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Go to Editorial ManagerThis narrative study provides an analytical and critical review of recent advancements (2019-2026) in the integration of IoT (Internet of Things) and AI (Artificial Intelligence) systems for fall detection and child health monitoring. Unlike prior studies, which concentrated on elderly care and monitoring, this study examines child-specific monitoring environments, including wearable, vision-based, and hybrid systems. It investigates new trends such as the combination of deep learning and interpretable AI with multimedia sensory input and peripheral or fuzzy computing. Data scarcity, real-world deployment limits, privacy concerns, and age-related changes are among the key challenges addressed. The paper identifies important research gaps and proposes future paths for sustainable, secure, and accessible intelligent child monitoring systems.
Artificial intelligence (AI) and ChatGPT-4 have versatile applications in both school children's education and university settings. Chat GPT-4 can be a valuable assistant for teachers in various ways. The model can utilize its comprehensive knowledge to provide additional information and concepts in diverse fields to help explain difficult topics. It can also provide extra exercises and questions to assist students in practicing and reinforcing their skills. With its predictive and linguistic generation capabilities, Chat GPT-4 can also offer review and editing for students' essays and research papers, aiming to improve the quality of their writing and expression. It can also guide students in the research process and the collection of reliable sources. Furthermore, the model can provide individual support by answering students' questions and guiding them through the learning process. It can also be used to create simulations and educational scenarios to enhance students' understanding and application of theoretical concepts in realistic contexts. It is worth mentioning that the model relies on the inputs it receives, so the teacher needs to play an active role in guiding and clarifying the ideas and information provided by the model, as well as in evaluating and monitoring students' progress.
Microwave photonic filters (MPFs) have been suggested as one solution to high-speed tunable wideband radio-frequency (RF) signal processing possessing unique characteristics relative to their all-electronic counterparts (or equivalents), both in bandwidth and tunability and insensitivity to electromagnetic interference. The article is a review of MPF design technologies and applications, and also contains relevant techniques in thermal, electrical and optical tuning as well as new methods founded on stimulated Brillouin scattering, optical frequency combs, and micro-ring resonators. The survey focuses on programmable optical processors, including liquid-crystal-on-silicon designs, arrayed waveguide gratings, and cascaded resonator designs, as arbitrary filters synthesis. Critical consideration is done on performance metrics which include bandwidth, selectivity, out-of-band rejection and tuning range, energy efficiency and other practical factors like stability in the environment and complexity of fabrication. The latest advances in reconfiguration with the help of artificial intelligence and machine learning are presented, and their significance in the optimization of adaptive and predictive filters is also highlighted. The paper also discusses the current constraints such as integration, power consumption, and environmental sensitivity and has provided directions of future achievability of compact, low-power and ultrafast and highly flexible MPFs to next-generation RF communication, radio-over-fiber, and cognitive radio systems. The survey should be used as a source of reference to the researchers and engineers who seek to improve the development, testing, and real-life application of the state of art technologies in the field of microwave photonic filtering.
As Internet of Things (IoT) devices continue to spread, they also create many new entry points for cyberattacks. Traditional security methods struggle to keep up, which makes smarter and more adaptive defenses necessary. This paper introduces an Artificial Intelligence (AI)–driven threat intelligence framework designed to improve intrusion detection in diverse IoT networks. The framework combines Machine Learning (ML) and Deep Learning (DL) models to detect malicious activity more accurately across different types of network traffic. To evaluate the approach, three widely used benchmark datasets—UNSW-NB15, CIC-IDS2017, and IoT-Botnet—were used. Experimental results show that the proposed hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model performs very well. It achieved 97% accuracy, a 0.95 F1-score, and a 0.98 Receiver Operating Characteristic – Area Under the Curve (ROC-AUC) on the UNSW-NB15 dataset, outperforming traditional ML models such as Random Forest, which reached 94% accuracy. While DL models provided better detection performance and stronger generalization, ML models proved to be much faster, with nearly three times lower inference latency—about 3 milliseconds per network flow. This makes them more suitable for real-time deployment at the IoT edge, where computing resources are limited. Overall, the proposed hybrid approach strikes a practical balance between detection accuracy and processing speed, offering a scalable and robust foundation for AI-based IoT threat intelligence in real-world environments.
The emergence of Large Language Models (LLMs) has opened new frontiers in artificial intelligence applications across multiple domains, including cybersecurity. This paper presents a comprehensive review of the role of LLMs in enhancing cyber defense mechanisms, with a particular focus on their effectiveness in identifying, mitigating, and responding to Advanced Persistent Threats (APTs) and other sophisticated cyber-attacks. We explore the integration of LLMs in threat intelligence, anomaly detection, automated incident response, and adversarial behavior analysis. By examining recent advancements, case studies, and state-of-the-art implementations, we highlight the strengths and limitations of current LLM-based approaches. Furthermore, we assess the challenges related to scalability, adversarial robustness, and ethical considerations inherent in deploying LLMs within cybersecurity infrastructures. The review concludes with future research directions, emphasizing the need for hybrid AI systems that combine LLMs with traditional rule-based and statistical methods to provide resilient and adaptive cybersecurity solutions in the face of evolving digital threats.
Predictive maintenance (PdM) is a key enabler of reliability and cost reduction in Industrial Internet of Things (IIoT) systems. However, conventional centralized learning approaches require raw data aggregation, raising serious concerns regarding data privacy, communication overhead, and single points of failure. Federated Learning (FL) addresses part of this challenge by enabling distributed model training, yet centralized FL architectures still rely on a central aggregator. This paper proposes a decentralized federated learning (DFL) framework based on a ring topology for multi-class predictive maintenance in IIoT environments. The proposed approach enables collaborative model training among distributed edge nodes without relying on a central server, thereby enhancing system robustness, scalability, and data privacy. A lightweight deep neural network (DNN) is employed to classify five machine operating states using multi-sensor data, including vibration, temperature, and current measurements. Extensive experiments are conducted using a real-world sensor dataset collected from three-phase AC motors under multiple fault conditions. The performance of the proposed DFL approach is systematically compared against a non-federated baseline and a centralized FL (CFL) scheme. Experimental results demonstrate that the decentralized framework achieves clas-sification performance comparable to centralized FL, with an accuracy of up to 99.83%, while eliminating the dependency on a central coordinator. The findings confirm the feasibility of decentralized federated learning for predictive maintenance applica-tions, particularly in distributed industrial settings where data privacy, fault tolerance, and communication efficiency are critical.
In an era shaped by rapid urbanization and digital transformation, smart cities have become a global imperative for sustainable, efficient, and citizen-centric development. This article analyzes Amman’s development into a smart city, highlighting its role as a model for emerging urban areas. Leveraging recent technologies such as AI, IoT, blockchain, and big data, Amman is actively transitioning from a traditional city to a smart one enhancing mobility, energy efficiency, education, healthcare, and citizen engagement. This study examines Amman’s smart city vision and roadmap, technological infrastructure, key application domains, implemented innovation projects, and global rankings. It also explores the challenges the city faces, future research opportunities across various domains, the role of software in urban development, and the critical factors contributing to Amman’s success as a smart city. This article serves as a vital reference for researchers, policymakers, urban planners, and practitioners aiming to shape next-generation smart cities. The case of Amman underscores how strategic governance, public-private collaboration, and the effective use of emerging technologies can accelerate sustainable urban transformation.
With the rapid popularization of 5G edge communication systems, their decentralized architecture and the ultra-large attack surface brought by massive access devices have created severe network security challenges. Although current artificial intelligence-based intrusion detection systems and conventional federated learning schemes can achieve high intrusion detection accuracy, they universally suffer from two major limitations: excessive computational overhead and vulnerability to poisoning attacks, which make them unable to adapt to resource-constrained edge deployment environments. To address this issue, this paper proposes a lightweight anti-poisoning federated learning framework for low-latency secure 5G edge communications, with two core design elements: First, it integrates lightweight GRU/CNN models and a trust-aware aggregation mechanism to weaken the negative impact of malicious model updates; second, it introduces a deviation-based poisoning detection strategy to identify and filter adversarial updates during the federated learning process. We used the public CICIDS2017 dataset to complete tests in a simulated 5G edge environment equipped with 100 to 1000 edge devices, covering multiple scenarios including DDoS, MITM, and poisoning attacks. The results show that compared with existing similar schemes, the proposed framework reaches a maximum detection accuracy of 98.3\%, reduces communication latency by 30-35\%, cuts computational overhead by 40-60\%, and sees only a negligible drop in accuracy under poisoning attacks. It can support a wide range of real-time 5G edge applications such as intelligent transportation, smart cities, and the industrial Internet of Things.
Smart cities represent a nexus where urban planning, engineering, digital technologies, and societal needs converge. In emerging economies such as Iraq, conventional top-down smart city models often fail to account for contextual realities, resulting in fragmented or unsustainable initiatives. This paper proposes a novel interdisciplinary smart city development framework that integrates Artificial Intelligence (AI)-based planning, engineering simulations, urban design heuristics, and insights from social sciences particularly those related to digital inclusion and governance. Leveraging publicly available datasets and simulation environments, we demonstrate that the proposed approach can reduce urban traffic congestion by up to 35%, improve equitable access to public services by over 30%, forecast energy demands with more than 85% accuracy, and detect cyber threats with a precision and recall of 85.7%. These results validate the feasibility of a modular, adaptable smart city blueprint that embeds cybersecurity and data governance principles from the outset offering a scalable alternative suited to the institutional and infrastructural realities of developing contexts like Iraq.
The combination of the Internet of Things (IoT) and Software-Defined Networking (SDN) has quickly revolutionized the world of industry, and also brought with it some severe security risks. Despite its recent success, Deep learning (DL) can be a powerful method for Network Intrusion Detection systems (NIDS), but often the computational and memory requirements are too high for edge devices. In this paper, a collaborative optimization framework with lightweight deep-learning IDS models suitable for SDN-IoT environments is proposed. We formalize and test three collaborative compression schemes based on combining pruning, clustering, and quantization, yet maintaining the structural advantages of each stage: Sparsity-Preserving Quantization-Aware Training (PQAT), Cluster-Preserving Quantization-Aware Training (CQAT), and Sparsity-and-Cluster-Preserving Quantization-Aware Training (PCQAT). The three schemes are evaluated on four canonical architectures (LSTM, RNN, DNN and CNN) on the proposed SICA dataset with respect to DDoS, Probe and Brute-Force (BFA).The results show that aggressive single-stage compression such as clustering can shrink model size by up to 94%, but at the cost of predictive confidence; PCQAT, by contrast, acts as a stabilizer that recovers this lost confidence. In particular, CNN-PCQAT and DNN-PCQAT deliver the most favorable trade-off, sustaining high detection accuracy on all three attack classes with inference latency below 0.01 ms. The framework therefore offers a practical, deployment-oriented guideline for implementing real-time IDS on resource-constrained SDN-IoT edge devices.
Harsh industrial environments such as oilfields present unique challenges to electronic systems, including extreme temperatures, limited connectivity, power constraints, and operational unpredictability. Traditional Internet of Things (IoT) deployments often fail to adapt in real-time, exposing systems to risks such as data loss, late anomaly detection, or critical failure. This paper proposes a lightweight, Artificial Intelligence (AI)-driven eSystem architecture tailored for such conditions, integrating edge intelligence, secure communication, and self-adaptive mechanisms. We demonstrate the framework's viability through simulating a case study of real-time sensor data from pipeline infrastructure, applying a Long Short-Term Memory (LSTM)-based anomaly detection model deployed at the edge. Results show significant improvements in detection latency, bandwidth efficiency, and system resilience. The framework offers a modular blueprint for deploying AI-enhanced eSystems across energy, mining, and remote critical infrastructure domains.