Results 1  10
of
294
Robust Distributed Network Localization with Noisy Range Measurements
, 2004
"... This paper describes a distributed, lineartime algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherw ..."
Abstract

Cited by 392 (21 self)
 Add to MetaCart
This paper describes a distributed, lineartime algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherwise corrupt localization computations. We formulate the localization problem as a twodimensional graph realization problem: given a planar graph with approximately known edge lengths, recover the Euclidean position of each vertex up to a global rotation and translation. This formulation is applicable to the localization of sensor networks in which each node can estimate the distance to each of its neighbors, but no absolute position reference such as GPS or fixed anchor nodes is available. We implemented the algorithm on a physical sensor network and empirically assessed its accuracy and performance. Also, in simulation, we demonstrate that the algorithm scales to large networks and handles realworld deployment geometries. Finally, we show how the algorithm supports localization of mobile nodes.
Signal Processing Techniques in NetworkAided Positioning: A Survey of StateoftheArt Positioning Designs
 IEEE Signal Processing Magazine
, 2005
"... [A survey of stateoftheart positioning designs] The U.S. Federal Communications Commission (FCC) requires that the precise location of all enhanced 911 (E911) callers be automatically determined. This requirement has motivated the development of cellularaided positioning. To facilitate emergency ..."
Abstract

Cited by 97 (3 self)
 Add to MetaCart
(Show Context)
[A survey of stateoftheart positioning designs] The U.S. Federal Communications Commission (FCC) requires that the precise location of all enhanced 911 (E911) callers be automatically determined. This requirement has motivated the development of cellularaided positioning. To facilitate emergency services, the FCC has mandated that 95 % of all handsets sold be location compatible by the end of December 2005 [1]. Wireless positioning has also been found very useful for other applications besides E911 service, ranging from vehicle navigation and network optimization to resource management and automated billing. Ubiquitous computing and locationaware computing also necessitate that we develop techniques for estimating the location of mobile users in both outdoor and indoor environments. Various positioning systems have been proposed for use in ubiquitous computing [2]. As an essential prerequisite for ubiquitous computing, mobile positioning techniques, linked with wireless networks, have increasingly provided mobile users with opportunities to access personal information, corporate data, and shared resources anytime, anywhere. Positioning systems can be grouped in many different ways, including indoor versus outdoor systems or cellular versus sensor network positioning designs, as shown in Figure 1.
Nonparametric belief propagation for selflocalization of sensor networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2005
"... Automatic selflocalization is a critical need for the effective use of adhoc sensor networks in military or civilian applications. In general, selflocalization involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. distance measurements b ..."
Abstract

Cited by 93 (3 self)
 Add to MetaCart
(Show Context)
Automatic selflocalization is a critical need for the effective use of adhoc sensor networks in military or civilian applications. In general, selflocalization involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. distance measurements between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of intersensor communication. We demonstrate that the information used for sensor localization is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then present and demonstrate the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multimodal uncertainty. Using simulations of small to moderatelysized sensor networks, we show that NBP may be made robust to outlier measurement errors by a simple model augmentation, and that judicious message construction can result in better estimates. Furthermore, we provide an analysis of NBP’s communications requirements, showing that typically only a few messages per sensor are required, and that even low bitrate approximations of these messages can have little or no performance impact.
Robust location distinction using temporal link signatures
 In MobiCom
, 2007
"... The ability of a receiver to determine when a transmitter has changed location is important for energy conservation in wireless sensor networks, for physical security of radiotagged objects, and for wireless network security in detection of replication attacks. In this paper, we propose using a meas ..."
Abstract

Cited by 82 (7 self)
 Add to MetaCart
(Show Context)
The ability of a receiver to determine when a transmitter has changed location is important for energy conservation in wireless sensor networks, for physical security of radiotagged objects, and for wireless network security in detection of replication attacks. In this paper, we propose using a measured temporal link signature to uniquely identify the link between a transmitter and a receiver. When the transmitter changes location, or if an attacker at a different location assumes the identity of the transmitter, the proposed link distinction algorithm reliably detects the change in the physical channel. This detection can be performed at a single receiver or collaboratively by multiple receivers. We record over 9,000 link signatures at different locations and over time to demonstrate that our method significantly increases the detection rate and reduces the false alarm rate, in comparison to existing methods.
Information fusion for wireless sensor networks
 Methods, models, and classifications, ACM Computing Surveys, Volume 39, Issue 3, Article 9
, 2007
"... ..."
Localization via ultrawideband radios
 IEEE Signal Processing Magazine
, 2005
"... A look at positioning aspects of future sensor networks. ..."
Abstract

Cited by 57 (6 self)
 Add to MetaCart
(Show Context)
A look at positioning aspects of future sensor networks.
Correlated link shadow fading in multihop wireless networks
 IEEE Trans. Wireless Commun
, 2009
"... Abstract—Accurate representation of the physical layer is required for analysis and simulation of multihop networking in sensor, ad hoc, and mesh networks. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. This ..."
Abstract

Cited by 55 (15 self)
 Add to MetaCart
(Show Context)
Abstract—Accurate representation of the physical layer is required for analysis and simulation of multihop networking in sensor, ad hoc, and mesh networks. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. This paper presents and analyzes a nonsitespecific statistical propagation model which accounts for the correlations that exist in shadow fading between links in multihop networks. We describe two measurement campaigns to measure a large number of multihop networks in an ensemble of environments. The measurements show statistically significant correlations among shadowing experienced on different links in the network, with correlation coefficients up to 0.33. Finally, we analyze multihop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater. Index Terms—Wireless sensor, ad hoc, mesh networks, shadowing, correlation, statistical channel model, wireless communication, measurement, performance I.
Distributed weightedmultidimensional scaling for node localization in sensor networks
 ACM Trans. Sens. Netw
, 2006
"... Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article introduces a scalable, distributed weightedmultidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally acc ..."
Abstract

Cited by 54 (0 self)
 Add to MetaCart
Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article introduces a scalable, distributed weightedmultidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function and then passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy efficiency of the proposed distributed method versus a centralized approach. For received signalstrength (RSS) based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to the CramérRao lower bound. Further, RSS and timeofarrival (TOA) channel measurements are used to demonstrate performance as good as the centralized maximumlikelihood estimator (MLE) in a realworld sensor network.
Effects of correlated shadowing: Connectivity, localization, and RF tomography
 In ACM/IEEE Information Processing in Sensor Networks (IPSN
, 2008
"... Unlike current models for radio channel shadowing indicate, realworld shadowing losses on different links in a network are not independent. The correlations have both detrimental and beneficial impacts on sensor, ad hoc, and mesh networks. First, the probability of network connectivity reduces when ..."
Abstract

Cited by 50 (16 self)
 Add to MetaCart
(Show Context)
Unlike current models for radio channel shadowing indicate, realworld shadowing losses on different links in a network are not independent. The correlations have both detrimental and beneficial impacts on sensor, ad hoc, and mesh networks. First, the probability of network connectivity reduces when link shadowing correlations are considered. Next, the variance bounds for sensor selflocalization change, and provide the insight that algorithms must infer localization information from link correlations in order to avoid significant degradation from correlated shadowing. Finally, a major benefit is that shadowing correlations between links enable the tomographic imaging of an environment from pairwise RSS measurements. This paper applies measurementbased models, and measurements themselves, to analyze and to verify both the benefits and drawbacks of correlated link shadowing. 1.
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 ..."
Abstract

Cited by 37 (6 self)
 Add to MetaCart
(Show Context)
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