IoT Quality Control for Data and Application Needs

The amount of Internet of Things (IoT) data is growing rapidly. Although there is a growing understanding of the quality of such data at the device and network level, important challenges in interpreting and evaluating the quality at informational and application levels remain to be explored. This article discusses some of these challenges and solutions of IoT systems at the different OSI layers to understand the factors affecting the quality of the overall system. With the help of two IoT-enabled digital health applications, the authors investigate the role of semantics in measuring the data quality of the system, as well as integrating multimodal data for clinical decision support. They also discuss the extension of IoT to the Internet of Everything by including human-in-the-loop to enhance the system accuracy. This paradigm shift through the confluence of sensors and data analytics can lead to accelerated innovation in applications by overcoming the limitations of the current systems, leading to unprecedented opportunities in healthcare.

Authors

• Tanvi Banerjee
• Amit Sheth University of South Carolina

Keywords

• Internet of Things
• Performance evaluation
• Semantics
• Data fusion
• Quality control
• Data Quality
• Decision Support
• Internet Of Things Devices
• Internet Of Things Systems
• World Applications
• Corrupted Data
• Healthcare Opportunities
• Obstructive Pulmonary Disease
• Service Quality
• Mobile App
• Lung Function
• Wearable
• Older Population
• Sensor Data
• Network Layer
• Frame Rate
• Emphysema
• Vital Capacity
• Actual Knowledge
• Depth Camera
• Application Layer
• Smart Home
• Home Systems
• Manifestations Of Aging
• IoT
• semantic data quality for OSI layers
• semantic data fusion for accurate system performance
• IoE inclusion for enhanced system performance
• intelligent systems
• artificial intelligence