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A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things

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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 re-configure 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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Dates and versions

hal-02615507 , version 1 (22-05-2020)
hal-02615507 , version 2 (02-06-2020)
hal-02615507 , version 3 (28-07-2020)

Identifiers

  • HAL Id : hal-02615507 , version 3

Cite

Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl. A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things. DEBS '20: 14th ACM International Conference on Distributed and Event-Based Systems, Jul 2020, Montreal, Canada. ⟨hal-02615507v3⟩
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