Cyber-Physical Systems (CPS) typically involve a number of networked systems that can watch over and control actual processes and objects. They share many similarities with the Internet of Things (IoT) applications, but CPS focuses on how physical devices, networking components, and computational processes are integrated. The Internet of Cyber-Physical Things is a new component of CPS as a result of their integration with IoT. Many applications like smart healthcare, smart home, smart grid, smart car, smart cities, and supply chains are made possible by the rapid and significant evolution of CPS, which has an impact on many elements of people’s way of living. As the foundation for current and upcoming smart services, these technologies will strengthen our essential infrastructure and they have a big impact on how we live our lives. IoT is one of the emerging technologies in the last decade and so many smart devices are developed and deployed to monitor things in real time. In this paper, we initially found the integration of CPS and IoT usability. We have mentioned the security challenges in CPS based on IoT applications. For implementation, we have considered smart home as an IoT application and tested how the activities of smart mobile phones can be captured. Experimental results show that smart devices are vulnerable to different attacks.
{"title":"Cyber Physical System: Security Challenges in Internet of Things System","authors":"Bhabendu Kumar Mohanta, Mohan Kumar Dehury, Badeea Al Sukhni, Niva Mohapatra","doi":"10.1109/I-SMAC55078.2022.9987256","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987256","url":null,"abstract":"Cyber-Physical Systems (CPS) typically involve a number of networked systems that can watch over and control actual processes and objects. They share many similarities with the Internet of Things (IoT) applications, but CPS focuses on how physical devices, networking components, and computational processes are integrated. The Internet of Cyber-Physical Things is a new component of CPS as a result of their integration with IoT. Many applications like smart healthcare, smart home, smart grid, smart car, smart cities, and supply chains are made possible by the rapid and significant evolution of CPS, which has an impact on many elements of people’s way of living. As the foundation for current and upcoming smart services, these technologies will strengthen our essential infrastructure and they have a big impact on how we live our lives. IoT is one of the emerging technologies in the last decade and so many smart devices are developed and deployed to monitor things in real time. In this paper, we initially found the integration of CPS and IoT usability. We have mentioned the security challenges in CPS based on IoT applications. For implementation, we have considered smart home as an IoT application and tested how the activities of smart mobile phones can be captured. Experimental results show that smart devices are vulnerable to different attacks.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115432627","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987327
Hui Wing Kuan, N. S. Lai
High electrical consumption in operating the factory has been a critical source of expense, especially for a frozen food warehouse. Hence, this project is proposing a solution by utilising Industrial IoT and Machine Learning to reduce the use of electricity. A simple prototype has been built by using ESPS266, DHT22 and Raspberry Pi, with the aid of NodeRed and TensorFlow for data collection and machine learning for prediction. The predicted temperature has obtained an accuracy of up to 98.24% for operating frozen food storage. Besides that, the efficiency of energy optimization forthe refrigeration compressor is up to 9 hours with the cost saved up to RM869.62 per year for 1HP.
{"title":"Condition Monitoring of Frozen Storage for Energy Optimization","authors":"Hui Wing Kuan, N. S. Lai","doi":"10.1109/I-SMAC55078.2022.9987327","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987327","url":null,"abstract":"High electrical consumption in operating the factory has been a critical source of expense, especially for a frozen food warehouse. Hence, this project is proposing a solution by utilising Industrial IoT and Machine Learning to reduce the use of electricity. A simple prototype has been built by using ESPS266, DHT22 and Raspberry Pi, with the aid of NodeRed and TensorFlow for data collection and machine learning for prediction. The predicted temperature has obtained an accuracy of up to 98.24% for operating frozen food storage. Besides that, the efficiency of energy optimization forthe refrigeration compressor is up to 9 hours with the cost saved up to RM869.62 per year for 1HP.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113986262","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987284
S. Banu, Syeeda Ayesha Mohmudiya, Noor Rahiba, Saniya Anmol
Because health and wellbeing are so important to human society, they should be among the first to benefit from emerging technologies like IoT. Dementia affects the elderly and persons with chronic diseases who must take their medications on time and without fail. In light of this, to track patients’ day-to-day activities, several Internet of Medical Things (IoMT) systems are connected to IoT networks. To overcome this, a smart medicine box has been developed for those people, who regularly take medicines and the prescription of their medicine is very long as it is hard to remember. This medicine box contains three sub pill boxes. Caregiver can setup time for these three sub pill boxes. Pill boxes are pre-loaded in the system which patient needs to take at given time which reduces caregiver’s responsibility towards giving the correct and timely consumption of medicines. When time of pill is set, pillbox will remind to take pill at a particular time and the pills required to take at that time comes out to the user to avoid confusion among medicines.
{"title":"IoT Enabled Patient Medicine Intake Tracking System-MEDIKIT","authors":"S. Banu, Syeeda Ayesha Mohmudiya, Noor Rahiba, Saniya Anmol","doi":"10.1109/I-SMAC55078.2022.9987284","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987284","url":null,"abstract":"Because health and wellbeing are so important to human society, they should be among the first to benefit from emerging technologies like IoT. Dementia affects the elderly and persons with chronic diseases who must take their medications on time and without fail. In light of this, to track patients’ day-to-day activities, several Internet of Medical Things (IoMT) systems are connected to IoT networks. To overcome this, a smart medicine box has been developed for those people, who regularly take medicines and the prescription of their medicine is very long as it is hard to remember. This medicine box contains three sub pill boxes. Caregiver can setup time for these three sub pill boxes. Pill boxes are pre-loaded in the system which patient needs to take at given time which reduces caregiver’s responsibility towards giving the correct and timely consumption of medicines. When time of pill is set, pillbox will remind to take pill at a particular time and the pills required to take at that time comes out to the user to avoid confusion among medicines.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115533851","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987297
Yee Jin Yeo, A. Balakrishnan, S. Selvaperumal, Illanur Muhaini Binti Mohd Nor
The main aim of this work is to develop a manually operated camera assisted firefighting robot with the capability to extinguish fire and controlled remotely by using an Android application. In this proposed work, a robot prototype was developed with the inclusion of camera module and relevant sensors. The robot was interfaced with Blynk IoT platform, which can be used by an Android device to control the robot. The performance of the developed robot is evaluated by testing the speed, water sprayer, sensors, fire extinguishment, and operating distance. The overall robot speed is lower than expected due to the condition of the test, which is 13.118 cm per second. The effective water sprayer area is 85 cm squared, that is considered as small due to the limited aiming angle. The overall sensors accuracy while considering several distances is 77.47%, which can be improved with omni-directional sensors. The fire extinguishment test proved that the robot is suitable for extinguishing spread type of fire. The optimal operating distance of the robot from the local server is from 0 to 26 meters, considering concrete walls as obstacles. Finally, the developed system has proved that the implementation of Android device and IoT platform is doable while retaining the core features such as live camera feed, fire detection, and fire extinguishment.
{"title":"Android Controlled Fire Fighter Robot Using IoT","authors":"Yee Jin Yeo, A. Balakrishnan, S. Selvaperumal, Illanur Muhaini Binti Mohd Nor","doi":"10.1109/I-SMAC55078.2022.9987297","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987297","url":null,"abstract":"The main aim of this work is to develop a manually operated camera assisted firefighting robot with the capability to extinguish fire and controlled remotely by using an Android application. In this proposed work, a robot prototype was developed with the inclusion of camera module and relevant sensors. The robot was interfaced with Blynk IoT platform, which can be used by an Android device to control the robot. The performance of the developed robot is evaluated by testing the speed, water sprayer, sensors, fire extinguishment, and operating distance. The overall robot speed is lower than expected due to the condition of the test, which is 13.118 cm per second. The effective water sprayer area is 85 cm squared, that is considered as small due to the limited aiming angle. The overall sensors accuracy while considering several distances is 77.47%, which can be improved with omni-directional sensors. The fire extinguishment test proved that the robot is suitable for extinguishing spread type of fire. The optimal operating distance of the robot from the local server is from 0 to 26 meters, considering concrete walls as obstacles. Finally, the developed system has proved that the implementation of Android device and IoT platform is doable while retaining the core features such as live camera feed, fire detection, and fire extinguishment.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114614152","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9986503
K. Sarmila, S. Manisekaran
Extensive development in networking and data communication among IoT devices has involved cloud computing in IoT environments to handle the ongoing data processing demands. The accelerated growth and integration of IoT and Cloud computing led to parallel expansion in the requirement of security and privacy of data at various levels of communication. Through communication with each other, these technologies aim at simplifying human life but are more vulnerable to different types of attacks. This paper focuses on building a knowledge base on various attacks on the IoT environment and highlights the importance of implementing data protection methodologies. Awareness of various threats is the initial step in providing sufficient protection to data. This paper recognizes research directions and challenges to integrate possible techniques and protective solutions to overcome malicious attacks in IoT and Cloud.
{"title":"Certain Investigation of Various Attacks and Vulnerabilites in IoT and Cloud Environment","authors":"K. Sarmila, S. Manisekaran","doi":"10.1109/I-SMAC55078.2022.9986503","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9986503","url":null,"abstract":"Extensive development in networking and data communication among IoT devices has involved cloud computing in IoT environments to handle the ongoing data processing demands. The accelerated growth and integration of IoT and Cloud computing led to parallel expansion in the requirement of security and privacy of data at various levels of communication. Through communication with each other, these technologies aim at simplifying human life but are more vulnerable to different types of attacks. This paper focuses on building a knowledge base on various attacks on the IoT environment and highlights the importance of implementing data protection methodologies. Awareness of various threats is the initial step in providing sufficient protection to data. This paper recognizes research directions and challenges to integrate possible techniques and protective solutions to overcome malicious attacks in IoT and Cloud.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117316285","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987330
Raman Sandhiya, T. V. Mohana, B. Jothi, Juhie Agarwal, N. Kulshrestha, S. Sandhiya
According to studies, there are approximately 850 million poultry birds across India, with an average of 30 million farmers working in the sector. In other words, a poultry farm is a trustworthy and long-term way to make money in India. However, managing a poultry farm is labour intensive due to the need for constant surveillance and control over a wide range of environmental factors. The actual implementation of this is significantly more complicated, expensive, and time-consuming. The paper suggested a smart poultry system that tries to provide the solution for all the issues. The health of poultry birds heavily relies on environmental parameters, so variables like temperature and humidity are measured and monitored continuously. The website was made so that poultry keepers may get reliable information about their birds’ health and use that information to take the appropriate measures. Moreover, in the event of a crisis, such as a fire or the illness of a single bird, the owner will receive a notification. It is also possible to gather information about the poultry in the specified timespan. The Firebase cloud is used for wireless monitoring and managing the poultry system. The suggested automatic smart poultry system will make the birds healthy and it indirectly helps the owners to increase their profit with minimal human effort.
{"title":"IoT based Smart Poultry to Produce a Healthy Environment","authors":"Raman Sandhiya, T. V. Mohana, B. Jothi, Juhie Agarwal, N. Kulshrestha, S. Sandhiya","doi":"10.1109/I-SMAC55078.2022.9987330","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987330","url":null,"abstract":"According to studies, there are approximately 850 million poultry birds across India, with an average of 30 million farmers working in the sector. In other words, a poultry farm is a trustworthy and long-term way to make money in India. However, managing a poultry farm is labour intensive due to the need for constant surveillance and control over a wide range of environmental factors. The actual implementation of this is significantly more complicated, expensive, and time-consuming. The paper suggested a smart poultry system that tries to provide the solution for all the issues. The health of poultry birds heavily relies on environmental parameters, so variables like temperature and humidity are measured and monitored continuously. The website was made so that poultry keepers may get reliable information about their birds’ health and use that information to take the appropriate measures. Moreover, in the event of a crisis, such as a fire or the illness of a single bird, the owner will receive a notification. It is also possible to gather information about the poultry in the specified timespan. The Firebase cloud is used for wireless monitoring and managing the poultry system. The suggested automatic smart poultry system will make the birds healthy and it indirectly helps the owners to increase their profit with minimal human effort.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122993312","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987398
D. J. N. Kumar, V. K, S. Sagar Imambi, P. V. Pramila, Ashok Kumar, Vijayabhaskar V
Rheumatoid arthritis, often known as rheumatoid, is an inflammatory condition brought on by the immune system’s malfunction.Various preliminary tests were proposed to predict this chronic illness. This study proposes a deep learning model which can detect the presence of rheumatoid by analyzing the thermal images of a person. For this purpose, the palms of the rheumatoid patients and the control group were scanned to produce a sample of thermal pictures of human hands. The efficiency of this training is then improved by preprocessing the thermal pictures. The CNN-LS TM approach is used to build a deep learning model. Then, to accurately forecast the presence of rheumatoid, this model is trained using thermal pictures. The training’s outcomes are noted and reviewed. Validation comes after training, and the outcomes of the validation are also tabulated. For simpler analysis, the findings are also plotted as graphs. The results show that as the number of epochs rises, accuracy, precision, and recall value all significantly increase. As the number of epochs rises, the loss value also falls. The model is then tested to determine the final values for each parameter after training and validation. The final accuracy score of the model is 92.78, while the loss score is 3.78, which is so minuscule as to occasionally be ignored. The model’s precision is 95.4%, and its recall value is 93.7%. This deep learning model can be utilized as a screening tool for rheumatoidbecause of its improved accuracy and precision values.
{"title":"DL-based Rheumatoid Arthritis Prediction using Thermal Images","authors":"D. J. N. Kumar, V. K, S. Sagar Imambi, P. V. Pramila, Ashok Kumar, Vijayabhaskar V","doi":"10.1109/I-SMAC55078.2022.9987398","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987398","url":null,"abstract":"Rheumatoid arthritis, often known as rheumatoid, is an inflammatory condition brought on by the immune system’s malfunction.Various preliminary tests were proposed to predict this chronic illness. This study proposes a deep learning model which can detect the presence of rheumatoid by analyzing the thermal images of a person. For this purpose, the palms of the rheumatoid patients and the control group were scanned to produce a sample of thermal pictures of human hands. The efficiency of this training is then improved by preprocessing the thermal pictures. The CNN-LS TM approach is used to build a deep learning model. Then, to accurately forecast the presence of rheumatoid, this model is trained using thermal pictures. The training’s outcomes are noted and reviewed. Validation comes after training, and the outcomes of the validation are also tabulated. For simpler analysis, the findings are also plotted as graphs. The results show that as the number of epochs rises, accuracy, precision, and recall value all significantly increase. As the number of epochs rises, the loss value also falls. The model is then tested to determine the final values for each parameter after training and validation. The final accuracy score of the model is 92.78, while the loss score is 3.78, which is so minuscule as to occasionally be ignored. The model’s precision is 95.4%, and its recall value is 93.7%. This deep learning model can be utilized as a screening tool for rheumatoidbecause of its improved accuracy and precision values.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123631515","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987431
S. Saradha, J. Asha, J. Sreemathy
Through the use of livestock, information sharing is becoming increasingly popular around the world. This study aims to see biometric face analysis be used on sheep recognition to improve sheep monitoring in the centralized database. Anchor-free region convolutional neural networks were used to detect sheep identities (AF-RCNN). Face recognition’s effectiveness as a biometric-based identification for sheep was studied utilizing reviews of face images using the deep earing approach. The method is standalone on a set of standardized facial photos from 50 sheep, using an augmentation strategy to expand the number of sheep images. The proposed method outperforms earlier methods for sheep recognition with high accuracy.
{"title":"A Deep Learning-based Framework for Sheep Identification System based on Facial Bio-Metrics Analysis","authors":"S. Saradha, J. Asha, J. Sreemathy","doi":"10.1109/I-SMAC55078.2022.9987431","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987431","url":null,"abstract":"Through the use of livestock, information sharing is becoming increasingly popular around the world. This study aims to see biometric face analysis be used on sheep recognition to improve sheep monitoring in the centralized database. Anchor-free region convolutional neural networks were used to detect sheep identities (AF-RCNN). Face recognition’s effectiveness as a biometric-based identification for sheep was studied utilizing reviews of face images using the deep earing approach. The method is standalone on a set of standardized facial photos from 50 sheep, using an augmentation strategy to expand the number of sheep images. The proposed method outperforms earlier methods for sheep recognition with high accuracy.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122133136","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987290
Fangqing Li
Aiming at the problem of the extension framework for complex data processing, this paper uses the CEP technology as a reference to propose a complex event big data generation method based on Bayesian networks. This method takes part of the real sample data as the research object, combines the experience of experts in related fields, gives the definition of complex event models, and uses algebraic expressions to describe the specific event information in the data set, such as event models such as cause and effect, sequence, selection, and coordination. Network communication relationship expansion based on multi-network integration uses multiple networks, analyzes the mapping between networks, and expands the connectivity of the network. The network communication relationship expansion based on named entity recognition extracts named entities that can expand the network from a single network. 11.2% reduction in complexity.
{"title":"Research on the Computer Complex Data Processing in the Big Data Era","authors":"Fangqing Li","doi":"10.1109/I-SMAC55078.2022.9987290","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987290","url":null,"abstract":"Aiming at the problem of the extension framework for complex data processing, this paper uses the CEP technology as a reference to propose a complex event big data generation method based on Bayesian networks. This method takes part of the real sample data as the research object, combines the experience of experts in related fields, gives the definition of complex event models, and uses algebraic expressions to describe the specific event information in the data set, such as event models such as cause and effect, sequence, selection, and coordination. Network communication relationship expansion based on multi-network integration uses multiple networks, analyzes the mapping between networks, and expands the connectivity of the network. The network communication relationship expansion based on named entity recognition extracts named entities that can expand the network from a single network. 11.2% reduction in complexity.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124295344","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987303
Simranjit Kaur, S. Vig
In this paper, an effective and efficient power hybrid power generation model is presented in which Maximum power point is tracked by using Fuzzy Logic Controller. The main objective of the proposed approach is to enhance the power capabilities of systems in order to fulfill the increasing load demand. To combat this task, a fuzzy based MPPT technique is implement in power generating system that takes two inputs. Furthermore, two optimization algorithms i.e. chaotic map and Differential Evolution (DE) are hybridized for optimizing the range of variables for two input functions of fuzzy model. The fitness value is calculated in terms of increase in power capabilities. Also, the proposed model utilized two energy sources i.e. Wind energy and solar energy for providing the necessary supply to customers during peak hours. A switching circuitry is also used in the proposed hybrid model for switching between two models when one is not able to generate electricity. The performance of the proposed fuzzy based approach is examined and validated by putting it in comparison with traditional ACO model in terms of their voltage, current and power generation abilities. In addition to this, analytical study is also conducted for wind and solar energy models to determine their abilities for generating power and satisfying load demands.
{"title":"Modeling of Hybrid Power Generation using FLC","authors":"Simranjit Kaur, S. Vig","doi":"10.1109/I-SMAC55078.2022.9987303","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987303","url":null,"abstract":"In this paper, an effective and efficient power hybrid power generation model is presented in which Maximum power point is tracked by using Fuzzy Logic Controller. The main objective of the proposed approach is to enhance the power capabilities of systems in order to fulfill the increasing load demand. To combat this task, a fuzzy based MPPT technique is implement in power generating system that takes two inputs. Furthermore, two optimization algorithms i.e. chaotic map and Differential Evolution (DE) are hybridized for optimizing the range of variables for two input functions of fuzzy model. The fitness value is calculated in terms of increase in power capabilities. Also, the proposed model utilized two energy sources i.e. Wind energy and solar energy for providing the necessary supply to customers during peak hours. A switching circuitry is also used in the proposed hybrid model for switching between two models when one is not able to generate electricity. The performance of the proposed fuzzy based approach is examined and validated by putting it in comparison with traditional ACO model in terms of their voltage, current and power generation abilities. In addition to this, analytical study is also conducted for wind and solar energy models to determine their abilities for generating power and satisfying load demands.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125175156","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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