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.
References:
[1] DFKI Intelligent Analytics for Massive Data Group, https://bit.ly/2LKoY4Y.
[2] DEBS 2020, https://2020.debs.org/
[3] "A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things," Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl, https://bit.ly/3gEAZa6