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    Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

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    Type
    Conference Paper
    Authors
    Douik, Ahmed S. cc
    Aly, Salah A.
    Al-Naffouri, Tareq Y. cc
    Alouini, Mohamed-Slim cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2017-08-31
    Online Publication Date
    2017-08-31
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/625750
    
    Metadata
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    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.
    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 Nature
    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
    DOI
    10.1007/978-3-319-64861-3_53
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-319-64861-3_53
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-64861-3_53
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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