An IoT Framework for detecting Movement within Indoor Environments.

Kevin Curran, Gary Mansell, Jack Curran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Tracking people indoors can be valuable in smart living scenarios such as tracking shoppers in a mall or in healthcare situations when tracking the movement of elderly patients can allow them to remain more independent. Determining accurate movement of people indoors is problematic however as there is no universal tracking system such as GPS which works indoors. Instead, a range of techniques are used based on technologies such as cameras, radio frequency identification, WiFi, Bluetooth, pressure pads and radar are used to track people and objects within indoor environments. The most common technologies for tracking are Bluetooth and WiFi. Many Internet of Things (IoT) devices support these protocols and can therefore act as beacons and hubs for movement detection indoor. We provide here an overview of an IoT focused framework which allows the plug and play of Bluetooth and WiFi devices in addition to integrating passive and active approaches to determining the movement of people indoors.
LanguageEnglish
Title of host publicationInternational Conference on Machine Learning for Networking (MLN’2018)
Publication statusPublished - 30 Nov 2018
EventInternational Conference on Machine Learning for Networking (MLN’2018) - France, Paris, France
Duration: 27 Nov 201829 Nov 2018

Conference

ConferenceInternational Conference on Machine Learning for Networking (MLN’2018)
CountryFrance
CityParis
Period27/11/1829/11/18

Fingerprint

Bluetooth
Shopping centers
Radio frequency identification (RFID)
Global positioning system
Radar
Cameras
Network protocols
Internet of things

Keywords

  • movement detection
  • indoor location
  • indoor location tracking

Cite this

Curran, K., Mansell, G., & Curran, J. (2018). An IoT Framework for detecting Movement within Indoor Environments. In International Conference on Machine Learning for Networking (MLN’2018)
Curran, Kevin ; Mansell, Gary ; Curran, Jack. / An IoT Framework for detecting Movement within Indoor Environments. International Conference on Machine Learning for Networking (MLN’2018). 2018.
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title = "An IoT Framework for detecting Movement within Indoor Environments.",
abstract = "Tracking people indoors can be valuable in smart living scenarios such as tracking shoppers in a mall or in healthcare situations when tracking the movement of elderly patients can allow them to remain more independent. Determining accurate movement of people indoors is problematic however as there is no universal tracking system such as GPS which works indoors. Instead, a range of techniques are used based on technologies such as cameras, radio frequency identification, WiFi, Bluetooth, pressure pads and radar are used to track people and objects within indoor environments. The most common technologies for tracking are Bluetooth and WiFi. Many Internet of Things (IoT) devices support these protocols and can therefore act as beacons and hubs for movement detection indoor. We provide here an overview of an IoT focused framework which allows the plug and play of Bluetooth and WiFi devices in addition to integrating passive and active approaches to determining the movement of people indoors.",
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author = "Kevin Curran and Gary Mansell and Jack Curran",
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Curran, K, Mansell, G & Curran, J 2018, An IoT Framework for detecting Movement within Indoor Environments. in International Conference on Machine Learning for Networking (MLN’2018). International Conference on Machine Learning for Networking (MLN’2018), Paris, France, 27/11/18.

An IoT Framework for detecting Movement within Indoor Environments. / Curran, Kevin; Mansell, Gary; Curran, Jack.

International Conference on Machine Learning for Networking (MLN’2018). 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Curran, Jack

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N2 - Tracking people indoors can be valuable in smart living scenarios such as tracking shoppers in a mall or in healthcare situations when tracking the movement of elderly patients can allow them to remain more independent. Determining accurate movement of people indoors is problematic however as there is no universal tracking system such as GPS which works indoors. Instead, a range of techniques are used based on technologies such as cameras, radio frequency identification, WiFi, Bluetooth, pressure pads and radar are used to track people and objects within indoor environments. The most common technologies for tracking are Bluetooth and WiFi. Many Internet of Things (IoT) devices support these protocols and can therefore act as beacons and hubs for movement detection indoor. We provide here an overview of an IoT focused framework which allows the plug and play of Bluetooth and WiFi devices in addition to integrating passive and active approaches to determining the movement of people indoors.

AB - Tracking people indoors can be valuable in smart living scenarios such as tracking shoppers in a mall or in healthcare situations when tracking the movement of elderly patients can allow them to remain more independent. Determining accurate movement of people indoors is problematic however as there is no universal tracking system such as GPS which works indoors. Instead, a range of techniques are used based on technologies such as cameras, radio frequency identification, WiFi, Bluetooth, pressure pads and radar are used to track people and objects within indoor environments. The most common technologies for tracking are Bluetooth and WiFi. Many Internet of Things (IoT) devices support these protocols and can therefore act as beacons and hubs for movement detection indoor. We provide here an overview of an IoT focused framework which allows the plug and play of Bluetooth and WiFi devices in addition to integrating passive and active approaches to determining the movement of people indoors.

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Curran K, Mansell G, Curran J. An IoT Framework for detecting Movement within Indoor Environments. In International Conference on Machine Learning for Networking (MLN’2018). 2018