Reasoning about RFID-tracked moving objects in symbolic indoor spaces

ABSTRACT

In recent years, indoor spatial data management has started to attract attention, partly due to the increasing use of receptor devices (e.g., RFID readers, and wireless sensor networks) in indoor, as well as outdoor spaces. There is thus a great need for a model that captures such spaces, their receptors, and provides powerful reasoning techniques on top. This paper reviews and extends a recent unified model of outdoor and indoor spaces and receptor deployments in these spaces. The extended model enables modelers to capture various information pieces from the physical world. On top of the extended model, this paper proposes and formalizes the route observability concept, and demonstrates its usefulness in enhancing the reading environment. The extended model also enables incorporating receptor data through a probabilistic trajectory-to-route translator. This translator first facilitates the tracking of moving objects enabling the search for them to be optimized, and second supports high-level reasoning about points of potential traffic (over)load, so-called bottleneck points. The functional analysis illustrates the behavior of the route observability function. The experimental evaluation shows the accuracy of the translator, and the quality of the inference and reasoning. The experiments are conducted on both synthetic data and uncleansed, real-world data obtained from RFID-tagged flight baggage.

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Index Terms

• Applied computing

  • Physical sciences and engineering

    • Mathematics and statistics

• Computing methodologies

  • Artificial intelligence

    • Knowledge representation and reasoning

      • Probabilistic reasoning

      • Vagueness and fuzzy logic

• Human-centered computing

  • Visualization

    • Visualization application domains

      • Geographic visualization

• Information systems

  • Information systems applications

    • Decision support systems

      • Data analytics

    • Spatial-temporal systems

• Mathematics of computing

  • Information theory

• Theory of computation

  • Theory and algorithms for application domains

    • Machine learning theory

      • Inductive inference