1 research outputs found
Rapid Node Cardinality Estimation in Heterogeneous Machine-to-Machine Networks
Machine-to-Machine (M2M) networks are an emerging technology with
applications in various fields, including smart grids, healthcare, vehicular
telematics and smart cities. Heterogeneous M2M networks contain different types
of nodes, e.g., nodes that send emergency, periodic, and normal type data. An
important problem is to rapidly estimate the number of active nodes of each
node type in every time frame in such a network. In this paper, we design two
schemes for estimating the active node cardinalities of each node type in a
heterogeneous M2M network with types of nodes, where is an
arbitrary integer. Our schemes consist of two phases-- in phase 1, coarse
estimates are computed, and in phase 2, these estimates are used to compute the
final estimates to the required accuracy. We analytically derive a condition
for one of our schemes that can be used to decide as to which of two possible
approaches should be used in phase 2 to minimize its execution time. The
expected number of time slots required to execute and the expected energy
consumption of each active node under one of our schemes are analysed. Using
simulations, we show that our proposed schemes require significantly fewer time
slots to execute compared to estimation schemes designed for a heterogeneous
M2M network in prior work, and also, compared to separately executing a
well-known estimation protocol designed for a homogeneous network in prior work
times to estimate the cardinalities of the node types, even though all
these schemes obtain estimates with the same accuracy.Comment: 14 pages, 21 figure