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32
Beyond Trilateration: On the Localizability of Wireless Ad-hoc Networks
"... Abstract — The proliferation of wireless and mobile devices has fostered the demand of context aware applications, in which location is often viewed as one of the most significant contexts. Classically, trilateration is widely employed for testing network localizability; even in many cases it wrongl ..."
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Cited by 42 (12 self)
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Abstract — The proliferation of wireless and mobile devices has fostered the demand of context aware applications, in which location is often viewed as one of the most significant contexts. Classically, trilateration is widely employed for testing network localizability; even in many cases it wrongly recognizes a localizable graph as non-localizable. In this study, we analyze the limitation of trilateration based approaches and propose a novel approach which inherits the simplicity and efficiency of trilateration, while at the same time improves the performance by identifying more localizable nodes. We prove the correctness and optimality of this design by showing that it is able to locally recognize all 1-hop localizable nodes. To validate this approach, a prototype system with 19 wireless sensors is deployed. Intensive and large-scale simulations are further conducted to evaluate the scalability and efficiency of our design. I.
Understanding Node Localizability of Wireless Ad-hoc Networks
"... Abstract — Location awareness is highly critical for wireless ad-hoc and sensor networks. Many efforts have been made to solve the problem of whether or not a network can be localized. Nevertheless, based on the data collected from a working sensor network, it is observed that the network is NOT alw ..."
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Cited by 16 (5 self)
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Abstract — Location awareness is highly critical for wireless ad-hoc and sensor networks. Many efforts have been made to solve the problem of whether or not a network can be localized. Nevertheless, based on the data collected from a working sensor network, it is observed that the network is NOT always entirely localizable. Theoretical analyses also suggest that, in most cases, it is unlikely that all nodes in a network are localizable, although a (large) portion of the nodes can be uniquely located. Existing studies merely examine whether or not a network is localizable as a whole; yet two fundamental questions remain unaddressed: First, given a network configuration, whether or not a specific node is localizable? Second, how many nodes in a network can be located and which are them? In this study, we analyze the limitation of previous works and propose a novel concept of node localizability. By deriving the necessary and sufficient conditions for node localizability, for the first time, it is possible to analyze how many nodes one can expect to locate in sparsely or moderately connected networks. To validate this design, we implement our solution on a real-world system and the experimental results show that node localizability provides useful guidelines for network deployment and other location-based services. I.
CASE: Connectivity-based Skeleton Extraction in Wireless Sensor Networks
, 2009
"... Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction has shown great impact on the performance of such services as location, routing, and path planning in sensor networks. Nonetheless, current stu ..."
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Cited by 13 (9 self)
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Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction has shown great impact on the performance of such services as location, routing, and path planning in sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, is not immediately applicable for the discrete and distributed sensor networks. In this paper we present CASE: a novel Connectivity-bAsed Skeleton Extraction algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, no centralized operation is required. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms state-of-the-art algorithms.
Connectivity-based sensor network localization with incremental delaunay refinement method
- In Proc. of the 28th Annual IEEE Conference on Computer Communications (INFOCOM’09
, 2009
"... Abstract—We study the anchor-free localization problem for a large-scale sensor network with a complex shape, knowing network connectivity information only. The main idea follows from our previous work [19] in which a subset of the nodes are selected as landmarks and the sensor field is partitioned ..."
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Cited by 11 (1 self)
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Abstract—We study the anchor-free localization problem for a large-scale sensor network with a complex shape, knowing network connectivity information only. The main idea follows from our previous work [19] in which a subset of the nodes are selected as landmarks and the sensor field is partitioned into Voronoi cells with all the nodes closest to the same landmark grouped into the same cell. We extract the combinatorial Delaunay complex as the dual complex of the landmark Voronoi diagram and embed the combinatorial Delaunay complex as a structural skeleton. In this paper we develop a new landmark selection algorithm with incremental Delaunay refinement method. This algorithm does not assume any knowledge of the network boundary and runs in a distributed manner to select landmarks incrementally until both the global rigidity property (the Delaunay complex is globally rigid and thus can be embedded uniquely) and the coverage property (every node is not far from the embedded Delaunay complex) are met. The new algorithm substantially improves the robustness and applicability of the original localization algorithm, especially in networks with very low average degree (even nonrigid networks) and complex shapes. I.
Approximate Convex Decomposition Based Localization in Wireless Sensor Networks
"... Abstract—Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. An important research direction for localization is to develop schemes using connectivity information only. These schemes primary apply h ..."
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Cited by 6 (4 self)
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Abstract—Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. An important research direction for localization is to develop schemes using connectivity information only. These schemes primary apply hop counts to distance estimation. Not surprisingly, they work well only when the network topology has a convex shape. In this paper, we develop a new Localization protocol based on Approximate Convex Decomposition (ACDL). It can calculate the node virtual locations for a large-scale sensor network with arbitrary shapes. The basic idea is to decompose the network into convex subregions. It is not straight-forward, however. We first examine the influential factors on the localization accuracy when the network is concave such as the sharpness of concave angle and the depth of the concave valley. We show that after decomposition, the
Beyond Triangle Inequality: Sifting Noisy and Outlier Distance Measurements for Localization
"... Abstract—Knowing accurate positions of nodes in wireless ad-hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically ..."
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Cited by 6 (0 self)
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Abstract—Knowing accurate positions of nodes in wireless ad-hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, triangle inequality is often employed in existing approaches. Our study shows that triangle inequality has a lot of limitations which make it far from accurate and reliable. In this study, we formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design a bilateration generic cycles based outlier detection algorithm, and examine its effectiveness and efficiency through a network prototype implementation of MicaZ motes as well as extensive simulations. The results shows that our design significantly improves the localization accuracy by wisely rejecting outliers. 1.
Geometric Algorithms for Sensor Networks
, 2011
"... Networked embedded sensors provide a unique opportunity for real time, large scale, high resolution environmental monitoring. Such systems are becoming ubiquitous across many activities important to our economy and life, from manufacturing and industrial sensing, to traffic and powergrid management, ..."
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Cited by 5 (0 self)
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Networked embedded sensors provide a unique opportunity for real time, large scale, high resolution environmental monitoring. Such systems are becoming ubiquitous across many activities important to our economy and life, from manufacturing and industrial sensing, to traffic and powergrid management, to wildlife, agriculture and environmental monitoring, to hospital operations and patient observation, all the
RSD: A Metric for Achieving Range-Free Localization beyond Connectivity
"... Abstract—Wireless sensor networks have been considered as a promising tool for many location-dependent applications. In such deployments, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free approaches depending only on rad ..."
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Cited by 5 (0 self)
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Abstract—Wireless sensor networks have been considered as a promising tool for many location-dependent applications. In such deployments, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free approaches depending only on radio connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to these limitations, this paper introduces a proximity metric called RSD to capture the distance relationships among 1-hop neighboring nodes in a range-free manner. With little overhead, RSD can be conveniently applied as a transparent supporting layer for state-of-the-art connectivity-based localization solutions to achieve better accuracy. We implemented RSD with three well-known algorithms and evaluated using two outdoor test beds: an 850-foot-long linear network with 54 MICAz motes, and a regular 2D network covering an area of 10,000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with a subhop resolution, and reduces localization errors by as much as 35 percent. In addition, simulations confirm its effectiveness for large-scale networks and reveal an interesting feature of robustness under unevenly distributed radio path loss.
Analysis of flip ambiguities for robust sensor network localization
- Vehicular Technology, IEEE Transactions on
"... Abstract—Erroneous local geometric realizations in some parts of a network due to the sensitivity to certain distance-measurement errors with respect to some neighboring sensor lo-cations is a major problem in wireless sensor-network localization, which may, in turn, affect the reliability of the lo ..."
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Cited by 5 (0 self)
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Abstract—Erroneous local geometric realizations in some parts of a network due to the sensitivity to certain distance-measurement errors with respect to some neighboring sensor lo-cations is a major problem in wireless sensor-network localization, which may, in turn, affect the reliability of the localization of the whole or a major portion of the sensor network. This phenomenon is well described using the notion of “flip ambiguity ” in rigid graph theory. In this paper, we present a formal geometric analysis of flip-ambiguity problems in planar sensor networks via quan-tification of the likelihood of flip ambiguities in arbitrary sensor neighborhood geometries. Based on this analysis, we establish a ro-bustness criterion to detect flip ambiguities in such neighborhood geometries. In addition to the analysis, the established robust-ness criterion is embedded in localization algorithms to enhance the reliability of the produced location estimates by eliminating neighborhoods with flip ambiguities from being included in the localization process. Index Terms—Flip ambiguities in WSN localization, robust WSN localization. I.
Component-Based Localization in Sparse Wireless Networks
"... Abstract—Localization is crucial for wireless ad hoc and sensor networks. As the distance-measurement ranges are often less than the communication ranges for many ranging systems, most communication-dense wireless networks are localization-sparse. Consequently, existing algorithms fail to provide ac ..."
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Cited by 4 (0 self)
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Abstract—Localization is crucial for wireless ad hoc and sensor networks. As the distance-measurement ranges are often less than the communication ranges for many ranging systems, most communication-dense wireless networks are localization-sparse. Consequently, existing algorithms fail to provide accurate localization supports. In order to address this issue, by introducing the concept of component, we group nodes into components so that nodes are able to better share ranging and anchor knowledge. Operating on the granularity of components, our design, CALL, relaxes two essential restrictions in localization: the node ordering and the anchor distribution. Compared to previous designs, CALL is proven to be able to locate the same number of nodes using the least information. We evaluate the effectiveness of CALL through extensive simulations. The results show that CALL locates 90 % nodes in a network with average degree 7.5 and 5 % anchors, which outperforms the state-of-the-art design Sweeps by about 40%. Index Terms—Component-based, finite mergence, localization, node-based, ranging-model-based estimation (RMBE).
