Data reduction in fog computing and internet of things: A systematic literature survey

Sadri, Ali Akbar; Rahmani, Amir Masoud; Saberikamarposhti, Morteza; Hosseinzadeh, Mehdi

doi:10.1016/j.iot.2022.100629

Cited by 24 publications

(3 citation statements)

References 47 publications

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“…Perhaps the internet of the data centre is not quick enough to meet the demands of a client. It cannot require the lag or downtime of a cloud service [34] provider link. Data centres may in these situations give them the benefit of directly linking the server with one cross-connection to the servers of the provider.…”

Section: Direct Connectionmentioning

confidence: 99%

Data Centre Infrastructure: Power Efficiency and Protection

Hussein¹,

Alrashd²,

Alabed³

et al. 2023

Latest Advances and New Visions of Ontology in Information Science

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The rapid expansion of e-commerce necessitates expanding the capacity and dependability of data centres in order to provide services with the proper level of quality. A green technology that has a lot of potentials to reduce CO2 emissions is optimization data centre design. However, a large data centre required a large amount of electricity because the capacity of the racks is higher, which required more potent cooling systems, power supplies, protection and security. These will increase the cost of the data centre and render it unusable for services. In this chapter, we provide a design for a tire-four data centre that will be situated in Cyberjaya, one of Malaysia’s best locations. This design’s primary goal is to offer highly functional and high-quality e-commerce services, particularly food delivery. Each component of the data centre has been carefully developed to deliver a range of services, including the administration of IT infrastructure, co-location, cooling system and protection. Additionally, advice and support have been given to guarantee that the suggested design outperforms competing data centres in terms of dependability, power efficiency and storage capacity. The analysis, synthesis, and evaluation of each element of the proposed data centre will be considered in this chapter.

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Section: Direct Connectionmentioning

confidence: 99%

Data Centre Infrastructure: Power Efficiency and Protection

Hussein¹,

Alrashd²,

Alabed³

et al. 2023

Latest Advances and New Visions of Ontology in Information Science

View full text Add to dashboard Cite

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“…Research on data collection and reduction in wireless sensor networks (WSNs) aims to reduce IoT nodes' energy consumption by reducing data transmission volume [3]. Data compression, data prediction, and data aggregation are the three main types of data reduction algorithms [4]. Data prediction is a more popular and effective strategy because it may achieve a substantial data reduction ratio in contrast with other strategies [5].…”

Section: Introductionmentioning

confidence: 99%

“…No data transmission is necessary if the difference between the predicted value and the collected value is smaller than the reduction threshold. If not, the remote processor receives the data sensor gathered, and the prediction model is updated [4]. As the reduced data increase, the reconstructed data become less accurate.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

Zhang,

Na,

Zhang

2023

Sensors

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With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy.

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ClusFC‐IoT: A clustering‐based approach for data reduction in fog‐cloud‐enabled IoT

Hemmati,

Rahmani

2024

Concurrency and Computation

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SummaryThe Internet of Things (IoT) is an ever‐expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog‐cloud‐enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC‐IoT, a novel two‐phase clustering‐based approach designed to optimize the management of IoT‐generated data. In the first phase, which is performed in the fog layer, we used the K‐means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K‐means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC‐IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC‐IoT very suitable for resource‐constrained IoT environments.

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Data reduction in fog computing and internet of things: A systematic literature survey

Cited by 24 publications

References 47 publications

Data Centre Infrastructure: Power Efficiency and Protection

Data Centre Infrastructure: Power Efficiency and Protection

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

ClusFC‐IoT: A clustering‐based approach for data reduction in fog‐cloud‐enabled IoT

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