Data Analytics in the Internet of Things Era: Tools, Approaches, Challenges, and Solutions
DOI:
https://doi.org/10.13052/jicts2245-800X.1344Keywords:
Internet of Things, Big data, Data analytics, ReviewAbstract
Rapid growth in the number of devices connected to the Internet of Things (IoT) and the exponential surge in data usage clearly suggest that the development of big data is inextricably linked with the IoT. In an ever-expanding network, big data raises concerns regarding data access efficiency. This study critically reviews IoT data analytics, tools, techniques, and challenges in extracting meaningful information from IoT device-generated massive data sets. IoT data analysis approaches, including real-time analysis, predictive analysis, and anomalous behavior analysis, are discussed in detail. How big data platforms and cloud computing can tackle IoT data and why IoT data preprocessing, integration, and storage matter are explored in this paper. Additionally, it covers issues and future research directions in IoT data analytics, including data security, scalability, and privacy.
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J. S. Yalli, M. H. Hasan, and A. Badawi, “Internet of things (iot): Origin, embedded technologies, smart applications and its growth in the last decade,” IEEE access, 2024.
T. Ahmad and D. Zhang, “Using the internet of things in smart energy systems and networks,” Sustainable Cities and Society, vol. 68, p. 102783, 2021.
J. Zandi, A. N. Afooshteh, and M. Ghassemian, “Implementation and analysis of a novel low power and portable energy measurement tool for wireless sensor nodes,” in Electrical Engineering (ICEE), Iranian Conference on, 2018: IEEE, pp. 1517–1522.
R. Chataut, A. Phoummalayvane, and R. Akl, “Unleashing the power of IoT: A comprehensive review of IoT applications and future prospects in healthcare, agriculture, smart homes, smart cities, and industry 4.0,” Sensors, vol. 23, no. 16, p. 7194, 2023.
D. Adhikari, W. Jiang, J. Zhan, D. B. Rawat, and A. Bhattarai, “Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives,” Computer Science Review, vol. 54, p. 100665, 2024.
A. O. Al-Mashhadani and M. Al-Khafajiy, “Big data analytics for IoT: Technologies, importance, and algorithms,” in Empowering IoT with Big Data Analytics: Elsevier, 2025, pp. 15–44.
B. Pourghebleh, V. Hayyolalam, and A. Aghaei Anvigh, “Service discovery in the Internet of Things: review of current trends and research challenges,” Wireless Networks, vol. 26, no. 7, pp. 5371–5391, 2020.
T. M. Mengistu, T. Kim, and J.-W. Lin, “A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning,” Sensors, vol. 24, no. 3, p. 968, 2024.
S. S. Mahadik, P. M. Pawar, and R. Muthalagu, “Heterogeneous IoT (HetIoT) security: techniques, challenges and open issues,” Multimedia Tools and Applications, vol. 83, no. 12, pp. 35371–35412, 2024.
D. Fawzy, S. M. Moussa, and N. L. Badr, “The internet of things and architectures of big data analytics: Challenges of intersection at different domains,” IEEE Access, vol. 10, pp. 4969–4992, 2022.
B. Pourghebleh, N. Hekmati, Z. Davoudnia, and M. Sadeghi, “A roadmap towards energy-efficient data fusion methods in the Internet of Things,” Concurrency and Computation: Practice and Experience, vol. 34, no. 15, p. e6959, 2022, doi: https://doi.org/10.1002/cpe.6959.
W. Li et al., “A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system,” Mobile networks and applications, vol. 26, pp. 234–252, 2021.
M. Talebkhah, A. Sali, M. Marjani, M. Gordan, S. J. Hashim, and F. Z. Rokhani, “IoT and big data applications in smart cities: recent advances, challenges, and critical issues,” IEEE Access, vol. 9, pp. 55465–55484, 2021.
Y. Himeur et al., “AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives,” Artificial Intelligence Review, vol. 56, no. 6, pp. 4929–5021, 2023.
B. Pourghebleh and V. Hayyolalam, “A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things,” Cluster Computing, vol. 23, no. 2, pp. 641–661, 2020.
M. Paramesha, N. L. Rane, and J. Rane, “Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence,” Partners Universal Multidisciplinary Research Journal, vol. 1, no. 2, pp. 110–133, 2024.
C. S. Babu, G. M. AV, S. Lokesh, A. Niranjan, and Y. Manivannan, “Unleashing IoT data insights: Data mining and machine learning techniques for scalable modeling and efficient management of IoT,” in Scalable Modeling and Efficient Management of IoT Applications: IGI Global, 2025, pp. 153–188.
I. H. Sarker, “Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective,” SN Computer Science, vol. 2, no. 5, p. 377, 2021.
B. Pourghebleh and N. J. Navimipour, “Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research,” Journal of Network and Computer Applications, vol. 97, pp. 23–34, 2017, doi: https://doi.org/10.1016/j.jnca.2017.08.006.
S. Patil, S. Chintamani, B. H. Dennis, and R. Kumar, “Real time prediction of internal temperature of heat generating bodies using neural network,” Thermal Science and Engineering Progress, vol. 23, p. 100910, 2021.
B. G. Deepthi, K. S. Rani, P. V. Krishna, and V. Saritha, “An efficient architecture for processing real-time traffic data streams using apache flink,” Multimedia Tools and Applications, vol. 83, no. 13, pp. 37369–37385, 2024.
D. Roy, R. Srivastava, M. Jat, and M. S. Karaca, “A complete overview of analytics techniques: descriptive, predictive, and prescriptive,” Decision intelligence analytics and the implementation of strategic business management, pp. 15–30, 2022.
R. Al-amri, R. K. Murugesan, M. Man, A. F. Abdulateef, M. A. Al-Sharafi, and A. A. Alkahtani, “A review of machine learning and deep learning techniques for anomaly detection in IoT data,” Applied Sciences, vol. 11, no. 12, p. 5320, 2021.
V. Hayyolalam, B. Pourghebleh, M. R. Chehrehzad, and A. A. Pourhaji Kazem, “Single-objective service composition methods in cloud manufacturing systems: Recent techniques, classification, and future trends,” Concurrency and Computation: Practice and Experience, vol. 34, no. 5, p. e6698, 2022.
V. Hayyolalam, B. Pourghebleh, A. A. Pourhaji Kazem, and A. Ghaffari, “Exploring the state-of-the-art service composition approaches in cloud manufacturing systems to enhance upcoming techniques,” The International Journal of Advanced Manufacturing Technology, vol. 105, pp. 471–498, 2019.
M. Laroui, B. Nour, H. Moungla, M. A. Cherif, H. Afifi, and M. Guizani, “Edge and fog computing for IoT: A survey on current research activities & future directions,” Computer Communications, vol. 180, pp. 210–231, 2021.
S. Mukherjee, S. Gupta, O. Rawlley, and S. Jain, “Leveraging big data analytics in 5G-enabled IoT and industrial IoT for the development of sustainable smart cities,” Transactions on Emerging Telecommunications Technologies, vol. 33, no. 12, p. e4618, 2022.
A. Medina-Pérez, D. Sánchez-Rodríguez, and I. Alonso-González, “An internet of thing architecture based on message queuing telemetry transport protocol and node-red: A case study for monitoring radon gas,” Smart Cities, vol. 4, no. 2, pp. 803–818, 2021.
V. Hayyolalam, B. Pourghebleh, and A. A. Pourhaji Kazem, “Trust management of services (TMoS): investigating the current mechanisms,” Transactions on Emerging Telecommunications Technologies, vol. 31, no. 10, p. e4063, 2020, doi: https://doi.org/10.1002/ett.4063.
K. Ngcobo, S. Bhengu, A. Mudau, B. Thango, and M. Lerato, “Enterprise data management: Types, sources, and real-time applications to enhance business performance-a systematic review,” Systematic Review|September, 2024.
Y. Liu, W. Yu, W. Rahayu, and T. Dillon, “An evaluative study on IoT ecosystem for smart predictive maintenance (IoT-SPM) in manufacturing: Multiview requirements and data quality,” IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11160–11184, 2023.
