Edge-assisted Parallel Uncertain Skyline Processing for Low-latency IoE Analysis

2025-08-06

AI Summary

To address the high communication overhead and analytical latency caused by uploading massive data from the Internet of Everything (IoE) to the cloud, this paper proposes an edge-cloud collaborative framework for parallel uncertain skyline processing. The framework dynamically constructs a skyline candidate set at edge nodes to enable local data pruning and optimization, while supporting parallel modeling and query processing of uncertain data. It innovatively integrates lightweight edge computation with global cloud-based optimization, significantly reducing transmission load and end-to-end latency. Experimental results demonstrate that the approach reduces processing latency by over 50% in two-dimensional settings, and maintains substantial performance advantages even in high-dimensional scenarios. These outcomes validate its effectiveness in enhancing real-time responsiveness, scalability, and uncertainty-aware modeling capabilities.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Due to the Internet of Everything (IoE), data generated in our life become larger. As a result, we need more effort to analyze the data and extract valuable information. In the cloud computing environment, all data analysis is done in the cloud, and the client only needs less computing power to handle some simple tasks. However, with the rapid increase in data volume, sending all data to the cloud via the Internet has become more expensive. The required cloud computing resources have also become larger. To solve this problem, edge computing is proposed. Edge is granted with more computation power to process data before sending it to the cloud. Therefore, the data transmitted over the Internet and the computing resources required by the cloud can be effectively reduced. In this work, we proposed an Edge-assisted Parallel Uncertain Skyline (EPUS) algorithm for emerging low-latency IoE analytic applications. We use the concept of skyline candidate set to prune data that are less likely to become the skyline data on the parallel edge computing nodes. With the candidate skyline set, each edge computing node only sends the information required to the server for updating the global skyline, which reduces the amount of data that transfer over the internet. According to the simulation results, the proposed method is better than two comparative methods, which reduces the latency of processing two-dimensional data by more than 50%. For high-dimensional data, the proposed EPUS method also outperforms the other existing methods.

Problem

• Reduce data transmission cost in cloud computing for IoE
• Optimize edge computing for low-latency skyline analysis
• Prune non-skyline data to minimize cloud resource usage

Innovation

• Edge-assisted parallel processing for IoE
• Skyline candidate set for data pruning
• Reduces latency and data transmission