The Internet of Things (IoT) is made up of interconnected devices that may be found in a variety of situations, including industrial, medical, consumer/home, transportation, and more. As IoT gets more popular and extensively used, the number of possible IoT devices grows exponentially. According to Statista, the world's linked gadgets number over 21.5 billion.
IoT devices, on the other hand, are frequently recognised for having basic, one-time functionalities. For example, a sensor on a manufacturing floor machine that records and transmits data from the machine.
While it is effective, it just does a single duty and adds no extra intellect.
Artificial intelligence (AI) can help IoT devices such as cameras, medical sensors, smart security systems, drones, and other gadgets become more intelligent and intuitive by gathering and analysing data, making choices, and then taking action.
The problem is that AI requires a lot of processing power, memory, and energy to do its sophisticated analyses and make these judgments. While this is good in a data centre with plenty of computing power, it has its drawbacks.
If the AI is housed in a data centre, the cloud and the solution must be connected to the internet, which means there may be a delay in the decision-making process as data goes from the device to the cloud and back. There may also be privacy concerns if the data is sensitive. To really unleash the next frontier of IoT, we must shift the AI decision-making process from the data centre to the device itself, allowing choices to be made instantaneously where the action occurs – what we refer to as the "edge." So, how can we bring AI to the edge, where it can be used to power additional IoT applications?
There are new platforms available today that can alter AI, especially deep neural networks (DNNs), to make it smaller, quicker, and less power-hungry than before. AI teams may focus on training their models for accuracy, or improved decision making, utilising such platforms, and then optimising the AI model so it can be deployed into limited hardware at the edge – all without losing the original accuracy. In other words, we must integrate AI into the actual world rather than the other way around.
Smart technology is becoming more prevalent in the transportation business.
Computer vision, or the use of cameras to gather picture data, is a fantastic illustration of how AI may be used at the edge of the IoT. Mini cameras may be used in a variety of situations and purposes. Trucking businesses, for example, are already using dashboard cameras to improve speed control and safety. If the truck exceeds the posted speed limit, the camera detects it and alerts the driver, as well as triggering a braking mechanism in the vehicle.
However, converting an AI model that was initially too big and impossible to operate on the CPUs already installed in (customers') vehicles posed a difficulty. Once the AI was converted into a smaller, lighter, and less power-hungry form factor, it was possible to use it on a speed control system, allowing trucking fleets to use it on a large scale. This sort of AI-driven smart camera technology might play a crucial part in the future of transportation as the transportation sector advances toward increasing usage of smart, technology-assisted, and even autonomous cars.
The AI system can operate 24 hours a day, seven days a week.
Another fantastic example is smart manufacturing, which uses the Internet of Things to allow automated factories that rely less on human inspections. Deep learning AI models may be utilised in a variety of categorization, detection, and segmentation applications, including automated visual inspection, defect identification, and more. This can assist a manufacturing business in scanning product samples for flaws using AI-driven smart cameras that have been taught to recognise both good and bad product samples and highlight them right away. This sort of AI system can operate 24 hours a day, seven days a week without getting tired or making mistakes, and it can prevent faulty items from ever making it to market.
Overall, (AI) optimization is the key to making AI on the edge a reality for connected devices in the IoT. Deep Neural Networks are becoming faster, smaller, and more energy efficient as a result of new AI-driven optimization advances, allowing AI to move from the cloud and data centres to the edge. This might open up huge potential for IoT systems, allowing linked devices to become smarter and more successful in a variety of areas, ultimately improving our daily lives.