Scalable Context-Awareness

  • Seng W. Loke
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


Since the pioneering work on context-aware computing by Schilit et al. [12] over two decades ago, there have been tremendous developments in context-aware mobile computing [1, 7], a mobile device is made aware of the current context of the user, including the circumstances or the surroundings as well as the user’s activity on the phone, or the phone’s current state (e.g., battery level, device properties and so on), and can take action based on such context information. Mobile sensing [4, 13] on the device is used to obtain information about the user, including the user’s location, objects nearby (e.g., via WiFi or Bluetooth scanning) as well as the current physical activity of the user (e.g., walking, on a bus, etc), i.e. the work on mobile activity recognition (e.g., [14]), and the current user interaction with the apps on the phone (e.g., what the user is looking at). A large range of data analysis techniques has been employed to process sensor data in order to learn to recognise activities—recently, Deep Convolutional Neural Networks have been employed achieving accuracy in recognition of up to 97–99% [5].


Mobile Device Sensor Data Smart City Current Physical Activity Social Media Data 
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

© The Author(s) 2017

Authors and Affiliations

  • Seng W. Loke
    • 1
  1. 1.School of Information TechnologyDeakin UniversityBurwoodAustralia

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