Cloud Computing Storage Backup and Recovery Strategy Based on Secure IoT and Spark

TLDR

Spatial data is abundant yet difficult to interpret, and even powerful standalone devices struggle to visualize it efficiently. The study aims to secure data and simplify user search and recovery by developing a cloud‑based backup and recovery strategy that integrates secure IoT and Spark. The authors employ secure IoT and Spark on a cloud platform, introduce cluster‑analysis and Ullman algorithms, and evaluate the system through experiments that analyze challenge‑response verification, packet counts, computational and communication costs, Spark method selection, platform throughput, and cache behavior. Experiments show loss rates below 5% across nodes, demonstrating that the system can reliably support application recovery.

Abstract

Spatial data occupies a large proportion of the large amount of data that is constantly emerging, but a large amount of spatial data cannot be directly understood by people. Even a highly configured stand-alone computing device can hardly meet the needs of visualization processing. In order to protect the security of data and facilitate for users the search for data and recover by mistake, this paper conducts a research on cloud computing storage backup and recovery strategies based on the secure Internet of Things and Spark platform. In the method part, this article introduces the security Internet of Things, Spark, and cloud computing backup and recovery related content and proposes cluster analysis and Ullman two algorithms. In the experimental part, this article explains the experimental environment and experimental objects and designs an experiment for data recovery. In the analysis part, this article analyzes the challenge-response-verification framework, the number of data packets, the cost of calculation and communication, the choice of Spark method, the throughput of different platforms, and the iteration and cache analysis. The experimental results show that the loss rate of database 1 in the fourth node is 0.4%, 2.4%, 1.6%, and 3.2% and the loss rate of each node is less than 5%, indicating that the system can respond to applications.