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18-03-2020 | Issue 6/2020

Wireless Networks 6/2020

Fast node cardinality estimation and cognitive MAC protocol design for heterogeneous machine-to-machine networks

Journal:
Wireless Networks > Issue 6/2020
Authors:
Sachin Kadam, Chaitanya S. Raut, Aman Deep Meena, Gaurav S. Kasbekar
Important notes
The contributions of S. Kadam and G. Kasbekar have been supported by SERB grant SB/S3/EECE/157/2016. A preliminary version of this paper [1] was presented at the IEEE GLOBECOM 2017 conference and was published in its proceedings (https://​doi.​org/​10.​1109/​GLOCOM.​2017.​8254618). A technical report corresponding to this paper is available online [2].
C. Raut: He worked on this research while he was with IIT Bombay. A. Meena: He worked on this research while he was with IIT Bombay.

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Abstract

We design two estimation schemes, Method I and Method II, for rapidly obtaining separate estimates of the number of active nodes of each traffic type in a heterogeneous machine-to-machine (M2M) network with T types of nodes (e.g., those that send emergency, periodic, normal type data etc.), where \(T\ge 2\) is an arbitrary integer. Method I is a simple scheme, and Method II is more sophisticated and outperforms Method I. Also, we design a medium access control (MAC) protocol that supports multi-channel operation for a heterogeneous M2M network with T types of nodes, operating as a secondary network using Cognitive Radio technology. In every time frame, our Cognitive MAC protocol uses the proposed estimation schemes to rapidly estimate the active node cardinality of each type, and uses these estimates to find the optimal contention probabilities to be used. We compute a closed form expression for the expected number of time slots required by Method I to execute, and a simple upper bound on it. Also, we analytically obtain expressions for the expected number of successful contentions per frame and the expected amount of energy consumed. Finally, we evaluate the performances of our proposed estimation schemes and Cognitive MAC protocol using simulations.

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