An RSSI-based wall prediction model for residential floor map construction

Xenofon Fafoutis, Evangelos Mellios, Niall Twomey, Tom Diethe, Geoffrey Hilton, Robert Piechocki

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)


In residential environments, floor maps, often required by location-based services, cannot be trivially acquired. Researchers have addressed the problem of automatic floor map construction in indoor environments using various modalities, such as inertial sensors, Radio Frequency (RF) fingerprinting and video cameras. Considering that some of these techniques are unavailable or impractical to implement in residential environments, in this paper, we focus on using RF signals to predict the number of walls between a wearable device and an access point. Using both supervised and unsupervised learning techniques on two data sets; a system-level data set of Bluetooth packets, and measurements on the signal attenuation, we construct wall prediction models that yield up to 91% identification rate. As a proof-of-concept, we also use the wall prediction models to infer the floor plan of a smart home deployment in a real residential environment.

Original languageEnglish
Title of host publication2015 IEEE 2nd World Forum on Internet of Things (WF-IoT 2015)
Subtitle of host publicationProceedings of a meeting held 14-16 December 2015, Milan, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509003655
ISBN (Print)9781509003679
Publication statusPublished - Apr 2016
Event2nd IEEE World Forum on Internet of Things, WF-IoT 2015 - Milan, Italy
Duration: 14 Dec 201516 Dec 2015


Conference2nd IEEE World Forum on Internet of Things, WF-IoT 2015

Structured keywords

  • Digital Health


  • bluetooth low energy
  • floor map construction
  • Wall prediction model
  • wearable systems


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