OPSitu: A Semantic-Web Based Situation Inference Tool Under Opportunistic Sensing Paradigm

Jiangtao Wang, Yasha Wang, Yuanduo He

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review


Opportunistic sensing becomes a competitive sensing paradigm nowadays. Instead of pre-deploying application-specific sensors, it makes use of sensors that just happen to be available to accomplish its sensing goal. In the opportunistic sensing paradigm, the sensors that can be utilized by a given application in a given time are unpredictable. This brings the Semantic-Web based situation inference approach, which is widely adopted in situation-aware applications, a major challenge, i.e., how to handle uncertainty of the availability and confidence of the sensing data. Although extending standard semantic-web languages may enable the situation inference to be compatible with the uncertainty, it also brings extra complexity to the languages and makes them hard to be learned. Unlike the existing works, this paper developed a situation inference tool, named OPSitu, which enables the situation inference rules to be written in the well accepted standard languages such as OWL and SWRL even under opportunistic sensing paradigm. An experiment is also described to demonstrate the validity of OPSitu.
Original languageEnglish
Title of host publicationMobile and Ubiquitous Systems
Subtitle of host publicationComputing, Networking, and Services. MobiQuitous 2013
Number of pages14
ISBN (Electronic)9783319115696
ISBN (Print)9783319115689
Publication statusPublished - 28 Sept 2014
Externally publishedYes

Publication series

NameLecture Notes of the Institute Computer Sciences, Social Informatics and Telecommunications Engineering


  • Semantic web
  • Situation inference
  • Opportunistic sensing


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