Robust Node Estimation and Topology Discovery Algorithm in Large-Scale Wireless Sensor Networks
Ahmed Douik, Salah A. Aly, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini

TL;DR
This paper presents a new statistical inference algorithm for estimating the number of nodes and their neighbors in large anonymous wireless sensor networks, achieving accuracy comparable to BLUE with limited queries.
Contribution
The work introduces a novel estimation algorithm that converges to BLUE and efficiently estimates network size and topology with minimal node queries.
Findings
Achieves 95% network estimation with small node queries.
Estimates converge to BLUE as queried nodes increase.
Effective in large-scale anonymous networks.
Abstract
This paper introduces a novel algorithm for cardinality, i.e., the number of nodes, estimation in large scale anonymous graphs using statistical inference methods. Applications of this work include estimating the number of sensor devices, online social users, active protein cells, etc. In anonymous graphs, each node possesses little or non-existing information on the network topology. In particular, this paper assumes that each node only knows its unique identifier. The aim is to estimate the cardinality of the graph and the neighbours of each node by querying a small portion of them. While the former allows the design of more efficient coding schemes for the network, the second provides a reliable way for routing packets. As a reference for comparison, this work considers the Best Linear Unbiased Estimators (BLUE). For dense graphs and specific running times, the proposed algorithm…
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Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Security in Wireless Sensor Networks · Indoor and Outdoor Localization Technologies
