TY - JOUR
T1 - Privacy-Preserving Database Assisted Spectrum Access for Industrial Internet of Things
T2 - A Distributed Learning Approach
AU - Zhang, Mengyuan
AU - Chen, Jiming
AU - He, Shibo
AU - Yang, Lei
AU - Gong, Xiaowen
AU - Zhang, Junshan
N1 - Funding Information:
Manuscript received October 23, 2018; revised March 21, 2019 and June 5, 2019; accepted August 7, 2019. Date of publication September 5, 2019; date of current version March 31, 2020. This work was supported in part by the NSFC under Grant 61629302 and in part by the U.S. NSF under Grant IIS-1838024. This article was presented in part at the GLOBECOM Global Communications Conference with the title of “Privacy-Preserving Database Assisted Spectrum Access: A Socially-Aware Distributed Learning Approach,” San Diego, CA, USA, Dec. 2015 [1]. (Corresponding author: Jiming Chen.) M. Zhang, J. Chen, and S. He are with the State Key Laboratory of Industrial Control Technology and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang University, Hangzhou 310027, China (e-mail:, zhang418@zju.edu.cn; cjm@zju.edu.cn; s18he@zju. edu.cn).
PY - 2020/8
Y1 - 2020/8
N2 - Industrial Internet of Things (IIoT) has been shown to be of great value to the deployment of smart industrial environment. With the immense growth of Internet of Things (IoT) devices, dynamic spectrum sharing is introduced, envisaged as a promising solution to the spectrum shortage in IIoT. Meanwhile, cyber-physical safety issue remains to be a great concern for the reliable operation of IIoT system. In this article, we consider the dynamic spectrum access in IIoT under a received signal strength-based adversarial localization attack. We employ a practical and effective power perturbation approach to mitigate the localization threat on the IoT devices and cast the privacy-preserving spectrum sharing problem as a stochastic channel selection game. To address the randomness induced by the power perturbation approach, we develop a two-timescale distributed learning algorithm that converges almost surely to the set of correlated equilibria of the game. The numerical results show the convergence of the algorithm and corroborate that the design of two-timescale learning process effectively alleviates the network throughput degradation brought by the power perturbation procedure.
AB - Industrial Internet of Things (IIoT) has been shown to be of great value to the deployment of smart industrial environment. With the immense growth of Internet of Things (IoT) devices, dynamic spectrum sharing is introduced, envisaged as a promising solution to the spectrum shortage in IIoT. Meanwhile, cyber-physical safety issue remains to be a great concern for the reliable operation of IIoT system. In this article, we consider the dynamic spectrum access in IIoT under a received signal strength-based adversarial localization attack. We employ a practical and effective power perturbation approach to mitigate the localization threat on the IoT devices and cast the privacy-preserving spectrum sharing problem as a stochastic channel selection game. To address the randomness induced by the power perturbation approach, we develop a two-timescale distributed learning algorithm that converges almost surely to the set of correlated equilibria of the game. The numerical results show the convergence of the algorithm and corroborate that the design of two-timescale learning process effectively alleviates the network throughput degradation brought by the power perturbation procedure.
KW - Distributed algorithm
KW - game theory
KW - industrial cyber-physical system security
KW - spectrum access
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U2 - 10.1109/TIE.2019.2938491
DO - 10.1109/TIE.2019.2938491
M3 - Article
AN - SCOPUS:85083211806
VL - 67
SP - 7094
EP - 7103
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 8
M1 - 8825819
ER -