In recent years, multi-axis robots are indispensable in automated factories due to the rapid development of Industry 4.0. Many related processes were required to have the increasing demand for accuracy, reproducibility, and abnormal detection. The monitoring function and immediate feedback for correction is more and more important. This present study integrated a highly sensitive lithium niobate (LiNbO₃) vibration sensor as a sensor node (SN) and architecture of wireless mesh network (WMN) to develop a monitoring system (MS) for the robotic arm. The advantages of the thin-film LiNbO₃ piezoelectric sensor were low-cost, high-sensitivity and good electrical compatibility. The experimental results obtained from the vibration platform show that the sensitivity achieved 50 mV/g and the reaction time within 1 ms. The results of on-site testing indicated that the SN could be configured on the relevant equipment quickly and detect the abnormal vibration in specific equipment effectively. Each SN could be used more than 10 h at the 80 Hz transmission rate under WMN architecture and the loss rate of transmission was less than 0.01% within 20 m.
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http://dx.doi.org/10.3390/s19030507 | DOI Listing |
Sensors (Basel)
October 2024
School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches.
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October 2024
Department of Interdisciplinary Information System, Graduate School of Smart Convergence, Kwangwoon University, Seoul 01897, Republic of Korea.
Cloud computing has recently gained widespread attention owing to its use in applications involving the Internet of Things (IoT). However, the transmission of massive volumes of data to a cloud server often results in overhead. Fog computing has emerged as a viable solution to address this issue.
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September 2024
Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK.
Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads.
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September 2024
Department of Radio Communications and Video Technology, Faculty of Telecommunications, Technical University of Sofia, 1000 Sofia, Bulgaria.
One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study.
View Article and Find Full Text PDFSAGE Open Med
August 2024
Department of Pharmaceutical Chemistry, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Quality has been a concern of the World Health Organization since its inception and is defined as fitness for use. Since our ancestors began trading several millennia ago, Falsified Pharmaceutical Products has been a recurring problem and still threatens economic stability and public health. Its definition various from country to country and according to World Health Organization, 2017, it is 'a product that is deliberately and fraudulently mislabelled with respect to identity and/or source'.
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