Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
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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.
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.
PublisherSpringer International Publishing
JournalProceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017
Conference/Event name3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017