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149
Localization for largescale underwater sensor networks
, 2006
"... Abstract. In this paper, we study the localization problem in largescale underwater sensor networks. The adverse aqueous environments, the node mobility, and the large network scale all pose new challenges, and most current localization schemes are not applicable. We propose a hierarchical approach ..."
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Cited by 46 (6 self)
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Abstract. In this paper, we study the localization problem in largescale underwater sensor networks. The adverse aqueous environments, the node mobility, and the large network scale all pose new challenges, and most current localization schemes are not applicable. We propose a hierarchical approach which divides the whole localization process into two subprocesses: anchor node localization and ordinary node localization. Many existing techniques can be used in the former. For the ordinary node localization process, we propose a distributed localization scheme which novelly integrates a 3dimensional Euclidean distance estimation method with a recursive location estimation method. Simulation results show that our proposed solution can achieve high localization coverage with relatively small localization error and low communication overhead in largescale 3dimensional underwater sensor networks. 1
Resilient localization for sensor networks in outdoor environments
 In International Conference on Distributed Computing Systems. IEEE Computer Society
, 2005
"... The process of determining the physical locations of nodes in a wireless sensor network is known as localization. Selflocalization is critical for largescale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent li ..."
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Cited by 39 (1 self)
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The process of determining the physical locations of nodes in a wireless sensor network is known as localization. Selflocalization is critical for largescale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardwaresoftware approach for acoustic ranging or radio interferometry to acquire internode distance measurements, and a resilient selflocalization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35m in grassy outdoor environments, achieving a distanceinvariant median measurement error of about 1 % (33cm). The localization algorithm is based on Least Squares Scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multidimensional scaling (MDS) algorithms against largemagnitude ranging errors and sparse range measurements: conditions that are common in largescale outdoor sensor
Using clustering information for sensor network localization
 in Proceedings of IEEE Conference on Distributed Computing in Sensor Systems (DCOSS 2005
, 2005
"... 0.1 Introduction Many wireless sensor network applications require information about the geographiclocation of each sensor node. Besides the typical application of correlating sensor readings with physical locations, approximate geographical localization is also neededfor applications such as locati ..."
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Cited by 36 (0 self)
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0.1 Introduction Many wireless sensor network applications require information about the geographiclocation of each sensor node. Besides the typical application of correlating sensor readings with physical locations, approximate geographical localization is also neededfor applications such as locationaided routing [2], geographic routing [3], geographic routing with imprecise geographic coordinates [4, 5], geographic hash tables [6], andfor many data aggregation applications.
Scalable Localization with Mobility Prediction for Underwater Sensor Networks
"... Abstract—Due to adverse aqueous environments, nonnegligible node mobility and large network scale, localization for largescale mobile underwater sensor networks is very challenging. In this paper, by utilizing the predictable mobility patterns of underwater objects, we propose a scheme, called Sca ..."
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Cited by 35 (7 self)
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Abstract—Due to adverse aqueous environments, nonnegligible node mobility and large network scale, localization for largescale mobile underwater sensor networks is very challenging. In this paper, by utilizing the predictable mobility patterns of underwater objects, we propose a scheme, called Scalable Localization scheme with Mobility Prediction (SLMP), for underwater sensor networks. In SLMP, localization is performed in a hierarchical way, and the whole localization process is divided into two parts: anchor node localization and ordinary node localization. During the localization process, every node predicts its future mobility pattern according to its past known location information, and it can estimate its future location based on its predicted mobility pattern. Anchor nodes with known locations in the network will control the whole localization process in order to balance the tradeoff between localization accuracy, localization coverage and communication cost. We conduct extensive simulations, and our results show that SLMP can greatly reduce localization communication cost while maintaining relatively high localization coverage and localization accuracy. I.
Pinpoint: An asynchronous timebased location determination system
 In Fourth International Conference on Mobile Systems, Applications, and Services (MobiSys 2006
, 2006
"... This paper presents the design, implementation and evaluation of the PinPoint location determination system. PinPoint is a distributed algorithm that enables a set of n nodes to determine the RF propagation delays between every pair of nodes, from which the internode distances and hence the spatia ..."
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Cited by 32 (2 self)
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This paper presents the design, implementation and evaluation of the PinPoint location determination system. PinPoint is a distributed algorithm that enables a set of n nodes to determine the RF propagation delays between every pair of nodes, from which the internode distances and hence the spatial topology can be readily determined. PinPoint does not require any calibration of the area of interest and thus is rapidly deployable. Unlike existing timeofarrival techniques, PinPoint does not require an infrastructure of accurate clocks (e.g., GPS) nor does it incur the o(n 2) message exchanges of “echoing ” techniques. PinPoint can work with nodes having inexpensive crystal oscillator clocks, and incurs a constant number of message exchanges per node to determine the location of n nodes. Each node’s clock is assumed to run reliably but asynchronously with respect to the other nodes, i.e., they can run at slightly different rates because of hardware (oscillator) inaccuracies. PinPoint provides a mathematical way to compensate for these clock differences in order to arrive at a very precise timestamp recovery that in turn leads to a precise distance determination. Moreover, each node is able to determine the clock characteristics of other nodes in its neighborhood allowing network synchronization. We present a prototype implementation for PinPoint and discusses the practical issues in implementing the mathematical framework and how PinPoint handles the different sources of error affecting its accuracy. Evaluation of the prototype in typical indoor and outdoor environments shows that PinPoint gives an average accuracy of four to six
Estimation bounds for localization
 in IEEE SECON
, 2004
"... for sensor networks. This paper studies the CramérRao lower bound (CRB) for two kinds of localization based on noisy range measurements. The first is Anchored Localization in which the estimated positions of at least 3 nodes are known in global coordinates. We show some basic invariances of the CRB ..."
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Cited by 30 (2 self)
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for sensor networks. This paper studies the CramérRao lower bound (CRB) for two kinds of localization based on noisy range measurements. The first is Anchored Localization in which the estimated positions of at least 3 nodes are known in global coordinates. We show some basic invariances of the CRB in this case and derive lower and upper bounds on the CRB which can be computed using only local information. The second is Anchorfree Localization where no absolute positions are known. Although the Fisher Information Matrix is singular, a CRBlike bound exists on the total estimation variance. Finally, for both cases we discuss how the bounds scale to large networks under different models of wireless signal propagation. Index Terms — CramérRao bound, localization, estimation bounds, ranging information, sensor networks.
Distance matrix reconstruction from incomplete distance information for sensor network localization
, 2005
"... Abstract — This paper focuses on the principled study of distance reconstruction for distancebased node localization. We address an important issue in node localization by showing that a highly incomplete set of internode distance measurements obtained in adhoc node deployments carries sufficient ..."
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Cited by 29 (1 self)
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Abstract — This paper focuses on the principled study of distance reconstruction for distancebased node localization. We address an important issue in node localization by showing that a highly incomplete set of internode distance measurements obtained in adhoc node deployments carries sufficient information for the accurate reconstruction of the missing distances, even in the presence of noise and sensor node failures. We provide an efficient and provably accurate algorithm for this reconstruction, and we show that the resulting error is bounded, decreasing at a rate that is inversely proportional to √ n, the square root of the number of nodes in the region of deployment. Although this result is applicable to many localization schemes, in this paper we illustrate its use in conjunction with the popular MultiDimensional Scaling algorithm. Our analysis reveals valuable insights and key factors to consider during the sensor network setup phase, to improve the quality of the position estimates. I.
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.
Distributed sensor network localization from local connectivity : performance analysis for the HopTerrain algorithm
 in SIGMETRICS’10: Proceedings of the 2010 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems
, 2010
"... Sensor localization from only connectivity information is a highly challenging problem. To this end, our result for the first time establishes an analytic bound on the performance of the popular MDSMAP algorithm based on multidimensional scaling. For a network consisting of n sensors positioned ran ..."
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Cited by 20 (8 self)
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Sensor localization from only connectivity information is a highly challenging problem. To this end, our result for the first time establishes an analytic bound on the performance of the popular MDSMAP algorithm based on multidimensional scaling. For a network consisting of n sensors positioned randomly on a unit square and a given radio range r = o(1), we show that resulting error is bounded, decreasing at a rate that is inversely proportional to r, when only connectivity information is given. The same bound holds for the rangebased model, when we have an approximate measurements for the distances, and the same algorithm can be applied without any modification. 1