Low-Cost Adaptive Monitoring Techniques for the Internet of Things

Demetris Trihinas, George Pallis, Marios Dikaiakos

Research output: Contribution to journalArticlepeer-review

Abstract

Internet-enabled physical devices with "smart" processing capabilities 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 flooding from devices scattered across multiple locations to the processing engines of almost all industry sectors. However, as the number of "things" surpasses the population of the technology-enabled world, real-time processing and energy-efficiency are great challenges of the big data era transitioning to IoT. In this article, we introduce a lightweight adaptive monitoring framework suitable for smart 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 monitoring stream. By accomplishing this, energy consumption and data volume are reduced, allowing IoT devices to preserve battery and ease processing on cloud computing and big data services. Experiments on real-world data from cloud services, internet security services, wearables and intelligent transportation services, show that our framework achieves a balance between efficiency and accuracy. Specifically, our framework reduces data volume by 74%, energy consumption by at least 71%, while achieving a greater than 89% accuracy.

Original languageEnglish
JournalIEEE Transactions on Services Computing
DOIs
Publication statusAccepted/In press - 22 Feb 2018
Externally publishedYes

Keywords

  • Adaptation models
  • Adaptive Monitoring
  • Adaptivity
  • Big Data
  • Cloud computing
  • Edge Computing
  • Energy consumption
  • Filtering
  • Internet of Things
  • Measurement
  • Monitoring
  • Runtime
  • Sampling

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