Enhancing emergency response systems through leveraging crowdsensing and heterogeneous data

Robust and prompt emergency response is a crucial service that smart cities should provide to citizens, communities, and corporations. Emergency management strategies that are currently supported by cities yield pre-determined protocols that can only handle well-understood incidents. However, there are incidents whose nature, shape, scale, and timing are not as predictable. The lack of adequate data management platforms to harvest emergency-related data from the proliferation of data sources scattered around a city is a major shortfall in current emergency response and risk assessment processes. We propose an improved information infrastructure to assist emergency personnel in responding effectively and proportionally to large-scale, distributed, unstructured natural and man-made hazards such as multi-vehicle accidents, outbreaks of human or animal diseases, major weather events, large fires, and terrorist attacks. The proposed infrastructure will crowdsource the multitude of human and physical sensing resources that can generate data about incidents (e.g. smartphones, sensors, vehicles, etc.) in order to build a comprehensive understanding of emergency situations and provide situational awareness and recommendations to emergency teams on the scene. Our infrastructure consists of three components: (1) large-scale crowdsensing and data quality valuation, (2) heterogeneous data integration and analytics, and (3) decision making, alternative generation and recommendations. Leveraging crowdsensing and heterogeneous data analytics will improve the response coordination to critical incidents and real-time incident management, which will contribute to saving lives and reducing injuries, improving the quality of life, and saving resources by deploying them more effectively.