Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249011
M. Nahas, M. Sabati, Rana Kanso, M. Azizi
Energy consumption of communication and cellular networks has recently received increased attention in the research community. Moreover, a large number of base stations (BSs) have been deployed in order to meet these demands on cellular traffic. This leads to considerable increase in the operational cost and contributes as a major portion of the energy consumption of cellular networks. Therefore, network operators are considering how to reduce the energy consumption and design “Green Communication”. The concept of Green Communication is to manage energy consumption and maintain reasonable costs while keeping high quality of service and benefits. In this paper, we present some techniques for reducing power consumption by developing solutions in terms of the network architecture and the base stations components. We use some of these solutions to develop and implement a novel algorithm that combines Cell Zooming and Heterogeneous Networks with many types of base station while taking into consideration user's mobility. The efficiency and the power management of our algorithm, especially in high traffic networks, are achieved and approved by simulations.
{"title":"Proactive algorithm to reduce power consumption and handovers in heterogeneous mobile networks","authors":"M. Nahas, M. Sabati, Rana Kanso, M. Azizi","doi":"10.1109/UEMCON.2017.8249011","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249011","url":null,"abstract":"Energy consumption of communication and cellular networks has recently received increased attention in the research community. Moreover, a large number of base stations (BSs) have been deployed in order to meet these demands on cellular traffic. This leads to considerable increase in the operational cost and contributes as a major portion of the energy consumption of cellular networks. Therefore, network operators are considering how to reduce the energy consumption and design “Green Communication”. The concept of Green Communication is to manage energy consumption and maintain reasonable costs while keeping high quality of service and benefits. In this paper, we present some techniques for reducing power consumption by developing solutions in terms of the network architecture and the base stations components. We use some of these solutions to develop and implement a novel algorithm that combines Cell Zooming and Heterogeneous Networks with many types of base station while taking into consideration user's mobility. The efficiency and the power management of our algorithm, especially in high traffic networks, are achieved and approved by simulations.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125557885","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249088
Deepak K. Tosh, S. Shetty, Xueping Liang, C. Kamhoua, L. Njilla
Blockchain has recently attracted tremendous interest due to its ability to enhance security and privacy through a immutable shared distributed ledger. Blockchain's ability to detect integrity violations are particularly key in providing assured data provenance in cloud platform. The practical adoption of blockchain will largely hinge on consensus protocols meeting performance and security guarantees. In this paper, we present the design issues for consensus protocols for blockchain based cloud provenance. We present the blockchain based data provenance framework for cloud. We find that there are performance and security challenges in adopting proof-of-work consensus protocol within this framework. We present unique design challenges and opportunities in developing proof-of-stake for data provenance in cloud platform.
{"title":"Consensus protocols for blockchain-based data provenance: Challenges and opportunities","authors":"Deepak K. Tosh, S. Shetty, Xueping Liang, C. Kamhoua, L. Njilla","doi":"10.1109/UEMCON.2017.8249088","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249088","url":null,"abstract":"Blockchain has recently attracted tremendous interest due to its ability to enhance security and privacy through a immutable shared distributed ledger. Blockchain's ability to detect integrity violations are particularly key in providing assured data provenance in cloud platform. The practical adoption of blockchain will largely hinge on consensus protocols meeting performance and security guarantees. In this paper, we present the design issues for consensus protocols for blockchain based cloud provenance. We present the blockchain based data provenance framework for cloud. We find that there are performance and security challenges in adopting proof-of-work consensus protocol within this framework. We present unique design challenges and opportunities in developing proof-of-stake for data provenance in cloud platform.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128990869","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249001
Kenichi Yoneda, Gary M. Weiss
Research on mobile sensor biometrics increased when mobile devices with powerful sensors, such as smartphones, became ubiquitous. However, existing studies are quite limited, especially with regard to the physical activities that are used to provide the biometric signature — many studies only consider a single activity. In this study, we provide the most comprehensive analysis of mobile biometrics to date. We evaluate eighteen physical activities and nine sensor combinations for their biometric efficacy (the accelerometer and gyroscope sensors from a smartphone and smartwatch are used). Our mobile biometric models are evaluated with respect to identification and authentication performance and are shown to achieve excellent results in both cases. Furthermore, our models perform well even when built from all eighteen activities without activity labels, which represents a big step towards achieving the goal of continuous biometrics using only a smartwatch and smartphone.
{"title":"Mobile sensor-based biometrics using common daily activities","authors":"Kenichi Yoneda, Gary M. Weiss","doi":"10.1109/UEMCON.2017.8249001","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249001","url":null,"abstract":"Research on mobile sensor biometrics increased when mobile devices with powerful sensors, such as smartphones, became ubiquitous. However, existing studies are quite limited, especially with regard to the physical activities that are used to provide the biometric signature — many studies only consider a single activity. In this study, we provide the most comprehensive analysis of mobile biometrics to date. We evaluate eighteen physical activities and nine sensor combinations for their biometric efficacy (the accelerometer and gyroscope sensors from a smartphone and smartwatch are used). Our mobile biometric models are evaluated with respect to identification and authentication performance and are shown to achieve excellent results in both cases. Furthermore, our models perform well even when built from all eighteen activities without activity labels, which represents a big step towards achieving the goal of continuous biometrics using only a smartwatch and smartphone.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127749290","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8248973
E. Balster, Andrew M. Kordik, F. Scarpino, Kerry L. Hill
In this paper, a number of pulse envelopes in a spotlight SAR simulation environment are tested. The simulation environment generates SAR phase history data from digital imagery so that the resulting SAR image formed from the phase history may be compared to the original with traditional image processing metrics such as peak signal-to-noise ratio (PSNR) and structured similarity index (SSIM). The results of this study show that a raised cosine envelope with rolloff factor β = 0.1 provides imagery of the best quality of the different envelopes tested, and provides an average of 1.06 dB gain in PSNR and 0.036 gain in SSIM when compared to a pulse with a rectangular envelope applied.
{"title":"Chirp pulse envelope evaluation for SAR image formation","authors":"E. Balster, Andrew M. Kordik, F. Scarpino, Kerry L. Hill","doi":"10.1109/UEMCON.2017.8248973","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8248973","url":null,"abstract":"In this paper, a number of pulse envelopes in a spotlight SAR simulation environment are tested. The simulation environment generates SAR phase history data from digital imagery so that the resulting SAR image formed from the phase history may be compared to the original with traditional image processing metrics such as peak signal-to-noise ratio (PSNR) and structured similarity index (SSIM). The results of this study show that a raised cosine envelope with rolloff factor β = 0.1 provides imagery of the best quality of the different envelopes tested, and provides an average of 1.06 dB gain in PSNR and 0.036 gain in SSIM when compared to a pulse with a rectangular envelope applied.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126124447","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8248993
C. Kwan, Bence Budavari, Minh Dao, Jin Zhou
In this paper, we present new sparsity based algorithms in generating a high resolution hyperspectral image by fusing a high resolution color image with a low resolution hyperspectral image. Mathematical formulation of the sparsity based approaches is presented. Comparison with other pansharpening algorithms using actual data has been carried out using two hyperspectral image data sets. Initial results are encouraging. Most importantly, the new sparsity formulation points to a new direction in generating high resolution hyperspectral images where the raw images may be noisy.
{"title":"New sparsity based pansharpening algorithms for hyperspectral images","authors":"C. Kwan, Bence Budavari, Minh Dao, Jin Zhou","doi":"10.1109/UEMCON.2017.8248993","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8248993","url":null,"abstract":"In this paper, we present new sparsity based algorithms in generating a high resolution hyperspectral image by fusing a high resolution color image with a low resolution hyperspectral image. Mathematical formulation of the sparsity based approaches is presented. Comparison with other pansharpening algorithms using actual data has been carried out using two hyperspectral image data sets. Initial results are encouraging. Most importantly, the new sparsity formulation points to a new direction in generating high resolution hyperspectral images where the raw images may be noisy.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127279719","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249053
Dayna Eidle, S. Ni, C. DeCusatis, Anthony Sager
There is a long-standing need for improved cybersecurity through automation of attack signature detection, classification, and response. In this paper, we present experimental test bed results from an implementation of autonomic control plane feedback based on the Observe, Orient, Decide, Act (OODA) framework. This test bed modeled the building blocks for a proposed zero trust cloud data center network. We present test results of trials in which identity management with automated threat response and packet-based authentication were combined with dynamic management of eight distinct network trust levels. The log parsing and orchestration software we created work alongside open source log management tools to coordinate and integrate threat response from firewalls, authentication gateways, and other network devices. Threat response times are measured and shown to be a significant improvement over conventional methods.
{"title":"Autonomic security for zero trust networks","authors":"Dayna Eidle, S. Ni, C. DeCusatis, Anthony Sager","doi":"10.1109/UEMCON.2017.8249053","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249053","url":null,"abstract":"There is a long-standing need for improved cybersecurity through automation of attack signature detection, classification, and response. In this paper, we present experimental test bed results from an implementation of autonomic control plane feedback based on the Observe, Orient, Decide, Act (OODA) framework. This test bed modeled the building blocks for a proposed zero trust cloud data center network. We present test results of trials in which identity management with automated threat response and packet-based authentication were combined with dynamic management of eight distinct network trust levels. The log parsing and orchestration software we created work alongside open source log management tools to coordinate and integrate threat response from firewalls, authentication gateways, and other network devices. Threat response times are measured and shown to be a significant improvement over conventional methods.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127326403","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249003
L. Ji, S. Kolhe, A. Clark
High performance computing (HPC) environments are becoming the norm for daily use. However, the resilience of these systems is questionable because their complex infrastructure makes troubleshooting both the location and cause of failures extremely difficult. These same reasons make HPCs prone to virulent activity. This paper presents a data analysis framework for analyzing ranges of failure observations as a result of malicious activity. Taking into account the internal reliability infrastructure, data network extrapolation is performed as a preprocessing tool that accurately calculates the normalized failure rates. Next, nonlinear regression is performed on the spectrum of observations taking into account the magnitude, growth rate, and midpoint behavior. Additionally, influence analysis is performed that considers outlying observations. The empirical results using a simulated supercomputing modeling and simulation framework show improvement, in terms of characterization performance, where approximately 91% of the nodes were properly characterized. The results of this work can be applied to develop robust task-scheduling frameworks within supercomputing architectures.
{"title":"Data analysis of cyber-activity within high performance computing environments","authors":"L. Ji, S. Kolhe, A. Clark","doi":"10.1109/UEMCON.2017.8249003","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249003","url":null,"abstract":"High performance computing (HPC) environments are becoming the norm for daily use. However, the resilience of these systems is questionable because their complex infrastructure makes troubleshooting both the location and cause of failures extremely difficult. These same reasons make HPCs prone to virulent activity. This paper presents a data analysis framework for analyzing ranges of failure observations as a result of malicious activity. Taking into account the internal reliability infrastructure, data network extrapolation is performed as a preprocessing tool that accurately calculates the normalized failure rates. Next, nonlinear regression is performed on the spectrum of observations taking into account the magnitude, growth rate, and midpoint behavior. Additionally, influence analysis is performed that considers outlying observations. The empirical results using a simulated supercomputing modeling and simulation framework show improvement, in terms of characterization performance, where approximately 91% of the nodes were properly characterized. The results of this work can be applied to develop robust task-scheduling frameworks within supercomputing architectures.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129219105","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249013
Alec Yenter, Abhishek Verma
Deep learning neural networks have made significant progress in the area of image and video analysis. This success of neural networks can be directed towards improvements in textual sentiment classification. In this paper, we describe a novel approach to sentiment analysis through the use of combined kernel from multiple branches of convolutional neural network (CNN) with Long Short-term Memory (LSTM) layers. Our combination of CNN and LSTM schemes produces a model with the highest reported accuracy on the Internet Movie Database (IMDb) review sentiment dataset. Additionally, we present multiple architecture variations of our proposed model to illustrate our attempts to increase accuracy while minimizing overfitting. We experiment with numerous regularization techniques, network structures, and kernel sizes to create five high-performing models for comparison. These models are capable of predicting the sentiment polarity of reviews from the IMDb dataset with accuracy above 89%. Firstly, the accuracy of our best performing proposed model surpasses the previously published models and secondly it vastly improves upon the baseline CNN+LSTM model. The capability of the combined kernel from multiple branches of CNN based LSTM architecture could also be lucrative towards other datasets for sentiment analysis or simply text classification. Furthermore, the proposed model has the potential in machine learning in video and audio.
{"title":"Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis","authors":"Alec Yenter, Abhishek Verma","doi":"10.1109/UEMCON.2017.8249013","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249013","url":null,"abstract":"Deep learning neural networks have made significant progress in the area of image and video analysis. This success of neural networks can be directed towards improvements in textual sentiment classification. In this paper, we describe a novel approach to sentiment analysis through the use of combined kernel from multiple branches of convolutional neural network (CNN) with Long Short-term Memory (LSTM) layers. Our combination of CNN and LSTM schemes produces a model with the highest reported accuracy on the Internet Movie Database (IMDb) review sentiment dataset. Additionally, we present multiple architecture variations of our proposed model to illustrate our attempts to increase accuracy while minimizing overfitting. We experiment with numerous regularization techniques, network structures, and kernel sizes to create five high-performing models for comparison. These models are capable of predicting the sentiment polarity of reviews from the IMDb dataset with accuracy above 89%. Firstly, the accuracy of our best performing proposed model surpasses the previously published models and secondly it vastly improves upon the baseline CNN+LSTM model. The capability of the combined kernel from multiple branches of CNN based LSTM architecture could also be lucrative towards other datasets for sentiment analysis or simply text classification. Furthermore, the proposed model has the potential in machine learning in video and audio.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131512031","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249106
Yi Wang, Brandon Saez, Joseph Szczechowicz, Jonathan Ruisi, Tyler Kraft, Stephen Toscano, Zachary Vacco, Kervins Nicolas
Smart cities and communities (S&CC) are experiencing transformational change across the nation and globally with the proliferation deployment of Internet of Things (IoT). Smart campus is an important part of S&CC. We built a smart campus IoT framework to address functionality, energy consumption, security and safety in a user-centric perspective. To achieve these, machine learning algorithms are employed in the hardware-software co-design process. During experiment, we focused on energy consumption management using support vector machine to learn the classroom schedule, so that the system can respond to dynamic change of the schedules. The experimental results show that our proposed framework can recognize the classroom regular events and irregular events with high accuracy.
{"title":"A smart campus internet of things framework","authors":"Yi Wang, Brandon Saez, Joseph Szczechowicz, Jonathan Ruisi, Tyler Kraft, Stephen Toscano, Zachary Vacco, Kervins Nicolas","doi":"10.1109/UEMCON.2017.8249106","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249106","url":null,"abstract":"Smart cities and communities (S&CC) are experiencing transformational change across the nation and globally with the proliferation deployment of Internet of Things (IoT). Smart campus is an important part of S&CC. We built a smart campus IoT framework to address functionality, energy consumption, security and safety in a user-centric perspective. To achieve these, machine learning algorithms are employed in the hardware-software co-design process. During experiment, we focused on energy consumption management using support vector machine to learn the classroom schedule, so that the system can respond to dynamic change of the schedules. The experimental results show that our proposed framework can recognize the classroom regular events and irregular events with high accuracy.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116155111","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8248978
J. Golbeck
Cable companies and Internet Service Providers have begun to offer public wifi hotspots run through customers' in-home wireless routers. This greatly expands the number of hotspots a company can offer, but it is often done with out the consent of the customers and sometimes without informing them that it is happening. This has led to a range of privacy and security concerns among the customers whose routers are used. In this paper, we analyze 501 online comments posted to news stories about this practice to develop a taxonomy of user concerns and identify their frequency. We found only about 19% of comments were unconcerned about the practice. Of those concerned, over 40% believed the practice was a violation of autonomy. Worries about quality of service impacts were similarly common. Issues of trust, legal liability, deceptive practices, and power were also quite common. We present these results and offer a discussion of the implications.
{"title":"User concerns with personal routers used as public Wi-fi hotspots","authors":"J. Golbeck","doi":"10.1109/UEMCON.2017.8248978","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8248978","url":null,"abstract":"Cable companies and Internet Service Providers have begun to offer public wifi hotspots run through customers' in-home wireless routers. This greatly expands the number of hotspots a company can offer, but it is often done with out the consent of the customers and sometimes without informing them that it is happening. This has led to a range of privacy and security concerns among the customers whose routers are used. In this paper, we analyze 501 online comments posted to news stories about this practice to develop a taxonomy of user concerns and identify their frequency. We found only about 19% of comments were unconcerned about the practice. Of those concerned, over 40% believed the practice was a violation of autonomy. Worries about quality of service impacts were similarly common. Issues of trust, legal liability, deceptive practices, and power were also quite common. We present these results and offer a discussion of the implications.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122805428","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：