Date: 2026-07-04 hits: 103
Artificial intelligence may seem distant, but it has already become part of our daily lives. Many people encounter AI when using voice-to-text assistants on their smartphones or fingerprint recognition applications every day. In IoT applications, AI helps identify patterns in edge devices and detect changes in relevant parameters. These IoT edge devices typically come equipped with sensors that can sense changes in environmental factors such as temperature and pressure.
Usually, simple embedded edge devices collect data via sensors in their environment and transmit this data to the cloud, where AI systems in cloud infrastructure analyze and infer from it. However, as the need for real-time decision-making in IoT implementations grows, so too do the demands for connectivity and data processing. It is not always feasible to send all data to the cloud for AI processing. This paper aims to explore how deploying AI at the edge can enhance IoT operation and implementation efficiency while reducing costs.
Exploring AI in IoT Solutions: Unlocking Infinite Potential
AI technology encompasses various techniques including machine learning, predictive analytics, and neural networks. Data collected from edge devices is labeled and then prepared by data engineers through pipelines for input into data models. These engineers possess specialized skills in creating software solutions around big data. Data scientists, proficient in mathematics, statistics, and programming languages like C and C++, develop AI models using machine learning algorithms fine-tuned for various known applications. These models ultimately take different forms such as neural networks, decision trees, or rule sets.
Machine learning is divided into supervised and unsupervised learning. Unsupervised learning (which provides only input variables without corresponding output variables) helps developers gain deeper insights into data, while supervised learning forms the basis of most practical machine learning. During the training phase of supervised machine learning, large data streams are mined to extract useful patterns or inferences through multiple computations for making predictions.
In the application phase of AI, data collected from edge devices can be input into selected models from available data models using standard frameworks like TensorFlow. The modeling process requires substantial data processing power, typically available at core node locations such as cloud sites and large data centers.
The deployment phase is where things get interesting. For example, edge devices can access software packages related to selected models from a shared resource library without heavy reliance on the cloud. In fields like health monitoring, edge computing benefits wearable devices that require unsupervised machine learning tailored to users. Additionally, custom applications needing rapid inference without prior learning often require significant data processing power, an area where edge AI excels.
In most cases, due to technical or energy constraints, not all data can be transmitted to the cloud where AI resides. Applications such as speech or video recognition require immediate identification and inference of content without communication delays. In some scenarios, deployments may lack stable connectivity, necessitating a scalable hybrid architecture where models are built in the cloud but inference tasks are executed at the edge. This approach only transmits a small amount of data to core node locations, optimizing bandwidth efficiency, reducing latency, and improving response speed.
How to Deploy Edge AI
The basic components of a typical edge AI model include hardware and software for capturing sensor data, software used for training models across different application scenarios, and application software for running AI models on IoT devices. Microservice software running on edge devices is responsible for initiating AI program packages on edge devices based on user requirements. Within edge devices, feature selection and transformation determined during the training phase are utilized. These models can be customized into appropriate functional combinations that can be expanded to include aggregation and engineered features.
Smart edge devices are deployed in battery-powered applications with narrow bandwidth and intermittent network connections. Consequently, edge device manufacturers are building sensors with integrated processing and storage capabilities, utilizing widely adopted low-speed communication protocols such as BLE, LoRa, and NB-IoT, which are compact and low-power.
Empowering IoT with Intelligence: The Advantages of Edge AI
While the complexity of such designs may make edge devices costly, the benefits far outweigh the associated expenses.
Beyond real-time rapid response, edge AI offers numerous significant advantages, such as higher security inherent in edge devices themselves and reduced data transmission between networks. Due to custom-built solutions for each application, edge AI is highly flexible. With inference capabilities pre-installed in edge devices, the requirements for operational and maintenance skills are lower.
In edge computing, developers can offload some complex operations to be executed by edge processors (such as routers, gateways, and servers) within the local network, distributing computing across the entire network. These edge processors offer good operational reliability due to local data storage and locally introduced intelligence, aiding deployment in areas with intermittent or no connectivity.
Generally, solving challenges through building machine learning models is a complex endeavor. Developers must manage vast amounts of model training data, select the best implementable algorithms, and manage cloud services for training models. Then, application developers use programming languages like Python to deploy these models into production environments. Smart edge device manufacturers will find that investing resources to implement AI at the edge from scratch is exceptionally challenging.
However, devices like Avnet's SmartEdge Agile bring good news to smart edge device manufacturers. The SmartEdge Agile IoT device comes equipped with various types of sensors and an integrated AI software stack. Through development platforms and software studios such as Brainium and Microsoft Azure Sphere, users can leverage existing AI algorithm databases to implement supervised and unsupervised machine learning and deploy models to devices without writing any code. They can also create small programs to view sensor values in real time and store this data for future use.
Indeed, AI can add complexity to an already intricate IoT landscape, and edge AI further doubles this complexity. But with the support of appropriate platforms and partners, developers can navigate this complexity and achieve innovations far beyond voice recognition and fingerprint identification.
