Design of Wireless Sensor Network for Real-Time Structural Health Monitoring

Giammarini, Marco; Isidori, Daniela; Concettoni, Enrico; Cristalli, Cristina; Fioravanti, Matteo; Pieralisi, Marco

doi:10.1109/ddecs.2015.45

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…SPO2 values that are less than 94% are threatening. • U-blox NEO-6M GPS Module: U-blox NEO-6M GPS Module [16] operates at 40 TO 85 • C temperature range, and it uses a supply voltage of 3.3 V. UART protocol is used to collect information of latitude and longitude. • Negative Temperature Coefficient thermistor: The input voltage for this negative temperature coefficient (NTC) thermistor [17]is in the range 3.3-5 V and operates at a temperature range of −25 to 80 • C with 0.1 • C precision.…”

Section: System Descriptionmentioning

confidence: 99%

Cost-Effective Device for Autonomous Monitoring of the Vitals for COVID-19 Asymptomatic Patients in Home Isolation Treatment

Ashwin

Menon

Devagopal

et al. 2021

Lecture Notes in Electrical Engineering

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As the number of COVID-19 cases keeps growing exponentially in the world, the use of the combination of wearable technology and IoT technologies opens up a wide variety of possibilities. An IoT-enabled healthcare device is useful for proper monitoring of COVID-19 patients to increase safety and reduce spreading. The healthcare device is connected to a large cloud network to obtain desirable solutions for predicting diseases at an early stage. This paper presents the design of a healthcare system that makes use of these technologies in a cost-effective and intuitive way which highlights the application of these technologies in the battle against the pandemic. The wearable can give real-time analysis reports of body vitals so that necessary precautions can be taken in case of infection. The wearable is designed in such a way that it can be used as a precautionary measure for people who are not infected with the virus and as a monitoring device for affected patients during the curse of their treatment. This low-cost design can not only be used to prevent the community spread of the virus but also for the early prediction of the disease.

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Section: System Descriptionmentioning

confidence: 99%

Cost-Effective Device for Autonomous Monitoring of the Vitals for COVID-19 Asymptomatic Patients in Home Isolation Treatment

Ashwin

Menon

Devagopal

et al. 2021

Lecture Notes in Electrical Engineering

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show abstract

“…SHM implementations using wireless sensors networks for Internet of Things (IoT) models [22,23] need improvement in their UC aspect, that is, there must be some algorithms and data processing that can assist Open System Interconnection (OSI) model, which should be application layer (layer 6), and presentation layer (layer 7) devices and applications. Deep Learning (DL) has been implemented on raspberry pi but still needs improvement for cloud compatibility and pairing with mathematical techniques mentioned in [24]. We believe that the role of SHM is very vital in reporting disasters and handling any abnormal and hazardous condition using seismic waves analysis through several signal processing algorithms, i.e., Frequency Domain Decomposition (FDD) and Eigen System Realization Algorithm (ERA), as defined in Reference [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Geographical Area Network—Structural Health Monitoring Utility Computing Model

Tariq

Tahir

Touati

et al. 2019

IJGI

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In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

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“…The SHM designs discussed in [12] is an acute process while taking into account cloud integration and real-time operations of machine learning algorithms. SHM implementations using wireless sensors networks for Internet of Things (IoT) models [13,14] need improvement in their UC aspect, that is, there must be some algorithms and data processing that can assist Open System Interconnection (OSI) model which should be application layer (layer 6), and presentation layer (layer 7) devices and applications.…”

Section: Introductionmentioning

confidence: 99%

Geographical Area Network—Structural Health Monitoring Utility Computing Model

Tariq¹,

Tahir²,

Touati³

et al. 2018

Preprint

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In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner. The proposed UCM consists of network-attached data drive that stores data from SHM logger, population count system and Geographic Information System (GIS) enhanced with a Cloud IoT data backup, display, and analysis server. The UCM using this data and data from building information systems applies a simple machine learning algorithm to generate real-time structure health and suggests re-planning of SHM units. The health of structure varies dynamically with disturbances created by higher occupancy and structure density per zone. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This was tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU simulated occupation and zone calculation models and then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

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Design of Wireless Sensor Network for Real-Time Structural Health Monitoring

Cited by 14 publications

References 10 publications

Cost-Effective Device for Autonomous Monitoring of the Vitals for COVID-19 Asymptomatic Patients in Home Isolation Treatment

Cost-Effective Device for Autonomous Monitoring of the Vitals for COVID-19 Asymptomatic Patients in Home Isolation Treatment

Geographical Area Network—Structural Health Monitoring Utility Computing Model

Geographical Area Network—Structural Health Monitoring Utility Computing Model

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