Framework for Error Detection & its Localization in Sensor Data Stream for reliable big sensor data analytics using Apache Spark Streaming
Govind Prasad Gupta, Jahanvi Khedwal · Procedia Computer Science · 2020
Internet of Things (IoT) is one of the big sources of Big Sensor data in which collected sensor readings often polluted due to corruption and losses of the sensor readings. In Big Sensor data analytics, fast and efficient detection of error and its localization is a challenging research issue. Most of the existing solutions for the error detection and localization in sensor data use offline techniques for detecting errors. In this paper, we propose a novel framework for online error detection and its localization which uses an online scheme for detecting and localizing errors in sensor data using latest big data processing tools such as Apache Spark streaming. Performance evaluation of the proposed framework is done using two different datasets such as real-time air quality dataset of the Raipur city and Intel sensor dataset in terms of false positive.