Journal article

Evolutionary Games for Correlation-Aware Clustering in Massive Machine-to-Machine Networks

Nicole Sawyer, Mehdi Naderi Soorki, Walid Saad, David B Smith, Ni Ding

IEEE Transactions on Communications | Institute of Electrical and Electronics Engineers | Published : 2019


In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-to-machine networks, MTDs are typically located within close proximity and gather correlated data, and, thus, clustering MTDs based on data correlation leads to a decrease in the number of redundant bits transmitted to the base station. The clustering problem is formulated as an evolutionary game, which models the interactions among a massive number of MTDs, in order to decrease MTD transmission power. A novel utility function that captures the tradeoff between minimizing the average MTD transmission power..

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University of Melbourne Researchers


Awarded by U.S. Office of Naval Research (ONR)

Funding Acknowledgements

This research is supported by an Australian Government Research Training Program (RTP) Scholarship, and by the U.S. Office of Naval Research (ONR) under Grant N00014-15-1-2709.