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Dergiler / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 2 Special Issue

IoT Generated Real-Time Big Data Analytics: A Systematic Literature Review

Timoteos Onur Özçelik(GALATASARAY UNIVERSITY)0000-0002-7241-2018
Sultan Nezihe Turhan(GALATASARAY UNIVERSITY, SCHOOL OF ENGINEERING AND TECHNOLOGY, COMPUTER ENGINEERING PR.)0000-0001-9763-0882
Sayfa
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Yayın Tarihi
2026-06-17
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It discusses integrating fog and edge computing into future architectures to address latency and resource constraints.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The Internet of Things (IoT) is a communication paradigm and a network of interconnected heterogeneous devices.
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Temel Bulgular & Veri:It generates large-scale, fast-changing, and differently formatted data, a phenomenon referred to as Big Data, which has become widely used with the rapid growth of IoT applications, especially since the end of the first decade of the 2000s.
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Sonuç & Katkı:It discusses integrating fog and edge computing into future architectures to address latency and resource constraints.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:2018 Verileri
⚙️Değişkenler:Internet of Things (IoT) · real-time big data analytics · systematic literature review

Abstract

The Internet of Things (IoT) is a communication paradigm and a network of interconnected heterogeneous devices. It generates large-scale, fast-changing, and differently formatted data, a phenomenon referred to as Big Data, which has become widely used with the rapid growth of IoT applications, especially since the end of the first decade of the 2000s. The Internet of Things (IoT) is a communication paradigm and a network of interconnected heterogeneous devices. It generates large-scale, fast-changing, and differently formatted data, which is referred to as Big Data and has become widely used with the rapid growth of IoT applications, especially since the end of the first decade of the 2000s. A critical characteristic of this data is its real-time nature, which—coupled with its volume, velocity, and variety—demands advanced real-time analytics to extract value. While existing reviews have explored the broader intersection of IoT and Big Data Analytics, this paper provides a systematic, PRISMA-guided review focusing specifically on real-time analytics in IoT devices. By restricting the scope to real-time streaming data, the study provides a detailed synthesis that complements existing broader surveys. Applying the PRISMA methodology, we selected and analyzed 33 relevant articles published between 2018 and 2024. Our analysis reveals that scalable architectures are crucial for real-time Big Data analytics and that integrating machine learning techniques is fundamental to enabling intelligent decision-making. The study also underscores the necessity of appropriate data processing tools. The primary challenge in this area is minimizing latency, followed by data heterogeneity, scalability, and resource constraints. Furthermore, the survey identifies the critical role of real-time analytics in areas such as healthcare and smart cities. It discusses integrating fog and edge computing into future architectures to address latency and resource constraints.

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Özçelik, T. O., Turhan, S. N. (2026). IoT Generated Real-Time Big Data Analytics: A Systematic Literature Review. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 2 Special Issue. https://doi.org/10.35377/saucis...1671167

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