Published papers

Many different approaches are currently used in the field of network integrated control systems known as the Smart Grid. We implemented an energy management system with the aim of developing a future standard for this area. It is difficult to connect a heterogeneous system containing different standards, even if standardized technologies are available. A metastandard concept is proposed and substantiated to overcome this problem. The concept was substantiated in an energy management system constructed at the Fukue port terminal building in Goto City, Nagasaki Prefecture. This system uses a variety of devices produced by seven different companies. An air-conditioning control system to reduce carbon dioxide emissions was implemented and substantiated as an application of the proposed system. Our results demonstrate the effects of a common platform and its advantages. © 2011 IEEE.

In recent years, there have been many attempts in smart agriculture to increase efficiency and profitability, especially in horticultural agriculture, where profitability is high. One of the measures to achieve this goal is to realize uniform quality by equalizing temperatures in greenhouses which have a huge influence on a process of growth. The most common method for measuring temperatures in greenhouses is the use of temperature sensors. However, to measure continuous temperature distribution by scattering temperature sensors, a large number of temperature sensors must be installed, a method that should be avoided because of its high cost. Therefore, the goal of this paper is to estimate the temperature of substances such as crops and soil in greenhouses, which are secondarily affected by the atmospheric temperature, at a low cost instead of measuring atmospheric temperatures in a costly way. Temperature sensors for substances must be directly attached to the target object to measure its surface temperature, which can lead to the deterioration in quality. In contrast, infrared array sensors can measure the surface temperature of materials from a distance. They have been increasingly used in recent years due to growing demand, and they can be used to measure the surface temperature of a wide range of objects in a greenhouse. However, infrared array sensors also have many operational problems, such as dirty lenses, and the measurement error is larger than that of temperature sensors. Therefore, this paper proposes a machine learning model that predicts continuous temperature distribution in the form of a 16 ×18 pixels heat map from a limited number of temperature sensors. Evaluation results show that our approach is useful in different greenhouse environments, including different airconditioning systems. In addition, the model is computationally inexpensive enough to run in practical fields with limited computational resources; therefore, it can be run on relatively inexpensive embedded terminals. As for the accuracy, the average error of the heat map obtained by the proposed model is as small as 0.28 [°C/pixel].

© 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.

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.

© 2019 by the authors. 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.

Energy Management System, including Demand Side Management, are contributing to energy saving. However, power demand for home consumers, unlike large power consumers, is considered difficult to control each electric device because these power demands depend on consumer's behavior. Therefore, predicting power demand eagerly requested. KNIVES (Keio University Network oriented Intelligent and Versatile Energy saving System) is an ICT based demand side management system and controls electric power by using distributed and cooperative power control algorithm. We propose a management system for households with a smart circuit breaker box, which can measure power consumption of each outlet and enables integrated control by using KNIVES. In addition, I propose prediction method of demand power in a household by using proposed system. © 2010 IEEE.

© 2016 IEEE. Automated metering infrastructure is employed widely in scientific fields as well as in industrial and commercial areas due to the development of wireless sensor network (WSN) technology. WSNs provide important features such as wireless multi-hop communication and they are easy to install everywhere; thus, extending the lifetime of WSNs is highly desirable. All WSN nodes consume a limited amount of energy from the battery during operations such as sensing, calculating, control, and communication and most of the power consumption is attributable to wireless communication. In this study, we propose energy consumption-oriented route selection (ECORS), which is a route selection algorithm that focuses on the remaining energy and energy consumption by WSN nodes. In ECORS, a sink node calculates all of the routes in the system by using the route lifetime (RL) as a metric according to the minimum residual energy (MRE) and the expected route cost (ERC). The route with the longest RL is selected by the proposed algorithm. By changing the route periodically using ECORS, the WSN system lasts 1.14 times longer as compared to that when the routes are fixed. We evaluated the performance of ECORS using an original WSN simulator. In realistic simulations, we measured the distance-packet error rate, current consumption, and discharge characteristic of a battery using actual sensor nodes assembled with an Arduino micro controller, XBee ZigBee wireless module, and lithium-polymer battery.

© 2016 IEEE. The number of connected devices - also known as Internet of Things (IoT) - is exponentially increasing. Such sensors and devices also appear in transportation systems giving some intelligence to roads, equipment and vehicles. Nowadays, it is possible to communicate with the environment in order to have better everyday services. Furthermore, the number of registered - public or private - Electric Vehicle (EVs) is continuously increasing. These vehicles, equipped with large battery, need to be charged and so, have a significant impact on power grids. However, these EVs can also be seen as energy sources. It is therefore important to be able to plan both the charge and discharge of EVs. Including these vehicles into Vehicle-to-Grid technology is a way to efficiently manage such pools of batteries. But, as a consequence, grid requires to have almost real-time data on these vehicles and especially their battery status. This paper studies an optimized data aggregation method for a fleet of electric buses. Each bus provides different type of information with different priority level. The efficiency of the studied method was evaluated with a simulation platform developed with ns-3. Simulation results - based on real route and bus stop positions - show that an optimal buffer size has been found to both satisfy transmission delays and optimize communications.

Structural Health Monitoring (SHM) system is a promising technology to determine the health condition of a structure and localize its damage. SHM system is widely used, especially in gigantic structures for its strong requirements of safety. Many researches about SHM system have been conducting. However, conventional SHM system did not take account of an accidental collapse caused by earthquake. As a result, there is a possibility that sensor nodes, network links, and a center server cause failures in processing or storing of data gathered in the building. These failures may lose important data that contains useful information for post-analyzing the collapse. The data includes when, where and why the structure is damaged and collapsed. Theses information have a great deal of potential for preventing similar damage or collapse and it will make great contribution to accelerate future structural researches. To solve the problem of data losses in a damage, data sharing system for SHM system is proposed. This system shares data within sensor nodes. The proposed system achieves the following three processes; node search, backup node selection and backup data transfer process. It is important to conduct these processes under the condition of limited resources due to small and not powerful sensor nodes. Backup node selection is an important process of this system. This paper mainly focused on this selection. Round-trip time, a size of free memory space of backup nodes and value of displacement caused by vibration are used for the selection. In this paper, the selection method based on displacement was checked. An experiment was conducted by using practical sensor nodes and acceleration data. From the result of this experiment, proposed system could share data by using selection method based on displacement. © 2010 IEEE.

© 2015 IEEE. Advanced metering infrastructure (AMI) systems have been developed to perform automated meter reading, reduce peak loads, and use energy efficiently. Two issues exist regarding this system. The first issue is the communication and handling of consumer data concerning electricity collected by power utilities. The second issue is the management of communication network resources and scheduling of metering to avoid congestions and communication errors. The major device for addressing these two issues is a concentrator that acts as a data relay point in an AMI system. The concentrator collects data from the meter and sends them through communication networks. This study discusses the aggregation methods of the concentrator with respect to the aforementioned two issues and proposes a method to reduce network utilization and message size on a server. The method concatenates small smart metering messages sent from relevant meters. The traditional method aggregates and concatenates messages without numerical processing. The proposed method processes messages at the concentrator to reduce total message size and calculation cost on the server. Moreover, the method that combines the traditional and proposed methods was evaluated by considering a real-world case. These methods were simulated by using an ns-3 network simulator to evaluate their efficiency in sending messages concerning the volume of power consumption to the server. The results of the simulations show that the proposed methods reduce message size by as much as 98.5% in some cases and, by means of the concentrator, shorten the communication time between meters and the server. The proposed method can help to reduce loads on networks and servers.

© 2015 IEEE. Content Delivery Networks (CDNs) constitute a major portion of Internet traffic. To cope with increasing demand for content, CDNs have deployed distributed infrastructures on Internet Service Providers (ISPs') networks. Most CDN systems optimize their traffic flow using Domain Name Systems. However, they do not collaborate with the ISPs, and the lack of collaboration limits performance such as end-user latency. 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. In this paper, the benefits of introducing an SoR to an ISP network for maintaining ISP-CDN collaboration is outlined. Furthermore, a prototype design of the proposed system is presented. Simulations clearly demonstrate the effectiveness of the proposed ISP-CDN collaboration, which yields a 30-50% reduction in end-user latency.

© 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%.

Conservation of energy has become a major concern all over the world. Especially in the domestic area, public buildings, and office buildings, energy supply systems have to be optimized swiftly balancing with the time-dependent energy demand. This paper discusses the exemplary energy assessment of the cogeneration and air-conditioning system on Keio University Shonan Fujisawa Campus with approximately 5,000 students and 200 faculty members. The purpose of the cogeneration system consisting of prime mover and exhaust heat recovery equipment is a conlribution to energy conservation. Exhaust heal of lhe prime mover can be reutilized in form of heated or chilled water by the exhaust heat recovery equipment. Optimized reutilization of exhaust heat shall bring the main benefit for energy conservation. Therefore, the effectiveness of the cogeneration system has been evaluated based on collected annual data of energy consumption on the campus. Furthermore, curtailment of air-conditioning systems during peak load caused by the congestion of the grid power has been examined. In order to optimize the energy management under these conditions, the forecast of power consumption has been discussed, and the relation of power consumption to air temperature has been examined. © 2006 IEEE.

© 2015 IEEE. Prevailing low energy buildings attracts lots of attention in the world. Many studies have contributed in introducing higher thermal efficiency towards rooms with low energy heating, ventilation, and air-conditioning (HVAC) systems. However, current HVAC systems do not consider CO2 concentration change and thermal contribution towards human bodies in a room. This paper presents a novel method to predict thermal dynamics, including person count. Occupancy data are dynamically estimated by CO2 concentration and thermal contribution from the human bodies. The model is formulated as a resistor-capacitor circuit (RC circuit) in the Modelica modeling language. All parameters in a simulation are identified using actual building data during the winter season in Japan. Results are validated using measured information of actual building environment, and the test results concluded an improvement of absolute percentage error by 0.16 % over the conventional model. From the test results, it was concluded that the moving average filter of 20 minutes was an appropriate mean time to represent the time delay value.

Deterioration of indoor condition is confirmed by decreasing efficiency of working in earlier study. Maintaining indoor condition is an important factor to control electric appliances in living space. This paper focuses on a trade-off relationship between power consumption and environmental amenity. In DSM (Demand Side Management) system, a control method for controlling electric appliances should aware environmental amenity. In addition, life styles of the system users differs request of demand control policy. In this policy, there are three elements of control, power consumption control, environment control, and user defined control. Proposed control method integrates these elements of the control policy. In addition, the proposed method enables to compare some operational methods. Simulation results suggest a possible beneficial effect of the proposed method in the environmental-amenity aware DSM. © 2010 IEEE.

Recently, many studies dealing with energy saving have been conducted from the viewpoint of mitigating global warming. However, most of them only consider energy saving and ignore the total environmental impact. It is necessary to provide a comfortable work space using efficient and optimized energy control, because excessive demand control can have a negative impact on the environment. It can also have a negative impact on working or learning efficiency. To provide a comfortable indoor environment, it is important to measure environmental indexes, including not only temperature, humidity, illuminance, and wind speed, but also CO2 concentration. Moreover, to forecast air conditioner demand, it is important to know the number of people in the controlled space. In this paper, we propose a new method to estimate the number of people in a ventilation-controlled environment by using a CO2 sensor. The proposed method will provide a demand control system that achieves more intelligent and accurate control of ventilation fans. © 2012 IEEE.

In this paper a novel cache-based packet-processing-engine (PPE) architecture that achieves high packet-processing throughput with low-power consumption is proposed and evaluated. As network packets of the same header information appear repeatedly in a short time, a special cache, the so called header-learning cache(HLC), memorizes the packet-processing method and enables most packets to skip the execution at the processing units array. The implementation of the cache-based PPE architecture, P-Gear, was designed. Real backbone network trace was used to evaluate the performance of it. This P-Gear can achieve over 80% cache hit rate using 4K/32K entry for access/core networks. Compared to conventional PPE, P-Gear can achieve 100-Gbps (gigabit per second) packet-processing throughput with only 36.5% of the die size and 32.6% of the power consumption required by the conventional PPE. © 2006 IEEE.

KNIVES (Keio University Network oriented Intelligent and Versatile Energy saving System) is an IT based supply and demand side management system that supports distributed and cooperative power control. In this study, KNIVES is constructed on a real experimental facility of microgrid. The facility includes some distributed power generator and controllable/uncontrollable loads. It is also connected with general power grid. Therefore, it can introduce the electric power from the power grid when the power supply is insufficient. An experiment of demand control was achieved in this facility and the affectivity of KNIVES was confirmed. In this experiment, error ratio of demand balancing with distributed and cooperative demand controls were evaluated and compared with that without the demand control. ©2009 IEEE.

Saving energy is indispensable in the solving of environmental problems such as global warming and resource depletion. In the household and office sectors, which are growing in terms of electric power consumption, it is desired to improve the efficiency of air conditioning because power consumption due to air conditioning is dominant particularly in summer when conditions of electricity usage are tight. However, the drawback with the excessive reduction in power consumption is the consequent reduction in indoor comfort levels and working efficiency. Therefore, it is important to reduce power consumption with comfort in mind. In our study, we carry out experiments on environmental measurement and control of an air conditioner by using the Keio university network oriented intelligent and versatile energy saving system (KNIVES). Three different control methods are used for evaluation of power consumption. The first method involves ON/OFF control of the air conditioner through its demand control terminal; this method is widely used. The second method involves infrared control in changing the temperature of the air conditioner by means of automatic infrared signals. The thirds method involves inverter control that changes the inverter power output of the air conditioner by using a special demand control unit. Power consumption and comfort are compared and evaluated for each of the three methods. © 2011 IEEE.

Network Management System (NMS) is a service that employs a variety of tools, applications, and devices to assist network administrators on monitoring and maintaining network. Keeping the network in high quality of service is the main purpose of NMS. This paper proposed a method to solve the network problem by making a prediction of failure based on network-data behavior. The prediction represented by conditional probability generated by Bayesian network. Bayesian network is a probability graphical model for representing the probabilistic relationship among a large number of variables and doing probabilistic inference with those variables. In order to describe how the prediction works, we discuss the prediction result by simulation on network congestion.