Data Processing and Management in IoT and Wireless Sensor Network

Sasirekha, S.; priya, A.Vijaya; Anita, T.; Sherubha, P.

doi:10.1088/1742-6596/1712/1/012002

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…The MAE is a linear value which means that the average weight of all the individual differences is equal. [19][20][21][22][23] The RMSE is a quadratic scoring rule that calculates the error's average magnitude. Each squared is the difference among the forecast and the related observed values and is then averaged over the sample.…”

Section: Performance Metricsmentioning

confidence: 99%

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Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

138

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In this research, a weather forecasting model based on machine learning is proposed for improving the accuracy and efficiency of forecasting. The aim of this research is to propose a weather prediction model for short-range prediction based on numerical data. Daily weather prediction includes the work of thousands of worldwide meteorologists and observers. Modernized computers make predictions more precise than ever, and earth-orbiting weather satellites capture pictures of clouds from space. However, in many cases, the forecast under many conditions is not accurate. Numerical weather prediction (NWP) is one of the popular methods for forecasting weather conditions. NWP is a major weather modeling tool for meteorologists which contributes to more accurate accuracy. In this research, the weather forecasting model uses the C5.0 algorithm with K-means clustering. The C5.0 is one of the better decision tree classifiers, and the decision tree is a great alternative for forecasting and prediction. The algorithm for clustering the K-means is used to combine identical data together. For this process, the clustering of K-means is initially applied to divide the dataset into the closest cluster of K. For training and testing, the meteorological data collection obtained from the database Modern-Era Historical Analysis for Research and Applications (MERRA) is used. The model's performance is assessed through MAE mean absolute error (MAE) and root mean square error (RMSE). And the proposed model is assessed with accuracy, sensitivity, and specificity for validation. The results obtained are compared with other current machine learning approaches, and the proposed model achieved predictive accuracy of 90.18%.

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Section: Performance Metricsmentioning

confidence: 99%

“…The MAE is the average of the absolute values of the differences among forecast and respective observation over the verification sample. The MAE is a linear value which means that the average weight of all the individual differences is equal 19–23 …”

Section: Performance Analysismentioning

confidence: 99%

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

138

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“…The performance of the ensemble model is computed with True Positive Rate (TPR), False Alarm Rate (FAR), precision, recall, F-measure, and accuracy. Here, accuracy depicts the percentage of appropriately predicted outcomes (class labels), precision is depicted as the ratio of appropriately classified victims, recall specifies the coverage proportion of victims, and F-measure is depicted as the weighted harmonic average of recall and precision [31][32][33][34][35]. These performance indicators are used to measure the classifier's performance.…”

Section: K-nn For Boosting Rssmentioning

confidence: 99%

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

Sadhasivam¹,

Valarmathie²,

Dinakaran³

2023

Intelligent Automation &Amp; Soft Computing

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With the vast advancements in Information Technology, the emergence of Online Social Networking (OSN) has also hit its peak and captured the attention of the young generation people. The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user. However, the attackers also target this height of OSN utilization, explicitly creating the clones of the user's account. Various clone detection mechanisms are designed based on social-network activities. For instance, monitoring the occurrence of clone edges is done to restrict the generation of clone activities. However, this assumption is unsuitable for a real-time environment and works optimally during the simulation process. This research concentrates on modeling and efficient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy. This model does not rely on assumptions. Here, an ensemble Adaptive Random Subspace is used for classifying the clone victims with k-Nearest Neighbour (k-NN) as a base classifier. The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results. When the weighted node's threshold value is higher, the trust establishment is terminated, and the clones are ranked and sorted in the higher place for termination. Thus, the victims are alert to the clone propagation over the online social networking end, and the validation is done using the MATLAB 2020a simulation environment. The model shows a better trade-off than existing approaches like Random Forest (RF), Naïve Bayes (NB), and the standard graph model. Various performance metrics like True Positive Rate (TPR), False Alarm Rate (FAR), Recall, Precision, F-measure, and ROC and run time analysis are evaluated to show the significance of the model.

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“…In order to detect the anomaly and to reduce the features, fuzzy detection system is used. FL approach requires expertise in the knowledge of formulation of the rule base, combination of the sets and de-fuzzing [28][29][30][31][32]. The reduced feature set namely hop count changes, Rx power and drop ratio assists to create the faster testing and testing process, to have lower resource consumptions and to maintain higher detection rate.…”

Section: Fuzzy Intrusion Detection Systemmentioning

confidence: 99%

Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

Reji¹,

Joseph²,

Thaiyalnayaki³

et al. 2023

Computer Systems Science and Engineering

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The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves, when the destination and source nodes are not in range of coverage. Because of its wireless type, it has lot of security concerns than an infrastructure networks. Wormhole attacks are one of the most serious security vulnerabilities in the network layers. It is simple to launch, even if there is no prior network experience. Signatures are the sole thing that preventive measures rely on. Intrusion detection systems (IDS) and other reactive measures detect all types of threats. The majority of IDS employ features from various network layers. One issue is calculating a huge layered features set from an ad-hoc network. This research implements genetic algorithm (GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks. For attack detection, the reduced feature set was put to a fuzzy logic system (FLS). The performance of proposed model was compared with principal component analysis (PCA) and statistical parametric mapping (SPM). Network performance analysis like delay, packet dropping ratio, normalized overhead, packet delivery ratio, average energy consumption, throughput, and control overhead are evaluated and the IDS performance parameters like detection ratio, accuracy, and false alarm rate are evaluated for validation of the proposed model. The proposed model achieves 95.5% in detection ratio with 96.8% accuracy and produces very less false alarm rate (FAR) of 14% when compared with existing techniques.

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Data Processing and Management in IoT and Wireless Sensor Network

Cited by 12 publications

References 7 publications

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

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