know when an oil well is at risk. In an emergency, sensors can be used to monitor and manage workers’ movements in order to evacuate or prepare for evacuation.
