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Abstract:The prevention and correct management of natural disaster event sequences play a key role in saving human lives. The availability of embedded and mobile smart computing systems opens new roads for the management of land and infrastructures by civil protection operators. To date, research has explored the use of social networks for the management of disasters connected to meteorological/hydrogeological events or earthquakes, but without emphasis on the importance of an integrated system. The main feature of the Whistland system proposed in this paper is to make synergistic use of augmented reality (AR), crowd-mapping (CM), social networks, the Internet of Things (IoT) and wireless sensor networks (WSN) by exploiting technologies and frameworks of Web 2.0 and GIS 2.0 to make informed decisions about the chain of events. The Whistland system is composed of a geo-server, a mobile application with AR and an analytics dashboard. The geo-server acts as the hub of the sensor and social networks. The abstracted concept in this sense is the transformation of the user domain into "intelligent sensors" for the whole scope of crisis management. The social network integration is made through an efficient pointer-like mechanism that keeps the storage requirement low through a mobile application based on an augmented reality engine and provides qualitative information that sensors are unable to capture. Real-time analyses, geo-searches and the capability to examine event histories with an augmented reality engine all help the stakeholders to understand better the state of the resources under observation/monitoring. The system has been extensively tested in the programmed maintenance of river basins, where it is necessary to log maintenance activities in order to keep the riverbank clean: a significant use-case in many countries affected by hydro-geological instability.

Figure 1: System overview.The roughness of the road surface affects driving safety and comfort. Having complete and up to date information on the state of the road network is essential for maintenance planning, but it entails time consuming and costly monitoring activities. As a matter of fact, maintenance interventions are mainly based on the results of case by case inspections prompted by drivers' reports. Moreover, qualitative observations are seldom supported by objective measures, making it difficult for administrators to collect data providing a clear perception of the actual priorities.Recent studies have shown that it is possible to exploit the triaxial accelerometers with which smartphones are equipped to obtain a sound indicator of road surface roughness [1, 2]. A crowdsensing system, called SmartRoadSense [3], has been developed to allow any driver to contribute with his/her smartphone in monitoring the status of the roads he/she travels by car.As shown in Fig. 1, Smartroadsense is composed of: a mobile application, a cloud-based backend, and a web portal. The application runs in background on any car-mounted Android smartphone, reads the accelerometer data at a freFigure 2: Road roughness map.quency of 100Hz, and computes roughness values once per second. All the values are associated with GPS coordinates and opportunistically sent to the server as soon as a suitable data connection is available. Traces are processed in cloud to compensate GPS inaccuracy and to aggregate data provided by different users. OpenStreetMap is used for map matching and aggregation, performed every 6 hours. Aggregated roughness estimates are then made publicly available online and displayed in color-scale on interactive maps (Fig. 2).The demo shows the entire system at work, allowing the audience to directly install and test the app on their smartphones and to follow the processing steps by looking inside databases and software components. The main purpose of the demo is to highlight the distributed and collaborative nature of SmartRoadSense, which handles a large quantity of data in real time exploiting mobile terminals and cloudbased resources.Demo trailer at http://smartroadsense.it/demo/.

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