WITHDRAWN: An efficient biometric based authenticated geographic opportunistic routing for IoT applications using secure wireless sensor network

Menaga, S.; Paruvathavardhini, J.; Pragaspathy, S.; Dhanapal, R.; Immanuel, D. Jebakumar

doi:10.1016/j.matpr.2021.01.241

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Using a balanced distribution of weak and hard data, which makes the data set, difficult instances are identified by out-of-bag handlers, so that when a sample is considered "hard" it is incorrectly classified by the ensemble. This hard data is always added to the next data set while easy data has little chance of getting into the dataset [20,[81][82][83]. Performance of the Bagging based hybrid models developed in this study is slightly better than other ML models such as LASSO (R2 = 0.911), Random Forest (R2 = 0.936) and SVM (R2 = 0.935) carried out by Nguyen et al [13] on Mekong River.…”

Section: Resultsmentioning

confidence: 75%

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Hoan¹,

Dũng²,

Thu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

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Section: Resultsmentioning

confidence: 75%

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Hoan¹,

Dũng²,

Thu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…Menaga et al [24] highlights about different routing protocols and simulation techniques for determining the ideal routing protocol. AODV and DSDV are investigated, with DSDV emerging as the best option for this simulation Network Simulator.…”

Section: Review Of Location-based Geographic Routing Protocolsmentioning

confidence: 99%

A Comprehensive Review of Geographic Routing Protocols in Wireless Sensor Network

Pandith¹,

Ramaswamy²,

Srikantaswamy³

et al. 2023

IDA

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To analyses the impact of high mobility, dynamic topologies, scalability and routing due to the more dynamic changes in network. To enhance mobile Ad-hoc network (MANET) self-organization capabilities by geographical routing algorithm during mobility. In this paper, a survey has been carried out on geographic routing protocols, such as hybrid routing, Greedy Routing, face-2 Algorithm, Perimeter Routing, quasi random deployment (QRD) techniques and time of arrival (TOA). An optimized multipath routing in wireless sensor network (WSN), energy utilization, detection of anonymous routing, node mobility prediction, data packet distribution strategies in WSN is analyzed. Geographic routing offers previous data packet information such as physical locations, packet elimination dependencies, storage capacity of topology, Associate costs and also identifies the dynamic behavior of nodes with respect to packets frequencies.

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“…Because of the non-contact nature of the FKP collection [73], users are more likely to accept it [74]. As such, FKP is regarded as one of the most feasible and effective personal identification technologies in the future [75][76][77][78][79].…”

Section: Introductionmentioning

confidence: 99%

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

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Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

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WITHDRAWN: An efficient biometric based authenticated geographic opportunistic routing for IoT applications using secure wireless sensor network

Cited by 6 publications

References 18 publications

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

A Comprehensive Review of Geographic Routing Protocols in Wireless Sensor Network

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

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