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A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things
Citation key GiouroukisDTZM20
Author Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl
Year 2020
Journal DEBS
Note A recording of the presentation is available here: https://www.youtube.com/watch?v=He_UmDEgPug

Presentation slides are available here: https://www.redaktion.tu-berlin.de/fileadmin/fg131/Conferences/Presentations/Giouroukis_DEBS-2020.pdf
Abstract The Internet of Things (IoT) represents one of the fastest emerging trends in the area of information and communication technology. The main challenge in the IoT is the timely gathering of data streams from potentially millions of sensors. In particular, those sensors are widely distributed, constantly in transit, highly heterogeneous, and unreliable. To gather data in such a dynamic environment efficiently, two techniques have emerged over the last decade: adaptive sampling and adaptive filtering. These techniques dynamically reconfigure rates and filter thresholds to trade-off data quality against resource utilization. In this paper, we survey representative, state-of-the-art algorithms to address scalability challenges in real-time and distributed sensor systems. To this end, we cover publications from top peer reviewed venues for a period larger than 12 years. For each algorithm, we point out advantages, disadvantages, assumptions, and limitations. Furthermore, we outline current research challenges, future research directions, and aim to support readers in their decision process when designing extremely distributed sensor systems.
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