Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks

By Chris Savarese, Jan M. Rabaey, Koen Langendoen. USENIX ATC, General Track, 2002

297 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b65686ce20954f621f160e3da301a0b0e8892950

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1. Tian He
2. Guoqiang Mao
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4. Henk Wymeersch
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7. Gang Zhou
8. Kamin Whitehouse

Citations in the corpus

• Range-free localization schemes for large scale sensor networks (2003, background): “Work to mitigate such errors such as robust range estimation ([11]), two-phase refinement positioning ([29], [31]), and parameter calibration ([34]) have been proposed to take advantage of averaging, smoothing, and alternate hybrid techniques to reduce error to within some acceptable limit.”
• A Survey of Architectures and Localization Techniques for Underwater Acoustic Sensor Networks (2011, background): “These techniques generally utilize connectivity information and they have been proposed for WSNs that can either afford fixed infrastructure or that can tolerate relatively high communication overhead [15]–[18].”
• Distributed localization in wireless sensor networks: a quantitative compariso (2003, unknown): “This approach was named DV-hop by Niculescu and Nath [10], and Hop-TERRAIN by Savarese et al. [11].”
• Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling (2004, background): “Savarese [28] try to improve the above approach by iteratively computing.”
• Localization in underwater sensor networks: survey and challenges (2006, methodology): “In [25], initial estimates are obtained using an algorithm similar to the range-free scheme DV-Hop (discussed in Section B.1.1).”
• Wireless sensor network localization techniques (2007, methodology): “All of the above three algorithms [87,96,95] have three phases [97]: (a) determination of the distances between non-anchor nodes and anchor nodes; (b) derivation of the location of at least some nonanchor nodes from their distances to the anchors; (c) refinement of the location estimates using measu”
• A Theory of Network Localization (2006, unknown): “The previous approaches can be classified into two types: coarse-grained [10], [11], [28], [42], [50], [57] and fine-grained [1], [7], [12], [18], [37], [44], [46], [47], [49], [52], [53], [56].”
• Localization with mobile anchor points in wireless sensor networks (2005, methodology): “The DV-Hop scheme can be enhanced with RSSI technology for location refinement [24].”
• The Cricket indoor location system (2005, background): “describe a two-phase, anchor-based, concurrent, localization algorithm [82].”
• A practical evaluation of radio signal strength for ranging-based localization (2007, methodology): “While APS is only one of many algorithms, we chose it for its simplicity and also because it represents a large class of algorithms that use shortest-path [29, 24, 33] or bounding-box [32, 27] approximations.”
• DV Based Positioning in Ad Hoc Networks (2003, background): “Theoretically, this process may continue untill all nodes get some position estimate (Savarese et al., 2001).”
• Robust distributed network localization with noisy range measurements (2004, methodology): “use a two-phase approach using connectivity for initial position estimates and trilateration for position refinement [17].”
• Cooperative Localization in Wireless Networks (2009, background): “The cooperative LS algorithm forms the basis of [30], [47], [48], [50], [53], [54], [56]–[58], and [61], as well as variations such as weighted LS, where signal metrics have an associated weight reflecting the quality of the estimate [ 105 ], and regularizedLS,wherecertainlocationsarepenalized[106].”