Imagine an industrial machine that can detect an anomaly before a breakdown occurs, or a healthcare device that can analyse data without continuously sending it to a remote server.
These applications are made possible in part by Edge AI, a technology that combines artificial intelligence and edge computing to process information close to where it is generated.
Traditionally, many digital systems send data to the cloud for processing and then receive a response. With Edge AI, however, some of that analysis can take place directly on devices or local computing systems.
This approach creates new opportunities for the Internet of Things (IoT), industrial automation, and technology research.
But how does it work in practice? What benefits does it offer, and where can it be applied?
Table of contents
What Is Edge AI and How Does It Work?
Edge AI, short for Edge Artificial Intelligence, is a technological approach that enables artificial intelligence algorithms to run directly on devices at the edge of a network or on computing infrastructure close to the data source.
The term edge refers to the point where information is generated, for example by sensors, cameras, machinery, or other connected devices.
To understand how Edge AI works, consider four steps:
- data collection: sensors and devices gather information from their surroundings;
- local processing: a processor or edge system analyzes the data without necessarily sending it to the cloud;
- application of artificial intelligence: machine learning algorithms identify anomalies, classify information, or generate predictions;
- application of artificial intelligence: machine learning algorithms identify anomalies, classify information, or generate predictions;
One example is a smart camera that can recognize specific objects using a locally installed computer vision model.
In this case, the system does not need to continuously transmit every image to a remote server to perform recognition.
Edge AI therefore brings AI processing capabilities closer to the physical world.
Edge AI vs. Cloud Computing: What Are the Differences?
To understand the potential of AI on the edge, it helps to compare it with cloud computing.
In cloud computing, data is generally transferred to remote infrastructure that provides computing power, storage, and software services.
With Edge AI, by contrast, at least some processing takes place close to the source of the information.
The main differences concern:
- processing location: in the cloud, processing primarily occurs on remote servers; with Edge AI, it takes place directly on devices or local nodes;
- latency: local processing can reduce the time needed to obtain a response;
- connectivity: some functions can continue operating even with limited connectivity;
- data transmission: Edge AI can reduce the amount of information sent over the network;
- available resources: the cloud generally offers greater computing capacity, while edge devices face hardware and energy constraints.
The two technologies are not necessarily alternatives.
A system can use the cloud to train complex models and then deploy them to edge devices, where they analyse new data.
This integration underpins the so-called Cloud-Edge-IoT continuum, a model that combines distributed computing resources.
To learn more about cloud computing, read the PMF article: Cloud computing: what it is and how it works (3 well-known examples)
What Are the Benefits of Edge AI?
Integrating artificial intelligence and IoT makes it possible to design systems that analyze information and support decisions directly in the field.
The main benefits of Edge AI include:
- reduced latency: local processing can speed up responses from intelligent systems;
- lower network usage: not all data needs to be continuously transferred to remote infrastructure;
- lower network usage: not all data needs to be continuously transferred to remote infrastructure;
- better data control: information can be processed locally, reducing the need to transfer it externally;
- support for automation: systems can recognize events and trigger responses based on their analysis;
- scalability of IoT applications: distributing some processing can help manage networks with large numbers of devices.
These benefits depend on the architecture used. Edge AI also requires careful attention to cybersecurity, device maintenance, and updates to AI models.
Where Is Edge AI Used? 5 Real-World Applications
Edge AI can be used in a variety of settings where data needs to be analyzed quickly or distributed smart devices are required.
1. Industry 4.0 and Predictive Maintenance
In industrial facilities, sensors installed on machinery can detect vibrations, temperatures, and other operating parameters.
AI algorithms used in IoT can analyze this information to identify unusual behavior and flag potential maintenance needs.
2. Smart Cities and Intelligent Mobility
Smart cities use connected systems to monitor traffic, manage infrastructure, and improve certain public services.
Edge AI can support local analysis of traffic flows and the detection of situations requiring intervention.
3. Digital Health and Medical Devices
Some healthcare devices can use local algorithms to process signals and physiological parameters.
This technology can support the development of smart monitoring tools, provided that applicable requirements for safety, reliability, and medical devices are met.
4. Precision Agriculture
Environmental sensors and smart devices can collect information about soil moisture, temperature, and crop conditions.
Local data processing can support smart irrigation systems and agricultural monitoring activities.
5. Environmental Monitoring
Edge AI can be integrated into sensor networks and observation tools to identify changes in environmental parameters.
For example, a system can detect anomalies in measurements and send only the most relevant information to a central platform.
These applications show how real-time data processing can contribute to the development of technology solutions across different sectors.
Edge AI and the Internet of Things: The Role of European Research
The integration of Edge AI, edge computing, and the Internet of Things is an important area of research for the European Union.
The European Commission promotes the development of the Cloud-Edge-IoT continuum to foster distributed, interoperable, and trustworthy digital infrastructure.
In particular, European research focuses on integrating computing capabilities, connectivity, artificial intelligence, and cybersecurity.
One example is the European project SovereignEdge.Cognit, which focuses on developing intelligent infrastructure to distribute and manage applications across the cloud-edge continuum.
This approach aims to make computing resources more flexible by allowing decisions about where data is processed to be based on application needs.
The evolution of these technologies creates opportunities for businesses, universities, and research centers involved in designing new digital systems.
Source: European Commission – Investing in Cloud, Edge and the Internet of Things
Further reading: CORDIS – Progressing towards the next-generation cloud-edge-IoT continuum
PMF: Research and Development at the Intersection of AI and IoT
The evolution of Edge AI highlights the importance of bringing together different areas of expertise when designing innovative technology solutions.
This is where PMF comes in: an R&D center active in Information and Communication Technology since 2003.
The company works across several technology areas, including artificial intelligence, the Internet of Things, big data, blockchain, and augmented and virtual reality.
Combining these capabilities is particularly relevant to developing applications that intelligently analyze information collected by connected devices and systems.
PMF’s experience includes PKU Smart Sensor, a project focused on developing and validating a point-of-care system for monitoring phenylalanine levels at home.
Although it is not presented as an Edge AI application, the project illustrates the company’s interest in integrating digital technologies, sensors, and monitoring tools.
A multidisciplinary research approach provides an important foundation for exploring further applications of artificial intelligence in IoT systems.
To explore the technologies behind these systems, you can also read the JOurnal article on neural networks and their use in research projects.
What Does the Future Hold for Edge AI?
As smart devices and connected systems become more widespread, finding efficient data processing solutions is increasingly important.
Edge AI offers one possible response to this need by distributing certain artificial intelligence functions closer to where they are needed.
Key challenges ahead include improving device energy efficiency, protecting information, ensuring system interoperability, and managing software updates.
Technology research will be essential to developing AI models that are increasingly efficient and adaptable to available resources.
The goal is not to replace the cloud, but to build infrastructure in which centralized and distributed processing work together.
Frequently Asked Questions About Edge AI
What Is Edge AI?
Edge AI is an approach that enables artificial intelligence algorithms to run directly on devices or infrastructure close to the data source, reducing the need to continuously send information to the cloud.
What Is the Difference Between Edge AI and Edge Computing?
Edge computing processes data close to where it originates. Edge AI uses this approach specifically to run artificial intelligence algorithms and models.
Can Edge AI Work Without an Internet Connection?
Some applications can operate without a continuous connection as long as the required models and resources are available locally. Updates, synchronization, and other functions may still require Internet access.
Which Sectors Use Edge AI?
The main applications include industry, intelligent mobility, digital health, precision agriculture, and environmental monitoring.
PMF: Turning Technology Research into Innovation
Edge AI shows how integrating artificial intelligence, data analytics, and the Internet of Things can open new opportunities for digital innovation.
Developing advanced technology solutions requires specialized expertise, research, and collaboration between organizations.
PMF participates in national and international research and development projects, contributing to innovation in the ICT sector.
Are you looking for a technology partner to develop an innovative project?
Explore PMF’s expertise and contact the team to discuss new opportunities for collaboration.
