1 research outputs found
Robust Node Estimation and Topology Discovery Algorithm in Large-Scale Wireless Sensor Networks
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 produces a
cardinality estimate proportional to the BLUE. Furthermore, for an arbitrary
number of iterations, the estimate converges to the BLUE as the number of
queried nodes tends to the total number of nodes in the network. Simulation
results confirm the theoretical results by revealing that, for a moderate
running time, asking a small group of nodes is sufficient to perform an
estimation of 95% of the whole network