Performance Evaluation of an AmI Testbed for Improving QoL: Evaluation Using Clustering Approach Considering Parallel Processing

  • Ryoichiro Obukata
  • Tetsuya Oda
  • Donald Elmazi
  • Makoto Ikeda
  • Leonard Barolli
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 2)


Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of k-means clustering algorithm considering sensing data. For evaluation we considered respiratory rate and heart rate metrics. We speeded up the k-means clustering algorithm by using parallel processing.


Cluster Center Parallel Processing Ubiquitous Computing Network Control System Ambient Intelligence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ryoichiro Obukata
    • 1
  • Tetsuya Oda
    • 2
  • Donald Elmazi
    • 1
  • Makoto Ikeda
    • 2
  • Leonard Barolli
    • 2
  1. 1.Graduate School of EngineeringFukuoka Institute of Technology (FIT)Higashi-KuJapan
  2. 2.Department of Information and Communication EngineeringFukuoka Institute of Technology (FIT)Higashi-KuJapan

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