The construction industry remains one of the most hazardous sectors, requiring innovative solutions to safeguard workers, especially in complex, dynamic outdoor environments. Digital Twin (DT) technology offers promising capabilities for real-time safety monitoring through virtual replicas of physical systems. However, existing DT frameworks rarely integrate comprehensive safety monitoring via a centralized model consolidation mechanism, such as Federated Learning (FL), which is explicitly tailored for multi-worker scenarios. Addressing this challenge, this paper proposes a FL-based Safety Monitoring Digital Twin (SMDT) framework designed to enhance multi-worker safety in resource-constrained settings. This enables real-time safety monitoring and control by representing on-site workers as virtual objects within a synchronized DT environment. A dynamic node selection mechanism based on client performance is employed to optimize global model convergence in FL. To validate the proposed approach, an edge computing-based experimental testbed using actual Raspberry Pi devices was implemented, using real-world construction safety data including worker status, weather conditions, and building structural parameters. Experimental results demonstrate the effectiveness of the proposed framework in significantly improving safety predictions and real-time monitoring efficiency. This research establishes a foundational work towards safer construction sites through intelligent, synchronized safety monitoring systems.

The rapid growth of the Federated Internet of Things ecosystem has introduced new challenges in achieving seamless connectivity and interoperability across heterogeneous IoT networks. The presence of heterogeneous platforms and protocols creates significant obstacles for effective communication among cross-silos federated IoT nodes. To tackle this challenge, we have developed Heterogeneous Federated Internet of Things (Hetero-FedIoT), an innovative rule-based interworking architecture enabling interoperability and seamless connectivity among heterogeneous federated IoT networks (oneM2M, OCF and EdgeX). Hetero-FedIoT offers a two-faceted solution to address these challenges. Firstly, it incorporates a rule-based interworking mechanism that fosters effective collaboration among Hetero-FedIoT networks. Additionally, it introduces a novel aggregation function capable of achieving accelerated convergence, effectively handling both system and statistical heterogeneity. By leveraging device proxies, Hetero-FedIoT enables interoperability among heterogeneous FedIoT networks by translating protocols from platform-native formats to a common format and vice versa. As a result, collaborative model training can be seamlessly conducted without the need to consider underlying frameworks. Additionally, the novel aggregation algorithm employed by Hetero-FedIoT empowers nodes to customize the complexity of local models according to their communication and computation capabilities. This is accomplished through the dynamic adjustment of hidden channel widths, ensuring that the overall performance of the global model remains unaffected. This groundbreaking Hetero-FedIoT architecture establishes a foundation for enhanced interoperability and optimal performance. Extensive evaluation of Hetero-FedIoT has demonstrated superior computational and communication efficiency over baseline schemes. The Hetero-FedIoT system revolutionizes decentralized training under heterogeneous conditions, fostering widespread adoption.

Greenhouses are a productive system that allows us to respond to the growing global demand for fresh and healthy food throughout the year, but the greenhouse environment is not easily controlled because its climate parameters are interrelated. However, the numbers of the actuator are operated parallelly to maintain the greenhouse environment; as a result, the energy consumption of greenhouses is high. In this study, we presented the optimization module by considering the outdoor environment with the aim of minimum energy consumption. Metaheuristic-based differential evaluation (DE) is used to optimize the climate parameters by considering indoor and outdoor environmental constraints. Furthermore, the long short-term memory (LSTM)-based inference model is offloaded on the Internet of Things (IoT) device to predict the next environmental situation. The objective function selects the optimal parameters within user preferences with minimum energy consumption based on the inferred parameter value. The open-source software framework IoTivity, implementing open connectivity foundation (OCF) technical standards, is used for the real-time connection between IoT devices and the IoT platform. Greenhouse owners can set the preferences based on the requirements of plants in the greenhouse by using a smart and remotely accessible Android-based interface. A fuzzy logic-based control module operates on an IoT device that maps the optimized parameters with the actuator and operates accordingly. The proposed model is analyzed, and the performance is evaluated in terms of energy consumption for each climate parameter and actuator in the greenhouse. The results show that the proposed mechanism saves 36% of energy.

The greenhouse industry has received great attention and experienced tremendous growth in the recent past across the globe. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. This demands an Internet of Things (IoT)-based smart solution for automation of greenhouse environment-related activities to ensure maintenance of the desired climate inside the greenhouse to maximize plant production with optimal resource utilization. To this end, several models are proposed in the literature that is based on a selected artificial intelligence (AI) algorithm which is once trained and then deployed. The drawback of such systems is that the trained models are fixed (locked) and, therefore, unable to adapt to dynamically changing conditions, which results in performance degradation. Second, the existing studies on the subject matter are focused on the individual key component (i.e., prediction, optimization, and control). In this article, a novel scheme is presented based on the integration of the key components, and the performance of prediction and optimization components is further enhanced through the exploitation of artificial neural network (ANN)-based learning modules to support autonomous greenhouse environment monitoring and control. For experimental analysis, the greenhouse environment is emulated through the mathematical formulation of essential greenhouse processes, considering the impact of actuators’ operations and external weather conditions. Real environmental data collected for Jeju Island, South Korea is used for model validation and result analysis. Proposed learning-based optimization scheme results are compared with two other schemes, i.e., baseline scheme and optimization scheme. Comparative analysis of the results shows that the proposed model maintains the desired indoor environment for maximizing plant production with reduced energy consumption, i.e., it achieves 61.97% reduced energy consumption than the baseline scheme, 11.73% better than the optimization scheme without learning modules. Furthermore, the proposed model achieves 67.96% and 12.56% reduction in cost when compared to the baseline scheme and optimization scheme without learning modules, respectively.

Presently, blockchain technology has been widely applied in various application domains such as the Internet of Things (IoT), supply chain management, healthcare, etc. So far, there has been much confusion about whether blockchain performs with scale, and admittedly, a lack of information about best practices that can improve the performance and scale. This paper proposes a novel blockchain network construction methodology to improve the performance of Hyperledger Fabric. As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of enterprise use cases from finance to governance. A comprehensive evaluation is performed by observing various configurable network components that can affect the blockchain performance. To demonstrate the significance of the proposed methodology, we set up the experiment environment for the baseline and the test network using optimized parameters, respectively. The experimental results indicate that the test network's performance is enhanced effectively compared to the baseline in transaction throughput and transaction latency.