Edge Computing in IoT: Real-Time Anomaly Detection

By - Sparsha U May 15, 2025 5 Minutes

The Internet of Things (IoT) has ushered in an era where billions of devices generate continuous streams of data—from temperature sensors in factories to smart meters in homes. However, sending all this data to the cloud for processing introduces latency, increases bandwidth consumption, and raises security concerns.

This is where Edge Computing transforms the game.

What is Edge Computing in IoT?

Edge computing refers to processing data at or near the source instead of relying on the cloud. This enables faster, more secure, and decentralized decision-making. Specifically, edge computing allows IoT devices to perform computation locally, resulting in:

• Reduced Latency – Enables immediate responses without round-trip delays to the cloud
• Improved Bandwidth Efficiency – Minimizes data transmitted over the internet
• Enhanced Data Privacy – Keeps sensitive data local
• Greater Resilience – Operates even without internet connectivity

Devices such as microcontrollers, Raspberry Pi, and edge gateways are commonly used for deploying edge solutions.

A Real-World Use Case: Anomaly Detection on the Edge

Consider an industrial motor that heats up during operation. Continuous temperature monitoring is essential to prevent overheating. By connecting a temperature sensor to a Raspberry Pi, you can create an intelligent edge system that performs real-time anomaly detection.

This setup monitors temperature continuously, detects unusual patterns—like sudden spikes—and triggers instant alerts, all without cloud dependency.

System Overview

The core components of this system are simple and accessible:

• A temperature sensor (e.g., DHT11) for real-time data collection
• A Raspberry Pi that handles data acquisition, processing, and alerting
• A TinyML model that learns normal temperature behavior and detects anomalies
• Python scripts to manage data handling, inference, and local alerting or visualization

How It Works

The Raspberry Pi reads temperature values from the sensor at regular intervals. During a brief training phase, the system learns what constitutes “normal” temperature behavior. Models like Isolation Forest or Support Vector Machines (SVM) can be trained directly on the Raspberry Pi.

Once trained, the model is deployed locally. Each new temperature reading is evaluated in real-time. If an anomaly is detected, the Raspberry Pi can trigger alerts—such as activating a buzzer, sending an MQTT message, or logging the event—all without sending data to the cloud.

Why This Is a True Edge + TinyML Application

This setup exemplifies a modern edge AI solution:

• Edge Computing: All processing happens on the Raspberry Pi.
• Real-Time Operation: Instant inference and alerting.
• TinyML: A compact model runs efficiently on a low-power device.
• Offline Capability: Operates fully without an internet connection.

This makes it ideal for environments like factories, farms, or remote locations where connectivity is unreliable and real-time responsiveness is crucial.

Key Benefits of Edge-Based Anomaly Detection

• Immediate fault detection without cloud-related delays
• Enhanced privacy, as sensitive data stays on-site
• Cost savings from reduced data transmission and storage
• Operational continuity, even when offline

Conclusion

Edge computing has evolved from a futuristic idea into a practical reality in modern IoT development. With tools like the Raspberry Pi and lightweight ML frameworks, developers can build responsive, intelligent, and privacy-conscious systems.

Anomaly detection using a temperature sensor is just one example. Similar approaches can be used for vibration analysis, audio-based leak detection, or image classification—executed directly at the data source.

As IoT and AI continue to converge, edge computing will play a pivotal role in designing, deploying, and managing intelligent systems.