Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150925
Vipul Narayan, Shashank Awasthi, Naushen Fatima, Mohammad Faiz, D. Bordoloi, R. Sandhu, Swapnita Srivastava
The lumpy skin disease epidemic in India claimed the lives of over 97,000 cattle in the three months from July to September 2022. It is spread by ticks and other blood-feeding insects, including some species of mosquito, ticks, and flies. It can also cause fever, skin nodules, and mortality, especially in animals who have never been exposed to the virus before. The spread of the illness might cause "substantial" and "severe" economic damage, according to FAO and WOAH. For the dairy business, the current epidemic in India has proven to be problematic. The severity of Lumpy Skin Disease (LSD) in cattle will be identified using the suggested Deep Learning (DL) methodology. Data capture, pre-processing, augmentation, segmentation, feature extraction, and classification are the six processes that comprise the proposed methodology.
{"title":"Severity of Lumpy Disease detection based on Deep Learning Technique","authors":"Vipul Narayan, Shashank Awasthi, Naushen Fatima, Mohammad Faiz, D. Bordoloi, R. Sandhu, Swapnita Srivastava","doi":"10.1109/ICDT57929.2023.10150925","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150925","url":null,"abstract":"The lumpy skin disease epidemic in India claimed the lives of over 97,000 cattle in the three months from July to September 2022. It is spread by ticks and other blood-feeding insects, including some species of mosquito, ticks, and flies. It can also cause fever, skin nodules, and mortality, especially in animals who have never been exposed to the virus before. The spread of the illness might cause \"substantial\" and \"severe\" economic damage, according to FAO and WOAH. For the dairy business, the current epidemic in India has proven to be problematic. The severity of Lumpy Skin Disease (LSD) in cattle will be identified using the suggested Deep Learning (DL) methodology. Data capture, pre-processing, augmentation, segmentation, feature extraction, and classification are the six processes that comprise the proposed methodology.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115264320","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151080
Sharath Kumar Jagannathan, Gulhan Bizel, Thomas Abraham J V, G. Kannan, Veeramalai Sankaradass, Durgaprasad Navulla
In analytics and business intelligence, there are a lot of things that can go wrong. Be it a report or a plan in the data based results must be consistent, verifiable, accurate to emerge and most importantly, acceptable to the end user. Contrary to the popular belief that the coexistence of multiple BI tools poses some major impediment to the advancement of high-quality analytics output, allowing multiple tools at the same time has several serious advantages in many ways. These are all examples of BI software and are more intelligent than relatively sophisticated tools. In this paper innovative business intelligence based analytics model was proposed to enhance the point of sale in commercial retail market. The practice of continuous integration, invented by software developers and borrowed by the analytics and business intelligence community, is an attempt to detect mistakes or errors early. The Point of sale systems occupies every retail space. Any company that has employees has some software that manages payroll, Sales reports are almost universal.
{"title":"A Smart Innovation of Business Intelligence Based Analytical Model by Using POS Based Deep Learning Model","authors":"Sharath Kumar Jagannathan, Gulhan Bizel, Thomas Abraham J V, G. Kannan, Veeramalai Sankaradass, Durgaprasad Navulla","doi":"10.1109/ICDT57929.2023.10151080","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151080","url":null,"abstract":"In analytics and business intelligence, there are a lot of things that can go wrong. Be it a report or a plan in the data based results must be consistent, verifiable, accurate to emerge and most importantly, acceptable to the end user. Contrary to the popular belief that the coexistence of multiple BI tools poses some major impediment to the advancement of high-quality analytics output, allowing multiple tools at the same time has several serious advantages in many ways. These are all examples of BI software and are more intelligent than relatively sophisticated tools. In this paper innovative business intelligence based analytics model was proposed to enhance the point of sale in commercial retail market. The practice of continuous integration, invented by software developers and borrowed by the analytics and business intelligence community, is an attempt to detect mistakes or errors early. The Point of sale systems occupies every retail space. Any company that has employees has some software that manages payroll, Sales reports are almost universal.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114681416","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150810
Himanshu Goyal, Muskan Kundnani, M. Singh, Nishant Gupta
This paper presents a study on the security challenges faced by online auction systems and proposes strategies to address them. The research methodology involved a literature review of existing studies. The main findings of the study suggest that the most common security threats faced by online auction systems include hacking, phishing, and fraud, which can have serious consequences for both buyers and sellers. To address these threats, the paper proposes a number of strategies, including the use of encryption, two-factor authentication, and secure payment systems. The study concludes that improved security measures in online auction systems can help to increase trust and confidence among users, which in turn can lead to greater participation and revenue for the industry. The implications of the study for the online auction industry and potential directions for future research are also discussed.
{"title":"Analysis, Design and Implementation of a Web- Based Online Auction System","authors":"Himanshu Goyal, Muskan Kundnani, M. Singh, Nishant Gupta","doi":"10.1109/ICDT57929.2023.10150810","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150810","url":null,"abstract":"This paper presents a study on the security challenges faced by online auction systems and proposes strategies to address them. The research methodology involved a literature review of existing studies. The main findings of the study suggest that the most common security threats faced by online auction systems include hacking, phishing, and fraud, which can have serious consequences for both buyers and sellers. To address these threats, the paper proposes a number of strategies, including the use of encryption, two-factor authentication, and secure payment systems. The study concludes that improved security measures in online auction systems can help to increase trust and confidence among users, which in turn can lead to greater participation and revenue for the industry. The implications of the study for the online auction industry and potential directions for future research are also discussed.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389766","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150615
Saslina Kamaruddin, Ani Munirah Mohammad, Nadia Nabila Mohd Saufi, Wan Rosalili Wan Rosli, M. B. Othman, Z. Hamin
AI is becoming increasingly important in cybersecurity. AI-based products detect risks and secure systems and data. Cybercriminals can use technology to launch more sophisticated attacks. AI-based security is in demand due to cyberattacks. With the adoption of AI technology, GDPR requires most countries to have legal measures to protect their citizens' data and privacy. Data protection and privacy issues arise when using AI technology. AI use must comply with GDPR, including obtaining consent for data processing, ensuring data accuracy, and giving individuals the right to access, correct, or delete their data. Organisations must also be transparent about how their AI makes decisions and not discriminate against individuals or groups. This study examines Malaysia's GDPR compliance on AI usage, data protection, and privacy in light of current concerns. This study analyses primary and secondary sources using doctrinal research. In 2022, Malaysia's banking, healthcare, and telecommunications sectors were hit by data breaches, indicating that AI is increasing data breaches. Thus, the government must examine citizen data protection and privacy concerns and re-examine its governance, including legal and regulatory mechanisms, to see if it conforms to international norms and consider reforms.
{"title":"Compliance to GDPR Data Protection and Privacy in Artificial Intelligence Technology: Legal and Ethical Ramifications in Malaysia","authors":"Saslina Kamaruddin, Ani Munirah Mohammad, Nadia Nabila Mohd Saufi, Wan Rosalili Wan Rosli, M. B. Othman, Z. Hamin","doi":"10.1109/ICDT57929.2023.10150615","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150615","url":null,"abstract":"AI is becoming increasingly important in cybersecurity. AI-based products detect risks and secure systems and data. Cybercriminals can use technology to launch more sophisticated attacks. AI-based security is in demand due to cyberattacks. With the adoption of AI technology, GDPR requires most countries to have legal measures to protect their citizens' data and privacy. Data protection and privacy issues arise when using AI technology. AI use must comply with GDPR, including obtaining consent for data processing, ensuring data accuracy, and giving individuals the right to access, correct, or delete their data. Organisations must also be transparent about how their AI makes decisions and not discriminate against individuals or groups. This study examines Malaysia's GDPR compliance on AI usage, data protection, and privacy in light of current concerns. This study analyses primary and secondary sources using doctrinal research. In 2022, Malaysia's banking, healthcare, and telecommunications sectors were hit by data breaches, indicating that AI is increasing data breaches. Thus, the government must examine citizen data protection and privacy concerns and re-examine its governance, including legal and regulatory mechanisms, to see if it conforms to international norms and consider reforms.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129123477","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150542
Annu Mishra, Pankaj Gupta, P. Tewari
Biomedical image segmentation is used widely for various diagnosis of various diseases and other medicinal purposes and help the radiologist and doctor fraternity to reduce their work and help them concentrate more on their research for new diseases. Researchers and medical practitioners use applications based on image segmentation for detecting abnormalities as well as analyzing the effect of certain deformations or deviations quantitatively. However, there are various issues faced while carrying out this task. The primary reason is the presence of inherent noise, the non-uniform intensity of the pixels, and other artifacts. The presence of artifacts not only limits the process of image segmentation but also increases the computational time for the segmentation process. In biomedical images, the problem is more complicated and recurrent. This is due to the different anatomical structures and multi-modal systems available. In this paper, a new algorithm is proposed where a modified fuzzy C-means (MFCM) clustering algorithm is integrated with Regularized Level set method to enhance the efficiency of the image segmentation process which improves the analysis exercise of the image processing system. The approach encompasses two crucial steps. Initially, the image is segmented using the Modified FCM. The MFCM approach has two basic updates with respect to the conventional FCM [1]. Firstly, we introduce a factor to the conventional FCM and secondly, Euclidean distance is replaced with the kernel-dependent distance measure. The factor increases the speed of computation of the FCM algorithm. Replacing the Euclidean distance with a kernel-dependent distance measure makes the algorithm more robust. After the initial segmentation, the Regularized Level Set method was used to refine the result and track the variation boundaries. The regularized level set method solves the reinitialization problem faced in the conventional level set method and enhances the capability and efficiency of the level set method. The combined approach not only enhances the computational speed but also helps to overcome the artifacts mentioned above.
{"title":"Biomedical Image Segmentation Using Integrated FCM Clustering Modified with Regularized Level Set Method","authors":"Annu Mishra, Pankaj Gupta, P. Tewari","doi":"10.1109/ICDT57929.2023.10150542","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150542","url":null,"abstract":"Biomedical image segmentation is used widely for various diagnosis of various diseases and other medicinal purposes and help the radiologist and doctor fraternity to reduce their work and help them concentrate more on their research for new diseases. Researchers and medical practitioners use applications based on image segmentation for detecting abnormalities as well as analyzing the effect of certain deformations or deviations quantitatively. However, there are various issues faced while carrying out this task. The primary reason is the presence of inherent noise, the non-uniform intensity of the pixels, and other artifacts. The presence of artifacts not only limits the process of image segmentation but also increases the computational time for the segmentation process. In biomedical images, the problem is more complicated and recurrent. This is due to the different anatomical structures and multi-modal systems available. In this paper, a new algorithm is proposed where a modified fuzzy C-means (MFCM) clustering algorithm is integrated with Regularized Level set method to enhance the efficiency of the image segmentation process which improves the analysis exercise of the image processing system. The approach encompasses two crucial steps. Initially, the image is segmented using the Modified FCM. The MFCM approach has two basic updates with respect to the conventional FCM [1]. Firstly, we introduce a factor to the conventional FCM and secondly, Euclidean distance is replaced with the kernel-dependent distance measure. The factor increases the speed of computation of the FCM algorithm. Replacing the Euclidean distance with a kernel-dependent distance measure makes the algorithm more robust. After the initial segmentation, the Regularized Level Set method was used to refine the result and track the variation boundaries. The regularized level set method solves the reinitialization problem faced in the conventional level set method and enhances the capability and efficiency of the level set method. The combined approach not only enhances the computational speed but also helps to overcome the artifacts mentioned above.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123385929","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151204
G. Verma, S. Prakash
There is a great advancement in the domain of Internet of medical Things (IoMT) including other domains of artificial intelligence, machine learning, deep learning which has an extensive possibility of exploring healthcare industry. The IoT devices like sensors, actuators and other devices gets connected to the internet and further they collect the data and store it to a specific location. For further processing of the data, the frameworks of machine learning, deep learning are utilized. These techniques help to get the clear insights of the patient’s health data which enables to know the current health status of the patient. Recently, the Covid-19 outbreak has occurred which has influenced millions of people across the globe. This virus has taken life of many people and the infection rate of this virus is still increasing day by day. Researchers and medical staffs are exploring advanced techniques to utilize medical images of the infected person using IoMT and deep learning frameworks so that the root cause can be explored. Different techniques deep neural networks have been explored in this work to detect Covid-19 infected persons which utilizes a chest X-ray dataset. A lot of challenges are there that are being faced by the researchers to detect Covid-19 infected patients from Chest X-ray images. This exhaustive literature review presents different frameworks of deep learning architectures and a comparative study has also been done addressing the recent methodologies, datasets, issues, research gaps and so on. Further, some pre-trained models based on CNN architectures like Xception, VGG16, VGG19 and so on are also discussed.
{"title":"Methodologies, Applications, and Challenges of Pneumonia Detection of Chest X-Ray images for COVID-19 using IoT-enabled Deep Learning","authors":"G. Verma, S. Prakash","doi":"10.1109/ICDT57929.2023.10151204","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151204","url":null,"abstract":"There is a great advancement in the domain of Internet of medical Things (IoMT) including other domains of artificial intelligence, machine learning, deep learning which has an extensive possibility of exploring healthcare industry. The IoT devices like sensors, actuators and other devices gets connected to the internet and further they collect the data and store it to a specific location. For further processing of the data, the frameworks of machine learning, deep learning are utilized. These techniques help to get the clear insights of the patient’s health data which enables to know the current health status of the patient. Recently, the Covid-19 outbreak has occurred which has influenced millions of people across the globe. This virus has taken life of many people and the infection rate of this virus is still increasing day by day. Researchers and medical staffs are exploring advanced techniques to utilize medical images of the infected person using IoMT and deep learning frameworks so that the root cause can be explored. Different techniques deep neural networks have been explored in this work to detect Covid-19 infected persons which utilizes a chest X-ray dataset. A lot of challenges are there that are being faced by the researchers to detect Covid-19 infected patients from Chest X-ray images. This exhaustive literature review presents different frameworks of deep learning architectures and a comparative study has also been done addressing the recent methodologies, datasets, issues, research gaps and so on. Further, some pre-trained models based on CNN architectures like Xception, VGG16, VGG19 and so on are also discussed.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121183883","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150811
Ankita Shukla, Izharuddin
Proactive illness diagnosis with AI and related technologies has been an intriguing and productive field in the last ten years. Cardiovascular illnesses are among the medical conditions that need regular monitoring. Arrhythmia, a type of coronary heart disease, is frequently observed by clinicians using electrocardiography (ECG). In humans, an ECG records electrical activity and cardiac rhythm. In recent decades, there has been a substantial surge in the use of neural networks to detect cardiovascular abnormalities. It has been shown that using the denoised signal as compared to the raw input signal increases the probability of better identification of arrhythmias. In this paper, rigorous, three-step preprocessing is done to improve classification accuracy. Firstly, denoising is done using a wavelet transform, then, for baseline artifact filtering, five filters have been applied to ECG signals, and lastly, an R peak is detected. A hybrid (CNN+LSTM) model is implemented to automate arrhythmia categorization on a denoised ECG signal. Comparative analysis demonstrates that the suggested model outperforms contemporary models in terms of various performance factors.
{"title":"Denoising ECG signals and their analysis using Hybrid Deep learning model","authors":"Ankita Shukla, Izharuddin","doi":"10.1109/ICDT57929.2023.10150811","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150811","url":null,"abstract":"Proactive illness diagnosis with AI and related technologies has been an intriguing and productive field in the last ten years. Cardiovascular illnesses are among the medical conditions that need regular monitoring. Arrhythmia, a type of coronary heart disease, is frequently observed by clinicians using electrocardiography (ECG). In humans, an ECG records electrical activity and cardiac rhythm. In recent decades, there has been a substantial surge in the use of neural networks to detect cardiovascular abnormalities. It has been shown that using the denoised signal as compared to the raw input signal increases the probability of better identification of arrhythmias. In this paper, rigorous, three-step preprocessing is done to improve classification accuracy. Firstly, denoising is done using a wavelet transform, then, for baseline artifact filtering, five filters have been applied to ECG signals, and lastly, an R peak is detected. A hybrid (CNN+LSTM) model is implemented to automate arrhythmia categorization on a denoised ECG signal. Comparative analysis demonstrates that the suggested model outperforms contemporary models in terms of various performance factors.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116331198","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150986
Raja Rambabu Thumati, L. A. J. Prabhu, Vijender Kumar Solanki
In today's Cloud environment there are number of issues related to cloud services such as data security, managing of resources. In this paper we are handling these issues by creating an eminent Framework using Python to audit all resources in the cloud platforms like AWS, Azure and GCP using AES and Diffie Hellman algorithm. There are works entitled on data security, which Handles, manages and mainly to store data using Triple DES, AES, RSA, BAT, HECC algorithms on specific cloud platforms. The proposed framework will imbibe AWS, Azure and GCP clouds and provide common platform to audit the resources before start using it and paying for it.
{"title":"Cloud Compliance Framework using Python","authors":"Raja Rambabu Thumati, L. A. J. Prabhu, Vijender Kumar Solanki","doi":"10.1109/ICDT57929.2023.10150986","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150986","url":null,"abstract":"In today's Cloud environment there are number of issues related to cloud services such as data security, managing of resources. In this paper we are handling these issues by creating an eminent Framework using Python to audit all resources in the cloud platforms like AWS, Azure and GCP using AES and Diffie Hellman algorithm. There are works entitled on data security, which Handles, manages and mainly to store data using Triple DES, AES, RSA, BAT, HECC algorithms on specific cloud platforms. The proposed framework will imbibe AWS, Azure and GCP clouds and provide common platform to audit the resources before start using it and paying for it.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124136372","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}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150818
Nikhat Parveen, S. S, B. Pattanaik, Balamurugan D
Recently, there has been a great deal of discussion about the Internet of Things (IoT), both in the classroom and in the field. Because the IoT relies on data, it can be challenging to compile all that data into a coherent whole. A high robust data transmission is the primary motivation for data aggregation systems, which seek to optimize delay, reliability, and energy consumption during data transmission. In this research, the study uses a hybrid data aggregation model for IoT that aims to improve the process of data aggregation for efficient data transmission in two different energy constrained networking applications. The entire simulation is conducted in a network simulator environment, and it is tested in terms of packet delivery rate and throughput. The results show an increase data rate and network throughput in various energy constrained applications.
{"title":"Data Aggregation in IOT Networks for Energy Constrained Applications","authors":"Nikhat Parveen, S. S, B. Pattanaik, Balamurugan D","doi":"10.1109/ICDT57929.2023.10150818","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150818","url":null,"abstract":"Recently, there has been a great deal of discussion about the Internet of Things (IoT), both in the classroom and in the field. Because the IoT relies on data, it can be challenging to compile all that data into a coherent whole. A high robust data transmission is the primary motivation for data aggregation systems, which seek to optimize delay, reliability, and energy consumption during data transmission. In this research, the study uses a hybrid data aggregation model for IoT that aims to improve the process of data aggregation for efficient data transmission in two different energy constrained networking applications. The entire simulation is conducted in a network simulator environment, and it is tested in terms of packet delivery rate and throughput. The results show an increase data rate and network throughput in various energy constrained applications.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128085208","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}
In recent years, scientists are becoming increasingly interested in the creation of nanosatellites. This interest is evident for a number of reasons, including safe development, extremely cheap cost, and small design, which reduces launch costs. Nanosatellites are made to carry out specific functions for researchers, such as satellite data, collection of data from space, etc. Nanosatellites are simpler to construct than conventional large-scale satellites. During various design phases, satellite developers face numerous difficulties. There are certain challenges in making nanosatellites. The creation of a communication link between the satellite and the ground station is another essential component; without it, spacecraft in Earth's orbit are useless objects. Designing uplink and downlink for nanosatellites is therefore one of the biggest problems. The most important phase in creating a connection between ground stations and nanosatellites is this one. An analytical assessment for the link budget computation with regard to the nanosatellite is provided in this publication.
{"title":"Designing Robust Uplink and Downlink Systems for Nanosatellites: An Overview of Key Technical and Operational Challenges","authors":"Surya Pratap, Sneha Agrawal, Abhishek Sharma, Himanshu Chaudhary, Praveen Kumar, Piyush Yadav, Vidushi Pandey, Ayush Singh","doi":"10.1109/ICDT57929.2023.10150621","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150621","url":null,"abstract":"In recent years, scientists are becoming increasingly interested in the creation of nanosatellites. This interest is evident for a number of reasons, including safe development, extremely cheap cost, and small design, which reduces launch costs. Nanosatellites are made to carry out specific functions for researchers, such as satellite data, collection of data from space, etc. Nanosatellites are simpler to construct than conventional large-scale satellites. During various design phases, satellite developers face numerous difficulties. There are certain challenges in making nanosatellites. The creation of a communication link between the satellite and the ground station is another essential component; without it, spacecraft in Earth's orbit are useless objects. Designing uplink and downlink for nanosatellites is therefore one of the biggest problems. The most important phase in creating a connection between ground stations and nanosatellites is this one. An analytical assessment for the link budget computation with regard to the nanosatellite is provided in this publication.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939176","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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