Five Revolutions in Geoinformatics – Caused by Geo-Sensor Networks Sven Schade and Alexander C. Walkowski Institute for Geoinformatics, University of Münster Robert-Koch-Str. 26-28 D-48149 Münster, Germany tel.: +49251-8333085, +49251-8330056 E-mail: schades@uni-muenster.de, walkowski@uni-muenster.de Abstract The traditional paradigm of observing environmental phenomena is based on a small number of fixed sensors. The deployment is carried out using a highly- controlled deployment strategy, which for example defines the deployment location and the calibration parameters. Currently, the observation process is moving from a centralized manner to a distributed and dense manner of observing phenomena. The emergence of distributed sensor networks results in a new data collection scheme, with continuous feeds from dense distributed sensors. Relating to these emerging distributed sensor networks, we expect five revolutions in understanding and handling of geographic information. Introduction The traditional paradigm of observing environmental phenomena is based on a small number of fixed sensors. The deployment is carried out using a highly- controlled deployment strategy, which for example defines the deployment location and the calibration parameters. Currently, the observation process is moving from a centralized manner – based on isolated sensors – to a distributed and dense manner of observing phenomena. Distributed sensor networks comprises of large number of sensors spread logically and connected through a communication network [1]. This results in a new data collection scheme, with continuous feeds from dense distributed sensors. A geo-sensor network can be defined as a distributed sensor network that monitors phenomena in geographic space and the geospatial content of the information gathered, aggregated and analyzed is fundamental. [2] We define a mobile geo-sensor network as a geo-sensor network whose sensor nodes are not fixed on a certain location. Instead the sensor nodes are either self-propelled or carried by agents. Such mobile geo-sensor networks provide an unprecedented way of monitoring environmental phenomena. Information derived from such observations will be the dominating source in the future. Relating to these, we expect five revolutions in understanding and handling of geographic information. We structure the rest of this paper as follows. In the next section a scenario of sensor use in an emergency case is introduced. This scenario illustrates the impact of the expected revolutions, which are outlined in the subsequent sections. We end with a conclusion and an outlook to the next steps to take towards our vision. Scenario Based on the following scenario that describes the current usage of sensor data we will explain the five expected revolutions. In the field of environmental monitoring the use of sensors is common practice. An emergency manager will survey the measurements observed by sensors. In the case a threshold is crossed the emergency manager will release an alarm. In the use case of air pollution monitoring the sensor network consists of fixed in-situ sensors. This scenario results in the following picture for the emergency manager: It is up to him to survey certain focal areas or objects (e.g. school, sports stadium). If the concentration of a pollutant exceeds the allowed threshold, he has to release an alarm. But due to the cost of the sensors it is not possible to place a sensor at each object of interest. Hence, the emergency manager requests data for a point in space (potentially also in time) for which no observations were made (Figure 1). Figure 1 Scenario based on the traditional observing paradigm Since it is unlikely that a sensor is directly located at the point of interest, a certain region in space and time is asked in a second step for time series for the sensors in the defined spatial region or - in the case of mobile sensors - for a trajectory of the sensor within the spatial region associated with the observations. It is up to the user to derive the desired information from the provided observation data. This scenario describes the current way of sensor data usage. The user does not get the desired information; he gets data observed at nearby locations (in space and time). Thus we call this pattern of sensor data usage data centric. GIS Ostrava 2006 http://gis.vsb.cz/gisvisions/2025/papers/schade.html 1 of 7 4/15/2015 10:58 AM