SafeCity: A Heterogeneous Mobile Crowd Sensing System for Urban Public Safety

Abstract

As important indicators of urban public safety, Public Safety and Environmental Security (PSES) is related to residents’ living security and greatly affects their quality of life and happiness index. Due to PSES characteristics such as diverse forms, wide distribution, and unpredictable occurrence times, traditional solutions consume huge manpower and time in the implementation process. Although some professional software and hardware systems have emerged to assist in solving the problems, there are still challenges such as limited sensing coverage and monotonous sensing modes, lack of interaction and understanding between systems and tasks, and scarcity of effective system architecture and functional modules. To meet these challenges, we design a PSES multi-terminal fusion system (SafeCity) based on the idea and technology of heterogeneous mobile crowd sensing. With collaboration among Humans, Machines, and Things (H-M-T), the proposed system makes full use of the idle mobility, sensing and computing resources in the city, and systematically provides a solution to the various PSES issues. Apart from the system architecture, functions, core mechanism, and algorithm libraries, the task execution flow is explained in depth through the description of several cases. We implement a prototype to verify the rationality and effectiveness of SafeCity. And comprehensive comparison and evaluation show that SafeCity is far superior to other solutions in terms of function, performance, and stability.