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An Intelligent Robust Networking Mechanism for the Internet of Things

In smart cities, the Internet of Things (IoT) consists of many low-power smart nodes. Its robustness is essential for protection of communication in data science against node failures caused by energy shortage or cyber-attacks. Scale-free networking topology, widely applied in IoT, is effectively resilient to random attacks but is vulnerable to malicious ones in which high-degree nodes are made to fail. The prohibitively high computational cost of existing robustness optimization algorithms is an obstacle to efficient topology self-optimization. To solve this problem, a novel robust networking model based on artificial intelligence is proposed to improve IoT topology robustness to protect its communication. Using the Back-Propagation neural network learning algorithm, the model extracts topology features from a dataset by supervised training. The experimental results show that the model achieves better prediction accuracy, thereby optimizing the topology with minimal computation overhead.

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Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.57 No.11 November 2019

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 91 - 95

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

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Subjek

MACHINE LEARNING
NEURAL NETWORKS
ROBUSTNESS
MATHEMATICAL MODEL
NETWORK TOPOLOGY
COMPUTATIONAL MODELING
TRAINING DATA

Info Detail Spesifik

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Pernyataan Tanggungjawab

Ning Chen, Tie Qiu, Xiaobo Zhou, Keqiu Li, Mohammed Atiquzzaman

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