Local map generation using position and communication history of mobile nodes.
ABSTRACT 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.
Conference Paper: Autonomous Recognition of Emergency Site by Wearable Sensors[Show abstract] [Hide abstract]
ABSTRACT: To support rescue activities of first responders is crucial at disaster sites. Especially, provisioning location and situation information is indispensable for those first responders to efficiently rescue injured people in unknown places with a lot of buildings such as private properties (like factories) and university campus. In such an extreme situation, seamless indoor/outdoor maps will be of substantial aid for the first responders. In this paper, we propose a method of creating an indoor/outdoor map by a first responder team. We assume some of them are equipped with range scanners and all the members have GPS and WiFi devices. Then the presence of obstacles and movable areas is estimated by combining information from different sources (like GPS, WiFi and range scanners) with different confident levels. Since these confident levels depend on scenarios and environments, we design an "adaptive information fusion" algorithm that automatically estimates the confident levels to optimize the precision of the generated map. We have demonstrated our method in several experiments with real sensor data.Green Computing and Communications (GreenCom), 2012 IEEE International Conference on; 01/2012
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: We propose an opportunistic ad hoc localization algorithm called Urban Pedestrians Localization (UPL), for estimating locations of mobile nodes in urban districts. The design principles of UPL are twofold. First, we assume that location landmarks are deployed sparsely due to deployment-cost constraints. Thus, most mobile nodes cannot expect to meet these location landmarks frequently. Each mobile node in UPL relies on location information received from its neighboring mobile nodes instead in order to estimate its area of presence in which the node is expected to exist. Although the area of presence of each mobile node becomes inexact as it moves, it can be used to reduce the areas of presence of the others. Second, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles for predicting the area of presence of mobile nodes accurately under mobility. This also helps to reduce each node's area of presence. The experimental results have shown that UPL could be limited to $(0.7r)$ positioning error in average, where $(r)$ denotes the radio range by the above two ideas.IEEE Transactions on Mobile Computing 01/2013; 12(5):1009-1022. · 2.40 Impact Factor