Local map generation using position and communication history of mobile nodes

Conference Paper · March 2010with8 Reads
DOI: 10.1109/PERCOM.2010.5466999 · Source: DBLP
Conference: Eigth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2010, March 29 - April 2, 2010, Mannheim, Germany
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.

    • "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. "
    [Show abstract] [Hide abstract] 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
    0Comments 5Citations
    • "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
    0Comments 1Citation
    • "Maps by simple image processing. Alternatively, we may use a map generation technique using both location information and ad hoc communication among nodes presented in [53]. When node i receives a hello message from node j at time t, node i immediately runs the UPL algorithm to update its area of presence. "
    [Show abstract] [Hide abstract] 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.
    Full-text · Article · May 2013 · IEEE Transactions on Mobile Computing
    0Comments 6Citations
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