User Recommendation for Data Sharing in Social Internet of Things

Area of Science:

• Computer Science
• Data Science
• Social Computing

Background:

• The Social Internet of Things (SIoT) integrates Internet of Things (IoT) devices with social networks, necessitating robust data exchange mechanisms.
• Establishing relationships between IoT devices and users is crucial for effective data sharing in SIoT environments.

Purpose of the Study:

• To propose a novel user recommendation scheme for facilitating data sharing in SIoT.
• To analyze interrelations between users and between users and IoT devices to enhance data sharing efficiency.

Main Methods:

• Analyzing user-device interactions to determine IoT device preferences.
• Analyzing user-user interactions to identify interest keywords.
• Calculating user similarity based on device preferences and interest keywords.
• Recommending top-N similar users for data sharing.

Main Results:

• The proposed scheme effectively identifies relevant users for data sharing.
• Performance evaluation using precision, recall, and F-measure validates the scheme's effectiveness.

Conclusions:

• The developed user recommendation scheme enhances data sharing in SIoT by leveraging user-device and user-user interaction analysis.
• The approach provides a scalable and effective method for connecting users with similar interests and device preferences for collaborative data exchange.