Abstract
The Internet of Things (IoT) has become widespread around the world. Since a large number of diverse devices, such as vehicles, household electrical appliances, smart phones, and environmental sensors are connected to the Internet, we can obtain a large volume of diverse IoT data, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoT big data</i> . The generation of IoT big data means that efficient analytic systems are needed for many application scenarios, for example, to optimize urban planning, solve air pollution problems, and improve business decisions. In this survey, we review current systems that can efficiently analyze IoT data. Existing systems can be categorized into batch and stream processing systems. We explore Hadoop- and Spark-based batch processing systems for spatiotemporal and trajectory data. We also review fog- and edge-aware stream processing systems. Although many existing systems can efficiently and effectively analyze specific data and tasks, no system exists that can handle all characteristics of IoT big data: volume, velocity, variety, veracity, and variability. We present some open issues and discuss the future of IoT big data analytic systems. This survey aims to help researchers and practitioners better understand current systems and develop new IoT big data analytic systems.
Highlights
S INCE Kevin Ashton first proposed the concept of the Internet of Things (IoT) in 1999, a diverse range of devices such as vehicles, smartphones, environmental sensors, and GPS sensors used with animals have been connected to the Internet
Cai et al [15] studied data storage systems in cloud computing for IoT big data. They mainly focused on the functionality of storage systems; by contrast, we focus on analytic systems
We review five stream processing systems that cooperate with fog and edge computing
Summary
Yuya Sasaki, Member, IEEE, Abstract—The Internet of Things (IoT) has become widespread around the world. The generation of IoT big data means that efficient analytic systems are needed for many application scenarios, for example, to optimize urban planning, solve air pollution problems, and improve business decisions. In this survey, we review current systems that can efficiently analyze IoT data. Existing systems can be categorized into batch and stream processing systems. We present some open issues and discuss the future of IoT big data analytic systems. This survey aims to help researchers and practitioners better understand current systems and develop new IoT big data analytic systems
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