A survey of adaptive sampling and filtering algorithms for the internet of things
Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl · 2020
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.