Using tracked mobile sensors to make maps of environmental effects

We present a study the results of a study of environmental carbon monoxide pollution that has uses a set of tracked, mobile pollution sensors. The motivating concept is that we will be able to map pollution and other properties of the real world a fine scale if we can deploy a large set of sensors with members of the general public who would carry them as they go about their normal everyday activities. To prove the viability of this concept we have to demonstrate that data gathered in an ad-hoc manner is reliable enough in order to allow us to build interesting geo-temporal maps. We present a trial using a small number of global positioning system-tracked CO sensors. From analysis of raw GPS logs we find some well-known spatial and temporal properties of CO. Further, by processing the GPS logs we can find fine-grained variations in pollution readings such as when crossing roads. We then discuss the space of possibilities that may be enabled by tracking sensors around the urban environment – both in getting at personal experience of properties of the environment and in making summative maps to predict future conditions. Although we present a study of CO, the techniques will be applicable to other environmental properties such as radio signal strength, noise, weather and so on

Erfassung von Innenraummodellen mittels Smartphones

Unter Verwendung von aufgezeichneten Bewegungsspuren eines Inertialsensors ist es möglich, ein Innenraummodell eines Gebäudes zu generieren. Das Innenraummodell unterscheidet hierbei Räume und Korridore. Die Bewegungsspuren werden durch auf dem Fuß platzierte Inertialsensoren per ZUPT erfasst. Es wird von einer Vielzahl an Benutzern ausgegangen, welche sich in alltäglichen Situationen durch Gebäude bewegen und mit den Sensoren ihres Smartphones opportunistisch Daten erfassen. Bewegungsspuren werden in gerade Segmente unterteilt. Durch eine Äquivalenzrelation wird festgestellt, ob sich der Benutzer beim Erfassen der Spuren auf demselben Korridor befunden hatte. Die Geometrie von Korridoren wird durch Quantile und die empirische Verteilungsfunktion bestimmt. Durch die Ausrichtung der Spuren anhand der Geometrie der Korridore, können überstehende Abschnitte durch geeignete Kriterien als Räume erkannt werden. Für die Evaluation wurden von vier Testpersonen über 200 Spuren in alltäglichen Szenarien aufgenommen. Wählt man aus diesen Spuren 90 aus, so werden im Durchschnitt über 90% aller Korridore des Stockwerks erkannt. In 65% der so generierten Innenraummodelle war die durchschnittliche Verschiebung der Korridore kleiner als 1,5m.It is possible to generate indoor models by using traces recorded by inertial measurement units. The generated indoor model distinguishes between rooms and corridors. Traces will be collected by foot-mounted inertial measurement units via ZUPT. The data will be collected in a crowd based approach via Smartphones and sensor units carried by users. Users will walk inside the building in all-day situations, collecting data opportunistically. The collected traces will be segmented into parts where the user walked straight. Using a equivalence relation, segments collected from the same corridor can be combined. Reconstructing the geometry of corridors will use quantiles and the empirical distribution function. Using a method to correct traces via the constructed corridor geometry, rooms can be found by protruding parts of traces. To evaluate the system, four volunteers collected over 200 traces in everyday scenarios. Choosing 90 out of them, in average 90% of all corridors will be found. In 65% of this constructed indoor models, the average shift of corridors was less than 1.5 m

Optimized acquisition of spatially distributed phenomena in public sensing systems

Nowadays, an increasing number of popular consumer electronics is shipped with a variety of sensors. The usage of these as a wireless sensing platform, where users are the key architectural component, and ubiquitous access to communication infrastructure has established a new application area called public sensing. We present an opportunistic public sensing system that allows for a flexible and efficient acquisition of sensor readings. This work considers the usage of smartphones as a sensor network in a model-driven sensor data acquisition. We focus on efficiency of query dissemination to mobile nodes, while retaining high effectiveness regarding defined sensing quality of collected data. We adopted and extended an existing geographic routing protocol to design an efficient com- munication system that executes model-driven data acquisition and is robust to changing sensors availability. We use in-network processing paradigm to efficiently distribute queries to mobile nodes and to collect results afterwards. The developed approach was simulated using OMNeT++ network simulator. To verify implemented algorithms and test the overall system performance, we run simulations in different scenarios and evaluate them using adequate cov- erage metrics. Moreover, we verify our intuitive extension to adopted routing protocol and show that it can have a strong impact on the efficiency of protocol in question

Geospatial Computing: Architectures and Algorithms for Mapping Applications

Beginning with the MapTube website (1), which was launched in 2007 for crowd-sourcing maps, this project investigates approaches to exploratory Geographic Information Systems (GIS) using web-based mapping, or ‘web GIS’. Users can log in to upload their own maps and overlay different layers of GIS data sets. This work looks into the theory behind how web-based mapping systems function and whether their performance can be modelled and predicted. One of the important questions when dealing with different geospatial data sets is how they relate to one another. Internet data stores provide another source of information, which can be exploited if more generic geospatial data mining techniques are developed. The identification of similarities between thousands of maps is a GIS technique that can give structure to the overall fabric of the data, once the problems of scalability and comparisons between different geographies are solved. After running MapTube for nine years to crowd-source data, this would mark a natural progression from visualisation of individual maps to wider questions about what additional knowledge can be discovered from the data collected. In the new ‘data science’ age, the introduction of real-time data sets introduces a new challenge for web-based mapping applications. The mapping of real-time geospatial systems is technically challenging, but has the potential to show inter-dependencies as they emerge in the time series. Combined geospatial and temporal data mining of realtime sources can provide archives of transport and environmental data from which to accurately model the systems under investigation. By using techniques from machine learning, the models can be built directly from the real-time data stream. These models can then be used for analysis and experimentation, being derived directly from city data. This then leads to an analysis of the behaviours of the interacting systems. (1) The MapTube website: http://www.maptube.org