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227
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 367 (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.
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 276 (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 Bits and Flops of the Nhop Multilateration Primitive For Node Localization Problems
, 2002
"... The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. Vital to the success of wireless microsensor networks is the ability of microsensors to "collectively perform sensi ..."
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Cited by 269 (3 self)
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The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. Vital to the success of wireless microsensor networks is the ability of microsensors to "collectively perform sensing and computation". In this paper, we study one of the fundamental challenges in sensor networks, node localization. The collaborative multilateration presented here, enables adhoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global nonlinear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large nonlinear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows finegrained localization to run on a low cost sensor node we have developed.
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 ..."
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Cited by 208 (12 self)
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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 ..."
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Cited by 167 (1 self)
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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.
Sensor Positioning in Wireless Adhoc Sensor Networks Using Multidimensional Scaling
, 2004
"... Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. In the paper, we first study some situations where most existing sensor positioning methods tend to fail to perform well, an example being when the topology of a sensor network is anisotropic. Then, we ..."
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Cited by 134 (0 self)
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Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. In the paper, we first study some situations where most existing sensor positioning methods tend to fail to perform well, an example being when the topology of a sensor network is anisotropic. Then, we explore the idea of using dimensionality reduction techniques to estimate sensors coordinates in two (or three) dimensional space, and we propose a distributed sensor positioning method based on multidimensional scaling technique to deal with these challenging conditions. Multidimensional scaling and coordinate alignment techniques are applied to recover positions of adjacent sensors. The estimated positions of the anchors are compared with their true physical positions and corrected, The positions of other sensors are corrected accordingly. With iterative adjustment, our method can overcome adverse network and terrain conditions, and generate accurate sensor position. We also propose an on demand sensor positioning method based on the above method.
JAM: A JammedArea Mapping Service for Sensor Networks
, 2003
"... Preventing denialofservice attacks in wireless sensor networks is difficult primarily because of the limited resources available to network nodes and the ease with which attacks are perpetrated. Rather than jeopardize design requirements which call for simple, inexpensive, massproducible devices, ..."
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Cited by 106 (2 self)
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Preventing denialofservice attacks in wireless sensor networks is difficult primarily because of the limited resources available to network nodes and the ease with which attacks are perpetrated. Rather than jeopardize design requirements which call for simple, inexpensive, massproducible devices, we propose a coping strategy that detects and maps jammed regions. We describe a mapping protocol for nodes that surround a jammer which allows network applications to reason about the region as an entity, rather than as a collection of broken links and congested nodes. This solution is enabled by a set of design principles: loose group semantics, eager eavesdropping, supremacy of local information, robustness to packet loss and failure, and early use of results. Performance results show that regions can be mapped in 1 – 5 seconds, fast enough for realtime response. With a moderately connected network, the protocol is robust to failure rates as high as 25 percent. 1.
Rigidity, Computation, and Randomization in Network Localization
 In Proceedings of IEEE INFOCOM ’04, Hong Kong
, 2004
"... In this paper we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigid ..."
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Cited by 102 (16 self)
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In this paper we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly.
A Theory of Network Localization
, 2004
"... In this paper we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigid ..."
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Cited by 102 (11 self)
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In this paper we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly.
Localization from connectivity in sensor networks
 IEEE Transactions on Parallel and Distributed Systems
, 2004
"... Abstract—We propose an approach that uses connectivity information—who is within communications range of whom—to derive the locations of nodes in a network. The approach can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor ..."
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Cited by 102 (4 self)
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Abstract—We propose an approach that uses connectivity information—who is within communications range of whom—to derive the locations of nodes in a network. The approach can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. It is based on multidimensional scaling (MDS), an efficient data analysis technique that takes Oðn 3 Þ time for a network of n nodes. Unlike previous approaches, MDS takes full advantage of connectivity or distance information between nodes that have yet to be localized. Two methods are presented: a simple method that builds a global map using MDS and a more complicated one that builds small local maps and then patches them together to form a global map. Furthermore, leastsquares optimization can be incorporated into the methods to further improve the solutions at the expense of additional computation. Through simulation studies on uniform as well as irregular networks, we show that the methods achieve more accurate solutions than previous methods, especially when there are few anchor nodes. They can even yield good relative maps when no anchor nodes are available. Index Terms—Wireless sensor networks, optimization, position estimation. 1