Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.

Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented.

Online clustering of evolving data streams presents unique challenges, particularly the problem of false merging, where distinct clusters are incorrectly combined during temporal overlap. Existing buffer-based and density-based algorithms, such as BOCEDS and CEC-Merge, lack mechanisms to adaptively distinguish transient overlaps from genuine merging events. To address this, we propose BOCEDS-ATI—a Buffer-based Online Clustering for Evolving Data Streams with Adaptive Time Interval algorithm. The core innovation is the adaptive time interval mechanism, which dynamically adjusts merging decisions based on the temporal persistence and relative velocity of cluster interactions. This allows the model to recognize and prevent false merging in continuously evolving data streams. Experimental evaluation on multiple synthetic and real-world datasets demonstrates that BOCEDS-ATI achieves superior clustering accuracy and robustness to dynamic drift compared with state-of-the-art algorithms, while maintaining linear time complexity suitable for real-time applications. The results confirm that incorporating localized temporal adaptation significantly improves the reliability of online clustering in non-stationary environments. Received: 17 July 2025 | Revised: 2 December 2025 | Accepted: 6 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Redhwan Al-amri: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Gamal Alkawsi: Validation, Project administration. Abdulnaser A. Hagar: Writing – review & editing. Luiz Fernando Capretz: Supervision, Funding acquisition.