A cloud-based system to protect against industrial multi-risk events

Industrial areas frequently present a high concentration of production operations which are source of anthropic risks. For this reason Smart Public Safety is receiving an increasing attention from industry, research and authorities. Moreover, due the consequences of global warming, these areas could be subject to risk events with increased probability with respect to the past. Information technologies enable an innovative approach towards safety management, which relies on the evolution of tools for environmental monitoring and citizens' interaction. This work presents the preliminary results of the Italian research project SIGMA - sensor Integrated System in cloud environment for the Advanced Multi-risk Management. The proposed system includes a continuous monitoring of the different information sources, thus reducing human control as much as possible. At the same time, the communication system manages multiple data flows in a flexible way, adapting itself to different working scenarios, enabling smarter applications. SIGMA intends to acquire, integrate and compute heterogeneous data, coming from various sensor networks in order to provide useful insights for the monitoring, forecasting and management of risk situations through services provided to citizens and businesses, both public and private. Based on the integration of different interoperating components, the system is able to provide a complete emergency management framework through simulations/optimizations and heterogeneous data manipulation tools. The prototype solution is detailed by an use case application in an industrial area located in the region of Sicily, Italy. In particular, web based modular applications connected through SIGMA allow the monitoring of the industrial environment through data gathering from different sensor networks, such as outdoor sensors mounted in the surroundings of large industrial areas, and support of the design of the logistics network aimed at covering the industrial risks.