Incredible Real-World Applications Prove the IoT Is Here to Stay
Industrial accidents? Air pollution?Actionable change is already occurring on the business application side of the IoT.
Yet, while many people debate whether little computers in wrist devices, clothing, refrigerators and vehicles will truly change our lives, actionable change is already occurring on the business application side of the IoT.
This includes real-world examples of industrial and enterprise IoT solutions already causing disruptive changes to numerous business segments and addressing previously unsolvable problems.
These exciting changes illustrate the true potential the IoT has to help the world become a better place while driving process and profit-performance improvements across business and government environments. Here are some of those scenarios:
Any discussion of the IoT's impact at this point, in 2017, has to include a prime area of growth, the smart city.
Forbes described, for instance, how municipal residents and their governments are seeing the real value the IoT can deliver, in the form of added safety from smart lighting, added convenience from improved transportation and parking systems and cost-savings from on-demand trash pickup and smart meters.
The IoT can also prevent accidents. Southern Company, an Atlanta-based power company, uses an early-warning detection system that issued an alert, for example, when a motor-coupling shim pack was coming loose. Not only would this problem have resulted in lost generation capacity, it would have damaged equipment, costing the company approximately $250,000.
As the primary factor behind the early-warning detection system, the IoT thus helped save the company considerable cost and avoided the tarnishing of its reputation.
Another great example of how the IoT is creating smart cities is the use of smart traffic lights that are helping to alleviate long-standing congestion issues in cities like Pittsburgh. Carnegie Mellon University researcher Stephen Smith has worked to add artificial intelligence to Pittsburgh's traffic signals so those signals can react to traffic conditions in real time.
These smart lights have been shown to decrease travel time by 25 percent, and reduce, by 30 percent, the need for cars to brake, or to idle, by over 40 percent. This research has prompted local administrative approval to add smart technology to more traffic lights and intersections across Pittsburgh.
The way this technology works is that artificial intelligence components learn local traffic patterns and conditions, build a timing plan and utilize predictive analysis to control when lights change. As vehicles become more andmore connected, the IoT will be further leveraged to alter driver behavior and perhaps make "traffic" a thing of the past.
Albuquerque, New Mexico, provides another example of how cities are evolving, as the location for one of the first machine networks. Powered by Ingenu's RPMA technology, this machine network "enables machines and devices to communicate with great efficiency, over great distances, more reliably than any other wireless technology."
This means that municipalities and government agencies will eventually be able to leverage these networks' always-available coverage to build their own IoT solutions. This industrial technology may become the foundation that finally propels wider adoption of the IoT on the enterprise, government and consumer levels.
Predictive analytics coming from the connections that the IoT creates among devices is another IoT factor. Predictive analytics means advantages for cities, businesses and government agencies.
For example, the IoT can be used to identify neighborhoods prone to fires or even crime. The result will be that available resources will be allocated more appropriately to address these problems.
Proactive steps mean problems can be targeted more precisely and their impact lessened. With fewer fires and lower crime rates, the quality of life, level of security and amount of available resources will grow.
One of the most interesting recent applications of predictive analytics has been the mapping of air pollution in order to better understand what its cause and effects are, and whether efforts to stop it are actually working.
Google Earth Outreach, Aclima, the Environmental Defense Fund and engineering researchers at the University of Texas at Austin have worked together on a yearlong project that includes a mobile mapping campaign designed to measure hyperlocal air pollution in Oakland, California.
The project aimed to determine the level of harm by collecting data through the use of IoT-connected vehicles and stationary monitors working together to measure pollution levels by city block and at street level. As Global Newswire explained, the project entailed a complex process driven by an IoT platform:
"The Aclima platform in the cars integrates sensing hardware, data management and computation, quality control, and visualization functions, facilitating extensive, routine measurements. The system continuously streams data to Aclima's cloud-based data processing and storage system where data is aggregated and analyzed. In addition to air quality measurement, the mobile platform digitizes and prepares each air-sample for geospatial visualization through an on-board data management system."
A network management system allowed scientists and engineers at UT Austin and Aclima to monitor conditions in real time, the article noted.
The IoT significantly boosts the effectiveness of technology that monitors and controls air pollution by providing a much more detailed picture, right down to the neighborhood level. In this way, the IoT augurs radical changes in the near future of governments and residents to change their localities' air quality.
Incredible changes on the horizon
Ingenu is just one of many companies confident about what the IoT has to offer. That's why Ingenu has become a leader in using the IoT as part of a nimble platform whose numerous features are furthering the growth of smart cities, the use of predictive analytics and the development of more industrial IoT applications.
DataRPM, meanwhile, is driving the predictive analysis capabilities that deliver numerous real-world examples. The company's Cognitive Predictive Maintenance Platform is helping companies prevent asset failures and breakdowns while increasing inventory and resource optimization, and lowering overall risk, quality and warranty issues.
According to DataRPM, predictive analysis by certain companies has meant that management need not spend as much time analyzing potential issues but instead address only those needs their IoT-powered systems alert them to.
DataRPM says that the result for these companies has been an average 300 percent increase in prediction accuracy on maintenance issues, and a cost savings of approximately $37 million.
Given that level of quantitative and qualitative evidence, then, it's hard to imagine why the Industrial IoT isn't here to stay. In fact, with these types of results, the IoT is sure to become a much larger part of more enterprise, government and business applications.
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