Online Risk Prediction for Indoor Moving Objects

Tanvir Ahmed, Torben Bach Pedersen, Toon Calders, Hua Lu

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review


Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency or inaccuracy at any step can make the bag risky, i.e., the bag may be delayed at the airport or sent to a wrong airport. In this paper, we propose a novel probabilistic approach for predicting the risk of an indoor moving object in real-time. We propose a probabilistic flow graph (PFG) and an aggregated probabilistic flow graph (APFG) that capture the historical object transitions and the durations of the transitions. In the graphs, the probabilistic information is stored in a set of histograms. Then we use the flow graphs for obtaining a risk score of an online object and use it for predicting its riskiness. The paper reports a comprehensive experimental study with multiple synthetic data sets and a real baggage tracking data set. The experimental results show that the proposed method can identify the risky objects very accurately when they approach the bottleneck locations on their paths and can significantly reduce the operation cost.
TitelIEEE 17th International Conference on Mobile Data Management, MDM 2016, Porto, Portugal, June 13-16, 2016
RedaktørerChi-Yin Chow, Prem Prakash Jayaraman, Wei Wu
Antal sider10
ForlagIEEE Computer Society Press
ISBN (Trykt)9781509008834
StatusUdgivet - 2016
Udgivet eksterntJa
Begivenhed17th IEEE International Conference on Mobile Data Management (MDM) - Porto, Portugal
Varighed: 13 jun. 201616 jun. 2016


Konference17th IEEE International Conference on Mobile Data Management (MDM)

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