Philipp Hertweck, Tobias Hellmund, Hylke van der Schaaf, Jürgen Moßgraber, & Jan-Wilhelm Blume. (2019). Management of Sensor Data with Open Standards. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: In an emergency, getting up-to-date information about the current situation is crucial to orchestrate an efficient response. Due to its objectivity, preciseness and comparability, time-series data offer broad possibilities to manage emergency incidents. Since the Internet of Things (IoT) is rapidly growing with an estimated number of 30 billion sensors in 2020, it offers excellent potential to collect time-series data for improving situational awareness. The IoT brings several challenges: caused by a splintered sensor manufacturer landscape, data comes in various structures, incompatible protocols and unclear semantics. To tackle these challenges a well-defined interface, from where uniform data can be queried, is necessary. The Open Geospatial Consortium (OGC) has recognized this demand and developed the SensorThings API standard, an open, unified way to interconnect devices throughout the IoT, which is implemented by the FRaunhofer-Opensource-SensorThings-Server (FROST). This paper presents the standard, its implementation and the application to the domain of crisis management.
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Jorge Vargas, Jonatan Rojas, Alejandra Inga, Wilder Mantilla, Hulber Añasco, Melanie Fatsia Basurto, et al. (2016). Towards Reliable Recurrent Disaster Forecasting Methods: Peruvian Earthquake Case. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: We are interested in recurrent disaster forecasts; these are events such as annual cyclones in the Caribbean, earthquakes along the Ring of Fire and so on. These crises, even small- or medium-sized, are, in fact, critical for the emergency response of humanitarian organizations inasmuch as the sum of casualties and losses attained are as deadly as those that are considered exceptional. The aim of our research is to show that it is possible to use traditional forecasting methods such as: causal methods (which include the use of linear regression functions, non-linear, multivariate, etc.), time series (which include simple moving average, weighted moving average, exponential smoothing, trend-adjusted exponential smoothing, etc.) and so on, if the historical data keeps, among other criteria, its patterns, frequency, and magnitude, in a sustainable manner. Finally, an example to forecast recurrent earthquakes in Peru is presented.
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