Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors

Nikhil Ravi; Raksha Ramakrishna; Hoi To Wai; Anna Scaglione

doi:10.1109/SPAWC.2018.8446031

Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors

Nikhil Ravi, Raksha Ramakrishna, Hoi To Wai, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians' actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not based on mathematical models, while most models used for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings. Our aim is to bridge these approaches, by capturing in the data model the swarming behavior of the network, including social interactions.

Original language	English (US)
Title of host publication	2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538635124
DOIs	https://doi.org/10.1109/SPAWC.2018.8446031
State	Published - Aug 24 2018
Event	19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece Duration: Jun 25 2018 → Jun 28 2018

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume	2018-June

Other

Other	19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Country/Territory	Greece
City	Kalamata
Period	6/25/18 → 6/28/18

Keywords

IoT
Viterbi training
crowd-dynamics
network inference
non-linear graph filter

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

Access to Document

10.1109/SPAWC.2018.8446031

Cite this

Ravi, N., Ramakrishna, R., Wai, H. T., & Scaglione, A. (2018). Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 Article 8446031 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC.2018.8446031

Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors. / Ravi, Nikhil; Ramakrishna, Raksha; Wai, Hoi To et al.
2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8446031 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2018-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ravi, N, Ramakrishna, R, Wai, HT & Scaglione, A 2018, Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors. in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018., 8446031, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018, Kalamata, Greece, 6/25/18. https://doi.org/10.1109/SPAWC.2018.8446031

Ravi N, Ramakrishna R, Wai HT, Scaglione A. Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8446031. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). doi: 10.1109/SPAWC.2018.8446031

Ravi, Nikhil ; Ramakrishna, Raksha ; Wai, Hoi To et al. / Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors. 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

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title = "Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors",

abstract = "In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians' actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not based on mathematical models, while most models used for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings. Our aim is to bridge these approaches, by capturing in the data model the swarming behavior of the network, including social interactions.",

keywords = "IoT, Viterbi training, crowd-dynamics, network inference, non-linear graph filter",

author = "Nikhil Ravi and Raksha Ramakrishna and Wai, {Hoi To} and Anna Scaglione",

note = "Funding Information: This work was supported in part by the National Science Foundation CCF-BSF 1714672 grant. Publisher Copyright: {\textcopyright} 2018 IEEE.; 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 ; Conference date: 25-06-2018 Through 28-06-2018",

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AB - In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians' actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not based on mathematical models, while most models used for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings. Our aim is to bridge these approaches, by capturing in the data model the swarming behavior of the network, including social interactions.

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Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors

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Keywords

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