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24
Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes
"... algorithm to locate sensors (with unknown locations) in 1, with respect to a minimal number of +1anchors with known locations. The sensors and anchors, nodes in the network, exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there a centralized fu ..."
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Cited by 37 (6 self)
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algorithm to locate sensors (with unknown locations) in 1, with respect to a minimal number of +1anchors with known locations. The sensors and anchors, nodes in the network, exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there a centralized fusion center to compute the sensors ’ locations. DILOC uses the barycentric coordinates of a node with respect to its neighbors; these coordinates are computed using the Cayley–Menger determinants, i.e., the determinants of matrices of internode distances. We show convergence of DILOC by associating with it an absorbing Markov chain whose absorbing states are the states of the anchors. We introduce a stochastic approximation version extending DILOC to random environments, i.e., when the communications among nodes is noisy, the communication links among neighbors may fail at random times, and the internodes distances are subject to errors. We show a.s. convergence of the modified DILOC and characterize the error between the true values of the sensors ’ locations and their final estimates given by DILOC. Numerical studies illustrate DILOC under a variety of deterministic and random operating conditions. Index Terms—Absorbing Markov chain, anchor, barycentric coordinates, Cayley–Menger determinant, distributed iterative
Robust decentralized source localization via averaging
 in Proc. IEEE ICASSP ’05
, 2005
"... We present a new approach to localizing an isotropic energy source using measurements from distributed sensors based on kernel averaging techniques. The location estimate is easily and efficiently calculated in a decentralized fashion. Statistical properties are derived for a very general measuremen ..."
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Cited by 27 (10 self)
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We present a new approach to localizing an isotropic energy source using measurements from distributed sensors based on kernel averaging techniques. The location estimate is easily and efficiently calculated in a decentralized fashion. Statistical properties are derived for a very general measurement model. Experiments suggest that the proposed estimator is much more robust and exhibits better performance characteristics than the popular least squares estimator under a variety of conditions. 1. LOCALIZATION VIA AVERAGING The problem of localizing and tracking an energyemitting source encompasses many of the challenging issues which commonly arise in wireless sensor network applications [1]. Consequently, this problem has recently received a great deal of attention. In [2], Chen et al. present an approach to source localization using
The Wireless Sensor Networks for CityWide Ambient Intelligence (WISEWAI) Project
 Sensors 2009
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A Modified Multidimensional Scaling with Embedded Particle Filter Algorithm for Cooperative Positioning of Vehicular Networks
"... Abstract—Vehicular communication technologies are on their way to be recognized as icons of modern societies. One important scientific challenge to the safety related applications of vehicular communication is indeed semiprecise positioning. Cooperative positioning is an idea for that purpose, and ..."
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Cited by 7 (5 self)
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Abstract—Vehicular communication technologies are on their way to be recognized as icons of modern societies. One important scientific challenge to the safety related applications of vehicular communication is indeed semiprecise positioning. Cooperative positioning is an idea for that purpose, and of course from research point of view is very attractive. From the practical point of view the attractiveness of cooperative positioning lies in its independence from any major additional infrastructure other than the vehicular communication systems. This paper introduces a new positioning algorithm for localization of mobile networks, in general, that nicely applies to vehicular networks. The algorithm is based on the well known multidimensional algorithm and shows remarkable performance compared to its counterparts in the vehicular positioning literature.
LowDensity wireless sensor networks for localization and tracking in critical environments
 2014 by the authors; licensee MDPI
"... Abstract—In this paper, the problem of localizing and tracking mobile nodes acting in a fixed wireless sensor network (WSN) is addressed. A strategy is proposed based on an empirical map of the received signalstrength distribution that is generated by the WSN and on a stochastic model of the mobile ..."
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Cited by 6 (0 self)
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Abstract—In this paper, the problem of localizing and tracking mobile nodes acting in a fixed wireless sensor network (WSN) is addressed. A strategy is proposed based on an empirical map of the received signalstrength distribution that is generated by the WSN and on a stochastic model of the mobilenode behavior. This approach results in being well suited for lowdensity setups and critical environments. The theoretical background and the architecture of the system are presented, together with simulations to validate the design phase. Also, the system is implemented into a realtime framework, and its performance is tested in an industrial indoor environment. Index Terms—Estimation theory, localization and tracking, packet loss, random walk model, stochastic modeling, wireless sensor networks. I.
An Efficient Gradient Descent Approach to Secure Localization in Resource Constrained Wireless Sensor Networks
"... Abstract—Many applications of wireless sensor networks require precise knowledge of the locations of constituent nodes. In these applications, it is desirable for the nodes to be able to autonomously determine their locations before they start sensing and transmitting data. Most localization algorit ..."
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Cited by 5 (1 self)
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Abstract—Many applications of wireless sensor networks require precise knowledge of the locations of constituent nodes. In these applications, it is desirable for the nodes to be able to autonomously determine their locations before they start sensing and transmitting data. Most localization algorithms use anchor nodes with known locations to determine the positions of the remaining nodes. However, these existing techniques often fail in hostile environments where some of the nodes may be compromised by adversaries and used to transmit misleading information aimed at preventing accurate localization of the remaining sensors. In this paper, a computationally efficient secure localization algorithm that withstands such attacks is described. The proposed algorithm combines iterative gradient descent with selective pruning of inconsistent measurements to achieve high localization accuracy. Results show that the proposed algorithm utilizes fewer computational resources and achieves an accuracy better than or comparable to that of existing schemes. The proposed secure localization algorithm can also be used in mobile sensor networks, where all nodes are moving, to estimate the relative locations of the nodes without relying on anchor nodes. Simulations demonstrate that the proposed algorithm can find the relative location map oftheentiremobilesensornetworkevenwhensomenodesare compromised and transmit false information. Index Terms—Gradient descent, mobile sensor networks (MSNs), secure localization, wireless sensor networks (WSNs). I.
M.: Localization using neural networks in wireless sensor networks
 In: MOBILWARE’08: Proceedings of the 1st international conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications, ICST
, 2008
"... Noisy distance measurements are a pervasive problem in localization in wireless sensor networks. Neural networks are not commonly used in localization, however, our experiments in this paper indicate neural networks are a viable option for solving localization problems. In this paper we qualitativel ..."
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Cited by 4 (0 self)
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Noisy distance measurements are a pervasive problem in localization in wireless sensor networks. Neural networks are not commonly used in localization, however, our experiments in this paper indicate neural networks are a viable option for solving localization problems. In this paper we qualitatively compare the performance of three different families of neural networks: MultiLayer Perceptron (MLP),
RF Localization and tracking of mobile nodes in Wireless Sensors Networks: Architectures, Algorithms and Experiments
"... Abstract. In this paper we address the problem of localizing, tracking and navigating mobile nodes associated to operators acting in a fixed wireless sensor network (WSN) using only RF information. We propose two alternative and somehow complementary strategies: the first one is based on an empirica ..."
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Cited by 3 (2 self)
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Abstract. In this paper we address the problem of localizing, tracking and navigating mobile nodes associated to operators acting in a fixed wireless sensor network (WSN) using only RF information. We propose two alternative and somehow complementary strategies: the first one is based on an empirical map of the Radio Signal Strength (RSS) distribution generated by the WSN and on the stochastic model of the behavior of the mobile nodes, while the second one is based on a maximum likelihood estimator and a radio channel model for the RSS. We compare the two approaches and highlight pros and cons for each of them. Finally, after implementing them into two realtime tracking systems, we analyze their performance on an experimental testbed in an industrial indoor environment. 1
Enhanced radio frequency (rf) collection with distributed wireless sensor networks
, 2007
"... NAVAL ..."