Facilitating the monitoring and management of structural health in civil infrastructures with an Edge/Fog/Cloud architecture

Martin, Charles L.; Garrido, Daniel; Llopis, Luis; Rubio, Bartolomé; Díáz, Manuel

doi:10.1016/j.csi.2021.103600

Cited by 25 publications

(15 citation statements)

References 22 publications

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“…The architecture model e-Health system is based on three main layers composed of the typical system model: end user layer, Edge layer and Cloud Layer as seen from the Figure 1 that illustrates the main components of the next generation e-Health System. Meanwhile, this architecture represents promising alternatives that complement the traditional system-based cloud architecture [18]. However, the centralized architectures produce delays and low performance, as well as security issues for connected devices and data that might affect human lives.…”

Section: E-health System Architecturementioning

confidence: 99%

Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems

2023

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In the new era, the trend of using wearable devices and smart accessories gained considerable popularity and become a necessity utility for human life due to their major role to keep monitoring health conditions and providing healthcare services. The combination of IoT networks with edge computing paradigms develops an intelligent e-health system that aims to monitor different real-time scenarios. The deployment of an e-health system exposes several challenges regarding the security and privacy aspects, particularly in the case of dealing with an enormous quantity of medical data and the risk presented by exchanging operations with external entities. In this paper a comprehensive presentation covered the basic topics of e-health system layers thus the advantages and limitations in terms of existing challenges has been mentioned, subsequently, adapted to the exposed cyber risk through the traditional systems in exchanging medical data, a discussion of the blockchain technology come over for new application opportunities, where this approach efficiently ensure the security of data transactions over the network, in addition, an overview outlined the main research works related to this technology. Therefore, a presentation study of diverse works reveals different security framework solutions related to e-health system’s layers, furthermore, uncovering the benefits of integrating intelligent technologies such as Machine Learning (supervised, and unsupervised types), Deep Learning, and Reinforcement Learning as well as introducing a comparison analysis of multiple AI algorithm models based on their efficiency for future deployment related security purposes to provide a smart healthcare monitoring system that meets patient needs. The end of this review highlighted further research directions and the actual open challenges regarding the e-Health system’s limitations.

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Section: E-health System Architecturementioning

confidence: 99%

Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems

2023

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“…Unlike traditional hypervisors, a lightweight virtualization technology implements process virtualization through the containers in an operating system. This allows the deployment of high-density containers by reducing the hardware overhead and the virtualization of virtual appliances on traditional hypervisors [5].…”

Section: Container Infrastructurementioning

confidence: 99%

“…In addition, the deployment, management, scaling, and networking of containerized applications are automated through Docker Swarm Orchestration, and computational resources, including hardware resources (e.g., CPU, memory, storage, and I/O), can be efficiently managed and easily accessed through the cloud. They can also be relocated or moved [5].…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing

Kim

Lee

et al. 2022

Applied Sciences

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Manufacturers are expanding their business-process innovation and customized manufacturing to reduce their information technology costs and increase their operational efficiency. Large companies are building enterprise-wide hybrid cloud platforms to further accelerate their digital transformation. Many companies are also introducing container virtualization technology to maximize their cloud transition and cloud benefits. However, small- and mid-sized manufacturers are struggling with their digital transformation owing to technological barriers. Herein, for small- and medium-sized manufacturing enterprises transitioning onto the cloud, we introduce a Docker Container application architecture, a customized container-based defect inspection machine-learning model for the AWS cloud environment developed for use in small manufacturing plants. By linking with open-source software, the development was improved and a datadog-based container monitoring system, built to enable real-time anomaly detection, was implemented.

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“…The multitude of these sensors collectively form a spatially distributed sensing network, capable of conducting extensive monitoring of the structure’s integrity [ 9 , 10 , 11 ]. With the Internet of Things (IoTs) technologies, the sensed data acquired by the nodes can be wirelessly transmitted to a remote base station or secured cloud sever for collation and analysis [ 12 , 13 , 14 ]. The modern SHM technology is emerging an important element of Industry 4.0, as it greatly enhances the efficiency of management for infrastructure and valuable assets with the desired automation of routine structural inspection activities.…”

Section: Introductionmentioning

confidence: 99%

Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process

Wong

Rabeek

Lai

et al. 2022

Sensors

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While the active ultrasonic method is an attractive structural health monitoring (SHM) technology, many practical issues such as weight of transducers and cables, energy consumption, reliability and cost of implementation are restraining its application. To overcome these challenges, an active ultrasonic SHM technology enabled by a direct-write transducer (DWT) array and edge computing process is proposed in this work. The operation feasibility of the monitoring function is demonstrated with Lamb wave excited and detected by a linear DWT array fabricated in situ from piezoelectric P(VDF-TrFE) polymer coating on an aluminum alloy plate with a simulated defect. The DWT array features lightweight, small profile, high conformability, and implementation scalability, whilst the edge-computing circuit dedicatedly designed for the active ultrasonic SHM is able to perform signal processing at the sensor nodes before wirelessly transmitting the data to a remote host device. The successful implementation of edge-computing processes is able to greatly decrease the amount of data to be transferred by 331 times and decrease the total energy consumption for the wireless module by 224 times. The results and analyses show that the combination of the piezoelectric DWT and edge-computing process provides a promising technical solution for realizing practical wireless active ultrasonic SHM system.

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Facilitating the monitoring and management of structural health in civil infrastructures with an Edge/Fog/Cloud architecture

Cited by 25 publications

References 22 publications

Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems

Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems

Design and Implementation of Cloud Docker Application Architecture Based on Machine Learning in Container Management for Smart Manufacturing

Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process

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