Jency Rubia J, Babitha Lincy R, E. Nithila, Sherin Shibi C, Rosi A
Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography prevents third party intervention over the data communication. The cryptography technology transforms the data into some other form to enhance security and robustness against the attacks. The thrust of enhancing the security among data transfer has been emerged ever since the need of Artificial Intelligence field came into a market. Therefore, modern way of computing cryptographic algorithm came into practice such as AES, 3DES, RSA, Diffie-Hellman and ECC. These public-key encryption techniques now in use are based on challenging discrete logarithms for elliptic curves and complex factorization. However, those two difficult problems can be effectively solved with the help of sufficient large-scale quantum computer. The Post Quantum Cryptography (PQC) aims to deal with an attacker who has a large-scale quantum computer. Therefore, it is essential to build a robust and secure cryptography algorithm against most vulnerable pre-quantum cryptography methods. That is called ‘Post Quantum Cryptography’. Therefore, the present crypto system needs to propose encryption key and signature size is very large.in addition to careful prediction of encryption/decryption time and amount of traffic over the communication wire is required. The post-quantum cryptography (PQC) article discusses different families of post-quantum cryptosystems, analyses the current status of the National Institute of Standards and Technology (NIST) post-quantum cryptography standardisation process, and looks at the difficulties faced by the PQC community.
{"title":"A Survey about Post Quantum Cryptography Methods","authors":"Jency Rubia J, Babitha Lincy R, E. Nithila, Sherin Shibi C, Rosi A","doi":"10.4108/eetiot.5099","DOIUrl":"https://doi.org/10.4108/eetiot.5099","url":null,"abstract":"Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography prevents third party intervention over the data communication. The cryptography technology transforms the data into some other form to enhance security and robustness against the attacks. The thrust of enhancing the security among data transfer has been emerged ever since the need of Artificial Intelligence field came into a market. Therefore, modern way of computing cryptographic algorithm came into practice such as AES, 3DES, RSA, Diffie-Hellman and ECC. These public-key encryption techniques now in use are based on challenging discrete logarithms for elliptic curves and complex factorization. However, those two difficult problems can be effectively solved with the help of sufficient large-scale quantum computer. The Post Quantum Cryptography (PQC) aims to deal with an attacker who has a large-scale quantum computer. Therefore, it is essential to build a robust and secure cryptography algorithm against most vulnerable pre-quantum cryptography methods. That is called ‘Post Quantum Cryptography’. Therefore, the present crypto system needs to propose encryption key and signature size is very large.in addition to careful prediction of encryption/decryption time and amount of traffic over the communication wire is required. The post-quantum cryptography (PQC) article discusses different families of post-quantum cryptosystems, analyses the current status of the National Institute of Standards and Technology (NIST) post-quantum cryptography standardisation process, and looks at the difficulties faced by the PQC community.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"61 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Cruz Antony, G. M. Karpura Dheepan, Veena K, Vellanki Vikas, Vuppala Satyamitra
In the realm of contemporary applications and everyday life, the significance of object recognition and classification cannot be overstated. A multitude of valuable domains, including G-lens technology, cancer prediction, Optical Character Recognition (OCR), Face Recognition, and more, heavily rely on the efficacy of image identification algorithms. Among these, Convolutional Neural Networks (CNN) have emerged as a cutting-edge technique that excels in its aptitude for feature extraction, offering pragmatic solutions to a diverse array of object recognition challenges. CNN's notable strength is underscored by its swifter execution, rendering it particularly advantageous for real-time processing. The domain of traffic sign recognition holds profound importance, especially in the development of practical applications like autonomous driving for vehicles such as Tesla, as well as in the realm of traffic surveillance. In this research endeavour, the focus was directed towards the Belgium Traffic Signs Dataset (BTS), an encompassing repository comprising a total of 62 distinct traffic signs. By employing a CNN model, a meticulously methodical approach was obtained commencing with a rigorous phase of data pre-processing. This preparatory stage was complemented by the strategic incorporation of residual blocks during model training, thereby enhancing the network's ability to glean intricate features from traffic sign images. Notably, our proposed methodology yielded a commendable accuracy rate of 94.25%, demonstrating the system's robust and proficient recognition capabilities. The distinctive prowess of our methodology shines through its substantial improvements in specific parameters compared to pre-existing techniques. Our approach thrives in terms of accuracy, capitalizing on CNN's rapid execution speed, and offering an efficient means of feature extraction. By effectively training on a diverse dataset encompassing 62 varied traffic signs, our model showcases a promising potential for real-world applications. The overarching analysis highlights the efficacy of our proposed technique, reaffirming its potency in achieving precise traffic sign recognition and positioning it as a viable solution for real-time scenarios and autonomous systems.
{"title":"Traffic sign recognition using CNN and Res-Net","authors":"J. Cruz Antony, G. M. Karpura Dheepan, Veena K, Vellanki Vikas, Vuppala Satyamitra","doi":"10.4108/eetiot.5098","DOIUrl":"https://doi.org/10.4108/eetiot.5098","url":null,"abstract":" \u0000In the realm of contemporary applications and everyday life, the significance of object recognition and classification cannot be overstated. A multitude of valuable domains, including G-lens technology, cancer prediction, Optical Character Recognition (OCR), Face Recognition, and more, heavily rely on the efficacy of image identification algorithms. Among these, Convolutional Neural Networks (CNN) have emerged as a cutting-edge technique that excels in its aptitude for feature extraction, offering pragmatic solutions to a diverse array of object recognition challenges. CNN's notable strength is underscored by its swifter execution, rendering it particularly advantageous for real-time processing. The domain of traffic sign recognition holds profound importance, especially in the development of practical applications like autonomous driving for vehicles such as Tesla, as well as in the realm of traffic surveillance. In this research endeavour, the focus was directed towards the Belgium Traffic Signs Dataset (BTS), an encompassing repository comprising a total of 62 distinct traffic signs. By employing a CNN model, a meticulously methodical approach was obtained commencing with a rigorous phase of data pre-processing. This preparatory stage was complemented by the strategic incorporation of residual blocks during model training, thereby enhancing the network's ability to glean intricate features from traffic sign images. Notably, our proposed methodology yielded a commendable accuracy rate of 94.25%, demonstrating the system's robust and proficient recognition capabilities. The distinctive prowess of our methodology shines through its substantial improvements in specific parameters compared to pre-existing techniques. Our approach thrives in terms of accuracy, capitalizing on CNN's rapid execution speed, and offering an efficient means of feature extraction. By effectively training on a diverse dataset encompassing 62 varied traffic signs, our model showcases a promising potential for real-world applications. The overarching analysis highlights the efficacy of our proposed technique, reaffirming its potency in achieving precise traffic sign recognition and positioning it as a viable solution for real-time scenarios and autonomous systems.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"119 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139785230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parameswaran Ramesh, Vidhya N, Panjavarnam B, Shabana Parveen M, Deepak Athipan A M B, B. P. T. V
INTRODUCTION: Around the world, individuals experience flooding more frequently than any other natural calamity. OBJECTIVES: The motivation behind this research is to provide an Internet of Things (IoT)-based early warning assistive system to enable monitoring of water logging levels in flood-affected areas. Further, the SSD-MobiNET V2 model is used in the developed system to detect and classify the objects that prevail in the flood zone. METHODS: The developed research is validated in a real-time scenario. To enable this, a customized embedded module is designed and developed using the Raspberry Pi 4 model B processor. The module uses (i) a pi-camera to capture the objects and (ii) an ultrasonic sensor to measure the water level in the flood area. RESULTS: The measured data and detected objects are periodically ported to the cloud and stored in the cloud database to enable remote monitoring and further processing. CONCLUSION: Also, whenever the level of waterlogged exceeds the threshold, an alert is sent to the concerned authorities in the form of an SMS, a phone call, or an email.
引言：在世界各地，人们遭遇洪灾的频率高于其他自然灾害。目标：这项研究的动机是提供一个基于物联网（IoT）的预警辅助系统，以监测受洪水影响地区的积水程度。此外，开发的系统还使用了 SSD-MobiNET V2 模型来检测洪水区域内的物体并对其进行分类。方法：所开发的研究在实时场景中进行了验证。为此，使用 Raspberry Pi 4 B 型处理器设计和开发了一个定制的嵌入式模块。该模块使用(i) pi 摄像头捕捉物体，(ii) 超声波传感器测量洪水区域的水位。结果：测量到的数据和检测到的物体会定期移植到云端，并存储在云数据库中，以便进行远程监控和进一步处理。结论：此外，只要内涝水位超过阈值，就会以短信、电话或电子邮件的形式向有关部门发出警报。
{"title":"I-CVSSDM: IoT Enabled Computer Vision Safety System for Disaster Management","authors":"Parameswaran Ramesh, Vidhya N, Panjavarnam B, Shabana Parveen M, Deepak Athipan A M B, B. P. T. V","doi":"10.4108/eetiot.5046","DOIUrl":"https://doi.org/10.4108/eetiot.5046","url":null,"abstract":"INTRODUCTION: Around the world, individuals experience flooding more frequently than any other natural calamity. \u0000OBJECTIVES: The motivation behind this research is to provide an Internet of Things (IoT)-based early warning assistive system to enable monitoring of water logging levels in flood-affected areas. Further, the SSD-MobiNET V2 model is used in the developed system to detect and classify the objects that prevail in the flood zone. \u0000METHODS: The developed research is validated in a real-time scenario. To enable this, a customized embedded module is designed and developed using the Raspberry Pi 4 model B processor. The module uses (i) a pi-camera to capture the objects and (ii) an ultrasonic sensor to measure the water level in the flood area. \u0000RESULTS: The measured data and detected objects are periodically ported to the cloud and stored in the cloud database to enable remote monitoring and further processing. \u0000CONCLUSION: Also, whenever the level of waterlogged exceeds the threshold, an alert is sent to the concerned authorities in the form of an SMS, a phone call, or an email.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"71 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139801460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parameswaran Ramesh, Vidhya N, Panjavarnam B, Shabana Parveen M, Deepak Athipan A M B, B. P. T. V
INTRODUCTION: Around the world, individuals experience flooding more frequently than any other natural calamity. OBJECTIVES: The motivation behind this research is to provide an Internet of Things (IoT)-based early warning assistive system to enable monitoring of water logging levels in flood-affected areas. Further, the SSD-MobiNET V2 model is used in the developed system to detect and classify the objects that prevail in the flood zone. METHODS: The developed research is validated in a real-time scenario. To enable this, a customized embedded module is designed and developed using the Raspberry Pi 4 model B processor. The module uses (i) a pi-camera to capture the objects and (ii) an ultrasonic sensor to measure the water level in the flood area. RESULTS: The measured data and detected objects are periodically ported to the cloud and stored in the cloud database to enable remote monitoring and further processing. CONCLUSION: Also, whenever the level of waterlogged exceeds the threshold, an alert is sent to the concerned authorities in the form of an SMS, a phone call, or an email.
引言：在世界各地，人们遭遇洪灾的频率高于其他自然灾害。目标：这项研究的动机是提供一个基于物联网（IoT）的预警辅助系统，以监测受洪水影响地区的积水程度。此外，开发的系统还使用了 SSD-MobiNET V2 模型来检测洪水区域内的物体并对其进行分类。方法：所开发的研究在实时场景中进行了验证。为此，使用 Raspberry Pi 4 B 型处理器设计和开发了一个定制的嵌入式模块。该模块使用(i) pi 摄像头捕捉物体，(ii) 超声波传感器测量洪水区域的水位。结果：测量到的数据和检测到的物体会定期移植到云端，并存储在云数据库中，以便进行远程监控和进一步处理。结论：此外，只要内涝水位超过阈值，就会以短信、电话或电子邮件的形式向有关部门发出警报。
{"title":"I-CVSSDM: IoT Enabled Computer Vision Safety System for Disaster Management","authors":"Parameswaran Ramesh, Vidhya N, Panjavarnam B, Shabana Parveen M, Deepak Athipan A M B, B. P. T. V","doi":"10.4108/eetiot.5046","DOIUrl":"https://doi.org/10.4108/eetiot.5046","url":null,"abstract":"INTRODUCTION: Around the world, individuals experience flooding more frequently than any other natural calamity. \u0000OBJECTIVES: The motivation behind this research is to provide an Internet of Things (IoT)-based early warning assistive system to enable monitoring of water logging levels in flood-affected areas. Further, the SSD-MobiNET V2 model is used in the developed system to detect and classify the objects that prevail in the flood zone. \u0000METHODS: The developed research is validated in a real-time scenario. To enable this, a customized embedded module is designed and developed using the Raspberry Pi 4 model B processor. The module uses (i) a pi-camera to capture the objects and (ii) an ultrasonic sensor to measure the water level in the flood area. \u0000RESULTS: The measured data and detected objects are periodically ported to the cloud and stored in the cloud database to enable remote monitoring and further processing. \u0000CONCLUSION: Also, whenever the level of waterlogged exceeds the threshold, an alert is sent to the concerned authorities in the form of an SMS, a phone call, or an email.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"138 1-3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139861522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Balasubramaniam S, Sandra Grace Nelson, Arishma M, Anjali S Rajan, Satheesh Kumar K
INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions. OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study. METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61 CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.
{"title":"Machine Learning based Disease and Pest detection in Agricultural Crops","authors":"Balasubramaniam S, Sandra Grace Nelson, Arishma M, Anjali S Rajan, Satheesh Kumar K","doi":"10.4108/eetiot.5049","DOIUrl":"https://doi.org/10.4108/eetiot.5049","url":null,"abstract":"INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions. \u0000OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study. \u0000METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). \u0000RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61 \u0000CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"19 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Balasubramaniam S, Sandra Grace Nelson, Arishma M, Anjali S Rajan, Satheesh Kumar K
INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions. OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study. METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61 CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.
{"title":"Machine Learning based Disease and Pest detection in Agricultural Crops","authors":"Balasubramaniam S, Sandra Grace Nelson, Arishma M, Anjali S Rajan, Satheesh Kumar K","doi":"10.4108/eetiot.5049","DOIUrl":"https://doi.org/10.4108/eetiot.5049","url":null,"abstract":"INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions. \u0000OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study. \u0000METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). \u0000RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61 \u0000CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"347 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research explores an encryption system that combines the Nth-degree Truncated Polynomial Ring Unit (NTRU) public key cryptosystem with Gaussian Graceful Labeling. This process assigns distinct labels to a graph's vertices, resulting in successive Gaussian integers. The NTRU method offers enhanced security and efficient key exchange. The communication encryption process depends on integers P, a, and b, with P being the largest prime number in the vertex labeling. The original receivers are the vertex labeling with the largest prime number coefficient, while all other receivers receive messages from the sender. A polynomial algebraic mixing system and a clustering principle based on the abecedarian probability proposition are used in NTRU encryption and decryption. The choice of relatively prime integers p and q in NTRU plays a role in the construction of polynomial rings used for encryption and decryption, with specific choices and properties designed to ensure scheme security.
这项研究探索了一种加密系统，它将 Nth 度截断多项式环单元（NTRU）公钥加密系统与高斯优雅标签（Gaussian Graceful Labeling）相结合。这一过程为图的顶点分配不同的标签，从而产生连续的高斯整数。NTRU 方法提供了更高的安全性和高效的密钥交换。通信加密过程取决于整数 P、a 和 b，其中 P 是顶点标记中最大的质数。原始接收者是具有最大质数系数的顶点标签，而其他所有接收者都从发送者那里接收信息。在 NTRU 加密和解密中使用了多项式代数混合系统和基于阿贝歇德概率命题的聚类原理。在 NTRU 中，相对素数 p 和 q 的选择对用于加密和解密的多项式环的构建起着重要作用，其特定选择和属性旨在确保方案的安全性。
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This research explores an encryption system that combines the Nth-degree Truncated Polynomial Ring Unit (NTRU) public key cryptosystem with Gaussian Graceful Labeling. This process assigns distinct labels to a graph's vertices, resulting in successive Gaussian integers. The NTRU method offers enhanced security and efficient key exchange. The communication encryption process depends on integers P, a, and b, with P being the largest prime number in the vertex labeling. The original receivers are the vertex labeling with the largest prime number coefficient, while all other receivers receive messages from the sender. A polynomial algebraic mixing system and a clustering principle based on the abecedarian probability proposition are used in NTRU encryption and decryption. The choice of relatively prime integers p and q in NTRU plays a role in the construction of polynomial rings used for encryption and decryption, with specific choices and properties designed to ensure scheme security.
这项研究探索了一种加密系统，它将 Nth 度截断多项式环单元（NTRU）公钥加密系统与高斯优雅标签（Gaussian Graceful Labeling）相结合。这一过程为图的顶点分配不同的标签，从而产生连续的高斯整数。NTRU 方法提供了更高的安全性和高效的密钥交换。通信加密过程取决于整数 P、a 和 b，其中 P 是顶点标记中最大的质数。原始接收者是具有最大质数系数的顶点标签，而其他所有接收者都从发送者那里接收信息。在 NTRU 加密和解密中使用了多项式代数混合系统和基于阿贝歇德概率命题的聚类原理。在 NTRU 中，相对素数 p 和 q 的选择对用于加密和解密的多项式环的构建起着重要作用，其特定选择和属性旨在确保方案的安全性。
{"title":"Enhanced Security in Public Key Cryptography: A Novel Approach Combining Gaussian Graceful Labeling and NTRU Public Key Cryptosystem","authors":"S. Kavitha, G. Jayalalitha, K. Sivaranjani","doi":"10.4108/eetiot.4992","DOIUrl":"https://doi.org/10.4108/eetiot.4992","url":null,"abstract":"This research explores an encryption system that combines the Nth-degree Truncated Polynomial Ring Unit (NTRU) public key cryptosystem with Gaussian Graceful Labeling. This process assigns distinct labels to a graph's vertices, resulting in successive Gaussian integers. The NTRU method offers enhanced security and efficient key exchange. The communication encryption process depends on integers P, a, and b, with P being the largest prime number in the vertex labeling. The original receivers are the vertex labeling with the largest prime number coefficient, while all other receivers receive messages from the sender. A polynomial algebraic mixing system and a clustering principle based on the abecedarian probability proposition are used in NTRU encryption and decryption. The choice of relatively prime integers p and q in NTRU plays a role in the construction of polynomial rings used for encryption and decryption, with specific choices and properties designed to ensure scheme security.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"13 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139873735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rhesus macaque trespassing is a widespread problem where wild Rhesus macaque monkeys enter human settlements and agricultural areas, causing various issues such as property damage, food theft, and health risks to humans. These primates also cause significant economic losses by raiding crops, damaging plants, and disrupting the natural balance of the ecosystem. To address this problem, a research paper proposes a technology-based solution called Smart Repeller, which uses ultrasonic sound waves and Calcium Carbide Cannon, along with computer vision technology and artificial intelligence to detect the presence of monkeys and activate repelling mechanisms automatically. The proposed device eliminates the need for human intervention, making it efficient and cost-effective. Our paper aims to demonstrate the feasibility and effectiveness of the proposed device through experimental studies and simulations, with the ultimate goal of providing a practical and scalable solution to mitigate the problem of Rhesus macaque trespassing in human settlements and agricultural areas.
{"title":"SMART REPELLER: A Smart system to prevent Rhesus Macaque Trespassing in Human Settlements and Agricultural Areas","authors":"Radha R, Balaji G, Anita X, Mridhula N","doi":"10.4108/eetiot.4809","DOIUrl":"https://doi.org/10.4108/eetiot.4809","url":null,"abstract":"Rhesus macaque trespassing is a widespread problem where wild Rhesus macaque monkeys enter human settlements and agricultural areas, causing various issues such as property damage, food theft, and health risks to humans. These primates also cause significant economic losses by raiding crops, damaging plants, and disrupting the natural balance of the ecosystem. To address this problem, a research paper proposes a technology-based solution called Smart Repeller, which uses ultrasonic sound waves and Calcium Carbide Cannon, along with computer vision technology and artificial intelligence to detect the presence of monkeys and activate repelling mechanisms automatically. The proposed device eliminates the need for human intervention, making it efficient and cost-effective. Our paper aims to demonstrate the feasibility and effectiveness of the proposed device through experimental studies and simulations, with the ultimate goal of providing a practical and scalable solution to mitigate the problem of Rhesus macaque trespassing in human settlements and agricultural areas.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manjula Devarakonda Venkata, Prashanth Donda, N. B. Madhavi, Pavitar Parkash Singh, A. Azhagu, Jaisudhan Pazhani, Shaik Rehana Banu
This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.
{"title":"Personalized recognition system in online shopping by using deep learning","authors":"Manjula Devarakonda Venkata, Prashanth Donda, N. B. Madhavi, Pavitar Parkash Singh, A. Azhagu, Jaisudhan Pazhani, Shaik Rehana Banu","doi":"10.4108/eetiot.4810","DOIUrl":"https://doi.org/10.4108/eetiot.4810","url":null,"abstract":"This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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