A privacy-enhancing and lightweight framework for device-free localization-based AIoT system

1. Device-Free Localization System Model :

This diagram illustrates a DFL system where wireless sensor nodes are deployed around a monitoring area. Each node functions as both a transmitter and receiver, creating a mesh network. The system detects and localizes individuals based on the variations in received signal strength (RSS) caused by their presence.

2. Fundamental Blocks of DFL in IoT Architecture :

This image presents the core components of a DFL system integrated within an IoT framework. It showcases the flow from data sensing using wireless sensor networks (WSNs) to data processing in cloud servers, culminating in user applications. This architecture emphasizes the importance of data communication and processing in achieving accurate localization.

3. Deep Learning-Based DFL Using ZigBee :

This diagram illustrates a DFL system that leverages ZigBee-based wireless sensor networks and deep learning techniques. The system collects RSS data, processes it through a convolutional neural network (CNN), and estimates the target's location without requiring them to carry any device.

4. Privacy-Preserving AIoT Localization Framework :

This framework integrates federated learning and blockchain technologies to enhance privacy in AIoT localization systems. By decentralizing data processing and ensuring secure model updates, the system mitigates privacy risks associated with centralized data storage.

Description :

A privacy-enhancing and lightweight framework for device-free localization-based AIoT systems leverages wireless signals (like Wi-Fi, ZigBee, or RF) to detect and track human presence without requiring individuals to carry any device. By integrating edge AI, lightweight models, and privacy-preserving techniques such as federated learning or differential privacy, the framework ensures real-time location tracking while safeguarding user data and minimizing computational overhead—ideal for smart homes, healthcare, and industrial IoT environments.

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Global Network & Technology Excellence Awards

Monday, May 12, 2025