Towards an architecture for big data analytics leveraging edge/fog paradigms

Díaz-de-Arcaya, Josu; Miñón, Raúl; Torre-Bastida, Ana I.

doi:10.1145/3344948.3344987

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…In addition to the edge layer, architectures may use a fog layer, which is an intermediate layer between edge devices and the cloud that enables cloud services such as data storage near the shop floor. In [49], the edge layer was used mainly for data collection, data transmission, and low-complexity analytics, while the fog layer was used to run more complex analytics and then send the processed and aggregated data to the cloud. In [11] and [50], the authors explored cloud capabilities to train complex models and push them as Docker microservices to the edge devices through a fog network.…”

Section: Architectural Perspectivementioning

confidence: 99%

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

2023

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A considerable part of enterprises’ total expenses is dedicated to maintenance interventions. Predictive maintenance (PdM) has appeared as a solution to decrease these costs; however, the necessity of end-to-end solutions in deploying predictive models and the fact that these models are often difficult to interpret by maintenance practitioners hinder the adoption of PdM approaches. In this work, we propose a flexible architecture for PdM to recommend maintenance actions. The proposed architecture is based on containerized microservices on intelligent edge devices together with a hybrid model which fuses generalized fault trees (GFTs) and anomaly detection. Results on injection molds carried out at OLI, a Portuguese company, show that the proposed solution is suitable for deploying predictive models and services such as data preprocessing, sensor management, and data flow control, among others. These services run near the shop floor, allowing for greater flexibility, as they may be remotely managed and customized according to the company’s requirements. The results of the GFT model show an estimated reduction of more than 63% in current maintenance costs, while the distribution of analytics tasks by the edge devices reduces the burden on the network, requiring only 0.2% of the current cloud storage.

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Section: Architectural Perspectivementioning

confidence: 99%

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

2023

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“…Data combination, integration, and aggregation are all components of data fusion from large, diverse datasets. An architecture for big data has been developed that spreads the data analytics over three layers, namely the cloud, the fog, and the edge, to exploit the strengths of each layer [43]. The edge layer controls the actuators and devices, the fog layer provides data aggregation and ingestion, and the cloud applies other analytics that needs high-performance computing resources.…”

Section: Handling Big Data In the Cloud And In The Fog Layermentioning

confidence: 99%

“…Therefore, many researchers have started monitoring stress using the IoMT. There are several datasets relevant to stress, including the multimodal SWELL knowledge-work dataset [43].…”

Section: Qos Requirements Of Healthcare Applicationsmentioning

confidence: 99%

Priority Detector and Classifier Techniques Based on ML for the IoMT

A. Alsemmeari,

Yehia Dahab,

Alturki

et al. 2023

Computers, Materials &Amp; Continua

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Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients' health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees' stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner. However, overwhelming the fog layer with extensive data will increase the load on fog nodes, leading to computational challenges. This study aims to reduce fog computation by proposing machine learning (ML) models with two phases. The first phase of the ML model assesses the priority of the situation based on the stress level. In the second phase, a classifier determines the cause of stress, which was either interruptions or time pressure while completing a task. This approach reduced the computation cost for the fog node, as only high-priority records were transferred to the fog. Low-priority records were forwarded to the cloud. Four ML approaches were compared in terms of accuracy and prediction speed: Knearest neighbors (KNN), a support vector machine (SVM), a bagged tree (BT), and an artificial neural network (ANN). In our experiments, ANN performed best in both phases because it scored an F 1 score of 99.97% and had the highest prediction speed compared with KNN, SVM, and BT.

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“…Interlayer communication plays an important role for effective resource management to enable coordination amongst various tasks and subtasks going inside resource manager to perform resource provisioning and scheduling [42]. In fog-edge computing model, there is a requirement of an effective communication among various layers such as cloud, fog, edge and IoT as shown in Figure 1, which can improve the quality of service in terms response time and latency [43].…”

Section: Interlayer Communicationmentioning

confidence: 99%

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

Gill

2021

EAI/Springer Innovations in Communication and Computing

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The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively. Further, fog and edge computing paradigms are introduced to improve user service by reducing latency and response time. This chapter presents a manifesto for modern fog and edge computing systems based on the current research trends. Further, architectures and applications of fog and edge computing are explained.Moreover, research opportunities and promising future directions are presented with respect to the new paradigms, which will be helpful for practitioners, researchers and academicians to continue their research.

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Towards an architecture for big data analytics leveraging edge/fog paradigms

Cited by 10 publications

References 10 publications

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

Priority Detector and Classifier Techniques Based on ML for the IoMT

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

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