ArticleSensors (Basel, Switzerland)2022
Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Research on Coverage Optimization in Wireless Sensor Networks Based on an Improved Sparrow Search Algorithm.Sensors (Basel, Switzerland) · 2026Article
- Prediction of Concrete Compressive Strength Based on ISSA-BPNN-AdaBoost.Materials (Basel, Switzerland) · 2024Article
- Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Digital-Twin-Assisted Edge-Computing Resource Allocation Based on the Whale Optimization Algorithm.Sensors (Basel, Switzerland) · 2022Article
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2 authors.
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Abstract
In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.
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