# Probabilistic Top-k Dominating Query Monitoring over Multiple Uncertain IoT Data Streams in Edge Computing Environments

## TL;DR

This paper presents a parallel probabilistic top-k dominating query method for uncertain IoT data streams in edge computing, significantly improving processing speed, reducing communication costs, and maintaining high accuracy.

## Contribution

It introduces a novel parallel approach for top-k dominating queries over uncertain IoT streams, optimizing for speed, cost, and accuracy in edge environments.

## Key findings

- Improves computation time by nearly 60%
- Reduces communication cost by about 20%
- Maintains high accuracy in most scenarios

## Abstract

Extracting the valuable features and information in Big Data has become one of the important research issues in Data Science. In most Internet of Things (IoT) applications, the collected data are uncertain and imprecise due to sensor device variations or transmission errors. In addition, the sensing data may change as time evolves. We refer an uncertain data stream as a dataset that has velocity, veracity, and volume properties simultaneously. This paper employs the parallelism in edge computing environments to facilitate the top-k dominating query process over multiple uncertain IoT data streams. The challenges of this problem include how to quickly update the result for processing uncertainty and reduce the computation cost as well as provide highly accurate results. By referring to the related existing papers for certain data, we provide an effective probabilistic top-k dominating query process on uncertain data streams, which can be parallelized easily. After discussing the properties of the proposed approach, we validate our methods through the complexity analysis and extensive simulated experiments. In comparison with the existing works, the experimental results indicate that our method can improve almost 60% computation time, reduce nearly 20% communication cost between servers, and provide highly accurate results in most scenarios.