The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.

In the rapidly evolving field of renewable energy, integrating Artificial Intelligence (AI) and the Internet of Things (IoT) has become a transformative strategy for improving solar energy monitoring and control. This paper provides a comprehensive survey of Artificial Intelligence of Things (AIoT) applications in solar energy, illustrating how IoT technologies enable real-time monitoring, system optimization through techniques such as Maximum Power Point Tracking (MPPT), solar tracking, and automated cleaning. Simultaneously, AI boosts these capabilities through energy forecasting, optimization, predictive maintenance, and fault detection, significantly enhancing system performance and reliability. This review highlights key advancements, challenges, and practical applications of AIoT in the solar energy sector, emphasizing its role in advancing energy efficiency and sustainability.

Industry 5.0 introduces a shift toward human-centric, sustainable, and resilient industrial ecosystems, emphasizing intelligent automation, collaboration, and adaptive operations. Predictive Maintenance (PdM) plays a critical role in this transition, addressing the limitations of traditional maintenance approaches in increasingly complex and data-driven environments. The convergence of Artificial Intelligence and the Industrial Internet of Things, referred to as the Artificial Intelligence of Things (AIoT), enables real-time sensing, learning, and decision-making for advanced fault detection, Remaining Useful Life estimation, and prescriptive maintenance actions. This study provides a systematic and structured review of AIoT-enabled PdM aligned with Industry 5.0 objectives. It presents a unified taxonomy integrating AI models, Industrial Internet of Things (IIoT) infrastructures, and AIoT architectures; reviews AI-driven techniques, sector-specific implementations in manufacturing, transportation, and energy; and analyzes emerging paradigms such as Edge–Cloud collaboration, federated learning, self-supervised learning, and digital twins for autonomous and privacy-preserving maintenance. Furthermore, this paper synthesizes strengths, limitations, and cross-industry challenges, and outlines future research directions centered on explainability, data quality and heterogeneity, resource-constrained intelligence, cybersecurity, and human–AI collaboration. By bridging technological advancements with Industry 5.0 principles, this review contributes a comprehensive foundation for the development of scalable, trustworthy, and next-generation AIoT-based predictive maintenance systems.

The increasing complexity and scale of Internet of Vehicles (IoV) networks pose significant security challenges, necessitating the development of advanced intrusion detection systems (IDS). Traditional IDS approaches, such as rule-based and signature-based methods, are often inadequate in detecting novel and sophisticated attacks due to their limited adaptability and dependency on predefined patterns. To overcome these limitations, machine learning (ML) and deep learning (DL)-based IDS have been introduced, offering better generalization and the ability to learn from data. However, these models can still struggle with zero-day attacks, require large volumes of labeled data, and may be vulnerable to adversarial examples. In response to these challenges, Generative AI-based IDS—leveraging models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers—have emerged as promising solutions that offer enhanced adaptability, synthetic data generation for training, and improved detection capabilities for evolving threats. This survey provides an overview of IoV architecture, vulnerabilities, and classical IDS techniques while focusing on the growing role of Generative AI in strengthening IoV security. It discusses the current landscape, highlights the key challenges, and outlines future research directions aimed at building more resilient and intelligent IDS for the IoV ecosystem.

The Industrial Internet of Things (IIoT) is central to modern industrial automation, yet its growing connectivity exposes critical systems to evolving cyber threats. Traditional intrusion detection methods struggle in IIoT environments due to class imbalance and limited adaptability to zero-day attacks. This systematic review evaluates generative AI techniques for IIoT intrusion detection and identifies deployment requirements for industrial environments. We searched five databases (IEEE Xplore, ACM Digital Library, Springer, ScienceDirect, and arXiv) for studies published between January 2019 and December 2025, applying predefined inclusion criteria. Following a systematic selection process (identification plus three progressive screening stages) across 342 records, 42 primary studies were included for systematic synthesis. We examined four GenAI paradigms—Generative Adversarial Networks, Transformers, Diffusion Models, and Variational Autoencoders—analyzing nine state-of-the-art frameworks through comparative performance analysis. Hybrid Transformer architectures (e.g., Transformer-GAN-AE) achieve the most consistent detection performance, while diffusion-based models (e.g., Diff-IDS) provide computational advantages for edge deployments. However, substantial variability in evaluation methodologies and limited reporting of statistical rigor indicate important gaps in current research practices. These findings inform the development of GenAI-driven strategies tailored to industrial infrastructure constraints and highlight key directions for advancing IIoT cybersecurity.