Pub Date : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284902
Anudeep Bonasu, S. Karmunchi, Nan Wang
The state of art Tomasulo's Algorithm is implemented in a quite different manner. It has multiple data bus lines for out-of-order execution to eliminate data hazards and even to minimize control and structural hazards at compile-time, that connects the reservation station to the execution units. And reservation station controls the instruction execution, is a decentralized scheduler a feature of CPU which allows register renaming to eliminate WAR/WAW hazards. But for this project with the use of multiple data bus lines will have multiple reservation stations to eliminate the structural and control hazards. Cache coherence protocol is about maintaining consistency among multiple local caches so that storing/fetching data will be easier in various cases for multiprocessor systems. So, this modified Tomasulo's algorithm is implemented in conjunction with caches for writing this result and to attain coherency.
{"title":"Design of Efficient Dynamic Scheduling of RISC Processor Instructions","authors":"Anudeep Bonasu, S. Karmunchi, Nan Wang","doi":"10.1109/IEMCON51383.2020.9284902","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284902","url":null,"abstract":"The state of art Tomasulo's Algorithm is implemented in a quite different manner. It has multiple data bus lines for out-of-order execution to eliminate data hazards and even to minimize control and structural hazards at compile-time, that connects the reservation station to the execution units. And reservation station controls the instruction execution, is a decentralized scheduler a feature of CPU which allows register renaming to eliminate WAR/WAW hazards. But for this project with the use of multiple data bus lines will have multiple reservation stations to eliminate the structural and control hazards. Cache coherence protocol is about maintaining consistency among multiple local caches so that storing/fetching data will be easier in various cases for multiprocessor systems. So, this modified Tomasulo's algorithm is implemented in conjunction with caches for writing this result and to attain coherency.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"43 1","pages":"0236-0240"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78705480","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284817
Sayan Seal, Aishee Sen, Ritodeep Mukerjee, A. Das
Conventionally, the process of taking attendance of students in a classroom is quite a laborious task, wherein either the teacher has to call out names of each individual student, or the student has to sign an attendance sheet. In recent times, due to the Covid-19 pandemic, special importance has been laid on facial recognition techniques, which are contact-free (unlike fingerprint scanners), and are in accordance with social distancing norms. In this paper, a software system automating the attendance-taking scheme is presented. This software integrates face detection, image processing and face recognition approaches to come up with a consolidated attendance system capable of overcoming the disadvantages of manual attendance. In the system, an end user has to first log in and subsequently, an IP camera (which is to be installed in the classroom) gets turned on, and the camera starts taking photographs of the classroom. The user can also manually upload images into the system, in case calculation of attendance is not immediately required. The Histogram of Oriented Gradients (HOG) approach is employed for the face detection mechanism in the proposed system. After a comparative performance analysis of different facial recognition techniques, the Local Binary Patterns Histograms (LBPH) method is chosen as the facial recognition procedure for the system. Once all the individual students have been recognised by comparison with the model built from the extracted faces, the final results are sorted according to date (similar to taking attendance of a class on a particular day) and stored in the database.
{"title":"An approach towards development of automated attendance system using face detection and recognition","authors":"Sayan Seal, Aishee Sen, Ritodeep Mukerjee, A. Das","doi":"10.1109/IEMCON51383.2020.9284817","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284817","url":null,"abstract":"Conventionally, the process of taking attendance of students in a classroom is quite a laborious task, wherein either the teacher has to call out names of each individual student, or the student has to sign an attendance sheet. In recent times, due to the Covid-19 pandemic, special importance has been laid on facial recognition techniques, which are contact-free (unlike fingerprint scanners), and are in accordance with social distancing norms. In this paper, a software system automating the attendance-taking scheme is presented. This software integrates face detection, image processing and face recognition approaches to come up with a consolidated attendance system capable of overcoming the disadvantages of manual attendance. In the system, an end user has to first log in and subsequently, an IP camera (which is to be installed in the classroom) gets turned on, and the camera starts taking photographs of the classroom. The user can also manually upload images into the system, in case calculation of attendance is not immediately required. The Histogram of Oriented Gradients (HOG) approach is employed for the face detection mechanism in the proposed system. After a comparative performance analysis of different facial recognition techniques, the Local Binary Patterns Histograms (LBPH) method is chosen as the facial recognition procedure for the system. Once all the individual students have been recognised by comparison with the model built from the extracted faces, the final results are sorted according to date (similar to taking attendance of a class on a particular day) and stored in the database.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"64 1","pages":"0333-0340"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86803602","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284932
M. W. Baidas
This paper considers the problem of offloading-efficiency maximization for a mobile edge computing (MEC) nonorthogonal multiple-access (NOMA) network with user clusters. The goal is to maximize network offloading-efficiency via joint power allocation, and computing resource allocation (J-PA-CRA), under the partial offloading mode, and subject to delay and transmit power constraints. However, the formulated problem happens to be non-convex, and thus is computationally-prohibitive. In turn, a low-complexity solution procedure is proposed, which optimally solves problem J-PA-CRA via successive convex approximation. Simulation results are provided to validate the proposed solution procedure, illustrating that it yields the optimal network offloading-efficiency, in comparison to the J-PA-CRA scheme (solved via a global optimization package), and superior to its OMA counterpart schemes (e.g. FDMA and TDMA).
{"title":"Offloading-Efficiency Maximization for Mobile Edge Computing in Clustered NOMA Networks","authors":"M. W. Baidas","doi":"10.1109/IEMCON51383.2020.9284932","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284932","url":null,"abstract":"This paper considers the problem of offloading-efficiency maximization for a mobile edge computing (MEC) nonorthogonal multiple-access (NOMA) network with user clusters. The goal is to maximize network offloading-efficiency via joint power allocation, and computing resource allocation (J-PA-CRA), under the partial offloading mode, and subject to delay and transmit power constraints. However, the formulated problem happens to be non-convex, and thus is computationally-prohibitive. In turn, a low-complexity solution procedure is proposed, which optimally solves problem J-PA-CRA via successive convex approximation. Simulation results are provided to validate the proposed solution procedure, illustrating that it yields the optimal network offloading-efficiency, in comparison to the J-PA-CRA scheme (solved via a global optimization package), and superior to its OMA counterpart schemes (e.g. FDMA and TDMA).","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"70 1","pages":"0101-0107"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83755324","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284950
B. Comar
This paper analyzes the performance of an LDPC code classification system that determines membership of code-words among 3 randomly generated binary LDPC codes. These codes all have the same codeword size and coderate. High classification accuracies are obtained with relatively small neural networks. The analysis presented here determines the accuracies of various look up tables and compares them to the performance of the neural networks.
{"title":"Analysis of CNN Based Schemes for LDPC Code Classification Using LUT Based Algorithms","authors":"B. Comar","doi":"10.1109/IEMCON51383.2020.9284950","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284950","url":null,"abstract":"This paper analyzes the performance of an LDPC code classification system that determines membership of code-words among 3 randomly generated binary LDPC codes. These codes all have the same codeword size and coderate. High classification accuracies are obtained with relatively small neural networks. The analysis presented here determines the accuracies of various look up tables and compares them to the performance of the neural networks.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"34 1","pages":"0492-0497"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86314543","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284866
B.T.N Perera, B. Jayarathne, T.G.G.M Dharmakeerthi, K.T.D.D.K Thanthilage, Y. Priyadarshana
21st century is far more advanced than the 20th century because of its new innovations along with the relevant technological mappings. Technology makes our day to day work easy. However, this has been led our simple life to be very complex. We have become really busy, money minded and most importantly we don't have time to spend with our families or thinking about ourselves. As Millennials form our childhood what we have experienced is the stress to be the best. The competition which has been generated around and among us cannot be handled; so, people have been depressed and this would let them even committing suicide. Therefore, a Learning Assistant for advanced level students, which has been named as “Shilpa”, would be a practical remedy and a companion to overcome such difficulties. Shilpa has been stepped forward to monitor students' stress levels and to help them understand their weak areas considering the curriculum of a particular subject. Once identifying a particularly weak area, Shilpa navigates the user to the summarized version of a weakly identified content of a lesson in which the user doesn't have to go through the entire course curriculum to improve his/her weak areas. The implemented system has been tested considering all the novel components, and an overall value of 0.81 has been experimented as per the precision. It can be concluded that this novel approach has achieved an overall 81% accuracy over the existing state-of-the-art baselines.
{"title":"Shilpa: A Novel Neural Based Approach for Measuring Human Stress Level","authors":"B.T.N Perera, B. Jayarathne, T.G.G.M Dharmakeerthi, K.T.D.D.K Thanthilage, Y. Priyadarshana","doi":"10.1109/IEMCON51383.2020.9284866","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284866","url":null,"abstract":"21st century is far more advanced than the 20th century because of its new innovations along with the relevant technological mappings. Technology makes our day to day work easy. However, this has been led our simple life to be very complex. We have become really busy, money minded and most importantly we don't have time to spend with our families or thinking about ourselves. As Millennials form our childhood what we have experienced is the stress to be the best. The competition which has been generated around and among us cannot be handled; so, people have been depressed and this would let them even committing suicide. Therefore, a Learning Assistant for advanced level students, which has been named as “Shilpa”, would be a practical remedy and a companion to overcome such difficulties. Shilpa has been stepped forward to monitor students' stress levels and to help them understand their weak areas considering the curriculum of a particular subject. Once identifying a particularly weak area, Shilpa navigates the user to the summarized version of a weakly identified content of a lesson in which the user doesn't have to go through the entire course curriculum to improve his/her weak areas. The implemented system has been tested considering all the novel components, and an overall value of 0.81 has been experimented as per the precision. It can be concluded that this novel approach has achieved an overall 81% accuracy over the existing state-of-the-art baselines.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"62 1","pages":"0308-0313"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83694809","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284943
Abhishek Maheshwari, U. Tuntoolavest, K. Fukawa
Since wireless communication systems supply a large amount of packet-sized (non-binary) data, a channel coding technique that can efficiently correct errors caused by channels is required. This paper focuses on systematic Low-Density Parity-Check (LDPC) code for non-binary codes with symbol size of at least 32 bits/symbol and investigates the transmission of non-binary data under different channel conditions through Message Passing Telemetry Transport (MQTT). Hard decision Message Passing-Vector Symbol Decoding (hMP-VSD) is used as a decoder of systematic LDPC. The encoder and decoder are implemented on the Raspberry Pi (R-pi) boards, and the wireless transmission is done by using the Wi-Fi module.
{"title":"Implementation of the Nonbinary Encoder and Decoder for Systematic Low Density Parity Check Codes on Raspberry-pi boards","authors":"Abhishek Maheshwari, U. Tuntoolavest, K. Fukawa","doi":"10.1109/IEMCON51383.2020.9284943","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284943","url":null,"abstract":"Since wireless communication systems supply a large amount of packet-sized (non-binary) data, a channel coding technique that can efficiently correct errors caused by channels is required. This paper focuses on systematic Low-Density Parity-Check (LDPC) code for non-binary codes with symbol size of at least 32 bits/symbol and investigates the transmission of non-binary data under different channel conditions through Message Passing Telemetry Transport (MQTT). Hard decision Message Passing-Vector Symbol Decoding (hMP-VSD) is used as a decoder of systematic LDPC. The encoder and decoder are implemented on the Raspberry Pi (R-pi) boards, and the wireless transmission is done by using the Wi-Fi module.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"50 1","pages":"0751-0756"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84267221","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284936
Reetodeep Hazra, Megha Banerjee, L. Badia
Breast cancer is one of the commonest cause of cancer deaths in women. It starts developing when threatening bumps start forming from the breast cells, and unfortunately most diagnoses happen in later stages, thus resulting in low chances of survival for the patient. So for early detection and prognosis, it is necessary to detect the benign or threatening nature of the bumps. In this paper, Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, in order to evaluate the attributes of a breast cancer development at beginning phases and classify it as malignant or benign. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%.
{"title":"Machine Learning for Breast Cancer Classification With ANN and Decision Tree","authors":"Reetodeep Hazra, Megha Banerjee, L. Badia","doi":"10.1109/IEMCON51383.2020.9284936","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284936","url":null,"abstract":"Breast cancer is one of the commonest cause of cancer deaths in women. It starts developing when threatening bumps start forming from the breast cells, and unfortunately most diagnoses happen in later stages, thus resulting in low chances of survival for the patient. So for early detection and prognosis, it is necessary to detect the benign or threatening nature of the bumps. In this paper, Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, in order to evaluate the attributes of a breast cancer development at beginning phases and classify it as malignant or benign. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"23 1","pages":"0522-0527"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83068999","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284923
Solomon Negussie Tesema, E. Bourennane
As much as an object detector should be accurate, it should be light and fast as well. However, current object detectors tend to be either inaccurate when lightweight or very slow and heavy when accurate. Accordingly, determining tolerable tradeoff between speed and accuracy of an object detector is not a simple task. One of the object detectors that have commendable balance of speed and accuracy is YOLOv2. YOLOv2 performs detection by dividing an input image into grids and training each grid cell to predict certain number of objects. In this paper we propose a new approach to even make YOLOv2 more fast and accurate. We re-purpose YOLOv2 into a dense object detector by using fine-grained grids, where a cell predicts only one object and its corresponding class and objectness confidence score. Our approach also trains the system to learn to pick a best fitting anchor box instead of the fixed anchor assignment during ground-truth annotation as used by YOLOv2. We will also introduce a new loss function to balance the overwhelming imbalance between the number of grids responsible of detecting an object and those that should not.
{"title":"DenseYOLO: Yet Faster, Lighter and More Accurate YOLO","authors":"Solomon Negussie Tesema, E. Bourennane","doi":"10.1109/IEMCON51383.2020.9284923","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284923","url":null,"abstract":"As much as an object detector should be accurate, it should be light and fast as well. However, current object detectors tend to be either inaccurate when lightweight or very slow and heavy when accurate. Accordingly, determining tolerable tradeoff between speed and accuracy of an object detector is not a simple task. One of the object detectors that have commendable balance of speed and accuracy is YOLOv2. YOLOv2 performs detection by dividing an input image into grids and training each grid cell to predict certain number of objects. In this paper we propose a new approach to even make YOLOv2 more fast and accurate. We re-purpose YOLOv2 into a dense object detector by using fine-grained grids, where a cell predicts only one object and its corresponding class and objectness confidence score. Our approach also trains the system to learn to pick a best fitting anchor box instead of the fixed anchor assignment during ground-truth annotation as used by YOLOv2. We will also introduce a new loss function to balance the overwhelming imbalance between the number of grids responsible of detecting an object and those that should not.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"57 1","pages":"0534-0539"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78270553","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284894
E. Ursini, Henry de Castro Lobo dos Santos, M. Okano
Due to pandemic Covid-19, which suddenly forced people to change their habits and stay in their homes for several weeks, the daily routines changed, people could no longer go to work or study, physical contact should be avoided, care with personal hygiene improved and all types of crowding avoided. This causes “home office” work to skyrocket and reach significant peaks. In this way, the demand for services related to Information and Communication Technology, ICT, has grown greatly. To manage the problems caused by the lack of resources needed to transport traffic on the network, SLA (Service Level Agreement) contracts are common, which the parties involved sign (the provider and the customer). Failure to comply with these contracts may result in a fine for the party that has not fulfilled it. This work proposes an approach to improve the dimensioning of Firewalls, in terms of their availability, to establish values as close as possible to the real ones so that there is neither an underestimation nor an overestimation of commitments agreed between the actors. In addition, this work proposes a way to approach this problem in a broader way, taking into account the Dependability, that is, Availability, Reliability and Maintainability.
{"title":"Improving the Availability of Firewalls with a View to Increasing ICT Consumption Due Covid-19","authors":"E. Ursini, Henry de Castro Lobo dos Santos, M. Okano","doi":"10.1109/IEMCON51383.2020.9284894","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284894","url":null,"abstract":"Due to pandemic Covid-19, which suddenly forced people to change their habits and stay in their homes for several weeks, the daily routines changed, people could no longer go to work or study, physical contact should be avoided, care with personal hygiene improved and all types of crowding avoided. This causes “home office” work to skyrocket and reach significant peaks. In this way, the demand for services related to Information and Communication Technology, ICT, has grown greatly. To manage the problems caused by the lack of resources needed to transport traffic on the network, SLA (Service Level Agreement) contracts are common, which the parties involved sign (the provider and the customer). Failure to comply with these contracts may result in a fine for the party that has not fulfilled it. This work proposes an approach to improve the dimensioning of Firewalls, in terms of their availability, to establish values as close as possible to the real ones so that there is neither an underestimation nor an overestimation of commitments agreed between the actors. In addition, this work proposes a way to approach this problem in a broader way, taking into account the Dependability, that is, Availability, Reliability and Maintainability.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"3 1","pages":"0647-0653"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81788126","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284890
Shashank P. Kumar, A. Pundir
A supply chain is getting an advantage from the technology's lucidity, less transaction expenses, and real-time data applications. Internet of Things (IoT) and Blockchain technology (BT) has gain remarkable attention by academician and industrial practitioner in recent years as these pair is disrupting a lot of businesses now. BT enables transactional security while IoT connects the physical and digital world with apps and sensors. In this study, a systematic literature review (SLR) is performed to explore the application of integrated IoT-BT in the supply chain and its implementation challenges. The different technologies listed are grouped into the supply chain's five functional fields i.e., responsiveness, intelligence, deductive, surveillance and operational excellence. The result reveals that IoT-BT is mostly used for surveillance purposes and to achieve operational excellence.
{"title":"Integration of IoT and Blockchain Technology for Enhancing Supply Chain Performance: A Review","authors":"Shashank P. Kumar, A. Pundir","doi":"10.1109/IEMCON51383.2020.9284890","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284890","url":null,"abstract":"A supply chain is getting an advantage from the technology's lucidity, less transaction expenses, and real-time data applications. Internet of Things (IoT) and Blockchain technology (BT) has gain remarkable attention by academician and industrial practitioner in recent years as these pair is disrupting a lot of businesses now. BT enables transactional security while IoT connects the physical and digital world with apps and sensors. In this study, a systematic literature review (SLR) is performed to explore the application of integrated IoT-BT in the supply chain and its implementation challenges. The different technologies listed are grouped into the supply chain's five functional fields i.e., responsiveness, intelligence, deductive, surveillance and operational excellence. The result reveals that IoT-BT is mostly used for surveillance purposes and to achieve operational excellence.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"8 1","pages":"0396-0401"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89197229","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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