Results 1  10
of
279
RangeFree Localization Schemes for Large Scale Sensor Networks
, 2003
"... Wireless Sensor Networks have been proposed for a multitude of locationdependent applications. For such systems, the cost and limitations of hardware on sensing nodes prevent the use of rangebased localization schemes that depend on absolute pointtopoint distance estimates. Because coarse accura ..."
Abstract

Cited by 518 (10 self)
 Add to MetaCart
Wireless Sensor Networks have been proposed for a multitude of locationdependent applications. For such systems, the cost and limitations of hardware on sensing nodes prevent the use of rangebased localization schemes that depend on absolute pointtopoint distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in rangefree localization are being pursued as a costeffective alternative to more expensive rangebased approaches. In this paper, we present APIT, a novel localization algorithm that is rangefree. We show that our APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three stateoftheart rangefree localization schemes to identify the preferable system configurations of each. In addition, we study the effect of location error on routing and tracking performance. We show that routing performance and tracking accuracy are not significantly affected by localization error when the error is less than 0.4 times the communication radio radius.
Distributed Localization in Wireless Sensor Networks: A Quantitative Comparison
, 2003
"... This paper studies the problem of determining the node locations in adhoc sensor networks. We compare three distributed localization algorithms (Adhoc positioning, Robust positioning, and Nhop multilateration) on a single simulation platform. The algorithms share a common, threephase structure: ..."
Abstract

Cited by 298 (7 self)
 Add to MetaCart
This paper studies the problem of determining the node locations in adhoc sensor networks. We compare three distributed localization algorithms (Adhoc positioning, Robust positioning, and Nhop multilateration) on a single simulation platform. The algorithms share a common, threephase structure: (1) determine nodeanchor distances, (2) compute node positions, and (3) optionally refine the positions through an iterative procedure. We present a detailed analysis comparing the various alternatives for each phase, as well as a headtohead comparison of the complete algorithms. The main conclusion is that no single algorithm performs best; which algorithm is to be preferred depends on the conditions (range errors, connectivity, anchor fraction, etc.). In each case, however, there is significant room for improving accuracy and/or increasing coverage.
Relative Location Estimation in Wireless Sensor Networks
, 2003
"... Selfconfiguration in wireless sensor networks is a general class of estimation problems which we study via the CramerRao bound (CRB).Specifically, we consider sensor location estimation when sensors measure received sig]P strengI (RSS) or timeofarrival (TOA) between themselves and neigboring sen ..."
Abstract

Cited by 298 (16 self)
 Add to MetaCart
Selfconfiguration in wireless sensor networks is a general class of estimation problems which we study via the CramerRao bound (CRB).Specifically, we consider sensor location estimation when sensors measure received sig]P strengI (RSS) or timeofarrival (TOA) between themselves and neigboring sensors. A small fraction of sensors in the network have known location while the remaining locations must be estimated. We derive CRBs and maximumlikelihood estimators (MLEs) under Gaussian and lognormal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaig in an indoor office area illustrates MLE performance. Finally, relative location estimation alg orithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1 m RMS location errorsusing TOA, and 1 m to 2 m RMS location errors using RSS.
Semidefinite Programming for Ad Hoc Wireless Sensor Network Localization
, 2004
"... We describe an SDP relaxation based method for the position estimation problem in wireless sensor networks. The optimization problem is set up so as to minimize the error in sensor positions to fit distance measures. Observable gauges are developed to check the quality of the point estimation of sen ..."
Abstract

Cited by 225 (14 self)
 Add to MetaCart
(Show Context)
We describe an SDP relaxation based method for the position estimation problem in wireless sensor networks. The optimization problem is set up so as to minimize the error in sensor positions to fit distance measures. Observable gauges are developed to check the quality of the point estimation of sensors or to detect erroneous sensors. The performance of this technique is highly satisfactory compared to other techniques. Very few anchor nodes are required to accurately estimate the position of all the unknown nodes in a network. Also the estimation errors are minimal even when the anchor nodes are not suitably placed within the network or the distance measurements are noisy.
Improved MDSbased localization
 In Proceedings of IEEE INFOCOM ’04, Hong Kong
, 2004
"... Abstract — It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDSMAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity informati ..."
Abstract

Cited by 180 (1 self)
 Add to MetaCart
Abstract — It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDSMAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information—who is within communications range of whom—to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDSMAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDSMAP method, which we call MDSMAP(P) standing for MDSMAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularlyshaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation. I.
Robust statistical methods for securing wireless localization in sensor networks
 In Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks (IPSN
, 2005
"... Abstract — Many sensor applications are being developed that require the location of wireless devices, and localization schemes have been developed to meet this need. However, as locationbased services become more prevalent, the localization infrastructure will become the target of malicious attack ..."
Abstract

Cited by 129 (4 self)
 Add to MetaCart
Abstract — Many sensor applications are being developed that require the location of wireless devices, and localization schemes have been developed to meet this need. However, as locationbased services become more prevalent, the localization infrastructure will become the target of malicious attacks. These attacks will not be conventional security threats, but rather threats that adversely affect the ability of localization schemes to provide trustworthy location information. This paper identifies a list of attacks that are unique to localization algorithms. Since these attacks are diverse in nature, and there may be many unforseen attacks that can bypass traditional security countermeasures, it is desirable to alter the underlying localization algorithms to be robust to intentionally corrupted measurements. In this paper, we develop robust statistical methods to make localization attacktolerant. We examine two broad classes of localization: triangulation and RFbased fingerprinting methods. For triangulationbased localization, we propose an adaptive least squares and least median squares position estimator that has the computational advantages of least squares in the absence of attacks and is capable of switching to a robust mode when being attacked. We introduce robustness to fingerprinting localization through the use of a medianbased distance metric. Finally, we evaluate our robust localization schemes under different threat conditions. I.
Theory of semidefinite programming for sensor network localization
 IN SODA05
, 2005
"... We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior–point algorithm theories to prove that the SDP localizes any network or graph th ..."
Abstract

Cited by 120 (9 self)
 Add to MetaCart
(Show Context)
We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior–point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we show, for the first time, that these networks can be localized in polynomial time. We also give a simple and efficient criterion for checking whether a given instance of the localization problem has a unique realization in R 2 using graph rigidity theory. Finally, we introduce a notion called strong localizability and show that the SDP model will identify all strongly localizable sub–networks in the input network.
Network Coverage Using Low DutyCycled Sensors: Random & Coordinated Sleep Algorithms
, 2004
"... This paper investigates the problem of providing network coverage using wireless sensors that operate on low duty cycles (measured by the percentage time a sensor is on or active), i.e., each sensor alternates between active and sleep states to conserve energy with an average sleep period (much) lon ..."
Abstract

Cited by 114 (0 self)
 Add to MetaCart
This paper investigates the problem of providing network coverage using wireless sensors that operate on low duty cycles (measured by the percentage time a sensor is on or active), i.e., each sensor alternates between active and sleep states to conserve energy with an average sleep period (much) longer than the active period. The dynamic change in topology as a result of such dutycycling has potentially disruptive effect on the operation and performance of the network. This is compensated by adding redundancy in the sensor deployment. In this paper we examine the fundamental relationship between the reduction in sensor duty cycle and the required level of redundancy for a fixed performance measure, and explore the design of good sensor sleep schedules. In particular, we consider two types of mechanisms, the random sleep type where each sensor keeps an activesleep schedule independent of another, and the coordinated sleep type where sensors coordinate with each other in reaching an activesleep schedule. Both types are studied within the context of providing network coverage. We present specific scheduling algorithms within each type, and illustrate their coverage and duty cycle properties via both analysis and simulation. We show with either type of sleep schedule the benefit of added redundancy saturates at some point in that the reduction in duty cycles starts to diminish beyond a certain threshold in deployment redundancy. We also show that at the expense of extra control overhead, a coordinated sleep schedule is more robust and can achieve higher duty cycle reduction with the same amount of redundancy compared to a random sleep schedule.
Localization of Wireless Sensor Networks with a Mobile Beacon
, 2003
"... Wireless sensor networks have the potential to become the pervasive sensing (and actuating) technology of the future. For many applications, a large number of inexpensive sensors is preferable to a few expensive ones. The large number of sensors in a sensor network and most application scenarios ..."
Abstract

Cited by 114 (2 self)
 Add to MetaCart
Wireless sensor networks have the potential to become the pervasive sensing (and actuating) technology of the future. For many applications, a large number of inexpensive sensors is preferable to a few expensive ones. The large number of sensors in a sensor network and most application scenarios preclude hand placement of the sensors. Determining the physical location of the sensors after they have been deployed is known as the problem of localization. In this paper, we present a localization technique based on a single mobile beacon aware of its position (e.g. by being equipped with a GPS receiver). Sensor nodes
Semidefinite programming based algorithms for sensor network localization
 ACM Transactions on Sensor Networks
, 2006
"... An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate distance information. The problem is set up to find a set of sensor positions such that given distance constraints are satisfied. The nonconvex constraints in the formulat ..."
Abstract

Cited by 113 (6 self)
 Add to MetaCart
An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate distance information. The problem is set up to find a set of sensor positions such that given distance constraints are satisfied. The nonconvex constraints in the formulation are then relaxed in order to yield a semidefinite program which can be solved efficiently. The basic model is extended in order to account for noisy distance information. In particular, a maximum likelihood based formulation and an interval based formulation are discussed. The SDP solution can then also be used as a starting point for steepest descent based local optimization techniques that can further refine the SDP solution. We also describe the extension of the basic method to develop an iterative distributed SDP method for solving very large scale semidefinite programs that arise out of localization problems for large dense networks and are intractable using centralized methods. The performance evaluation of the technique with regard to estimation accuracy and computation time is also presented by the means of extensive simulations. Our SDP scheme also seems to be applicable to solving other Euclidean geometry problems where points are locally connected.