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494
Geographyinformed Energy Conservation for Ad Hoc Routing
 ACM MOBICOM
, 2001
"... We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks. GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning off unnecessary nodes, keeping a constant level of routing fidelity. GA ..."
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Cited by 1045 (21 self)
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We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks. GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning off unnecessary nodes, keeping a constant level of routing fidelity. GAF moderates this policy using application and systemlevel information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use. GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR. Analysis and simulation studies of GAF show that it can consume 40% to 60% less energy than an unmodified ad hoc routing protocol. Moreover, simulations of GAF suggest that network lifetime increases proportionally to node density; in one example, a fourfold increase in node density leads to network lifetime increase for 3 to 6 times (depending on the mobility pattern). More generally, GAF is an example of adaptive fidelity, a technique proposed for extending the lifetime of selfconfiguring systems by exploiting redundancy to conserve energy while maintaining application fidelity.
Location Systems for Ubiquitous Computing
, 2001
"... This survey and taxonomy of location systems for mobilecomputing applications describes... ..."
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Cited by 954 (19 self)
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This survey and taxonomy of location systems for mobilecomputing applications describes...
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 ..."
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Cited by 525 (8 self)
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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.
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 ..."
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Cited by 403 (20 self)
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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.
Robust Positioning Algorithms for Distributed AdHoc Wireless Sensor Networks
, 2002
"... A distributed algorithm for determining the positions of nodes in an adhoc, wireless sensor network is explained in detail. Details regarding the implementation of such an algorithm are also discussed. Experimentation is performed on networks containing 400 nodes randomly placed within a square are ..."
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Cited by 383 (9 self)
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A distributed algorithm for determining the positions of nodes in an adhoc, wireless sensor network is explained in detail. Details regarding the implementation of such an algorithm are also discussed. Experimentation is performed on networks containing 400 nodes randomly placed within a square area, and resulting error magnitudes are represented as percentages of each node's radio range. In scenarios with 5% errors in distance measurements, 5% anchor node population (nodes with known locations), and average connectivity levels between neighbors of 7 nodes, the algorithm is shown to have errors less than 33% on average. It is also shown that, given an average connectivity of at least 12 nodes and 10% anchors, the algorithm performs well with up to 40% errors in distance measurements.
Localization from Mere Connectivity
 In Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
, 2003
"... 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. We present an algorithm that uses connectivity information— who is within communications range of whom—to derive the locat ..."
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Cited by 367 (9 self)
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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. We present an algorithm that uses connectivity information— who is within communications range of whom—to derive the locations of the nodes in the network. The method can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. The algorithm is based on multidimensional scaling, a data analysis technique that takes O(n 3) time for a network of n nodes. Through simulation studies, we demonstrate that the algorithm is more robust to measurement error than previous proposals, especially when nodes are positioned relatively uniformly throughout the plane. Furthermore, it can achieve comparable results using many fewer anchor nodes than previous methods, and even yields relative coordinates when no anchor nodes are available.
Organizing a Global Coordinate System from Local Information on an Amorphous Computer
, 1999
"... This paper demonstrates that it is possible to generate a reasonably accurate coordinate system on randomly distributed processors, using only local information and local communication. By coordinate system we imply that each element assigns itself a logical coordinate that maps to its global phy ..."
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Cited by 339 (7 self)
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This paper demonstrates that it is possible to generate a reasonably accurate coordinate system on randomly distributed processors, using only local information and local communication. By coordinate system we imply that each element assigns itself a logical coordinate that maps to its global physical location, starting with no apriori knowledge of position or orientation. The algorithm presented is inspired by biological systems that use chemical gradients to determine the position of cells [12]. Extensive analysis and simulation results are presented. Two key results are: there is a critical minimum average neighborhood size of 15 for good accuracy and there is a fundamental limit on the resolution of any coordinate system determined strictly from local communication. We also demonstrate that using this algorithm, random distributions of processors produce significantly better accuracy than regular processor grids  such as those used by cellular automata. This has implications for discrete models of biology as well as for building smart sensor arrays.
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 ..."
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Cited by 305 (16 self)
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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.
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: ..."
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Cited by 302 (7 self)
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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.
The Cricket Compass for ContextAware Mobile Applications
, 2000
"... The abilit y to determine the orien tation of a device is of fundamental importancein con texta w areand locationdependent mobile computing. By analogy to a traditional compass, knowledge of orientation through the ####### # ### #### attached to a mobile device enhances various applications, inclu ..."
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Cited by 247 (5 self)
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The abilit y to determine the orien tation of a device is of fundamental importancein con texta w areand locationdependent mobile computing. By analogy to a traditional compass, knowledge of orientation through the ####### # ### #### attached to a mobile device enhances various applications, including ecientway nding and navigation, directional service disco very,and \augmentedrealit y" displays. Our compass infrastructure enhances the spatial inference capabilit yof the Cric ketindoor location system [20], and enables new pervasiv e computing applications.