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An Ensemble Learning Based Approach to Position Falsification Detection in Internet of Vehicles
Goodness Oluchi Anyanwu,Cosmas Ifeanyi Nwakanma,Jae-Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Misbehaviour detection is seen as an important development to guarantee that vehicles are certified on the IoV network. To solve this, a recent dataset known as the VeReMi dataset was created. In this paper, we presented an Optimized Ensemble Neural Network approach using MATLAB R2019b. To validate the idea in this work, three other ensemble learning algorithms were investigated to show the best performed. The result shows that the proposed optimized scheme (AdaBoostM2) outperformed other state-of-the-art algorithms as well as related works with an accuracy of 99:6%.
Optimized Ensemble Learning Algorithm for Hidden Malicious Traffic Detection in VANET
Goodness Oluchi Anyanwu,Jae-Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
The growth in intelligent transport system comes the challenge of monitoring transportation data traffic for a secured and cost-efficient system Securing the system requires the incorporation of the IoT and Artificial Intelligence as security is of primary importance to connected vehicles. For a secured vehicular space, this work proposed an Intrusion Detection Architecture (IDA) to identify traffic from hidden networks. This novel technique has been developed to avoid illegitimate traffic from passing hidden malicious messages across various equipment on VANET. Optimal simulation parameters were selected and the output demonstrates the efficacy of the proposed Ensemble algorithm which achieved a detection accuracy of 99.2% and a Minimum Classification Error of 291.
Resolution-Aware Deep Learning Model for Emergency Communication in Smart Homes using Thermal Sensor
Goodness Oluchi Anyanwu,Cosmas Ifeanyi Nwakanma,Adinda Riztia Putri,JeongHan Kim,Gihwan Hwang,Jae-Min Lee,Dong-Seong Kim 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
Due to the development in sensor technologies and smart homes, Emergency and Activity Detection (EAD) has become a growing research issue as there is a need to support safety and security in homes. In this work, the impact of three different thermal sensor resolutions was investigated for EAD. The design of the system includes three parts: data acquisition, EAD and the emergency alert system. An alert system is considered reliable if the sensing model can mitigate the introduction of noise by the sensors or noisy environments. Research in this domain has seen the adoption of sensors with different resolutions. However, not much work has been done in developing resolution-aware models considering the impact of sensor resolutions on both the quality of data and the performance of the classification models. In this work, a CNN model was developed for EAD from datasets of various sensors with diverse resolutions. The results showed that the proposed model exhibited resilience in handling the error that may occur from the impact of sensor resolution for classification of normal daily living activity and emergency in a smart home.
Goodness Oluchi Anyanwu,Cosmas Ifeanyi Nwakanma,Jae Min Lee,Dong-Seong Kim 한국통신학회 2023 ICT Express Vol.9 No.1
Detection of nodes disseminating false data is a prerequisite for effective deployment of Internet of Vehicles (IoV) services. This work proposed a novel hyper-tuned ensemble Random Forest (Ens. RF) algorithm to detect false basic safety messages in IoV. Performance evaluation was done using the Vehicular Reference Misbehavior (VeReMi) dataset comprising data-centric misbehavior evaluation for vehicular networks. For validation, a comparative analysis of the performance of the proposed “Ens. RF” model, five machine learning algorithms implemented in this work, and state-of-the-art ML models from related literature was presented. The performance metrics considered are time efficiency and validation accuracy for overall misbehavior classification. Also, the results confirmed the irrelevance of data balancing in real-life scenarios. Finally, we assess the performance of our proposed system for detecting each falsification scenario using precision and recall. The result shows that the proposed algorithm outperformed others with a validation accuracy of 99.60% and a negligible 604 misclassifications out of 153,730 points.
A review of thermal array sensor-based activity detection in smart spaces using AI
Nwakanma, Cosmas Ifeanyi,Anyanwu Goodness Oluchi,AHAKONYE LOVE ALLEN CHIJIOKE,Lee, Jae-Min,김동성 한국통신학회 2024 ICT Express Vol.10 No.2
Nowadays, research works into the dynamic and static human activities on Smart spaces abounds. Artificial Intelligence (AI) and low cost non-privacy invasive ambient sensors have made this ubiquitous. This review presents a state-of-the-art analysis, performance evaluation, and future research direction. One of the aims of activity recognition (especially that of humans) systems using thermal sensors and AI is the safety of persons in Smart spaces. In a Smart home, human activity detection systems are put in place to ensure the safety of persons in such an environment. This system should have the ability to monitor issues like fall detection, a common home-related accident. In this work, a review of trends in thermal sensor deployment, an appraisal of the popular datasets, AI algorithms, testbeds, and critical challenges of the recent works was provided to direct the research focus. In addition, a summary of AI models and their performance under various sensor resolutions was presented.
Thermal Sensor-Based Activity Detection in Smart Spaces using GentleBoost Optimized Classifier
Cosmas Ifeanyi Nwakanma,Goodness Oluchi Anyanwu,Adinda Riztia Putri,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
On the smart factory shop floor, the safety of persons can be enhanced with an effective human activity detection system. This system should have the ability to monitor issues like fall detection which is a common work-related accident. In this work, we have used a public dataset that is based on a thermal array (ambient) sensor for the detection and classification of falls on the smart factory shop floor. The performance of the proposed optimized ensemble learning in MATLAB R2019b shows an accuracy of 100%, and a loss value of 0.00015642 using the minimum classification error plot.
LSTM-Based Human Fall Detection using Thermal Array Sensor
Adinda Riztia Putri,Goodness Oluchi Anyanwu,Mareska Pratiwi Maharani,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Accidental fall may lead to numerous serious and deadly injures. Existing fall detection systems mostly use cameras and are considered a privacy-intrusive approach. Thermal array sensors are considered a privacy-friendly device that does not raises discomforts for users. In this study, we simulate a fall detection system using a thermal array sensor with three different algorithms: CNN, LSTM, and CNN-LSTM. Our result shows that LSTM has the best accuracy among other algorithms by 99.96%.