The Internet of Things (IoT) connects billions of heterogeneous devices, necessitating lightweight, efficient, and secure communication protocols to support a diverse range of use cases. While physical and network-layer technologies enable connectivity, transport and application-layer protocols determine how IoT devices exchange, manage, and secure information. The diverse and constrained nature of IoT devices presents a challenge in selecting appropriate communication protocols, with no one-size-fits-all solution existing. This article provides a comprehensive review of key transport and application protocols in IoT, including MQTT, MQTT-SN, CoAP, LwM2M, AMQP, XMPP, WebSockets, HTTP/HTTPS, and OPC UA. Each protocol is examined in terms of its design principles, communication patterns, reliability mechanisms, and security features. The discussion highlights their suitability for different deployment scenarios, ranging from resource-constrained sensor networks to industrial automation and cloud-integrated consumer devices. By mapping protocol characteristics to IoT requirements, such as scalability, interoperability, power efficiency, and manageability, the article provides guidelines for selecting the optimal protocol stack to optimize IoT system performance and long-term sustainability. Our analysis reveals that while MQTT dominates cloud telemetry, CoAP and LwM2M are superior in IP-based constrained networks, and emerging solutions like OSCORE are critical for end-to-end security.

This study explores pollution detection and classification in the Shatt al-Arab River using advanced image processing techniques. Our proposed system integrates Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The Shatt al-Arab River in Basra, Iraq, faces increasing pollution from human activities, including oil spills, debris, and wastewater. We conducted extensive surveys of the river and its tributaries using a DJI Mavic drone, amassing over 1000 images to train machine learning models. The results indicate that RF excels with 94% accuracy for oil spills, 92% for wastewater, and 95% for debris. SVM also performs well, achieving 92%, 88%, and 94% accuracy for the respective pollutants. KNN, though insightful, lags with 85%, 89%, and 86% accuracy. Trained on this novel image dataset, these models show promising accuracy in detecting various pollution types from drone footage.

Low-cost geared DC motors are widely used in mobile robotics and embedded mechatronic systems; however, their control remains challenging due to dead-zone nonlinearities, friction, low encoder resolution, motor asymmetry, and supply voltage variations. Biological organisms routinely perform motor control in the presence of similar uncertainties by relying on approximate reasoning and adaptive responses rather than precise mathematical models. This paper develops and experimentally validates a practical bio-inspired control architecture for low-cost geared DC motors operating under severe sensing and actuator limitations. The proposed controller combines fuzzy inference, dead-zone compensation, and a ramp-start mechanism to emulate the gradual and adaptive nature of biological motor responses. Instead of relying on an accurate plant model, control actions are generated through linguistic rules that mimic human-like decision-making based on speed error and error variation. The controller is implemented on an Arduino-based differential-drive robotic platform equipped with low-resolution optical encoders. Experimental results demonstrate that the proposed bio-inspired approach effectively mitigates startup stall, reduces oscillatory behavior caused by measurement quantization, and maintains stable speed regulation despite actuator variability and battery voltage fluctuations. The study shows that biologically inspired fuzzy control provides a practical and computationally efficient alternative to conventional PID methods for low-cost robotic systems characterized by significant uncertainty and nonlinear behavior.

The transition from Industry 4.0 to Industry 5.0 has led to a rapid increase in scientific publications addressing human–machine collaboration, artificial intelligence and sustainable manufacturing, which makes it difficult to obtain a coherent overview of the main research directions and global collaboration patterns in this field. This study aims to clarify the development of Industry 5.0 by combining a conceptual overview of its enabling technologies with a bibliometric analysis of scientific articles indexed in the Web of Science Core Collection. The paper outlines the role of key technologies such as computer vision, machine learning, edge computing and collaborative robots in supporting adaptive, human-centred and resource-efficient production systems. The bibliometric component examines publication trends, country-level research activity and thematic relationships between keywords in order to identify dominant topics and emerging areas of interest. The results indicate a concentration of research output in countries with strong investments in advanced manufacturing and artificial intelligence, as well as a growing association between Industry 5.0 and themes such as human–robot collaboration, predictive maintenance, digital twins and ethical aspects of intelligent systems. The analysis also highlights the appearance of new research clusters related to workforce adaptation, resilient supply chains and sustainable production strategies. By providing both a synthesis of technological developments and a quantitative mapping of the literature, this study offers an updated overview of how Industry 5.0 is currently interpreted within the scientific community and supports the identification of future research directions and industrial implementation priorities.