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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15621
Title: A fast and scalable crowd sensing based trajectory tracking system
Authors: Niyogi, Rajdeep
Kulshrestha T.
Patel D.
Prasad S.
Patel P.
Xia Y.
Sureka A.
Ucar B.
Kothapalli K.
Govindaraju M.
Goel S.
Halappanavar M.
Madduri K.
Saxena V.
Alum S.
Kalyanararnan A.
Barnas M.
Published in: Proceedings of 2017 10th International Conference on Contemporary Computing, IC3 2017
Abstract: Crowd Sensing collects users' local knowledge such as local information, ambient context, and traffic conditions, etc., using their sensor-enabled devices. The collected information is further aggregated and transferred to the cloud for detailed analysis, such as places / friends recommendation, human behavior, criminal activities, etc. These tracking and monitoring systems must be scalable, fast, and easy to deploy to meet the requirements of a real-time system. In this paper, we propose a fast and scalable crowdsensing based trajectory tracking system which can track any person having the smartphone and can provide a complete analysis of her visited locations in a given time span. We use the Redis in-memory database and XMPP at the sensing units for fast data retrieval and exchange. When a person moves to a new location, WebSocket server updates that person's new location automatically among all sensing units to make the system analysis in real-time. We develop and deploy a real prototype testbed in IIT Roorkee campus and evaluate it extensively to demonstrate the efficiency and scalability of our proposed system. © 2017 IEEE.
Citation: Proceedings of 2017 10th International Conference on Contemporary Computing, IC3 2017, (2018), 1- 6
URI: https://doi.org/10.1109/IC3.2017.8284303
http://repository.iitr.ac.in/handle/123456789/15621
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: access points
Crowd Sensing
human behavior
MAC
probe requests
SSID
trajectory analysis
Wi-Fi
ISBN: 9.78154E+12
Author Scopus IDs: 35100732400
55583922600
57190488069
Author Affiliations: Niyogi, R., Department of CSE, IIT Roorkee, India
Kulshrestha, T., Department of CSE, IIT Roorkee, India
Patel, D., IBM T. J. Watson Research Center USA, United States
Funding Details: ACKNOWLEDGMENT We would like to thank Divya Saxena (Research Scholar, IIT Roorkee) for her significant contributions in this work. We would also thank to RailTel IITR Centre of Excellence in Telecom (RICET) for providing us financial support under the project grant RCI-763(2)-ECD.
Appears in Collections:Conference Publications [CS]

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