Traffic forecasting is an attractive research direction of fundamental importance that underpins next-generation traffic management. While recent progress has been driven by deep learning techniques, existing approaches often rely on historical observation to make predictions and exhibit fragility in handling rare but critical scenarios such as holiday congestion, thereby limiting their practical applicability in real-world traffic management processes. To fill this gap, we propose a novel framework as an actionable solution for practical traffic management scenarios. Our method is based on two key insights: a) route search data encodes rich travel intentions in holiday and b) traffic forecasting should be formulated as a conditional generation task to deal with the inherent challenge from bias dataset. We develop a diffusion-based model and train it on large-scale datasets containing 450 million route search records and three years of traffic data. Our method demonstrates remarkable capacity to forecast challenging holiday traffic states (speed, traffic volume and congestion), with superior performance compared to state-of-the-art deep learning baselines and experts.

Large-scale human mobility data (e.g. GPS data) encodes valuable information interested by various fields. Extracting stay and movement behaviors from noisy positioning record sequences is a critical preliminary step to utilize human mobility data. For the past two decades, this processing has been founded on a simple intuition proposed by Hariharan and Zheng et al.[18, 48], which uses manually selected parameters to make recursive, rule-based classification as to whether data points in a positioning record sequence constitute noise, move, or stay. This de facto processing approach, despite its simplicity, is inherently sensitive to parameter choice and suffers from the low efficiency of sequential processing. These inherent limitations make it practically infeasible, when confronted with the large-scale, fine-grained human mobility datasets in industry. To address this fundamental problem in human mobility data utilization, we innotatively rethink the distinction in representation patterns of noise/stay/move within the positioning record sequence from the lens of topological persistence, culminating in a novel pipeline for robust and efficient human mobility data processing. This is grounded in our empirical observation that topological persistence features of stay/move/noise exhibit robust and generalizable discriminability across variations in parameter choice, individuals, and geographical regions. By introducing the Laplacian to simplify the computation of topological persistence features, our processing pipeline is capable to exploit GPUs' parallel capacity for efficient processing. Experiments on real-world GPS datasets totaling up to thousand billion data points demonstrate that our method produces processing results comparable to those of human annotators, while requiring only 10% of the time consumed by previous approaches. We further show that our method is scalable for cumulative data volume and remains effective in identifying stay/move behaviors that traditional techniques consistently fail to handle, even under conditions of severe positioning errors and diverse behavioral patterns.

Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they are often affected by nonperiodic public events, such as disasters and occasional celebrations. Since regular human mobility patterns are affected by these events, estimating their causal effects is critical to accurate mobility predictions. News articles provide unique perspectives on these events, though processing them is a challenge. In this study, we propose a causality based prediction model, CausalMob, to analyze the causal effects of public events. We first utilize large language models (LLMs) to extract human intentions from news and transform them into features that act as causal treatments. Next, the model learns representations of spatio-temporal regional covariates from multiple data sources to serve as confounders for causal inference. Finally, we present a causal effect estimation framework to ensure that event features remain independent of confounders during prediction. Based on large-scale real-world data, the experimental results show that the proposed model excels in human mobility prediction, outperforming state-of-the-art models.

This paper presents a novel method for transforming large-scale historical expressway route search records into a three-dimensional (3D) Origin-Destination (OD) map, enabling data compression, efficient spatiotemporal sampling and statistical analysis. The study analyzed over 380 million expressway route search logs to investigate online search behavior related to tourist destinations. Several expressway interchanges (ICs) near popular attractions, such as those associated with spring flower viewing, autumn foliage and winter skiing, are examined and visualized. The results reveal strong correlations between search volume trends and the duration of peak tourism seasons. This approach leverages cyberspace behavioral data as a leading indicator of physical movement, providing a proactive tool for traffic management and tourism planning.

This paper presents a simulation for traffic evacuation during railway disruptions to enhance urban resilience. The research focuses on large-scale railway networks and provides flexible simulation settings to accommodate multiple node or line failures. The evacuation optimization model is mathematically formulated using matrix computation and nonlinear programming. The simulation integrates railway lines operated by various companies, along with external geographical features of the network. Furthermore, to address computational complexity in large-scale graph networks, a subgraph partitioning solution is employed for computation acceleration. The model is evaluated using the extensive railway network of Greater Tokyo. Data collection included both railway network structure and real-world GPS footfall data to estimate the number of station-area visitors for simulation input and evaluation purposes. Several evacuation scenarios were simulated for major stations including Tokyo, Shinjuku, Shibuya and so on. The results demonstrate that both evacuation passenger flow (EPF) and average travel time (ATT) during emergencies were successfully optimized, while remaining within the capacity constraints of neighboring stations and the targeted disruption recovery times.

Traffic states typically exhibit regular spatio-temporal patterns that support accurate prediction. However, traffic accidents can disrupt these patterns and introduce irregular fluctuations that challenge forecasting models. Previous studies have focused on predicting accident hotspots, risks, or traffic states using simple accident features. Yet, because traffic accidents are infrequent and exhibit spatio-temporal biases relative to collected traffic sensor data, they may significantly or only slightly affect traffic states, posing challenges for accurate prediction. In this study, we propose a large language model (LLM) enhanced Causality-aware Traffic accidents post-effects prediction (LLeCaT) to uncover the causal relationship between traffic states and accidents. Our model integrates fine-tuned LLMs to extract semantic information from accident records and estimate their effects on future traffic states following their occurrence. To validate the effectiveness of our proposed method, we collected and processed publicly available anonymized traffic accident records, traffic state datasets, and environmental information. We conducted extensive experiments demonstrating its superiority over all baselines. Additional ablation studies and case analyses further confirm the validity of the estimated causal effects.

Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportation planning. However, the inclusion of diverse external features, alongside the complexities of spatial relationships and temporal uncertainties, significantly increases the complexity of forecasting models. Additionally, traditional approaches have handled data preprocessing separately from the learning model, leading to inefficiencies caused by repeated trials of preprocessing and training. In this study, we propose a federated architecture capable of learning directly from raw data with varying features and time granularities or lengths. The model adopts a unified design that accommodates different feature types, time scales, and temporal periods. Our experiments focus on federating route search records and begin by processing raw data within the model framework. Unlike traditional models, this approach integrates the data federation phase into the learning process, enabling compatibility with various time frequencies and input/output configurations. The accuracy of the proposed model is demonstrated through evaluations using diverse learning patterns and parameter settings. The results show that online search log data is useful for forecasting long-term traffic, highlighting the model’s adaptability and efficiency.

As governments increasingly rely on digital data to enhance public services, it is crucial to rethink how this data is utilized to better meet the needs of citizens. This paper emphasizes the transformative potential of structured data semantics in improving service delivery at both local and national levels. Focusing on the case of Noto Peninsula, Japan, which was affected by a recent earthquake, we explore how the implementation of the Universal Menu facilitated efficient access to vital services for affected residents. By examining this case, the paper underscores the need for a standardized approach to data organization that allows municipalities to tailor services effectively to the unique needs of their communities. Drawing on examples from other countries, we argue that embracing structured data semantics not only enhances the responsiveness of government services but also empowers citizens to easily identify and access the support they require. The conclusion reinforces the necessity of this paradigm shift for fostering resilient and citizen-centric governance in an increasingly digital world.

In recent years, data spaces have emerged as a framework for secure data collaboration. Data spaces are distributed data collaboration infrastructure systems that enable data collaboration among stakeholders. One of the requirements for data space systems is the technical enforcement of governance policies for the shared data. The objective of this study is to propose a new policy definition language, DSPOL, as a stepping stone toward realizing the enforcement of data governance for the practical use of data spaces. DSPOL can describe the constraints that must be followed in data space systems when accessing and using data, and when distributing deliverables. It has verification and validation functions to ensure that the policy description is as the writer intended and that there are no inconsistencies in the policy. We defined the state transitions of data usage in data user environments, developed a model of the execution infrastructure in data space systems, and formulated the contents of the describable policies. We implemented DSPOL and its verification and validation functions. Examples of policy descriptions based on supposed scenarios were shown, and examples of verification and validation were presented.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis.

Smart buildings incorporate various emerging Internet of Things (loT) applications for comprehensive management of energy efficiency, human comfort, automation and security. However, the development of a knowledge extraction framework for human activities is fundamental. Currently, there is a lack of a unified and practical framework for modeling heterogeneous sensor data within buildings. In this paper, we propose a practical inference framework for extracting status-to-event knowledge within smart building. Our proposal includes IoT-based API integration, ontology model design, and time probability dependent knowledge extraction methods. We leveraged the Building Topology Ontology (BOT) to construct spatial relations among sensors and spaces within the building. Additionally, we utilized Apache Jena Fuseki's SPARQL server for storing and querying RDF triple data. Two types of knowledge could be extracted: timestamp-based probability for abnormal event detection and time interval-based probability for conjunction of multiple events. We conducted experiments over a 78-day period in a real smart building environment, collecting data on light and elevator states for evaluation. The evaluation revealed several inferred events, such as room occupancy, elevator trajectory tracking, and the conjunction of both events. The numerical values of detected event counts and probability demonstrate the potential for automatic control in the smart building.

Recently, smart meters have been widely used as social infrastructure, making it easy to obtain electricity power consumption data. Electricity power consumption data has high potential in healthcare since it reflects various activities in daily life. We have been focusing on the potential of smart meters and studying frailty detection model using electricity power consumption data; frailty is a condition of reduced function and health in the elderly, also said to be reversible, which means the elderly can return to robust conditions by implementing appropriate interventions. Therefore, detecting frailty at an early stage and implementing intervention makes it possible to extend healthy life expectancy. Our previous research showed that frailty detection is possible with acceptable accuracy only using electricity power consumption data from smart meters. However, for real-world implementation, it is crucial to guarantee the robustness of the model performance. Moreover, implementing interventions after detecting frailty and ongoing monitoring is essential to extending healthy life expectancy. Given these points, we aim to guarantee the robustness of frailty detection model and verify the effectiveness of frailty detection model as a post-intervention monitoring tool. As a result of case study conducted in three regions, we demonstrated that the proposed frailty detection model has seasonal and regional robustness, showing acceptable performance in various seasons and regions. We also confirmed that it is possible to observe the elderly’s condition by tracking the outputs of the proposed model. Through our research, we showed the feasibility of frailty detection using smart meters and the possibility of a healthcare platform using smart meters.

Data utilization for efficiency and optimization is increasing in many fields, and it is expected to create new value by sharing data among multiple stakeholders. In addition to domain-specific data platforms, which are already prevalent in Europe and Japan, cross-domain data platforms are being developed to enable interoperable and sovereign data exchange across different domains. However, through an extensive survey, we found that as the requirements for data exchange vary with regulations and/or applications, data platform initiatives and the software tools they offer are scattered. Thus, we need an experimental environment where various data exchange platform technologies can be learned, developed, and tested in one place. The objective of this paper is to propose the establishment of the International Testbed for Dataspace Technology (ITDT), an academic and industrial testbed for data platform technology. The ITDT intends to provide an environment that facilitates the development and testing of interoperability, portability, and customizability for data platform technologies. We expect the ITDT to promote the advancement of data platforms, international collaboration, and the formation of a neutral technical community. This paper presents the requirements, architectural design, and future plans for the ITDT.

Many applications utilizing big data are being used every day, and the importance of data utilization is increasing day by day. Data spaces, a federated data management system, is gaining increasing attention in Europe and Japan. To promote data spaces into real applications and increase data exchange between companies and individuals, the data exchanged over data spaces must be well protected. To achieve that goal, we propose to apply homomorphic encryption scheme to data spaces, propose its architecture, and analyze its advantages. Furthermore, we also investigate the feasibility of the proposed architecture based on results of calculation of GHG emission of a self-driving electric vehicle in the real-world experiment.

Long-term traffic prediction is essential for both road managers and users to prepare for future congestion. However, most existing studies have only focused on short-term prediction. Moreover, few studies have effectively incorporated external data into long-term traffic prediction, even though traffic conditions are complexly influenced by various spatiotemporal factors. In this paper, we propose a novel method that utilizes online search log data for long-term traffic prediction on expressways. Online search logs reflect drivers’ travel intentions and external factors, such as weather conditions and events, which cannot be represented by historical traffic data. Based on a new analysis of the correlation between online search log data and real-world traffic, we use online search log data as potential future traffic volume in an LSTM-based encoder-decoder model. Experiments using a real-world dataset on an expressway known for frequent congestion show that the use of online search log data improves the metrics of MAE, RMSE, and MAPE in next-day traffic volume prediction by 8.1%, 12.5%, and 7.2% on average, respectively. Similarly, in speed prediction, the MAE, RMSE, and MAPE are reduced by 3.7%, 2.1%, and 11.8%, respectively. It is also shown that online search log data is particularly effective in predicting irregular congestion caused by sudden increases in traffic demand.

With applications demanding increased amount of data, edge computing, where data are processed on the edge, is gaining popularity. Edge devices can store a limited amount of data and need to extract important data from the collected data. On-line interpolation-based data management (O-IDM) is an efficient method for managing time series data on an edge. O-IDM extracts and stores data to represent its overall shape. However, using this method becomes computationally expensive when the amount of the retained data is large. In this paper, we propose fast-O-IDM (FO-IDM), which can perform the same data extraction as O-IDM at a lower computational cost. Furthermore, we propose dynamic representative data management (D-RDM), which is a multiple time series management method that applies FO-IDM. D-RDM can efficiently hold the characteristics of multiple time series by dynamically controlling the capacity based on the data features. The extraction accuracy and computational costs of D-RDM are evaluated via comparison with other management methods. The experimental results confirmed that D-RDM could extract the data more accurately than the other methods, reducing the data amount required to convey the feature of the data by up to 25.0 percentage points compared to naive O-IDM. In addition, when the number of time series and the amount of the retained data were 3 and 30,000 respectively, the processing time of D-RDM was reduced by 91.2 percentage point compared to O-IDM, indicating that the computational cost of D-RDM is sufficiently small for use in edge devices.

Owing to the widespread adoption of the Internet of Things, a vast amount of sensor information is being acquired in real time. Accordingly, the communication cost of data from edge devices is increasing. Compressed sensing (CS), a data compression method that can be used on edge devices, has been attracting attention as a method to reduce communication costs. In CS, estimating the appropriate compression ratio is important. There is a method to adaptively estimate the compression ratio for the acquired data using reinforcement learning (RL). However, the computational costs associated with existing RL methods that can be utilized on edges are often high. In this study, we developed an efficient RL method for edge devices, referred to as the actor–critic online sequential extreme learning machine (AC-OSELM), and a system to compress data by estimating an appropriate compression ratio on the edge using AC-OSELM. The performance of the proposed method in estimating the compression ratio is evaluated by comparing it with other RL methods for edge devices. The experimental results indicate that AC-OSELM demonstrated the same or better compression performance and faster compression ratio estimation than the existing methods.

With the widespread implementation of smart meter infrastructure, various applications have been proposed and implemented beyond energy usage monitoring and savings. In particular, there has been a growing interest in using smart meter data for daily life monitoring and remote care systems by visualizing human activities based on electricity, gas, and water usage. Previous research has demonstrated the feasibility of activity detection using an individual smart meter dataset. However, research on the effectiveness of using multiple smart meter datasets is limited. This paper proposes a model for Activities of Daily Living (ADLs) detection using multiple smart meter datasets (i.e., gas and electricity). To evaluate the performance of the proposed approach, a case study was conducted with 47 households for one month, collecting electricity data, gas data, and ground truth data about ADLs. The case study results showed that our proposed approach, which uses multiple smart meter datasets, enables more accurate activity detection.

Data is the most important property for bringing innovation and digital transformation. In Japan, data-driven society is one of the most important concept for the future society referred in Society 5.0, DFFT (Data Free Flow with Trust), and National Data Strategy of Japan. Several public and/or industrial domains have already established data ecosystem including data sharing platforms. However, there are many scenarios and applications which cannot be supported by the domain-specific data platforms. For example, disaster response, tourism, GHG (Greenhouse Gas) management such as the Scope 3 protocol, supply chain management support, and smart city are among them. So, we need a cross-domain data-exchange platform to support these applications. The objective of this paper is to propose "DATA-EX (data exchange)", which is a platform which is under development and will be operated by Data Society Alliance (DSA), and works as an infrastructure for cross-domain data exchange consisting of open standard specifications and guidelines for data exchange and platform operation, software systems based on the standards, and governance rules for the operation of DATA-EX. This paper presents definition, objectives, basic strategies, and architecture of DATA-EX, and its current status and future plan.

Predicting future people’s behavior can significantly impact various industries. Intelligent transportation system (ITS) advancement, in particular, depends on the ability to predict traffic congestion. If we can do so, we can encourage people to alter their behavior, which reduces traffic congestion, traffic accidents, travel times, and CO2emissions while also promoting the development of applications like dynamic pricing. However, predicting traffic congestion a few days ahead is challenging owing to its spatial and temporal dependence and its nature of being susceptible to external factors, such as weather, local events, and the pandemic of infectious diseases. For these reasons, previous studies have been limited to predicting the next few minutes to a few hours. To address this limitation, we propose using search log data of the toll route search service owned by East Nippon Expressway Co., Ltd. (NEXCO East), which operates expressway services in Japan, as these data are available several days before the prediction and comprehensively explain multiple external factors. We show that search log data can contribute to predicting people’s behavior by verifying the improvement in the accuracy of traffic congestion prediction.

In this study, a method of multi-weighted graphs learning for passenger count prediction in railway networks, is presented. Traffic prediction can provide significant insights for railway system optimization, urban planning, smart city development, etc. However, affected by various factors, including spatial, temporal, and other external ones, traffic prediction on railway networks remains a critical task because of the complexity of the railway networks. To achieve high learning performance of the models and discover the correlation between the models and features, we proposed various heterogenerous weighted graphs for the passenger count prediction. Six types of weight graphs, that is, connection graph, distance graph, correlation graph, and their fused weight graphs were proposed to fully construct the spatial and geometrical features within the entire railway network. Two representative types of graph neural networks, that is, the graph convolutional network (GCN) and graph attention network (GAT) were implemented for evaluation. The evaluation results demonstrate that the proposed GAT model learning on the correlation graph achieves the best performance, as it can reduce the metrics of mean absolute error (MAE), root mean square error (RSME), and mean absolute percentage error metrics (MAPE) on average by 19.7%, 6.9%, 27.9% respectively. Finally, the importance and effectiveness of the models with corresponding weight graphs were also investigated and explained. It also provides the interpretability of the traffic prediction tasks on the railway network.

In recent years, a vast amount of sensor information has been generated as a result of the widespread use of IoT devices. However, with the explosion of data volume, it has become difficult to process all the data centrally on cloud servers. Therefore, it has become important to process data at edge devices where it is acquired. As edge devices cannot store all the acquired data, it is necessary to select and store the important data from the acquired data. In this paper, we propose an online data management method for time-series data called O-IDM, which is designed to extract data to preserve characteristics. O-IDM can extract data necessary to approximate the original data with a small computational cost. We evaluated the extraction performance of the proposed method by comparing it with other data extraction methods using the degree of deviation between the input data and polygonal line approximation base on the extracted data. The experimental results show that the proposed method was able to extract data with high accuracy. Further, the proposed approach was able to reduce the amount of data transmission required to convey the characteristics of the data by up to 66.7 percentage point compared to the naïve time-based sampling method.

Group exercise is more effective for gaining motivation than exercising alone, but it can be difficult to always find such partners. In this paper, we explore the experiences that joggers have with a virtual partner instead of a human partner and report on the results of two controlled experiments evaluating our approach. In Study 1, we investigated how participants felt and how their behav-ior changed when they jogged indoors with a human partner or with a virtual partner compared to solitary jogging. The virtual partner was represented either as a full-body, limb-only, or a point-light avatar displayed on smartglasses. In Study 2, we investigated the differences between the three representations as virtual partners for casual joggers in an outdoor setting. Based on our results, we propose implications for the design of virtual runners as casual jogging partners and speculate on their relationship with human users.

With the increase of the number of the elderly, healthcare systems to support the daily life and wellbeing of the elderly attracted attention. Especially, frailty syndrome is one of the most significant challenges faced by many countries because of its high association with mortality and hospitalization. Recently, with the progress of ICT (Information and Communication Technology), many frailty detection models which use sensors were proposed. However, many of them require very high costs caused by the installation and management of sensors. Therefore, the objective of this study is to propose a machine learning-based frailty detection model using only electric power consumption data from smart meter, which uses no other devices such as sensors. Also, we examined the feasibility of our model through a case study, in which we have conducted on 24 elderly people. As a result of a cast study, we could detect frailty with 82% accuracy, 77% precision, 84% recall, and 80% f-score for a 2-class classification problem (frailty or non-frailty). The results of our study show that more elderly people can receive frailty diagnoses through smart meters. Moreover, since frailty is a reversible condition that could be restored to a healthy status with early and appropriate intervention, our model has potential to extend the healthy expectancy of the elderly.

Most building energy is consumed in heating, ventilation and air conditioning (HVAC) processes, reported that 30% of building energy consumption is inefficient. Therefore, for energy-saving and managing Indoor Air Quality (IAQ), smart buildings have been proposed that can provide optimal environment to indoor users. Smart buildings measure CO2 for IAQ by default and realize energy-savings for using accumulated data. In this paper, we propose a method of estimating the new value, the number of occupants, using the data that is basically measured in smart building. To estimate indoor occupancy details, we use a combination of indoor state features and IAQ parameters such as room temperature and CO2. Moreover, we evaluate 5 methods (kNN, GP, RF, BR, MLP) that use and do not use indoor environmental state variables for two cases (normal and meeting). For Mean Absolute Error (MAE) of the MLP method using the state values, the normal case was improved from 0.58 to 0.36 and the parameter decreased from 0.8 to 0.59 in the meeting case.

20% of home parcel delivery is redelivery due to absence, which is estimated to cost $ billions a year in Japan. On the other hand, government is proceeding initiative to install smart meters for all households in Tokyo by 2020, and for all households in Japan by 2024. Considering those two factors, in this research, we built up future occupancy predictor for households with valid accuracy from electricity usage data which can be obtained from a smart meter, and we showed how a new routing algorithm equipped with this predictor can reduce home parcel absent delivery by 87.5%. Significant reduction of absent delivery indicates a great amount of time saved for the industry.

The development of ambient assisted living environments requires the growth of the associated technologies of the inside devices. In this article, we propose the Intellevator, an intelligent elevator system in smart buildings that could pro-actively provide passengers assistive information based on the real-time context sensing and awareness. We firstly introduce the embedded sensing approach to upgrade a conventional elevator to become Internet of Things-enabled. Then, after transferring and increasing the computational capability to the agent server, we prototyped an end-user centered application that provides the real-time context visualization and intelligence for assisting users, such as the advices of riding at other floor for reducing passengers' waiting time. We experimented this proposal by integrating with a real conventional elevator and conducted a 22-participants attended case study for evaluation. The results demonstrated that the system usability and user experience of the Intellevator is beyond that of the existing conventional elevator. Furthermore, valuable feedbacks for advice making and context visualization have been collected; they will serve as an informed reference for developing the future elevator system.

With the rapid advances of Internet of Things (IoT) technologies in recent years, more devices around us are becoming connected to the Internet. Such devices are often equipped with web API, which allows them to work as a whole in smart buildings or cities, not as a standalone object that conventional devices used to be. In order to make these device API practical, security cannot be disregarded because the impact of a security breach in physical objects tends to be larger, as it directly affects the physical environment we live in. However, enforcing security for such devices is challenging, because it may consist of heterogeneous devices including those with limited resources. Additionally, devices in smart building environment may be attached or removed at any time dynamically. In order to fill up this gap, we present an access control framework for API-enabled devices in smart buildings. To accommodate a dynamic environment in smart buildings, we split security manager as a third party in the system. Complex security computations are delegated to the security manager so that resource-constrained devices can be securely handled as well in our framework. To guarantee only authorized user can access the device, the device is forced to ask security manager to evaluate requests. The proposed framework is implemented in the real smart building, which consists of more than 300 devices and its performance is evaluated by conducting a various of experiments. The average of response time has less than 0.1 seconds, which indicates that the proposed framework is reasonable to be practically used in a real smart building.

Improving energy efficiency of smart buildings becomes more complex in the era of Internet of Things (IoT) where networked devices supply unprecedented high-resolution energy related information. As data acquisition and device control services are integrated into the building management platform by using web-based RESTful APIs, fine-grained context-based automatic control ability based on sensed and metered situations using a programmable approach is enabled. Rule-based approaches have been applied to design controllers for an individual device or a sub-system in buildings. However, the rules are static and usually mixed with the application logics. To improve energy efficiency by allowing smart devices to work in a collaborative way, a flexible programming architecture, which involves all the energy related factors, is required for complex context-based control of devices. In this paper, we propose DCRDL, a rule definition language that enables energy control policy designers to write flexible rules to control the behaviors of devices based on various levels of energy related information. We conducted several experiments with policies written in DCRDL in a real smart building, and the result shows that DCRDL is an efficient tool for situation-based device control for energy efficiency in IoT-enabled smart buildings.

Spatial information is very important because context such as location information is one of the key issues to realize ubiquitous spatial infrastructure. However, there are many specifications for spatial information model and establishing a common infrastructure for spatial information model is difficult. To solve the problem, we propose a new spatial information model based on ucR. Our model has high scalability because it is based on dynamic linking. We use triplestore to build our database, and its schema is more flexible and extensible than relational database schema. Moreover, we propose a flexible method to create new geographic identifiers by using combination of registered geographic identifiers. In addition, we realize the interoperability between our model and other spatial information models.

Lateral distance between two traffic participants is an important context for intelligent transportation systems for realizing a collision prediction system. Obtaining lateral distance requires costly hardware, prohibiting traffic participants such as pedestrians and/or bicyclists to receive benefits of lateral distance information for securing their safety. In this paper, a time-of-arrival (TOA) based bilateration is proposed for estimating relative lateral distance using ultra-wideband impulse radio (UWB-IR). Ranging error of UWB-IR systems affecting the estimation accuracy of lateral distance is coped with applying a low pass filter to angular data. Experimental results show that the estimation error is reduced from 8 m to 3 m when the relative distance between a target vehicle and a pedestrian is less than 15 m, using a portable UWB-IR system with 30 cm ranging accuracy.

An ultra-small, high-data-rate, low-power 1-cc computer (OCCC) with an UWB-IR (ultra-wideband impulse-radio) transceiver was developed for a wireless sensor network. Thanks to bear-chip implementation and a flexible printed circuit board, the size of the computer is only 1 cm3. To achieve 10-Mbps data rate, a middle-class 32-bit microcontroller, which has both a bus interface and a USB 2.0 controller, was selected. Low-power techniques, such as transition of microcontroller status to standby mode by using an external real-time clock during wait times, power shutdown of halted circuits, and detailed control of UWB-IR transceiver status, are applied. The effect of these low-power techniques is verified by measuring the time history of current consumption of the OCCC. It was confirmed that the OCCC can provide wireless communication at a transmission rate of 258 kbps over a distance of 30 m.

In achieving the mobility assistance for the individuals with different disabilities, it is required to recognize places with different granularities defined based on human interest, which is hard to be accomplished using traditional GPS-based approaches. In order to meet this requirement, a space-identifying ubiquitous infrastructure designed upon the concept of the universal design has been proposed, which is capable of constructing a service that is aware of places of human interest using ubiquitous computing technologies. Upon this infrastructure, two case studies are performed in the existing civil space, consisting of a pedestrian navigation service for the individuals with different needs, and a mobility assistant service for the visually-impaired. Through these case studies, the possibilities of the proposed infrastructure are discussed.

The key issue in achieving a pedestrian navigation service is to grasp the spatial context of pedestrians, which is mainly characterized by the places of their interest. We have developed a pedestrian navigation system uNavi, based on our proposed infrastructure that is capable of associating information services to the places of human interest. This paper focuses on the implementation and deployment of the system. Our current implementation realizes a detailed and timely navigation, by presenting landmark-based instruction of guidance using the spatial semantics stored locally. In addition, thanks to the feature of flexible definition of places achieved by our infrastructure, uNavi has a potential to assist the mobility of the disabled. Through the case studies of several large-scale deployments, the feasibility and effectiveness of our approach is discussed in this paper.

In this paper, we propose a robust natural language interface called CASIS for controlling devices in an intelligent environment. CASIS is novel in a sense that it integrates physical context acquired from the sensors embedded in the environment with traditionally used context to reduce the system error rate and disambiguate deictic references and elliptical inputs. The n-best result of the speech recognizer is re-ranked by a score calculated using a Bayesian network consisting of information from the input utterance and context. In our prototype system that uses device states, brightness, speaker location, chair occupancy, speech direction and action history as context, the system error rate has been reduced by 41% compared to a baseline system that does not leverage on context information.

This paper proposes a distributed shared memory, Window Real-Object (WROL which facilitates the construction of GUI applications with a set of cooperating parallel units running on multiple machines.To support unit cooperation, the WRO provides a shared data structure storing interaction objects displayed in a window among multiple machines.It also provides an event mechanism called absfrac[ everm to support control transfer among these units.Abstract events are generated when the shared data structure is updated, and invoke the units sharing the WRo.Both the function and mechanism of the WRO are tailored for GUI applications.The WRO provides spatia~addressing to the data structure, which is implemented efficiently using an R-tree.It also adopts aflli-replication algorithm as a shared-memory coherenee scheme.Consequently, the WRO can be implemented with adequate performance on a usual workstation environment.