A crowdsensing platform for mass surveillance

Nowadays festivals and city events attract huge number of visitors and unfortunately in large masses a minor panic could have incalculable consequences despite of the organizers' efforts to do everything for participants' safety. Therefore we have developed an integrated mass surveillance system based on crowdsensing data, where the system helps authorities and organizers in information gathering about the actual dynamics of the crowd. Based on real time and representative information, they are able to perform fast and targeted interventions.

[1]  Scott Shenker,et al.  Discretized streams: fault-tolerant streaming computation at scale , 2013, SOSP.

[2]  P G Gipps,et al.  A micro simulation model for pedestrian flows , 1985 .

[3]  Evangelia Mitleton-Kelly,et al.  Co-evolution of Intelligent Socio-technical Systems , 2013 .

[4]  Andreas Schadschneider,et al.  Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics , 2002 .

[5]  L. F. Henderson,et al.  The Statistics of Crowd Fluids , 1971, Nature.

[6]  Victor J. Blue,et al.  Cellular automata microsimulation for modeling bi-directional pedestrian walkways , 2001 .

[7]  Vilmos Simon,et al.  Crowdsensing Solutions in Smart Cities towards a Networked Society , 2015, IOT 2015.

[8]  Deborah Estrin,et al.  SensLoc: sensing everyday places and paths using less energy , 2010, SenSys '10.

[9]  Zygmunt J. Haas,et al.  Predictive distance-based mobility management for PCS networks , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[10]  A. Schadschneider Cellular Automaton Approach to Pedestrian Dynamics - Theory , 2001, cond-mat/0112117.

[11]  Wenjian Yu,et al.  Modeling crowd turbulence by many-particle simulations. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Mikolaj Leszczuk,et al.  Automated Detection of Firearms and Knives in a CCTV Image , 2016, Sensors.

[13]  Dirk Helbing,et al.  Dynamics of crowd disasters: an empirical study. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[15]  Jianzhong Zhang,et al.  An efficient outdoor localization method for smartphones , 2014, 2014 23rd International Conference on Computer Communication and Networks (ICCCN).

[16]  Wei Wang,et al.  Empirical investigation on safety constraints of merging pedestrian crowd through macroscopic and microscopic analysis. , 2016, Accident; analysis and prevention.

[17]  Dirk Helbing A Fluid-Dynamic Model for the Movement of Pedestrians , 1992, Complex Syst..

[18]  Guihai Chen,et al.  APT: Accurate outdoor pedestrian tracking with smartphones , 2013, 2013 Proceedings IEEE INFOCOM.

[19]  Coretta Phillips A review of CCTV evaluations: crime reduction effects and attitudes to its use , 1999 .

[20]  Timothy W. McLain,et al.  Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs , 2006, Proceedings of the IEEE.

[21]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[22]  Yoshihiro Ishibashi,et al.  Jamming Transition in Cellular Automaton Models for Pedestrians on Passageway , 1999 .