In order to alleviate the traffic burden, there has been researching on helper equipments (HEs) in wireless networks. In this paper, a novel caching scheme with HEs in Device-to-Device (D2D) communication is studied. The cost of the system is formulated to find the optimal number of HEs in D2D communication, in which the impact of mobility, cache size, number of devices are all taken into account. Due to the fact that with the increase of the number of HEs, the D2D transmission coverage and the system cost increases accordingly, we model the cost function and prove that it is a convex problem. By minimizing the cost, the optimal number of HEs is found out. We apply the optimal number of HEs to existing caching strategies, then can prove that the system performance will become better. Finally, the simulation shows that the mobility of HEs can extend D2D transmission coverage and especially improve the offloading performance. In addition, by applying the optimal number of HEs, different caching strategies can achieve higher cache hit rate at the minimum cost.
{"title":"Analysis on Caching Strategy for Device-to-Device Communication with Multiple Helpers","authors":"Hui Song, Qunying Wu, Zhikai Liu, Feng Ke, Daru Pan, Xian Zhou","doi":"10.1109/SmartIoT49966.2020.00030","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00030","url":null,"abstract":"In order to alleviate the traffic burden, there has been researching on helper equipments (HEs) in wireless networks. In this paper, a novel caching scheme with HEs in Device-to-Device (D2D) communication is studied. The cost of the system is formulated to find the optimal number of HEs in D2D communication, in which the impact of mobility, cache size, number of devices are all taken into account. Due to the fact that with the increase of the number of HEs, the D2D transmission coverage and the system cost increases accordingly, we model the cost function and prove that it is a convex problem. By minimizing the cost, the optimal number of HEs is found out. We apply the optimal number of HEs to existing caching strategies, then can prove that the system performance will become better. Finally, the simulation shows that the mobility of HEs can extend D2D transmission coverage and especially improve the offloading performance. In addition, by applying the optimal number of HEs, different caching strategies can achieve higher cache hit rate at the minimum cost.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129996725","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00036
Ze Xu, Sanxing Cao
In the Internet era, electronic voting has replaced traditional paper voting forms with advantages of low cost, high work efficiency, and low errors. A blockchain as a trustworthy and secure decentralized and distributed network provides new ideas for electronic voting schemes. The paper proposes an electronic voting scheme based on a distributed SM2 encryption scheme with differential privacy mechanism, designing and implementing smart contracts based on the proposed secure voting scheme. The performance of the proposed voting scheme is tested on the Ethereum’s private chain, and its test contents include the computing cost and network consumption of key methods. The test results show that the blockchain-based voting scheme designed in this paper has good performance, and it also proves the feasibility and correctness of the voting scheme.
{"title":"Efficient Privacy-Preserving Electronic Voting Scheme Based on Blockchain","authors":"Ze Xu, Sanxing Cao","doi":"10.1109/SmartIoT49966.2020.00036","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00036","url":null,"abstract":"In the Internet era, electronic voting has replaced traditional paper voting forms with advantages of low cost, high work efficiency, and low errors. A blockchain as a trustworthy and secure decentralized and distributed network provides new ideas for electronic voting schemes. The paper proposes an electronic voting scheme based on a distributed SM2 encryption scheme with differential privacy mechanism, designing and implementing smart contracts based on the proposed secure voting scheme. The performance of the proposed voting scheme is tested on the Ethereum’s private chain, and its test contents include the computing cost and network consumption of key methods. The test results show that the blockchain-based voting scheme designed in this paper has good performance, and it also proves the feasibility and correctness of the voting scheme.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124068783","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00062
Shaojun Jiang, Yilin He
We developed a low power monitor for medical drip infusion monitoring system, it can operate for a year, and longer than other designs. The monitoring system consists of many drip infusion monitoring nodes and a monitoring center, and transmits the message by the Zigbee wireless sensor network. The capacitive sensor in the monitor node differentiates that air and water are different dielectric material, and has a feature of low power consumption. The status of the drip infusion tube is detected by the capacitive sensor, the monitor transmits the alarm message to the monitoring center when there is no water in the drip infusion tube, the nurses can handle quickly the drip infusion task.
{"title":"A low power circuit for medical drip infusion monitoring system","authors":"Shaojun Jiang, Yilin He","doi":"10.1109/SmartIoT49966.2020.00062","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00062","url":null,"abstract":"We developed a low power monitor for medical drip infusion monitoring system, it can operate for a year, and longer than other designs. The monitoring system consists of many drip infusion monitoring nodes and a monitoring center, and transmits the message by the Zigbee wireless sensor network. The capacitive sensor in the monitor node differentiates that air and water are different dielectric material, and has a feature of low power consumption. The status of the drip infusion tube is detected by the capacitive sensor, the monitor transmits the alarm message to the monitoring center when there is no water in the drip infusion tube, the nurses can handle quickly the drip infusion task.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758382","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00054
Juan Wen, Zhiyong Yang, Lei Jin
With the rapid population ageing and increase of the elderly who live alone, there is a growing demand for intelligent monitoring, especially fall detection systems. In this paper, based on received signal strength (RSS) and machine learning algorithm, a fall detection method is proposed. It using multi-domain features, including time-domain and wavelet-domain, and Boost algorithm trains a model to discriminate fall and other actions, such as, sit, stand and squat. The experimental results show that the proposed method can identify falls well.
{"title":"Wireless Signal Based Elderly Fall Detection Using XGboost Algorithm","authors":"Juan Wen, Zhiyong Yang, Lei Jin","doi":"10.1109/SmartIoT49966.2020.00054","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00054","url":null,"abstract":"With the rapid population ageing and increase of the elderly who live alone, there is a growing demand for intelligent monitoring, especially fall detection systems. In this paper, based on received signal strength (RSS) and machine learning algorithm, a fall detection method is proposed. It using multi-domain features, including time-domain and wavelet-domain, and Boost algorithm trains a model to discriminate fall and other actions, such as, sit, stand and squat. The experimental results show that the proposed method can identify falls well.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"347 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134298463","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00034
Shihong Gao
Neural network pruning techniques have been widely used due to their little deterioration to test set accuracy while removing a great amount of weights in a network. Recent research [1] has shown that pruning impacts classification of classes and images differently even in one task. In this paper, we dive more along this line and find that different kinds of pruning methods will have different influences on classes and images, but pruning methods belonging to the same family will have a similar influence. Specifically, using iterative L1 unstructured pruning gets the least deviation for classes' accuracy from the overall accuracy and structured pruning is more likely to lead to high deviation. These findings show that choice of pruning methods can be quite nuanced and should be treated cautiously before it is used in sensitive domains.
{"title":"A Discover of Class and Image Level Variance Between Different Pruning Methods on Convolutional Neural Networks","authors":"Shihong Gao","doi":"10.1109/SmartIoT49966.2020.00034","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00034","url":null,"abstract":"Neural network pruning techniques have been widely used due to their little deterioration to test set accuracy while removing a great amount of weights in a network. Recent research [1] has shown that pruning impacts classification of classes and images differently even in one task. In this paper, we dive more along this line and find that different kinds of pruning methods will have different influences on classes and images, but pruning methods belonging to the same family will have a similar influence. Specifically, using iterative L1 unstructured pruning gets the least deviation for classes' accuracy from the overall accuracy and structured pruning is more likely to lead to high deviation. These findings show that choice of pruning methods can be quite nuanced and should be treated cautiously before it is used in sensitive domains.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463201","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00013
Haoshi Ren, Lihai Nie, Hongyun Gao, Laiping Zhao, J. Diao
In modern data center networks (DCNs), failures of network devices always occur and it is difficult to localize these failures. Our key observation is that latency data can reflect and profile network status. We can use this information to resolve issues like network failure localization.In this paper, we present NetCruiser, a system that is able to localize failures by learning from latency data. It can both measure and collect latency data to monitor the status of the whole network and pinpoint which switch or router encounters a failure. And we design a data structure to handle these latency data. With the construction of this data structure, we build a machine learning model to infer where issue occurs. Therefore, by the usage of this system, it answers the question about which switch encounters a failure in network. Our experimental evaluation has validated both the efficiency and effectiveness of our approach. Our system can be widely applied to both inter-DC network and intra-DC network.
{"title":"NetCruiser: Localize Network Failures by Learning from Latency Data","authors":"Haoshi Ren, Lihai Nie, Hongyun Gao, Laiping Zhao, J. Diao","doi":"10.1109/SmartIoT49966.2020.00013","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00013","url":null,"abstract":"In modern data center networks (DCNs), failures of network devices always occur and it is difficult to localize these failures. Our key observation is that latency data can reflect and profile network status. We can use this information to resolve issues like network failure localization.In this paper, we present NetCruiser, a system that is able to localize failures by learning from latency data. It can both measure and collect latency data to monitor the status of the whole network and pinpoint which switch or router encounters a failure. And we design a data structure to handle these latency data. With the construction of this data structure, we build a machine learning model to infer where issue occurs. Therefore, by the usage of this system, it answers the question about which switch encounters a failure in network. Our experimental evaluation has validated both the efficiency and effectiveness of our approach. Our system can be widely applied to both inter-DC network and intra-DC network.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803053","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00042
Stavros A. Viktoros, M. Michael, M. Polycarpou
The recent advances in the area of Internet-of-Things (IoT) have allowed for the implementation of complex large-scale Cyber-Physical Systems (CPSs). This phenomenon calls for efficient and scalable solutions for the new challenges being introduced. Sensor fault diagnosis has emerged as a priority in various IoT-enabled CPSs, especially for critical infrastructure applications where multiple IoT devices might be in use. In this work, we examine the problem of building a compact fault dictionary which allows for efficient real-time model-based multiple sensor fault detection and isolation. The problem under consideration is formulated as a combinatorial set problem and then efficiently encoded using Zero-suppressed binary Decision Diagrams (ZDDs), which are specialized data structures based on Boolean theory. The proposed approach is highly scalable with respect to the total number of sensor fault scenarios considered. Using the respective ZDD as a fault dictionary reduces the memory requirements by several orders of magnitude when compared to the conventional approach. This is achieved while allowing the fault isolation process to occur in linear time to the size of the dictionary. Our experimental results show that it takes between 0.002s to 0.012s for performing the fault isolation process in the range of tested systems.
{"title":"Compact Fault Dictionaries for Efficient Sensor Fault Diagnosis in IoT-enabled CPSs","authors":"Stavros A. Viktoros, M. Michael, M. Polycarpou","doi":"10.1109/SmartIoT49966.2020.00042","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00042","url":null,"abstract":"The recent advances in the area of Internet-of-Things (IoT) have allowed for the implementation of complex large-scale Cyber-Physical Systems (CPSs). This phenomenon calls for efficient and scalable solutions for the new challenges being introduced. Sensor fault diagnosis has emerged as a priority in various IoT-enabled CPSs, especially for critical infrastructure applications where multiple IoT devices might be in use. In this work, we examine the problem of building a compact fault dictionary which allows for efficient real-time model-based multiple sensor fault detection and isolation. The problem under consideration is formulated as a combinatorial set problem and then efficiently encoded using Zero-suppressed binary Decision Diagrams (ZDDs), which are specialized data structures based on Boolean theory. The proposed approach is highly scalable with respect to the total number of sensor fault scenarios considered. Using the respective ZDD as a fault dictionary reduces the memory requirements by several orders of magnitude when compared to the conventional approach. This is achieved while allowing the fault isolation process to occur in linear time to the size of the dictionary. Our experimental results show that it takes between 0.002s to 0.012s for performing the fault isolation process in the range of tested systems.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116171484","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00032
Zhicheng Li, Lixin Ji, Shuxin Liu, Jinsong Li
Link prediction, which utilizes the information of endpoint and network structure to predict the unknown links between two nodes, has attracted much attention in recent years. The network topological attributes contain the structure attributes and node attributes. However, some existing methods focus on the node attributes, while others focus on the structure attributes. To solve this problem, we propose a prediction method based on attributes fusion which combines node attributes and structure attributes. In our proposed method, we first analyze the structural attributes based on common neighbors in directed networks and define the structural attribute similarity. Then the similarity contribution of the influence of the common neighbors to the predicted nonadjacent nodes is analyzed. Experimental results on 9 directed networks show that our proposed index achieves higher performance than existing mainstream baselines under the precision evaluation.
{"title":"A New Link Prediction in Directed Networks Based on Attributes Fusion","authors":"Zhicheng Li, Lixin Ji, Shuxin Liu, Jinsong Li","doi":"10.1109/SmartIoT49966.2020.00032","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00032","url":null,"abstract":"Link prediction, which utilizes the information of endpoint and network structure to predict the unknown links between two nodes, has attracted much attention in recent years. The network topological attributes contain the structure attributes and node attributes. However, some existing methods focus on the node attributes, while others focus on the structure attributes. To solve this problem, we propose a prediction method based on attributes fusion which combines node attributes and structure attributes. In our proposed method, we first analyze the structural attributes based on common neighbors in directed networks and define the structural attribute similarity. Then the similarity contribution of the influence of the common neighbors to the predicted nonadjacent nodes is analyzed. Experimental results on 9 directed networks show that our proposed index achieves higher performance than existing mainstream baselines under the precision evaluation.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123694948","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00019
S. Sicari, A. Rizzardi, A. Coen-Porisini
People are increasingly surrounded by a connected world, where they can gather and share information everywhere, at anytime, and by means of a variety of devices, belonging to the so-called Internet of Things (IoT) network. IoT technologies and applications are spreading in different scenarios, ranging from every-day life activities to business ones. The presence of a huge amount of data, continuously transmitted over the network, brings relevant issues in terms of scalability. Hence, a proper network infrastructure must be put in action, in order to efficiently manage the information. A middleware layer could be a potential solution for overcoming such an issue, and to cope with interoperability. In literature, many architectures have been proposed in the last years, but little attention has been paid to how to decentralize as much as possible all the network’s components and tasks, in order to cover a wider area, avoiding single points of failure, while guaranteeing efficiency. Moreover, the interest of different stakeholders is not adequately considered yet. In this sense, fog computing represents a viable approach, which is adopted in this paper for the realization of a highly distributed and security-aware IoT middleware, aimed at operating without the need of a central coordinating unit and at allowing the participation of multiple stakeholders in the same IoT infrastructure. The proposed solution exploits the functionalities provided by the MQTT protocol, and its potentialities, besides the architectural features, are evaluated by means of a simple yet real test-bed, in terms of computing effort and latency.
{"title":"Increasing the pervasiveness of the IoT: fog computing coupled with pub&sub and security","authors":"S. Sicari, A. Rizzardi, A. Coen-Porisini","doi":"10.1109/SmartIoT49966.2020.00019","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00019","url":null,"abstract":"People are increasingly surrounded by a connected world, where they can gather and share information everywhere, at anytime, and by means of a variety of devices, belonging to the so-called Internet of Things (IoT) network. IoT technologies and applications are spreading in different scenarios, ranging from every-day life activities to business ones. The presence of a huge amount of data, continuously transmitted over the network, brings relevant issues in terms of scalability. Hence, a proper network infrastructure must be put in action, in order to efficiently manage the information. A middleware layer could be a potential solution for overcoming such an issue, and to cope with interoperability. In literature, many architectures have been proposed in the last years, but little attention has been paid to how to decentralize as much as possible all the network’s components and tasks, in order to cover a wider area, avoiding single points of failure, while guaranteeing efficiency. Moreover, the interest of different stakeholders is not adequately considered yet. In this sense, fog computing represents a viable approach, which is adopted in this paper for the realization of a highly distributed and security-aware IoT middleware, aimed at operating without the need of a central coordinating unit and at allowing the participation of multiple stakeholders in the same IoT infrastructure. The proposed solution exploits the functionalities provided by the MQTT protocol, and its potentialities, besides the architectural features, are evaluated by means of a simple yet real test-bed, in terms of computing effort and latency.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130449601","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00012
Muhammad Luthfi, S. Goto, Osamu Ytshi
Consensus building process for enterprise digital transformation is a significant approach on the implementation of Internet of Things (IoT) solutions through product lifecycle management (PLM). When we improve the consensus building process, it is important to find any latent opinions and hidden dialog patterns analyzing discussion activities by stakeholders. Several approaches have been proposed in forms of instructions and frameworks such as causal model of Consensus Building Theory (CBT) and short-term intensive workshop in strategy planning phase of Product Lifecycle Management (PLM) process. This paper will analyze a new approach to improve consensus building process by summarizing discussion activity. The proposed method is done by performing data augmentation and topic modeling with the help of background knowledge on discussion activity held within industrial engineering context. Our method produces a complete summarization of discussion activity that consists of topic distribution and distribution similarity between topics. We also found that the usage of data augmentation and background knowledge will improve topic quality. We validate our findings to a professional consultant and conclude that our approach gives an adequate contribution towards summarizing discussion activity that might improve consensus building process.
{"title":"Analysis on the Usage of Topic Model with Background Knowledge inside Discussion Activity in Industrial Engineering Context","authors":"Muhammad Luthfi, S. Goto, Osamu Ytshi","doi":"10.1109/SmartIoT49966.2020.00012","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00012","url":null,"abstract":"Consensus building process for enterprise digital transformation is a significant approach on the implementation of Internet of Things (IoT) solutions through product lifecycle management (PLM). When we improve the consensus building process, it is important to find any latent opinions and hidden dialog patterns analyzing discussion activities by stakeholders. Several approaches have been proposed in forms of instructions and frameworks such as causal model of Consensus Building Theory (CBT) and short-term intensive workshop in strategy planning phase of Product Lifecycle Management (PLM) process. This paper will analyze a new approach to improve consensus building process by summarizing discussion activity. The proposed method is done by performing data augmentation and topic modeling with the help of background knowledge on discussion activity held within industrial engineering context. Our method produces a complete summarization of discussion activity that consists of topic distribution and distribution similarity between topics. We also found that the usage of data augmentation and background knowledge will improve topic quality. We validate our findings to a professional consultant and conclude that our approach gives an adequate contribution towards summarizing discussion activity that might improve consensus building process.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114110787","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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