AIoT: Artificial Intelligence and IoT Transforming the Market
#iot, #iiot, #ai
When AI and IoT join forces, a whole new level of efficiency, performance, and value becomes possible. That level is AIoT, and it’s already being used in many industrial applications today. What are those applications, what future possibilities does it bring to the IoT market, and what does it mean for us in Teltonika Networks?
Most IoT use cases are very similar in structure: a given workflow is currently inefficient in some way, or outright incapable of adapting to Industry 4.0 standards. IoT connectivity allows for efficiency and automation, which are a tried-and-true way of making any slice of the market take a confident step into the world of tomorrow. They are called solutions because that is exactly what they do – solve real, industrial challenges by acting as a transformative technological force that opens up new possibilities for innovation and growth.
We live in an era where another transformative technology is quickly gaining momentum alongside IoT, and is a perfect match to work alongside: artificial intelligence (AI). This term refers to computer systems that can perform problem-solving, learning, pattern recognition, and decision-making using algorithms and adaptive computational models. AI systems often leverage techniques such as machine learning, deep learning, and natural language processing to emulate cognitive functions, allowing them to continuously improve and refine their performance over time.
IoT is great at making sure real-time data is readily available, and AI is great at processing that data. The result is clear: when working as a team, the two technologies create a connected ecosystem capable of catapulting industries into whole new levels of efficiency, performance, and value. That system is AIoT, and if you don’t know about it yet – you should.
Applications of AIoT Today
AI plays an essential role in many present-day IoT applications, optimizing the performance of connected devices and systems across different industries.
One of those industries is smart cities. AI-powered IoT solutions are driving the development of smart cities by enabling the efficient management of urban systems, such as traffic control, waste management, air or water quality, utility billing, and public safety. AI helps analyze data collected from sensors and devices, providing real-time insights for optimizing city operations and enhancing the quality of life of you and me. This market is predicted to grow from $648 billion in 2020 to over $6.06 trillion in 2030, at a CAGR of 25.2%.
The energy sector has also been making great use of AIoT, adopting the technology in different ways. AIoT makes smart grids more efficient and resilient, which can optimize electricity distribution, reduce power outages, and improve the overall efficiency of energy systems. This includes optimizing the integration of renewable energy sources like solar and wind power into the grid, as AI algorithms can predict fluctuations in renewable energy production based on weather data – and adjust grid operations accordingly. AIoT also allows for predicting consumer energy consumption patterns and improving energy storage systems management, which helps ensure optimized grid operations.
Another industry currently benefiting from AIoT is agriculture, primarily in the form of precision farming. Heralded as “the next agricultural revolution”, precision farming takes advantage of the proliferation of IoT tools and devices already used in agriculture and yields optimization out of them. For example, IoT sensors collect data on soil moisture, temperature, and nutrient levels, allowing AI algorithms to determine the optimal time for planting, watering, and harvesting crops.
Applications of AIoT Tomorrow
Despite all of these applications, the integration of AI and IoT is still in its early stages and has numerous opportunities for further growth and development. One such opportunity is Edge AI – a combination of edge computing and artificial intelligence. In a nutshell, edge computing is an emerging computing paradigm in which data processing is performed closer to where the data is generated – the “edge” of a network of smart devices rather than the cloud server – resulting in greater processing speeds and volumes.
With Edge AI, artificial intelligence is built into these networks, allowing for AI processing to take place on the edge. This allows the devices to make decisions in milliseconds, based on the data they collected themselves and machine learning. In practice, this technology is expected to take predictive maintenance to the next level, ensuring that localized and isolated IoT networks can predict their own equipment failure and act to prevent it. This can be the network of smart homes and factories, self-driving vehicles, energy grids, AI-powered healthcare solutions, and more.
The move away from cloud computing towards edge computing also tackles one of the issues with cloud-based solutions: security. Nearly 50% of all data breaches in 2022 took place in the cloud, with the average data breach cost being $4.24 million for private clouds and $5.02 million for public ones. However, if data is processed on the edge instead of on the cloud, it can be automatically deleted, partially or completely, without ever leaving the local network.
Apart from cybersecurity, physical security using video surveillance will also be augmented. Smart security systems could process data in real-time to detect security threats by detecting abnormal behaviors, and access systems will have more effective ways to identify an individual via several parameters at the same time.
The Teltonika Networks Perspective
A lot of this may sound very theoretical to some, but at Teltonika Networks, we observe the evolution of AIoT with great care. According to our Head of R&D, Mantas Čižauskas, while the uses for AI in the IoT market are numerous and promise a great deal of value, there is an equally great likelihood that this technology will be used for more shady purposes. According to him, “things like data analysis for the purpose of user profiling, personalized advertisement, and citizen surveillance would indeed require far less resources, but that would make them used more widely.” The question becomes not what the data is capable of, but who has access to data, and what do they choose to do with it.
But he also points out that there is a silver lining. “AI can make the complicated network setup process easier for users. AI can help quickly and correctly configure a network, and even opens up the door to dynamic configuration.” In terms of products, RMS would see the most amount of benefit from these technologies, as “the number of connected devices and amount of data being collected make it a fertile ground for AIoT-powered innovation and growth.” He concluded that at the very least, “a growing use of AI in the market would only push us to further enhance the security of Teltonika Networks products.