Unmanned aerial vehicles (UAVs) are now considered one of the best remote sensing techniques for gathering data over large areas. They are now being used in the industry sector as sensing tools for proactively solving or preventing many issues, besides quantifying production and helping to make decisions. UAVs are a highly consistent technological platform for efficient and cost-effective data collection and event monitoring. The industrial Internet of things (IIoT) sends data from systems that monitor and control the physical world to data processing systems that cloud computing has shown to be important tools for meeting processing requirements. In fog computing, the IoT gateway links different objects to the internet. It can operate as a joint interface for different networks and support different communication protocols. A great deal of effort has been put into developing UAVs and multi-UAV systems. This paper introduces a smart IIoT monitoring and control system based on an unmanned aerial vehicle that uses cloud computing services and exploits fog computing as the bridge between IIoT layers. Its novelty lies in the fact that the UAV is automatically integrated into an industrial control system through an IoT gateway platform, while UAV photos are systematically and instantly computed and analyzed in the cloud. Visual supervision of the plant by drones and cloud services is integrated in real-time into the control loop of the industrial control system. As a proof of concept, the platform was used in a case study in an industrial concrete plant. The results obtained clearly illustrate the feasibility of the proposed platform in providing a reliable and efficient system for UAV remote control to improve product quality and reduce waste. For this, we studied the communication latency between the different IIoT layers in different IoT gateways.

The construction industry, traditionally lagging behind in terms of digitalization, faces significant challenges in its transition to Industry 4.0, which is characterized by the use of advanced technologies such as artificial intelligence (AI), the Industrial Internet of Things (IIoT), and cloud computing. This article presents the development and implementation of an AI-based virtual assistant, designed to optimize the operation and maintenance of concrete production plants. The assistant helps reduce the margin of human error, improve operational efficiency, and facilitate continuous training for operators. These advancements foster a more collaborative and digitalized environment, while also generating environmental, economic, and social benefits: reduced material and energy waste, lower carbon footprint, increased workplace safety, and strengthened organizational resilience. The results show high accuracy in voice transcription (96%) and a 100% success rate in responding to technical queries, demonstrating its effectiveness as a support tool in industrial settings. Based on these findings, it is concluded that the incorporation of AI-based virtual assistants promotes a more sustainable and responsible production model, aligned with the Sustainable Development Goals of the 2030 Agenda, and anticipates the principles of Industry 5.0 by promoting symbiotic collaboration between humans and technology. This innovation represents a key advancement in transforming the concrete industry, contributing to productivity, environmental sustainability, and workplace well-being in the sector.

Accessing operational information across industrial systems (ERP, MES, SCADA, PLC) in concrete plants requires 15–30 min and specialized knowledge. This work addresses this accessibility gap by developing a conversational AI system that democratizes industrial information access through natural language. A five-layer cognitive architecture was implemented integrating the Mistral-7B model quantized in GGUF Q4_0 format (3.82 GB) on a Raspberry Pi 5, Spanish speech recognition/synthesis, and heterogeneous industrial protocols (OPC UA, MQTT, REST API) across all automation pyramid levels. Experimental validation at Frumecar S.L. (Murcia, Spain) characterized performance, thermal stability, and reliability. Results show response times of 14.19 s (simple queries, SD = 7.56 s), 16.45 s (moderate, SD = 6.40 s), and 23.24 s (complex multilevel, SD = 6.59 s), representing 26–77× improvement over manual methods. The system maintained average temperature of 69.3 °C (peak 79.6 °C), preserving 5.4 °C margin below throttling threshold. Communication latencies averaged 8.93 ms across 10,163 readings (<1% of total latency). During 30 min of autonomous operation, 100% reliability was achieved with 39 successful queries. These findings demonstrate the viability of deploying quantized LLMs on low-cost edge hardware, enabling cognitive democratization of industrial information while ensuring data privacy and cloud independence.