This study aims to achieve packet processing in the edge domain while maintaining network transparency to meet diverse service requirements in smart communities. Since traditional packet-based control lacks flexibility, we propose a content-based fine-grained flow management method that enables control at the level of individual content segments within packets. To address the limited processing resources of on-path nodes, we introduce an out-of-path transparent add-on that offloads processing to external nodes and implement a mechanism for dynamically rewriting Ack and Seq numbers to maintain TCP session integrity. Implementation and evaluation on Mininet confirmed that the proposed methods achieve effective flow management with minimal impact on network latency.

Prior FPGA-based trajectory anonymization relied exclusively on shortest-path computations, failing to capture realistic travel behavior and reducing data utility. This paper introduces a history-aware trajectory k-anonymization methodology with an FPGA architecture integrating parallel history-based searches, custom fixed-point counting, and shortest-path finding. Our system prioritizes behaviorally common routes, achieving real-time throughput exceeding 6,000 records/s, improving data retention by up to 1.2%, and better preserving major arterial roads.

In edge computing environments, challenges in utilizing FPGAs as computational resources in edge nodes include the separation of expertise among FPGA designers, network engineers, and IoT engineers, as well as issues of logic reusability and flexible configuration. A GUI-based framework using Node-RED is proposed, leveraging the reconfigurability of FPGAs and dynamic network configuration to enable collaboration among engineers from different fields.The proposed framework utilizes the Processing System (PS) and Programmable Logic (PL) of SoC FPGAs, allowing visual operation of FPGA nodes on Node-RED. When FPGA nodes are connected consecutively, direct data transfer between FPGAs is possible without passing through Node-RED, minimizing communication latency and overhead. Furthermore, by dynamically configuring network settings instead of hard-coding them into the logic, the same logic design can be reused in different network environments, reducing development costs and enabling rapid service deployment.In evaluation experiments, communication latency between Node-RED and FPGAs was measured in both two-point and three-point communication scenarios. The results confirmed that the additional latency introduced by the proposed framework is minimal and does not compromise the processing speed advantage of hardware accelerators. Additionally, resource evaluation showed that sufficient resources can be secured, indicating the possibility of implementing additional applications.With this framework, FPGA designers can focus on logic design without considering network or communication partners, while IoT engineers can use FPGAs as applications without knowledge of FPGA or coding. This improves the practicality and reusability of FPGAs in edge computing environments and demonstrates the potential for more engineers to participate in FPGA-based application development.

Accurate, memory-efficient forecasting of greenhouse energy loads and indoor environmental conditions is essential for demand management in modern horticultural facilities. This study introduces μRWKV, a lightweight variant of the Receptance-Weighted Key-Value (RWKV) network tailored to multivariate time-series inference. μRWKV replaces the token embedding and softmax layers of natural language models with linear projections and regression heads. At the same time, it retains RWKV-v2’s inherent recurrent Time-Mixing and Channel-Mixing blocks, enabling sequential prediction during inference. These modifications produce an effectively constant inference memory footprint that remained below 11 MB across all tested sequence lengths, and training time scaled smoothly with model dimension. We trained and evaluated μRWKV on winter measurements from a Venlo-type greenhouse, using outdoor temperature, accumulated solar radiation, and temporal encodings to predict indoor temperature, electricity demand, and gas consumption. Compared with tuned LSTM and GRU baselines, μRWKV matched mean absolute error (MAE) and achieved the lowest difference in mean absolute rate of change (DMARC), indicating superior fidelity to short-term fluctuations. These fine-grained forecasts can support real-time load shifting and peak shaving, underscoring μRWKV’s practical value for energy-aware greenhouse control.

Digital twins (DTs) are virtual replicas of physical assets, systems, and processes that enable real-time monitoring, simulation, and analysis to improve decision-making, optimize operations, increase efficiency, and achieve cost savings across various Internet of Things (IoT) applications. This paper introduces an IEEE 1451-based smart transducer digital twin framework (DTF) for IoT real-time monitoring and control applications. The framework consists of an IEEE 1451-based smart transducer physical twin (PT), its corresponding digital twin (DT), and a bidirectional information exchange between them for real-time monitoring and control. In this paper, the IEEE 1451.0 and P1451.1.6-based smart transducer DT is developed using the Node-RED platform and message queue telemetry transport (MQTT) protocol. The PT is developed using embedded devices, and a sensor and actuator development kit. The PT and DT communicate using 1451.0 and P1451.1.6 messages to exchange temperature sensor data for controlling the temperature of a smart home in real-time. The monitoring and control results of the smart home are provided in the paper to test and validate that the physical twin and digital twin work correctly.

The Internet of Things (IoT) is highly heterogeneous regarding smart sensors/devices, connectivity, communication protocols, and data formats. The major challenges of IoT ecosystems are fragmentation (or disintegration) and cross-domain interoperability. To overcome the challenges of interoperability, standardized interfaces and protocols, harmonized interactions, and interoperability testing are essential. This paper introduces a harmonization model of interactions among IoT devices with diverse interfaces/connectivity and communication protocols to achieve interoperability by harmonizing their interactions. A harmonization implementation of interactions of the IEEE 1451-based IoT devices and MODBUS devices has been provided in the paper to test and verify that the proposed harmonization model works well. This harmonization model and implementation of interactions among IoT devices will provide a solid foundation for the IEEE P1451.99 standard specification.

IoT applications require secure communication methods that protect personal information contained in communication data. This study focuses on MQTT, a low-cost protocol used for IoT communication, and proposes a mechanism to anonymize communication data between IoT and clients. MQTT is a publish-subscribe model of communication where a broker handles many-to-many communications among clients. Due to the concentration of communications on the broker, it is efficient to anonymize data there. Therefore, the proposed mechanism performs differential privacy anonymization of communication data on the MQTT broker. We also propose a mechanism to anonymize data according to anonymization criteria required by senders and receivers using topic names and user properties, which are features of MQTT. We implemented the proposed mechanism in an FPGA-based MQTT broker and confirmed that it achieves the same throughput and low latency as regular MQTT communication and satisfies IoT applications such as power control and automated driving that require sub-millisecond latency.

The network infrastructure in a smart community uses the cloud and compute servers, and the edge and fog areas to perform processing in the optimal place. Nodes in the edge and fog areas have various processing capacities, and there must be load-balancing mechanisms to meet the requirements of applications. However, Internet of Things (IoT) devices are generally small, computationally constrained, and numerous, so reconfiguration and functionality updates for the network must be kept to a minimum. In addition, if nodes establish connections with other nodes to exchange their information, the number of connections will be large, and communication costs will be high. In this study, we use nodes called “Distributor” that can monitor data flow through the network without changing the existing infrastructure. Piggyback technique is used to add information onto the packets flowing in the network. Distributors add the information to the packets and exchange information to each other without establishing connections with other nodes, and they perform load balancing based on the information. By changing and migrating the processing locations among Distributors, IoT devices can continue to communicate with other nodes even during load balancing. Experiments confirmed that load balancing could be performed to prepare for sudden load increases and that the impact of piggybacking on occupied bandwidth and processing delay was 0.177% and 0.06%, respectively. We also confirmed that sensor data can be sent without reconnecting communications, even during load balancing.

In IoT applications, service provision commonly leverages the cloud infrastructure. However, the processing system at the edge is expected recently. It can process private information collected in factories and other locations without exposing it externally by using a distributed networked system. Typically, this private information is encrypted, and it can only be decrypted in the cloud. It has been difficult to provide the processing system at the edge. This study proposed and implemented the system to enable secure, Transport Layer Security (TLS) 1.3-compliant, and authentication-compliant processing under an encrypted communication environment between the terminal and the cloud, based on the system to realize edge processing called a transparent add-on. It also defines communication protocols. The latency introduced by the introduction of the proposed system is equivalent to the delay caused by adding one router to the network path. Therefore, the proposed system does not cause any harm to the provision of services after it is implemented.

Recently, smart agriculture utilizing Information and Communication Technology (ICT) and robotics has gained attention. The Institute of Electrical and Electronics Engineers (IEEE) Standards Association is developing standard P2992 for smart agricultural data design. Research on crop feature representation exists but lacks universal methods and real-world application. This study proposes a method to represent crop growth and environments in JavaScript Object Notation (JSON) format based on P2992, applied to cooperative farmers’ greenhouse management. The method includes feature extraction, environment data acquisition, and JSON representation. As a result of the designed application, flower detection achieved 84.5% conformance and 86.1% recall, with a data size reduction of over 99% compared to traditional methods. Applications using temperature and crop data show potential for effective cultivation and sales management by using simple JSONata scripts, suggesting P2992's practicality and usefulness in real-world environments.

There are many challenges for Internet of Things (IoT) sensor networks including the lack of robust standards, diverse wireline and wireless connectivity, interoperability, security, and privacy. Addressing these challenges, the Institute of Electrical and Electronics Engineers (IEEE) P1451.0 standard defines network services, transducer services, transducer electronic data sheets (TEDS) format, and a security framework to achieve sensor data security and interoperability for IoT applications. This paper proposes a security solution for IEEE P1451.1.6-based sensor networks for IoT applications utilizing the security framework defined in IEEE P1451.0. The proposed solution includes an architecture, a security policy with six security levels, security standards, and security TEDS. Further, this paper introduces a new service to update access control lists (ACLs) to regulate the access for topic names by the applications and provides an implementation of the security TEDS for IEEE P1451.1.6-based sensor networks. The paper also illustrates how to access security TEDS that contain metadata on security standards to achieve sensor data security and interoperability.

Recently, the increased utilization of data has led to the development of various services. However, using data necessitates anonymization techniques for privacy. multi-input k-anonymizer unit (MIKU) [1], a facial image anonymization method proposed by Nakamura et al., anonymizes images by averaging the latent vectors of facial images in the latent space of StyleGAN [2]. However, MIKU has two limitations: (1) it only targets single-face images, and (2) it merely calculates the centroid of the latent vectors between the original images, thus not preserving the statistical information of the original image dataset. Therefore, this paper proposes a framework to realize a k-anonymization method for photos containing multiple faces. The anonymization process uses a proposed framework comprising multiple modified artificial intelligence applications. First, identifiers, quasi-identifiers, and sensitive attributes are specified for a photo with multiple faces. This paper proposes three methods for generalizing age, one of the quasi-identifiers. The first is the random method, which replaces the age of the original image with the age of a random individual. The second is the uniform distribution method, which assigns ages based on the uniform distribution of age ranges after anonymization. The third is a kernel method that assigns ages using random numbers obtained from a probability density function computed by kernel density estimation. The experiments resulted in an Fréchet inception distance (FID) [2] equivalent to MIKU without any observed difference for the specified sensitive attributes. Furthermore, the most effective results were obtained using the kernel method, indicating that the proposed method is effective for the k-anonymization of facial images.

This study proposes a pioneering approach for anomaly detection in smart factories through the analysis of equipment acoustic data, leveraging the StyleGAN2-ADA deep generative model, originally developed for facial image generation, to analyze acoustic data. This method shows the potential to accurately estimate the severity and type of unusual sounds, allowing for early identification of equipment faults or wear and tear, and subsequently estimating their operational lifespan. Specifically, acoustic data from small motors are converted into Mel spectrograms, which are treated as images. By training the StyleGAN2-ADA model with these spectrograms and analyzing the resulting latent vectors, the technique classifies the nature and extent of anomalies in previously unseen acoustic data. Using unsupervised learning to analyze anomalous sounds holds significant potential for improving industrial monitoring and maintenance planning.

In recent years, there has been an increasing demand for accurate measurement of the spatial dispersion of air temperature in air conditioning control for indoor environments. This spatial dispersion of temperature is caused by the unevenness of air conditioning equipment, local temperature gradients due to human activities, and local heat exchange with outdoor air, all of which hinder the provision of a comfortable environment. This issue requires resolution in various fields, including the agricultural sector, where it can negatively impact crop growth. To address this issue, it is necessary to measure air temperature at multiple points, although the number of sensors that can be installed is typically limited, and the temperature at a single point is typically used as a representation of the temperature in the space. Additionally, heat maps using infrared array sensors can acquire temperature distribution in a narrow area; however, they cannot directly determine air temperature as they only measure the surface temperature of an object. Therefore, we propose a deep learning-based method that utilizes both heat maps acquired from infrared array sensors and air temperature data obtained from environmental sensors to estimate air temperature. We evaluate its effectiveness by varying the experimental environment, the model structure, and the resolution and number of heat maps added as input. Furthermore, we assess the usefulness of utilizing a Graphics Processing Unit (GPU), which is a cost-effective solution that can be installed on low-resolution sensors and edge devices. The outcomes indicate that the proposed model can attain an estimation accuracy of under 1 mean squared error (MSE) in approximately 200 seconds, while also exhibiting practical feasibility for deployment on edge devices, including residential abodes and agricultural premises.

It is vital to establish a suitable thermal environment for greenhouses to improve production quality for crop cultivation. However, in greenhouse environments, uneven temperature, caused by the influence of outside temperature, leads to unevenness in air conditioning and results in sub-optimized crop quality and yield. This paper introduces a real-time temperature monitoring and airflow controlling system using IEEE P1451.0 and P1451.1.6 standards to balance and improve greenhouse environments. The controlling system consists of three components: an infrared array of smart temperature sensors (STS) placed inside and outside the greenhouse, a smart airflow controller (SAC), and a temperature monitoring and airflow control application (TMACA). The network communications among STS, TMACA, and SAC are based on IEEE P1451.0 and P1451.1.6 standard network services using the message queuing telemetry transport (MQTT) protocol. In the system, the TMACA can monitor the temperatures inside and outside the greenhouse using two STS, process and estimate the temperature differences inside the greenhouse, and then control and equalize the temperature of the greenhouse using the SAC based on the temperature differences to balance and improve the greenhouse environment.

In recent years, there has been growing interest in using big data for machine learning applications. However, as data are often distributed across multiple clients, privacy and physical limitations can make it difficult to aggregate and use these data on centralized servers. Federated learning and split neural networks enable machine learning models to be built without requiring the aggregation of distributed data. These methods have received increased attention in the industrial sector as a means of eliminating impediments to privacy preservation in data sharing across various institutions, such as smart factories. One critical aspect of implementing such methods in practice is analyzing the time variation of traffic and estimating the throughput required for operationalizing machine learning. Previous studies have formulated traffic characteristics without considering packet overhead and measured only the total traffic and the throughput per iteration; such an approach is inadequate from a traffic monitoring perspective. To address these issues, this study conducted an empirical investigation of traffic patterns under realistic conditions, with a particular focus on quantifying traffic and latency. The results of this study suggest that due to the dynamic nature of throughput, it is essential to measure traffic using time units that can accommodate the temporal variability of traffic.

In a smart community, which is an initiative to create an efficient regional society by leveraging information and communications technology (ICT), various data exchanges and data processing are performed. Owing to the demand for network transparency in data processing, packet rewriting is required at network edges. In the packet rewriting process, packets need to be analyzed after obtaining the necessary permissions. A conventional method for packet analysis is using a 5-tuple. While this method can analyze packets by a service user, it cannot analyze packets by a service. Moreover, service provision needs to take place at the appropriate locations with respect to the privacy level, and service provision locations need to be flexible enough to change even subsequent to the commencement of services. To address this issue, there is a need to enable positional flexibility in determining the location for service provision between the data source and cloud. In this study, assuming that the permission to analyze packets had been obtained within the smart community, we implemented a framework that could analyze not only 5-tuple but also layer-7 (L7) information and rewrite packet contents based on the analysis results. The process performed at the edge first analyzed the header of the incoming packet to determine whether the packet was subject to L7 information analysis. If the packet was subject to analysis, the L7 information was analyzed to determine whether the packet was subject to rewriting. A packet subject to rewriting were rewritten and forwarded accordingly. For this series of packet process, we used the Data Plane Development Kit (DPDK), a tool that could speed up packet processing. The framework was applied to the anonymization process, and the delay due to the anonymization process was 0.195 ms. With this framework, it is possible to achieve the packet content rewriting process at any point between the IoT device and the cloud by modifying the location of the edge node.

Short-term hot water consumption forecasting in an individual household is important to realize energy-efficient hot water management while meeting users' comfort. However, consumption traits in an individual household contain much irregularity, and that gives additional challenges to accurate forecasting. This paper proposes an Attention-PVS model to tackle this issue, which focuses on consumption values with similar past consumption trends. In this study, verification experiments were conducted on a real-household hot water consumption dataset collected by Electricity of France. The model was evaluated with its consumption forecasting error with MAE, MSE, and RMSE metrics and computational costs. As for the computational cost, experiments were conducted on NVIDIA Jetson Nano (Jetson) to validate the applicability to embedded systems. The results revealed that the proposed model performs consumption forecasting with comparable accuracy to other state-of-the-art models. Additionally, while LSTM scored lower error than the proposed model, Attention-PVS performed training and inference on Jetson in shorter times than LSTM.

Industrial Internet-of-Things (IIoT) technology has been rigorously developed in recent years, moving towards the ambitious goal of industry 4.0, Network Automation. However, there are a few critical challenges regarding the implementation of a reliable IIoT ecosystem for different applications; security, battery life, and bandwidth are controversial challenges. All the challenges regarding IIoT are mainly struggling within the edge computing smaller box of the big picture in which a cloud is an upstream object while sensors and actuators act as downstream devices. Therefore, having a reliable IIoT ecosystem necessitates focusing on the whole IIoT’s challenges in the edge computing smaller box; first realizing the vital, practical demands of a specific IIoT application, and then defining the compatible protocols to form the effective edge computing configuration. This paper reviews four IIoT case study applications with their specific requirements and their counterpart sensor/actuator properties to find the appropriate edge computing protocols satisfying their demands.

In smart communities which refers to communities that leverage the use of technologies to benefit their citizens, it is necessary to ensure data interoperability among services and secure data operations. Particularly, it is necessary to manage the data path of services based on edge computing to consider the level of privacy between regions and at different network layers. FIWARE, a smart city operating system(OS) that has been recently deployed in several cities, provides an application programming interface (API) based on common rules to ensure interoperability and to enable coordination and distribution of services and data. However, the data distribution control component is assumed to be placed in the cloud, and it does not have a distribution management mechanism. In this study, within the architecture of FIWARE, we have built a mechanism that manages data paths on an edge by using tags of dataflows which is associated with the personal information in the packets. Additionally, we implemented a benchmark and measured a processing delay for gathering personal information. In the simulation environment, the processing time delay was 0.05 seconds by using proposed system when the control component is placed in the egde area.

This paper introduces an implementation of a standard-based sensor network for the Internet of Things (IoT) applications adhering to the Institute of Electrical and Electronics Engineers (IEEE) P1451.0 and P1451.1.6 draft standards. The architecture of standards-based sensor networks consists of P1451.0 and P1451.1.6-based wide-area networks (WAN), P1451.0, and P1451.5.X-based wireless local area networks (WLANs), and P1451.0 and P1451.2-based wired local-area networks (LANs). A few case studies P1451.0 and P1451.1.6 WAN are provided with preliminary results to verify some network service specifications of the sensor network based on P1451.0 network services and P1451.1.6 MQTT interfaces in order to verify and improve draft specifications helping to achieve interoperability.

Anomaly detection is one of the key applications of data utilization in smart factories, particularly in monitoring factory facilities. Early detection and resolution of anomalies, such as system failures, can lead to cost reduction and quality stabilization. One of the targets of abnormality detection applications in the industry section is a refrigerator unit used in food processing factories and warehouses. Anomalies in the early stages in refrigerator units appear in the operating sounds, which can enable their detection. In this study, we propose a method for detecting abnormal sound, extracting abnormal frequency components, and identifying the direction of the abnormal sound source. To identify the direction of the anomalous sound source, multi-channel sound recorded by a microphone array is used. To the best of our knowledge, no method has yet been proposed for anomaly sound detection using multi-channel acoustic data. In the proposed method, anomaly scores calculated in each channel of the microphone array are aggregated to determine whether the entire data is anomalous or not. Anomalous sounds were extracted from the anomaly data using a deep generative model. The extracted anomalous sounds were used to localize the sound source and the direction of the anomalous source was identified. The proposed method improved the precision of anomaly sound detection while maintaining the recall rate of a conservative comparison method. Using the proposed method, anomalous sounds were extracted from the anomaly data, and their arrival directions were identified.

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].

In recent years, there has been an increase in the demand for three-dimensional data of geospatial information in various fields, such as automatic driving and disaster prevention. Beyond5G (above 5thgeneration network), which features wideband, low latency, reliable connection, allows for the easily collection of spatial information of many points and times from camera-equipped vehicles, such as self-driving cars and drones. Spatiotemporal information at any location and at any time can be provided depending on users’ requests. However, there are instances where the target point’s spatiotemporal information and the request’s target time have not been collected. Therefore, appropriate data completion is required to provide this service seamlessly. To create 3D models using photogrammetry, images are required; therefore, multiple videos that captured the streets near our university were prepared and split into frames. Then, those images were stored in our local storage. The information of the recording date, weather, file path to the image, and location (latitude and longitude) were saved to our database per frame. When users request spatiotemporal information, this system commences searching for images that meet the requirements. When the number of matched images are sufficient, the 3D model is created. When it is insufficient, the data are complemented appropriately.

This paper introduces the time synchronization approaches to the Institute of Electrical and Electronics Engineers (IEEE) P1451.0 standard-based sensor networks for Internet of Things (IoT) applications. A time synchronization architecture of IEEE P1451.0 standard-based sensor networks is described including two-level time synchronization systems in IEEE P1451.0 and P1451.1.X standards-based wide-area network (WAN) and IEEE P1451.0 and P1451.5.X standards-based local area networks (LANs). However, this paper mainly focuses on the time synchronization approach of IEEE P1451.0 and P1451.1.6 standards-based WANs and provides two implementations of time synchronization of IEEE P1451.0 and P1451.1.6 using wireline and wireless networks with their preliminary results to verify that the time synchronization approach of IEEE P1451.1.6 functions properly. In addition, the time synchronization transducer electronic data sheets (TEDS) of P1451.1.6 is described.

As various data services are provided to realize Society 5.0, the usage of personal data is estimated to increase along with the explosive increase in data traffic. It is important that the protection of privacy keeps pace with increases in the exchange of data containing personal information. Starting from the enforcement of the General Data Protection Regulation (GDPR), stricter privacy protection regulations are expanding to more countries. These restrictions require that personal data should be hidden or anonymized before they are propagated over the network. The secondary usage of data is assumed in smart communities, and systems that can protect privacy are required for secure network infrastructures. In this study, we propose an edge-based computing platform that manages the privacy of users on the network of a smart community. For the platform, we prepared three models: The Basic, Preceding Packet, and Piggyback models. These are considered OpenFlow models, and the network efficiency for each was evaluated.

Home energy management system (HEMS), enabled by the development of the Internet of Things (IoT), issue behavior change recommendations to encourage residents to reduce their energy consumption. Receiving these suggestions from HEMS makes it easier for them to set specific reduction goals and raise their awareness of energy saving. This feedback will lead to effective power reduction in the household sector. However, each user has unique preferences, and uniformly generated recommendations may not be followed if they do not match the preferences of the specific user. In addition, frequent recommendations that are not aligned with their preferences may stress users and decrease their motivation to reduce energy consumption. This paper presents a practical method of making behavior change recommendations reflecting users’ response rates and considering their stress. Targeting the action of opening a window, we illustrate how our system induces behavioral change. To increase the users’ response rate and reduce their stress, we adjust the recommendation for each user from two perspectives. First, assuming that users open windows mainly depending on the external temperature, humidity, wind, and weather, we introduce the k-nearest neighbors (k-NN) classification using these parameters as the explanatory variables to predict the possibility that the user accepts the window-opening recommendation. Generating recommendations only when the predicted probability is high enables building a unique recommendation system considering user preferences. Second, if the recommendations are sent frequently, users may become tired of following them; this leads to a situation in which users ignore recommendations or turn off their notifications. To avoid such a situation, we propose adjusting the delivery interval according to the users’ response rate. When we schedule the notification cycle, we introduce a forgetting curve, assuming that the users’ stress on the recommendation decreases over time. We conducted a simulation using historical weather data. The response rate and thermal sensation of users with different variations were set, and the delivery timing of the recommendation was changed according to these factors. The proposed methods are expected to effectively generate behavioral changes by having users take medium- to long-term initiatives without lowering their motivation.

Depending on the location of the air conditioner and the shape of a room, air-conditioning control may be inefficient resulting in temperature imbalance. When attempting to solve this problem, it is vital to understand the spatial structure of a room (including its size and shape) and the location of air conditioners and then automatically control the airflow and direction according to the structure. However, such a method for recognizing spatial structures has not yet been established. In this paper, we propose a spatial recognition method using stereo infrared array sensors (SIRA sensors) installed in an air conditioner. Our system detects objects in the obtained thermal images and estimates their distances using triangulation. In addition, the room's size and shape are estimated based on the assumption that the room size lies within the detection range. The distances to the front and left/right walls were estimated in one-meter-wide classes. The estimation accuracy was compared using two types of IRA sensors: thermopile array sensors and thermal diode infrared sensors. Regarding the distance estimation of persons from the captured stereo thermal images, the average error rate was 12.5% for both types. The distance to each wall was estimated within a 1 m error range for the thermal diode infrared sensor. Moreover, applications of the proposed spatial recognition to air-conditioning control were demonstrated. Specifically, we propose a method to control the airflow direction and volume by considering the room’s geometry. An L-shaped room was modeled and simulated. From the results, the spatial recognition reduced the unevenness in temperature by adjusting the airflow based on the room shape. These results indicate that the proposed method can be practically used for spatial recognition to efficiently improve user comfort by controlling air-conditioning based on the spatial structure and eliminating uneven temperature.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

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 into 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. RPC can improve the cache miss rate, throughput, and energy-efficiency of PPC systems by 15.3%, 17.9%, and 17.8%, respectively.

The development of IoT has led to the creation of a data-enriched environment that enables data gathering by using distributed sensors and terminals. However, in this environment, the cost of data analysis has increased. Machine learning has gained attention for reducing the cost because enabling automatic data analysis, as well as multidimensional data, is expected. However, for enormous data, such as Big Data, we still have to pay costs. Therefore, selecting feature values when using machine learning technology is essential, especially as inputs of a classifier. Selecting the feature values increases its estimation accuracy. Moreover, the time cost, as well as calculation cost, needs consideration for the actual time-critical use of machine learning, especially in its learning process. Therefore, in this study, we proposed an algorithm that selected suitable feature values in required time. The proposed method consists of two stages: stepwise input selection stage using ANOVA and feature deletion stage according to the contribution rate of the features to estimate accuracy. These selection and deletion processes continue until the required processing time. We confirmed the efficiency of the proposed method by using an environment of a crystallization process in a factory and a household's occupancy estimation. A comparison with the original stepwise input method proved that the proposed method improved the estimation accuracy by 2%and 5% in the estimation of the substance amount of the crystallization process and household's occupancy, respectively.

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.

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). ILM is referred to a technique that a sensor installed for each load. In NILM 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.

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 TMk-anonymity according to the definition.

In recent years, there have been rapid increases in the number of network-connected devices such as computers, smartphones, and Internet of Things devices. Thus, large amounts of data have been accumulated such as locational data, website search histories, and power usage data. These data are used in various types of services. However, these data cannot be used easily for secondary purposes in some countries because of privacy problems. Therefore, privacy protection is necessary to apply these data in secondary uses where data anonymization is the usual solution. Many conventional methods are used for anonymizing power usage data, but the conventional method has three problems. First, it cannot anonymize time-series data. Second, the information loss is so large in the conventional method that the anonymized data are no longer suitable for secondary uses. Third, the conventional method cannot preserve the type of electrical appliance used. In this study, we propose a method for anonymizing power demand data, where sparse coding is used to solve the three problems that affect the conventional method. The proposed method can anonymize time series-data and it allows data to be analyzed at a chosen time. The proposed method was used to anonymize power usage data from the Urban Design Center Misono (UDCMi) and the experimental error rate decreased compared with the conventional method. The dictionary produced using the proposed method represents the electrical appliance data.

Smart community is the extended concept of the smart grid. Many services, including, but not limited to infrastructure services are deployed in the smart community. Some of the services in the smart community are time critical and require low latency between requests and responses. However, commonlyused cloud computing architecture is not designed with an emphasis on the latency. Therefore, the network architecture for low latency required services are necessary. This paper proposes a serviceproviding network for smart communities. In the proposed network, service-oriented router is utilized to provide the service in intermediate nodes between a cloud server and an end node. By responding to therequest at the intermediate node instead of the cloud where is close to the end devices, the latency is reduced. To demonstrate the feasibility of the proposed network, a simulation of the proposed network with two time-critical services were carried out in the network simulator ns-3. The ancillary service and intersection accident avoidance service are chosen to be considered in the simulation since they are attention-grabbing time-critical services for the smart community. The reduction of both latency and variation of the latency in both services was observed in the simulation. For the ancillary service, the proposed method has reduced the average latency to 10.85 ms that is low enough to provide stable service. The result shows the feasibility of application task allocation between intermediate SoR nodes based on a latency requirement in the smart community.

Recently, data traffic on the Internet has increased due to the rapid growth of various Internet-based services. The convergence of user requests means that servers are overloaded. To solve this problem, service providers generally install multiple servers and distribute requests using a load balancer. The existing load balancing algorithms do not estimate the size of the load of unknown requests. However, the requested contents are heterogeneous and complex, so the size of the load is dependent on the servers and the contents of the requests. In this study, we propose a load balancing algorithm that distributes the requests based on estimates of the processing time, which avoids mismatches between the characteristics of servers and the request contents. The processing time for requests is estimated based on the requested contents by online machine learning, and a strategy to cover the latency of machine learning is proposed and partially conducted. To test the algorithm, we built a model of multiple database servers and performed an experiment using real log data for database requests. The simulation results showed that the proposed algorithm reduced the average processing time for requests by 94.5% compared with round robin and by 28.3% compared with least connections.

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%.

Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., CO2) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.

Recent trends in the applications of batteries in the built environment are improving the efficiency of batteries and lowering costs. This paper introduces various types of battery technologies such as sodium sulfur, lithium ion, flow and lead acid batteries and discusses their models. Various applications of batteries such as adaptive battery systems, Battery Electrical Vehicles (BEVs), Battery Energy Storage Systems (BESS), the Internet of Things (IoT), and Smart Grid and Smart Environment applications are also discussed. In their selection and use of batteries, scholars are ultimately looking to maximize occupant comfort whilst keeping costs low and optimizing the energy efficiency of buildings. This paper will provide a review of current trends in this field.

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%.

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

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

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

The Internet of Things (IOT) continues to transform the world, and in many countries is now an integral part of our everyday lives-influencing everything from the way that we intercommunicate to how we conduct business. Innovators continue to find ways to integrate IOT into uses as far flung as fashion to medicine. This survey looks at how IOT is currently being integrated into the built environment for the purpose of saving energy and improving occupants' livelihoods. In particular, it reviews three technologies that have received a lot of attention in the literature as the future of an IOT mediated built environment. Based on this literature, predictions are made of the likely trends in regards to the future scholarship on these technologies.

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.

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.

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.

In this paper, an effective low-cost heating, ventilation, and air conditioning (HVAC) control system incorporating a new sensor is proposed. This research builds upon our previous system, which achieved efficient control of the air conditioning (AC) for a large room in a library. By detecting whether or not individual chairs were occupied and regulating the AC near unoccupied chairs, the system reduced the overall power consumption. This detection was achieved using pressure sensors installed in the chairs. However, the sensors required battery power to measure voltage and transmit data. The batteries needed to be replaced within several months, and thus the system required frequent maintenance. In this paper, we introduce a self-powered sensor. The sensor integrates an energy-harvesting switch and a wireless transmitter. The action of pressing or releasing the switch generates sufficient electricity to power the device, and this enables long-term operation without replacing or charging the batteries. When these sensors are integrated into chairs, occupancy status can be detected without additional power or regular maintenance. The proposed improved system with battery-free chair sensors is evaluated in an eight-day experiment conducted at a library in Kurihara, Miyagi Prefecture, Japan. The energy efficiency evaluation indicates that the proposed system can reduce electricity consumption by 18.3%. The predicted mean vote (PMV) values for the environment in the library are determined to assess the comfort level, and these values confirm that the proposed system is capable of maintaining occupant comfort. In the experiment, wireless sensor network is built through the Library to the Laboratory. All sensing data (e.g. temperature, humidity occupancy, status, power consumption, air conditioning status) are managed on the Cloud.

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 CO2concentration 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 CO2concentration 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.

In a data center, the main challenge is dealing with the power consumption of IT equipment and air conditioning (AC) units. To reduce the total energy in a data center, many techniques and approaches have been proposed. These strategies focused on the idle power of servers or controlling AC units while considering the temperature of server racks independently. In this paper, an active controlled shutter that enables the control of cooling airflow was proposed while considering the temperature of servers independently. By using the proposed method, wasted cooling energy could be eliminated. In a simulation of the proposed method, a conventional task assignment method was used, and start-up delays were considered. Additionally, the effect of the active controlled shutter was evaluated. The shutter blocks off cool air from cold aisles by observing the temperature of a server. As a result, the cooling power of the AC can be reduced by preventing the excessive cooling of idle or shut-down servers. An experimental result shows that the proposed method reduced the cooling energy by 22.8 %.

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.

Heating, ventilation, and air conditioning (HVAC) control is used to effectively reduce energy consumption in a home energy management system (HEMS). For the HVAC control, it is necessary to evaluate the indoor comfort level because the HVAC operation is significantly related to indoor environmental conditions. Although there are some indices for indoor comfort, individuals have different comfort ranges. A questionnaire is one way to gather data on individual comfort ranges. However, it requires contributions from users. Therefore, we proposed a new survey method that varied the intervals of the questions addressed to users to reduce the number of responses needed. Additionally, a comfort estimation method was proposed that did not require a questionnaire. This method analyzes the user's operation of the HVAC system, such as touching its on/off switch. An HEMS with the capability of utilizing these two methods was installed in a laboratory and 16 houses in Miyagi prefecture, Japan. An experiment using this HEMS was conducted, and it was able to reduce the number of feedbacks needed to maintain each person's comfort by an average of 32.2%. The HEMS provided the same accuracy as hourly questionnaires. Namely, it confirmed that the estimation based on the air conditioner operations was almost the same as that based on an hourly questionnaire.

Homomorphic encryption for smart grids has been investigated in many studies. It is possible to estimate the total power consumption in an area without knowing the consumption data of individual households. In the case of demand response (DR), it is important to calculate the total electric power consumption in an area because DR reports are published accordingly to reduce peak power consumption when the demand is high. However, the published data may reveal private information about residents, such as the timings of specific activities (leaving from and returning home), and device details. To overcome this problem, we propose a method specialized to enable energy providers to securely share electric power consumption data. The proposed method uses a self-organizing map (SOM), which is an unsupervised learning method. In order to share power consumption data while preserving privacy, the SOM is shared without the raw data. In this framework, a target accuracy of nearly 3% is achieved, while actual data are not published by any company.

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%.

Many network services including intrusion detection and recommendation provide their services by analyzing information acquired from network transactions. A careful analysis of these data can reveal valuable information when deep packet inspection is performed. Since these packet analyses generate sensitive information from enormous volumes of transmitted data, the requirement for data anonymization has been discussed. There have been many studies of anonymization techniques and their implementation in software applications. However, limited research has been undertaken regarding hardware-based anonymizers. This paper proposes and evaluates a RAM-based anonymization architecture that maintains both high throughput and a low information-loss ratio.

Increasing carbon dioxide emissions have given rise to global warming. Therefore, the need to reduce these emissions is being widely discussed. Decreasing energy consumption by efficient energy use is required to directly influence carbon dioxide emissions. Recently, energy consumption in the civilian sector is rising in Japan. To address this problem, the introduction of a Home Energy Management System (HEMS) is effective because it enables the control of power consumption and an economical energy use. Controlled heating, ventilation, and air conditioning (HVAC) is an essential function to be implemented in HEMS. HVAC control systems are now commercial and several HEMS experiments with HVAC control have been conducted in Japan. However, only few experiments have been conducted in cold districts because peak energy consumption predominantly occurs in summer. Nevertheless, from a carbon dioxide emissions reduction viewpoint, in such districts, the use of kerosene fan heaters, which are popular, becomes a dominant source of emissions around the year, but particularly in winter. In this study, we constructed a HEMS at 16 houses in a cold district, with the collecting function of environmental conditions, heating appliances status, the amount electric/oil energy consumption, and with the control function of HVAC systems. In particular, specially designed fully-controlled and monitored kerosene fan heaters are introduced. To confirm the effectiveness of the proposed HEMS, an energy saving experiment was conducted in which the use of kerosene fan heaters was controlled on the basis of the acquired environmental information, without degrading the room comfort. A reduction in carbon dioxide emissions was monitored along with the comfort level. This is the first experiment which integrates kerosene fan heaters into HEMS. This study demonstrated the ability of autonomous heater control to reduce the burden of residents and the usefulness of HEMS in Japan's cold district.

Heating, ventilation, and air conditioning (HVAC) are important factors for a building energy management system (BEMS). Efficient control of the air conditioning (AC) is required to maintain both energy efficiency and human comfort. However, AC control systems are expensive to install in buildings. In this study, a cost-effective AC control system intended for a library is proposed; it is also applicable to other facilities such as offices and meeting spaces. This system uses seating sensors for maintaining both energy efficiency and human comfort. Conventional AC control systems generally use expensive motion sensors or vision sensors as well as temperature and humidity sensors to acquire information on both the location and the comfort of users and to provide location-based control to users. In order to reduce sensor-based cost, the proposed system uses low-cost pressure sensors and maintains both energy efficiency and the comfort level. For detecting seat occupancy and locations, low-cost pressure sensors and low-power communication nodes are attached to chairs and they transmit seating data to the database of a server. A programmable logic controller is used to control the proposed AC system according to the information stored in the database. The system is evaluated using a 10-day experiment conducted at a library in Kurihara City in Miyagi prefecture of Japan. The results of energy efficiency evaluation in this experiment show that the proposed system can reduce electricity consumption by 18.3%. Through measurement of the predicted mean vote values of users of the library, the proposed system is found to be capable of maintaining user comfort.

Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.

The Future Internet architecture would be advanced and flexible depending on service requirements by program. The concept of Software-Defined Network (SDN) has a possibility to enable innovation while hiding much of the complexity on the networking design. SDN is mainly used by OpenFlow in real scenario, which can use up to layer-4. In this paper, we propose SDN which consider up to layer-7. To maintain flexible network and to get maximum benefit from networks, Service-oriented Router (SoR) was introduced. A SoR enables layer-2 to layer-7 information to be captured, analyzed, and stored and has a high-throughput database(DB) and is able to analyze all transactions on its interfaces. In addition, SoRs can provide APIs to access stored contents in order to enrich services. In our system, we make network programmable by considering layer-7 information, which can not be captured by using OpenFlow method. This feature will make future SDN more effective and convenient.

Energy consumption is continuing to increase, especially the consumption of fossil fuels. It is important, therefore, to make use of our limited energy sources and reduce carbon dioxide emission. Some natural energy resources, such as solar power, have been well studied; however, they require complex systems and introduce energy fluctuations. On the other hand, demand side management (DSM) can also be studied from the consumer's point of view. One of the challenges associated with DSM is reducing the energy consumption of pumps in commercial buildings. Although water cycling systems have been well studied by large water departments, the knowledge is not generally used in most commercial facilities. Therefore, this study aims to control a water circulation pump to reduce energy consumption while maintaining water quality. This approach is applicable to buildings with pools or hot springs that are expensive to operate and maintain. Automation of water circulation systems has been well studied only by water departments. However, general water purification systems can be expensive and are highly complex. For these reasons, this paper constructs a low-cost automatic water management system to reduce management costs. An experiment was conducted to demonstrate an automatic water management system that will effectively cut water management costs using low-cost sensors. We also experimentally obtained some parameters to facilitate automatic water management. Finally, the experimental system was found to reduce electricity consumption by 8%/day and reduce the electric peak by 30%.

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.

Recently, there has been high demand for an efficient energy management system that decreases total energy consumption. The Great East Japan Earthquake of March 2011 resulted in the shutdown of nuclear power plants in Japan, thus limiting the supply of power. Since then, the need for an energy management system has become more pronounced in Japan. Such a situation warrants efficient energy consumption through the use of an energy management system. When heating, ventilation, and air conditioning are restricted, especially under peak-cut or peak-leveling control of electric power consumption, it should be ensured that the living environment is not uncomfortable for humans. However, in the existing methods, living environment comfort has not been considered practically in terms of the fundamental parameters, namely, volume, window size, and number of people in a given room, despite the considerable influence of these parameters on room amenity. In this study, we proposed a new control method that considers these parameters for maintaining the amenity index in indoor environments at a set value by predicting the predicted mean vote value. Furthermore, we set up a building energy management system in an office building in Kanagawa, Japan, to compare the proposed control method with an existing one. Two different cases were considered in this experiment: control using an existing method and control using the proposed method. The results show that the proposed method maintains the amenity index of the room at the desired level, while reducing peak and total power consumption levels.

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.

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 CO2concentration. 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 CO2sensor. The proposed method will provide a demand control system that achieves more intelligent and accurate control of ventilation fans.

Currently, electricity generation requires both fossil energy resources and the supply of renewable energy. In addition, efficient energy management is necessary. This need became more pronounced in Japan after the Great East Japan Earthquake of March 2011. Since then, Japan's power supply has been limited because of the shutdown of nuclear power plants, and this has led to several problems. To deal with this problem, the effective use of renewable energies such as photovoltaic and wind power is required. Since these energy sources are unstable, batteries need to be introduced in the power grid for power supply leveling. However, the financial burden of introducing batteries on the demand side, such as in houses or buildings, is very high. To solve this problem, the required battery capacity needs to be reduced by controlling the air conditioners used by consumers in order to facilitate the peek-shift effect. In this study, we constructed a real-time simulation using a hardware-in-the-loop (HIL) system that can control air conditioners through power leveling with batteries. We propose two different cooperative demand control methods for comparison. These methods consider consumer comfort and the state of charge (SoC) of the batteries. The aim of this simulation was to compare three cases: no control, battery control, and control of both the air conditioner and the battery by a cooperative demand control method. This simulation showed that the cooperative control method achieves power consumption reduction and power leveling at the same time, which proves that the proposed method can reduce the battery capacity required for power leveling.

This paper proposes a service-oriented communication platform enabling the provision of scalable smart community services. The proposed platform is managed using Extensible Markup Language (XML) that has advantages in terms of flexibility, cost-effective implementation and affinity with databases. In practice, the platform is embedded into distributed communication nodes, e.g. routers, switches and home gateways, and both existing IP services and smart community services are provided over public networks. In this research, as an application, a building energy management system (BEMS) using the platform is demonstrated. The feasibility of the network architecture with the platform is discussed based on experimental results of data acquisition and control in the BEMS demonstration.

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.

In recent years, instead of wired communication, wireless communication technology has been utilized in many applications. Particularly, sensor networks are used in cases such as environmental monitoring, security service, space exploration etc. In this paper, motion planning and keeping connectivity of multiple robots distributed in a ZigBee based sensor networks are discussed in order to sense their unknown surroundings. The multiple robots distributed in a ZigBee based sensor network exchange information each other over the wireless sensor network and accomplish cooperative tasks in a closed area. In a rescue task, it changes dynamically according to the continuously shifting situation. In this case, the robots need to keep connectivity between other robots in order to accomplish the tasks in the real world. The proposed method enables mobile robot to be controlled under non-holonomic constraint and it is controlled based on the indicator of communication connectivity, which is related to the quality of communication as a real-time and real world application.

In this paper, “Martini,” a network interface controller chip for our original network called RHiNET is described. Martini is designed to provide high-bandwidth and low-latency communication with small overhead. To obtain high performance communication, protected user-level zero-copy RDMA communication functions are completely implemented by a hardwired logic. Also, to reduce the communication latency efficiently, we have proposed PIO-based communication mechanisms called “On-the-fly (OTF)” and have implemented them on Martini. The evaluation results show that Martini connected to a 64bit/66MHz PCI-bus achieves 470MByte/s maximum bidirectional bandwidth and 1.74 μsec minimum latency on host-to-host memory copying.

This paper describes with the design of a communication network necessary for demand response systems adapted to the requirements of the Japanese power grid. Based on KNIVES controllers and network devices an exemplary redundant hierarchical tree structure is proposed to meet high demands on reliability, scalability and dynamic adaptability in order to be capable to secure the wholesale competition. Therefore essential information for energy management has to be secured, managed, processed and delivered. Using Internet for communication, the network has to guarantee reliability facing a high variety of network availability. This approach focuses on redundant data management combined with non local control loops of the power supply system, which shall guarantee a functional safe and high-performance microgrid.

An ultra high-speed Ethernet subsystem, which realizes 100-Gb/s throughput and transmission up to 40 km, is examined for next-generation metro-area networks. A parallel link of 12 10-Gb/s synchronized parallel optical lanes is proposed. The 10 optical lanes are used to transmit 10-bit parallel data. The one of redundant lanes transmits a forward error correction code ((132b, 140b) Hamming code) to achieve highly-reliable (BER < 10-12) data transmission, and the other lane transmits a parity data used for the fault-lane recovery. Here, a 64B/66B code-sequence-based de-skewing mechanism is proposed, and its effectiveness to realize low-latency compensation of the inter-lane skew (< 80 ns) is shown. We have implemented the 100-Gb-Ethernet interface architectures into FPGA circuits, and confirmed the performance of 100 Gb/s data communication with compact 385-kgates circuit size, which is practically small for implementation in a single LSI circuit.

The VCore [1](*) based design methodology, which has been developed at the VCDS (**) Project, is a SoC design methodology using VCores. A VCore is a reusable, high level abstracted design component. We have developed the VCore based design methodology and the VCDS tool prototype. We used the developed tool and did a trial SoC design. The design result showed that SoC design productivity improved using the proposed methodology.

In this paper, we propose a novel architecture synthesis method for SoC using VCores. VCores are reusable and configurable high-level descriptions. An initial SoC architecture, which consists of a CPU, buses, and peripherals, is generated based on an architecture template. The hardware and software tradeoff is possible on the architecture model after assignment of software VCores or hardware VCores. The assignment is based on the results of the architecture’s performance estimation. We present a prototype of the synthesis for SoC architecture using VCores and an architecture level design experiment using this prototype. 1.

RHiNET-2/NI is a network interface for a parallel and distributed computing system with network connected PCs. The core of the network interface is an ASIC network controller chip Martini, which provides low-latency and large-bandwidth communication. Evaluation results show that it achieves almost full bandwidth of the 66MHz/64bit PCI bus, which is much larger than that of Myrinet-2000. The performance of a small prototype parallel system achieves almost linear speed up.

Recursive Diagonal Torus (RDT), a class of interconnection network is proposed for massively parallel computers with up to 2/sup 16/ nodes. By making the best use of a recursively structured diagonal mesh (torus) connection, the RDT has a smaller diameter (e.g., it is 11 for 2/sup 10/ nodes) with a smaller number of links per node (i.e., 8 links per node) than those of the hypercube. A simple routing algorithm, called vector routing, which is near-optimal and easy to implement is also proposed. Although the congestion on upper rank tori sometimes degrades the performance under the random traffic, the RDT provides much better performance than that of a 2D/3D torus in most cases and, under hot spot traffic, the RDT provides much better performance than that of a 2D/3D/4D torus. The RDT router chip which provides a message multicast for maintaining cache consistency is available. Using the 0.5 /spl mu/m BICMOS SOG technology, versatile functions, including hierarchical multicasting, combining acknowledge packets, shooting down/restart mechanism, and time-out/setup mechanisms, work at a 60 MHz clock rate.

The communication architecture of the DIMMnet-1 network interface, based on MEMOnet, is described. MEMOnet is an architecture consisting of a network interface plugged into a memory slot. The DIMMnet-1 prototype will have two banks of PC133 based SO-DIMM slots and an 8 Gbps full duplex optical link or two 448 MB/s full duplex LVDS channel links. The software overhead incurred to generate a message is only I CPU cycle and the estimated hardware delay is less than 100 ns using the atomic on-the-fly sending with header TLB. The estimated achievable communication bandwidth with block on-the-fly sending with protection stampable window memory is 440 MB/s which was observed in our experiments writing to the DIMM area with a write combining attribute. This is 3.3 times higher than the maximum bandwidth of PCI. This high performance distributed computing environment is available using economical personal computers with DIMM slots.

The Real World Computing Partnership (RWCP) has developed a local area system network (LASN) called RHiNET-1 (RWCP High-performance NETwork, version 1) using 1.33-Gbps optical interconnections for high-performance computing using personal computers distributed in an office or laboratory environment. The network interface, RHiNET-1/NI, uses a complex programmable logic device (CPLD) based protocol controller to provide an easy evaluation platform for various protocols. It fits in a 32-bit/33-MHz PCI bus. The switch, RHiNET-1/SW, consists of a single-chip CMOS switch and external SRAM. It provides low-latency, reliable communication with a flexible topology design. We are currently evaluating protocols on RHiNET-1. RHiNET-1 will enable a new form of high-performance computing environment. We are also developing the second implementation, RHiNET-2. RHiNET-2/NI will support a 64-bit/66-MHz PCI bus. RHiNET-2/SW is an 8-Gbps/port 8/spl times/8 single-chip ASIC switch. The aggregate bandwidth of RHiNET-2/SW is 64 Gbps.

The RDT network router chip is a versatile router for the massively parallel computer prototype JUMP-1. The major goal of this project is to establish techniques for building an efficient distributed shared memory on a massively parallel processor. For this purpose, the reduced hierarchical bit-map directory (RHBD) schemes are used for efficient cache management of the distributed shared memory. In order to implement (RHBD) schemes efficiently, we proposed a novel interconnection network RDT (recursive diagonal torus), and developed a sophisticated router chip for the RDT which equips a hierarchical multicast mechanism without deadlock and acknowledge combining mechanism. By using the 0.5/spl mu/BiCMOS SOG technology it can transfer all packets synchronized with a unique CPU clock(60MHz). Long coaxial cables are directly driven with the ECL interface of this chip. The mixed design approach with schematic and VHDL permits the development of the complicated chip with 90,522 gates in a year.