Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2017-08-31Online Publication Date
2017-08-31Print Publication Date
2018Permanent link to this record
http://hdl.handle.net/10754/625750
Metadata
Show full item recordAbstract
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 NatureJournal
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017Conference/Event name
3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017Additional Links
https://link.springer.com/chapter/10.1007%2F978-3-319-64861-3_53ae974a485f413a2113503eed53cd6c53
10.1007/978-3-319-64861-3_53