The rapid development of the Internet of Things (IoT) has drawn significant attention from both industry and academia, driven by the integration of cloud computing, big data analytics, machine learning, and cyber-physical systems in manufacturing. Programmable Logic Controllers (PLCs), long central to industrial control systems, have evolved from basic feedback control devices to advanced components capable of networking and data exchange through IoT technologies. The Industrial Internet of Things (IIoT) refers to intelligent automation systems that continuously monitor critical parameters and respond to changes in real time. The integration of IoT with PLCs is transforming industrial automation by enabling remote real-time monitoring, data-driven decision-making, and predictive maintenance through advanced analytics. IIoT technologies enhance manufacturing performance and offer strategic value across sectors. Understanding their impact involves examining current research, including technology assessments and application-based case studies. This study provides an overview of PLC systems evolving into IIoT frameworks, with a focus on implementing proportional-integral (PI) control using the Siemens S7-300. Designed for precise and consistent temperature regulation, this approach enhances process efficiency and product quality, making it highly suitable for industrial and manufacturing environments.
Smart city applications demand lightweight, efficient and dependable communication protocols to facilitate the functioning of resource-limited Internet of Things (IoT) devices. This work performs an extensive empirical study of the three most prominent IoT standards; Message Queuing Telemetry Transport (MQTT), the Constrained Application Protocol (CoAP) and Hypertext Transfer Protocol (HTTP) by emulating real-world smart city use cases using a Raspberry Pi based testbed. The primary metrics based on which the protocols are analyzed are latency, message overhead, delivery rate and energy consumption. ANOVA and Tukey's HSD tests are used to determine the statistical significance of experimental data. The test results indicate that CoAP under (QoS-1 reliability) shows the least latency and energy consumption and MQTT due to its support for Quality of Service (QoS) is the most reliable. Among the others, HTTP is in general performance terms certainly at the bottom of all metrics mainly for its verbosity and synchronous nature. The paper then also suggests a decision flowchart for developers to choose the suitable protocol according to application requirements. The results are more than just numbers on a graph, and the research can be deployed for advice for protocol selection in practice, where this study helps identify issues with encryption overhead (over 75\%) while showcasing multi-hop network scalability and adaptive switch mechanisms as areas that remain to be resolved. Such findings can be used as a basis for design approaches to construct secure, efficient and scalable communication protocols in urban IoT settings.
The integration of Deep Learning (DL) techniques with the Internet of Things (IoT) has emerged as a transformative paradigm in the advancement of smart healthcare systems. Numerous recent studies have investigated the convergence of these technologies, demonstrating their potential in improving healthcare delivery, patient monitoring, and clinical decision-making. The ongoing evolution of Industry 5.0 in parallel with the deployment of 5G communication networks has further facilitated the development of intelligent, cost-effective, and highly responsive sensors. These innovations enable continuous and real-time monitoring of patients’ health conditions, a capability that was not feasible within the constraints of traditional healthcare models. Smart health monitoring systems have thus introduced significant improvements in terms of speed, affordability, reliability, and accessibility of medical services, particularly in remote or underserved regions. Moreover, the application of Deep Learning and Machine Learning algorithms in health data analysis has played a pivotal role in achieving preventive healthcare, reducing mortality risks, and enabling personalized treatment strategies. Such methods have also enhanced the early detection of chronic diseases, which previously posed considerable diagnostic challenges. To further optimize scalability and cost-efficiency, cloud computing and distributed storage solutions have been incorporated, ensuring secure and real-time data availability. This review therefore provides a comprehensive perspective on smart healthcare innovations, emphasizing the role of intelligent systems, recent advancements, and persisting challenges in the domain of digital health monitoring.
The rapid expansion of smart home and smart city technologies has introduced a complex array of interconnected Internet of Things (IoT) devices, exposing both cyber and physical infrastructures to a growing spectrum of security threats. Traditional cybersecurity models are insufficient to address the dynamic and distributed nature of modern cyber-physical environments, particularly in emerging economies where standardized security frameworks are often lacking. This research proposes a unified, hybrid cyber-physical security framework tailored for smart home and smart city IoT systems. Leveraging publicly available datasets such as UNSW-NB15, TON_IoT, and CICIDS2019, we simulate various attack vectors and evaluate a multi-layered intrusion detection system (IDS) that combines both signature-based and anomaly-based machine learning models. The proposed framework is validated using simulated network topologies built with NS-3 and Cooja, focusing on performance metrics including detection accuracy, false-positive rate, and computational overhead. Results demonstrate that our hybrid approach achieves over 95% accuracy in detecting complex multi-stage attacks, while maintaining scalability and adaptability across different IoT environments. The findings contribute to the development of more secure, resilient, and context-aware smart infrastructure systems offering a practical foundation for real-world deployment in smart cities and connected home ecosystems, especially within developing regions such as Iraq.
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.
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.