From Sensors to Solutions: IoT and AIoT for Real-World Impact

The rapid evolution of the Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) is fundamentally reshaping how real-world systems are sensed, analyzed, and optimized across scientific and engineering domains. IoT technologies enable large-scale, continuous data acquisition through interconnected sensors and devices embedded in physical environments, while AIoT integrates artificial intelligence into these systems to enable intelligent perception, learning, decision-making, and autonomous action at the edge and in the cloud. Together, IoT and AIoT are driving innovation in diverse application areas, including healthcare, agriculture, environmental monitoring, smart cities, industrial automation, energy systems, and transportation.Despite significant advances, a persistent gap remains between laboratory-scale research and reliable, scalable real-world deployment. Many IoT and AIoT solutions demonstrate strong performance in controlled or simulated environments but face challenges related to scalability, robustness, interoperability, energy efficiency, security, privacy, and ethical considerations when deployed in dynamic, resource-constrained, and heterogeneous real-world settings. Moreover, translating IoT and AIoT technologies across multiple scientific disciplines requires integrated approaches that combine sensing hardware, communication infrastructures, data analytics, machine learning, edge intelligence, and domain-specific expertise.Recent progress in edge AI, federated and distributed learning, low-power computing, digital twins, and privacy-preserving analytics has created new opportunities to close this gap. These advances enable intelligent systems that not only sense and transmit data, but also learn continuously, adapt to changing environments, and support real-time, mission-critical decision-making across domains.The goal of this Research Topic is to highlight advances in IoT and AIoT technologies that demonstrate validated real-world impact and cross-disciplinary translation. It aims to bring together research that moves beyond conceptual designs or simulations, emphasizing deployed, pilot-tested, or field-validated systems. By focusing on practical implementations and measurable outcomes, this topic seeks to advance understanding of how IoT and AIoT solutions can be effectively designed, deployed, and translated across scientific and application domains.This Research Topic welcomes contributions that address the design, implementation, deployment, and evaluation of IoT and AIoT systems in real-world environments. We particularly encourage interdisciplinary studies that integrate intelligent sensing, edge and cloud analytics, machine learning, computer vision, deep learning and cyber-physical systems to solve real scientific, societal, or industrial problems. Emphasis is placed on research that demonstrates scalability, robustness, interoperability, and long-term operation, as well as lessons learned from real-world deployments.Topics of interest include, but are not limited to: 1. Real-world deployment and validation of IoT and AIoT systems. 2. Edge AI and intelligent sensing under resource constraints.3. AIoT applications cross discipline application domains such as healthcare, agriculture, environmental science, energy, transportation, and smart cities.4. Federated learning, distributed intelligence, and privacy-preserving AI in IoT ecosystems. 5. Role of IoT and AIoT in developing digital twins with real-world industrial use-cases.6. Human-centered, ethical, and trustworthy AIoT systems. 7. Integration of Generative AI with IoT and AIoT systems. Topic editors: Khalid Elgazzar, Umair Iqbal Source: https://www.frontiersin.org/research-topics/78602/from-sensors-to-solutions-iot-and-aiot-for-real-world-impact