A 3d-localization and terrain modeling technique for wireless sensor networks

AbdelSalam, Hady S.; Olariu, Stephan

doi:10.1145/1540343.1540351

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…The individual solution is then identified using simple statistical techniques. The overall approach is a generalization in the R m space of techniques used in the traditional lighthouse beacon positioning, and recently in positioning in metric and non-metric spaces; for instance, positioning of a cellular phone in GSM and WiFi cells [1,3], and of earthquake epicenters [9]. This process is broadly compatible to the conventional least squares adjustment ( Figure 1) and it is stochastic, because we assume that measurements contain errors defined by a set of parameters " j .…”

Section: Discussionmentioning

confidence: 99%

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Adjustment of highly non-linear redundant systems of equations using a numerical, topology-based approach

Saltogianni

Stiros

2012

Journal of Applied Geodesy

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The adjustment of systems of highly non-linear, redundant equations f j , deriving from observations of certain geophysical processes and geodetic data cannot be based on conventional least-squares techniques, and is based on various numerical inversion techniques. Still these techniques lead to solutions trapped in local minima, to correlated estimates and to solution with poor error control. To overcome these problems, we propose an alternative numerical-topological approach inspired by lighthouse beacon navigation, usually used in 2-D, low-accuracy applications. In our approach, an m-dimensional grid G of points around the real solution (an m-dimensional vector) is at first specified. Then, for each equation f j an uncertainty " j is assigned to the corresponding measurement l j , and the sets A j G of the grid points which satisfy the condition jf j l j j < " j are detected. This process is repeated for all equations, and the common section A of the sets of grid points A j is defined. From this set of grid points, which define a space including the real solution, we compute its center of weight, which corresponds to an estimate of the solution, and its variance-covariance matrix. An optimal solution can be obtained through optimization of the uncertainty " j in each observation. The efficiency of the overall process was assessed in comparison with conventional least squares adjustment.

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Section: Discussionmentioning

confidence: 99%

“…defines a subspace of G which contains the solution of the system of equations(1). Selecting the suitable values of k j , the minimal space A defined by grid points can be defined in analogy to Figures 1 (e)-(f).The most likely (estimated) solution of the system of equations is defined by the center of gravity N x D .…”

mentioning

confidence: 99%

Adjustment of highly non-linear redundant systems of equations using a numerical, topology-based approach

Saltogianni

Stiros

2012

Journal of Applied Geodesy

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“…The simulation displayed that this modification was effective to get the better performance in locating time and accuracy. Literature [9] selected 3 nearest beacons around the unknown node to estimate the position of the unknown node. It adopted the triangulation to divide the whole three-dimensional space in order to build the model and acquire the location.…”

Section: Related Workmentioning

confidence: 99%

A RSSI-weighted refinement method of IAPIT-3D

Liu

Zhang

Shu

et al. 2011

Proceedings of 2011 International Conference on Computer Science and Network Technology

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The location information is very essential to monitoring activities in wireless sensor networks. According to the low accuracy of IAPIT-3D (Improved Approximate Point in Tetrahedron) in beacon-sparse environment, we propose a RSSI weighted refinement method. It reduces the occurrence probability of the "in-to-out error" by utilizing the improved GRID SCAN. Moreover, the RSSI-weighted method improves the accuracy and decreases the probability of "in-to-out error" simultaneously. The simulation reveals that this method enhances the accuracy relatively to IAPIT-3D and is robust to the low node density.

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“…Analysis on localization schemes focusing on the 3-D space is of particular interests to real applications of WSNs, especially when the difference between 2-D space and 3-D space is significant. For instance, irregularity of the radio transmission has been investigated in 2-D space, while its counterpart in 3-D space remains to be unknown [68,69].…”

Section: -Dimension Localizationmentioning

confidence: 99%

Algorithmic Aspects of Sensor Localization

Das

Wang

Ghosh

et al. 2010

Monographs in Theoretical Computer Science. An EATCS Series

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Identifying locations of nodes in wireless sensor networks (WSNs) is critical to both network operations and most application level tasks. Sensor nodes equipped with geographical positioning system (GPS) devices are aware of their locations at a precision level of few meters. However, installing GPS devices on a large number of sensor nodes is not only expensive but affects the form factor of these nodes. Moreover, GPS-based localization is not applicable in the indoor environments such as buildings. There exists an extensive body of research literature that aims at obtaining absolute locations as well as relative spatial locations of nodes in a WSN without requiring specialized hardware at large scale. The typical approach consists of employing only a limited number of anchor nodes that are aware of their own locations, and then trying to infer locations of non-anchor nodes using graph theoretic, geometric, statistical, optimization and machine learning techniques. Thus, the literature represents a very rich ensemble of algorithmic techniques applicable to low power, highly distributed nodes with resource-optimal computations. In this chapter we take a close look at the algorithmic aspects of various important localization techniques for WSNs.

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A 3d-localization and terrain modeling technique for wireless sensor networks

Cited by 13 publications

References 20 publications

Adjustment of highly non-linear redundant systems of equations using a numerical, topology-based approach

Adjustment of highly non-linear redundant systems of equations using a numerical, topology-based approach

A RSSI-weighted refinement method of IAPIT-3D

Algorithmic Aspects of Sensor Localization

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