Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2014
Abstract
Real-time monitoring of groups and their rich contexts will be a key building block for futuristic, group-aware mobile services. In this paper, we propose GruMon, a fast and accurate group monitoring system for dense and complex urban spaces. GruMon meets the performance criteria of precise group detection at low latencies by overcoming two critical challenges of practical urban spaces, namely (a) the high density of crowds, and (b) the imprecise location information available indoors. Using a host of novel features extracted from commodity smartphone sensors, GruMon can detect over 80% of the groups, with 97% precision, using 10 minutes latency windows, even in venues with limited or no location information. Moreover, in venues where location information is available, GruMon improves the detection latency by up to 20% using semantic information and additional sensors to complement traditional spatio-temporal clustering approaches. We evaluated GruMon on data collected from 258 shopping episodes from 154 real participants, in two large shopping complexes in Korea and Singapore. We also tested GruMon on a large-scale dataset from an international airport (containing ≈37K+ unlabelled location traces per day) and a live deployment at our university, and showed both GruMon's potential performance at scale and various scalability challenges for real-world dense environment deployments.
Keywords
Clustering, Context monitoring, Indoor localization, Smartphone sensors, Social groups
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
SenSys '14: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems: November 3-6, 2014, Memphis, TN
First Page
46
Last Page
60
ISBN
9781450331432
Identifier
10.1145/2668332.2668340
Publisher
ACM
City or Country
New York
Citation
SEN, Rijurekha; LEE, Youngki; JAYARAJAH, Kasthuri; BALAN, Rajesh Krishna; and MISRA, Archan.
GruMon: Fast and Accurate Group Monitoring for Heterogeneous Urban Spaces. (2014). SenSys '14: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems: November 3-6, 2014, Memphis, TN. 46-60.
Available at: https://ink.library.smu.edu.sg/sis_research/2656
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2668332.2668340