Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053471
Jayashree M, Rachana P, Ashin Kunjumon, Meena Thamban, Athul Roy
Nowadays when a accident occurs people are afraid or create a major chaos while informing the emergency services, or a accident gets unnoticed and eventually when the emergency services arrive its too late. Using the already in-place and functioning CCTV infrastructure, a complete system has been developed to actively detect all kinds of accidents on the road and alert the necessary personal, for a accident the nearest police station, hospitals, general ambulances and the registrant of the vehicle in accident and their emergency contacts, for a hit and run case the vehicle number of the accused vehicle can be provided to the police.
{"title":"Convolutional Neural Networks (CNN)-based Vehicle Crash Detection and Alert System","authors":"Jayashree M, Rachana P, Ashin Kunjumon, Meena Thamban, Athul Roy","doi":"10.1109/IDCIoT56793.2023.10053471","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053471","url":null,"abstract":"Nowadays when a accident occurs people are afraid or create a major chaos while informing the emergency services, or a accident gets unnoticed and eventually when the emergency services arrive its too late. Using the already in-place and functioning CCTV infrastructure, a complete system has been developed to actively detect all kinds of accidents on the road and alert the necessary personal, for a accident the nearest police station, hospitals, general ambulances and the registrant of the vehicle in accident and their emergency contacts, for a hit and run case the vehicle number of the accused vehicle can be provided to the police.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"48 1","pages":"161-164"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80743537","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/IDCIoT56793.2023.10053459
Surekha Chalnewad, Arati Manjaramkar
A license plate is alphanumeric rectangular plate. It is fixed on the vehicle and used for identification of the vehicle. Generally, huge numbers of vehicles move-on the road which is the major issue of concern in identifying the vehicle(s) owner, registration place of vehicle, address, etc. The automatic license plate detection is one of the solutions for such kind of problems. There are numerous methodologies available for license plate detection, but certain factors like speed of vehicles, language used on license plate, non-uniform letter effects on license plate, etc. makes the task of recognition difficult. The license plate detection system has many applications like payment of parking fees; toll fee on the highway; traffic monitoring system; border security system; signal system, etc. This research work proposes a novel license plate detection technique with the extension of Sobel mask. In proposed system, first step is acquisition of image. Second step is to detect the vehicle from the acquired image. In third step, segmentation of license plate from vehicle image is done. Finally, neural network classifier is used to classify the vehicle(s) license plate. The proposed system gives promising, robust, and efficient results for license plate detection. Proposed system achieves accuracy of 98% is achieved in detecting the license plate.
{"title":"Detection and Classification of License Plate by Neural Network Classifier","authors":"Surekha Chalnewad, Arati Manjaramkar","doi":"10.1109/IDCIoT56793.2023.10053459","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053459","url":null,"abstract":"A license plate is alphanumeric rectangular plate. It is fixed on the vehicle and used for identification of the vehicle. Generally, huge numbers of vehicles move-on the road which is the major issue of concern in identifying the vehicle(s) owner, registration place of vehicle, address, etc. The automatic license plate detection is one of the solutions for such kind of problems. There are numerous methodologies available for license plate detection, but certain factors like speed of vehicles, language used on license plate, non-uniform letter effects on license plate, etc. makes the task of recognition difficult. The license plate detection system has many applications like payment of parking fees; toll fee on the highway; traffic monitoring system; border security system; signal system, etc. This research work proposes a novel license plate detection technique with the extension of Sobel mask. In proposed system, first step is acquisition of image. Second step is to detect the vehicle from the acquired image. In third step, segmentation of license plate from vehicle image is done. Finally, neural network classifier is used to classify the vehicle(s) license plate. The proposed system gives promising, robust, and efficient results for license plate detection. Proposed system achieves accuracy of 98% is achieved in detecting the license plate.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"41 1","pages":"531-535"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79844832","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/IDCIoT56793.2023.10053425
U. Sakthi, Thomas M. Chen, Mithileysh Sathiyanarayanan
Suicide is a very critical and important issue in modern society. Suicide is the third-leading cause of death for college and high school students. Social media allows students in the digital environment to share their suicidal ideas and thoughts with others. Accurate and early detection and prevention of suicidal ideation in students can save the students' lives. To identify the risk factor for suicidal attempts, a suitable method of analysing the suicidal behaviour of students using their sentiment text posted on social media can be used. This paper presents an optimized Dragonfly algorithm (DFA) using a Deep Belief Network (DBN) for the automatic detection of suicidal ideation in students. In our CyberHelp Solution, the proposed DFA-based DBN model analyses student social media data, predicts suicidal behavior, and treats students appropriately. The sentiment analysis performs automated categorization of online messages and makes accurate predictions of the student’s suicidal behaviors. The dragonfly heuristic optimization algorithm is used for tuning the hyperparameter in the deep belief network. The proposed DFA-DBN technique has been implemented to predict suicidal ideation in students with a higher accuracy of 95.5% compared with other classification models.
{"title":"CyberHelp: Sentiment Analysis on Social Media Data Using Deep Belief Network to Predict Suicidal Ideation of Students","authors":"U. Sakthi, Thomas M. Chen, Mithileysh Sathiyanarayanan","doi":"10.1109/IDCIoT56793.2023.10053425","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053425","url":null,"abstract":"Suicide is a very critical and important issue in modern society. Suicide is the third-leading cause of death for college and high school students. Social media allows students in the digital environment to share their suicidal ideas and thoughts with others. Accurate and early detection and prevention of suicidal ideation in students can save the students' lives. To identify the risk factor for suicidal attempts, a suitable method of analysing the suicidal behaviour of students using their sentiment text posted on social media can be used. This paper presents an optimized Dragonfly algorithm (DFA) using a Deep Belief Network (DBN) for the automatic detection of suicidal ideation in students. In our CyberHelp Solution, the proposed DFA-based DBN model analyses student social media data, predicts suicidal behavior, and treats students appropriately. The sentiment analysis performs automated categorization of online messages and makes accurate predictions of the student’s suicidal behaviors. The dragonfly heuristic optimization algorithm is used for tuning the hyperparameter in the deep belief network. The proposed DFA-DBN technique has been implemented to predict suicidal ideation in students with a higher accuracy of 95.5% compared with other classification models.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"9 1","pages":"206-211"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78908706","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/IDCIoT56793.2023.10053524
W. Rajan Babu, M. Sundaram, A. Kavithamani, S. Sam Karthik, N. Abinaya, V. Bharath Choudry
Most of the machines are driven by Induction motor nowadays. Induction motor gets failure due to various reasons. This fault mostly occurs in the stator. By measuring the current of a motor and comparing it to a fixed value, any fault can be detected. Different kind of faults exhibits different types of electrical current profile. The nature of this current is measured by a Power Quality Analyzer and converted into waveforms and spectrums. By looking closely at these three-phase current readings, one can predict when a machine is about to fail. Motor Stator Current Profile (MSCP) based method is proposed to identify the different stator faults.
{"title":"MSCP Based Stator Fault Identification in Induction Motor Using Power Quality Analyzer","authors":"W. Rajan Babu, M. Sundaram, A. Kavithamani, S. Sam Karthik, N. Abinaya, V. Bharath Choudry","doi":"10.1109/IDCIoT56793.2023.10053524","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053524","url":null,"abstract":"Most of the machines are driven by Induction motor nowadays. Induction motor gets failure due to various reasons. This fault mostly occurs in the stator. By measuring the current of a motor and comparing it to a fixed value, any fault can be detected. Different kind of faults exhibits different types of electrical current profile. The nature of this current is measured by a Power Quality Analyzer and converted into waveforms and spectrums. By looking closely at these three-phase current readings, one can predict when a machine is about to fail. Motor Stator Current Profile (MSCP) based method is proposed to identify the different stator faults.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"33 1","pages":"1001-1005"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85655892","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/IDCIoT56793.2023.10053513
Surya Pandey, A. K, M. R. Shaikh, Dhanush Y P, Yajat Vishwakarma
The process and functioning of data integration is termed as combining information from several sources to provide users with a coherent perspective. The fundamental idea behind data integration is to open up data and make it simpler for individuals and systems to access, utilize, and process. The process of converting data from one format to another, typically from that of a source system into that required by a destination system, is known as data transformation. Data transformation is a component of the majority of data integration and management processes, including data manipulation and data warehousing. Many organizations carry out data transformation and integration because they have requirements with respect to data usage that is important in every situation. This paper proposes an architecture that reduces manual work and abstracts the decisions to be made in the integration and transformation process. This approach can lower the risk of human error and result in significant financial savings for various organizations. A modular approach is followed to ease these complex tasks and get desired results.
{"title":"Data Integration and Transformation using Artificial Intelligence","authors":"Surya Pandey, A. K, M. R. Shaikh, Dhanush Y P, Yajat Vishwakarma","doi":"10.1109/IDCIoT56793.2023.10053513","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053513","url":null,"abstract":"The process and functioning of data integration is termed as combining information from several sources to provide users with a coherent perspective. The fundamental idea behind data integration is to open up data and make it simpler for individuals and systems to access, utilize, and process. The process of converting data from one format to another, typically from that of a source system into that required by a destination system, is known as data transformation. Data transformation is a component of the majority of data integration and management processes, including data manipulation and data warehousing. Many organizations carry out data transformation and integration because they have requirements with respect to data usage that is important in every situation. This paper proposes an architecture that reduces manual work and abstracts the decisions to be made in the integration and transformation process. This approach can lower the risk of human error and result in significant financial savings for various organizations. A modular approach is followed to ease these complex tasks and get desired results.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"7 1","pages":"844-849"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79203934","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":"73 1","pages":"746-751"},"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":"16 1","pages":"919-924"},"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":"80 1","pages":"451-456"},"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":"34 1","pages":"771-776"},"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":"9 1","pages":"59-62"},"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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