Integration of Smart Sensors in Next-Generation Engineering Systems

Recent advances in smart nanosensors and IoT-based systems for real-time diagnostic monitoring.

The integration of smart nanosensor systems with artificial intelligence (AI) and the Internet of Things (IoT) is transforming diagnostic monitoring in biomedical, environmental, and industrial applications. These advancements facilitate real-time data acquisition, continuous physiological tracking, and point-of-care diagnostics, significantly enhancing early disease detection and personalized care. Innovations in nanomaterials, such as graphene, quantum dots, and plasmonic nanoparticles, have bolstered sensing capabilities, allowing for high sensitivity, rapid responses, and the selective detection of important biomarkers. However, challenges remain in translating these nanosensor technologies from the lab to real-world applications, including reliability, stability, integration with digital platforms, data security, power supply for ongoing monitoring, and clinical biocompatibility. Frequently, current systems operate in silos, lacking coordination between sensing, data processing, and decision-making components. This study proposes a systems integration framework that unifies smart nanosensors, AI-driven analytics, and the IoT into a single platform for continuous health monitoring. The main findings reveal that nanosensor-enabled systems can significantly enhance diagnostic performance, with graphene-based nanosensors detecting C-reactive protein in human serum at concentrations near 27 pM, and plasmonic sensors offering swift, portable detection. Additionally, electrochemical immunosensors achieve sensitivity for oral cancer biomarkers at concentrations as low as 1.424 fg mL-1 within just 120 seconds. The novel contribution of this review is the proposed systems-integration approach that unifies smart nanosensors, AI-driven analytics, and IoT into a cohesive platform for continuous health monitoring and data-driven decision-making in healthcare, showcasing the potential for improved diagnostic efficiency and patient outcomes. By proposing a multi-layered systems integration framework, this review synthesizes current advances in smart nanosensors, AI, and IoT into a unified architecture that addresses translational challenges and supports the development of intelligent, real-time, and patient-centered healthcare systems.

Intelligent medical sensors for smart healthcare and precision medicine.

Clinical translation remains limited by data security and privacy risks, insufficient standardization and regulatory alignment, long-term stability and biocompatibility concerns, and uneven validation maturity across technologies.

Energy Harvesting for IoT and Edge-Enabled Building Automation Systems: A Review of Technologies, Applications and Future Challenges

Smart buildings increasingly depend on dense, distributed sensing infrastructures to improve energy efficiency, indoor environmental quality and operational flexibility. However, large-scale IoT/WSN deployment is still constrained by wiring effort, battery maintenance and limited access to sensing locations. Energy harvesting (EH) offers a promising approach toward low-maintenance and partly autonomous sensing, but its practical value in building automation depends on more than the output of individual transducers. This article presents a structured review of EH for IoT/WSN and edge-enabled building automation, focusing on smart-building, Building Management System (BMS) and Building Automation and Control System (BACS) contexts. Light-based, thermoelectric, mechanical, RF/wireless-power-transfer and hybrid harvesting technologies are interpreted through a system-oriented chain linking energy sources, power management, storage, communication, adaptive operation, gateways, diagnostics and edge intelligence. The synthesis shows that EH is most promising for low-duty-cycle environmental monitoring, envelope and façade sensing, occupancy and human–building interaction, airflow-related sensing, technical monitoring and retrofit automation. The main challenges concern the transition from device autonomy to sensing-service autonomy, complete-node evaluation under real building conditions, interoperability with supervisory systems and diagnostic interpretation of intermittent operation. Further research is also needed on lifecycle value assessment and safe transferability toward remote, temporary, resilient and closed ecological infrastructure applications.

Smart dust technology in distributed sensor networks: architecture, energy strategies, and emerging challenges

Smart Dust technology represents a new class of distributed sensor networks based on large populations of autonomous micronodes with volumes on the order of a few cubic millimeters. This paper provides a structured overview of the functional architecture of such nodes, including MEMS-based sensing modules, ultra-low-power processing cores, and heterogeneous communication interfaces, as well as integrated power-management subsystems designed for operation under extreme energy constraints. Particular attention is given to energy-management strategies such as duty cycling, event-driven activation, and multimodal energy harvesting, together with the role of TinyML-based local inference in reducing communication overhead at the network edge. The discussion further addresses fundamental physical limits of miniaturization, reliability and calibration issues, security and privacy challenges in large-scale autonomous deployments, and the prospects of biodegradable platforms for environmentally sustainable implementations, highlighting the need for interdisciplinary co-design across microsystems engineering, materials science, and machine learning.

Future Generation Computer

Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations

The rapid advancement of Industry 4.0 has accelerated the development of intelligent and autonomous smart factories powered by Industrial Internet of Things (IIoT) devices, cyber-physical systems (CPS), and advanced manufacturing technologies. Although cloud computing offers significant computational and storage capabilities, it suffers from latency, bandwidth limitations, privacy concerns, and delayed decision-making in time-critical industrial environments. This paper proposes an Integrated Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations, combining edge computing, Artificial Intelligence (AI), digital twins, and predictive analytics to enable real-time local data processing with seamless cloud integration. The framework employs machine learning for anomaly detection, deep learning for automated quality inspection, reinforcement learning for adaptive production scheduling, and predictive models for equipment health monitoring. Secure communication protocols enhance data protection and system reliability. Experimental results demonstrate improved latency, prediction accuracy, manufacturing efficiency, energy utilization, fault detection, and operational resilience compared with conventional cloud-based approaches. The proposed architecture provides a scalable and sustainable solution for next-generation autonomous smart factories, improving equipment reliability, reducing operational costs, and increasing manufacturing productivity.