Pub Date : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181310
Chia-Wei Tsai, Chun-Wei Yang, Feng-Ling Hsu, Hsih-Min Tang, N. Fan, Cheng-Yang Lin
Solar is an important energy resource at present, and thus how to generate power efficiently by using solar is the crucial research topics in next generation power system. Among these research topics, managing and maintaining the solar panels for avoiding the situation which cannot generate power due to damage is also an interesting issue. Because the cost of developing the solar plant is expensive and needing the extra-cost to maintain solar, how to maintain the solar panels effectively is another important issue. In this study, an anomaly detection mechanism with using the semi-supervision learning model is proposed to pre-identify whether the solar panel will occur the abnormal events or not. In the anomaly detection mechanism, this study uses the clustering algorithm to filter the normal events, and then adopts the neuron network model, Autoencoder, to develop the classificator. This study takes the data collected from a 500kW solar power plant to train models and verify the feasibility of the proposed anomaly detection mechanism.
{"title":"Anomaly Detection Mechanism for Solar Generation using Semi-supervision Learning Model","authors":"Chia-Wei Tsai, Chun-Wei Yang, Feng-Ling Hsu, Hsih-Min Tang, N. Fan, Cheng-Yang Lin","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181310","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181310","url":null,"abstract":"Solar is an important energy resource at present, and thus how to generate power efficiently by using solar is the crucial research topics in next generation power system. Among these research topics, managing and maintaining the solar panels for avoiding the situation which cannot generate power due to damage is also an interesting issue. Because the cost of developing the solar plant is expensive and needing the extra-cost to maintain solar, how to maintain the solar panels effectively is another important issue. In this study, an anomaly detection mechanism with using the semi-supervision learning model is proposed to pre-identify whether the solar panel will occur the abnormal events or not. In the anomaly detection mechanism, this study uses the clustering algorithm to filter the normal events, and then adopts the neuron network model, Autoencoder, to develop the classificator. This study takes the data collected from a 500kW solar power plant to train models and verify the feasibility of the proposed anomaly detection mechanism.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115236669","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}
This paper deals with an algorithm for the easy identification or classification of pathological diseases in plant species via a mobile or web application. The entire system is an intelligent framework that enables users to identify a pathological disease via a deep learning and computer vision based smart system – A user merely needs to open the app, click a picture, and view the result. Input for the system can be either an image or live video feed of the plant species, and the result is in the form of a bounding box with the name of the identified pathological disease and the accuracy of the identification. Once the identification is accurately done the user can get more insights into the cause of the disease and how to do a proper medication.For this experimental research purpose, we are targeting five pathological diseases: Blister Blight in Tea, Citrus Canker, Early Blight, Late Blight, Powdery Mildew in Cucurbitaceae. This paper illustrates how the solution is built using deep learning and computer vision algorithms powered by the Intel® Distribution of Open VINO™ toolkit Model Optimizer.
本文研究了一种通过移动或web应用程序轻松识别或分类植物物种病理疾病的算法。整个系统是一个智能框架，用户只需打开应用程序，点击图片，查看结果，就可以通过基于深度学习和计算机视觉的智能系统识别病理疾病。系统的输入可以是植物物种的图像或实时视频，结果以边界框的形式显示，其中包含已识别的病理疾病的名称和识别的准确性。一旦准确地进行了识别，用户就可以更深入地了解疾病的原因以及如何进行适当的药物治疗。本次实验研究的目标是5种病理疾病:茶叶水疱病、柑橘溃疡病、早疫病、晚疫病、葫芦科白粉病。本文说明了如何使用深度学习和计算机视觉算法构建解决方案，这些算法由Intel®Distribution of Open VINO™toolkit Model Optimizer提供支持。
{"title":"Identification of Pathological Disease in Plants using Deep Neural Networks - Powered by Intel® Distribution of OpenVINO™ Toolkit","authors":"Risab Biswas, Avirup Basu, Abhishek Nandy, Arkaprova Deb, Roshni Chowdhury, Debashree Chanda","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181339","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181339","url":null,"abstract":"This paper deals with an algorithm for the easy identification or classification of pathological diseases in plant species via a mobile or web application. The entire system is an intelligent framework that enables users to identify a pathological disease via a deep learning and computer vision based smart system – A user merely needs to open the app, click a picture, and view the result. Input for the system can be either an image or live video feed of the plant species, and the result is in the form of a bounding box with the name of the identified pathological disease and the accuracy of the identification. Once the identification is accurately done the user can get more insights into the cause of the disease and how to do a proper medication.For this experimental research purpose, we are targeting five pathological diseases: Blister Blight in Tea, Citrus Canker, Early Blight, Late Blight, Powdery Mildew in Cucurbitaceae. This paper illustrates how the solution is built using deep learning and computer vision algorithms powered by the Intel® Distribution of Open VINO™ toolkit Model Optimizer.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126076458","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 : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181311
Shanshan Meng, W. Chu
Recycling is already a significant work for all countries. Among the work needed for recycling, garbage classification is the most fundamental step to enable cost-efficient recycling. In this paper, we attempt to identify single garbage object in images and classify it into one of the recycling categories. We study several approaches and provide comprehensive evaluation. The models we used include support vector machines (SVM) with HOG features, simple convolutional neural network (CNN), and CNN with residual blocks. According to the evaluation results, we conclude that simple CNN networks with or without residual blocks show promising performances. Thanks to deep learning techniques, the garbage classification problem for the target database can be effectively solved.
{"title":"A Study of Garbage Classification with Convolutional Neural Networks","authors":"Shanshan Meng, W. Chu","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181311","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181311","url":null,"abstract":"Recycling is already a significant work for all countries. Among the work needed for recycling, garbage classification is the most fundamental step to enable cost-efficient recycling. In this paper, we attempt to identify single garbage object in images and classify it into one of the recycling categories. We study several approaches and provide comprehensive evaluation. The models we used include support vector machines (SVM) with HOG features, simple convolutional neural network (CNN), and CNN with residual blocks. According to the evaluation results, we conclude that simple CNN networks with or without residual blocks show promising performances. Thanks to deep learning techniques, the garbage classification problem for the target database can be effectively solved.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122535563","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 : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181322
Sahil Nazir Pottoo, R. Goyal, Amit Gupta
The evolving expertise in optical wireless communication (OWC) and free space optics (FSO) proposes numerous benefits above conventional radio network owing to unlicensed bandwidth, low installation time, affordable cost, high data rate and insusceptibility to electromagnetic interference. In this paper, two wireless optical communication systems are investigated, one with FSO channel and another with OWC channel. Both the systems have been analyzed using quality factor and bit error rate as performance metrics. The mathematical model for received optical power and Pointing error has been taken into account for system considerations. It was observed that superior quality factor and minimum bit error rate for long link distance (80 km) was achieved with OWC channel while FSO did well only for short range (800 m) communication.
{"title":"Performance Investigation of Optical Communication System using FSO and OWC Channel","authors":"Sahil Nazir Pottoo, R. Goyal, Amit Gupta","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181322","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181322","url":null,"abstract":"The evolving expertise in optical wireless communication (OWC) and free space optics (FSO) proposes numerous benefits above conventional radio network owing to unlicensed bandwidth, low installation time, affordable cost, high data rate and insusceptibility to electromagnetic interference. In this paper, two wireless optical communication systems are investigated, one with FSO channel and another with OWC channel. Both the systems have been analyzed using quality factor and bit error rate as performance metrics. The mathematical model for received optical power and Pointing error has been taken into account for system considerations. It was observed that superior quality factor and minimum bit error rate for long link distance (80 km) was achieved with OWC channel while FSO did well only for short range (800 m) communication.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114423588","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 : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181345
Tanisha Dey Roy, Jaiteg Singh
This paper presents data set of three commercial physiological sensors i.e. Electrocardiogram (ECG), Galvanic Skin Response (GSR), and Pulse sensor. The paper focuses on experiment to recognize human behavior using these body sensors. An experiment was done with participation of 12 users to observe human behavior i.e. Happy and Neutral mood. Users were asked to watch advertisement video based on comedy and actionscenes. During the implementation some variations were observed in the data-set while users were watching the videos. The results have been discussed at the end of the paper based on the data-set of 12 participants.
{"title":"Human Behavior Recognition using Body Sensors based on WBSNs","authors":"Tanisha Dey Roy, Jaiteg Singh","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181345","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181345","url":null,"abstract":"This paper presents data set of three commercial physiological sensors i.e. Electrocardiogram (ECG), Galvanic Skin Response (GSR), and Pulse sensor. The paper focuses on experiment to recognize human behavior using these body sensors. An experiment was done with participation of 12 users to observe human behavior i.e. Happy and Neutral mood. Users were asked to watch advertisement video based on comedy and actionscenes. During the implementation some variations were observed in the data-set while users were watching the videos. The results have been discussed at the end of the paper based on the data-set of 12 participants.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130418743","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 : 2020-02-01DOI: 10.1109/indo-taiwanican48429.2020.9181307
Indo-Taiwan Ican
{"title":"Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN 2020)","authors":"Indo-Taiwan Ican","doi":"10.1109/indo-taiwanican48429.2020.9181307","DOIUrl":"https://doi.org/10.1109/indo-taiwanican48429.2020.9181307","url":null,"abstract":"","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122281220","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 : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181319
Gagan Abbot, Dhruv Sharma
VLSI architecture design of DSP focuses on designtechniques for the realization of a dedicated Very Large Scale Integrated (VLSI) systems for signal processing, image processing, and other communication applications. The VLSI design techniques and systolic architectures will be used for exploring the speed-area-power tradeoffs for different DSP applications. Memory-based structures are a pertinent and fitting choice for a large number of signal processing implementations that implicate multiplication with a certain set of coefficients. In this paper, however, we show a memory-based approach that can be advantageous for reduced-latency and area reducing implementations in which memory processing time is shorter than the normal computation-time effectuated in traditional multipliers. The key factor of our paper is lookup table (LUT) optimization which reduces the area and power, also a pipelined version of the memory-based multiplier reduces the combinational path delay.
{"title":"Area Efficient Memory-Based Even-Multiple-Storage Multiplier for Higher Input","authors":"Gagan Abbot, Dhruv Sharma","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181319","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181319","url":null,"abstract":"VLSI architecture design of DSP focuses on designtechniques for the realization of a dedicated Very Large Scale Integrated (VLSI) systems for signal processing, image processing, and other communication applications. The VLSI design techniques and systolic architectures will be used for exploring the speed-area-power tradeoffs for different DSP applications. Memory-based structures are a pertinent and fitting choice for a large number of signal processing implementations that implicate multiplication with a certain set of coefficients. In this paper, however, we show a memory-based approach that can be advantageous for reduced-latency and area reducing implementations in which memory processing time is shorter than the normal computation-time effectuated in traditional multipliers. The key factor of our paper is lookup table (LUT) optimization which reduces the area and power, also a pipelined version of the memory-based multiplier reduces the combinational path delay.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131497770","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 : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181360
Preeti, R. Kaur, Damanpreet Singh
Clustering is one of the essential techniques in wireless sensor network (WSN). Clustering is done to achieve the energy efficiency, improve network lifetime and the scalability of the network. The sensor nodes (SNs) in the network are arranged into various small clusters and each cluster is assigned with a cluster head (CH). Cluster formation is mandatory objective for maximizing the network lifetime to conserve energy. In this work, the problem of clustering is formulated in accordance with dissimilarity factor. The network nodes are deployed and clusters are formed randomly for a large area network. The selection of CHs done dynamically on the basis of residual maximum energy and performance is optimized on the basis of energy consumption. In this paper clustering techniques such as Mean-shift, Fuzzy C Mean (FCM), K-mean (KMEAN) and Hierarchal clustering (HC) are simulated and the results are compared on the basis of dissimilarity factor. HC is showing better results in comparison to the other clustering algorithms. The performance comparison of various clustering techniques is used to find a better formation algorithm for WSN. Better clustering with the proposed HC algorithm will provide better communication in a cost effective manner.
{"title":"Performance Evaluation of Clustering Techniques in Wireless Sensor Networks","authors":"Preeti, R. Kaur, Damanpreet Singh","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181360","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181360","url":null,"abstract":"Clustering is one of the essential techniques in wireless sensor network (WSN). Clustering is done to achieve the energy efficiency, improve network lifetime and the scalability of the network. The sensor nodes (SNs) in the network are arranged into various small clusters and each cluster is assigned with a cluster head (CH). Cluster formation is mandatory objective for maximizing the network lifetime to conserve energy. In this work, the problem of clustering is formulated in accordance with dissimilarity factor. The network nodes are deployed and clusters are formed randomly for a large area network. The selection of CHs done dynamically on the basis of residual maximum energy and performance is optimized on the basis of energy consumption. In this paper clustering techniques such as Mean-shift, Fuzzy C Mean (FCM), K-mean (KMEAN) and Hierarchal clustering (HC) are simulated and the results are compared on the basis of dissimilarity factor. HC is showing better results in comparison to the other clustering algorithms. The performance comparison of various clustering techniques is used to find a better formation algorithm for WSN. Better clustering with the proposed HC algorithm will provide better communication in a cost effective manner.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131304368","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 : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181326
Arpita, Pardeep Kumar, Kanwal Garg
Preparation of data prior to information retrieval is an important task to perform so as to gather accurate results efficiently. Preprocessing is an approach that helps to make data ready for mining algorithms. Aim of this research is to club all the techniques of cleaning for preprocessing of opinion bearing text in one single model. Besides, entire process of preprocessing for textual data is furnished in two steps for this work. First phase is of data collection and the second includes cleaning of data. Further, the paper endows insight of all the functionalities incorporated for cleaning process.
{"title":"Data Cleaning of Raw Tweets for Sentiment Analysis","authors":"Arpita, Pardeep Kumar, Kanwal Garg","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181326","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181326","url":null,"abstract":"Preparation of data prior to information retrieval is an important task to perform so as to gather accurate results efficiently. Preprocessing is an approach that helps to make data ready for mining algorithms. Aim of this research is to club all the techniques of cleaning for preprocessing of opinion bearing text in one single model. Besides, entire process of preprocessing for textual data is furnished in two steps for this work. First phase is of data collection and the second includes cleaning of data. Further, the paper endows insight of all the functionalities incorporated for cleaning process.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115717076","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 : 2020-02-01DOI: 10.1109/Indo-TaiwanICAN48429.2020.9181363
Kuang-Hui Chi, I. Kustiawan
When a mobile station (MS) performs handover among base stations or femtocells, data streaming services are likely to be disrupted due to connectivity reset or connection transfer. As a remedy, we develop a means to enhance handover by caching users' data such as security contexts or video clips a priori at network sites where the MS is likely to migrate. Each such site with foreknowledge of the user can thus complete handover sooner than would otherwise be possible by bypassing parts of procedures when the MS arrives. Complementary to well-known schemes, our approach extends the use of current cache model to allow for recency information and distinct processing delay. By introducing a form of data admission control to prevent low-penalty stations from contending for limited storage space, our approach enables high-penalty stations to experience fewer cache misses. Further, the control is exercised adaptively in light of network dynamics. Moderate cache size is suggested to accommodate sufficient data, so as to trade cache hits for a saving of storage demand. Performance results show that our approach outperforms counterpart schemes generally by over 20% in terms of handover delays. Our development lends itself to emerging 5G telecommunications networks.
{"title":"Handover-Supporting Streamlined Networking","authors":"Kuang-Hui Chi, I. Kustiawan","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181363","DOIUrl":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181363","url":null,"abstract":"When a mobile station (MS) performs handover among base stations or femtocells, data streaming services are likely to be disrupted due to connectivity reset or connection transfer. As a remedy, we develop a means to enhance handover by caching users' data such as security contexts or video clips a priori at network sites where the MS is likely to migrate. Each such site with foreknowledge of the user can thus complete handover sooner than would otherwise be possible by bypassing parts of procedures when the MS arrives. Complementary to well-known schemes, our approach extends the use of current cache model to allow for recency information and distinct processing delay. By introducing a form of data admission control to prevent low-penalty stations from contending for limited storage space, our approach enables high-penalty stations to experience fewer cache misses. Further, the control is exercised adaptively in light of network dynamics. Moderate cache size is suggested to accommodate sufficient data, so as to trade cache hits for a saving of storage demand. Performance results show that our approach outperforms counterpart schemes generally by over 20% in terms of handover delays. Our development lends itself to emerging 5G telecommunications networks.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114716013","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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