7,973 research outputs found
Robust Component-based Network Localization with Noisy Range Measurements
Accurate and robust localization is crucial for wireless ad-hoc and sensor
networks. Among the localization techniques, component-based methods advance
themselves for conquering network sparseness and anchor sparseness. But
component-based methods are sensitive to ranging noises, which may cause a huge
accumulated error either in component realization or merging process. This
paper presents three results for robust component-based localization under
ranging noises. (1) For a rigid graph component, a novel method is proposed to
evaluate the graph's possible number of flip ambiguities under noises. In
particular, graph's \emph{MInimal sepaRators that are neaRly cOllineaR
(MIRROR)} is presented as the cause of flip ambiguity, and the number of
MIRRORs indicates the possible number of flip ambiguities under noise. (2) Then
the sensitivity of a graph's local deforming regarding ranging noises is
investigated by perturbation analysis. A novel Ranging Sensitivity Matrix (RSM)
is proposed to estimate the node location perturbations due to ranging noises.
(3) By evaluating component robustness via the flipping and the local deforming
risks, a Robust Component Generation and Realization (RCGR) algorithm is
developed, which generates components based on the robustness metrics. RCGR was
evaluated by simulations, which showed much better noise resistance and
locating accuracy improvements than state-of-the-art of component-based
localization algorithms.Comment: 9 pages, 15 figures, ICCCN 2018, Hangzhou, Chin
Eigenvector Synchronization, Graph Rigidity and the Molecule Problem
The graph realization problem has received a great deal of attention in
recent years, due to its importance in applications such as wireless sensor
networks and structural biology. In this paper, we extend on previous work and
propose the 3D-ASAP algorithm, for the graph realization problem in
, given a sparse and noisy set of distance measurements. 3D-ASAP
is a divide and conquer, non-incremental and non-iterative algorithm, which
integrates local distance information into a global structure determination.
Our approach starts with identifying, for every node, a subgraph of its 1-hop
neighborhood graph, which can be accurately embedded in its own coordinate
system. In the noise-free case, the computed coordinates of the sensors in each
patch must agree with their global positioning up to some unknown rigid motion,
that is, up to translation, rotation and possibly reflection. In other words,
to every patch there corresponds an element of the Euclidean group Euc(3) of
rigid transformations in , and the goal is to estimate the group
elements that will properly align all the patches in a globally consistent way.
Furthermore, 3D-ASAP successfully incorporates information specific to the
molecule problem in structural biology, in particular information on known
substructures and their orientation. In addition, we also propose 3D-SP-ASAP, a
faster version of 3D-ASAP, which uses a spectral partitioning algorithm as a
preprocessing step for dividing the initial graph into smaller subgraphs. Our
extensive numerical simulations show that 3D-ASAP and 3D-SP-ASAP are very
robust to high levels of noise in the measured distances and to sparse
connectivity in the measurement graph, and compare favorably to similar
state-of-the art localization algorithms.Comment: 49 pages, 8 figure
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
This paper deals with the rotation synchronization problem, which arises in
global registration of 3D point-sets and in structure from motion. The problem
is formulated in an unprecedented way as a "low-rank and sparse" matrix
decomposition that handles both outliers and missing data. A minimization
strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against
state-of-the-art algorithms on simulated and real data. The results show that
R-GoDec is the fastest among the robust algorithms.Comment: The material contained in this paper is part of a manuscript
submitted to CVI
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
Methods and strategies of object localization
An important property of an intelligent robot is to be able to determine the location of an object in 3-D space. A general object localization system structure is proposed, some important issues on localization discussed, and an overview given for current available object localization algorithms and systems. The algorithms reviewed are characterized by their feature extracting and matching strategies; the range finding methods; the types of locatable objects; and the mathematical formulating methods
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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