面向物联网自适应数据压缩算法降传输量研究OA

The growth in the volume and types of Internet of Things(IoT)data presents significant security challenges,as traditional security measures struggle to meet the demands.We propose a privacy-preserving framework for IoT big data analytics that integrates federated learning with lightweight cryptography techniques.By employing a novel aggregation algorithm,adaptive learning rates,and a hierarchical architecture,the framework enhances the privacy and efficiency of big data analytics;it provides lightweight encryption schemes to reduce computational load and energy consumption;it designs the SEPP-IoT communication protocol to address challenges in federated learning;it proposes an adaptive data compression algorithm to reduce communication overhead;it integrates multiple mechanisms to enhance system fault tolerance and anti-interference capabilities;and it introduces a trust management system to evaluate the credibility of IoT nodes.Experimental results demonstrate that the proposed method outperforms existing approaches in terms of model accuracy,communication overhead,resource utilization,fault tolerance,and privacy protection,achieving higher detection rates against IoT attacks.However,the study has limitations.In the future,studies can be conducted in areas such as the application of machine learning in trust management and fault detection,enhancing privacy protection without reducing model performance,and verifying the fea-sibility of large-scale complex networks.