Intelligent Sensing in Multiagent-Based Wireless Sensor Network for Bridge Condition Monitoring System

Putra, Seno Adi; Trilaksono, Bambang Riyanto; Riyansyah, Muhammad; Laila, Dina Shona; Harsoyo, Agung; Kistijantoro, Achmad Imam

doi:10.1109/jiot.2019.2901796

Cited by 30 publications

(14 citation statements)

References 35 publications

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“…Wireless sensors have been widely applied in structural health monitoring of concrete structures [ 37 , 66 , 86 , 102 , 103 ]. Apart from concrete, wireless sensors have been extensively applied in greenhouses for smart farming applications.…”

Section: Sensors For Health Monitoring Of Agricultural Structuresmentioning

confidence: 99%

“…The two case studies do not yield conclusive evidence because the structures served specific functions and were exposed to unique environmental forces, which are absent in other areas. For example, Federici et al [ 103 ] observations were confined to a single location (Basilica S. Maria di Collemaggio); this means that the impact of distance, structural interferences, and terrain on the structure was not adequately delineated. Even though the findings and observations made by Abruzzese et al [ 44 ], Keller et al [ 45 ], and Federici et al [ 102 ] are not conclusive, they offer useful insights on the application of wireless sensors in structural health monitoring in various environments.…”

Section: Sensors For Health Monitoring Of Agricultural Structuresmentioning

confidence: 99%

“…Similar to other types of sensors, there are critical impediments to the widespread application of the sensors, including design challenges for long-term application and customized control algorithms. Other constraints include the possibility of data flooding in wireless sensor networks (WSN) [ 102 , 103 ], poor fault tolerance of the wireless sensor network, potential power interruptions, and lack of seamless communication between the central devices and the peripheral sensors [ 44 ]. The listed drawbacks are but a microcosm of the potential challenges that might arise from wireless sensors’ widespread use.…”

Section: Sensors For Health Monitoring Of Agricultural Structuresmentioning

confidence: 99%

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Sensors for Structural Health Monitoring of Agricultural Structures

Maraveas

Bartzanas

2021

Sensors

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The health diagnosis of agricultural structures is critical to detecting damages such as cracks in concrete, corrosion, spalling, and delamination. Agricultural structures are susceptible to environmental degradation due to frequent exposure to water, organic effluent, farm chemicals, structural loading, and unloading. Various sensors have been employed for accurate and real-time monitoring of agricultural building structures, including electrochemical, ultrasonic, fiber-optic, piezoelectric, wireless, fiber Bragg grating sensors, and self-sensing concrete. The cost–benefits of each type of sensor and utility in a farm environment are explored in the review. Current literature suggests that the functionality of sensors has improved with progress in technology. Notable improvements made with the progress in technology include better accuracy of the measurements, reduction of signal-to-noise ratio, and transmission speed, and the deployment of machine learning, deep learning, and artificial intelligence in smart IoT-based agriculture. Key challenges include inconsistent installation of sensors in farm structures, technical constraints, and lack of support infrastructure, awareness, and preference for traditional inspection methods.

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Section: Sensors For Health Monitoring Of Agricultural Structuresmentioning

confidence: 99%

Section: Sensors For Health Monitoring Of Agricultural Structuresmentioning

confidence: 99%

Section: Sensors For Health Monitoring Of Agricultural Structuresmentioning

confidence: 99%

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Sensors for Structural Health Monitoring of Agricultural Structures

Maraveas

Bartzanas

2021

Sensors

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“…In addition, the sensor node can be deployed on a sealed space that a wired system is unable to set up. WSNs have recently been widely applied to monitor conditions such as coal mines [4], mechanical equipment [5], bridge condition monitoring [6], and wind power generation [7].…”

Section: Introductionmentioning

confidence: 99%

Path Perception Synchronization Trigger Method for Dual Mode Multiplexing Mechanical Vibration Wireless Sensor Networks

Hao¹,

Tang

Huang

et al. 2024

IEEE Access

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Synchronization triggers for mechanical-vibration wireless sensor networks play an important role in data acquisition and the analysis of multiple nodes. At present, most nodes are designed using a single protocol, which makes it difficult to achieve a balance between network establishment and synchronization. Meanwhile, such synchronization trigger methods rely on a clock, beacon, or communication between nodes. A method based on a clock and beacon requires frequent timestamps, which reduces the synchronization trigger accuracy and increases the power consumption. The communication between nodes requires these nodes to be at a close distance; otherwise, the node cannot receive synchronization information. Aiming at the above problems, a path perception synchronization trigger method based on dual-mode multiplexing and cluster networks is proposed. First, the node is designed using dual mode multiplexing. Second, an adjoint networking method is proposed to construct the cluster network topology. Next, a path perception time measurement method based on the terminal device and terminal assistant measurement without clock synchronization, beacon synchronization, and communication between nodes is discussed. Finally, a maximum time deviation compensation and time block synchronization trigger measurement method are proposed to solve the problems of a time delay compensation and system instruction interruption. The experimental results show that the maximum time error of the path perception synchronization trigger is between 50 and 60 μs, and the minimum time error is between 10 and 16 μs, which significantly improves the synchronization triggering accuracy under conditions that do not rely on clock synchronization, beacon synchronization, or communicating between nodes.

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“…Experiments show that this method can reduce data transmission by 95% compared with direct transmission of raw data. Besides machine condition monitoring and fault diagnosis, WSNs also plays an irreplaceable role in the fields of structure monitoring [17][18][19], home automation [20], mass transportation [21][22] and security prevention [23].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Using Wireless Sensor Networks and Convolutional Neural Network

Huang

2020

Int. J. Onl. Eng.

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Rolling bearings are widely used in modern production equipment. Effective bearing fault diagnosis method will improve the reliability of the machinery and increase its operating efficiency. In this paper, a novel fault diagnosis method based on WSN and CNN has been proposed to fully utilize the strong fault classification capability of CNN and the inherent merits of WSNs, such as relatively low cost, convenience of installation, and ease of relocation. The feasibility and effectiveness of proposed system are evaluated using the vibration data sets of seven motor operating conditions released by the Case Western Reserve University Bearing Data Center. The experimental results show the fault diagnosis accuracy of the proposed approach can reach 97.6%.

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Intelligent Sensing in Multiagent-Based Wireless Sensor Network for Bridge Condition Monitoring System

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it..

Cited by 30 publications

References 35 publications

Sensors for Structural Health Monitoring of Agricultural Structures

Sensors for Structural Health Monitoring of Agricultural Structures

Path Perception Synchronization Trigger Method for Dual Mode Multiplexing Mechanical Vibration Wireless Sensor Networks

Fault Diagnosis of Rolling Bearing Using Wireless Sensor Networks and Convolutional Neural Network

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