Indoor mobility semantics annotation using coupled conditional Markov networks

Huan Li, Hua Lu, Muhammad Aamir Cheema, Lidan Shou, Gang Chen

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2 Citations (Scopus)


Indoor mobility semantics analytics can greatly benefit many pertinent applications. Existing semantic annotation methods mainly focus on outdoor space and require extra knowledge such as POI category or human activity regularity. However, these conditions are difficult to meet in indoor venues with relatively small extents but complex topology. This work studies the annotation of indoor mobility semantics that describe an object's mobility event (what ) at a semantic indoor region (where ) during a time period (when ). A coupled conditional Markov network (C2MN) is proposed with a set of feature functions carefully designed by incorporating indoor topology and mobility behaviors. C2MN is able to capture probabilistic dependencies among positioning records, semantic regions, and mobility events jointly. Nevertheless, the correlation of regions and events hinders the parameters learning. Therefore, we devise an alternate learning algorithm to enable the parameter learning over correlated variables. The extensive experiments demonstrate that our C2MN-based semantic annotation is efficient and effective on both real and synthetic indoor mobility data.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
EditorsMurat Kantarcioglu, Dimitrios Gunopulos, S. Sudarshan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages12
ISBN (Electronic)9781728129037
ISBN (Print)9781728129044
Publication statusPublished - 2020
EventIEEE International Conference on Data Engineering 2020 - Online, Virtual, Dallas, United States of America
Duration: 20 Apr 202024 Apr 2020
Conference number: 36th (Proceedings) (Website)

Publication series

NameProceedings - International Conference on Data Engineering
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
ISSN (Print)1084-4627
ISSN (Electronic)2375-026X


ConferenceIEEE International Conference on Data Engineering 2020
Abbreviated titleICDE 2020
Country/TerritoryUnited States of America
Internet address

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