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%.
Distributed Ledger Technology (DLT) brings a set of opportunities for the Internet of Things (IoT), which leads to innovative solutions for existing components at all levels of existing architectures. IOTA Tangle has the potential to overcome current technical challenges identified for the IoT domain, such as data processing, infrastructure scalability, security, and privacy. Scaling is a serious challenge that influences the deployment of IoT applications. We propose a Scalable Distributed Intelligence Tangle-based approach (SDIT), which aims to address the scalability problem in IoT by adapting the IOTA Tangle architecture. It allows the seamless integration of new IoT devices across different applications. In addition, we describe an offloading mechanism to perform proof-of-work (PoW) computation in an energy-efficient way. A set of experiments has been conducted to prove the feasibility of the Tangle in achieving better scalability, while maintaining energy efficiency. The results indicate that our proposed solution provides highly-scalable and energy efficient transaction processing for IoT DLT applications, when compared with an existing DAG-based distributed ledger approach.
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