A paper by data management systems researchers in the Database Systems and Information Management (DIMA) Group at TU Berlin and the Intelligent Analytics for Massive Data (IAM) Group at DFKI (the German Research Institute for Artificial Intelligence) has been accepted for presentation at the 14th ACM International Conference on Distributed and Event-Based Systems (DEBS 2020), 13. – 17. July 2020 in Montreal, Canada.
The paper “A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things”, authored by D. Giouroukis et al. gathers representative, state-of-the-art algorithms to address scalability challenges in real-time and distributed sensor systems. To gather data timely and efficiently, the authors focus on two techniques, namely adaptive sampling and adaptive filtering. The paper outlines current research challenges for the IoT, future research directions, and aims to support researchers in their decision making process when designing distributed sensor systems.
THE PAPER IN DETAIL:
Authors: Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl
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.
Authors: Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl
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.
References:
[1] TU Berlin Database Systems & Information Management Group, https://www.dima.tu-berlin.de/.
[2] DFKI Intelligent Analytics for Massive Data Group, https://bit.ly/2LKoY4Y.
[3] DEBS 2020, https://2020.debs.org/