On the Utilization of Heterogeneous Sensors and System Adaptability for Opportunistic Activity and Context Recognition

  • Marc Kurz (Speaker)

Activity: Talk or presentationContributed talkunknown

Description

Opportunistic activity and context recognition systems draw from the characteristic to utilize sensors as they happen to be available instead of predefining a fixed sensing infrastructure at design time of the system. Thus, the kinds and modalities of sensors are not predefined. Sensors of different types and working characteristics shall be used equally if the delivered environmental quantity is useful for executing a recognition task. This heterogeneity in the sensing infrastructure and the lack of a defined sensor infrastructure motivates the utilization of sensor abstractions and sensor self-descriptions for identifying and configuring sensors according to recognition tasks. This paper describes how sensors of different kinds can be accessed in a common way, and how they can be utilized at runtime by using their self-descriptions. The different steps within the lifecycle of sensor descriptions are described to understand the powerful concepts of self-describing sensors and sensor abstractions. Furthermore, a prototypical framework realizing the vision of opportunistic activity recognition is presented together with a discussion of subsequent steps to adapt the system to different application domains.
Period28 May 2013
Event titleFifth International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE2013)
Event typeConference
LocationSpainShow on map

Fields of science

  • 202017 Embedded systems
  • 102019 Machine learning
  • 102009 Computer simulation
  • 102 Computer Sciences
  • 211912 Product design
  • 211902 Assistive technologies
  • 102020 Medical informatics
  • 102022 Software development
  • 102021 Pervasive computing
  • 102013 Human-computer interaction
  • 102025 Distributed systems

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Engineering and Natural Sciences (in general)