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.

翻译：随着万物互联（IoE）的发展，我们生活中产生的数据变得日益庞大。因此，我们需要投入更多精力来分析数据并提取有价值的信息。在云计算环境中，所有数据分析均在云端完成，客户端仅需较少的计算能力来处理一些简单任务。然而，随着数据量的快速增长，通过互联网将所有数据发送到云端已变得成本高昂。所需的云计算资源也变得更为庞大。为解决此问题，边缘计算应运而生。边缘设备被赋予更强的计算能力，以便在将数据发送到云端之前对其进行处理。因此，可以有效减少通过互联网传输的数据量以及云端所需的计算资源。在本工作中，我们提出了一种面向新兴低延迟万物互联分析应用的边缘辅助并行不确定天际线（EPUS）算法。我们利用天际线候选集的概念，在并行边缘计算节点上修剪那些不太可能成为天际线数据的数据。借助候选天际线集，每个边缘计算节点仅向服务器发送更新全局天际线所需的信息，从而减少了互联网上的数据传输量。仿真结果表明，所提方法优于两种对比方法，将处理二维数据的延迟降低了50%以上。对于高维数据，所提出的EPUS方法也优于其他现有方法。