Adaptive beacon placement

By Nirupama Bulusu, Deborah Estrin, John S. Heidemann. Proceedings 21st International Conference on Distributed Computing Systems, 2001

351 citations recorded by the source corpus.

Beacon placement strongly affects the quality of spatial localization, a critical service for context-aware applications in wireless sensor networks; yet this aspect of localization has received little attention. Fixed beacon placement approaches such as uniform and very dense placement are not always viable and will be inadequate in very noisy environments in which sensor networks may be expected to operate (with high terrain and propagation uncertainties). We motivate the need for empirically adaptive beacon placement and outline a general approach based on exploration and instrumentation of the terrain conditions by a mobile human or robot agent. We design, evaluate and analyze three novel adaptive beacon placement algorithms using this approach for localization based on RF-proximity. In our evaluation, we find that beacon density rather than noise level has a more significant impact on beacon placement algorithms. Our beacon placement algorithms are applicable to a low (beacon) density regime of operation. Noise makes moderate density regimes more improvable.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/367ff504e259686d634fdbcec10cacaece4affb2

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Citations in the corpus

Sensor Networks : An Overview (2002, background): “Figure 4 [5] illustrates the Max algorithm.”
An energy efficient hierarchical clustering algorithm for wireless sensor networks (2003, background): “I. INTRODUCTION Recent advances in wireless communications and microelectro-mechanical systems have motivated the development of extremely small, low-cost sensors that possess sensing, signal processing and wireless communication capabilities.”
ASCENT: adaptive self-configuring sensor networks topologies (2004, methodology): “Bulusu et. al. [ 4 ], have proposed different algorithms for incremental beacon placement in sensor networks.”
Sensor placement for effective coverage and surveillance in distributed sensor networks (2003, background): “A number of techniques for both fine and coarse-grained localization have been proposed recently [6, 10].”
Sensor placement for grid coverage under imprecise detections (2002, background): “A number of techniques for both fine and coarse-grained localization have been proposed recently [11, 14, 15].”
Energy-efficient deployment of Intelligent Mobile sensor networks (2005, background): “’s work [3] is somewhat similar to the deployment problem that is considered here.”
Sensor deployment and target localization based on virtual forces (2003, background): “In the area of adaptive beacon placement and spatial localization, a number of techniques have been proposed for both fine-grained and coarse-grained localization [12].”
An Incremental Self-Deployment Algorithm for Mobile Sensor Networks (2002, methodology): “The deploymentproblem describedhereis similar to that described by Bulusu et al [2], who considerthe problem of adapti ve beaconplacementfor localizationin large-scalewirelesssensornetworks.”
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms (2009, background): “(2009) consider coverage problems where the density function is unknown, Lekien and Leonard (2009) propose centralized laws for non-uniform coverage using cartograms, de Silva and Ghrist (2007) study static coverage problems with minimal assumptions on the capabilities of individual sensors using al”
Using proximity and quantized RSS for sensor localization in wireless networks (2003, neutral): “wireless sensor networks [29][8][17][18][5][6].”
Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling (2004, background): “The second class of positioning methods relies on a large amount of sensor nodes with positions known densely distributed in a sensor network [4], [5], [6].”
The holes problem in wireless sensor networks: a survey (2005, background): “in [46], can help achieve differentiated multiple coverage in priority areas of a target region by attracting the mobile sensors if the existing density of deployment cannot support the application requirements.”
Distributed online localization in sensor networks using a moving target (2004, methodology): “[3] showed that a simpler centroid-based approach can be used with the reference beacons.”