Method of aggregating temporal multimodal streaming data from iot devices based on reactive programming


  • Lukianets M.O.
  • Sulema Y.S.



groupings, IoT, streaming data, reactive programming, multimodal data.


Recent advancements in Internet of Things (IoT) technologies have led to an exponential increase in the generation of temporal multimodal streaming data from various IoT devices. Aggregating and processing this data efficiently poses significant challenges, including scalability, reliability, and synchronization issues. This paper proposes a novel method for aggregating temporal multimodal streaming data from IoT devices based on reactive programming principles. We begin by analyzing recent research and publications in the field to identify existing challenges and limitations in aggregating temporal data from IoT devices. The key problem addressed in this study is the efficient aggregation of data streams while ensuring scalability, reliability, and synchronization across different devices. The primary objective of this research is to develop a method that can hierarchically organize and aggregate temporal data streams from IoT devices while addressing the challenges of scalability and reliability. To achieve this, we leverage reactive programming techniques, specifically the GroupJoin operation, which allows for the seamless integration of data streams from multiple sources. Our method involves initializing the aggregation process by passing relevant data streams from two devices to the GroupJoin operation and configuring the settings for operation. Additionally, we introduce flexibility in the aggregation process by allowing for the hierarchical structuring of data streams based on physical or logical criteria. Furthermore, we discuss the implementation of our proposed method using the Rx library for .NET, which provides implementations for various programming languages, ensuring its adaptability across different platforms. We benchmark the performance of our method against a naive implementation, demonstrating its efficiency and scalability in aggregating temporal data streams from IoT devices. In conclusion, our proposed method offers a scalable and reliable solution for aggregating temporal multimodal streaming data from IoT devices. By leveraging reactive programming principles, we address key challenges in data aggregation and provide a flexible method for organizing and processing data streams efficiently.


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