論文 - 西　宏章

Industrial Edge Computing: Enabling Embedded Intelligence

Dai W., Nishi H., Vyatkin V., Huang V., Shi Y., Guan X.

IEEE Industrial Electronics Magazine （IEEE Industrial Electronics Magazine）  13 （ 4 ） 48 - 56 2019年12月

ISSN  19324529

　概要を見る

The term industrial edge computing is used to describe a distributed platform that integrates communication, computation, and storage resources for performing real-time applications that can be directly accessed from the cloud. A step toward the industrial Internet revolution, industrial edge computing is designed to facilitate agile connectivity, real-time control, and data optimization, while enabling intelligent applications, ensuring tight security, and protecting privacy. Industrial edge computing makes use of what is known as edge computing nodes (ECNs), which bridge the gap between the physical world and the digital world by acting as smart gateways for assets, services, and systems. The IEEE P2805 Standards are being developed for defining protocols for self-management, data acquisition, and machine learning through cloud-edge collaboration on ECNs.

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Face image anonymization as an application of multidimensional data K-anonymizer

Nakamura T., Sakuma Y., Nishi H.

Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019 （Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019）     155 - 161 2019年11月

ISSN  9781728152684

　概要を見る

Recently, the development of data communication networks and advances in computer processing capacity have significantly increased the amount of data to be managed. Although future innovations using data are expected, privacy violation of data has become a problem. For example, image disclosure in Social Networking Service may result in infringement of portrait rights. Previously, studies on anonymization have been conducted to disclose data for protecting personal information. However, conventional anonymization, even for high-dimensional data such as faced images, uses an averaging operation in anonymization and does not consider complex relationships between dimensions. Therefore, the loss of semantic meaning increases because it is caused by the anonymization process, assuming an Euclidean data space. We herein propose Multidimensional Inputs K-anonymizing Unit(MIKU), an anonymization algorithm focusing on face image anonymization as a typical example of high-dimensional datMIKU enables anonymization that retains the image quality. As the comparison method directly anonymizes images, the quality of images is deteriorated. MIKU considers the relation between dimensions using the latent space of StyleGAN. The effect of using latent space was confirmed by comparing the quantitative results of Fréchet Inception Distance(FID) and the qualitative evaluation of the output image with the comparison method. The quantitative results by FID proved that MIKU achieved better performance when K-anonymity was large. When K-anonymity is small, we clarified that the image quality of the comparison method such as FID could not be measured correctly and was overestimated because of the effect of the inception model's linearity. In addition, the qualitative evaluation of anonymized images shows that MIKU generates a more natural image in the contour and hair expressions than the comparison method, and the anonymized images of MIKU contain no unnatural edge lines on a face such as those generated in the comparison method.

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Exploring Variability in IoT Data for Human Activity Recognition

Sakuma Y., Kleisarchaki S., Gurgen L., Nishi H.

IECON Proceedings (Industrial Electronics Conference) （IECON Proceedings (Industrial Electronics Conference)）  2019-October   5312 - 5318 2019年10月

ISSN  9781728148786

　概要を見る

Human Activity Recognition (HAR) is a well-studied scientific area that has gained much traction with the rise of Internet of Things (IoT). Despite the interest in HAR for a wide spectrum of domains (technological, medical, etc.) only a few works exist, which study the variability in IoT data. To correctly perceive this variability, it is essential to dynamically model the evolving context of daily-life activities. Additionally, it is required to reduce the calculation cost of HAR, which is crucial for security and real-time applications. For the purpose of dynamically modeling, three context-aware approaches are formalized along with a context-free baseline. This study demonstrates improvements in terms of both of accuracy and calculation cost by considering variability in IoT data; our experimental study on real datasets reduced calculation cost by 20% while increasing accuracy by 20%.

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Practical Estimation Method of Thermal Sensation Using an Infrared Array Sensor

Saito S., Nishi H.

IECON Proceedings (Industrial Electronics Conference) （IECON Proceedings (Industrial Electronics Conference)）  2019-October   497 - 502 2019年10月

ISSN  9781728148786

　概要を見る

In heating, ventilation, and air conditioning control, it is crucial to maintain the comfort of residents. Thermal comfort is typically assessed using the predicted mean vote (PMV) index. PMV depends on six factors: air temperature, mean radiant temperature, air velocity, air humidity, metabolic rate, and clothing insulation. Although PMV can be estimated by measuring these factors directly, this process is costly because multiple sensors are required. Furthermore, measuring metabolic rate and clothing insulation is especially costly because expensive and complex sensors are required. To solve these problems, this paper proposes a practical method for estimating PMV by estimating metabolic rate and clothing insulation using a low-cost infrared array (IrA) sensor. In this study, an IrA sensor called 'Grid-EYE' is adopted. PMV parameters other than air velocity and humidity can be measured when the proposed method and an IrA sensor are implemented in an air conditioner. Human detection is done using the temperature map captured by the sensor and their PMV values are estimated individually. Heat sources around people are also detected and their influence on PMV estimation is evaluated. Practical experiments demonstrate the validity of the proposed method by providing estimated PMV values close to theoretical values and real sensations. Therefore, the proposed method can contribute to providing comfortable living spaces and improving energy consumption and amenities efficiently.

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Implementation of content-based anonymization edge router on netfpga

Fukuhara A., Iwai T., Sakuma Y., Nishi H.

Proceedings - 2019 IEEE 13th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019 （Proceedings - 2019 IEEE 13th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2019）     123 - 128 2019年10月

ISSN  9781728148823

　概要を見る

In recent years, a large number of Internet of Things (IoT) devices have appeared. Accordingly, various services using data from such devices have been proposed. However, the collected raw data include private information, and thus, privacy problems arise. Data anonymization is a method for removing privacy-sensitive information from raw data. Data anonymization for IoT data services should satisfy the following requirements. First, the raw data should be anonymized between a device and the cloud server. Second, the anonymization methods and the destinations of the collected data should be flexibly configured, as they depend on data types and agreements with data suppliers. Third, network transparency is necessary for ease of installation. However, conventional data anonymization systems do not satisfy these requirements. We propose anonymization hardware that functions as a network router on network edges. It directly anonymizes data in network packets. Moreover, it decides the destination IP address of the packets and anonymizes data based on their content. For high-Throughput and low-power processing of the packets, the proposed hardware was implemented by using a field-programmable gate array. The throughput of the proposed hardware achieved 10 Gbps wire speed, and the power consumption was lower than that of software implementation.

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Dynamic load balancing using actual workload traces based on central processing unit temperatures

Nakajo Y., Athavale J., Yoda M., Joshi Y., Nishi H.

Journal of Electronic Packaging, Transactions of the ASME （Journal of Electronic Packaging, Transactions of the ASME）  141 （ 3 ）  2019年09月

ISSN  10437398

　概要を見る

With the rapid growth in demand for distributed computing, data centers are a critical physical component of the "cloud."Recent studies show that the energy consumption of data centers for both cooling and computing keeps increasing, and the growth in server power densities makes it ever more challenging to keep the servers below their maximum operating temperature. This paper presents a new dynamic load-balancing approach based on individual server central processing unit (CPU) temperatures. In this approach, a load balancer assigns a task in real time to a server based on the objective to keep the CPU temperatures below a maximum value. Experimental studies are conducted in a single rack based on production workload traces of Google clusters. This study also compares the performance of this method with two other load balancing approaches, Round Robin, and a CPU utilization-based method in terms of temperature distributions, local fan rotation speeds, system loads, and server processing times. Furthermore, we investigate how the effect of the proposed load balancing changes with different assumed applications run on servers. The results indicate that this new method can more effectively reduce both server CPU temperatures and local fan rotation speed in a rack especially for the most of web applications.

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Estimation of building thermal performance using simple sensors and air conditioners

Sakuma Y., Nishi H.

Energies （Energies）  12 （ 15 ）  2019年07月

ISSN  1996-1073

　概要を見る

Energy and environmental problems have attracted attention worldwide. Energy consumption in residential sectors accounts for a large percentage of total consumption. Several retrofit schemes, which insulate building envelopes to increase energy efficiency, have been adapted to address residential energy problems. However, these schemes often fail to balance the installment cost with savings from the retrofits. To maximize the benefit, selecting houses with low thermal performance by a cost-effective method is inevitable. Therefore, an accurate, low-cost, and undemanding housing assessment method is required. This paper proposes a thermal performance assessment method for residential housing. The proposed method enables assessments under the existing conditions of residential housings and only requires a simple and affordable monitoring system of power meters for an air conditioner (AC), simple sensors (three thermometers at most), a BLE beacon, and smartphone application. The proposed method is evaluated thoroughly by using both simulation and experimental data. Analysis of estimation errors is also conducted. Our method shows that the accuracy achieved with the proposed three-room model is 9.8% (relative error) for the simulation data. Assessments on the experimental data also show that our proposed method achieved Ua value estimations using a low-cost system, satisfying the requirements of housing assessments for retrofits.

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Energy-efficient task distribution using neural network temperature prediction in a data center

Omori M., Nakajo Y., Yoda M., Joshi Y., Nishi H.

IEEE International Conference on Industrial Informatics (INDIN) （IEEE International Conference on Industrial Informatics (INDIN)）  2019-July   1429 - 1434 2019年07月

ISSN  9781728129273

　概要を見る

The growing demand for computing resources leads to a serious problem of excessive energy consumption in data centers. In recent studies, energy consumption of both computing and cooling equipment is drawing attention. For improving the energy efficiency of cooling equipment such as computer room air conditioners (CRACs), it is neccesary to predict temperatures in data centers and to optimize thermal management in data centers. In this study, we propose a temperature prediction method for servers in a data center using a neural network. We used the prediction result for distributing task targeting temperature-based load balancing. First, we conducted an experiment in a real data center to evaluate the prediction accuracy of the proposed method. We then simulated task distribution based on the predicted temperatures and compared the maximum CPU temperature with a non-predictive approach. The results indicated that the proposed method can reduce future CPU temperatures successfully compared to the non-predictive approach, though in exchange for high computational cost.

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Network transparent fog-based IoT platform for industrial IoT

Morishima R., Nishi H.

IEEE International Conference on Industrial Informatics (INDIN) （IEEE International Conference on Industrial Informatics (INDIN)）  2019-July   920 - 925 2019年07月

ISSN  9781728129273

　概要を見る

There has been rapid growth in the number of Internet of Things (IoT) devices that produce a large volume of data such as location data, temperature data, and power usage data. These data are used in various types of services on IoT platforms. IoT services such as autonomous vehicles and factory automation system have the following three requirements. First, confidential data such as machine ID, power usage, and location information need to be locally anonymized before they are sent to a cloud server. Second, these applications require low latency response of less than 10 ms. Third, the bandwidth usage that an enormous number of IoT devices generate needs to be efficiently reduced to alleviate the burden on the cloud server. However, existing IoT platforms have limitations on the configuration of applications because most of them are cloud-based. In this study, we propose a fog-based IoT platform, where fog nodes achieve network transparency for the IoT devices and the cloud server. The network-transparent machine can be installed without modification of the existing network configuration and routing. Fog nodes transparently achieve the three requirements: anonymization of specific data in packets, real-time feedback, and reduction in the bandwidth usage.

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Optimized service function path selection for iot devices using virtual network function performance data

Abeysiriwardhana W., Wijekoon J., Nishi H.

International Conference on Information Networking （International Conference on Information Networking）  2019-January   165 - 170 2019年05月

ISSN  9781538683507

　概要を見る

Software defined networking (SDN) and network function virtualization (NFV) are proposed as software based applications to cater to smart services requirements of smart communities. The services are linked together to support different sets of clients using service function chaining (SFC). Service functions (SFs) in an SFC must be distributed among available computing resources by creating a service function path (SFP) allowing resource management and optimal SF execution. This paper proposes a novel SFP allocation algorithm considering the computation capabilities of hardware resources while minimizing the completion time of SFCs. The proposed algorithm shows 10% performance increment compared to recently developed algorithms such as nearly optimal service function path and optimal service function selection algorithms.

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Error correction method considering fog and edge computing environment

Matsui K., Nishi H.

Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019 （Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019）     517 - 521 2019年05月

ISSN  9781538685006

　概要を見る

This study aims to execute error correction service of sensor data at fog and edge, which are the computing environments for distributing the concentrated processes in a cloud by using hierarchical computing resources. Service integrity of error correction was evaluated as an essential application of fog and edge computing environment, which can use the locality of fog and edge. Errors or outliers in data may cause a serious problem in system controlling and visualization application for behavior change when by using the malformed data or visualization of the data. The proposed methods can solve the data congestion problems at a cloud in the future IoT era.

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Feature extraction and resident number prediction method using power consumption data

Yoshida M., Imanishi T., Nishi H.

IEEJ Transactions on Electronics, Information and Systems （IEEJ Transactions on Electronics, Information and Systems）  139 （ 3 ） 227 - 236 2019年

ISSN  03854221

　概要を見る

A large amount of power consumption information generated from private houses is being aggregated nowadays, particularly by the spread of smart electric meters. Applications that utilize these data are widely studied, and several services have been proposed. In order to utilize the power demand information effectively, an appropriate feature extraction and selection method is necessary. In this paper, a feature extraction method for power consumption information is proposed. Extracted features are used to predict the “number of household members (number of residents)” using typical machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighborhood (k-NN), and Random Forest (RF). The number of residents represents significant information for the marketing departments of several industries such as real estate and construction industries. The proposed feature extraction method consists of two steps: feature variable generation and feature variable selection. In the feature variable generation step, we have used both fundamental statics and an ARMA model to generate 33 feature variables. In the feature variable selection step, the extracted feature variables are first ranked by applying Analysis of Variance (ANOVA). An appropriate feature variable set is selected by assessing several combinations of the 33 features, based on proposed extended algorithm of Recursive Feature Elimination (RFE). Our overall feature extraction method is evaluated based on the prediction accuracy using extracted feature variables. Compared with the accuracy using feature variables extracted by conventional methods, the accuracy is improved by 6.78%, 4.98%, and 8.11% for k-NN, SVM, and RF, respectively, and we have successfully proven validity of our proposition.

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Software-accelerated Service-oriented Router for Edge and Fog Service Enhancement Using Advanced Stream Content Analysis

Shanaka W.A., Abeysiriwardhana P., Wijekoon J., Tennekoon R.L., Nishi H.

IEEJ Transactions on Electronics, Information and Systems （IEEJ Transactions on Electronics, Information and Systems）  139 （ 8 ） 891 - 899 2019年

ISSN  03854221

　概要を見る

Fog services have been gaining momentum due to the demand generated by IoT-based services that require data and services to be provided at the edge of the network. This phenomenon creates several problems for Internet systems, such as security, and data management. Particularly, edge and fog computing addresses these problems and provides new services by solving these issues. To this end, a service-oriented router (SoR) was proposed and extended to provide edge and fog services by performing authorized stream content analysis (ASCA). Packet Capture (Libpcap) library was used to develop the initial implementation of the SoR to perform ASCA using conventional hardware and software platforms. However, Libpcap implementation together with conventional hardware demonstrated limited performance. Consequently, this paper proposes a method using Intel technologies such as the Intel Data Plane Development Kit and Hyperscan to accelerate stream processing and string matching on SoRs. The paper compares both implementations of SoRs and evaluates the performance with regard to packet throughput, CPU utilization, and memory usage. Furthermore, the latency of major functions is evaluated under Libpcap-based and DPDK-based SoR implementations. The results demonstrate an increase of more than 0.8 Gbps in bandwidth in a 1 Gbps link using less hardware utilization when SoR is implemented using Intel technologies compared to the original Libpcap implementation.

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Image analysis on the flexural behaviors of RC beams with artificial damage and the effects of repair

Kota Matsue, Takashi Matsumoto, Ko Kakuma, Hiroaki Nishi

INTERNATIONAL CONFERENCE ON SUSTAINABLE CIVIL ENGINEERING STRUCTURES AND CONSTRUCTION MATERIALS (SCESCM 2018) （E D P SCIENCES）  258 2019年

ISSN  2261-236X

　概要を見る

In cold and snowy regions, frost damage happened in an RC bridge deck slab leads to horizontally layered cracks, which results in the disintegration of cover concrete. Since this deterioration mode occurs inside an RC slab, it is usually difficult to detect them. It is possible that the slab performances have already decreased when the deteriorations appear on the exterior surface. As a preliminary step towards the establishment of a method for evaluating the structural performance of such a damaged RC slab, the purpose of this paper is to investigate the relationship between deteriorations and structural behaviors and to study the effect of an epoxy injection repair. For simplification, RC beams are prepared with artificial cracks that simulate deteriorations and these were made by the use of expansive agent. These beams are tested in flexure. Since the damage is volumetric, image analysis is useful to capture the two-dimensional deformations. It is shown that with different deterioration depths that is a part of the compression side of the beam or full of the height, there are differences about the deformation behaviors, the repair effects and the distributions of maximum and minimum principal strain.

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Hardware for Accelerating Anonymization Transparent to Network

Shohata S., Nakamura Y., Nishi H.

Proceedings - 2018 6th International Symposium on Computing and Networking, CANDAR 2018 （Proceedings - 2018 6th International Symposium on Computing and Networking, CANDAR 2018）     181 - 187 2018年12月

ISSN  9781538691823

　概要を見る

With the increase in the number of Internet of Things (IoT) devices, many applications using the data accumulated from IoT devices have been proposed. Consequently, privacy violation due to data disclosure is becoming a problem. Data anonymization is a technique for publishing data while preserving data privacy and is an effective method for solving the problem of privacy violation. There are several requirements for implementing anonymization systems suitable for IoT devices, where the amount of hardware and software resources, function extensibility, and processing throughput are limited. To satisfy these requirements, anonymization hardware for anonymizing data packets from IoT devices is proposed. The proposed hardware is allocated on the intermediate location of network and anonymizes the data packet stream from IoT devices transparently. The proposed anonymization process does not affect data communication or routing. This transparency allows an additional anonymization function to be installed on all types of IoT devices without modifying the devices. The proposed hardware allows IoT devices to anonymize the data packet stream. To reduce the latency and achieve transparent anonymization at a low cost, the proposed anonymization hardware was implemented using a field-programmable gate array. The throughput and latency of the testbed of the proposed hardware were 1,286 Mbps and 330 ns, respectively. The power consumption of the proposed hardware was lower than that for a software implementation.

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An overview of technologies for lower energy consumption in smart buildings

Moayedi S., AlJuheshi F., Almaghrebi A., Haase J., Nishi H., Tsang K., Alahmad M.

Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society （Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society）     4693 - 4698 2018年12月

ISSN  9781509066841

　概要を見る

In the last decade, the significant development of smart building technologies has led to the formation of various energy sensing and monitoring applications. Energy monitoring of appliances relies on techniques such as Intrusive Load Monitoring (ILM) and Non-Intrusive Load Monitoring (NILM). I L M is referred to a technique that a sensor installed for each load. In N I L M method, disaggregation of measured energy of all appliances at utility service entry is the main goal to provide a simple and cost-effective method of monitoring the appliances like sequence time domain reflectometry (STDR). This manuscript provides an overview of developments in energy consumption sensing and monitoring in three key areas of Internet of things (IoT), WSN and STDR for the advancement of smart building technology. This paper also provides some research directions for smart home of the future.

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TMk-anonymity: Perturbation-based data anonymization method for improving effectiveness of secondary use

Nakamura T., Nishi H.

Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society （Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society）     3138 - 3143 2018年12月

ISSN  9781509066841

　概要を見る

The recent emergence of smartphones, cloud computing, and the Internet of Things has brought about the explosion of data creation. By collating and merging these enormous data with other information, services that use information become more sophisticated and advanced. However, at the same time, the consideration of privacy violations caused by such merging is indispensable. Various anonymization methods have been proposed to preserve privacy. The conventional perturbation-based anonymization method of location data adds comparatively larger noise, and the larger noise makes it difficult to utilize the data effectively for secondary use. In this research, to solve these problems, we first clarified the definition of privacy preservation and then propose TMfc-anonymity according to the definition.

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Self-organizing map using classification method for services in multilayer computing environments

Iwai T., Ohno Y., Niwa A., Nakamura Y., Sakai K., Matsui K., Nishi H.

Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society （Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society）     4193 - 4198 2018年12月

ISSN  9781509066841

　概要を見る

The increasing amount of data running in cloud-computing environments has started inflating networks. To solve the problems caused by network inflation (e.g., latency and privacy), new types of computing environments with multiple layers have been proposed. However, service placement inside these multilayer computing environments has not been proposed. Nodes inside multilayer computing environments have different preferences, and the services deployed also have restrictions on deployment. Therefore, services must be placed carefully inside the computing environment. To place these services, we introduce a service classification method according to their properties and restrictions. However, when accommodating dynamic placement, rapid classification is needed to avoid serious damage caused by restriction changes. Therefore, we propose a classifying method using k-Nearest Neighbor Classification (k-NN). In addition, to accelerate the process, we use a dimension reduction method called Self-Organizing Maps (SOM) to preprocess the data. The proposed classification method is expected to be used as the primary step in service placement. The method will supply service placers with the identification of which layer services should be deployed.

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FROG: A Packet Hop Count based DDoS Countermeasure in NDN

Nakatsuka Y., Wijekoon J., Nishi H.

Proceedings - IEEE Symposium on Computers and Communications （Proceedings - IEEE Symposium on Computers and Communications）  2018-June   492 - 497 2018年11月

ISSN  9781538669501

　概要を見る

Named Data Networking (NDN) is a promising inter-networking paradigm that focus on content rather than hosts and their physical locations. In NDN Consumers issue Interests for Contents. Producers generate a content in response to each received interest and such content is routed back to the requesting consumer. When compared to IP, NDN brings advantages such as better throughput and lower latency, because routers are able to cache popular contents and satisfy interests for such contents locally. However, before being considered a viable approach, NDN should offer security services that are ideally better, but at least equivalent to current mechanisms in IP.In this regard, mechanisms to prevent DDoS are of paramount importance. In this work we propose FROG: a simple yet effective Interest Flooding Attack (IFA) detection and mitigation method. FROG runs on routers that are directly connected to NDN consumers and monitors packet hop counts. It then calculates mean and variance using stored hop counts to distinguish attackers from legitimate users. We use the NDN simulator ndnSIM to evaluate FROG's effectiveness. Our results show that FROG improves resilience against DDoS attacks. In particular, during an attack, legitimate users can still receive 75% of requested contents. Without FROG this number decreases to 50%.

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Improving Road Traffic Management by A Model-Based Simulation

Harahap E., Wijekoon J., Purnamasari P., Darmawan D., Ceha R., Nishi H.

Proceedings - 2018 4th International Conference on Science and Technology, ICST 2018 （Proceedings - 2018 4th International Conference on Science and Technology, ICST 2018）  2018年11月

ISSN  9781538658130

　概要を見る

Road traffic is a supporting medium that is essential to various means of land transportation that connect various locations and even large cities. Smooth traffic is a good parameter of the management of a city, also as a sign to determine the technological and economic development in the city. As the rate of civilization grows, as well as the number of people, the traffic becomes more crowded, so that congestion occurs in to an alarming level. The solution to solve the problem is by applying some methods as well as traffic engineering. However, implementing such methods without proper planning will lead to higher costs and lesser optimal results. In this article, we proposed a traffic simulation system called as 'LINTAS', a simulator tool in an attempt to resolve the traffic jam. The exact method and traffic engineering, before it is tested in a real situation, is first simulated through the LINTAS system, so that it can be known its effectiveness. LINTAS are built using the SimEvents toolbox and runs on MATLAB-Simulink software. The LINTAS simulator is performed based on the mathematical sciences, especially the field of queueing theory.

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