Quality inspection and defect detection remain critical challenges across diverse industrial applications. Driven by advancements in Deep Learning, Convolutional Neural Networks (CNNs) have revolutionized Computer Vision, enabling breakthroughs in image analysis tasks like classification and object detection. CNNs’ feature learning and classification capabilities have made industrial defect detection through Machine Vision one of their most impactful applications. This article aims to showcase practical applications of CNN models for surface defect detection across various industrial scenarios, from pallet racks to display screens. The review explores object detection methodologies and suitable hardware platforms for deploying CNN-based architectures. The growing Industry 4.0 adoption necessitates enhancing quality inspection processes. The main results demonstrate CNNs’ efficacy in automating defect detection, achieving high accuracy and real-time performance across different surfaces. However, challenges like limited datasets, computational complexity, and domain-specific nuances require further research. Overall, this review acknowledges CNNs’ potential as a transformative technology for industrial vision applications, with practical implications ranging from quality control enhancement to cost reductions and process optimization.

Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision-based autonomous rack inspection framework centered around YOLOv7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a mean average precision of 91.1%.

In recent years, Information and Communications Technology (ICT) has rapidly advanced, bringing numerous benefits to the lives of many individuals and organisations. Technologies such as Internet of Things (loT) solutions, Cloud-Based Services (CBSs), Cyber-Physical Systems (CPSs) and mobile devices have brought many benefits to technologically-advanced societies. As a result, commercial transactions and governmental services have rapidly grown, revolutionising the life styles of many individuals living in these societies. While technological advancements undoubtedly present many advantages, at the same time they pose new security threats. As a result, the number of cases that necessitate Digital Forensic Investigations (DFIs) are on the rise, culminating in the creation of a backlog of cases for law enforcement agencies (LEAs) worldwide. Therefore, it is of paramount importance that new research approaches be adopted to deal with these security threats. To this end, this paper evaluates the existing set of circumstances surrounding the field of Digital Forensics (DF). Our research study makes two important contributions to the field of DF. First, it analyses the most difficult technical challenges that need to be considered by both LEAs and Digital Forensic Experts (DFEs). Second, it proposes important specific future research directions, the undertaking of which can assist both LEAs and DFEs in adopting a new approach to combating cyber-attacks.

The Internet of Things (IoT) has had a tremendous impact on the evolution and adoption of information and communication technology. In the modern world, data are generated by individuals and collected automatically by physical objects that are fitted with electronics, sensors, and network connectivity. IoT sensor networks have become integral aspects of environmental monitoring systems. However, data collected from IoT sensor devices are usually incomplete due to various reasons such as sensor failures, drifts, network faults and various other operational issues. The presence of incomplete or missing values can substantially affect the calibration of on-field environmental sensors. The aim of this study is to identify efficient missing data imputation techniques that will ensure accurate calibration of sensors. To achieve this, we propose an efficient and robust imputation technique based on k-means clustering that is capable of selecting the best imputation technique for missing data imputation. We then evaluate the accuracy of our proposed technique against other techniques and test their effect on various calibration processes for data collected from on-field low-cost environmental sensors in urban air pollution monitoring stations. To test the efficiency of the imputation techniques, we simulated missing data rates at 10–40% and also considered missing values occurring over consecutive periods of time (1 day, 1 week and 1 month). Overall, our proposed BFMVI model recorded the best imputation accuracy (0.011758 RMSE for 10% missing data and 0.169418 RMSE at 40% missing data) compared to the other techniques (kNearest-Neighbour (kNN), Regression Imputation (RI), Expectation Maximization (EM) and MissForest techniques) when evaluated using different performance indicators. Moreover, the results show a trade-off between imputation accuracy and computational complexity with benchmark techniques showing a low computational complexity at the expense of accuracy when compared with our proposed technique.

Widespread adoption of smart IoT devices is accelerating research for new techniques to make IoT applications secure, scalable, energy-efficient, and capable of working in mission-critical use cases, which require an ability to function offline. In this context, the novel combination of distributed ledger technology (DLT) and distributed intelligence (DI) is seen as a practical route towards the decentralisation of IoT architectures. This paper surveys DI techniques in IoT and commences by briefly explaining the need for DI, by proposing a comprehensive taxonomy of DI in IoT. This taxonomy is then used to review existing techniques and to investigate current challenges that require careful attention and consideration. Based on the taxonomy, IoT DI techniques can be classified into five categories based on the factors that support distributed functionality and data acquisition: cloud-computing, mist-computing, distributed-ledger-technology, service-oriented-computing and hybrid. Existing techniques are compared and categorized mainly based on related challenges, and the level of intelligence supported. We evaluate more than thirty current research efforts in this area. We define many significant functionalities that should be supported by DI frameworks and solutions. Our work assists system architects and developers to select the correct low-level communication techniques in an integrated IoT-to-DLT-to-cloud system architecture. The benefits and shortcomings of different DI approaches are presented, which will inspire future work into automatic hybridization and adaptation of DI mechanisms. Finally, open research issues for distributed intelligence in IoT are discussed.