Pub Date : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987413
R. Krishnaswamy, A. Titus, G. Gengalakshmi., S. Srinivasan, J. Manikandan
Positron Emission Tomography (PET) is suggested for its high potential Deep Learning (DL) diagnostic imaging with a profound learning approach. The network training is done using clear images but reconstructing the low resolution images using Poisson operation. In training the Convolutional Neural Networks (CNN) at a default noise level, a major issue for their generic applicability is the noise level discrepancy. The noise level varies considerably in each iteration reduces the overall efficiency. The results and measured efficiency loss in different noise environments with various noise levels due to inadequate current trials is also presented. To fix this problem, a local linear fitting function is represented before improving the image quality. It indicates that the resulting approach is resilient to noise levels despite the network being educated at a fixed noise level. The proposed protocol is demonstrated to exceed traditional approaches based on total variance and penalty by mean and standard deviation via simulations and trials.
{"title":"Deep Learning Features Restoration and Regional Longitudinal Fitting of Computed Tomography Images using Convolution Neural Network","authors":"R. Krishnaswamy, A. Titus, G. Gengalakshmi., S. Srinivasan, J. Manikandan","doi":"10.1109/I-SMAC55078.2022.9987413","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987413","url":null,"abstract":"Positron Emission Tomography (PET) is suggested for its high potential Deep Learning (DL) diagnostic imaging with a profound learning approach. The network training is done using clear images but reconstructing the low resolution images using Poisson operation. In training the Convolutional Neural Networks (CNN) at a default noise level, a major issue for their generic applicability is the noise level discrepancy. The noise level varies considerably in each iteration reduces the overall efficiency. The results and measured efficiency loss in different noise environments with various noise levels due to inadequate current trials is also presented. To fix this problem, a local linear fitting function is represented before improving the image quality. It indicates that the resulting approach is resilient to noise levels despite the network being educated at a fixed noise level. The proposed protocol is demonstrated to exceed traditional approaches based on total variance and penalty by mean and standard deviation via simulations and trials.","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":"126509895","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-SMAC55078.2022.9987341
Juanjuan Luo
Particle Array, Texture Expansion method, HDR high dynamic texture application, VRAY layered rendering setting, photon file application that can store radiosity information, application of dynamic system in some areas, etc. are some examples of wiring principle and 3D modeling. The module realizes the functions of human-computer interaction and image display, and the entity editing module realizes the editing function of each entity in the scene and transmits the real-time rendering and editing results through data transmission and displays them. This part is realized by the rendering engine, and this method avoids the complexity. It has very low requirements on hardware equipment and realizes automatic 3D reconstruction of virtual scenes based on sequence images.
{"title":"Software Design of 3D Animation Scene based on Virtual Image Modeling Algorithm","authors":"Juanjuan Luo","doi":"10.1109/I-SMAC55078.2022.9987341","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987341","url":null,"abstract":"Particle Array, Texture Expansion method, HDR high dynamic texture application, VRAY layered rendering setting, photon file application that can store radiosity information, application of dynamic system in some areas, etc. are some examples of wiring principle and 3D modeling. The module realizes the functions of human-computer interaction and image display, and the entity editing module realizes the editing function of each entity in the scene and transmits the real-time rendering and editing results through data transmission and displays them. This part is realized by the rendering engine, and this method avoids the complexity. It has very low requirements on hardware equipment and realizes automatic 3D reconstruction of virtual scenes based on sequence images.","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":"130545232","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-SMAC55078.2022.9987378
C. Latha, S. Bhuvaneswari, K. Soujanya
Forecasting is still a potential area of research, particularly in the stock market. Any forecasting model must overcome the subjective nature of the factors that affect market oscillation. Current fuzzy models have made an effort throughout the years to improve financial market forecasting accuracy. The fuzzy returns of the phenomena under study contribute to reducing the subjective nature of the financial market, particularly with respect to the effect of human emotions. These are based on large part on fuzzy sets. Fuzzy sets, on the other hand, may not fully satisfy or characterize the ambiguity of the data since they are unable to depict the level of neutrality of time series. Existing fuzzy inference systems’ reliance on a univariate framework is another important and crucial shortcoming. However, the time series that are part of a prediction problem frequently interact with one another. Given these factors, it is important to create a hybrid fuzzy system for a time series prediction issue that is built on fresh fuzzy sets and a collection of fuzzy logic relations. In this context, this research suggests a hybrid fuzzy time-series forecasting model (HFTSF) on the Standard & Poor Bombay Stock Exchange Information Technology (S& P BSE IT) index, for the prediction of time-series data. This model boosts the chances of getting better forecasts. The validation techniques such as root mean square error, mean square error, and mean absolute error were used in terms of validating the predicting outcomes.
{"title":"Stock Price Prediction using HFTSF Algorithm","authors":"C. Latha, S. Bhuvaneswari, K. Soujanya","doi":"10.1109/I-SMAC55078.2022.9987378","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987378","url":null,"abstract":"Forecasting is still a potential area of research, particularly in the stock market. Any forecasting model must overcome the subjective nature of the factors that affect market oscillation. Current fuzzy models have made an effort throughout the years to improve financial market forecasting accuracy. The fuzzy returns of the phenomena under study contribute to reducing the subjective nature of the financial market, particularly with respect to the effect of human emotions. These are based on large part on fuzzy sets. Fuzzy sets, on the other hand, may not fully satisfy or characterize the ambiguity of the data since they are unable to depict the level of neutrality of time series. Existing fuzzy inference systems’ reliance on a univariate framework is another important and crucial shortcoming. However, the time series that are part of a prediction problem frequently interact with one another. Given these factors, it is important to create a hybrid fuzzy system for a time series prediction issue that is built on fresh fuzzy sets and a collection of fuzzy logic relations. In this context, this research suggests a hybrid fuzzy time-series forecasting model (HFTSF) on the Standard & Poor Bombay Stock Exchange Information Technology (S& P BSE IT) index, for the prediction of time-series data. This model boosts the chances of getting better forecasts. The validation techniques such as root mean square error, mean square error, and mean absolute error were used in terms of validating the predicting outcomes.","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":"131594874","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-SMAC55078.2022.9987316
C. Padmavathy, V. Akshaya, R. Menaha, S. Raja
Node lifetime is an important factor in wireless sensor networks as the entire lifetime of the network depends on the individual nodes. Researchers pay more attention towards enhancement of node lifetime through various deployment models. Rather than concentrating over node deployment, efficient clustering, data aggregation in wireless sensor networks enhances the node and network lifetime, minimize the energy utilization, reduces network congestion and identifies an optimal route for better load balancing. Clustering approaches considers the parameters like residual energy of node, communication range, distance between node and sink. Specifically, cluster head selection and replacement is a crucial part in clustering which directly relates to energy management of network. Considering these facts, an energy efficient clustering approach to enhance node lifetime through hybrid adaptive neuro fuzzy inference system (ANFIS) is proposed in this research work. Conventional models are compared with proposed hybrid approach to demonstrate the superior performance.
{"title":"Hybrid Cluster Head Selection Approach for Node Lifetime Enhancement in Wireless Sensor Networks","authors":"C. Padmavathy, V. Akshaya, R. Menaha, S. Raja","doi":"10.1109/I-SMAC55078.2022.9987316","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987316","url":null,"abstract":"Node lifetime is an important factor in wireless sensor networks as the entire lifetime of the network depends on the individual nodes. Researchers pay more attention towards enhancement of node lifetime through various deployment models. Rather than concentrating over node deployment, efficient clustering, data aggregation in wireless sensor networks enhances the node and network lifetime, minimize the energy utilization, reduces network congestion and identifies an optimal route for better load balancing. Clustering approaches considers the parameters like residual energy of node, communication range, distance between node and sink. Specifically, cluster head selection and replacement is a crucial part in clustering which directly relates to energy management of network. Considering these facts, an energy efficient clustering approach to enhance node lifetime through hybrid adaptive neuro fuzzy inference system (ANFIS) is proposed in this research work. Conventional models are compared with proposed hybrid approach to demonstrate the superior performance.","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":"128683785","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-SMAC55078.2022.9987302
M. P. Varghese, T. Muthumanickam
Due to the vast amount of data collected and the very high level of complexity in VLSI design and manufacturing, the implementation using machine learning can be used in physical design has increased significantly. ML can be used to increase the abstraction level that is obtained from complex simulations based on physics models and provide results that represent a significant level of quality. Computer science techniques such as pattern matching and machine learning can reduce the design time of VLSI circuits by working with large datasets.
{"title":"Machine Learning Approaches for Electronic Design Automation in IC Design Flow","authors":"M. P. Varghese, T. Muthumanickam","doi":"10.1109/I-SMAC55078.2022.9987302","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987302","url":null,"abstract":"Due to the vast amount of data collected and the very high level of complexity in VLSI design and manufacturing, the implementation using machine learning can be used in physical design has increased significantly. ML can be used to increase the abstraction level that is obtained from complex simulations based on physics models and provide results that represent a significant level of quality. Computer science techniques such as pattern matching and machine learning can reduce the design time of VLSI circuits by working with large datasets.","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":"115993576","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-SMAC55078.2022.9987395
V. Mohanavel, M. Tamilselvi, G. Ramkumar, R. Prabu, G. Anitha
Large bandwidth and more mobility are only two reasons why wireless and mobile networks are fast overtaking wired ones as the preferred mode of connectivity. Heterogeneous networks refer to systems that consist of many independent networks, each of which has its own unique set of protocols and characteristics. Due to their density and complexity, such dense small-cell heterogeneous networks currently consume a lot of power; thus, in order to tackle climate change, we require power information security. A Modified Deep Reinforcement Learning (MDRL) approach may offer an on-demand automated approach with short inference time for NP-hard network communication problems including radio resource distribution, identification, and battery preservation. We examine the DRL algorithm’s applicability to a multi-objective issue. A paradigm for hopeful nonlinear assistance that is founded on the entertainer paradigm and explores repeatedly for potential answers to the multiobjective issue we have given. Throughput and energy savings achieved by our algorithm are equivalent to those of currently used approaches, according to the findings of our tests.
{"title":"Deep Reinforcement Learning for Energy Efficient Routing and Throughput Maximization in Various Networks","authors":"V. Mohanavel, M. Tamilselvi, G. Ramkumar, R. Prabu, G. Anitha","doi":"10.1109/I-SMAC55078.2022.9987395","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987395","url":null,"abstract":"Large bandwidth and more mobility are only two reasons why wireless and mobile networks are fast overtaking wired ones as the preferred mode of connectivity. Heterogeneous networks refer to systems that consist of many independent networks, each of which has its own unique set of protocols and characteristics. Due to their density and complexity, such dense small-cell heterogeneous networks currently consume a lot of power; thus, in order to tackle climate change, we require power information security. A Modified Deep Reinforcement Learning (MDRL) approach may offer an on-demand automated approach with short inference time for NP-hard network communication problems including radio resource distribution, identification, and battery preservation. We examine the DRL algorithm’s applicability to a multi-objective issue. A paradigm for hopeful nonlinear assistance that is founded on the entertainer paradigm and explores repeatedly for potential answers to the multiobjective issue we have given. Throughput and energy savings achieved by our algorithm are equivalent to those of currently used approaches, according to the findings of our tests.","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":"122588092","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-SMAC55078.2022.9987418
Yi-Heng Mao, Man Zhang
The image recognition rate is reduced. Based on the monochromatic atmospheric scattering model and the prior law of dark primary colors, a new algorithm for the saturation of the HS I color model for visual perception is proposed to achieve image dehazing. For the minimum pixel point of the dehazed image, the maximum value and the minimum value are used. Estimate. It is conduded that “high efficiency, energy saving, green low carbon, clean and environmental protection” is the inevitable direction of the future development of DC welding power sources. Using the good 3D image generation function of the OpenGL graphics standard, the special finite element simulation system for the steel pipe tension reduction process developed by Yanshan University is based on the SketchUp platform. where the target is.
{"title":"Application of Image Saturation Enhancement Algorithm based on OpenGL Aided Design System","authors":"Yi-Heng Mao, Man Zhang","doi":"10.1109/I-SMAC55078.2022.9987418","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987418","url":null,"abstract":"The image recognition rate is reduced. Based on the monochromatic atmospheric scattering model and the prior law of dark primary colors, a new algorithm for the saturation of the HS I color model for visual perception is proposed to achieve image dehazing. For the minimum pixel point of the dehazed image, the maximum value and the minimum value are used. Estimate. It is conduded that “high efficiency, energy saving, green low carbon, clean and environmental protection” is the inevitable direction of the future development of DC welding power sources. Using the good 3D image generation function of the OpenGL graphics standard, the special finite element simulation system for the steel pipe tension reduction process developed by Yanshan University is based on the SketchUp platform. where the target is.","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":"120994916","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-SMAC55078.2022.9987393
Anshul Jindal, Jiby Mariya Jose, S. Benedict, M. Gerndt
The ongoing accomplishments in the decades-long realization of computer vision have infused new dimensions in various research areas such as smart mobility, smart healthcare, education, finance, and so forth. Research works relating to automated object detection, deep learning-assisted data pipelines, and energy-efficient end-to-end solutions have enabled newer perceptions among researchers, albeit the existence of challenges. This paper proposes an object detection system using energy-efficient Long Range (LoRA) communication media on edge nodes such as Raspberry Pi, Coral DevBoard, and Nvidia Jetson Nano. The proposed approach utilized energy-efficient methods to collaboratively offload object detection-related tasks such as capturing images, training images, and inferring objects across a compendium of computing nodes using LoRA. In addition, this research study has attempted to reveal the inference capabilities of images on three different edge nodes. The proposed work has achieved a power difference of at least 1.2 watts during the inference period of the deep learning models without challenging the prediction accuracy with respect to the base model.
{"title":"LoRa-Powered Energy-Effcient Object Detection Mechanism in Edge Computing Nodes","authors":"Anshul Jindal, Jiby Mariya Jose, S. Benedict, M. Gerndt","doi":"10.1109/I-SMAC55078.2022.9987393","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987393","url":null,"abstract":"The ongoing accomplishments in the decades-long realization of computer vision have infused new dimensions in various research areas such as smart mobility, smart healthcare, education, finance, and so forth. Research works relating to automated object detection, deep learning-assisted data pipelines, and energy-efficient end-to-end solutions have enabled newer perceptions among researchers, albeit the existence of challenges. This paper proposes an object detection system using energy-efficient Long Range (LoRA) communication media on edge nodes such as Raspberry Pi, Coral DevBoard, and Nvidia Jetson Nano. The proposed approach utilized energy-efficient methods to collaboratively offload object detection-related tasks such as capturing images, training images, and inferring objects across a compendium of computing nodes using LoRA. In addition, this research study has attempted to reveal the inference capabilities of images on three different edge nodes. The proposed work has achieved a power difference of at least 1.2 watts during the inference period of the deep learning models without challenging the prediction accuracy with respect to the base model.","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":"126557822","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-SMAC55078.2022.9987352
J. M. Sahayaraj, K. Gunasekaran, S. Verma, P. Ramesh, G. Murugesan
The purpose of this research is to develop protocols for the underutilized channels of primary user usage with scant transmission and minimal interference associating with the secondary users. This has been achieved with large scale fading channels using discrete time queues. Flow level analysis has been made by appropriate queuing model and packet level analysis has done with NS2 simulator. CRTTP uses the channel selection procedure based on utilization, throughput and minimal drop rate whereas the response time denotes whether to increment or decrement transmission power control. Cognitive Radio based Temporal Transmission Protocol Single Channel (CRTTP-SC) denies transmission if sustainable routing parameter does not by cognitive user. Cognitive Radio based Temporal Transmission Protocol Single Channel Receiver Capacity (CRTTP-SCRC). CRTTP-SCRC protocol calculates the channel utilization, drop rate and receiver capacity after which it determines whether to prolong transmission or to refrain from transmission. Cognitive Radio based Temporal Transmission Protocol Multiple Channel (CRTTP MC) uses exponential distribution with inter-arrival time of packets with appropriate transmission power assigned to each channel. Cognitive Radio based Temporal Transmission Protocol Multiple Channel Collision Avoidance (CRTTP-MCCA) assigning hyper exponential distribution with inter arrival time of packets for optimizing the usage of lesser utilized channel. Comparison has been done with simulations for single channel protocols of CRTTP and multiple channel protocols of CRTTP.
{"title":"Resource Allocation and Information Exchange of Cognitive user Connectivity with Minimal Interference using Simulation Analysis","authors":"J. M. Sahayaraj, K. Gunasekaran, S. Verma, P. Ramesh, G. Murugesan","doi":"10.1109/I-SMAC55078.2022.9987352","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987352","url":null,"abstract":"The purpose of this research is to develop protocols for the underutilized channels of primary user usage with scant transmission and minimal interference associating with the secondary users. This has been achieved with large scale fading channels using discrete time queues. Flow level analysis has been made by appropriate queuing model and packet level analysis has done with NS2 simulator. CRTTP uses the channel selection procedure based on utilization, throughput and minimal drop rate whereas the response time denotes whether to increment or decrement transmission power control. Cognitive Radio based Temporal Transmission Protocol Single Channel (CRTTP-SC) denies transmission if sustainable routing parameter does not by cognitive user. Cognitive Radio based Temporal Transmission Protocol Single Channel Receiver Capacity (CRTTP-SCRC). CRTTP-SCRC protocol calculates the channel utilization, drop rate and receiver capacity after which it determines whether to prolong transmission or to refrain from transmission. Cognitive Radio based Temporal Transmission Protocol Multiple Channel (CRTTP MC) uses exponential distribution with inter-arrival time of packets with appropriate transmission power assigned to each channel. Cognitive Radio based Temporal Transmission Protocol Multiple Channel Collision Avoidance (CRTTP-MCCA) assigning hyper exponential distribution with inter arrival time of packets for optimizing the usage of lesser utilized channel. Comparison has been done with simulations for single channel protocols of CRTTP and multiple channel protocols of CRTTP.","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":"128002305","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-SMAC55078.2022.9987348
Yihang Wang
In order to comprehensively improve the overall quality of project construction, this article should combine the data analysis of the Internet of Thing, integrate various factors, and actively implement a complete supervision and control system to ensure that the comprehensive level of the project can meet expectations. Pay attention to the business structure and application structure, and give play to the advantages of the intelligent framework application system. Apply the theory of "top-level design" to establish an overall framework of intelligent top-level design of engineering construction quality management with "one platform, multiple systems, seamless, and all-round" as the core; from business architecture, data architecture, application architecture, technical architecture, and security The overall architecture is discussed in five aspects including architecture, with data standards as a starting point, management methods as a booster, and an intelligent platform as an implementation carrier to realize "automatic data collection and real-time upload."
{"title":"Construction of an Intelligent Platform based on the Perspective of IoT Data Analysis","authors":"Yihang Wang","doi":"10.1109/I-SMAC55078.2022.9987348","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987348","url":null,"abstract":"In order to comprehensively improve the overall quality of project construction, this article should combine the data analysis of the Internet of Thing, integrate various factors, and actively implement a complete supervision and control system to ensure that the comprehensive level of the project can meet expectations. Pay attention to the business structure and application structure, and give play to the advantages of the intelligent framework application system. Apply the theory of \"top-level design\" to establish an overall framework of intelligent top-level design of engineering construction quality management with \"one platform, multiple systems, seamless, and all-round\" as the core; from business architecture, data architecture, application architecture, technical architecture, and security The overall architecture is discussed in five aspects including architecture, with data standards as a starting point, management methods as a booster, and an intelligent platform as an implementation carrier to realize \"automatic data collection and real-time upload.\"","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":"133995814","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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