The Internet of Things (IoT) has fans and skeptics. Both have given their own reasons why they may or may not continue. A commitment: issues related to standard practices and security.
However, many people are arguing whether wrist devices, clothes, refrigerators and small computers in the car have really changed our lives, but actual changes have taken place in the business applications of the Internet of Things. of
This includes practical examples of industrial and enterprise IoT solutions that address issues that have caused devastating changes in many business areas and were previously unsolvable.
These exciting changes show that while the Internet of Things is helping to make the world a better place, it can also drive real-world improvements in process and profit performance across the enterprise and government environments. Some of these scenarios are:
The discussion about the impact of the Internet of Things at this time in 2017 should include smart cities, which are key areas of growth.
For example, Forbes is a real way for local residents and governments to improve the security through smart lighting, improve convenience by improving transportation and parking systems, and reduce costs by collecting garbage on demand to provide the Internet of Things. Explains how to view the value of smart meters.
The Internet of Things can also prevent accidents. The Atlanta-based power company Southern Company uses an early warning system that alerts when, for example, the motor coupler shim pack is loose. This problem not only lost power generation capacity, but also damaged the equipment, making the company cost about $250,000.
As a major factor in early warning detection systems, the Internet of Things has thus helped save company costs and avoid a decline in reputation.
Another good example of how to use the Internet of Things to create smart cities is the use of intelligent traffic lights to help alleviate the long-standing traffic congestion problems in cities such as Pittsburgh. Steven Smith, a researcher at Carnegie Mellon University, added artificial intelligence to the Pittsburgh traffic lights so they could react to traffic lights in real time.
These smart lights have been proven to reduce travel time by 25% and reduce the need for car braking or idle speed by more than 40%. The study prompted local governments to approve the addition of smart technology to more traffic lights and intersections throughout Pittsburgh.
The technology works by artificial intelligence components learning local traffic patterns and conditions, creating timing plans and using predictive analytics to control the change time of the lights. As the connection between vehicles becomes more and more tight, the Internet of Things will be further utilized, the driver’s behavior will change, and “traffic” may have become a thing of the past.
Albuquerque, New Mexico, as one of the first machine networks, shows another example of urban development. This machine network, powered by Ingenu’s RPMA technology, “makes machines and devices more reliable than other wireless technologies, enabling long-distance and reliable communication.”
This means that municipalities and government agencies can take advantage of the reach of these networks and ultimately build their own IoT solutions.
This industrial technology may eventually become the basis for the widespread adoption of IoT at the corporate, government and consumer levels.
The predictive analysis generated by the connection established between the devices in the Internet of Things is another Internet of Things factor. Predictive analytics means bringing benefits to cities, businesses and government agencies.
For example, you can use IoT to identify areas that are prone to fire and crime. As a result, the available resources can be better allocated to solve these problems.
Proactive procedures can help you more accurately target and mitigate impact. With fewer fires and lower crime rates, quality of life, safety levels and the amount of resources available will increase.
One of the most interesting applications of predictive analytics in recent times is air pollution mapping to better understand whether its causes and effects and efforts to stop air pollution really work.
Google Earth Outreach, Aclima, the Environmental Protection Fund and engineering researchers at the University of Texas at Austin collaborated on a one-year project that included mobile mapping to measure ultra-local air pollution in Oakland, California.
The goal of the project is to determine the level of harmfulness by collecting data using vehicles connected to IoT blocks and fixed monitors and measuring pollution levels at the urban and street levels. As explained by the Global News Agency.