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Special issue on recent advancements in machine learning
algorithms for internet of things
· Naveen Chilamkurti
© Springer Science+Business Media, LLC, part of Springer Nature 2018
In recent years, wireless sensor networks are continuously generating a massive amount of
data. Processing this huge amount of data is not possible by traditional tools and technologies.
Hence, there is a need for scalable machine learning algorithms to process such massive
amount of data. In recent years, scalable machine learning algorithms are developed to process
the massive IoT data. This special issue focused on innovations in scalable machine learning
algorithms and embedded system development.
The ﬁrst paper, entitled QOS Distributed Routing Protocol for Mobile Ad-Hoc Wireless
Networks Using Intelligent Packet Carrying Systems, by Murugeswari and Rathi, proposes an
intelligent packet carrying algorithm; it provides a tracking mechanism that tracks nodes in
rural places. The effectiveness and reliability has been calculated and the results are obtained
using OPNET simulator.
The second paper, entitled TEE based Session Key Establishment Protocol for Secure
Infotainment Systems, by Sungbum Lee and Jong-Hyouk Lee, proposes a session key estab-
lishment protocol using Elliptic Curve Cryptography. The proposed protocol enables secure
authentication and key distribution between a user device and a telematics control unit.
Third paper, entitled A novel Gini Index Decision Tree Data Mining Method with Neural
Network Classiﬁers for Prediction of Heart Disease, by K. Mathan et al., proposes an altered
University of California, Davis, USA
LaTrobe University, Melbourne, Australia
Chung Hua University, Hsin-chu, Taiwan