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Erschienen in:
Weighted Dissemination of Bundles in Probabilistic Spray and Wait Routing Protocol Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Design and Analysis of IoT-Based System for Crowd Density Estimation Techniques

verfasst von : Ajitesh Kumar, Mona Kumari

Erschienen in: Advances in Data and Information Sciences

Verlag: Springer Singapore

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Abstract

In this paper, we present an IoT-based solution that can reduce the complexity of crowd estimation. About the human crowd estimation many techniques are in existence but nowadays more work is going on in this field because this is era of IoT and most of the organization is shifted toward IoT-based system. So in our proposed system we are using the Raspberry Pi-3 which are having quad-core processor that can be very useful and gives better result and accurate number even when the humans are very close to each other. This IoT-based model can easily be implemented in crowded areas and monitor the same. The camera module in this model also helps to differentiate between human and other bodies. As this is a mobile model, it can be easily fixed on the walls of street light and in the time of darkness or in night the camera captures clear images for process in the presence of street light. So that this model gives better result almost 70% better result in compare to exiting approaches.

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Literatur
1.
Boukerche, A., Oliveira, H. A. B. F., Nakamura, E. F., & Loureiro, A. A. F. (2007). Localization systems for wireless sensor networks. IEEE Wireless Communications, 14(6), 6–12.CrossRef
2.
Ni, L. M., Liu, Y., Lau, Y. C., & Patil, A. P. (2003). LANDMARC: Indoor location sensing using active RFID. In Proceedings of the 1st IEEE International Conference on Pervasive Computing and Communications (PerCom’03) (pp. 407–415). Fort Worth, Tx, USA: IEEE. Retrieved March 2003.
3.
Dian, Z., & Ni, L. M. (2009). Dynamic clustering for tracking multiple transceiver-free objects. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom’09), Galveston, Tx, USA (pp. 1–8). Retrieved March 2009.
4.
Arai, M., Kawamura, H., & Suzuki, K. (2010). Estimation of ZigBee’sRSSI fluctuated by crowd behavior in indoor space. In Proceedings of the SICE Annual Conference (SICE’10), Taipei, Taiwan (pp. 696–701). Retrieved August 2010.
5.
Xu, C., Firner, B., & Moore, R. S., et al. (2013). SCPL: Indoor device free multi-subject counting and localization using radio signal strength. In: Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN’13), Philadelphia, Pa, USA (pp. 79–90). Retrieved April 2013.
6.
Huang, C.-N., & Chan, C.-T. (2011). ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI. Procedia Computer Science, 5, 58–65.CrossRef
7.
Bahl, P., & Padmanabhan, V. N. (2000). RADAR: An in-building RF based user location and tracking system. In Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’00) (Vol. 2, pp. 775–784). Tel Aviv, Israel: IEEE. Retrieved March 2000.
8.
Oka, A., & Lampe, L. (2010). Distributed target tracking using signal strength measurements by a wireless sensor network. IEEE Journal on Selected Areas in Communications, 28(7), 1006–1015.CrossRef
9.
Liu, X.-L., Chen, Y.-G., Jing, X.-R., & Chen, Y.-W. (2010). Design of experiment method for microsatellite system simulation and optimization. In Proceedings of the International Conference on Computational and Information Sciences (ICCIS’10) (pp. 1200–1203), Chengdu, China: IEEE. Retrieved December 2010.
10.
Litvinski, O., & Gherbi, A. (2013). Open stack scheduler evaluation using design of experiment approach. In Proceedings of the 16th IEEE International Symposium on Object/Component/Service Oriented Real-Time Distributed Computing (ISORC’13), Paderborn, Germany (pp. 1–7). Retrieved June 2013.
11.
Fadhlullah, S. Y., & Ismail, W. (2015). Solar energy harvesting design framework for 3.3 V small and low-powered devices in wireless sensor network. In Proceedings of the 1st International Conference on Telemetric and Future Generation Networks (pp. 89–94). Kuala Lumpur, Malaysia: IEEE. Retrieved May 2015.
12.
Curtis, S., Guy, S. J. Zafar, B., & Manocha, D. VirtualTawaf: A case study in simulating the behavior of dense, heterogeneous crowds. In Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV’11) (pp. 128–135). IEEE.
13.
Karamouzas, I., Skinner, B., & Guy, S. J. (2014). Universal power law governing pedestrian interactions. Physical Review Letters, 113(23), Article ID 238701.
Metadaten
Titel
Design and Analysis of IoT-Based System for Crowd Density Estimation Techniques
verfasst von
Ajitesh Kumar
Mona Kumari
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Advances in Data and Information Sciences

Print ISBN: 978-981-15-0693-2

Electronic ISBN: 978-981-15-0694-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-0694-9_29

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