IoT Generated Real-Time Big Data Analytics: A Systematic Literature Review
Sakarya University Journal of Computer and Information Sciences, cilt.9, sa.2, ss.634-649, 2026 (Scopus, TRDizin)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 9 Sayı: 2
- Basım Tarihi: 2026
- Doi Numarası: 10.35377/saucis...1671167
- Dergi Adı: Sakarya University Journal of Computer and Information Sciences
- Derginin Tarandığı İndeksler: Scopus, Applied Science & Technology Source, Central & Eastern European Academic Source (CEEAS), Directory of Open Access Journals, TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.634-649
- Anahtar Kelimeler: Big data infrastructure, Internet of Things (IoT), real-time big data analytics, systematic literature review
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Galatasaray Üniversitesi Adresli: Evet
Özet
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. 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.