## 1 Introduction  
The paramount requirement for effective disaster mitigation, preparation and response is the provision of relevant, timely, accurate and adequately spatially-resolved information. Such information will need to cover both the natural hazard environment (e.g., an area's climate, seismicity, landscape, etc. ), possible contributions from, e.g., heavy industry (e.g., potential for toxic material release), and the nature and extent of the area of interest's built environment. However, this leads to the problems of, first, identifying what parameters need to be measured, second, how to interpret and communicate these measurements, and third, what is the most effective (and cost efficient) means of undertaking these data-gathering actions (including the establishment of the required measuring networks). This is especially important in those regions where there is a lack of such information, or where there are major changes occurring in the environment, e.g., rapid urbanization in developing countries, where available information may quickly become out-of-date. In addition, those environments that experience a number of different natural hazard types (e.g., earthquakes, landslides, windstorms, flooding) would require multiple parameters to be measured. The aim of this paper is to present an integrated system for measuring a range of parameters that are relevant to different components of the so-called risk equation. Risk in general may be described as being a function of hazard (e.g., earthquake-induced ground motion), exposure (e.g., the number and structural characteristics of buildings within a given area, but also segments of the population, lifelines or the natural environment) and the vulnerability of the exposed elements to the considered hazard. Knowledge of each of these elements calls upon specific types of information. For example, understanding the hazard requires records of the frequency of events of a given size (e.g., earthquake magnitude, flood water levels), exposure requires the extent and distribution of the elements of interest, be it population density (and the associated demographics), industrial facilities and lifelines and other infrastructure, while vulnerability describes how each element is affected by an event of a given size. The role of the system presented in this work is to undertake measurements either continuously, as in the case of structural health monitoring and early warning, or for specific tasks, e.g., local site amplification assessments, monitoring following a seismic event, that will contribute to the disaster mitigation and response decision-making process. An earlier generation of smart sensors, termed Self-Organizing Seismic Early Warning Information Network (SOSEWIN, [1]) were developed by GFZ in collaboration with the Humboldt University within the framework of the EC-funded Seismic eArly warning For EuRope project (SAFER; [[http://cordis.europa.eu/project/rcn/81386_en.html](http://cordis.europa.eu/project/rcn/81386_en.html)]([http://cordis.europa.eu/project/rcn/81386_en.html](http://cordis.europa.eu/project/rcn/81386_en.html))). Based on this experience, a new generation of smart sensors have been designed that extend the capabilities of the SOSEWIN technology into an industry-grade solution. The Multi-Parameter Wireless Sensing system (MPwise, [2]) presented in this work will contribute to the provision of some of the required information, focusing on the hazard and vulnerability components of the risk equation. The system allows several types of sensors to be combined with the node unit to acquire data in real time and, when combined with the software platform, is capable of real-time processing. While such a system could be used for long-term risk assessment, it may also be employed to obtain knowledge about an evolving situation, for example, the change in the fragility of buildings due to damage arising from a seismic sequence.