As the Internet of Things (IoT) gets smarter, the volume of data produced by sensors is steadily increasing. According to International Data Corporation (IDC), by 2025, there will be 41.6 billion connected IoT devices, or “things,” generating 79.4 zettabytes (ZB) of data.
Sensing this approaching data storm, more and more businesses are now adopting Artificial Intelligence (AI) to mine meaningful insights, process data, and further strengthen their IoT applications. Machine learning, a vital part of AI, has now become a robust analytical tool that streamlines data processing.
When AI meets IoT
With a rapid surge of investments, new products and enterprise deployments, AI is opening up new business opportunities in IoT. Businesses are now formulating IoT strategies, evaluating new IoT projects and seeking to get more value from an existing IoT ecosystem like never before.
But how do we maximize the potentials of AI and IoT?
When we combine AI and IoT, we get AIoT—the artificial intelligence of things, a newly coined term which helps businesses achieve more efficient IoT operations, improve human-machine interactions and enhance their data management and analytics capabilities. Simply put, for instance, you can think IoT devices as the digital nervous system, while Artificial Intelligence is the brain that processes information, makes decisions and controls the system.
To shed some clarity, let’s take another example.
When devices such as wearable devices, virtual assistants and sensors interact with other devices over the internet, collect and process data, you have the Internet of Things. On the flip side, when you have AI added to IoT, those devices can analyze data, learn and make decisions based on that data without your assistance. This simply makes them ‘intelligent devices’.
Pairing AI with IoT comes with a wide array of business benefits. It enables organizations to enhance operational efficiency, manage risk more efficiently and roll out new products and services. Let’s dive deep into each one of these.
Enhanced Operational Efficiency
In an IoT environment, Machine Learning can effectively predict equipment failure. It can predict anomalies and identify parameters that need to be adjusted to maintain seamless and optimum outcome. This is achieved by analyzing constant, real-time data streams to detect patterns that the human eye fails to notice.
Efficient Risk Management
The AI-IoT duopoly is helping companies better understand, predict and mitigate a variety of risks involved in operating a variety of equipments. Businesses also leverage the power of automation for rapid response, enabling them to focus on and better manage areas such as finance, safe working environment, and cyber threats.
Release of New Products and Services
AI powered IoT devices unlock endless possibilities for businesses as well as humans. With rapid advancements in Natural language processing (NLP), people now speak with machines, rather than requiring a human operator to get their issues resolved.
Amazon Alexa is a great example. It is capable of voice interaction, music playback, making to-do lists, setting alarms, providing real-time information, and more. Alexa can also control several smart devices, making itself an efficient home automation system. In addition, AI controlled drones can bring new opportunities in surveillance that didn’t exist before.
Let’s dive deep into some of the most common, yet practical applications of AI-powered IoT.
One of the latest applications of AIoT in agriculture is an intelligent insect pest monitoring system. By keeping track of the population density of insect pests in a farm, data driven strategies can be developed for implementing Integrated Pest Management (IPM).
This can be achieved by deploying insect wireless imaging, and environmental sensor nodes. Each node includes an embedded system, RGB camera, temperature-humidity sensor and light intensity sensor.
Smart Traffic Management
Traffic monitoring by drones is an innovative application of AI powered IoT. In major cities, drones can monitor traffic in real-time and make adjustments to the traffic flow. This can help reduce congestion during peak hours.
Drones, when deployed, monitors a large area and transmits real-time traffic data. AI analyzes this data and adjusts the speed limits and traffic light timings accordingly. AI makes decisions without any human intervention.
Smart Office Surveillance, Access Control
Most companies these days choose to install a network of smart sensors in their office premises. These sensors detect the number of persons present in a particular area and adjust the temperatures and lighting accordingly to improve energy efficiency.
Facial recognition, powered by a series of connected cameras and AI, is another technology used for access control and surveillance. Images are taken and compared against a database in real-time to determine who should be granted access to a building. This can also be applied for employee attendance, to make it a hassle-free process.
The Way Ahead for AIoT
In terms of exploring the practical applications of AI in IoT, we have just managed to touch the tip of an iceberg. Gartner pinpoints – by 2022, more than 80% of enterprise IoT projects will have an AI component. In years to come, businesses are expected to evolve further by leveraging the widespread availability of smart devices, application of AI and by solving specific business problems.