Unlocking Iot Stream Data Potential with Adaptive Strategies for Uncertainty

Abstract

Users benefit greatly from the rapid development of technology and the combination of effective methods. IoT uses many sensors to collect massive amounts of data in transportation, agriculture, healthcare, smart grid, smart home, and other applications. IoT applications collect uncertain and inaccurate data due to transmission errors or sensor device variations. The sensing data changes over time, and the dataset's veracity, velocity, and volume uncertainty affect the data stream. This review paper covers effective IoT stream data uncertainty resolution methods. Machine learning, computational intelligence, and natural language processing have also been proposed to address IoT stream data uncertainty. We will also review existing literature on uncertainty in IoT stream data and the various methods used to solve it. In the end, this review paper will survey uncertainty issues in IoT Stream Data to guide researchers.