Pub Date : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298182
H. Alasti
An efficient accelerated learning algorithm is proposed and discussed for tracking of spatially correlated signals in ad-hoc wireless sensor networks. The proposed algorithm is low-cost and computationally efficient. It models an unknown, spatially correlated signal using a number of its contour lines at equally spaced levels. In the proposed algorithm, each sensor is modeled as one neuron in a neural network. The accelerated learning’s agent is implemented at the fusion center (FC). The algorithm is performed in two phases of spatial modeling and spatial tracking. In spatial modeling phase that accelerated learning is implemented, the algorithm discovers the model parameters. In spatial tracking phase, the model parameters are updated to track the varying, unknown spatial signal. Those sensors (neurons) that their observation are in a given margin of at least one of the contour levels, report their filtered observations to the FC. The FC updates the model parameters based on the reported observations and returns the model features to the sensor network for the next iteration step. The performance evaluation results show that the proposed accelerated learning is low cost and converges faster than single layer machine learning approach. The modeling performance, convergence speed and the cost of the proposed algorithm are compared with those of single layer machine learning algorithm. The algorithm is proposed for environmental monitoring.
{"title":"An Efficient Accelerated Learning Algorithm For Tracking Of Unknown, Spatially Correlated Signals In Ad-Hoc Wireless Sensor Networks","authors":"H. Alasti","doi":"10.1109/UEMCON51285.2020.9298182","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298182","url":null,"abstract":"An efficient accelerated learning algorithm is proposed and discussed for tracking of spatially correlated signals in ad-hoc wireless sensor networks. The proposed algorithm is low-cost and computationally efficient. It models an unknown, spatially correlated signal using a number of its contour lines at equally spaced levels. In the proposed algorithm, each sensor is modeled as one neuron in a neural network. The accelerated learning’s agent is implemented at the fusion center (FC). The algorithm is performed in two phases of spatial modeling and spatial tracking. In spatial modeling phase that accelerated learning is implemented, the algorithm discovers the model parameters. In spatial tracking phase, the model parameters are updated to track the varying, unknown spatial signal. Those sensors (neurons) that their observation are in a given margin of at least one of the contour levels, report their filtered observations to the FC. The FC updates the model parameters based on the reported observations and returns the model features to the sensor network for the next iteration step. The performance evaluation results show that the proposed accelerated learning is low cost and converges faster than single layer machine learning approach. The modeling performance, convergence speed and the cost of the proposed algorithm are compared with those of single layer machine learning algorithm. The algorithm is proposed for environmental monitoring.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131425337","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298155
Naheem Olakunle Adesina, A. Srivastava
There are tremendous improvements in performance of transistor in CMOS technology by scaling down its size. However, there are various challenges, such as short channel effects (SCE), that are associated with miniaturization. FinFET technology is a promising technique to overcome these issues because it offers better electrostatic control of the channel than planar CMOS transistor as the technology scales down. In this work, we have proposed a phase locked loop (PLL) design with FinFET and memristor. The resistive and capacitive (R-C) components of loop filter are replaced with memristor and memcapacitor, respectively, in order to minimize the die area and reduce power consumption. The designed PLL produces a tuning range of 0.25 - 1.60 GHz at center frequency of 1 GHz with 2.05 mW average power consumption. The voltage-controlled oscillator (VCO), which contributes majorly to the total phase noise in phase locked loop, has a phase noise -135.2 dBc/Hz at 1 MHz offset frequency. In addition, the PLL shows high reliability with wide variations in temperature.
{"title":"A 250 MHz-to-1.6 GHz Phase Locked Loop Design in Hybrid FinFET-Memristor Technology","authors":"Naheem Olakunle Adesina, A. Srivastava","doi":"10.1109/UEMCON51285.2020.9298155","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298155","url":null,"abstract":"There are tremendous improvements in performance of transistor in CMOS technology by scaling down its size. However, there are various challenges, such as short channel effects (SCE), that are associated with miniaturization. FinFET technology is a promising technique to overcome these issues because it offers better electrostatic control of the channel than planar CMOS transistor as the technology scales down. In this work, we have proposed a phase locked loop (PLL) design with FinFET and memristor. The resistive and capacitive (R-C) components of loop filter are replaced with memristor and memcapacitor, respectively, in order to minimize the die area and reduce power consumption. The designed PLL produces a tuning range of 0.25 - 1.60 GHz at center frequency of 1 GHz with 2.05 mW average power consumption. The voltage-controlled oscillator (VCO), which contributes majorly to the total phase noise in phase locked loop, has a phase noise -135.2 dBc/Hz at 1 MHz offset frequency. In addition, the PLL shows high reliability with wide variations in temperature.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133002792","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298112
Ayad N. Bihnam, Xian Liu
In this letter, the performance analysis of Cloud Radio Access Network (C-RAN) is investigated in terms of Ergodic Capacity (EC) under the well-known Nakagami-m fading channel. In this study, the C-RAN model is based on two nearest Remote Radio Head (2-RRH) association. The user is considered at the center of a disk equipped with a single antenna while each RRH has L antennas. The outage probability equation is defined and a closed form formula of EC has been derived with arbitrary path loss exponent. Numerical and analytical results show nonlinear behavior of channel capacity versus signal to noise ratio.
{"title":"Performance Analysis of Cloud Radio Access Networks with Nakagami-m Fading Channel","authors":"Ayad N. Bihnam, Xian Liu","doi":"10.1109/UEMCON51285.2020.9298112","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298112","url":null,"abstract":"In this letter, the performance analysis of Cloud Radio Access Network (C-RAN) is investigated in terms of Ergodic Capacity (EC) under the well-known Nakagami-m fading channel. In this study, the C-RAN model is based on two nearest Remote Radio Head (2-RRH) association. The user is considered at the center of a disk equipped with a single antenna while each RRH has L antennas. The outage probability equation is defined and a closed form formula of EC has been derived with arbitrary path loss exponent. Numerical and analytical results show nonlinear behavior of channel capacity versus signal to noise ratio.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131341396","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298179
Ahmed E. Elshafey, Soumaia Ahmed Al Ayyat, S. Aly
In modern opportunistic networks, network operations can be improved through knowledge of spatial information of low and high density areas, predictions of the mobility of nodes in the space, as well as the spatial distribution of nodes. Such information can be used to adapt forwarding decisions. In this paper, we introduce an efficient opportunistic spatial clustering algorithm, OPSCAN (Opportunistic Spatial Clustering of Applications with Noise). Based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a density-based clustering algorithm that discovers arbitrary-shaped clusters in a dataset and distinguishes noise points. OPSCAN is capable of clustering network nodes into high density clusters, while maintaining sparse areas of nodes between clusters. Clusters share spatial information of the network such as area density, mobility statistics and information about other clusters and their nodes. Knowledge of edge nodes in the clusters is also made available for utilization in more efficient forwarding decisions. Simulations show that our algorithm is capable of producing dense, homogeneous clusters and accurately outlining cluster edges. We have used the Silhouette Coefficient to measure cluster homogeneity against density-based clustering algorithms DBSCAN and ST-DBSCAN (Spatial-Temporal DBSCAN), a DBSCAN-based spatial-temporal variant on "GeoLife" dataset. We have found OPSCAN outperforms DBSCAN by a coefficient of 0.81 to 0.73 for the same minimum distance, under-performing ST-DBSCAN by 0.87 to 0.81 for that distance. OPSCAN requires only two inputs as compared to four for ST-DBSCAN. As the distance parameter is increased, OPSCAN produces homogeneous clusters more closely to ST-DBSCAN.
在现代机会网络中，可以通过了解低密度和高密度区域的空间信息，预测空间中节点的移动性以及节点的空间分布来改进网络运营。这些信息可以用来调整转发决策。本文介绍了一种高效的机会空间聚类算法OPSCAN (opportunistic spatial clustering of Applications with Noise)。DBSCAN (Density-Based Spatial Clustering of Applications with Noise)是一种基于密度的聚类算法，可以发现数据集中任意形状的聚类并区分噪声点。OPSCAN能够将网络节点聚为高密度簇，同时保持簇间节点的稀疏区域。集群共享网络的空间信息，如区域密度、流动性统计数据以及其他集群及其节点的信息。集群中边缘节点的知识也可用于更有效的转发决策。仿真结果表明，该算法能够生成密集、均匀的聚类，并能准确地勾勒出聚类的边缘。我们使用廓形系数来衡量基于密度的聚类算法DBSCAN和ST-DBSCAN(时空DBSCAN)的聚类同质性，ST-DBSCAN是基于DBSCAN的“GeoLife”数据集的时空变体。我们发现，对于相同的最小距离，OPSCAN的性能优于DBSCAN的系数为0.81至0.73，而ST-DBSCAN的性能差为0.87至0.81。OPSCAN只需要两个输入，而ST-DBSCAN需要四个输入。随着距离参数的增加，OPSCAN产生的均匀簇更接近ST-DBSCAN。
{"title":"OPSCAN: Density-based Spatial Clustering in Opportunistic Networks","authors":"Ahmed E. Elshafey, Soumaia Ahmed Al Ayyat, S. Aly","doi":"10.1109/UEMCON51285.2020.9298179","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298179","url":null,"abstract":"In modern opportunistic networks, network operations can be improved through knowledge of spatial information of low and high density areas, predictions of the mobility of nodes in the space, as well as the spatial distribution of nodes. Such information can be used to adapt forwarding decisions. In this paper, we introduce an efficient opportunistic spatial clustering algorithm, OPSCAN (Opportunistic Spatial Clustering of Applications with Noise). Based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a density-based clustering algorithm that discovers arbitrary-shaped clusters in a dataset and distinguishes noise points. OPSCAN is capable of clustering network nodes into high density clusters, while maintaining sparse areas of nodes between clusters. Clusters share spatial information of the network such as area density, mobility statistics and information about other clusters and their nodes. Knowledge of edge nodes in the clusters is also made available for utilization in more efficient forwarding decisions. Simulations show that our algorithm is capable of producing dense, homogeneous clusters and accurately outlining cluster edges. We have used the Silhouette Coefficient to measure cluster homogeneity against density-based clustering algorithms DBSCAN and ST-DBSCAN (Spatial-Temporal DBSCAN), a DBSCAN-based spatial-temporal variant on \"GeoLife\" dataset. We have found OPSCAN outperforms DBSCAN by a coefficient of 0.81 to 0.73 for the same minimum distance, under-performing ST-DBSCAN by 0.87 to 0.81 for that distance. OPSCAN requires only two inputs as compared to four for ST-DBSCAN. As the distance parameter is increased, OPSCAN produces homogeneous clusters more closely to ST-DBSCAN.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115558992","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298039
G. Baldini, Raimondo Giuliani, M. Gemo
Odometer fraud is a serious offense in the automotive sector and indicates the disconnection, resetting, or alteration of a vehicle’s odometer and the related sensor with the intent to change the number of miles/Kms indicated or recorded to report false information. This paper focuses specifically on the threat scenario where the odometer sensor (i.e., Hall Sensor) is manipulated or replaced to implement an odometer fraud. This paper proposes a technique to mitigate odometer fraud by performing a physical layer authentication of the Hall Sensor, which takes in consideration the limitation of the in-vehicle networks and microprocessors. In particular, the Discrete Hartley Transform (DHT) in combination with machine learning algorithms is used to perform the authentication on an experimental data set of 12 Hall Sensors, which has been collected by the authors. The results shows that features extracted with DHT have more discriminating power than the original time domain and the frequency domain representations based on the Fast Fourier Transform (FFT) especially in presence of noise.
{"title":"Mitigation of Odometer Fraud for In-Vehicle Security Using the Discrete Hartley Transform","authors":"G. Baldini, Raimondo Giuliani, M. Gemo","doi":"10.1109/UEMCON51285.2020.9298039","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298039","url":null,"abstract":"Odometer fraud is a serious offense in the automotive sector and indicates the disconnection, resetting, or alteration of a vehicle’s odometer and the related sensor with the intent to change the number of miles/Kms indicated or recorded to report false information. This paper focuses specifically on the threat scenario where the odometer sensor (i.e., Hall Sensor) is manipulated or replaced to implement an odometer fraud. This paper proposes a technique to mitigate odometer fraud by performing a physical layer authentication of the Hall Sensor, which takes in consideration the limitation of the in-vehicle networks and microprocessors. In particular, the Discrete Hartley Transform (DHT) in combination with machine learning algorithms is used to perform the authentication on an experimental data set of 12 Hall Sensors, which has been collected by the authors. The results shows that features extracted with DHT have more discriminating power than the original time domain and the frequency domain representations based on the Fast Fourier Transform (FFT) especially in presence of noise.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115580345","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298160
V. Manthena, S. Miryala, G. Deptuch, G. Carini
This paper presents a low jitter dual-path charge-pump phase locked loop (PLL) synthesizer in a CMOS 65-nm process for quantum readout applications. The PLL incorporates a programmable dual charge-pump and a loop filter with both proportional and integral paths that can be driven independently providing flexible control of the loop bandwidth to achieve low jitter performance. The design is implemented at 300 K and critical blocks like voltage-controlled oscillator (VCO) and charge-pump (CP) are analyzed at 77 K based on the characterized results. The LC-VCO is realized with the class-C NMOS only architecture with 5-bit coarse control and quadrature signals are generated with poly phase filter. The VCO is designed with the tuning range of 1 GHz around the center frequency of 6 GHz with Phase Noise of -123 dBc/Hz and -132 dBc/Hz at 1MHz offset at 300 K and 77 K temperature. The simulated PLL rms jitter is 125 fs at 6 GHz with a power consumption of 8 mW at the 1.2 V power supply.
{"title":"A 1.2-V 6-GHz Dual-Path Charge-Pump PLL Frequency Synthesizer for Quantum Control and Readout in CMOS 65-nm Process","authors":"V. Manthena, S. Miryala, G. Deptuch, G. Carini","doi":"10.1109/UEMCON51285.2020.9298160","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298160","url":null,"abstract":"This paper presents a low jitter dual-path charge-pump phase locked loop (PLL) synthesizer in a CMOS 65-nm process for quantum readout applications. The PLL incorporates a programmable dual charge-pump and a loop filter with both proportional and integral paths that can be driven independently providing flexible control of the loop bandwidth to achieve low jitter performance. The design is implemented at 300 K and critical blocks like voltage-controlled oscillator (VCO) and charge-pump (CP) are analyzed at 77 K based on the characterized results. The LC-VCO is realized with the class-C NMOS only architecture with 5-bit coarse control and quadrature signals are generated with poly phase filter. The VCO is designed with the tuning range of 1 GHz around the center frequency of 6 GHz with Phase Noise of -123 dBc/Hz and -132 dBc/Hz at 1MHz offset at 300 K and 77 K temperature. The simulated PLL rms jitter is 125 fs at 6 GHz with a power consumption of 8 mW at the 1.2 V power supply.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122883680","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298105
Siwei Zhao, Sanyami Shah, Kishan N. Patel, Nickyta Patel, Vachana Shetty, Michal Aibin
The Elastic Optical Network is a technology that offers versatile conversion of modulation format, allowing for more effective use of spectrum resources compared to the traditional fixed grid in WDM networks. Additionally, the content-oriented services offered by geographically distributed data centres raise a need for cost-effective and scalable data delivery. In this paper, we discuss Routing, Modulation and Spectrum Allocation (RMSA) in content-oriented networks, based on the Elastic Optical Network. We propose a new adaptive modulation, regenerator and distance-aware algorithm. Our findings discover an interesting trade-off between the request blocking and regenerator use.
{"title":"Adaptive Modulation Regenerator and Distance Aware Algorithm for Dynamic Routing in Elastic Optical Networks","authors":"Siwei Zhao, Sanyami Shah, Kishan N. Patel, Nickyta Patel, Vachana Shetty, Michal Aibin","doi":"10.1109/UEMCON51285.2020.9298105","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298105","url":null,"abstract":"The Elastic Optical Network is a technology that offers versatile conversion of modulation format, allowing for more effective use of spectrum resources compared to the traditional fixed grid in WDM networks. Additionally, the content-oriented services offered by geographically distributed data centres raise a need for cost-effective and scalable data delivery. In this paper, we discuss Routing, Modulation and Spectrum Allocation (RMSA) in content-oriented networks, based on the Elastic Optical Network. We propose a new adaptive modulation, regenerator and distance-aware algorithm. Our findings discover an interesting trade-off between the request blocking and regenerator use.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128429898","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298153
Soo-Yeon Ji, Bong-Keun Jeong, C. Kamhoua, Nandi O. Leslie, D. Jeong
Analyzing network traffic data to detect suspicious network activities requires tremendous efforts because of continuously changing network traffic patterns and intrusion scenarios. Numerous research has been devoted to the task of identifying network anomalies while maintaining excellent performances. However, most studies focus on identifying network attacks without considering their temporal domain. Time information is useful for discovering patterns in network activities and understanding the changes in network traffic over time. This paper introduces an approach to discover network traffic patterns with time series analysis to estimate the level of attack risks. Classification is performed with machine learning techniques to assess the estimated attack risks. Findings from this study can increase the capability to detect network intrusions by analyzing the behaviors of temporal data and estimating their attack risks.
{"title":"Estimating Attack Risk of Network Activities in Temporal Domain: A Wavelet Transform Approach","authors":"Soo-Yeon Ji, Bong-Keun Jeong, C. Kamhoua, Nandi O. Leslie, D. Jeong","doi":"10.1109/UEMCON51285.2020.9298153","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298153","url":null,"abstract":"Analyzing network traffic data to detect suspicious network activities requires tremendous efforts because of continuously changing network traffic patterns and intrusion scenarios. Numerous research has been devoted to the task of identifying network anomalies while maintaining excellent performances. However, most studies focus on identifying network attacks without considering their temporal domain. Time information is useful for discovering patterns in network activities and understanding the changes in network traffic over time. This paper introduces an approach to discover network traffic patterns with time series analysis to estimate the level of attack risks. Classification is performed with machine learning techniques to assess the estimated attack risks. Findings from this study can increase the capability to detect network intrusions by analyzing the behaviors of temporal data and estimating their attack risks.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485980","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298110
Daniel DeBruno, Kyle Moissinac, Joseph Severt, Abdelrahman Elleithy
Highly sensitive information systems need a simple and secure method of authentication. To supplement standard login procedures, we can implement multiple variations of two-factor authentication through radio waves, based on specific use cases. This allows us to enforce a proximity-based authentication system allowing for a physical separation of users at work and home. We can also ensure fine-grained access control for systems requiring a small number of privileged users. We will demonstrate two possible implementations, both using forms of public-key cryptography. The first scheme broadcasts a time-synchronized token to all available computers in a geographic area, and the second scheme uses public and private key pairs for digitally signed access request over-the-air.
{"title":"Radio-Factor Authentication: Identity Management over the 900MHz Band","authors":"Daniel DeBruno, Kyle Moissinac, Joseph Severt, Abdelrahman Elleithy","doi":"10.1109/UEMCON51285.2020.9298110","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298110","url":null,"abstract":"Highly sensitive information systems need a simple and secure method of authentication. To supplement standard login procedures, we can implement multiple variations of two-factor authentication through radio waves, based on specific use cases. This allows us to enforce a proximity-based authentication system allowing for a physical separation of users at work and home. We can also ensure fine-grained access control for systems requiring a small number of privileged users. We will demonstrate two possible implementations, both using forms of public-key cryptography. The first scheme broadcasts a time-synchronized token to all available computers in a geographic area, and the second scheme uses public and private key pairs for digitally signed access request over-the-air.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122043503","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 : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298151
Salam Ismaeel, M. Kamaludeen, A. Miri
Technology becomes more and more involved in the learning process of K-12 schools. The digital requirements must be assessed for cost-effective and efficient (a reasonable download wait time) access to the Internet. Nowadays, there is an increase in usage of the amount of data incoming and outgoing based on students' activities. This needs to be increased to guarantee ready access and effective usage of technology in K-12 school environments. This paper outlines a useful standard management unit called Digital Resource Unit (DRU), which identifies the ability to be securely connected to the internet and transfer digital contents free from impediments or prying eyes.
{"title":"Secure Digital Service as a Utility and the Unit of Measure","authors":"Salam Ismaeel, M. Kamaludeen, A. Miri","doi":"10.1109/UEMCON51285.2020.9298151","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298151","url":null,"abstract":"Technology becomes more and more involved in the learning process of K-12 schools. The digital requirements must be assessed for cost-effective and efficient (a reasonable download wait time) access to the Internet. Nowadays, there is an increase in usage of the amount of data incoming and outgoing based on students' activities. This needs to be increased to guarantee ready access and effective usage of technology in K-12 school environments. This paper outlines a useful standard management unit called Digital Resource Unit (DRU), which identifies the ability to be securely connected to the internet and transfer digital contents free from impediments or prying eyes.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131051521","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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