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The Department of Computer Science at the University of Cyprus cordially invites you to the Seminar entitled:

AdaM: an Adaptive Monitoring Framework for Sampling and Filtering on IoT Devices

Speaker: Mr. Demetris Trihinas
Affiliation: University of Cyprus, Cyprus
Category: Seminar
Location: Room 148, Faculty of Pure and Applied Sciences (FST-01), 1 University Avenue, 2109 Nicosia, Cyprus (directions)
Date: Friday, October 23, 2015
Time: 15:00-16:00 EET
Host: Marios Dikaiakos (mdd-AT-cs.ucy.ac.cy)
URL: https://www.cs.ucy.ac.cy/colloquium/presentations.php#cs.ucy.pres.2015.trihinas

Abstract:
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 talk, we introduce AdaM, a lightweight adaptive monitoring framework capable of supporting data streaming engines running even on 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 metric evolution and variability of the metric stream. By accomplishing this, energy consumption and the amount of data generated is reduced, allowing the IoT device to preserve battery and ease processing at the base station or a central endpoint, while still preserving data accuracy. To achieve this, AdaM incorporates two algorithms, one for adaptive sampling and one for adaptive filtering. Both algorithms provide one-step ahead estimations, adjusting the sampling rate and the filter range based on the confidence of each algorithm to correctly estimate what will happen next in the metric stream. Specific consideration is taken such that our algorithms immediately identify abrupt transient changes in the metric evolution. Most importantly, AdaM runs on the source device without any additional communication to a central management endpoint and excessive profiling to determine framework parameters. In this paper, we present a thorough evaluation of AdaM by comparing it to other IoT adaptive techniques, with a testbed that utilizes publicly available real-world datasets. Results show, that AdaM is capable of reducing data volume by 74%, energy consumption by at least 71% while preserving a greater than 89% accuracy.

Short Bio:
Demetris is a Computer Science PhD Candidate at the University of Cyprus. He additionally holds a Computer Science MSc from the University of Cyprus and a Dipl-Ing in Electrical and Computer Engineering from the National Technical University of Athens (NTUA). Demetris lives, breathes and thinks in the elastic “cloud” and is always open for a challenge to tweak the performance and scalability of distributed systems and apps. He is the person behind the JCatascopia cloud monitoring system and is also a member of the Cloud Application Management Framework which is now an official Eclipse IDE project. His work is published in IEEE/ACM conferences such as CCGrid, BigData, ICSOC, ICWE and EuroPar.

  Other Presentations Web: https://www.cs.ucy.ac.cy/colloquium/presentations.php
  Colloquia Web: https://www.cs.ucy.ac.cy/colloquium/
  Calendar: https://www.cs.ucy.ac.cy/colloquium/schedule/cs.ucy.pres.2015.trihinas.ics