  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.
