An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Area of Science:

• Computer Science
• Data Science
• Internet of Things

Background:

• The Internet of Things (IoT) enables solutions for smart cities, healthcare, and environmental monitoring.
• Real-time IoT sensor data present challenges including data quality issues and high resource costs.
• Effective data processing, fusion, and analysis are crucial for leveraging IoT data.

Purpose of the Study:

• To provide a comprehensive overview of techniques for processing, fusing, and analyzing IoT sensor data.
• To address the challenges associated with real-time IoT sensor data.
• To explore the integration of IoT data analysis with emerging computing paradigms.

Main Methods:

• Data processing techniques: denoising, outlier detection, missing data imputation, and aggregation.
• Data fusion methods: direct fusion, associated feature extraction, and identity declaration.
• Integration with cloud, fog, and edge computing for IoT sensor data analysis.

Main Results:

• The paper details essential data processing steps to handle unclean sensor data.
• It elaborates on various data fusion strategies to combine information from multiple sources.
• The study discusses the integration of IoT data analysis with advanced computing infrastructures.

Conclusions:

• This work presents a complete overview of IoT sensor data processing, fusion, and analysis techniques.
• Addressing data quality and resource constraints is key for effective IoT applications.
• Integrating IoT data analysis with cloud, fog, and edge computing offers solutions to current challenges.