A Framework for Smart Capacity Estimation at Crowded Area using WSN

Dynamic crowd causes many challenges including estimation of crowd capacity in a restricted area. It’s one of the problems for estimating the crowd capacity when crowd strength increases or decreases (fluctuates) at different time (peak and off-peak time) and during different activities. This may cause difficulty in managing the crowd according to capacity of the area, time and activity. To overcome the issue of capacity estimation according to the space or area available, this paper proposed a conceptual framework to calculate the number of persons in a definite zone or level and calculate the remaining capacity in the area (Zone and level). IoT, cloud computing and WSN are used to estimate the remaining capacity in the restricted area according to the time and activity.

[1]  Sonia Hashish,et al.  Efficient wireless sensor network rings overlay for crowd management in Arafat area of Makkah , 2015, 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES).

[2]  Zhu Wang,et al.  Mobile Crowd Sensing and Computing , 2015, ACM Comput. Surv..

[3]  Asadullah Shah,et al.  A Security-Based Survey and Classification of Cloud Architectures, State of Art and Future Directions , 2013, 2013 International Conference on Advanced Computer Science Applications and Technologies.

[4]  Hao Wu,et al.  A Survey on Localization in Wireless Sensor Networks , 2011 .

[5]  Sumit Goyal,et al.  Public vs Private vs Hybrid vs Community - Cloud Computing: A Critical Review , 2014 .

[6]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[7]  Niranjan Lal,et al.  A Tree Based Routing Protocol for Mobile Sensor Networks (MSNs) , 2010 .

[8]  Venkata Lakshmi,et al.  A Survey on Wireless Sensor Networks for Smart Grid , 2015 .

[9]  Rongbo Zhu,et al.  Energy-Aware Distributed Intelligent Data Gathering Algorithm in Wireless Sensor Networks , 2011, Int. J. Distributed Sens. Networks.

[10]  C. Mala,et al.  Analysis of ECC for application specific WSN security , 2015, 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[11]  Hanan Elazhary,et al.  Cloud-based Context-aware Mobile Applications and Framework for Hajj and Umrah Management , 2017 .

[12]  Michael Lettenmeier,et al.  Transport reduction by crowdsourced deliveries – a library case in Finland , 2016 .

[13]  Asadullah Shah,et al.  WSN based sensing model for smart crowd movement with identification: a conceptual model , 2016 .

[14]  Marimuthu Palaniswami,et al.  An Information Framework for Creating a Smart City Through Internet of Things , 2014, IEEE Internet of Things Journal.

[15]  Robbert van Renesse,et al.  Mission-Critical Cloud Computing for Critical Infrastructures , 2017, Smart Grids.

[16]  John Panneerselvam,et al.  A Cloud-Based Sustainable Business Model for Effective ICT Provision in Higher Education , 2015, 2015 IEEE Symposium on Service-Oriented System Engineering.

[17]  Abdul Hanan Abdullah,et al.  A Comprehensive Study of Data Collection Schemes Using Mobile Sinks in Wireless Sensor Networks , 2014, Sensors.

[18]  Wu He,et al.  Developing Vehicular Data Cloud Services in the IoT Environment , 2014, IEEE Transactions on Industrial Informatics.

[19]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[20]  José Ramón Gil-García,et al.  Understanding Smart Cities: An Integrative Framework , 2012, HICSS.

[21]  Asadullah Shah,et al.  A framework for smart estimation of demand-supply for crowdsource management using WSN , 2017, ICC.

[22]  Sunilkumar S. Manvi,et al.  Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey , 2014, J. Netw. Comput. Appl..