Edge device fleet management is the process of overseeing and controlling a group of devices operating at the edge of a network, such as sensors, cameras, or smart appliances. It involves tasks like monitoring device health, updating software, configuring settings, and ensuring security across all devices. This management helps organisations keep their devices running smoothly,…
Category: Edge Computing
Hybrid Edge-Cloud Architectures
Hybrid edge-cloud architectures combine local computing at the edge of a network, such as devices or sensors, with powerful processing in central cloud data centres. This setup allows data to be handled quickly and securely close to where it is generated, while still using the cloud for tasks that need more storage or complex analysis….
Edge AI for Industrial IoT
Edge AI for Industrial IoT refers to using artificial intelligence directly on devices and sensors at industrial sites, rather than sending all data to a central server or cloud. This allows machines to analyse information and make decisions instantly, reducing delays and often improving privacy. It is especially useful in factories, warehouses, and energy plants…
Lightweight Model Architectures for Edge Devices
Lightweight model architectures for edge devices are specially designed artificial intelligence models that use less memory, computing power and energy. These models are made to work efficiently on devices like smartphones, sensors and cameras, which have limited resources compared to powerful computers or servers. The goal is to enable AI functions, such as recognising objects…
Prompt Caching at Edge
Prompt caching at edge refers to storing the results of frequently used AI prompts on servers located close to users, known as edge servers. This approach reduces the need to send identical requests to central servers, saving time and network resources. By keeping commonly requested data nearby, users experience faster response times and less delay…
Field Data Logger
A field data logger is an electronic device used to automatically record measurements such as temperature, humidity, or pressure in outdoor or remote environments. It collects data over time without the need for constant human supervision, storing the information for later analysis. Field data loggers are often used in scientific research, agriculture, and environmental monitoring.
Geo-Fencing System
A geo-fencing system is a technology that uses GPS, RFID, Wi-Fi, or mobile data to create a virtual boundary around a specific real-world location. When a device enters or leaves this area, the system can trigger actions like sending alerts, enabling features, or restricting access. Geo-fencing is commonly used for location-based services, security, and automation…
TinyML Deployment Strategies
TinyML deployment strategies refer to the methods and best practices used to run machine learning models on very small, resource-constrained devices such as microcontrollers and sensors. These strategies focus on making models small enough to fit limited memory and efficient enough to run on minimal processing power. They also involve optimising power consumption and ensuring…
Edge Inference Optimization
Edge inference optimisation refers to making artificial intelligence models run more efficiently on devices like smartphones, cameras, or sensors, rather than relying on distant servers. This process involves reducing the size of models, speeding up their response times, and lowering power consumption so they can work well on hardware with limited resources. The goal is…
TinyML Optimization
TinyML optimisation is the process of making machine learning models smaller, faster, and more efficient so they can run on tiny, low-power devices like sensors or microcontrollers. It involves techniques to reduce memory use, improve speed, and lower energy consumption without losing too much accuracy. This lets smart features work on devices that do not…