Local map generation using position and communication history of mobile nodes.
Grad. Sch. of Inf. Sci. & Technol., Osaka Univ., Suita, JapanDOI: 10.1109/PERCOM.2010.5466999 Conference: Eigth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2010, March 29 - April 2, 2010, Mannheim, Germany
In this paper, we propose an algorithm to estimate 2D shapes and positions of obstacles such as buildings using GPS and wireless communication history of mobile nodes. Our algorithm enables quick recognition of geography, which is required in broader types of activities such as rescue activities in emergency situations. Nevertheless, detailed building maps might not be immediately available in private regions such as large factories, warehouses and universities, or prepared maps might not be effective due to collapse of buildings or roads in disaster situations. Some methodologies adopt range measurement sensors like infra-red and laser sensors or cameras. However, they require dedicated hardware and actions for the measurement. Meanwhile, the proposed method can create a rough 2D view of buildings and roads using only wireless communication history between mobile nodes and position history from GPS receivers. The results from the experiment conducted in 150 mÃ190 m region on our university campus assuming rescue and treatment actions by 15 members have shown that our method could generate a local map with 85% accuracy within 350 seconds. We have also validated the performance of our algorithm by simulations with various settings.
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ABSTRACT: Large accidents and disasters in crowded regions such as business districts and universities may create a large number of patients, and first responders need to recognize the presence and location of buildings for their efficient rescue operations. In this paper, we propose an algorithm to estimate the two-dimensional (2D) shapes and positions of buildings, simultaneously using GPS logs and wireless communication logs of mobile nodes. The algorithm is easy to implement since it only needs general wireless devices such as smartphones. The results from the experiments conducted assuming rescue operation scenarios have shown that the proposed method could quickly generate a map with 85% accuracy.Pervasive and Mobile Computing 12/2010; 6(6):623-641. DOI:10.1016/j.pmcj.2010.06.001 · 2.08 Impact Factor
Conference Paper: Sensing and Classifying Impairments of GPS Reception on Mobile Devices[Show abstract] [Hide abstract]
ABSTRACT: Positioning using GPS receivers is a primary sensing modality in many areas of pervasive computing. However, previous work has not considered how people’s body impacts the availability and accuracy of GPS positioning and for means to sense such impacts. We present results that the GPS performance degradation on modern smart phones for different hand grip styles and body placements can cause signal strength drops as high as 10-16 dB and double the positioning error. Furthermore, existing phone applications designed to help users identify sources of GPS performance impairment are restricted to show raw signal statistics. To help both users as well as application systems in understanding and mitigating body and environment-induced effects, we propose a method for sensing the current sources of GPS reception impairment in terms of body, urban and indoor conditions. We present results that show that the proposed autonomous method can identify and differentiate such sources, and thus also user environments and phone postures, with reasonable accuracy, while relying solely on GPS receiver data as it is available on most modern smart phones.Pervasive Computing - 9th International Conference, Pervasive 2011, San Francisco, CA, USA, June 12-15, 2011. Proceedings; 01/2011
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ABSTRACT: 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.IEEE 36th Conference on Local Computer Networks, LCN 2011, Bonn, Germany, October 4-7, 2011; 10/2011
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