TY - JOUR
T1 - A Feedback Control-Based Crowd Dynamics Management in IoT System
AU - Kawamoto, Yuichi
AU - Yamada, Naoto
AU - Nishiyama, Hiroki
AU - Kato, Nei
AU - Shimizu, Yoshitaka
AU - Zheng, Yao
N1 - Funding Information:
This work was supported in part by the Strategic International Research Cooperative Program, Japan Science and Technology Agency. The work of Y. Zheng was supported by the NSF under Grant CNS-1405747.
Funding Information:
Manuscript received April 17, 2017; revised June 10, 2017; accepted June 25, 2017. Date of publication July 11, 2017; date of current version October 9, 2017. This work was supported in part by the Strategic International Research Cooperative Program, Japan Science and Technology Agency. The work of Y. Zheng was supported by the NSF under Grant CNS-1405747. (Corresponding author: Yuichi Kawamoto.) Y. Kawamoto, N. Yamada, H. Nishiyama, and N. Kato are with the Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan (e-mail: youpsan@it.is.tohoku.ac.jp; naoto.yamada@it.is.tohoku.ac.jp; hiroki.nishiyama.1983@ieee.org; kato@it.is.tohoku.ac.jp).
Publisher Copyright:
© 2014 IEEE.
PY - 2017/10
Y1 - 2017/10
N2 - The development of technologies related to the Internet of Things (IoT) provides a new perspective on applications pertaining to smart cities. Smart city applications focus on resolving issues facing people in everyday life, and have attracted a considerable amount of research interest. The typical issue encountered in such places of daily use, such as stations, shopping malls, and stadiums is crowd dynamics management. Therefore, we focus on crowd dynamics management to resolve the problem of congestion using IoT technologies. Real-time crowd dynamics management can be achieved by gathering information relating to congestion and propose less crowded places. Although many crowd dynamics management applications have been proposed in various scenarios and many models have been devised to this end, a general model for evaluating the control effectiveness of crowd dynamics management has not yet been developed in IoT research. Therefore, in this paper, we propose a model to evaluate the performance of crowd dynamics management applications. In other words, the objective of this paper is to present the proof-of-concept of control effectiveness of crowd dynamics management. Our model uses feedback control theory, and enables an integrated evaluation of the control effectiveness of crowd dynamics management methods under various scenarios. We also provide extensive numerical results to verify the effectiveness of the model.
AB - The development of technologies related to the Internet of Things (IoT) provides a new perspective on applications pertaining to smart cities. Smart city applications focus on resolving issues facing people in everyday life, and have attracted a considerable amount of research interest. The typical issue encountered in such places of daily use, such as stations, shopping malls, and stadiums is crowd dynamics management. Therefore, we focus on crowd dynamics management to resolve the problem of congestion using IoT technologies. Real-time crowd dynamics management can be achieved by gathering information relating to congestion and propose less crowded places. Although many crowd dynamics management applications have been proposed in various scenarios and many models have been devised to this end, a general model for evaluating the control effectiveness of crowd dynamics management has not yet been developed in IoT research. Therefore, in this paper, we propose a model to evaluate the performance of crowd dynamics management applications. In other words, the objective of this paper is to present the proof-of-concept of control effectiveness of crowd dynamics management. Our model uses feedback control theory, and enables an integrated evaluation of the control effectiveness of crowd dynamics management methods under various scenarios. We also provide extensive numerical results to verify the effectiveness of the model.
KW - Control theory
KW - Internet of Things (IoT)
KW - crowd control
KW - feedback
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U2 - 10.1109/JIOT.2017.2724642
DO - 10.1109/JIOT.2017.2724642
M3 - Article
AN - SCOPUS:85023203655
SN - 2327-4662
VL - 4
SP - 1466
EP - 1476
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 5
M1 - 7972937
ER -