2005
DOI: 10.1016/j.adhoc.2004.08.013
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Planar graph routing on geographical clusters

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Cited by 20 publications
(31 citation statements)
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“…As perimeter routing (and other recovery techniques using planarized graphs) requires longer path length in networks with higher node density this problem becomes worse with increasing node densities [12]. Perimeter routing may also take unnecessarily long paths when traversing faces in the ''wrong'' direction.…”
Section: The Euclidian Distance Metricmentioning
confidence: 98%
See 1 more Smart Citation
“…As perimeter routing (and other recovery techniques using planarized graphs) requires longer path length in networks with higher node density this problem becomes worse with increasing node densities [12]. Perimeter routing may also take unnecessarily long paths when traversing faces in the ''wrong'' direction.…”
Section: The Euclidian Distance Metricmentioning
confidence: 98%
“…Perimeter routing (and similar recovery methods) generally requires longer path lengths as the node density increases [12]. Thus, the benefits of LBLSP are expected to increase with increasing node densities.…”
Section: Performance Evaluationmentioning
confidence: 99%
“…This identifier could be globally unique for the cell, perhaps derived from its geographical location, but this is not necessary. Allocating identifiers to cells is beyond the scope of DCAP but is addressed in the literature [19].…”
Section: B Intra-and Extra-cellular Discriminationmentioning
confidence: 99%
“…Greedy routing might get stuck at convex network boundaries or at network voids inside the monitored area where sensor nodes can't be deployed (e.g. a lake inside the monitored area) [4].…”
Section: Related Workmentioning
confidence: 99%
“…In this work we will relate sensing and communication range of sensing covered networks in order to derive a theoretical result about the delivery guarantee of a geographical cluster based routing algorithm which was recently described in [4]. Related geographic routing algorithms, the concept of geographical clusters, and finally the algorithm analyzed in this work will be described in the next Section.…”
Section: Introductionmentioning
confidence: 98%