物联网网络的区块链变异信息瓶颈

Qiong Wu;Le Kuai;Pingyi Fan;Qiang Fan;Junhui Zhao;Jiangzhou Wang
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摘要

在物联网(IoT)网络中,用户设备感知的数据量可能非常巨大,从而导致严重的网络拥塞。要解决这一问题,智能数据压缩至关重要。变异信息瓶颈(VIB)方法与机器学习相结合,可用于训练编码器和解码器,从而大幅减少所需的传输数据大小。然而,VIB 存在计算负担和网络不安全问题。在本文中,我们提出了一种支持区块链的 VIB(BVIB)方法,以减轻计算负担,同时保证网络安全。使用 Python 和 C++ 进行的大量仿真表明,BVIB 在时间和 CPU 周期成本、互信息和攻击下的准确性方面分别比 VIB 高出 36%、22% 和 57%。
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Blockchain-Enabled Variational Information Bottleneck for IoT Networks
In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion. To solve this problem, intelligent data compression is critical. The variational information bottleneck (VIB) approach, combined with machine learning, can be employed to train the encoder and decoder, so that the required transmission data size can be reduced significantly. However, VIB suffers from the computing burden and network insecurity. In this letter, we propose a blockchain-enabled VIB (BVIB) approach to relieve the computing burden while guaranteeing network security. Extensive simulations conducted by Python and C++ demonstrate that BVIB outperforms VIB by 36%, 22% and 57% in terms of time and CPU cycles cost, mutual information, and accuracy under attack, respectively.
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2024 Index IEEE Networking Letters Vol. 6 Table of Contents IEEE Networking Letters Publication Information IEEE Networking Letters Society Information Editorial SI on Advances in AI for 6G Networks
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