AdaM: An adaptive monitoring framework for sampling and filtering on IoT devices
Demetris Trihinas, George Pallis, Marios D. Dikaiakos · 2015
Real-time data processing while the velocity and volume of data generated keep increasing, as well as, energy-efficiency are great challenges of big data streaming which have transitioned to the Internet of Things (IoT) realm. In this paper, we introduce AdaM, a lightweight adaptive monitoring framework for smart battery-powered IoT devices with limited processing capabilities. AdaM, inexpensively and in place dynamically adapts the monitoring intensity and the amount of data disseminated through the network based on the current evolution and variability of the metric stream. Results on real-world testbeds, show that AdaM achieves a balance between efficiency and accuracy. Specifically, AdaM is capable of reducing data volume by 74%, energy consumption by at least 71%, while preserving a greater than 89% accuracy.