Wideband Code Division Multiple Access (WCDMA) systems and Orthogonal Frequency Division Multiple Access (OFDMA) technique were the basic of modern wireless systems aimed to provide enriched services. But the channel impairments always put a limit on modern systems that also includes AC-MIMO Radio, 802.11ac and LTE/VoLTE. Here, the conduct of WCDMA and OFDMA primarily based totally structures is analyzed via way of means of widely recognized primary Gaussian Approximation (GA) in which interference and noise to the gadget is generated via way of means of suggest and variance approximations of noise power. In order to generate the faded transmitted signal Weibull, Rayleigh, Rician and Nakagami distributions have been applied to systems. OFDMA and WCDMA system performances for different fading environments have been observed by error rate graphs. It is validated that inclusion of fading in the system increases error rate and the performance of OFDMA system is much better than WCDMA system.
{"title":"Gaussian Approximation based WCDMA and OFDMA System Performance Investigation for Various Fading Channels","authors":"Parveen Singla, Vikas Gupta, Rinkesh Mittal, Ramanpreet Kaur, Jaskirat Kaur","doi":"10.1109/IDCIoT56793.2023.10053401","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053401","url":null,"abstract":"Wideband Code Division Multiple Access (WCDMA) systems and Orthogonal Frequency Division Multiple Access (OFDMA) technique were the basic of modern wireless systems aimed to provide enriched services. But the channel impairments always put a limit on modern systems that also includes AC-MIMO Radio, 802.11ac and LTE/VoLTE. Here, the conduct of WCDMA and OFDMA primarily based totally structures is analyzed via way of means of widely recognized primary Gaussian Approximation (GA) in which interference and noise to the gadget is generated via way of means of suggest and variance approximations of noise power. In order to generate the faded transmitted signal Weibull, Rayleigh, Rician and Nakagami distributions have been applied to systems. OFDMA and WCDMA system performances for different fading environments have been observed by error rate graphs. It is validated that inclusion of fading in the system increases error rate and the performance of OFDMA system is much better than WCDMA system.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76976332","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10052783
Khushi Gupta, Siddhartha Choubey, Y. N, P. William, V. N., Chaitanya P. Kale
Detecting driver drowsiness is a huge crucial problem in the sector of accident-avoidance technologies, so the development of an innovative intelligent system came into the picture. The system also prioritized safety concerns such as informing the victim and avoiding yawning. The technique for this system is a machine learning-based sophisticated algorithm that can identify the driver's facial expressions and quantify the rate of driver sleepiness. This may be avoided by activating an alarm that causes the driver to become alert when he or she becomes fatigued. The Eye Aspects Ratio (EAR) is used to recognize the system’s drowsiness rate by calculating the facial plot localization which extracts and gives the drowsiness rate.Current approaches, however, have significant shortcomings due to the considerable unpredictability of surrounding conditions. Poor lighting may impair the camera's ability to precisely measure the driver's face and eye. This will affect image processing analysis which corresponds to late detection or no detection, tendering the technique in accuracy and efficiency. Numerous strategies were investigated and analyzed to determine the optimal technique with the maximum accuracy for detecting driver tiredness. In this paper, the implementation of a real-time system is proposed that requires a camera to automatically trace and process the victim’s eye using Dlib Python, and OpenCV. The driver's eye area is continually monitored and computed to assess drowsiness before generating an output alarm to notify the driver.
{"title":"Implementation of Motorist Weariness Detection System using a Conventional Object Recognition Technique","authors":"Khushi Gupta, Siddhartha Choubey, Y. N, P. William, V. N., Chaitanya P. Kale","doi":"10.1109/IDCIoT56793.2023.10052783","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10052783","url":null,"abstract":"Detecting driver drowsiness is a huge crucial problem in the sector of accident-avoidance technologies, so the development of an innovative intelligent system came into the picture. The system also prioritized safety concerns such as informing the victim and avoiding yawning. The technique for this system is a machine learning-based sophisticated algorithm that can identify the driver's facial expressions and quantify the rate of driver sleepiness. This may be avoided by activating an alarm that causes the driver to become alert when he or she becomes fatigued. The Eye Aspects Ratio (EAR) is used to recognize the system’s drowsiness rate by calculating the facial plot localization which extracts and gives the drowsiness rate.Current approaches, however, have significant shortcomings due to the considerable unpredictability of surrounding conditions. Poor lighting may impair the camera's ability to precisely measure the driver's face and eye. This will affect image processing analysis which corresponds to late detection or no detection, tendering the technique in accuracy and efficiency. Numerous strategies were investigated and analyzed to determine the optimal technique with the maximum accuracy for detecting driver tiredness. In this paper, the implementation of a real-time system is proposed that requires a camera to automatically trace and process the victim’s eye using Dlib Python, and OpenCV. The driver's eye area is continually monitored and computed to assess drowsiness before generating an output alarm to notify the driver.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74807212","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053483
K. Laxminarayanamma, R. Krishnaiah, P. Sammulal
Cancer disease prediction based on neurological characteristics of cancer patients is gaining a significant research attention in recent times. The role of data in the processing and analysis of neurological features is critical, and the main goal is to efficiently extract neurological features from cancer patients' data. Random extraction of neurological features from cancer patient data is a new research initiative. Convolutional Neural Networks (CNN) is a promising approach in various healthcare applications to efficiently perform the data processing tasks. Some CNN-based approaches have been proposed to perform efficient cancer disease prediction using remotely sensed neurological features. Cancer disease extraction based on MPDCNN is one of the best CNN approaches used for extracting features and perform disease prediction from Geo-Fan-2 (GF-2) sensing cancer patient data. However, due to its sparse arrangement of optimal boundary, exact neurological features and high amount of training time, it is insufficient to investigate and automate the neurological feature extraction process from the cancer patient's data. A Novel Optimized Multi Feature Contour based Hierarchical Neural Network (NOMFCHNN) is proposed to improve the automatic neurological feature prediction process. NOMFCHNN is made up of expanding neural network features and layers related to inception, which contains the data about network localization, and this approach uses optimal and exact neurological feature matching with extended feature extraction. This method also employs contour map optimization to identify contours based on globalization of cancer patient data along with the output of the identified contour being transmitted to the next identified contour in the selected hierarchical region. Furthermore, the proposed approach evaluates the low- resolution term in cancer patient's data to gain knowledge from the cancer patient's data by obtaining the prediction results of neighbouring optimal and exact neurological features to eliminate small changes or errors. A multi scale feature Prediction module is used to eliminate feature inconsistency between the encoding and decoding phases of the prediction process in order to identify better contours of neurological features from remote sensing cancer patient's data. Extensive experiments on combined repository cancer patient data show that the proposed methodology improves the prediction accuracy and other parameters when compared to the other state-of-the-art methods used to remotely analyze the neurological features.
{"title":"Advanced Optimized Counter based Hierarchal Model to Predict Cancer’s Disease from Cancer Patients Neurological Features","authors":"K. Laxminarayanamma, R. Krishnaiah, P. Sammulal","doi":"10.1109/IDCIoT56793.2023.10053483","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053483","url":null,"abstract":"Cancer disease prediction based on neurological characteristics of cancer patients is gaining a significant research attention in recent times. The role of data in the processing and analysis of neurological features is critical, and the main goal is to efficiently extract neurological features from cancer patients' data. Random extraction of neurological features from cancer patient data is a new research initiative. Convolutional Neural Networks (CNN) is a promising approach in various healthcare applications to efficiently perform the data processing tasks. Some CNN-based approaches have been proposed to perform efficient cancer disease prediction using remotely sensed neurological features. Cancer disease extraction based on MPDCNN is one of the best CNN approaches used for extracting features and perform disease prediction from Geo-Fan-2 (GF-2) sensing cancer patient data. However, due to its sparse arrangement of optimal boundary, exact neurological features and high amount of training time, it is insufficient to investigate and automate the neurological feature extraction process from the cancer patient's data. A Novel Optimized Multi Feature Contour based Hierarchical Neural Network (NOMFCHNN) is proposed to improve the automatic neurological feature prediction process. NOMFCHNN is made up of expanding neural network features and layers related to inception, which contains the data about network localization, and this approach uses optimal and exact neurological feature matching with extended feature extraction. This method also employs contour map optimization to identify contours based on globalization of cancer patient data along with the output of the identified contour being transmitted to the next identified contour in the selected hierarchical region. Furthermore, the proposed approach evaluates the low- resolution term in cancer patient's data to gain knowledge from the cancer patient's data by obtaining the prediction results of neighbouring optimal and exact neurological features to eliminate small changes or errors. A multi scale feature Prediction module is used to eliminate feature inconsistency between the encoding and decoding phases of the prediction process in order to identify better contours of neurological features from remote sensing cancer patient's data. Extensive experiments on combined repository cancer patient data show that the proposed methodology improves the prediction accuracy and other parameters when compared to the other state-of-the-art methods used to remotely analyze the neurological features.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89093475","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053547
Swaminathan K, V. Ravindran, R. Ponraj, S. Venkatasubramanian, K. Chandrasekaran, S. Ragunathan
Modern wireless technology demands the implementation of preset Sensor nodes for a structured wireless network. The network has sensor nodes for surveillance or environmental sensing, which wirelessly transmit data to a collection point. Therefore, data transfer must be protected by preventing external intrusion attacks. This will be handled by designing an effective intrusion detection system proposed as a Composite Intrusion detection system (CIDS). It is suitable for a network in heterogeneous network structure with a capable of identifying externals attacks like flooding of data's, sending unwanted data packets and changing the destination node. For routing of data packets between the nodes, minimum power utilization with changeable cluster heading method is used. The activities of sensor nodes will be monitored and a dataset is formed on the basis of the node’s activity. It is known as Network Databases (NDB). Using this dataset, the intrusion attacks will be identified by using Artificial Neural Network (ANN). ANN will be trained with a predefined dataset for the effective identification of external attacks. The proposed CIDS methodology shows the high accuracy of identifying the external attacks on the sensor networks when comparing to the previous designed system in all the types of attacks.
{"title":"A Novel Composite Intrusion Detection System (CIDS) for Wireless Sensor Network","authors":"Swaminathan K, V. Ravindran, R. Ponraj, S. Venkatasubramanian, K. Chandrasekaran, S. Ragunathan","doi":"10.1109/IDCIoT56793.2023.10053547","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053547","url":null,"abstract":"Modern wireless technology demands the implementation of preset Sensor nodes for a structured wireless network. The network has sensor nodes for surveillance or environmental sensing, which wirelessly transmit data to a collection point. Therefore, data transfer must be protected by preventing external intrusion attacks. This will be handled by designing an effective intrusion detection system proposed as a Composite Intrusion detection system (CIDS). It is suitable for a network in heterogeneous network structure with a capable of identifying externals attacks like flooding of data's, sending unwanted data packets and changing the destination node. For routing of data packets between the nodes, minimum power utilization with changeable cluster heading method is used. The activities of sensor nodes will be monitored and a dataset is formed on the basis of the node’s activity. It is known as Network Databases (NDB). Using this dataset, the intrusion attacks will be identified by using Artificial Neural Network (ANN). ANN will be trained with a predefined dataset for the effective identification of external attacks. The proposed CIDS methodology shows the high accuracy of identifying the external attacks on the sensor networks when comparing to the previous designed system in all the types of attacks.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84560241","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053501
P. William, Y. N, V. M. Tidake, Snehal Sumit Gondkar, Chetana. R, K. Vengatesan
The phrase "personality" refers to an individual's distinct mode of thought, action, and behaviour Personality is a collection of feelings, thoughts, and aspirations that may be seen in the way people interact with one another. Behavioural features that separate one person from another and may be clearly seen when interacting with individuals in one's immediate surroundings and social group are included in this category of traits. To improve good healthy discourse, a variety of ways for evaluating candidate personalities based on the meaning of their textual message have been developed. According to the research, the textual content of interview responses to conventional interview questions is an effective measure for predicting a person's personality attribute. Nowadays, personality prediction has garnered considerable interest. It analyses user activity and displays their ideas, feelings, and so on. Historically, defining a personality trait was a laborious process. Thus, automated prediction is required for a big number of users. Different algorithms, data sources, and feature sets are used in various techniques. As a way to gauge someone's personality, personality prediction has evolved into an important topic of research in both psychology and computer science. Candidate personality traits may be classified using a word embedding model, which is the subject of this article.
{"title":"Framework for Implementation of Personality Inventory Model on Natural Language Processing with Personality Traits Analysis","authors":"P. William, Y. N, V. M. Tidake, Snehal Sumit Gondkar, Chetana. R, K. Vengatesan","doi":"10.1109/IDCIoT56793.2023.10053501","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053501","url":null,"abstract":"The phrase \"personality\" refers to an individual's distinct mode of thought, action, and behaviour Personality is a collection of feelings, thoughts, and aspirations that may be seen in the way people interact with one another. Behavioural features that separate one person from another and may be clearly seen when interacting with individuals in one's immediate surroundings and social group are included in this category of traits. To improve good healthy discourse, a variety of ways for evaluating candidate personalities based on the meaning of their textual message have been developed. According to the research, the textual content of interview responses to conventional interview questions is an effective measure for predicting a person's personality attribute. Nowadays, personality prediction has garnered considerable interest. It analyses user activity and displays their ideas, feelings, and so on. Historically, defining a personality trait was a laborious process. Thus, automated prediction is required for a big number of users. Different algorithms, data sources, and feature sets are used in various techniques. As a way to gauge someone's personality, personality prediction has evolved into an important topic of research in both psychology and computer science. Candidate personality traits may be classified using a word embedding model, which is the subject of this article.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72897136","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053433
N. M. Reddy, Chodagam Srinivas, Peruri Naga Sai Varsha, Sypureddy Srujana, Nadimpalli Saipriya, Rayi Sai Ganesh
Generally, a large power system consists of small interlinked power systems. These small systems are known as single-area systems and the entire large power system is known as a multi-area system. As technology is evolving day by day, the smart loads on power systems have been increasing. Due to this, the sudden addition and rejection of load take place which causes the deviation of frequency in the system. This scenario leads to a raise of uncertainties in the system so these can be reduced by using SSSC (Static Synchronous Series Compensator) device which belongs to the FACTS (Flexible AC Transmission System) devices. The main aim of this research work is to reduce frequency deviations in multi-area systems by using SSSC devices. Hence, the frequency deviation is reduced during load uncertainties. The results are then obtained through MATLAB/SIMULINK.
{"title":"Minimization of Frequency Deviations in Multi-Area Power System with SSSC","authors":"N. M. Reddy, Chodagam Srinivas, Peruri Naga Sai Varsha, Sypureddy Srujana, Nadimpalli Saipriya, Rayi Sai Ganesh","doi":"10.1109/IDCIoT56793.2023.10053433","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053433","url":null,"abstract":"Generally, a large power system consists of small interlinked power systems. These small systems are known as single-area systems and the entire large power system is known as a multi-area system. As technology is evolving day by day, the smart loads on power systems have been increasing. Due to this, the sudden addition and rejection of load take place which causes the deviation of frequency in the system. This scenario leads to a raise of uncertainties in the system so these can be reduced by using SSSC (Static Synchronous Series Compensator) device which belongs to the FACTS (Flexible AC Transmission System) devices. The main aim of this research work is to reduce frequency deviations in multi-area systems by using SSSC devices. Hence, the frequency deviation is reduced during load uncertainties. The results are then obtained through MATLAB/SIMULINK.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75196953","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053477
C. Aravindan, R. Vasuki
Diabetic retinopathy is the term used to describe the damage to the blood vessels in the retina of the human eye. The symptoms of diabetic retinopathy are blurriness, difficulty in vision and even blindness can occur. The blood vessels in the retina of the human eye have been damaged over time, which has an impact on the person’s ability to see. It is a cumulative problem in the modern world. Diabetic retinopathy has four stages, including mild, moderate, and severe non proliferative and proliferative. To reduce the effects of diabetic retinopathy are early diagnosis is necessary. Thus, by using artificial intelligence and image processing, the early stage of diabetic retinopathy can be detected. This leads to faster and easier screening of disorder for both the patients and ophthalmologists.
{"title":"Detection and Classification of Early Stage Diabetic Retinopathy using Artificial Intelligence and Image Processing","authors":"C. Aravindan, R. Vasuki","doi":"10.1109/IDCIoT56793.2023.10053477","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053477","url":null,"abstract":"Diabetic retinopathy is the term used to describe the damage to the blood vessels in the retina of the human eye. The symptoms of diabetic retinopathy are blurriness, difficulty in vision and even blindness can occur. The blood vessels in the retina of the human eye have been damaged over time, which has an impact on the person’s ability to see. It is a cumulative problem in the modern world. Diabetic retinopathy has four stages, including mild, moderate, and severe non proliferative and proliferative. To reduce the effects of diabetic retinopathy are early diagnosis is necessary. Thus, by using artificial intelligence and image processing, the early stage of diabetic retinopathy can be detected. This leads to faster and easier screening of disorder for both the patients and ophthalmologists.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86884822","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10052784
G. Thiyagarajan, P. S
What students do in a self-paced online learning environment is a "black box". The instructor has limited interactions with students and a restricted understanding of how students are progressing in their studies. A technology, sophisticated enough to predict the outcome of the student in an online learning environment was widely adopted in Predictive Learning Analytics. In the past, research on predictive learning analytics has emphasized predicting learning outcomes rather than facilitating instructors and students in decision-making or analyzing student behavior. This research study employed a predictive process monitoring technique to analyze the student’s event logs in an online learning and online test environment to predict the next activity the student is going to perform and the remaining time to complete the course or test. The Long Short Term Memory neural network approach is used in this work to predict the next activity of the running case by analyzing the sequence of historical data and Apromore to predict the completion time of a case. By employing the predictive monitoring of learning processes, new insights are developed to analyze students’ behavior in real-time and is achievable.
{"title":"Predictive Monitoring of Learning Processes","authors":"G. Thiyagarajan, P. S","doi":"10.1109/IDCIoT56793.2023.10052784","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10052784","url":null,"abstract":"What students do in a self-paced online learning environment is a \"black box\". The instructor has limited interactions with students and a restricted understanding of how students are progressing in their studies. A technology, sophisticated enough to predict the outcome of the student in an online learning environment was widely adopted in Predictive Learning Analytics. In the past, research on predictive learning analytics has emphasized predicting learning outcomes rather than facilitating instructors and students in decision-making or analyzing student behavior. This research study employed a predictive process monitoring technique to analyze the student’s event logs in an online learning and online test environment to predict the next activity the student is going to perform and the remaining time to complete the course or test. The Long Short Term Memory neural network approach is used in this work to predict the next activity of the running case by analyzing the sequence of historical data and Apromore to predict the completion time of a case. By employing the predictive monitoring of learning processes, new insights are developed to analyze students’ behavior in real-time and is achievable.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84103837","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053431
Nerella Venkata Pragna, Jothiga Srinivasan, Greeshma. M, Anala Jeyendra Sri Vishnu, Rithvik Polavarapu, Aparna Mohanty
The occurrence of floods is unavoidable due to the varying climatic and environmental conditions of a country like India. Flooding can be disastrous for both life and the economy, thus the presence of a flood monitoring and alert system becomes vital. A conventional weather monitoring system is not sufficient, since it is not quick and efficient. When a flood occurs, the authorities must spend a myriad of funds on food rations and emergency necessities. Even though the upscaled current flood monitoring systems in practice are situated in vital areas, it can be noticed that flash floods are still ubiquitous. In crucial times, drones play a major role in providing quick and efficient responses. During floods, it assists to map the impact caused, and to predict the damages to various life forms, properties, and lands. On that account, a First-Person View drone, which mainly incorporates an electronic speed controller and flight controller, that can be used for surveilling the areas of the flood has been designed in this work.
{"title":"Flood Surveillance using FPV drones","authors":"Nerella Venkata Pragna, Jothiga Srinivasan, Greeshma. M, Anala Jeyendra Sri Vishnu, Rithvik Polavarapu, Aparna Mohanty","doi":"10.1109/IDCIoT56793.2023.10053431","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053431","url":null,"abstract":"The occurrence of floods is unavoidable due to the varying climatic and environmental conditions of a country like India. Flooding can be disastrous for both life and the economy, thus the presence of a flood monitoring and alert system becomes vital. A conventional weather monitoring system is not sufficient, since it is not quick and efficient. When a flood occurs, the authorities must spend a myriad of funds on food rations and emergency necessities. Even though the upscaled current flood monitoring systems in practice are situated in vital areas, it can be noticed that flash floods are still ubiquitous. In crucial times, drones play a major role in providing quick and efficient responses. During floods, it assists to map the impact caused, and to predict the damages to various life forms, properties, and lands. On that account, a First-Person View drone, which mainly incorporates an electronic speed controller and flight controller, that can be used for surveilling the areas of the flood has been designed in this work.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82725252","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053482
Meet Kumari
An optical fiber communication may not be a favorable choice in geographical restriction areas for next generation Internet of Things (IoT) based networks at minimum cost, high data rate and long-range transmission. To reduce the cost of fiber cable, short range wireless links, to enhance the mobility and the system bandwidth, a hybrid fiber-Visible Light Communication (VLC) system is employed to access the information anywhere and anytime with less delay. In this work, a white Light Emitting Diode (LED) based fiber-VLC system has been presented. The results depict that the proposed work allows 40Gbps transmission rate, fiber range of 10km and VLC range of 800m. Also, at an optimum transmitter and receiver aperture diameters of 10cm and 10cm respectively, the desired system performance can be received. The proposed fiber-VLC system offers long range distance and high data rate under the presence of noise and interference. Besides this, the proposed system is a superior system when compared to other related work.
{"title":"Modeling of IoT based High-Speed Hybrid Fiber-Optical Wireless Communication System","authors":"Meet Kumari","doi":"10.1109/IDCIoT56793.2023.10053482","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053482","url":null,"abstract":"An optical fiber communication may not be a favorable choice in geographical restriction areas for next generation Internet of Things (IoT) based networks at minimum cost, high data rate and long-range transmission. To reduce the cost of fiber cable, short range wireless links, to enhance the mobility and the system bandwidth, a hybrid fiber-Visible Light Communication (VLC) system is employed to access the information anywhere and anytime with less delay. In this work, a white Light Emitting Diode (LED) based fiber-VLC system has been presented. The results depict that the proposed work allows 40Gbps transmission rate, fiber range of 10km and VLC range of 800m. Also, at an optimum transmitter and receiver aperture diameters of 10cm and 10cm respectively, the desired system performance can be received. The proposed fiber-VLC system offers long range distance and high data rate under the presence of noise and interference. Besides this, the proposed system is a superior system when compared to other related work.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84262003","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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