Real-Time Information Exchange Strategy for Large Data Volumes Based on IoT.

Area of Science:

• Computer Science
• Data Engineering
• Internet of Things

Background:

• The Internet of Things (IoT) generates massive amounts of real-time sensory data.
• Efficient storage and retrieval of this big data are critical for IoT applications.
• Existing methods struggle with the high velocity and volume of micro-sensory data.

Purpose of the Study:

• To propose a novel real-time information exchange strategy for big data in IoT environments.
• To develop an optimized sensory data storage method to improve near real-time data handling.
• To enhance the reliability and security of data transmission in IoT systems.

Main Methods:

• Developed a primitive sensory data storage method (TSBPS) using spatial-temporal chunking preprocessing.
• Implemented a partitioned data model with micro-perceptual and perceptual data layers for storage and query optimization.
• Designed a high-concurrency, TCP-based socket transmission framework with RSA asymmetric encryption and data integrity verification.

Main Results:

• Substantially improved the speed of near real-time storage and writing of micro-sensory data.
• Achieved effective parameter collection, classification, and aggregation processing for diverse devices.
• Ensured reliable and secure data transmission through a custom-designed protocol.

Conclusions:

• The proposed TSBPS method offers significant performance improvements for IoT big data storage.
• The integrated approach enhances the overall efficiency, reliability, and security of IoT data management.
• The convergence of big data and IoT promises advancements in societal intelligence.