4.8 Article

Toward Secure IoT Networks in Healthcare Applications: A Game-Theoretic Anti-Jamming Framework

期刊

IEEE INTERNET OF THINGS JOURNAL
卷 9, 期 20, 页码 19615-19633

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3170382

关键词

Jamming; Games; Resource management; Interference; Internet of Things; Signal to noise ratio; Servers; Anti-jamming; constrained devices; gaming theory; Internet of Things (IoT) networks; power allocation (PA); resource-constrained networks

资金

  1. National Priorities Research Program (NPRP) from the Qatar National Research Fund [NPRP13S-0205-200270]

向作者/读者索取更多资源

This article proposes an anti-jamming power allocation strategy for health monitoring IoT networks using game theory and artificial neural networks. It also introduces novel data population and balancing techniques to enhance the efficiency of the strategy in combating jamming attacks. Furthermore, time and spatial diversities are exploited to enhance network security.
The Internet of Things (IoT) is used to interconnect a massive number of heterogeneous resource-constrained smart devices. This makes such networks exposed to various types of malicious attacks. In particular, jamming attacks are among the most common harmful attacks to IoT networks. Therefore, an anti-jamming power allocation (PA) strategy is first proposed in this article for health monitoring IoT networks by exploiting the game theory to minimize the worst case jamming effect under multichannel fading. This strategy uses an iterative algorithm based on gradient descent to identify the Nash Equilibrium (NE) of the game. An artificial neural network (ANN) model is also proposed to accelerate the convergence of the algorithm making it more suitable for IoT networks. Furthermore, novel data population (DP), extension, and balancing techniques are proposed to enhance the efficiency of the proposed strategy in combating jamming attacks even for network configurations that were never used in the training phase. In addition, time and spatial diversities are exploited using a heterogeneous iterative algorithm to enhance the security of the network.

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