Purpose The evolution of computer vision algorithms has enabled us to replace redundant tasks, such as continuous monitoring with simple camera installations. This paper proposes a lightweight crack detection mechanism for deployment on mobile phones to automate the structural health monitoring (SHM) task in construction. Traditional sensor-based and human expert-guided monitoring tools are costly and subjective in nature. Thus, automatic detection increases performance with substantially fewer resources. Design/methodology/approach To provide a stable solution for crack detection, the authors work with two pipelines: one for identifying cracks from images and the second for identifying cracks directly from a mobile feed, and propose a novel ensemble of MobileNetv2 and UNet for crack detection in images and use YOLOv5n to detect cracks in a live stream. For training and testing, the authors create a comprehensive dataset of 9K images with different annotation schemes, where 5,600 images were annotated for image classification, 1,200 image masks were created for segmentation, and 2,200 boundary box annotations were made for object detection. Findings The proposed model achieved 98% accuracy for the MobileNetv2 module; 97% accuracy for the UNet module, and a mAP@0.5 of 79.9 was achieved on the YOLOv5n module of the proposed data set after training. The model is then converted to a lightweight TFLite model and deployed on Android with the proposed Android application. The code and data are available at Link to the cited article. Research limitations/implications The deviation in angle leads to error susceptibility in crack severity. The effect of weather on images for crack detection is very limited. The cross-training of the surfaces also results in low crack detection accuracy by the YOLO model. Practical implications This study is useful for the automated continuous image-based structural health management for critical structures. Originality/value This work provides a practical and real-time solution to the complex problem of crack detection and SHM. Including advanced vision models with lightweight Android-based applications, the solution benefits site engineers and surveyors.

Purpose In modern agriculture, the efficient completion of agricultural tasks is essential for maintaining crop health and maximizing yield. This paper aims to efficiently deal with the completion of tasks in an agricultural field using multi-robot coordination. It also performs an extensive performance analysis of the communication protocols to be used for multi-robot coordination for the field work. Design/methodology/approach The authors propose a multi-robot approach that uses Internet of Things (IoT) communication protocols and is designed to optimize tasks in the agricultural field by coordinating multiple robots to ensure that the task is completed efficiently thus improve operational efficiency across agricultural fields. Findings The proposed system demonstrates that using multiple robots with integrated IoT protocols can lead to significant improvements in agricultural field work. It helps to enhance the overall efficiency of the task to be completed. Originality/value This work introduces a multi-robot approach for agricultural field work, incorporating a combination of IoT communication protocols. It contributes to the field by addressing the complexity of managing the tasks in agricultural fields through an intelligent, coordinated robotic solution.

Purpose The purpose of the study concludes detecting address resolution protocol (ARP) Spoofing attack in software-defined network (SDN) architecture meanwhile using different machine learning models to evaluate their effectiveness. Design/methodology/approach This research originates from building a SDN topology and researching into its changes under ARP Spoofing attack. Based on that, the authors propose four features which show obvious abnormalities in network under attack stage. The data collected from SDN controller is used to build a data set, which is then put into different machine learning models, which are: Artificial Neuron network (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN-LSTM and Gated Recurrent Unit (GRU). Findings After applying this proposal in simulation and experimental environments, they achieve impressive performance metrics. In simulation environment, the GRU model stands out with the highest accuracy of 98.94%. In real environments, the CNN-LSTM model leads with a recall of 98.38% and an F1-Score of 98.57%, while the LSTM model has the highest precision (98.8%). The GRU model also performs strongly in real scenarios with a high accuracy of 97.65%. ANN, despite its reliability, struggles with lower recall and F1-Score across both environments. Originality/value This analysis emphasizes the importance of the proposed features when applied to different models and their high potential to conduct in practical environment.

PurposeOne of the open questions is how to define a complete semantic description of an Internet of Things (IoT) system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system.Design/methodology/approachOne of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system.FindingsOne of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system.Originality/valueOne of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system.

Purpose This paper aims to build an indoor positioning system capable of tracking residents and caregivers and feeding a novel information system with this data. This information system introduces three main interesting modules: a compact data structure to efficiently store the gathered data, an activity deduction module that add semantics to the raw captured trajectories and a user-friendly interface able to display this spatio-temporal information. Design/methodology/approach Their proposal was built following an iterative and incremental development. Nursing home managers cooperated in the requirement composition phase, while some residents and caregivers contributed testing the system with their real trajectories. Findings Their contribution is a functional information system that can evaluate the quality of healthcare provided to residents and assess the efficiency of the nursing home as a business. This information system includes a novel state-of-the-art compact data structure to efficiently work with the captured data. Originality/value A new system has been designed and implemented using a wide range of technologies to support the necessities of a real enterprise. The rudimentary position tracking systems were upgraded to a feasible automatic indoor positioning solution. The amount of information generated by this new strategy is dealt with compact data structures, reducing space usage without jeopardizing query times. Those compressed positions are tagged with semantic information that enables individual activity analysis. Last, a user-friendly interface lets the final user interact with the gathered and calculated information. As future work, the authors plan to improve the integration of this new information system with other systems already in use in indoor mobile workforce environments.