論文

© 2016 The Institute of Electrical Engineers of Japan. A large fraction of the Internet traffic is organized by the Content Delivery Networks (CDNs). CDNs redirect endusers to an appropriate server using Domain Name Servers (DNSs). Meanwhile, in future networks, it is anticipated that network routers will be equipped with more processing power and storage modules for providing most effective end-user services. From this viewpoint, a Service-oriented Router (SoR) is introduced to accelerate content-based services. This paper presents a novel method, DNS and SoR Collaborative Redirection (DSCR), to accelerate the end-user redirection. DSCR uses SoRs to collect and store network and server state information by placing SoRs at edges of the ISP network. The collected information is then used by the DSCR to leverage the DNS-based end-user redirection. We present the design and a prototype implementation of DSCR and, show how both SoR and DNS jointly can take advantage of existing Internet architecture to accelerate end-user redirection. We used a detailed simulation to evaluate the proposed method using various strategies. Simulations clearly demonstrate the effectiveness of the collaborative method, which yields 5-10% reduction in end-user latency when compared with DNS-based redirection.

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.

In recent years, while internet of things (IoT) devices and cloud computing environments have been penetrated, the increase in communication latency and concentration of traffic caused by data centers has deteriorated the quality of some network services. Message Queuing Telemetry Transport (MQTT) is a well-known protocol for exchanging messages between dedicated applications. MQTT employs a publish–subscribe model in which a broker mediates communication between IoT devices and applications. To improve service quality, the processing latency of the broker should be reduced. Moreover, the broker's throughput must be increased. IoT devices, in particular, must maintain a highly accurate time to provide some IoT services. This study proposed hardware implementation of MQTT broker and evaluated the accuracy of an MQTT-based time synchronization method. The proposed MQTT architecture's hardware resource utilization is also provided. We confirmed that our implementation achieved high throughput, low latency, and low jitter MQTT broker, which satisfies the smart city services for automated drive and power control, which must be less than a few milliseconds. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

© 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.

© 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.

In smart communities, data are aggregated, and various services are provided based on it. When considering the optimization of service delivery through cooperative processing between the edge and the cloud in smart communities, it is essential to realize network transparent edge services due to the constraints of not touching end devices in order to improve user convenience and additional cost reduction. Moreover, considering the minimization of service provision and service delivery latency at the edge, it is desirable to use field-programmable gate arrays (FPGAs), which have lower power consumption, lower latency, and higher throughput than conventional processors. Additionally, to advance towards even lower latency, the distribution of processing load is important, and service migration is necessary for this purpose. Service migration is generally used to increase service availability, and even for edge services, service migration is important because the distribution of processing and the location where the service should be provided changes over time due to user mobility. When considering network transparency, service migration should be performed on the communication path between the end device and the cloud. Furthermore, to enhance service transparency, it is essential to maintain communication consistency for continuous service provision and minimize service downtime. Therefore, this study proposes a migration mechanism with FPGAs to satisfy these requirements. As an example of a real application, we target a network-transparent data anonymization application to protect the privacy of data providers and evaluate the proposed architecture based on the reality of latency and other factors. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A service-oriented router (SoR) is a new router architecture that provides rich application-layer services to Internet users by extracting information from network traffic. SoR performs stream reconstruction and selection using string matching. After on-the-fly reconstruction of stream data, SoR extracts the required information using a software-based character string analyzer. For wire-rate string matching and for design flexibility, a software-based string-matching accelerator is required. A graphics processing unit (GPU) is a promising solution as a conventional cost-effective accelerator for use in a high-end router. Existing string-matching methods do not consider the multiple-stream processing and effective thread management required in a network application. We propose an accelerated string-matching method that is customized for a router and implemented a task controller that improves thread distribution depending on the status of core processes. © 2013 IEEE.

In this paper, a scheme for a haptic communication system is proposed by using an FPGA (Field Programmable Gate Array) and real-time network framework, RTnet. In the haptic communication system, the FPGA is used as a motion control processor, and the Linux-based real-time network framework is applied to the communication line between a master and a slave robot. The FPGA can execute control calculations in a short interval. Sending a large number of packets in a short period is, however, not acceptable for the communication line. In the haptic communication system, the FPGA execute the control calculation in every 50 μs, and the packets are sent in every 1 ms to prevent network congestion. The degrees-of-freedom(DOF) of the haptic device can be increased without increasing the number of sending packets, since the data of the each DOF is packed into one packet in a Linux module. In this paper, a master-slave haptic communication system is developed. The master side consists of a haptic device, whereas the slave side consists of a virtual haptic device and a virtual environment. The validity of the haptic communication system is shown through an experiment. ©2009 IEEE.

In late years, techniques and instruments for endoscopic surgery have been developed. The endoscopic surgery is a minimally invasive surgery, hence the Quality of Life (QOL) of patients can be improved. The endoscopic surgery still has, however, great difficulty for surgeons despite the development of the techniques and the instruments. To solve the problem, it has been studied to apply the technology of robotics to the endoscopic surgery. In this paper, a multi-degree of freedom haptic endoscopic surgery robot is developed aiming at the practical application of applying the robotics technology to the endoscopic surgery. The developed robot is a master-slave type robot, and a bilateral control system based on acceleration control is implemented. To achieve a short sampling period of the control calculation, FPGAs are utilized as processors. The validity of the developed robot is verified through some experimental results. © 2008 IEEE.

With the recent growth in the quantity and value of data, data holders have come to realize the importance of being able to utilize information that is otherwise abandoned or concealed. In this situation, they face the difficulty of publishing data without revealing private information. One of the methods used to protect private information when publishing data is privacy-preserving method based on constraints known as k-anonymity and l-diversity. In this paper, we propose a hardware architecture composed of Ternary Content Addressable Memory (TCAM) and a cache mechanism to efficiently reduce the time required for executing the methods. An evaluation proves that an implementation of the proposed architecture on a reconfigurable device performs approximately 10-50 times faster than a RAM-based architecture and up to 60% of the information loss can be eliminated by using the cache mechanism. © 2012 IEEE.

© 2016 IEEE. Smart City is a promising infrastructure for our near future community systems. It is predicted that more and more sensor devices and computing resources will be deployed in access networks because of the Internet of Things (IoT). This movement can create a wide variety of new applications with different requirements of delay, privacy and locality. Smart community dictates less than 10ms communication as a control interval in order not to disturb the lifeline systems, such as power supply. As such, future devices should have the following attributes: low-power, low-latency, and flexibility for various applications. In this paper, we propose an energy-aware, low-latency, and flexible local device, such as gateway or coordinator, which integrates an SoC FPGA with dynamic partial reconfiguration (DPR) feature to grapple with data anonymization. The data anonymization is important to protect private information in smart community because domestic human activities can be, for example, estimated by exploiting electric power consumption data, which is known as nonintrusive load monitoring (NILM). However, anonymization operations usually require processing cost at the order of O(n2) where n denotes the number of data in a privacy processing window. In case of encapsulating privacy information in a local network, privacy preserving computation should be achieved in a local device. Hence, we have implemented four primitive operations using DPR for various anonymization purposes. The proposed system achieves 18-328 times better performance and 10-200 times lower power consumption in every primitive anonymization method than Raspberry Pi 2 Model B.

For rich network services, it is indispensable to reconstruct TCP stream in the middle of the network. In this reconstruction function, managing a large number of streams is a crucial as an embedded hardware for achieving high-throughput processing. In this case, hashing is a well-used solution as an ID key of TCP streams. Since CRC hashes have simple tree structure of XOR logics and are perfectly composed of combination logics, they can easily be hardware implemented and enables low-latency calculation of the hash values. Therefore, CRC hash has been used in network applications where low-latency and high speed operations are necessary. Some of their usages include data identification, load balancing, encryption, and checksum. Although CRC hashes may be well suited for hardware implementation, it is not the top choice when collision rates and distribution of hash values are considered. For some network workloads, CRC hashing shows very uneven distribution of hash values and can lead to decrease in the overall performance of network systems. In this study, two hashes, Jenkins hash and MurmurHash, are implemented on a FPGA as TCP connection manager and evaluated for their aptitude for hashing used in hardware-based network applications. © 2013 IEEE.

As a peak-cut or peak leveling control, heating, ventilation, and air conditioning (HVAC) are essential method for controlling the demand of electric power consumption. However, it is important to consider environmental amenity when designing control systems because the control may cause the deterioration of the amenity. In existing methods, metabolic equivalent (MET) values are not considered. Measurement and processing of MET values can be costly, requiring special infrastructure to both measure the values and communicate these values with a home energy management system (HEMS) or building energy management system (BEMS). This paper proposes a HVAC control method that considers the tradeoff between environmental amenity and energy conservation using the Keio University network oriented intelligent and versatile energy management system (KNIVES). The proposed method uses an iPod touch or iPhone application to obtain accurate measurements of both the MET value and the predicted mean vote (PMV). The accuracy of the PMV values after incorporation of MET estimates was confirmed experimentally. A HEMS experiment was conducted and a reduction in energy consumption was observed, demonstrating the usefulness of both the application and the proposed HVAC control method.

Short-term load forecasting plays an essential role in appliance control in households and demand response at the neighborhood or community level. When load forecasting is simultaneously performed at different levels in a smart community, a central method that directly aggregates raw household data may incur a security issue. However, an individual method that builds separate models for each level requires additional data collection. We propose a distributed load forecasting framework that is secure, data efficient, and resource efficient. Our proposed method shares the latent variables, the reduced expression of the input data, of the household models without revealing the raw data. The latent variables are aggregated and fed to the neighborhood model, and during inference, input data collection can be abbreviated in the neighborhood. Because household models provide informative latent variables, the number of model parameters for the neighborhood model can be reduced. Our proposed method is evaluated using real electricity consumption data for six different models under 24 h and 1 h ahead forecasting. Our proposed method with the long short-term memory, multi-layer perceptron, and convolution neural network-based models presents the degradation in average mean absolute percentage error 3.68% and 5.16% at most for household and neighborhood forecasting, respectively, compared to the individual method. At the same time, it reduces the neighborhood's model size by approximately 53%.

© 2016 IEEE. In recent years, studies of DPI have been carried out actively. HTTP packets, which are a kind of DPI target, include GZIP compressed packets, and multi-streamed GZIP compressed HTTP cannot be analyzed directly on routers. Moreover, wire-rate processing is required to achieve on-router analysis. In this paper, HTTP decompressing architecture on routers supporting 40Gbps network is considered, and three mechanisms, which are parallelized architecture, cache architecture and piggy-back method, were proposed for achieving higher throughput. Hardware cost simulations by using Verilog HDL confirms it can achieve 10Gbps throughput at low circuit costs.

© 2014 IEEE. Deep Packet Inspection (DPI) has become crucial for providing rich internet services, such as intrusion and phishing protection, but the use of DPI raises concerns for protecting the privacy of internet users. In this paper, a RAM-based hardware anonymizer is proposed for implementation on a Virtex-5 FPGA device. The results of the hardware anonymizer showed that the proposed architecture reduced circuit usage by 40%.

[Purpose] The purpose of our study was to compare the effects of hand bathing using plain water and water supplemented with inorganic salt and carbonated gas and to assess the hyperthermic effects of performing finger flexion-extension exercise while bathing in water with carbonated gas and inorganic salt and without water. [Subjects and Methods] Sixteen healthy, young males were subjected to plain water bathing, CO2 bathing, kineto-CO2 bathing, or no bathing. CO2 bathing involved bathing in a solution of artificial bath additives including inorganic salts and carbon dioxide. Partial bathing of the hand was implemented for 20 minutes at 41 degrees C. The concentration of carbonic gas was set at 33 ppm. In the kineto-CO2 bathing condition, finger flexion-extension exercise was performed at 60 laps per minute in the same solution used in CO2 bathing. The control group engaged in the same exercise as those in the kineto-CO2 bathing group, but without bathing. [Results] A significant increase in deep-body temperature was observed in the CO2 bathing and kineto-CO2 bathing conditions compared with both the plain water bathing and control condition. [Conclusion] Significantly heightened hyperthermic effects were observed when finger flexion-extension exercise was performed during CO2 bathing.

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.

Copyright © 2016 EAI. This paper proposes to implement a domain name system (DNS) module to network simulator-3 (ns-3). The ns-3 is one of the well-known Internet simulators that provides the ideal simulation environment for network research. Despite its advantages, the ns-3 is not yet possesses a module to simulate DNS, which is an essential module required for Internet-oriented simulations. To this end, this paper implements a DNS module for ns-3 according to the design notes provided in RFC 1035. Moreover, this paper presents the implementation notes of the proposed ns-3 DNS module including its class structures, protocol message structures, name server application development, and host-to-IP resolver development. The proposed ns-3 DNS module is evaluated in terms of host-to-IP mapping, recursive DNS resolution, DNS cache management, and internationalized domain name support.

© 2015 IEEE. Recently, efficient energy management systems have been in high demand to reduce overall energy consumption. In particular, energy consumption in the civilian sector is increasing considerably. Therefore, the effective use of energy in this sector should be emphasized. To do so, the introduction of a Home Energy Management System (HEMS) is effective because such systems allow households to control power consumption, generation, and charging. HEMSs are designed to provide services that are appropriate from the perspective of the user. However, in order to control a variety of consumer electronics devices, existing middleware does not consider descriptive and operational abilities when designing HEMS applications. In this paper, we propose an HEMS control mechanism that uses a combination of control statements written in Extensive Markup Language. These control statements, or «HEMS modules,» comprise three types: Trigger, Condition, and Event. The modules can connect with each other to express event transitions. The proposed middleware facilitates the description and control of HEMS devices for users. The proposed middleware was implemented in an embedded HEMS controller and evaluated in a real household environment.