Pub Date : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9334008
Youbin Jeon, Hosung Baek, Sangheon Pack
To cope with limited capabilities of mobile devices, task offloading in distributed edge computing (DEC) environments is perceived as a promising solution. However, the mobility of devices makes the task offloading a more challenging issue. In this paper, we investigate mobility-awareness for optimal task offloading in DEC environments. To this end, we formulate an optimization problem to minimize the response time of offloaded tasks. Simulation results demonstrate that the mobility-aware task offloading scheme can reduce the response time by 14% $sim 21$% compared with the conventional task offloading schemes without any mobility-awareness.
{"title":"Mobility-Aware Optimal Task Offloading in Distributed Edge Computing","authors":"Youbin Jeon, Hosung Baek, Sangheon Pack","doi":"10.1109/ICOIN50884.2021.9334008","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9334008","url":null,"abstract":"To cope with limited capabilities of mobile devices, task offloading in distributed edge computing (DEC) environments is perceived as a promising solution. However, the mobility of devices makes the task offloading a more challenging issue. In this paper, we investigate mobility-awareness for optimal task offloading in DEC environments. To this end, we formulate an optimization problem to minimize the response time of offloaded tasks. Simulation results demonstrate that the mobility-aware task offloading scheme can reduce the response time by 14% $sim 21$% compared with the conventional task offloading schemes without any mobility-awareness.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"60 1","pages":"65-68"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83592095","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 : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9334021
Rahul Kumar, Ankur Gupta, Harkirat Singh Arora, B. Raman
Gliomas originates in glial cells and recognized as one of the most malignant and dangerous brain tumors and categories into two major classes i.e., High Grade Glioma (HGG) and Low Grade Glioma (LGG). Out of both, HGG tumors are more aggressive. Classification of grade of glioma is a crucial task for deciding the treatment therapy and estimating survival period of patient. In this work, a computational approach based on Radiomics and machine learning algorithms, namely GRGE, is proposed to discriminate between HGG and LGG. The approach, GRGE, has performed better than several state-of-art methods proposed in the literature for glioma classification.
{"title":"GRGE: Detection of Gliomas Using Radiomics, GA Features and Extremely Randomized Trees","authors":"Rahul Kumar, Ankur Gupta, Harkirat Singh Arora, B. Raman","doi":"10.1109/ICOIN50884.2021.9334021","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9334021","url":null,"abstract":"Gliomas originates in glial cells and recognized as one of the most malignant and dangerous brain tumors and categories into two major classes i.e., High Grade Glioma (HGG) and Low Grade Glioma (LGG). Out of both, HGG tumors are more aggressive. Classification of grade of glioma is a crucial task for deciding the treatment therapy and estimating survival period of patient. In this work, a computational approach based on Radiomics and machine learning algorithms, namely GRGE, is proposed to discriminate between HGG and LGG. The approach, GRGE, has performed better than several state-of-art methods proposed in the literature for glioma classification.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"20 1","pages":"379-384"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84445942","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 : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9333876
Su-keun Kwak, Joohyung Lee
As the development of smart cities, energy management systems have been changing from centralized management systems to distributed energy management systems for better energy efficiency. In the distributed energy management systems, while producing the energy from distributed users, there can be two types of users such that 1) users who have surplus energy generations and 2) users who lack energy generations compared to their demands. In this paper, to alleviate such imbalance of energy generation between distributed users, a peer to peer (P2P) based energy trading platform is proposed. Specifically, blockchain is one of emerging solutions in which transactions can be made reliably to achieve P2P transactions without any centralized broker intervention. Correspondingly, we implement the P2P energy trading platform under Ethereum’s smart contract for reliable trading. The energy generated from distributed users at the proposed platform can be traded by utilizing the characteristics of Decentralize Application. Specifically, we provide the details of implementations of the proposed platform, which includes both hardware platform and software platform. Further, we establish a web page and an mobile application for monitoring the transaction information such as transaction details and energy prices, which can enhance users’ accessibility. Finally, the demonstration of process of energy trading via web interface is represented.
{"title":"Implementation of Blockchain based P2P Energy Trading Platform","authors":"Su-keun Kwak, Joohyung Lee","doi":"10.1109/ICOIN50884.2021.9333876","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333876","url":null,"abstract":"As the development of smart cities, energy management systems have been changing from centralized management systems to distributed energy management systems for better energy efficiency. In the distributed energy management systems, while producing the energy from distributed users, there can be two types of users such that 1) users who have surplus energy generations and 2) users who lack energy generations compared to their demands. In this paper, to alleviate such imbalance of energy generation between distributed users, a peer to peer (P2P) based energy trading platform is proposed. Specifically, blockchain is one of emerging solutions in which transactions can be made reliably to achieve P2P transactions without any centralized broker intervention. Correspondingly, we implement the P2P energy trading platform under Ethereum’s smart contract for reliable trading. The energy generated from distributed users at the proposed platform can be traded by utilizing the characteristics of Decentralize Application. Specifically, we provide the details of implementations of the proposed platform, which includes both hardware platform and software platform. Further, we establish a web page and an mobile application for monitoring the transaction information such as transaction details and energy prices, which can enhance users’ accessibility. Finally, the demonstration of process of energy trading via web interface is represented.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"70 1","pages":"5-7"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83734944","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 : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9333867
Ashar Ahmad, Muhammad Saad, Joongheon Kim, Daehun Nyang, David A. Mohaisen
Blockchain-based audit systems use “Practical Byzantine Fault Tolerance” (PBFT) consensus protocol which suffers from a high message complexity and low scalability. Alternatives to PBFT have not been tested in blockchain-based audit systems since no blockchain testbed supports the execution and benchmarking of different consensus protocols in a unified testing environment. In this paper, we address this gap by developing a blockchain testbed capable of executing and testing five consensus protocols in a blockchain network; namely PBFT, Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Elapsed Time (PoET), and Clique. We carry out performance evaluation of those consensus algorithms using data from a real-world audit system. Our results show that the Clique protocol is best suited for blockchain-based audit systems, based on scalability features.
{"title":"Performance Evaluation of Consensus Protocols in Blockchain-based Audit Systems","authors":"Ashar Ahmad, Muhammad Saad, Joongheon Kim, Daehun Nyang, David A. Mohaisen","doi":"10.1109/ICOIN50884.2021.9333867","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333867","url":null,"abstract":"Blockchain-based audit systems use “Practical Byzantine Fault Tolerance” (PBFT) consensus protocol which suffers from a high message complexity and low scalability. Alternatives to PBFT have not been tested in blockchain-based audit systems since no blockchain testbed supports the execution and benchmarking of different consensus protocols in a unified testing environment. In this paper, we address this gap by developing a blockchain testbed capable of executing and testing five consensus protocols in a blockchain network; namely PBFT, Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Elapsed Time (PoET), and Clique. We carry out performance evaluation of those consensus algorithms using data from a real-world audit system. Our results show that the Clique protocol is best suited for blockchain-based audit systems, based on scalability features.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"49 1","pages":"654-656"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87612612","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 : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9333957
Chalee Thammarat, Chian Techapanupreeda
Mobile payment protocols have attracted widespread attention over the past decade, due to advancements in digital technology. The use of these protocols in online industries can dramatically improve the quality of online services. However, the central issue of concern when utilizing these types of systems is their accountability, which ensures trust between the parties involved in payment transactions. It is, therefore, vital for researchers to investigate how to handle the accountability of mobile payment protocols. In this research, we introduce a secure mobile payment protocol to overcome this problem. Our payment protocol combines all the necessary security features, such as confidentiality, integrity, authentication, and authorization that are required to build trust among parties. In other words, is the properties of mutual authentication and non-repudiation are ensured, thus providing accountability. Our approach can resolve any conflicts that may arise in payment transactions between parties. To prove that the proposed protocol is correct and complete, we use the Scyther and AVISPA tools to verify our approach formally.
{"title":"A Secure Mobile Payment Protocol for Handling Accountability with Formal Verification","authors":"Chalee Thammarat, Chian Techapanupreeda","doi":"10.1109/ICOIN50884.2021.9333957","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333957","url":null,"abstract":"Mobile payment protocols have attracted widespread attention over the past decade, due to advancements in digital technology. The use of these protocols in online industries can dramatically improve the quality of online services. However, the central issue of concern when utilizing these types of systems is their accountability, which ensures trust between the parties involved in payment transactions. It is, therefore, vital for researchers to investigate how to handle the accountability of mobile payment protocols. In this research, we introduce a secure mobile payment protocol to overcome this problem. Our payment protocol combines all the necessary security features, such as confidentiality, integrity, authentication, and authorization that are required to build trust among parties. In other words, is the properties of mutual authentication and non-repudiation are ensured, thus providing accountability. Our approach can resolve any conflicts that may arise in payment transactions between parties. To prove that the proposed protocol is correct and complete, we use the Scyther and AVISPA tools to verify our approach formally.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"25 1","pages":"249-254"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80000002","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 : 2021-01-13DOI: 10.1109/icoin50884.2021.9333887
{"title":"ICOIN 2021 Front Matter","authors":"","doi":"10.1109/icoin50884.2021.9333887","DOIUrl":"https://doi.org/10.1109/icoin50884.2021.9333887","url":null,"abstract":"","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79399566","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 : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9333895
Soyi Jung, Won Joon Yun, Joongheon Kim, Jae-Hyun Kim
This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big-data processing in a distributed manner. In order to realize UAV-based aerial surveillance or mobile cellular services, seamless and robust wireless charging mechanisms are required for delivering energy sources from charging infrastructure (i.e., charging towers) to UAVs for the consistent operations of the UAVs in the sky. For actively and intelligently managing the charging towers, MADRL-based energy management system (EMS) is proposed and designed for energy trading among the energy storage systems those are equipped with charging towers. If the required energy for charging UAVs is not enough, the purchasing energy from utility company is desired which takes high consts. The main purpose of MADRL-based EMS learning is for minimizing purchasing energy from outside utility company for minimizing operational costs. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.
{"title":"Infrastructure-Assisted Cooperative Multi-UAV Deep Reinforcement Energy Trading Learning for Big-Data Processing","authors":"Soyi Jung, Won Joon Yun, Joongheon Kim, Jae-Hyun Kim","doi":"10.1109/ICOIN50884.2021.9333895","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333895","url":null,"abstract":"This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big-data processing in a distributed manner. In order to realize UAV-based aerial surveillance or mobile cellular services, seamless and robust wireless charging mechanisms are required for delivering energy sources from charging infrastructure (i.e., charging towers) to UAVs for the consistent operations of the UAVs in the sky. For actively and intelligently managing the charging towers, MADRL-based energy management system (EMS) is proposed and designed for energy trading among the energy storage systems those are equipped with charging towers. If the required energy for charging UAVs is not enough, the purchasing energy from utility company is desired which takes high consts. The main purpose of MADRL-based EMS learning is for minimizing purchasing energy from outside utility company for minimizing operational costs. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"99 1","pages":"159-162"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79486441","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 : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9334012
Cong Tran, Won-Yong Shin
Due to practical reasons such as limited resources and privacy settings specified by users on social media, most network data tend to be only partially observed with both missing nodes and missing edges. Thus, it is of paramount importance to infer the missing parts of the networks since incomplete network data may severely degrade the performance of downstream analyses. In this paper, we provide a comprehensive survey on network completion, which is a more challenging task than the well-studied low-rank matrix completion problem in the sense that a row and a column of an adjacency matrix shall be entirely unobservable when a node is completely missing from the given network. Specifically, we first define the problem of network completion. Then, we review two state-of-the-art algorithms for discovering the missing part of an underlying network, namely KronEM and DeepNC. We also show a performance comparison between the two algorithms via experimental evaluation. Finally, we discuss the potentials and limitations of the two algorithms.
{"title":"Network Completion: Beyond Matrix Completion","authors":"Cong Tran, Won-Yong Shin","doi":"10.1109/ICOIN50884.2021.9334012","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9334012","url":null,"abstract":"Due to practical reasons such as limited resources and privacy settings specified by users on social media, most network data tend to be only partially observed with both missing nodes and missing edges. Thus, it is of paramount importance to infer the missing parts of the networks since incomplete network data may severely degrade the performance of downstream analyses. In this paper, we provide a comprehensive survey on network completion, which is a more challenging task than the well-studied low-rank matrix completion problem in the sense that a row and a column of an adjacency matrix shall be entirely unobservable when a node is completely missing from the given network. Specifically, we first define the problem of network completion. Then, we review two state-of-the-art algorithms for discovering the missing part of an underlying network, namely KronEM and DeepNC. We also show a performance comparison between the two algorithms via experimental evaluation. Finally, we discuss the potentials and limitations of the two algorithms.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"136 1","pages":"667-670"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75778800","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 : 2021-01-13DOI: 10.1109/ICOIN50884.2021.9333906
Seunghyeok Oh, Jaeho Choi, Jong-Kook Kim, Joongheon Kim
Convolutional Neural Network (CNN) is a breakthrough learning model that shows outstanding performance in computer vision and deep learning applications. However, it is a relatively burdened model in terms of learning speed and resource usage compared to other learning models when the learning scale becomes large. Quantum Convolutional Neural Network (QCNN) is a novel model as a potential solution using quantum computers to handle this problem. Quantum computers with a limited number of usable qubits needs a resource-efficient method to process large-scale data at once. In addition, Quantum Random Access Memory (QRAM) can store the large data to qubits logarithmically using superposition and entanglement. The QRAM algorithm can design a new QCNN model that can efficiently process in massive data. This paper proposes a more resource and depth efficient model for larger-sized input data and the number of output channels using the QRAM algorithm and efficiently extracting features.
{"title":"Quantum Convolutional Neural Network for Resource-Efficient Image Classification: A Quantum Random Access Memory (QRAM) Approach","authors":"Seunghyeok Oh, Jaeho Choi, Jong-Kook Kim, Joongheon Kim","doi":"10.1109/ICOIN50884.2021.9333906","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333906","url":null,"abstract":"Convolutional Neural Network (CNN) is a breakthrough learning model that shows outstanding performance in computer vision and deep learning applications. However, it is a relatively burdened model in terms of learning speed and resource usage compared to other learning models when the learning scale becomes large. Quantum Convolutional Neural Network (QCNN) is a novel model as a potential solution using quantum computers to handle this problem. Quantum computers with a limited number of usable qubits needs a resource-efficient method to process large-scale data at once. In addition, Quantum Random Access Memory (QRAM) can store the large data to qubits logarithmically using superposition and entanglement. The QRAM algorithm can design a new QCNN model that can efficiently process in massive data. This paper proposes a more resource and depth efficient model for larger-sized input data and the number of output channels using the QRAM algorithm and efficiently extracting features.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"147 1","pages":"50-52"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91448407","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 : 2021-01-13DOI: 10.1109/icoin50884.2021.9334031
{"title":"ICOIN 2021 Organizing Committee Memberse","authors":"","doi":"10.1109/icoin50884.2021.9334031","DOIUrl":"https://doi.org/10.1109/icoin50884.2021.9334031","url":null,"abstract":"","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73456287","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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