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BeepBeep: A High Accuracy Acoustic Ranging System using COTS Mobile Devices
"... We present the design, implementation, and evaluation of BeepBeep, a highaccuracy acousticbased ranging system. It operates in a spontaneous, adhoc, and devicetodevice context without leveraging any preplanned infrastructure. It is a pure softwarebased solution and uses only the most basic se ..."
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Cited by 77 (3 self)
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We present the design, implementation, and evaluation of BeepBeep, a highaccuracy acousticbased ranging system. It operates in a spontaneous, adhoc, and devicetodevice context without leveraging any preplanned infrastructure. It is a pure softwarebased solution and uses only the most basic set of commodity hardware – a speaker, a microphone, and some form of devicetodevice communication – so that it is readily applicable to many lowcost sensor platforms and to most commercialofftheshelf mobile devices like cell phones and PDAs. It achieves high accuracy through a combination of three techniques: twoway sensing, selfrecording, and sample counting. The basic idea is the following. To estimate the range between two devices, each will emit a speciallydesigned sound signal (“Beep”) and collect a simultaneous recording from its microphone. Each recording should contain two such beeps, one from its own speaker and the other from its peer. By counting the number of samples between these two beeps and exchanging the time duration information with its peer, each device can derive the twoway time of flight of the beeps at the granularity of sound sampling rate. This technique cleverly avoids many sources of inaccuracy found in other typical timeofarrival schemes, such as clock synchronization, nonrealtime handling, software delays, etc. Our experiments on two common cell phone models have shown that we can achieve around one or two centimeters accuracy within a range of more than ten meters, despite a series of technical challenges in implementing the idea.
Distributed control of robotic networks: a mathematical approach to motion coordination algorithms
, 2009
"... (i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 ..."
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Cited by 38 (1 self)
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(i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 “Distributed Control of Robotic Networks ” by F. Bullo, J. Cortés and S. Martínez
Computational Intelligence in Wireless Sensor Networks: A Survey
 IEEE COMMUNICATIONS SURVEYS & TUTORIALS
, 2011
"... Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms o ..."
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Cited by 37 (0 self)
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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.
Particle swarm optimization in wirelesssensor networks: A brief survey
 IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
, 2011
"... Abstract—Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensio ..."
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Cited by 31 (0 self)
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Abstract—Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bioinspired techniques. Particle swarm optimization (PSO) is a simple, effective and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering and dataaggregation. This paper outlines issues in WSNs, introduces PSO and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues. Index Terms—clustering, dataaggregation, localization, optimal deployment, PSO, Wireless sensor networks
Distributed ImageBased 3D Localization of Camera Sensor Networks
"... Abstract — We consider the problem of distributed estimation of the poses of N cameras in a camera sensor network using image measurements only. The relative rotation and translation (up to a scale factor) between pairs of neighboring cameras can be estimated using standard computer vision technique ..."
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Cited by 26 (4 self)
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Abstract — We consider the problem of distributed estimation of the poses of N cameras in a camera sensor network using image measurements only. The relative rotation and translation (up to a scale factor) between pairs of neighboring cameras can be estimated using standard computer vision techniques. However, due to noise in the image measurements, these estimates may not be globally consistent. We address this problem by minimizing a cost function on SE(3) N in a distributed fashion using a generalization of the classical consensus algorithm for averaging Euclidean data. We also derive a condition for convergence, which relates the stepsize of the consensus algorithm and the degree of the camera network graph. While our methods are designed with the camera sensor network application in mind, our results are applicable to other localization problems in a more general setting. We also provide synthetic simulations to test the validity of our approach. I.
Optimality analysis of sensortarget geometries in passive localization: Part 1  Bearingonly localization
 In ISSNIP’07
, 2007
"... In this paper we characterize the relative sensortarget geometry in R2 in terms of potential localization performance for timeofarrival based localization. Our aim is to characterize those relative sensortarget geometries which minimize the relative CramerRao lower bound. 1. ..."
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Cited by 25 (7 self)
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In this paper we characterize the relative sensortarget geometry in R2 in terms of potential localization performance for timeofarrival based localization. Our aim is to characterize those relative sensortarget geometries which minimize the relative CramerRao lower bound. 1.
Sensor network localization by eigenvector synchronization over the Euclidean group
 In press
"... We present a new approach to localization of sensors from noisy measurements of a subset of their Euclidean distances. Our algorithm starts by finding, embedding and aligning uniquely realizable subsets of neighboring sensors called patches. In the noisefree case, each patch agrees with its global ..."
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Cited by 25 (15 self)
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We present a new approach to localization of sensors from noisy measurements of a subset of their Euclidean distances. Our algorithm starts by finding, embedding and aligning uniquely realizable subsets of neighboring sensors called patches. In the noisefree case, each patch agrees with its global positioning up to an unknown rigid motion of translation, rotation and possibly reflection. The reflections and rotations are estimated using the recently developed eigenvector synchronization algorithm, while the translations are estimated by solving an overdetermined linear system. The algorithm is scalable as the number of nodes increases, and can be implemented in a distributed fashion. Extensive numerical experiments show that it compares favorably to other existing algorithms in terms of robustness to noise, sparse connectivity and running time. While our approach is applicable to higher dimensions, in the current paper we focus on the two dimensional case.
On frame and orientation localization for relative sensing networks
, 2008
"... Abstract — We develop a novel localization theory for planar networks of nodes that measure each other’s relative position, i.e., we assume that nodes do not have the ability to perform measurements expressed in a common reference frame. We begin with some basic definitions of frame localizability a ..."
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Cited by 22 (3 self)
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Abstract — We develop a novel localization theory for planar networks of nodes that measure each other’s relative position, i.e., we assume that nodes do not have the ability to perform measurements expressed in a common reference frame. We begin with some basic definitions of frame localizability and orientation localizability. Based on some key kinematic relationships, we characterize orientation localizability for networks with angleofarrival sensing. We then address the orientation localization problem in the presence of noisy measurements. Our first algorithm computes a leastsquare estimate of the unknown node orientations in a ring network given angleofarrival sensing. For arbitrary connected graphs, our second algorithm exploits kinematic relationships among the orientation of node in loops in order to reduce the effect of noise. We establish the convergence of the algorithm, and through some simulations we show that the algorithm reduces the meansquare error due to the noisy measurements. I.
2010): “Quality of trilateration: Confidencebased iterative localization
 IEEE Transactions on Parallel and Distributed Systems
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Localization from Incomplete Noisy Distance Measurements
"... Abstract—We consider the problem of positioning a cloud of points in the Euclidean space R d, from noisy measurements of a subset of pairwise distances. This task has applications in various areas, such as sensor network localizations, NMR spectroscopy of proteins, and molecular conformation. Also, ..."
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Cited by 21 (0 self)
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Abstract—We consider the problem of positioning a cloud of points in the Euclidean space R d, from noisy measurements of a subset of pairwise distances. This task has applications in various areas, such as sensor network localizations, NMR spectroscopy of proteins, and molecular conformation. Also, it is closely related to dimensionality reduction problems and manifold learning, where the goal is to learn the underlying global geometry of a data set using measured local (or partial) metric information. Here we propose a reconstruction algorithm based on a semidefinite programming approach. For a random geometric graph model and uniformly bounded noise, we provide a precise characterization of the algorithm’s performance: In the noiseless case, we find a radius r0 beyond which the algorithm reconstructs the exact positions (up to rigid transformations). In the presence of noise, we obtain upper and lower bounds on the reconstruction error that match up to a factor that depends only on the dimension d, and the average degree of the nodes in the graph. I.