Comprehensive Survey of Sensor Data Verification in Internet of Things

Abstract

The Internet of Things (IoT) has been a critical and emerging technology platform enabling the development and deployment of smart devices to solve real-world challenges and issues. In addition, IoT applications have gathered various and numerous data from various sources, such as sensor data from IoT devices, system data, status information of IoT devices, and others. However, the quality of sensor data should be thoroughly verified to exploit the benefits of sensor data fully. Due to the importance of sensor data quality, we comprehensively provide a survey on the verification of IoT sensor data in this paper. Thus, we conduct a survey of sensor data verification in IoT regarding six aspects. We focus on: 1) anomaly classification; 2) sensor data verification frameworks; 3) sensor data verification methods (including anomaly detection and anomaly correction); 4) evaluation methods to assess sensor data verification methods; 5) technology and tools to build sensor data verification systems; and 6) challenges and future research directions. In addition, we identify advantages and disadvantages of the methods in anomaly detection and correction. Hence, this survey provides a comprehensive and better understanding of sensor data verification in IoT, building background knowledge in related topics.

Keywords

• Anomaly classification
• anomaly detection
• big sensor data
• decision making
• faulty sensor data
• Internet of Things