Overflow remote warning using improved fuzzy c-means clustering in IoT monitoring system based on multi-access edge computing

Liang, Haibo; Liu, Gang; Gao, Jianchong; Khan, Muhammad Junaid

doi:10.1007/s00521-019-04540-y

Cited by 12 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clustering of sensor nodes, which can provide superior performance, is one of the greatest methods for energy efficiency in IoT [47] [48]. Topological limits, QoS, and battery are the key obstacles in improving the WSN efficiency [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model

Gorikapudi¹,

Kondaveeti²

2023

Preprint

View full text Add to dashboard Cite

Internet of Things (IoT) is a networking paradigm shift that aims to link almost everything on the world. Due to the limited form of smart devices, energy-efficient routing would be critical to their effective implementation. Clustering techniques divide a network's nodes into groups or clusters, each of which is led by a specified node known as the cluster head. Clustering methods have been presented in the area of Wireless Sensor Networks (WSN), while their application in IoT might solve comparable problems. By transferring a major portion of communication overhead to the cluster head, clustering would promote topology maintenance and energy efficient routing. Therefore, this article aims to establish a novel clustering model in IoT, in which the cluster head selection is performed via a proposed Sandpiper Optimization with Cycle Crossover Process (SOCCP) model. Moreover, this cluster head selection includes certain constraints including (i) Distance (ii) Energy (iii) security (based on the risk levels), and (iv) Cluster Radius, respectively. At first, the clustering is done by the Optimized Fuzzy C-Means (FCM) algorithm. Here, the determination of the cluster head and cluster radius is tuned optimally via an adopted SOCCP model. At the end, the results of the adopted technique are computed to extant based on various metrics including alive node analysis, risk analysis, distance analysis, cluster radius analysis, as well as normalized energy analysis, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model

Gorikapudi¹,

Kondaveeti²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…7,8 Topological limits, Quality of Service (QoS), and battery are the key obstacles to improving the WSN efficiency. 9,10 Due to the requirement of screen nodes in the vicinity, WSNs 11 have gained widespread prominence since the advent of the IoT. Furthermore, optimizing resources in complex systems is critical for maximizing energy resource optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Clustering of sensor nodes, which can provide superior performance, is one of the greatest methods for energy efficiency in the IoT 7,8 . Topological limits, Quality of Service (QoS), and battery are the key obstacles to improving the WSN efficiency 9,10 …”

Section: Introductionmentioning

confidence: 99%

A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

Gorikapudi,

Kondaveeti

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryThe Internet of Things (IoT) is a paradigm change in networking that aspires to connect almost everything on the globe. Clustering methods have been presented in the context of wireless sensor networks, and their application to the IoT could solve similar problems. However, existing clustering protocols have issues with the clustering structure that affect their performance, and some approaches pose a risk of privacy leakage during the data handling process. Therefore, this article aims to establish a novel secure and energy‐aware clustering model in the IoT, in which cluster head selection is performed via a proposed sandpiper optimization with cycle crossover process (SOCCP) model. Moreover, this cluster head selection includes certain constraints, including distance, energy, security, and cluster radius, respectively. The clustering is done by the optimized fuzzy C‐means algorithm. Here, the determination of the cluster head and cluster radius is tuned optimally via an adopted SOCCP model. Final results of the employed technique are calculated using a variety of metrics, such as alive node analysis, risk analysis, distance analysis, cluster radius analysis, as well as normalized energy analysis, respectively.

show abstract

“…In the process of oil and gas field exploration and development, overflow, as the most common downhole construction safety accident, is not only risky and sudden, but once it is not identified and effectively controlled in time, it can further lead to a series of malicious accidents, endangering the lives of people and property. [1][2][3] The safety of life and property is at risk. With the continuous development and popularization of big data and artificial intelligence, various intelligent oil and gas development technologies are gradually emerging.…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Intelligent Prediction of Drilling Overflow Based on BiLSTM-BP Neural Network

Zhou,

Zhang

2023

AJST

View full text Add to dashboard Cite

The prevention and control of overflow is an important task to ensure the safe and efficient development of oil and gas fields, and the traditional overflow monitoring methods have the defects of insufficient real-time and low reliability. This paper proposes an early overflow intelligent identification method based on BiLSTM-BP neural network to address the problems of insufficient real-time and low reliability of traditional monitoring means; the lack of downhole drilling measurement data affects the prediction accuracy of multi-source data fusion. Firstly, the input parameters of the model are optimized through correlation analysis; secondly, the hidden features and complex change laws in the overflow monitoring data are learned by using bi-directional long and short-term memory network to realize the repair of downhole missing data; finally, the BP neural network realizes the early overflow intelligent identification. The test set data and field test results of a test well in Bozhong oilfield show that the early overflow intelligent identification method based on BiLSTM-BP neural network constructed in this paper can realize the intelligent identification of early overflow using multiple monitoring parameters of surface integrated logging, high-precision flowmeter at the wellhead and downhole drilling, and the identification accuracy reaches 97.02%, which is 1.23% better than the existing model and It can identify the overflow more quickly while ensuring higher accuracy. It provides a key theoretical model for early overflow identification technology from traditional identification to intelligent identification.

show abstract

Overflow remote warning using improved fuzzy c-means clustering in IoT monitoring system based on multi-access edge computing

Cited by 12 publications

References 24 publications

Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model

Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model

A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

An Identification Method for Intelligent Prediction of Drilling Overflow Based on BiLSTM-BP Neural Network

Contact Info

Product

Resources

About