Future Outlook of IoT

Machine learning is a key factor in the development of IoT because the large amount of structured and unstructured data generated by IoT devices cannot be managed under human control. Therefore, data can be collected and streamlined through machine learning algorithms to find its true value. This will be achieved on two levels:

The emergence of a large number of IoT devices also brings some problems. This situation makes large-scale management and device monitoring very important, and bottlenecks will be created in the process of using IoT to provide data. In this regard, machine learning mentioned above can solve some problems. In addition to machine learning, technologies such as sensor fusion can also reduce the uncertainty of collected data by fusing different sources. Automatic computing can help devices achieve a higher degree of self-management and reduce cloud-level overhead when processing data provided by potentially billions of devices.

Security is both an area of ​​innovation that can do a lot for the future of the Internet of Things, and a problem. This problem includes not only data security, but also access security and overall management security for a large number of potential endpoint devices. One of the more serious problems brought about by the Internet of Things is that a large number of devices share the same software. Once these software have vulnerabilities, attackers only need to do very little to invade a large number of devices and build a botnet. This can only be addressed through device self-management, that is, the device monitors and protects itself when creating updates.