Datenschutzerklärung|Data Privacy
Impressum

02.03.2016
A. Borusan

15.03.2016, 16 Uhr c.t. TU Berlin, EN building, seminar room EN 719 (7th floor), Einsteinufer 17, 10587 Berlin: "Low-Cost Adaptive Monitoring Techniques for the Internet of Things" (Dimitris Trihinas, PhD Candidate, University of Cyprus)

Sensors and actuators with “smart” processing capabilities embedded in battery-powered and internet-enabled physical devices are becoming the tools for understanding the complexity of the global inter-connected world we inhabit. The Internet of Things (IoT) churns tremendous amounts of data with continuous data streams flooding from devices scattered across multiple locations to the processing engines of almost all industry sectors for analysis. However, as the number of “things” surpasses the population of the technology-enabled world, real-time processing while data volume keeps increasing and energy-efficiency are great challenges of the big data era transitioning to IoT. In this talk, we introduce a lightweight adaptive monitoring framework suitable for smart battery-powered IoT devices with limited processing capabilities. Our framework, 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. By accomplishing this, energy consumption and data volume are reduced, allowing IoT devices to preserve battery and ease processing on data engines. Experiments on real-world data from physical servers, internet security services, wearables and intelligent transportation systems, show that our framework achieves a balance between efficiency and accuracy. Specifically, our framework is capable of reducing data volume by 74%, energy consumption by at least 71%, while preserving a greater than 89% accuracy.