Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

Handle URI:
http://hdl.handle.net/10754/625750
Title:
Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks
Authors:
Douik, Ahmed; Aly, Salah A.; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
Consider a large-scale anonymous wireless sensor network with unknown cardinality. In such graphs, each node has no information about the network topology and only possesses a unique identifier. This paper introduces a novel distributed algorithm for cardinality estimation and topology discovery, i.e., estimating the number of node and structure of the graph, by querying a small number of nodes and performing statistical inference methods. While the cardinality estimation allows the design of more efficient coding schemes for the network, the topology discovery provides a reliable way for routing packets. The proposed algorithm is shown to produce a cardinality estimate proportional to the best linear unbiased estimator for dense graphs and specific running times. Simulation results attest the theoretical results and reveal that, for a reasonable running time, querying a small group of nodes is sufficient to perform an estimation of 95% of the whole network. Applications of this work include estimating the number of Internet of Things (IoT) sensor devices, online social users, active protein cells, etc.
KAUST Department:
King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
Citation:
Douik A, Aly SA, Al-Naffouri TY, Alouini M-S (2017) Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks. Advances in Intelligent Systems and Computing: 569–578. Available: http://dx.doi.org/10.1007/978-3-319-64861-3_53.
Publisher:
Springer International Publishing
Journal:
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017
Conference/Event name:
3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017
Issue Date:
30-Aug-2017
DOI:
10.1007/978-3-319-64861-3_53
Type:
Conference Paper
ISSN:
2194-5357; 2194-5365
Additional Links:
https://link.springer.com/chapter/10.1007%2F978-3-319-64861-3_53
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorDouik, Ahmeden
dc.contributor.authorAly, Salah A.en
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2017-10-03T12:49:37Z-
dc.date.available2017-10-03T12:49:37Z-
dc.date.issued2017-08-30en
dc.identifier.citationDouik A, Aly SA, Al-Naffouri TY, Alouini M-S (2017) Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks. Advances in Intelligent Systems and Computing: 569–578. Available: http://dx.doi.org/10.1007/978-3-319-64861-3_53.en
dc.identifier.issn2194-5357en
dc.identifier.issn2194-5365en
dc.identifier.doi10.1007/978-3-319-64861-3_53en
dc.identifier.urihttp://hdl.handle.net/10754/625750-
dc.description.abstractConsider a large-scale anonymous wireless sensor network with unknown cardinality. In such graphs, each node has no information about the network topology and only possesses a unique identifier. This paper introduces a novel distributed algorithm for cardinality estimation and topology discovery, i.e., estimating the number of node and structure of the graph, by querying a small number of nodes and performing statistical inference methods. While the cardinality estimation allows the design of more efficient coding schemes for the network, the topology discovery provides a reliable way for routing packets. The proposed algorithm is shown to produce a cardinality estimate proportional to the best linear unbiased estimator for dense graphs and specific running times. Simulation results attest the theoretical results and reveal that, for a reasonable running time, querying a small group of nodes is sufficient to perform an estimation of 95% of the whole network. Applications of this work include estimating the number of Internet of Things (IoT) sensor devices, online social users, active protein cells, etc.en
dc.publisherSpringer International Publishingen
dc.relation.urlhttps://link.springer.com/chapter/10.1007%2F978-3-319-64861-3_53en
dc.subjectAnonymous networksen
dc.subjectSensor networksen
dc.subjectCardinality estimationen
dc.subjectBigDataen
dc.subjectIoTen
dc.titleCardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networksen
dc.typeConference Paperen
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabiaen
dc.identifier.journalProceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017en
dc.conference.date2017-09-09 to 2017-09-11en
dc.conference.name3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017en
dc.conference.locationCairo, EGYen
dc.contributor.institutionCalifornia Institute of Technology (Caltech), Pasadena, USAen
dc.contributor.institutionScientific Research Group Egypt (SRGE), Giza, Egypten
kaust.authorAl-Naffouri, Tareq Y.en
kaust.authorAlouini, Mohamed-Slimen
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