Details of a Researcher - Nishi, Hiroaki

Papers - Nishi, Hiroaki

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Efficient energy utilization based on task distribution and cooling airflow management in a data center

Yusuke Nakajo, Tomomichi Noguchi, Hiroaki Nishi

Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society (IEEE) 2017-January 7171 - 7176 2017.12

Accepted, ISSN 1553-572X

© 2017 IEEE. With the recent emergence of smartphones, cloud computing, and the Internet of Things (IoT), our society has become more dependent on the Internet. In these circumstances, increasing energy consumption in data centers is becoming a crucial problem worldwide and data center managers are required to run them efficiently in terms of energy consumption. This study aims to reduce cooling airflow energy by achieving appropriate task distribution and adding a shutter control system, which reduces the energy consumption of an air-conditioner. In most cases, servers tend to be unnecessarily cooled at low temperatures, even when their exhaust temperatures are not high. Our proposed method solves this problem by using shutter control and introducing a task allocation method. We built an experimental rack model and implemented our proposed control system, validating it with a real HTTP data request. The results show that our experimental system reduces the cooling airflow energy by 4.4%.

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Novel infrastructure with common API using docker for scaling the degree of platforms for smart community services

Tatsuki Miura, Janaka L. Wijekoon, Shanaka Prageeth, Hiroaki Nishi

Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017 (IEEE) 474 - 479 2017.11

ISSN 1935-4576

© 2017 IEEE. The development of smart communities has diversified not only service execution platforms but also the resolvers of multiple service requirements, each of which has different requirements in terms of processing delay, anonymity, computational cost, the amount of data at a given level of granularity, etc. To meet these requirements, an infrastructure that easily performs service migration and provides services with the correct processing nodes using IP-independent distributed processing methods such as Authorized Stream Contents Analysis (ASCA) is becoming a pressing need of smart communities. ASCA is an advanced method of analyzing packet streams and filtering necessary packet streams according to the marker tags in the contents of the streams under the Opt-In manner. Moreover, smart communities require that every service be able to perform ASCA and gather necessary data because of the diversified nature of the services. Consequently, in this paper, we have implemented a service infrastructure using a Docker container that facilitates service migration and provides services with a common Application Programming Interface (API) using ASCA. The API provides a process throughput of over 60 Gbps on a Docker container using Zero-Copy mechanism.

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Application of blind watermarking method for secondary use on smart community

大野雄太, 仁和瞭, 西宏章

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 (電子情報通信学会) 117 ( 273 ) 191 - 196 2017.11

ISSN 0913-5685

Proposal and Evaluation Techniques to Determine Characteristics of Importance with Saccharides Crystallization Data

湯山要海, 吉田将大, 服部一裕, 村並広章, 田中基雅, 西宏章

電気学会情報システム研究会資料 (電気学会) 2017 ( 51 ) 1 - 4 2017.11

Decentralized hybrid switching control of multiconverter MVDC shipboard power systems

Dominik Mildt, Ryogo Kubo

Proceedings of the 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017 2017.10

Accepted

スマートコミュニティとは何か –実証例から見る社会問題解決策としての可能性-

西宏章

Journal of Human and Environmental Symbiosis, 環境共生 31 3 - 12 2017.09

Research paper (scientific journal)

Design of Smart Community : Its possibility as a solution for social problems from case study perspectives

西宏章

Journal of Human and Environmental Symbiosis, 環境共生 (日本環境共生学会本部事務局) 31 3 - 12 2017.09

ISSN 1346-3489

Time-series decomposition of power demand data to extract uncertain features

Tomoya Imanishi, Masahiro Yoshida, Janaka Wijekoon, Hiroaki Nishi

IEEE International Symposium on Industrial Electronics (IEEE) 1535 - 1540 2017.08

ISSN 2163-5137

© 2017 IEEE. The spread of smart meters means that a large amount of power demand information from private houses is being collected around the world. Owing to the development of smart city infrastructure, the use of standardized frameworks for extracting features from power demand information has become vital. In this paper, we propose a novel decomposition approach useful for extracting feature values from power demand information from a house. Energy consumption was monitored for multiple houses for one month in Japan with a sampling duration of 30 minutes, which is a standard sampling time of smart meters in Japan. First, periodic characteristics were detected for 24 hours based on autocorrelation analysis. Then, the monitored information was decomposed into four components: standby power, trends, and periodic and residual parts. The distribution of the residual part is similar to a Gaussian distribution, so the behavior of the residual part was parameterized using variance and average. Trend, periodic, and residual components were clustered by means of k-means clustering in order to aggregate the difference in behaviors. There was no periodic component in the residual part according to auto-correlation analysis. Nevertheless, some clusters had a relatively large variance, which means that abnormal power demand occurred frequently in datasets. The amount of variance and climate correlation was analyzed, and the fact detected that large scale events disturb usual daily life-styles, from the viewpoint of energy usage. Last, these features were compared with actual customer information. In the evaluation, family structure and floor space were utilized to prove the effectiveness of the proposed decomposition approach. The evaluation proved that this decomposition method could extract uncertainty features from power demand information.

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Feature extraction and background information detection method using power demand

Masahiro Yoshida, Tomoya Imanishi, Hiroaki Nishi

IEEE International Symposium on Industrial Electronics (IEEE) 1336 - 1341 2017.08

ISSN 2163-5137

© 2017 IEEE. Recently, many electricity retailers have been aggregating consumer power demand information from smart meter infrastructure, and applications that utilize these data are widely studied. For example, the estimation of customers' background information using their power demand information has attracted much interest; this information can be utilized in the marketing field for background-targeted advertising. In order to utilize power demand information effectively, an appropriate feature extraction method must be applied. In this paper, appropriate data extraction methods specific to power demand information are proposed. In the experiment, power demand data for Kawasaki city were used, and 19 feature data were extracted using the proposed method. The utility of the extracted features was assessed through the performance of classification estimation for two background information types, family structure and floor space. The classification problems are solved by applying two typical machine-learning algorithms, the support vector machine and k-nearest neighbor. In particular, analysis of variance (ANOVA) was applied to the 19 feature data, which were ranked according to the F value. Then, the n (n = [1, 2, 19]) best feature data were used as the input step by step, and the score for each condition was computed to derive the best feature set. According to the results, some of the feature data were considered to be irrelevant, and the best feature data set was successfully selected. Furthermore, thee scores when raw data were input were also computed and compared with the scores when the best feature data set was used. As a result, the performance was better when using processed data instead of raw data.

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Novel Method to Watermark Anonymized Data for Data Publishing

Yuichi Nakamura, Yoshimichi Nakatsuka, and NISHI HIROAKI

IEICE Transactions on Information and Systems (IEICE) E100-D ( 8 ) 1671 - 1679 2017.08

Research paper (scientific journal), ISSN 0916-8532

Copyright © 2017 The Institute of Electronics, Information and Communication Engineers. In this study, an anonymization infrastructure for the secondary use of data is proposed. The proposed infrastructure can publish data that includes privacy information while preserving the privacy by using anonymization techniques. The infrastructure considers a situation where ill-motivated users redistribute the data without authorization. Therefore, we propose a watermarking method for anonymized data to solve this problem. The proposed method is implemented, and the proposed method's tolerance against attacks is evaluated.

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Implementation and Evaluation of an FPGA-based Network Data Anonymizer

Yuichi Nakamura, Sota Sawaguchi and HIROAKI NISHI

Journal of Information ProcessingIEEJ Transactions on Electrical and Electronic Engineering, IEEJ Trans 2017; 12(S1); S134-S140 (Wiley Online Library.) 12 ( S1 ) S124 - S140 2017.06

Joint Work

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Effectiveness of Service-oriented Router for ISP-CDN Collaboration

Janaka L. Wijekoon, Eriwn H. Harahap, Rajitha L. Tennekoon,NISHI HIROAKI

Journal of Information Processing (Information Processing Society of Japan) 25 ( 25 ) 44 - 45 2017.01

Research paper (scientific journal), Accepted, ISSN 0387-5806

© 2017 Information Processing Society of Japan. This article discusses a novel method to strengthen the collaboration between Internet service providers (ISPs) and content delivery networks (CDNs). CDNs are becoming the primary data delivery method in information communication technology environments because information sharing via networks is becoming the driving force of the future Internet. Moreover, it is anticipated that network routers will be equipped with additional processing power and storage modules for providing efficient end-user services. Consequently, this article studies the effectiveness of introducing a Service-oriented Router (SoR) to strengthen the ISP-CDN collaboration to leverage DNS-based request redirection in CDNs. In contrast, the proposed method yields better performance in user redirection and network resource utilization, suggesting that using SoR may a future business model which addresses adequate ISP-CDN collaboration.

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Feature expression of frequency transform regarding daily power demand information

Tomoya Imanishi, Rajitha Tennekoon, Hiroaki Nishi

Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017 (Association for Computing Machinery, Inc) 23 - 23 2017.01

Accepted

© 2017 ACM. Owing to the introduction of an energy-monitoring device called the "smart meter," a large amount of power-demand information regarding private houses is being stored around the world through the development of smart grids. Such electric consumption data include a variety of features related to the structure of the customer's home, as well as their family structure and lifestyle. To capture the meaningful features from the power-demand information, a proper extraction method should be considered such as a discrete Fourier transform (DFT) or discrete cosine transform (DCT). This paper proposes the use of two frequency analysis methods, i.e., DFT and DCT, toward power-demand information. The converted power-demand information is transformed using three expression methods. A power-demand dataset was gathered from more than 100 houses in Yokohama, Japan, over a two-year period. The numbers of feature values in these three expression methods were compared by estimating the floor space of each customer's house with two support vector classification (SVC) values using both linear and RBF kernels. As a result, the proposed expression method performed better than the other two feature expression methods. The mean accuracy of this expression method is better than the other two by 20%, and the highest level of accuracy in terms of the floor-space estimation is greater than 95% when using liner SVC (L2-SVC). The accuracy shows a distribution of 50% to 100% regardless of the number of selected frequencies transformed using a DFT and DCT.

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Temperature-based request distribution for effective CRAC and equipment life-cycle extension

Yusuke Nakajo, Hiroaki Nishi

ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems, InterPACK 2017, collocated with the ASME 2017 Conference on Information Storage and Processing Systems (AMER SOC MECHANICAL ENGINEERS) 2017

Accepted, ISSN 2378-8267

© 2017 ASME. With the growth of Internet-based services, data centers must deal with increasing data traffic. Load-balancing technologies can help data centers process data effectively and stably; however, the current load-balancing methods do not take into account the heat generated by servers. Excessive heat can increase the failure rate of IT devices and the energy consumption of air conditioning systems, both of which lead to higher data center maintenance costs. This paper aims to simultaneously increase the coefficient of performance (COP) of the data center's air-conditioning equipment and decrease the semiconductor-based equipment failure rate. To do so-and, consequently, reduce the operation and maintenance costs-we propose a novel request distribution system based on servertemperature and evaluate the proposed system by creating a thermal model of a data center. As a result, it is suggested that using the proposed load-balancing method the semiconductor failure rate can be reduced by 32 % when compared with the common round-robin distribution method, and by 19 % when compared with a load-balancing method based on CPU utilization. Moreover, the COP of the air-conditioning equipment obtained with the proposed method is recognized to be higher than those obtained with either the round-robin or the CPUutilization- based methods.

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Coordination middleware for secure wireless sensor networks

Yuichi Nakamura, Maxime Louvel, Hiroaki Nishi

IECON Proceedings (Industrial Electronics Conference) (IEEE) 0 6931 - 6936 2016.12

Accepted, ISSN 1553-572X

© 2016 IEEE. Wireless sensor networks (WSNs) are implemented in various Internet-of-Things applications such as energy management systems. As the applications may involve personal information, they must be protected from attackers attempting to read information or control network devices. Research on WSN security is essential to protect WSNs from attacks. Studies in such research domains propose solutions against the attacks. However, they focus mainly on the security measures rather than on their ease in implementation in WSNs. In this paper, we propose a coordination middleware that provides an environment for constructing updatable WSNs for security. The middleware is based on LINC, a rule-based coordination middleware. The proposed approach allows the development of WSNs and attaches or detaches security modules when required. We implemented three security modules on LINC and on a real network, as case studies. Moreover, we evaluated the implementation costs while comparing the case studies.

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Shutter control for cooling air flow management in data center servers

Tomomichi Noguchi, Janaka Wijekoon, Yogendra Joshi, Minami Yoda, Hiroaki Nishi

IECON Proceedings (Industrial Electronics Conference) (IEEE) 5591 - 5596 2016.12

Accepted, ISSN 1553-572X

© 2016 IEEE. The growth of energy consumption in data centers is becoming one of the significant problems all over the world. The power usage of IT devices and air-conditioning (AC) units accounts for up to three-quarters of total energy consumption in typical data centers. This study discusses the effectiveness of using shutter-controlled air flow management to control cold air flow into the servers. It can impact the power consumption of both, the servers and the AC units. A raised floor data center has two types of aisles - cold and hot. In most cases, excessive volumes of cold air are pumped into the servers using cold aisles. We are proposing the utilization of a shutter to control the air flow and reduce wastage while preventing damage due to increased CPU temperature. The shutter is intended to control the air flow and maintain the temperature appropriately according to the workload of the servers. We evaluated the proposed system for controlling the flow of air into the servers. The results show that cold air volumes can be reduced by up to 92% while maintaining appropriate control of CPU temperature.

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Analysis and implementation of WSN with Route Selection considering Energy Consumption

Tadanori Matsui, Hiroaki Nishi

2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 (IEEE) 1 - 7 2016.12

Accepted, ISSN 2373-6836

© 2016 IEEE. In a smart city infrastructure, Wireless Sensor Network (WSN) is the most important technique to collect environmental data to provide services. WSN becomes more and more popular research area because of its usefulness and huge demand. Since energy resource in WSN is limited, the design of a WSN system should consider energy effectiveness as well as fulfilling application requirements. The effectiveness of the system totally depends on its application and hardware specifications; thus, the routing algorithm of WSN must be optimized to achieve the energy effective communication. The proposed algorithm Energy Consumption-oriented Route Selection (ECORS) is the route selection algorithm attempting to prolong the system lifetime, especially for data collection application. In this paper, the suitable mechanism for data collection is proposed with focusing on implementation, which includes the actual packet transactions mechanism and the improved function for selecting routes in an assumed environment. By modifying the parameters with the thought of implementation, proposed mechanism can extend the lifetime 2.03 times longer than the fixed routing scenario. Furthermore, the simulation result is analyzed in detail with focusing on state of charge variation on each node. Moreover, the new indicator to depict the relationship between lifetime expansion rate and parameters used for the algorithm is introduced. By using this indicator, lifetime expansion rate can be assumed without simulation, which is helpful for implementation.

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Automated generation method of recommendation for effective energy utilization as a HEMS service

Takahiro Hosoe, Tadanori Matsui, Hiroaki Nishi

2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 (IEEE) 74 - 79 2016.12

Accepted, ISSN 2373-6836

© 2016 IEEE. Smart Grids and Home Energy Management System (HEMS) have been propagated by energy liberalization, and there is a demand for services, which are based on analysis of energy consumption data. For instance, a recommendation on effective utilization of home appliances in order to reduce power consumption. However, it is computationally expensive to analyze data in order to provide an energy-saving handbook, which recommends low-carbon life and is written in a natural language. This kind of service is called recommendation service. Existing automated recommendation services are constrained by the range of data usage, especially when using local information, such as status of surroundings, weather, residents' behavior, etc. The proposed method of automated generation of recommendation considers this background knowledge and information by using clustering methods. The result of a questionnaire which compared a handmade recommendation with the proposed fully-automated recommendation showed that 80% of the residents selected the automated recommendation because of its appropriateness.

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Feature extraction and classification using power demand information

Tomoya Imanishi, Rajitha Tennekoon, Hiroaki Nishi

2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 (IEEE) 92 - 97 2016.12

Accepted, ISSN 2373-6836

© 2016 IEEE. Electrical load monitoring, by means of a smart meter, is getting more and more popular these days. Power demand information from smart meters is drawing attention among researchers, since it could be applied for power demand control. Providing attractive services with smart meters encourage electricity retailers to utilize demand side management, which could be a solution for energy-related problems in our society. In this paper, a novel service is proposed by classifying private information from the household electricity usage. The private information is estimated using feature vectors extracted from time series analysis of power demand information. In order to extract feature vectors effectively, two extraction methods were proposed: simple statistical method, and Discrete Fourier Transform (DFT) based extraction method. Then, Support Vector Machines (SVMs) classifier is carried out after the optimization of hyper-parameters. As the estimated information, both family structure and floor space were selected. The classification result is evaluated using F-measure and accuracy. As a result, the accuracy of DFT-based classification was superior to the statistical method for detecting the floor space in a house.

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Influence of noise-based perturbation on recommendation application

Yuichi Nakamura, Takahiro Hosoe, Hiroaki Nishi

2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 (IEEE) 14 - 19 2016.12

Accepted, ISSN 2373-6836

© 2016 IEEE. A typical service provided by a Smart City is a Home Energy Management System (HEMS), an automated system that uses sensor networks to collect usage of home appliances and devices such that the collected data are accessible by the local government. For example, Smart meters, installed within individual homes, can send measures of electricity consumption for analysis in the context of the global community. Such data can be used by a forecasting tool to avoid electrical outage and improve the overall management of electricity usage by the community. However, the data collected may be protected by individual privacy laws and it is possible that the network may be violated by intruders to gain access to the lifestyle of individuals within their home. For example, an intruder may be able to identify active appliances and the number of individuals present within the residence. There is a requirement to protect the data emanating from these sensors to preserve this individual privacy information. Thus, a data concierge service capable of analyzing individual HEMS data is required; such a service would improve the lifestyle by allowing access to HEMS data by a third-party analyst. Although such a service may not require raw data, one must evaluate the trade-off between the preservation of privacy information and the effectiveness of the impact of the data services on the entire community. In this paper, we implemented a prototype application in conjunction with two noise-based perturbations. The application recommends improvements to household electricity usage based on its measured electricity consumption collected by smart meters. The perturbations provide a method to support Privacy Preserving Data Mining (PPDM) techniques. Evaluation of the performance results of the prototype applications illustrates the influence of the PPDM in improving the accuracy of said application.

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