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
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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 05/2013; 12(5):1009-1022. · 2.91 Impact Factor
Conference Paper: Energy-efficient trajectory tracking for mobile devices.[Show abstract] [Hide abstract]
ABSTRACT: Emergent location-aware applications often require tracking trajectories of mobile devices over a long period of time. To be useful, the tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, when trajectory information needs to be sent to a remote server, on-device simplification of the trajectories is needed to reduce the amount of data transmission. While there has recently been a lot of work on energy-efficient position tracking, the energy-efficient tracking of trajectories has not been addressed in previous work. In this paper we propose a novel on-device sensor management strategy and a set of trajectory updating protocols which intelligently determine when to sample different sensors (accelerometer, compass and GPS) and when data should be simplified and sent to a remote server. The system is configurable with regards to accuracy requirements and provides a unified framework for both position and trajectory tracking. We demonstrate the effectiveness of our approach by emulation experiments on real world data sets collected from different modes of transportation (walking, running, biking and commuting by car) as well as by validating with a real-world deployment. The results demonstrate that our approach is able to provide considerable savings in the battery consumption compared to a state-of-the-art position tracking system while at the same time maintaining the accuracy of the resulting trajectory, i.e., support of specific accuracy requirements and different types of applications can be ensured.Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys 2011), Bethesda, MD, USA, June 28 - July 01, 2011; 01/2011