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, smart cities etc. 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 an estimation scheme for estimating the active node cardinalities of each node type in a heterogeneous M2M network with three types of nodes. Our scheme consists of two phases- in phase 1, coarse estimates are computed and these estimates are used to compute the final estimates to the required accuracy level in phase 2. We analytically derive a condition that can be used to decide as to which of two possible approaches is to be used in phase 2. Using simulations, we show that our proposed scheme requires significantly fewer time slots to execute compared to separately executing a well-known estimation protocol designed for a homogeneous network in prior work thrice to estimate the cardinalities of the three node types, even though both these schemes obtain estimates with the same accuracy.