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Graphical Properties of Easily Localizable Sensor Networks
, 2006
"... The sensor network localization problem is one of determining the Euclidean positions of all sensors in a network given knowledge of the Euclidean positions of some, and knowledge of a number of intersensor distances. This paper identifies graphical properties which can ensure unique localizability ..."
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The sensor network localization problem is one of determining the Euclidean positions of all sensors in a network given knowledge of the Euclidean positions of some, and knowledge of a number of intersensor distances. This paper identifies graphical properties which can ensure unique localizability, and further sets of properties which can ensure not only unique localizability but also provide guarantees on the associated computational complexity, which can even be linear in the number of sensors on occasions. Sensor networks with minimal connectedness properties in which sensor transmit powers can be increased to increase the sensing radius lend themselves to the acquiring of the needed graphical properties. Results are presented for networks in both two and three dimensions.
Optimization Schemes for Wireless sensor network Localization
 Int. J. Appl. Math. Comput. Sci., 2009
"... Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Selforganization and localization capabilities are one of the most important requirements in sensor networks. This paper provides an overview of centralized distancebased al ..."
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Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Selforganization and localization capabilities are one of the most important requirements in sensor networks. This paper provides an overview of centralized distancebased algorithms for estimating the positions of nodes in a sensor network. We discuss and compare three approaches: semidefinite programming, simulated annealing and twophase stochastic optimization—a hybrid scheme that we have proposed. We analyze the properties of all listed methods and report the results of numerical tests. Particular attention is paid to our technique—the twophase method—that uses a combination of trilateration, and stochastic optimization for performing sensor localization. We describe its performance in the case of centralized and distributed implementations.
Analysis of flip ambiguities for robust sensor network localization
 Vehicular Technology, IEEE Transactions on
"... Abstract—Erroneous local geometric realizations in some parts of a network due to the sensitivity to certain distancemeasurement errors with respect to some neighboring sensor locations is a major problem in wireless sensornetwork localization, which may, in turn, affect the reliability of the lo ..."
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Abstract—Erroneous local geometric realizations in some parts of a network due to the sensitivity to certain distancemeasurement errors with respect to some neighboring sensor locations is a major problem in wireless sensornetwork localization, which may, in turn, affect the reliability of the localization of the whole or a major portion of the sensor network. This phenomenon is well described using the notion of “flip ambiguity ” in rigid graph theory. In this paper, we present a formal geometric analysis of flipambiguity problems in planar sensor networks via quantification of the likelihood of flip ambiguities in arbitrary sensor neighborhood geometries. Based on this analysis, we establish a robustness criterion to detect flip ambiguities in such neighborhood geometries. In addition to the analysis, the established robustness criterion is embedded in localization algorithms to enhance the reliability of the produced location estimates by eliminating neighborhoods with flip ambiguities from being included in the localization process. Index Terms—Flip ambiguities in WSN localization, robust WSN localization. I.
A Novel Subspace Approach for Cooperative Localization in Wireless Sensor Networks Using Range Measurements
"... Abstract—Estimating the positions of sensor nodes is a fundamental and crucial problem in wireless sensor networks. In this paper, three novel subspace methods for node localization in a fully connected network are devised with the use of range measurements. Biases and mean square errors of the sens ..."
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Abstract—Estimating the positions of sensor nodes is a fundamental and crucial problem in wireless sensor networks. In this paper, three novel subspace methods for node localization in a fully connected network are devised with the use of range measurements. Biases and mean square errors of the sensor node position estimates are also derived. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional subspace positioning method, namely, classical multidimensional scaling, as well as CramérRao lower bound. Index Terms—Position estimation, rangebased measurements, subspace method, wireless sensor networks.
Article GPSFree Localization Algorithm for Wireless Sensor Networks
, 2010
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Robust Localization from Incomplete Local Information
"... We consider the problem of localizing wireless devices in an adhoc network embedded in a ddimensional Euclidean space. Obtaining a good estimate of where wireless devices are located is crucial in wireless network applications including environment monitoring, geographic routing and topology cont ..."
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We consider the problem of localizing wireless devices in an adhoc network embedded in a ddimensional Euclidean space. Obtaining a good estimate of where wireless devices are located is crucial in wireless network applications including environment monitoring, geographic routing and topology control. When the positions of the devices are unknown and only local distance information is given, we need to infer the positions from these local distance measurements. This problem is particularly challenging when we only have access to measurements that have limited accuracy and are incomplete. We consider the extreme case of this limitation on the available information, namely only the connectivity information is available, i.e., we only know whether a pair of nodes is within a fixed detection range of each other or not, and no information is known about how far apart they are. Further, to account for detection failures, we assume that even if a pair of devices is within the detection range, it fails to detect the presence of one another with some probability and this probability of failure depends on how far apart those devices are. Given this limited information, we investigate the performance of a centralized positioning algorithm MDSMAP introduced by Shang et al. [3], and a distributed positioning algorithm HOPTERRAIN introduced by Savarese et al. [4]. In particular, for a network consisting of n devices positioned randomly, we provide a bound on the resulting error for both algorithms. We show that the error is bounded, decreasing at a rate that is proportional to RCritical/R, where RCritical is the critical detection range when the resulting random network starts to be connected, and R is the detection range of each device.
Localization in Wireless Sensor Networks by Cross Entropy Method
, 2012
"... Abstract—Wireless sensor network localization technique remains an open research topic due to its challenges on reducing the location estimation error and cost of the localization algorithm itself. For a large mobile network localization cost becomes increasingly important due to the exponential in ..."
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Abstract—Wireless sensor network localization technique remains an open research topic due to its challenges on reducing the location estimation error and cost of the localization algorithm itself. For a large mobile network localization cost becomes increasingly important due to the exponential increment of the algorithmic cost. Conversely sacrificing localization accuracy to a great extent is not acceptable at all. To address the localization problem of wireless sensor network this paper presents a novel algorithm based on crossentropy (CE) method. The proposed centralized algorithm estimates location information of the nodes based on the measured distances of the neighboring nodes. The algorithm minimizes the estimated location error by using the CE method. Simulation results compare the proposed CE approach with DVHop and Simulated Annealingbased localizations and show that this approach provides a balance between the accuracy and cost. When compared with DVHop, the CE approach is costlier but much more accurate. When compared with Simulated Annealingbased method, this approach offers the same level of accuracy but is significantly less costly. I.
On the Optimal Performance of Collaborative Position Location
 IEEE Trans. Wireless Comm
, 2010
"... Abstract—In this paper, we investigate the optimal performance of collaborative position location. In particular, we develop a branchandbound (BB) solution search strategy, coupled with the reformulation linearization technique (RLT), to solve the maximum likelihood estimation (MLE) problem for co ..."
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Abstract—In this paper, we investigate the optimal performance of collaborative position location. In particular, we develop a branchandbound (BB) solution search strategy, coupled with the reformulation linearization technique (RLT), to solve the maximum likelihood estimation (MLE) problem for collaborative position location, which is in general a nonlinear and nonconvex optimization problem. Compared with existing work which has only approximately solved the MLE problem, our approach is guaranteed to produce the (1 −
LOCALIZATION IN WIRELESS SENSOR NETWORKS: CLASSIFICATION AND EVALUATION OF TECHNIQUES
"... Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices. These devices are networked through setting up a Wireless Sensor Network (WSN). Sensors that form a WSN are expected to be remotely deployed in large numbers and to selfo ..."
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Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices. These devices are networked through setting up a Wireless Sensor Network (WSN). Sensors that form a WSN are expected to be remotely deployed in large numbers and to selforganize to perform distributed sensing and acting tasks. WSNs are growing rapidly in both size and complexity, and it is becoming increasingly difficult to develop and investigate such large and complex systems. In this paper we provide a brief introduction to WSN applications, i.e., properties, limitations and basic issues related to WSN design and development. We focus on an important aspect of the design: accurate localization of devices that form the network. The paper presents an overview of localization strategies and attempts to classify different techniques. A set of properties by which localization systems are evaluated are examined. We then describe a number of existing localization systems, and discuss the results of performance evaluation of some of them through simulation and experiments using a testbed implementation.
Localization in Wireless Sensor Networks by Constrained Simultaneous Perturbation Stochastic Approximation Technique
"... Abstract—Localization of sensor networks poses an immense challenge and is considered as a hot research topic in recent days. To address the accuracy on localization this paper proposes constrained simultaneous perturbation stochastic approximation (SPSA) based localization techniques for wireless s ..."
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Abstract—Localization of sensor networks poses an immense challenge and is considered as a hot research topic in recent days. To address the accuracy on localization this paper proposes constrained simultaneous perturbation stochastic approximation (SPSA) based localization techniques for wireless sensor networks. A simple centralized localization algorithm using SPSA technique that estimates the location of the nonanchor nodes based on minimizing the summation of the estimated error of all neighbors is the basic building block of the proposed localization technique. This category of localization technique incurs errors often referred as flip ambiguity. The improvement of the simple SPSA based localization is made by modifying the algorithm to a constrained optimization technique using penalty function method where the correction on the flipped node is made by penalizing the identified flips by the penalty function. Simulation results demonstrate the superiority of the proposed SPSA algorithm compared to its closest counterpart, namely, the simulated annealing (SA) based localization algorithm. Index Terms—wireless sensor network, localization, constrained optimization, simultaneous perturbation stochastic approximation I.