The role of big data analytics in Internet of Things

By Ejaz Ahmed, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Majid Iqbal Khan, Abdelmuttlib Ibrahim Abdalla ahmed, Muhammad Imran, Athanasios V. Vasilakos. Comput. Networks, 2017

542 citations recorded by the source corpus.

The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/ac469d36498a54ecaa129c6ef4dc09745bfa3b03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

The Role of Big Data Analytics in Industrial Internet of Things (2019, unknown): “Although IIoT [19–24] and BDA [13, 25–31] have been widely studied separately, only a few studies including [32] have explored the convergence of the two domains.”
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies (2020, background): “Several real-time data analytics schemes have been proposed for IoT systems [160], [161], but the QoS requirement for H-IoT differs from other IoT scenarios.”
Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey (2020, background): “follows [32]: 1) Acquisition: Involves collecting raw data.”
Edge Computing in 5G: A Review (2019, methodology): “Subsequently, the outcomes of the local data analysis are used for decision making [59].”
Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective (2020, unknown): “[3] explored the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “three: descriptive, predictive and perspective analytics [52].”
Internet of Things and Big Data as enablers for business digitalization strategies (2020, background): “Hence, IoT and data analysis are part of the same phenomenon (Ahmed et al., 2017; Ranjan et al., 2018): Companies should be able to equip themselves with competitive infrastructures that can analyze the data derived from IoT.”
Big Data Analytics in Operations Management (2018, background): “…al. 2015), an update of the development in big data analytics for business applications and risk analysis (Choi et al. 2017a), a taxonomy of the literature on IoTs-supported big data analytics (Ahmed et al. 2017), and an examination of big data analytics for risk analysis (Choi and Lambert 2017).”
Social media big data analytics: A survey (2019, unknown): “The most related clas-si ﬁ cation found in the literature is discussed in (Ahmed et al., 2017), which focused on the big data analytics in the Internet of things.”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “IoT NIDS need to be experienced with ML algorithms and big data [133], [160] strategies to update their training model in real-time, in a streaming detection.”