Real-Time Insights from Connected Devices

Data Acquisition & Sensor Networks

IoT systems rely on a network of sensors collecting raw data. These sensors can measure temperature, pressure, humidity, location, motion, and more. The accuracy and reliability of the sensor directly impact the quality of the monitored data.

Sensor networks often employ wireless communication protocols like Wi-Fi, Bluetooth Low Energy (BLE), Zigbee, or cellular to transmit data to a central processing unit.

Edge Processing & Data Reduction

Due to bandwidth constraints and latency requirements, raw sensor data is often processed at the ‘edge’ – close to the source. This involves filtering, aggregation, and pre-processing of the data.

Edge devices, such as microcontrollers or dedicated gateways, can perform real-time analytics, reducing the volume of data transmitted to the cloud.

Data Reduction = Raw Data * Compression Factor

Cloud Integration & Storage

Processed data is frequently sent to a cloud platform for long-term storage, advanced analytics, and visualization. Cloud services offer scalability and accessibility.

Popular IoT platforms (e.g., AWS IoT Core, Azure IoT Hub) provide tools for device management, data ingestion, and integration with other enterprise systems.

Anomaly Detection & Alerting

Monitoring systems should be configured to detect anomalies – deviations from expected behavior. This could include unusual temperature spikes or unexpected motion patterns.

Algorithms like statistical process control (SPC) and machine learning models can identify these anomalies in real-time, triggering alerts for immediate action.

Anomaly Score = |Actual Value - Expected Value| / Expected Value

Frequently asked questions

Try it live

Everything above runs in your browser — open Inverse Kinematics (FABRIK) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.