Machine learning approaches for underwater sensor network parameter prediction

Uyan, Osman Gokhan; Akbaş, Ayhan; Güngör, Vehbi Çağrı

doi:10.1016/j.adhoc.2023.103139

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…Sevgi and Kocyigit [5] proposed a novel framework for the optimal deployment of WSNs from the connectivity perspective, particularly for random deployments. Overall, these studies contribute to the understanding and optimization of WSN deployments for connectivity, coverage, reliability, and lifetime, opening avenues for further research in the field [6], [7].…”

Section: Introductionmentioning

confidence: 90%

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

AKBAŞ,

BUYRUKOĞLU

2023

Balkan Journal of Electrical and Computer Engineering

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Wireless sensor networks (WSNs) are widely used in various fields, and their deployment is critical to ensure area coverage and full network connectivity to achieve the maximum network lifetime. In this study, we present a mixed-integer programming (MIP) model that deeply investigates deployment parameters to optimize lifetime and analyze network connectivity. We further analyze the obtained results using Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms to achieve higher accuracy rates. Our evaluation shows that the DBN outperforms the DNN with an accuracy rate of 81.2%, precision of 81.2%, recall of 99.1%, and an F1-Score of 0.78. We also utilize two different datasets to justify the efficiency of the DBN in this research. The findings of this study emphasize the validity of our DBN algorithm and encourage further research into lifetime optimization and connectivity analysis in WSNs.

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Section: Introductionmentioning

confidence: 90%

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

AKBAŞ,

BUYRUKOĞLU

2023

Balkan Journal of Electrical and Computer Engineering

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“…Research has shown that machine learning methods have been widely applied to energy consumption modeling [1,2] , such as electricity [3] , wastewater treatment [4] , complex networks [5] , sensor parameters [6] , and more. Of course, there are also many applications in neural networks.…”

Section: Related Researchmentioning

confidence: 99%

Analysis and Prediction of Energy Consumption in Neural Networks Based on Machine Learning

2024

AJCIS

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In the current technological development, convolutional neural networks have become an important tool for computer vision tasks, especially in mobile devices. However, executing related tasks using complex neural network models often leads to high energy consumption issues. Therefore, energy modeling for neural networks becomes crucial. Through energy modeling, we can better understand the energy consumption of neural networks, and subsequently carry out targeted energy optimization to reduce the energy consumption of devices when performing tasks. This experiment selected nine feature variables from three levels of convolutional neural networks, and used five machine learning algorithms to model the energy consumption of convolutional neural networks. The five machine learning methods are Support Vector Regression (SVR), Neural Network (NN), Decision Tree (DT), Random Forest (RF), and Adaptive Boosting (AdaBoost). To select the best modeling method, this paper introduces Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to evaluate the models. The experiment proves that Adaptive Boosting (AdaBoost) has the lowest MSE, RMSE, and MAE, therefore it is the optimal model in this experiment.

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“…There are numerous studies in the literature on intrusion detection [1]- [3]. Altunay and Albayrak [4] utilized the CIC IDS2018 dataset which was created by the Canadian Cybersecurity Institute (CSE) and other synthetically generated datasets.…”

Section: Related Workmentioning

confidence: 99%

Hibrit Sık Kullanılan Öğe Kümeleme ile Makine Öğrenmesi Tabanlı Ağ Sızma Tespiti

FİRAT,

BAKAL,

AKBAŞ

2023

Journal of Polytechnic

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With the development and expansion of computer networks day by day and the diversity of software developed, the damage that possible attacks can cause is increasing beyond the predictions. Intrusion Detection Systems (STS/IDS) are one of the effective defense tools against these potential attacks that are constantly increasing and diversifying. The ultimate goal is to train these systems with various artificial intelligence methods, to detect subsequent attacks in real time and to take the necessary precautions. In this study, classical feature selection methods and Frequent Item Set Mining were used in feature selection in a hybrid model, and it was aimed to classify network traffic data for normal and attack by using many machine learning methods, including Logistic Regression, with the final features obtained. The method uses a data set originally containing 85 features to make a decision while making this classification. These attributes are extracted using CICFlowMeter from a PCAP file where network traffic is recorded. The results show that the proposed method in the study classifies more than 225000 records in the data set with a success rate of 97.68%.

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Machine learning approaches for underwater sensor network parameter prediction

Cited by 12 publications

References 15 publications

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

Analysis and Prediction of Energy Consumption in Neural Networks Based on Machine Learning

Hibrit Sık Kullanılan Öğe Kümeleme ile Makine Öğrenmesi Tabanlı Ağ Sızma Tespiti

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