Details of a Researcher - Nishi, Hiroaki

Papers - Nishi, Hiroaki

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Standards and Interoperability in Industrial Electronics - A Trending View.

Victor Huang, Hiroaki Nishi, António Espírito-Santo 0001, Allen Chen, Dietmar Bruckner

19th IEEE International Conference on Industrial Informatics(INDIN) (IEEE) 2021-July 1 - 6 2021

ISSN 19354576

With the active development of IES in standards since the mid-2010s, the society has made considerable progress with multiple standards' developments. This paper presents the results of engaging in standards' development within and across borders of an IEEE society. In particular, the hands-on INTEROP Plugfests, coupled with the CoEs, provide platforms to create ideas for standards, develop standards, initiate interoperability among multiple vendors, providing competitive time-to-market advantage for involved industry partners.

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Network Transparent Decrypting of Cryptographic Stream Considering Service Provision at the Edge.

Hiroki Hiraga, Hiroaki Nishi

19th IEEE International Conference on Industrial Informatics(INDIN) (IEEE) 2021-July 1 - 6 2021

ISSN 19354576

The spread of Internet of Things (IoT) devices and high-speed communications, such as 5G, makes their services rich and diverse. Therefore, it is desirable to perform functions of rich services transparently and use edge computing environments flexibly at intermediate locations on the Internet, from the perspective of a network system. When this type of edge computing environment is achieved, IoT nodes as end devices of the Internet can fully utilize edge computing systems and cloud systems without any change, such as switching destination IP addresses between them, along with protocol maintenance for the switching. However, when the data transfer in the communication is encrypted, a decryption method is necessary at the edge, to realize these transparent edge services. In this study, a transparent common key-exchanging method with cloud service has been proposed as the destination node of a communication pair, to transparently decrypt a secure sockets layer-encrypted communication stream at the edge area. This enables end devices to be free from any changes and updates to communicate with the destination node.

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Consistency Guaranteed Multi Container Migration for Smart Community Network Services

S WA, R Morishima, T Miura, H Nishi

IEEJ Transactions on Electronics, Information and Systems 141 (12), 1453-1461 2021

Consistency Guaranteed Multi Container Migration for Smart Community Network Services

Abeysiriwardhana W.A.P.S., Morishima R., Miura T., Nishi H.

IEEJ Transactions on Electronics, Information and Systems (IEEJ Transactions on Electronics, Information and Systems) 141 ( 12 ) 1453 - 1461 2021

ISSN 03854221

Smart Community (SC) uses IoT sensors to provide smart grid control, traffic management, and similar IoT services. These services expect to run at the network edge or fog layer to provide low latency network services, encapsulate citizens' private information, support low-cost IoT terminals from cyber-attacks, and support other cutting-edge fog and edge services. SC edge is a service platform that supports edge/fog services for IoT terminals by using Docker containers. Initially, SC edge/fog computing nodes did not support the function of service migration. However, service migration is necessary to support remote deployment and service distribution in SC networks. The existing container migration techniques focus mainly on resource utilization. However, SC services should handle loss-free data stream processing and order-preservation of network packets to gather IoT sensor data after migration. In addition, SC services require one-to-many migration to support high throughput loads when required. Therefore, this paper focuses on enhancing SC service flexibility by introducing migration for relocatable and network consistency guaranteed containerized services. SC edge proposes multiple container migration techniques that are suitable for network services. The proposed techniques can improve resource consumption, guarantee network traffic consistency, and apply one-to-many migration patterns. Layer leveraging migration (LLM) reduces the overall migration time by 10.8% for an elastic search Docker container than available Docker migration methods. Additionally, consistency guaranteed migration (CGM) is proposed to guarantee network consistency. However, CGM consumes additional resources compared to LLM for IoT data management. Finally, One to N Consistency Guaranteed Migration (O2NCGM) is proposed to support one-to-many migration with data consistency that shows similar performance to CGM.

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Consistency Guaranteed Multi Container Migration for Smart Community Network Services

W. A.P.Shanaka Abeysiriwardhana, Ryo Morishima, Tatsuki Miura, Hiroaki Nishi

IEEJ Transactions on Electronics, Information and Systems 141 ( 12 ) 1453 - 1461 2021

ISSN 0385-4221

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ICSIM 2021: 2021 The 4th International Conference on Software Engineering and Information Management, Yokohama Japan, January 16-18, 2021

ICSIM (ACM) 2021

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Standards and Interoperability in Industrial Electronics–A Trending View

V Huang, H Nishi, A Espírito-Santo, A Chen, D Bruckner

2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 1-6 2021

Preface

Nishi, H.

ACM International Conference Proceeding Series VII - VIII 2021

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RPC: An Approach for Reducing Compulsory Misses in Packet Processing Cache.

Hayato Yamaki, Hiroaki Nishi, Shinobu Miwa, Hiroki Honda

IEICE Trans. Inf. Syst. (IEICE Transactions on Information and Systems) E103D ( 12 ) 2590 - 2599 2020.12

ISSN 09168532

We propose a technique to reduce compulsory misses of packet processing cache (PPC), which largely affects both throughput and energy of core routers. Rather than prefetching data, our technique called response prediction cache (RPC) speculatively stores predicted data in PPC without additional access to the low-throughput and power-consuming memory (i.e., TCAM). RPC predicts the data related to a response flow at the arrival of the corresponding request flow, based on the request-response model of internet communications. Our experimental results with 11 real-network traces show that RPC can reduce the PPC miss rate by 13.4% in upstream and 47.6% in downstream on average when we suppose three-layer PPC. Moreover, we extend RPC to adaptive RPC (A-RPC) that selects the use of RPC in each direction within a core router for further improvement in PPC misses. Finally, we show that A-RPC can achieve 1.38x table-lookup throughput with 74% energy consumption per packet, when compared to conventional PPC.

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FPGA Context-based Live Migration Maintaining Network Consistency

Fukuhara A., Shohata S., Nishi H.

Proceedings - 2020 8th International Symposium on Computing and Networking Workshops, CANDARW 2020 (Proceedings - 2020 8th International Symposium on Computing and Networking Workshops, CANDARW 2020) 81 - 86 2020.11

ISSN 9781728199191

Based on the development of networking services and the emergence of various computing environments, such as edge and cloud environments, appropriate service allocation based on processing delays, computational resources, data volumes, and other requirements has become crucial. To support dynamic load shifting and reduce service downtime, service allocation should be performed using live migration, which maintains network consistency. There have been many studies on live migration techniques and their implementation in software applications. However, limited research has been reported on field-programmable gate array (FPGA)-based transparent service migration. This paper proposes and evaluates a transparent live-migration architecture for an FPGA computing system that maintains network consistency before and after migration and enables effective migration without any software or hardware extensions on communicating edges or cloud nodes. The proposed architecture achieves sufficient throughput and sufficiently low downtime to be introduced into cloud and edge nodes.

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Application Protocol Conversion Corresponding to Various IoT Protocols

Saito K., Nishi H.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference)) 2020-October 5219 - 5225 2020.10

ISSN 9781728154145

In Internet of Things (IoT) networks, devices use various application protocols, such as MQ telemetry transport (MQTT), constrained application protocol (CoAP), and extensible messaging and presence protocol (XMPP). However, because of the various protocols used, devices adopting different protocols cannot communicate mutually; thus, service interoperability issues arise. To enhance service interoperability, protocol conversion is required. Because different protocols are used in IoT networks, they are required to correspond to various protocols and have high extensibility for protocol conversion. In this paper, a protocol conversion method that satisfies these requirements is proposed. The proposed method converts packets into a middle format before converting them into target protocols. Conversion rules are described for each protocol; thus, the proposed method has high extensibility. We confirmed that clients that use the proposed conversion method could successfully connect to other devices using different protocols and communicate mutually through servers. The protocols included MQTT, CoAP, XMPP, and SMTP. Furthermore, the throughput degradation caused by the conversion process is small.

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Energy Optimization Technologies in Smart Homes

Moayedi S., Almaghrebi A., Haase J., Nishi H., Zucker G., Aljuhaishi N., Alahmad M.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference)) 2020-October 1974 - 1979 2020.10

ISSN 9781728154145

Energy optimization in the built environment is receiving more attention in the last decade. This leads to remarkable technological advancements in energy monitoring, data communication, energy storage, control, and data analysis applications. Energy monitoring can be aggregate (Non-Intrusive load monitoring -NILM) or disaggregate (Intrusive load monitoring -ILM) based on the location in which the monitoring takes place. Sensors communicate via wired or wireless means, and several protocols are developed to support communications in smart homes. This real-time data can be sent to remote controllers for further analysis, training and prediction applications using Internet of Things (IoT) that provides a cost-effective solution for energy management systems. This manuscript presents an overview of current innovation in smart homes for the advancement of building technology.

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Smart community edge: Stream processing edge computing node for smart community services

W. A.Shanaka P. Abeysiriwardhana, Janaka L. Wijekoon, Hiroaki Nishi

IEEJ Transactions on Electronics, Information and Systems (IEEJ Transactions on Electronics, Information and Systems) 140 ( 9 ) 1030 - 1039 2020.09

ISSN 03854221

© 2020 The Institute of Electrical Engineers of Japan. A smart community utilizes information technology to interconnect and manage community infrastructures. Smart community networks should support a large number of Internet of Things (IoT) devices in community infrastructures to provide services such as smart grids and health monitoring systems. In comparison to cloud-based solutions, smart community services can be deployed in the edge computing area to reduce service latency and to encapsulate private and local information. Furthermore, smart community services can leverage network virtualization technologies to support IoT network services at the edge. A service-oriented container-based solution that processes data streams from IoT sensors using conventional hardware will improve the compatibility and latency of these virtualized network services at the edge. To this end, a software-based edge computing node, namely, the smart community edge (SCE), was proposed to develop a platform for smart community services. SCE supports data-tapping applications, especially for IoT devices, and has a stream processing feature with a comparatively shorter processing delay. This tapping and processing function was named multi-service authorized stream content analysis. SCE captures network stream data and enables service applications using shared memory buffers for a shorter processing delay. SCE supports services as Docker containers to provide remote deployment, service compatibility, and service isolation. SCE allows IoT services to run at the edge through conventional hardware devices, thus, reducing the service latency for delay-sensitive services, which approximately require to sustain latency less than 10 ms. The proposed SCE achieves 10 Gbps bandwidth with a 16 core server when compared to the f-stack library with a 5 Gbps bandwidth. SCE deployment on conventional hardware devices shows its capability of operating at 1-10 Gbps line rates to support up to eight services at 500 Mbps data bandwidth per service, while keeping the overall latency below 1 ms. Therefore, SCE provides a platform for delay-sensitive IoT services at the network edge.

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Anomaly Detection Based on Histogram Methodology and Factor Analysis Using LightGBM for Cooling Systems

Yanabe T., Nishi H., Hashimoto M.

IEEE Symposium on Emerging Technologies and Factory Automation, ETFA (IEEE Symposium on Emerging Technologies and Factory Automation, ETFA) 2020-September 952 - 958 2020.09

ISSN 9781728189567

The development of the Internet of Things (IoT) has created an environment in which numerous sensors and actuators are connected to the Internet. Machines and management systems in factories use data from such sensors and actuators to improve their work efficiency, and are essential parts of today's smart factories. The vision of a smart factory is based on the concept of Industry 4.0 (I4.0), a subset of the fourth industrial revolution, in which smart factories support the operator and maintenance processes of the factory from an I4.0 perspective. The analysis of big data gathered by IoT devices in factories, particularly for the use of anomaly detection, can aid in achieving product quality stabilization. For example, if a large refrigerator in a warehouse breaks down, the quality of stock food will deteriorate, and food loss may become significant. In the case of anomaly detection, machine status monitoring and accident prediction are required to reduce the operation and maintenance costs. Furthermore, the introduction cost of such systems can be reduced by generalizing them (the systems). However, the data types as well as the sensor and actuator types, differ between factories. Therefore, nonparametric statistical methods are required for anomaly detection. By contrast, factor analysis requires a costless method, one that does not require an overhaul of machinery. Consequently, it is necessary to adopt a machine learning-based method using sampled data. In this study, we proposed a method of anomaly detection and factor analysis for cooling systems in smart factories using appropriate methodologies for detection and analysis. The proposed method consists of two phases: anomaly detection and factor analysis. In the anomaly detection stage, Gaussian kernel density estimation was used to calculate the occurrence distribution. Two types of anomaly scores, cumulative density value and KL divergence, were defined. The probability distribution was estimated with a constant window frame to reflect a tendency to increase. In the factor analysis stage, target values were predicted using LightGBM. The factor of abnormalities was detected by comparing the results of two predictions: one using all the features, and the other using the data, which excluded a factor to detect the contribution of the factor.

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Representation of Plant Structure using XML and Its Application to Cultivation Management

Saito S., Yuyama K., Ishii M., Huang V., Nishi H.

IEEE Symposium on Emerging Technologies and Factory Automation, ETFA (IEEE Symposium on Emerging Technologies and Factory Automation, ETFA) 2020-September 125 - 131 2020.09

ISSN 9781728189567

The agricultural population in Japan is aging and shrinking. Therefore, it is necessary to improve the efficiency of farm work. Additionally, it takes many years to learn the skills required for farming, which discourages many young people from joining the field. As a countermeasure to this problem, smart agriculture technology is gaining increasing attention. Smart agriculture is an approach to increasing the quantity and quality of crops by leveraging advanced technologies. In smart agriculture, a large amount of data are collected from various sensors and internet of things devices. As the use of information and communication technology in agriculture spreads, the demand for data sharing between different agricultural systems increases accordingly. However, electronic data exchange interfaces for agricultural data are not standardized. Therefore, in Japan, it has been recommended to unify data formats using extensible markup language (XML). In this study, we focused on data regarding crop growth states and developed a method for representing plant structure using XML. In the proposed method, branches and fruits are extracted from plant images and their connections are expressed using the hierarchical structure of XML. Compared to conventional management of crop growth states based on images, the proposed method significantly reduces data size. Furthermore, because XML elements can be easily searched and sorted using XPath and XQuery, the XML format makes data easy to utilize for many services. For example, by counting numbers of fruits, profits and work times can be predicted. Additionally, data regarding plant structure is useful for directing farmers or robots to harvest fruits. The proposed method contributes to improving productivity and helps inexperienced farmers.

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Video Object Detection Method Using Single-Frame Detection and Motion Vector Tracking

Nohara M., Nishi H.

IEEE International Conference on Industrial Informatics (INDIN) (IEEE International Conference on Industrial Informatics (INDIN)) 2020-July 119 - 125 2020.07

ISSN 9781728149646

Video traffic on the Internet has been increasing rapidly and accounts for a large percentage of the total traffic. To process the increasing number of videos, edge computing is preferable for load balancing and bandwidth reduction. However, edge areas have less computational resources than cloud areas, and high-performance GPUs for processing videos at high speed are not always present. Therefore, a memory-saving and high-Throughput video analysis method is necessary for analyzing videos in edge areas. In this paper, a video object detection method using single-frame detection and motion vector tracking is proposed. This method is classified as a pixel and compressed domain analysis method and is realized by compensating motion using the motion vectors that already exist in the compressed domain. This method is divided into two processes: CNN-based object detection and motion vector-based object detection. In addition, a network-Transparent platform for video reconstruction in edge areas is constructed. The network-Transparent service can be installed without modifying the existing end-device network settings, network configuration, and routing. The platform enables video object detection services to be added on without modification of these settings.

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Low-Information-Loss Anonymization of Trajectory Data Considering Map Information

Hashimoto M., Morishima R., Nishi H.

IEEE International Symposium on Industrial Electronics (IEEE International Symposium on Industrial Electronics) 2020-June 499 - 504 2020.06

ISSN 9781728156354

Preserving an individual's privacy when publishing data are essential, and anonymization has been getting attention as the solution. When anonymizing data, it is necessary to contemplate the possibilities of linkage with other data which can lead to privacy violation. Trajectory data are one of the data, which contains personal data. Consequently, various anonymization methods of trajectory data have been considered by researchers. However, most research handle trajectory data as polylines connecting location data or as a sequence of location data. In other words, it lacks on considering the connection with map data. In this paper, we will consider the anonymization of trajectory data of moving users matched according to map data, which we will be calling pathing data. According to k-anonymity principle, data can be published if there are k of the same data. We will use k-anonymity principle to quantitively judge the risk of privacy violation and propose two methods that can fulfill the anonymization requirements with low data loss. The two methods are Map Matching to Node (MMtoN) and Map matching to Edge (MMtoE), which judges k-anonymity by segments of pathing data.

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Airflow Direction Control of Air Conditioners Using Deep Reinforcement Learning

Sakuma Y., Nishi H.

Proceedings of 2020 SICE International Symposium on Control Systems, SICE ISCS 2020 (Proceedings of 2020 SICE International Symposium on Control Systems, SICE ISCS 2020) 61 - 68 2020.03

ISSN 9784907764647

Achieving a uniform comfort within an indoor housing environment is important for health and productivity while saving the energy consumption of a Heating, Ventilation, and Air Conditioners (HVAC) device. The optimal control of an HVAC system is a well-studied area. While many works explore the optimal temperature set-point, a few works consider effective airflow direction control. This work proposes an airflow direction control method that aims uniform comfort of the indoor environment using a deep reinforcement learning (DRL) approach. We implemented our proposed DRL framework using computational fluid dynamics (CFD) simulation software. Our proposed method was evaluated for comfort and energy consumption. The experimental results show the improvements for our proposed method in comfort by 21.3 % while reducing energy consumption by 34.5 % for the average than the baseline method.

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Precision variable anonymization method supporting transprecision computing

Harada K., Charles H.P., Nishi H.

International Conference on Advanced Communication Technology, ICACT (International Conference on Advanced Communication Technology, ICACT) 2020 35 - 42 2020.02

ISSN 9791188428045

Recently, the number of Internet of Things (IoT) sensors has been increasing rapidly; hence, various data are gathered. As a secondary use of the data, they are useful in providing new services, such as the demand response service in the Smart Grid. However, data services cause several problems in preserving privacy and during computation. This study focuses on these two significant problems. First, the invasion of privacy while using the data to provide such new services is problematic. A lot of private information is available in the data. For example, power consumption data may reveal the lifestyle of the residents, and the technique of obtaining information is known as nonintrusive load monitoring. Second, the penetration of IoT devices and sensors increases the computational and communicating energy consumption for processing the data and for providing various services using the data. In this paper, a new method is proposed to solve these two problems. This method is based on the fact that the anonymization process reduces the amount of information itself, as well as the quantity of computational resources required. This leads to a trade-off between anonymization level and computational cost. For example, raw data have a maximum amount of information and maximum computational cost. In contrast, fully generalized data (all zero data) have minimum amount of information and minimum computational cost. Compared to the conventional method, the proposed method demonstrated lower precision and a higher error rate. Therefore, the proposed method aims to control the trade-off and enables the provision of anonymized data with less information, the required anonymity level, and low computational cost compared to the conventional method. The proposed method is practiced using power consumption data gathered from the Urban Design Center Misiono (UDCMi) and the demand response service is evaluated as an experiment using the data. In this evaluation, a simple model of energy consumption was used in the calculation, which uses the required bit width of the arithmetic logic unit (ALU) for providing the service. The computational efficiency of the proposed method was increased by 60% when k = 2 and by 65% when k = 3, 4, 5, 6 compared to the conventional method. The method can also maintain an acceptable range of service error. The transprecision platform can restrict energy consumption by reducing the bit width of the data. Therefore, the proposed anonymization method can also reduce energy consumption by achieving lower usage of the ALU based on the transprecision architecture.

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IoT-based monitoring for smart community

Nishi, H., Nakamura, Y.

Urban Systems Design: Creating Sustainable Smart Cities in the Internet of Things Era 335 - 344 2020

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