Going further to explain What is the internet of things and why does it matter for future ? The Internet of Things also known as IoT isn’t new: tech companies have been discussing the idea for decades, and the first internet-connected toaster was unveiled at a conference in 1989.
At its core, IoT is simple, it’s about connecting devices over the internet, letting them talk to us, applications, and each other. The popular, example is the smart fridge what if your fridge could tell you it was out of milk, texting you if its internal cameras saw there was none left, other example that you can see your garage in real time from internet and if something happen bad or suppose you forgot to close the door then you can close that from internet or can trigger alarm.
IoT is more than smart homes and connected appliances, however. It scales up to include smart cities think about connected traffic signals or smart bins that signal when they need to be emptied – and industry, with connected sensors for everything from tracking parts to monitoring crops.
The Internet of Things is being used in following sectors :-
1. Health care
Smart pills and connected monitoring patches are already available, highlighting the life-saving potential of IoT, and many people are already strapping smartwatches or fitness bands to their wrists to track their steps or heartbeat while on a run.
2. Self driving car and Vehicles
By 2020, there will be 250 million IoT connected cars on the world’s streets, which will cut down on traffic accidents solely due to the multitude of eternally aware sensors and smart apps. Vehicles will avoid collisions by communicating their position on the road to each other. Furthermore, maintaining perfect spacing between vehicles will ease traffic congestion.
Each year, 1.3 million people are killed in traffic accidents. A full 90% of these are caused by human error, which is often sparked by physical and emotional factors such as stress, fatigue, recklessness, or distracted driving. Smart cars will eliminate this human factor. Vehicles connected and controlled by apps and sensors will be permanently alert and responsive to changing conditions.
3. Home Automation
Homes of the 21st century will become more and more self controlled and automated due to the comfort it provides, especially when employed in a private home. A home automation system is a means that allow users to control electric appliances of varying kind. Many existing, well-established home automation systems are based on wired communication.
This does not pose a problem until the system is planned well in advance and installed during the physical construction of the building.
4. Artificial Intelligence(AI)
Artificial Intelligence: The field of artificial intelligence is the study and design of intelligent agents able to perform tasks that require human intelligence, such as visual perception, speech recognition, and decision-making. In order to pass the Turing test, intelligence must be able to reason, represent knowledge, plan, learn, communicate in natural language and integrate all these skills towards a common goal.
Machine Learning: The sub field of machine learning grew out of the effort of building artificial intelligence. Under the “learning” trait of AI, machine learning is the sub field that learns and adapts automatically through experience. It focuses on prediction, based on known properties learned from the training data. The origin of machine learning can be traced back to the development of neural network model and later to the decision tree method. Supervised and unsupervised learning algorithms are used to predict the outcome based on the data.
Data Mining: The field of data mining grew out of Knowledge Discovery in Databases (KDD), where data mining represents the analysis step of the KDD process. Data mining focuses on the discovery of previously unknown properties in the data. It originated from research on efficient algorithm for mining association rules in large databases, which then spurred other research on discovering patterns and more efficient mining algorithms.
Machine learning and data mining overlap in many ways. Data mining uses many machine learning methods, but often with a slightly different goal in mind. The difference between machine learning and data mining is that in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge while in KDD the key task is the discovery of previously unknown knowledge. Unlike machine learning, in KDD, supervised methods cannot be used due to the unavailability of training data.