Pub Date : 2020-10-28DOI: 10.1109/UEMCON51285.2020.9298170
Shafinaz Islam, Damian Valles, M. Forstner
Audio signal analysis has become prominent in biological domains toward applications in detecting endangered or threatened species like Houston toad and Crawfish frog. Researchers at Texas State University and Texas A&M University are working on a project to rescue these species and understanding the causes of their decline. Currently the researchers are using an Automated Recording Device (ARD), Toadphone 1, an embedded solution designed for only Houston toad call detection. However, this device's software solution has shown limited success in identifying toad calls consequent of high false-positive rates. This paper experimented with a modified software solution for existing ARD, which is capable of detecting Houston toad and Crawfish frog calls with decreased false-positive rates. Six experiments to detect the calls were designed by using thirty-nine Mel-Frequency Cepstral Coefficients (MFCCs) with delta and delta-delta coefficients and sixteen Spectral Sub-band Centroids (SSCs) as audio features within Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) as the classifiers. Results show that LSTM as the classifier with thirty-nine MFCCs audio features, and a 20% validation split produces the highest accuracy for detecting Houston toad and Crawfish frog calls. This architecture has gained 84.7% training, 82.05% validation accuracy, and 84.2% test accuracy with 91.4% test accuracy on Houston toad call and 77.1% on Crawfish frog call.
{"title":"Performance Analysis and Evaluation of LSTM and GRU Architectures for Houston toad and Crawfish frog Call Detection","authors":"Shafinaz Islam, Damian Valles, M. Forstner","doi":"10.1109/UEMCON51285.2020.9298170","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298170","url":null,"abstract":"Audio signal analysis has become prominent in biological domains toward applications in detecting endangered or threatened species like Houston toad and Crawfish frog. Researchers at Texas State University and Texas A&M University are working on a project to rescue these species and understanding the causes of their decline. Currently the researchers are using an Automated Recording Device (ARD), Toadphone 1, an embedded solution designed for only Houston toad call detection. However, this device's software solution has shown limited success in identifying toad calls consequent of high false-positive rates. This paper experimented with a modified software solution for existing ARD, which is capable of detecting Houston toad and Crawfish frog calls with decreased false-positive rates. Six experiments to detect the calls were designed by using thirty-nine Mel-Frequency Cepstral Coefficients (MFCCs) with delta and delta-delta coefficients and sixteen Spectral Sub-band Centroids (SSCs) as audio features within Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) as the classifiers. Results show that LSTM as the classifier with thirty-nine MFCCs audio features, and a 20% validation split produces the highest accuracy for detecting Houston toad and Crawfish frog calls. This architecture has gained 84.7% training, 82.05% validation accuracy, and 84.2% test accuracy with 91.4% test accuracy on Houston toad call and 77.1% on Crawfish frog call.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"45 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":"133779193","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/UEMCON51285.2020.9298180
Hong Tien Vu, Quang Minh Dinh, D. Nguyen, M. Le
In this paper, we present a novel configuration of dual band rectifier for RF energy harvesting application. Unlike traditional dual band rectifiers which employ only one rectifier with the ability to operate at two different frequencies, the proposed design consists of two separated rectifiers, each operates at a distinct frequency, thus simplify the design process significantly. The two rectifiers are connected to the multiband receiving antenna via a microstrip diplexer which carries out the task of distributing the collected power at each frequency to the corresponding rectifier. Numerical simulation and measurement are carried out to evaluate the design, showing a simulated AC - DC conversion efficiency of 52% at 1.8 GHz and 46% at 2.6 GHz and a measured efficiency around 40% for both frequencies under -10 dBm low input power.
{"title":"Simple Dual Band Rectifier Based on Diplexer for Ambient RF Energy Harvesting Application","authors":"Hong Tien Vu, Quang Minh Dinh, D. Nguyen, M. Le","doi":"10.1109/UEMCON51285.2020.9298180","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298180","url":null,"abstract":"In this paper, we present a novel configuration of dual band rectifier for RF energy harvesting application. Unlike traditional dual band rectifiers which employ only one rectifier with the ability to operate at two different frequencies, the proposed design consists of two separated rectifiers, each operates at a distinct frequency, thus simplify the design process significantly. The two rectifiers are connected to the multiband receiving antenna via a microstrip diplexer which carries out the task of distributing the collected power at each frequency to the corresponding rectifier. Numerical simulation and measurement are carried out to evaluate the design, showing a simulated AC - DC conversion efficiency of 52% at 1.8 GHz and 46% at 2.6 GHz and a measured efficiency around 40% for both frequencies under -10 dBm low input power.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"64 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":"130323886","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/UEMCON51285.2020.9298041
Carl Haberfeld, A. Sheta, M. Hossain, H. Turabieh, S. Surani
In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface (GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis Obstructive Sleep Apnea (OSA) based on demographic data such as: gender, age, height, BMI, neck circumference, waist, etc., allowing a tentative diagnosis of OSA without the need for overnight tests. The developed smart phone application can diagnosis sleep apnea using a model trained with 620 samples collected from a sleep center in Corpus Christi, TX. Two machine learning classifiers (i.e., Logistic Regression (LR) and Support Vector Machine (SVM)) were used to diagnosis OSA. Our preliminary results show that at-home OSA screening is indeed possible, and that our application is effective method for covering large numbers of undiagnosed cases.
{"title":"SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models","authors":"Carl Haberfeld, A. Sheta, M. Hossain, H. Turabieh, S. Surani","doi":"10.1109/UEMCON51285.2020.9298041","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298041","url":null,"abstract":"In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface (GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis Obstructive Sleep Apnea (OSA) based on demographic data such as: gender, age, height, BMI, neck circumference, waist, etc., allowing a tentative diagnosis of OSA without the need for overnight tests. The developed smart phone application can diagnosis sleep apnea using a model trained with 620 samples collected from a sleep center in Corpus Christi, TX. Two machine learning classifiers (i.e., Logistic Regression (LR) and Support Vector Machine (SVM)) were used to diagnosis OSA. Our preliminary results show that at-home OSA screening is indeed possible, and that our application is effective method for covering large numbers of undiagnosed cases.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"67 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":"116786074","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/UEMCON51285.2020.9298088
Takumi Shiohara, T. Murase
In this research, we propose a velocity-adaptive contention window (CW) control method that reduces the maximum delay under dynamic interference on automobiles with IEEE802.11 wireless LAN communication. The method is developed for mission-critical communications for tiny periodic data. In the proposed method, the average backoff time (random wait time) is reduced as the vehicle velocity decreases, and this is done to reduce the maximum delay in situations where the influence of interference is large. Additionally, increasing the average contention window at the time of retransmission is prohibited, and the window size is fixed. Developing the proposed method, we focused on the condition in which, the slower the velocity of the vehicle is, the smaller the distance to the surrounding vehicles (and therefore the greater the amount of interference). Furthermore, we did not focus on the fact that the CW size is not optimal; instead, we focused on interference as the main cause of retransmission. This can reduce the delay determined by the number of retransmissions (the number of transmission failures) and the backoff time. To research the effect of the proposed method, we evaluated the performance of a sensor network in a vehicle using a model that causes interference when other vehicles pass near the vehicle at various velocities. The effectiveness of the proposed method was clarified by comparing the conventional method with fixed control for interference and the proposed method with control according to vehicle velocity.
{"title":"QoS Control for Mission-critical Communication on Vehicles with IEEE802.11 Wireless LAN under Dynamic Interference","authors":"Takumi Shiohara, T. Murase","doi":"10.1109/UEMCON51285.2020.9298088","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298088","url":null,"abstract":"In this research, we propose a velocity-adaptive contention window (CW) control method that reduces the maximum delay under dynamic interference on automobiles with IEEE802.11 wireless LAN communication. The method is developed for mission-critical communications for tiny periodic data. In the proposed method, the average backoff time (random wait time) is reduced as the vehicle velocity decreases, and this is done to reduce the maximum delay in situations where the influence of interference is large. Additionally, increasing the average contention window at the time of retransmission is prohibited, and the window size is fixed. Developing the proposed method, we focused on the condition in which, the slower the velocity of the vehicle is, the smaller the distance to the surrounding vehicles (and therefore the greater the amount of interference). Furthermore, we did not focus on the fact that the CW size is not optimal; instead, we focused on interference as the main cause of retransmission. This can reduce the delay determined by the number of retransmissions (the number of transmission failures) and the backoff time. To research the effect of the proposed method, we evaluated the performance of a sensor network in a vehicle using a model that causes interference when other vehicles pass near the vehicle at various velocities. The effectiveness of the proposed method was clarified by comparing the conventional method with fixed control for interference and the proposed method with control according to vehicle velocity.","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":"122555236","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/UEMCON51285.2020.9298133
Michal Aibin
Nowadays, artificial intelligence provides an excellent opportunity for scientists to improve the efficiency of resource allocation in communication networks. In this paper, we focus on applying two methods: Long-Short Term Memory and Monte Carlo Tree Search, to solve the problem of cloud resource allocation in dynamic, real-time traffic scenarios. We use a framework of Software Defined Elastic Optical Networks and cloud resources available from Amazon Web Services. Results show that the application of Monte Carlo Tree Search and Long-Short Term Memory provides superior performance, which is an excellent opportunity for network operators to achieve better utilization of their networks, with lower operational costs.
如今，人工智能为科学家提高通信网络资源配置效率提供了绝佳的机会。在本文中，我们重点应用长短期记忆和蒙特卡罗树搜索两种方法来解决动态实时交通场景下的云资源分配问题。我们使用软件定义弹性光网络框架和Amazon Web Services提供的云资源。结果表明，蒙特卡罗树搜索和长短期记忆的应用提供了优越的性能，这为网络运营商提供了一个很好的机会，可以更好地利用他们的网络，降低运营成本。
{"title":"LSTM for Cloud Data Centers Resource Allocation in Software-Defined Optical Networks","authors":"Michal Aibin","doi":"10.1109/UEMCON51285.2020.9298133","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298133","url":null,"abstract":"Nowadays, artificial intelligence provides an excellent opportunity for scientists to improve the efficiency of resource allocation in communication networks. In this paper, we focus on applying two methods: Long-Short Term Memory and Monte Carlo Tree Search, to solve the problem of cloud resource allocation in dynamic, real-time traffic scenarios. We use a framework of Software Defined Elastic Optical Networks and cloud resources available from Amazon Web Services. Results show that the application of Monte Carlo Tree Search and Long-Short Term Memory provides superior performance, which is an excellent opportunity for network operators to achieve better utilization of their networks, with lower operational costs.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"18 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":"115451903","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/UEMCON51285.2020.9298068
Colin Galen, Robert Steele
It has been recognized that machine learning-based malware detection models, trained on features statically extractable from binary executable files, offer a number of promising benefits, such as the ability to detect malware that has not been previously encountered and an ability to re-train and adapt over time as threats evolve. Nevertheless, many academic studies of machine learning-based malware detection consider and evaluate performance on datasets that do not evolve with time, although it is recognized in practice that malware detection models will necessarily deteriorate in performance over time due to the emergence of novel malware threats. In this study, we make use of a large dataset comprised of the features extracted from malware/goodware executable samples in the very common Portable Executable (PE) format, that are orderable by time of first appearance, to analyze the deterioration of machine learning-based malware detection models over time from training. Of the large number of models we trained and then evaluated on later occurring subsets of the dataset, we note the relative strength of Random Forest to maintain predictive performance into the future. We then consider in greater depth, Random Forest-based models for malware detection, considering Random Forest hyperparameter choices to achieve better maintenance of performance and discuss the significance of the findings for PE malware detection approaches.
{"title":"Performance Maintenance Over Time of Random Forest-based Malware Detection Models","authors":"Colin Galen, Robert Steele","doi":"10.1109/UEMCON51285.2020.9298068","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298068","url":null,"abstract":"It has been recognized that machine learning-based malware detection models, trained on features statically extractable from binary executable files, offer a number of promising benefits, such as the ability to detect malware that has not been previously encountered and an ability to re-train and adapt over time as threats evolve. Nevertheless, many academic studies of machine learning-based malware detection consider and evaluate performance on datasets that do not evolve with time, although it is recognized in practice that malware detection models will necessarily deteriorate in performance over time due to the emergence of novel malware threats. In this study, we make use of a large dataset comprised of the features extracted from malware/goodware executable samples in the very common Portable Executable (PE) format, that are orderable by time of first appearance, to analyze the deterioration of machine learning-based malware detection models over time from training. Of the large number of models we trained and then evaluated on later occurring subsets of the dataset, we note the relative strength of Random Forest to maintain predictive performance into the future. We then consider in greater depth, Random Forest-based models for malware detection, considering Random Forest hyperparameter choices to achieve better maintenance of performance and discuss the significance of the findings for PE malware detection approaches.","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":"115674598","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/UEMCON51285.2020.9298065
Omar A. Zargelin, Fadel M. Lashhab, Walid K. A. Hasan
Wireless sensor networks (WSNs) have shown promise in a broad range of applications. One of the primary challenges in leveraging them lies in gathering accurate position information for the deployed sensors while minimizing power cost. Through analyzing the error associated with acquiring such position information, we developed several novel localization methods based on modeling the analyzed error and applying rigorous mathematical and statistical principles in order to produce improved location estimates compared with existing methods. The methods presented herein have been utilized for a one-dimensional space for proof-of-concept, simplicity of presentation, and to illustrate how viable, single-dimensional applications can be approached. These methods utilize only two mobile beacons that can be mounted to a vehicle, rather than a costly, large array. The primary measurement taken to perform localizations is received signal strength (RSS). Unlike many previously existing methods, the techniques presented herein utilize practical, realistic assumptions and were progressively designed to mitigate incrementally discovered limitations. To exercise and analyze the developed methods, a multiple-layered simulation environment was developed in tandem. The approach, developed methodologies, and software infrastructure presented herein provide a framework for future endeavors within the field of wireless sensor networks.
{"title":"Localization Methods based on Error Analysis and Modeling in One Dimension","authors":"Omar A. Zargelin, Fadel M. Lashhab, Walid K. A. Hasan","doi":"10.1109/UEMCON51285.2020.9298065","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298065","url":null,"abstract":"Wireless sensor networks (WSNs) have shown promise in a broad range of applications. One of the primary challenges in leveraging them lies in gathering accurate position information for the deployed sensors while minimizing power cost. Through analyzing the error associated with acquiring such position information, we developed several novel localization methods based on modeling the analyzed error and applying rigorous mathematical and statistical principles in order to produce improved location estimates compared with existing methods. The methods presented herein have been utilized for a one-dimensional space for proof-of-concept, simplicity of presentation, and to illustrate how viable, single-dimensional applications can be approached. These methods utilize only two mobile beacons that can be mounted to a vehicle, rather than a costly, large array. The primary measurement taken to perform localizations is received signal strength (RSS). Unlike many previously existing methods, the techniques presented herein utilize practical, realistic assumptions and were progressively designed to mitigate incrementally discovered limitations. To exercise and analyze the developed methods, a multiple-layered simulation environment was developed in tandem. The approach, developed methodologies, and software infrastructure presented herein provide a framework for future endeavors within the field of wireless sensor networks.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"14 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":"127151261","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/UEMCON51285.2020.9298058
Hong Yu, Joushua Lorrain, Fanming Liu, C. Lo
The multiple embedded sensors of the cross-platform in Internet of Things (IoT) have influenced the aspects of human life, especially for smart home system. Android, iOS and Window OS are the leading representative in the terminal operating system of networks. As one of the applications in IoT, a smart home system included the cross-platforms with various software and hardware is programmable to enhance the network motive efficiently. In this paper, we discuss a cross-platform with the embedded devices such as sensors to support the realization of a smart home system, the network technologies of a smart home network, the varieties of devices and circuits, software-based systems and standards.
{"title":"Cross-platform for the development environment of smart home system","authors":"Hong Yu, Joushua Lorrain, Fanming Liu, C. Lo","doi":"10.1109/UEMCON51285.2020.9298058","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298058","url":null,"abstract":"The multiple embedded sensors of the cross-platform in Internet of Things (IoT) have influenced the aspects of human life, especially for smart home system. Android, iOS and Window OS are the leading representative in the terminal operating system of networks. As one of the applications in IoT, a smart home system included the cross-platforms with various software and hardware is programmable to enhance the network motive efficiently. In this paper, we discuss a cross-platform with the embedded devices such as sensors to support the realization of a smart home system, the network technologies of a smart home network, the varieties of devices and circuits, software-based systems and standards.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"75 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":"127362092","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/UEMCON51285.2020.9298072
M. Levy
Data center risk assessment must provide an understanding of the risks that the mission critical facility is exposed to. This paper proposes a novel framework for data center site risk assessment, as an important tool for data center due diligence. The proposed methodology is aimed at standardizing a process to help quantify and prioritize external risks to enable comparisons. It consists of three steps: risk identification, risk analysis, and risk evaluation. The risk analysis incorporates the infrastructure resiliency rating and a data center site risk metric to quantify the weighted risk level, as well as criteria based on standards, best practices and expert knowledge. Based on the results from the risk assessment, the risk level may be treated, to adjust it to the desired level. The risk assessment is a way to better communicate and understand risks associated to the data center location, and help evaluate mitigation strategies.
{"title":"A Novel Framework for Data Center Risk Assessment","authors":"M. Levy","doi":"10.1109/UEMCON51285.2020.9298072","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298072","url":null,"abstract":"Data center risk assessment must provide an understanding of the risks that the mission critical facility is exposed to. This paper proposes a novel framework for data center site risk assessment, as an important tool for data center due diligence. The proposed methodology is aimed at standardizing a process to help quantify and prioritize external risks to enable comparisons. It consists of three steps: risk identification, risk analysis, and risk evaluation. The risk analysis incorporates the infrastructure resiliency rating and a data center site risk metric to quantify the weighted risk level, as well as criteria based on standards, best practices and expert knowledge. Based on the results from the risk assessment, the risk level may be treated, to adjust it to the desired level. The risk assessment is a way to better communicate and understand risks associated to the data center location, and help evaluate mitigation strategies.","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":"126742087","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/UEMCON51285.2020.9298061
D. Mechta, S. Harous
Data aggregation is an energy-saving technology in wireless sensor networks (WSNs). Because of the high density of nodes in sensor networks, many nodes discover the same data (in many cases with a large quantity), leading to a lot of energy consumption and may be packets loss. These challenges can be resolved by using a data collection policy when routing packets from the source nodes to the base station (BS). Researchers are still struggling to choose an effective and appropriate data collection method from the current WSN literature. In this paper, we propose an energy-aware Huffman coding-based LEACH protocol for WSN (HC-LEACH). The experiment results show the effectiveness of the proposed scheme in enhancing energy consumption by approximately 38% compared to LEACH.
{"title":"HC-LEACH: Huffman Coding-based energy-efficient LEACH protocol for WSN","authors":"D. Mechta, S. Harous","doi":"10.1109/UEMCON51285.2020.9298061","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298061","url":null,"abstract":"Data aggregation is an energy-saving technology in wireless sensor networks (WSNs). Because of the high density of nodes in sensor networks, many nodes discover the same data (in many cases with a large quantity), leading to a lot of energy consumption and may be packets loss. These challenges can be resolved by using a data collection policy when routing packets from the source nodes to the base station (BS). Researchers are still struggling to choose an effective and appropriate data collection method from the current WSN literature. In this paper, we propose an energy-aware Huffman coding-based LEACH protocol for WSN (HC-LEACH). The experiment results show the effectiveness of the proposed scheme in enhancing energy consumption by approximately 38% compared to LEACH.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"87 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":"123620723","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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