Distributed Data Stream Management Strategies in Internet of Things Network Architecture to Improve QoS Parameters: A Systematic Review

Subject Areas : New technologies in distributed systems and algorithmic computing

abbas jalilvand ^{1 *} , Mohammad Farrokhzadeh Ajirlou ²

1 - Assistant Professor – Islamic Azad University, Karaj Branch – Department of Computer Science – Karaj – Iran
2 -

Received: 2025-11-29 Accepted : 2025-12-16 Published : 2025-12-21

Keywords: Internet of Things (IoT), Data Stream, Distributed Architecture, Quality of Service (QoS), Distributed Computing, Systematic Review,

Abstract :

The Internet of Things (IoT) has emerged as one of the most transformative concepts in information technology, aiming to connect physical objects to the internet and enable them to collect, transmit, and share data. This extensive framework has led to the development of networks of smart devices capable of autonomous interaction with each other and their surrounding environments. In recent years, the advent of decentralized approaches within IoT has significantly expanded its capabilities in data management and distributed collaboration among systems. These approaches, leveraging distributed computing, cloud computing, and service-oriented architectures, facilitate real-time and scalable data processing. However, with the dramatic increase in the number and diversity of connected devices, the massive volume of generated data poses substantial challenges in terms of data storage, processing, and stream management. This paper adopts a comprehensive review approach to examine and analyze existing methods for distributed data stream management within IoT network infrastructures. The various methodologies are evaluated based on their architecture, efficiency, quality of service (QoS) considerations, and implementation challenges. Furthermore, the strengths and limitations of each category of approaches are elucidated. The objective of this review is to provide a holistic overview of the current state of research in distributed data management for IoT, thereby identifying future research directions aimed at enhancing the performance and efficiency of these complex systems.

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