While location-based services are already well established in outdoor scenarios, they are still not available in indoor environments. The reason for this can be found in two open problems: First, there is still no off-the-shelf indoor positioning system for mobile devices and, second, indoor maps are not publicly available for most buildings. While there is an extensive body of work on the first problem, the efficient creation of indoor maps remains an open challenge. We tackle the indoor mapping challenge in our MapGENIE approach that automatically derives indoor maps from traces collected by pedestrians moving around in a building. Since the trace data is collected in the background from the pedestrians' mobile devices, MapGENIE avoids the labor-intensive task of traditional indoor map creation and increases the efficiency of indoor mapping. To enhance the map building process, MapGE-NIE leverages exterior information about the building and uses grammars to encode structural information about the building. Hence, in contrast to existing work, our approach works without any user interaction and only needs a small amount of traces to derive the indoor map of a building. To demonstrate the performance of MapGENIE, we implemented our system using Android and a foot-mounted IMU to collect traces from volunteers. We show that using our grammar approach, compared to a purely trace-based approach we can identify up to four times as many rooms in a building while at the same time achieving a consistently lower error in the size of detected rooms.
ABSTRACT:As spatial grammars have proven successful and efficient to deliver LOD3 models, the next challenge is their extension to indoor applications, leading to LOD4 models. Therefore, a combined indoor grammar for the automatic generation of indoor models from erroneous and incomplete observation data is presented. In building interiors where inaccurate observation data is available, the grammar can be used to make the reconstruction process robust, and verify the reconstructed geometries. In unobserved building interiors, the grammar can generate hypotheses about possible indoor geometries matching the style of the rest of the building. The grammar combines concepts from L-systems and split grammars. It is designed in such way that it can be derived from observation data fully automatically. Thus, manual predefinitions of the grammar rules usually required to tune the grammar to a specific building style, become obsolete. The potential benefit of using our grammar as support for indoor modeling is evaluated based on an example where the grammar has been applied to automatically generate an indoor model from erroneous and incomplete traces gathered by foot-mounted MEMS/IMU positioning systems.
Recent forecasts predict that the amount of cellular data traffic will significantly increase within the next few years. The reason for this trend is on the one hand the high growth rate of mobile Internet users and on the other hand the growing popularity of high bandwidth streaming applications. Given the fact that cellular networks (e.g. UMTS) have only limited capacity, the existing network infrastructure will soon reach its limits. As a result, the concept of traffic offloading attracts more and more attention in research since it aims at the reduction of cellular traffic by shifting it to local-area networks like Wi-Fi. One particular form of traffic offloading is known as opportunistic traffic offloading and follows the basic idea to shift traffic from the cellular network to the level of inter-device communication of mobile devices. To perform opportunistic traffic offloading in an efficient way, assumptions about the prospective inter-device connectivity of the mobile devices have to be made. In general, the more inter-device connections are possible the more traffic can be offloaded. To utilize this fact, we developed the TOMP system. TOMP is the first opportunistic traffic offloading system that uses movement predictions of mobile users to analyze the prospective inter-device connectivity. In this paper we propose three different metrics for analyzing movement predictions and present an algorithm, which uses these metrics to utilize an efficient opportunistic traffic offloading. To evaluate TOMP, we show by simulation that we can save up to 40% of cellular messages in comparison to a typical cellular network.
With the increasing proliferation of small and cheap GPS receivers, a new way of generating road maps could be witnessed over the last few years. Participatory mapping approaches like OpenStreetMap introduced a way to generate road maps collaboratively from scratch. Moreover, automatic mapping algorithms were proposed, which automatically infer road maps from a set of given GPS traces. Nevertheless, one of the main problems of these maps is their unknown quality in terms of accuracy, which makes them unreliable and, therefore, not applicable for the use in critical scenarios.To address this issue, we propose MapCorrect: An automatic map correction and validation system. MapCorrect automatically collects GPS traces from people's mobile devices to correct a given road map and validate it by identifying those parts of the map that are accurately mapped with respect to some user provided quality requirements. Since fixing a GPS position is a battery draining operation, the collection of GPS data raises concerns about the energy consumption of the participating mobile devices. We tackle this issue by introducing an optimized sensing mechanism that gives the mobile devices notifications indicating those parts of the map that are considered as sufficiently mapped and, therefore, require no further GPS data for their validation. Furthermore, we show by simulation that using this approach up to 50% of the mobile phones' energy can be saved while not impairing the effectiveness of the map correction and validation process at all.
Utilizing peoples' mobile devices for gathering sensor data has attracted a lot of attention within the last few years. As a result, a great variety of systems for sensing environmental phenomena like temperature or noise have been proposed. However, most of these systems do not take into account that mobile devices have only limited energy resources. For instance, an often assumed prerequisite is that mobile devices are always aware of their position. Given the fact that a position fix is a very energy consuming operation, continuous positioning would quickly drain a device's battery. Since the owners of the mobile devices will not tolerate a significant reduction of the devices' battery lifetime, such an approach is not suitable. To address this issue we present PSense, a flexible system for efficiently gathering sensor data with mobile devices. By avoiding unnecessary position fixes, PSense reduces the energy consumption of mobile devices by up to 70% compared to existing mobile sensing approaches. This is achieved by introducing an adaptive positioning mechanism and by utilizing energy efficient short-range communication to exchange position related information.
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