Conference Paper

Local map generation using position and communication history of mobile nodes.

Grad. Sch. of Inf. Sci. & Technol., Osaka Univ., Suita, Japan
DOI: 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
Source: DBLP

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 m×190 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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    • "In increasingly many applications, however, knowledge about the location of the device is not sufficient, but information about the movement history or trajectory of the device is essential. Examples of applications that depend on trajectories rather than just on the current position include sports trackers that log running paths [16], shared ride recommenders [1], health care applications that visualize daily patterns or habits of patients [29], and collaborative sensing applications for generating maps [23], monitoring environmental impact [24] or mapping cycling experiences [8]. Continuous sensing of the user's position rapidly depletes the battery of a mobile device. "

    Full-text · Article · Jan 2014
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    • "In increasingly many applications, however, knowledge about the location of the device is not sufficient, but information about the movement history or trajectory of the device is essential. Examples of applications that depend on trajectories rather than just on the current position include sports trackers that log running paths [16], shared ride recommenders [1], health care applications that visualize daily patterns or habits of patients [29], and collaborative sensing applications for generating maps [23], monitoring environmental impact [24] or mapping cycling experiences [8]. "
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    ABSTRACT: Many mobile location-aware applications require the sampling of trajectory data accurately over an extended period of time. However, continuous trajectory tracking poses new challenges to the overall battery life of the device, and thus novel energy-efficient sensor management strategies are necessary for improving the lifetime of such applications. Additionally, such sensor management strategies are required to provide a high and application-adjustable level of robustness regardless of the user's transportation mode. In this article, we extend and further analyze the sensor management strategies of the EnTrackedT system that intelligently determines when to sample different on-device sensors (e.g., accelerometer, compass and GPS) for trajectory tracking. Specifically, we propose the concept of situational bounding to improve and parameterize the robustness of sensor management strategies for trajectory tracking. We demonstrate the effectiveness of our proposed approach by performing a series of emulation experiments on real world data sets collected from different modes of transportation (including walking, running, biking and commuting by car) on mobile devices from two different platforms. Thorough experimental analyses indicate that our system can save significant amounts of battery power compared to the state-of-the-art position tracking systems, while simultaneously maintaining robustness and accuracy bounds as required by diverse location-aware applications.
    Full-text · Article · Jan 2014 · IEEE Transactions on Mobile Computing
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    • "However, often not only knowledge about its current position is required, but also the tracking of its trajectory over longer time intervals. Examples of applications that depend on trajectories rather than just on the current position are sports trackers that log, e.g., running paths [12], shared ride recommenders [1], health care applications that visualize daily patterns and habits of patients [22], and collaborative sensing applications that, e.g., generate maps [18], monitor the environmental impact [19], or map cycling experiences [6]. "
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    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.
    Full-text · Conference Paper · Jan 2011
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