What are the application scenarios after the integration of artificial intelligence and the Internet of Things?

The technology trends of artificial intelligence (AI) and the Internet of Things (IoT) have begun to merge, and the industry has named this trend artificial intelligence Internet of Things (AIoT). Artificial intelligence moves from the cloud to the edge, providing solutions to the bandwidth and security issues that have hindered wider adoption of IoT in key markets. If the history of technology development is a reliable guide to the future, there are at least two more phases of this convergence to take place over the next few years.

#The Internet of Things has attracted a lot of interest recently, but for many applications, two important questions arise. One is security; the data flowing through the network from IoT devices and control over the devices themselves rely heavily on adequate security from cyberattacks. As threats continue to evolve and become more intense, security requires IoT developers to continually increase vigilance and mitigation. At the same time, many potential users are put off using IoT technology due to uncertainty about the security of systems and data.

The second issue limiting IoT adoption is the bandwidth required to send data to the cloud for processing. As the number of installed devices increases and the amount of data involved increases, IoT deployments are being constrained by the bandwidth resources and costs involved in data collection. This becomes even more concerning as AI becomes an increasingly important element in extracting value from all data.

The importance of artificial intelligence in data processing has grown significantly as traditional data processing techniques become increasingly cumbersome. Developing and coding efficient algorithms to extract useful information from large amounts of data requires time and application expertise that many potential users lack. It can also result in software that is brittle and difficult to maintain and modify as requirements change. Artificial intelligence, specifically machine learning (ML), allows processors to develop their own algorithms based on training to achieve desired results, rather than relying on expert analysis and software development. Furthermore, with additional training, AI algorithms can easily adapt to new requirements.

The latest trend in moving artificial intelligence to the edge is to bring these two technologies together. Extracting information from IoT data currently mainly occurs in the cloud, but if most or all of the information can be extracted locally, bandwidth and security issues are less important. With artificial intelligence running in IoT devices, there is little need to send large amounts of raw data over the network; only concise conclusions need to be communicated. With less communication traffic, network security is easier to enhance and maintain. Local AI can even help improve device security by inspecting incoming traffic for signs of tampering.

Predictive maintenance of industrial machinery is an application where the convergence of AI and IoT will continue to evolve.

AIoT appears to be following a development path similar to how microprocessors evolved in the 1980s. Processing begins with separate devices that handle different tasks: general-purpose processors, memories, serial interface peripherals, parallel interface peripherals, etc. These eventually integrated device tasks into single-chip microcontrollers, which then evolved into dedicated microcontrollers for specific applications. AIoT looks to follow the same path.

Currently, AIoT designs use processors supplemented by general-purpose AI acceleration and AI middleware. Processors equipped with AI acceleration are also beginning to appear. If history is to repeat itself, the next phase of AIoT will be the evolution of AI-enhanced processors tailored for specific applications.

For a custom device to be economically viable, it needs to meet the common needs of a range of subject-related applications. Such applications are already starting to become visible. One such topic is predictive maintenance. Artificial intelligence combined with IoT sensors on industrial machinery is helping users identify abnormal patterns in vibration and current draw that are precursors to equipment failure. The benefits of placing AI local to sensor devices include reduced data bandwidth and latency, as well as the ability to isolate device responses from its network connection. Dedicated predictive maintenance AIoT devices will serve a huge market.

The second topic is voice control. The popularity of voice assistants like Siri and Alexa has prompted consumers to demand voice control capabilities in a variety of devices. Dedicated voice-controlled AIoT devices will help resolve bandwidth and latency issues and help ensure functionality during unstable connections. Today, the number of potential uses for such a device is staggering.

There are other potential topics to address with specialized AIoT devices. Environmental sensing for industrial safety and building management is one of them. Chemical process control is another issue. Self-driving car systems are the third. The fourth type is a camera that identifies specific targets. No doubt there will be more to come.

Artificial intelligence technology appears to be here to stay, and the next step forward – as with processing technology – will be the development of specialized equipment for key markets. In addition to this, the industry is most likely to develop configurable AI accelerators that can be customized according to their applications, so that the benefits of AIoT can effectively reach more and smaller markets.

There are still many technical challenges to overcome. Device size and power consumption have always been fringe issues, and AI needs to do more to solve them. When using AI, development tools can do more to simplify the application development work. Developers need to learn more about artificial intelligence as an alternative approach to app development. But if history is any guide, these challenges will soon be overcome.

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