The prevailing single-source paradigm in wireless sensing produces specialized models that are unscalable and generalize poorly to new tasks. Multi-source pre-training offers a path toward a generalist backbone but poses challenges including task heterogeneity, data redundancy, structural incompatibility, and the lack of a general-purpose pre-training objective. To address these issues, we propose WiSwiss, a comprehensive self-supervised multi-source pre-training framework that learns a general-purpose backbone for each modality. WiSwiss integrates semantic deduplication for dataset curation and a transformation-invariant pre-training objective. Experiments show that WiSwiss outperforms models trained from scratch, improving WiFi and mmWave performance by 4.5% and 10.3%, respectively, while reducing fine-tuning data requirements by 22.2% and 28.6%. We also present a qualitative study of scaling laws, showing that gains are task-dependent and that larger models require sufficiently large and diverse pre-training corpora to achieve substantial improvements.

Voice Activity Projection (VAP) has been actively studied to enable natural turn-taking in spoken dialogue systems, relying primarily on acoustic features. Visual cues such as head movements are also known to contribute to turn-taking prediction; however, camera-based approaches are affected by placement and lighting conditions and are not always reliably available to dialogue systems. As a camera-independent approach for directly capturing head motion, earable devices offer a promising solution. In this study, we propose Sensor-Augmented VAP, a framework that integrates in-ear inertial measurement unit (IMU) signals with a pre-trained VAP model via a lightweight residual fusion module. To validate our proposed method, we collected a dataset pairing conversational audio with in-ear IMU data, comprising 12 dyadic Japanese dialogues recorded using microphones and earbuds. Experiments in speaker-independent and speaker-dependent settings demonstrate that IMU fusion consistently improves weighted F1 score for shift detection and reduces VAP loss over the audio-only baseline. These results confirm that head-motion cues are effective for enhancing turn-taking prediction.

Deep learning-based object detection has seen a surge in applications for sensing systems on mobile devices. In this context, objects are identified and tracked across video frames, facilitating the calculation of associated events of interest. A significant research challenge refers to the acceleration of processing speed, which is constrained by deep learning-based object detection due to its intensive resource requirements. This paper focuses on a typical mobile sensing scenario, wherein sequences of frames containing objects of interest are sparsely dispersed throughout the video stream. Given that many of the frames lack objects, allocating substantial computational resources to detect them becomes inefficient. In light of this, we propose a stochastic scheduling algorithm, JumpQ. JumpQ performs per-frame detection when anticipating the presence of objects in the current frames. Consecutive negative detections prompt a transition to intermittent detection with a probability that undergoes further decay if the negative detection persists until reaching a predefined limit. Upon a positive detection, JumpQ swiftly reverts to per-frame detection and retraces a specific number of previously buffered frames to ensure the inclusion of potentially missed true frames. A comprehensive experimental study using the garbage bag counting technique was conducted to show the efficiency of JumpQ in accelerating the processing speed by nearly 1.92 times while maintaining a negligible impact on sensing accuracy.

In Smart Things and smart city applications, IoT devices generate large amounts of data and deep learning technologies are used to acquire useful information from it. Based on the kind of application and data, there are various non-functional requirements, such as low latency for information presentation by MR and privacy for video processing. To serve these requirements, a computing platform needs to make appropriate use of computing resources, namely Cloud, Fog, and Dew. However, there are some technical challenges in designing such a platform: i) transparently satisfying application QoS; ii) running the application across various hardware and OSes without modification; iii) sharing the application context taking into account the validity of values (temporal locality) and the data privacy (spatial locality). In this paper, we introduce Laqista, a novel Cloud-Fog-Dew computing platform. Laqista serves applications in a serverless manner via the Edgeless API, which schedules requests and abstracts the details of the platform. Applications are separated into Logics and Models, which are converted to lightweight, platform-agnostic formats such as WebAssembly and ONNX, respectively. Additionally, the Context Store synchronizes application context among the nodes, handling the privacy and validity of data. We developed a prototype implementation of Laqista in Rust and evaluated its performance. Experimental results show that the Laqista design has practical performance and is applicable to real-time applications such as video processing and MR.

In this work, we propose a novel model that focuses on object features by combining object detection with CNN and LSTM networks. In recent years, a multitude of deep learning-based methods for Visual Localization, have been extensively researched. However, conventional methods do not adequately account for object-level features. Therefore, it is difficult to use indoors where similar objects appear frequently. Our method applies CNN for feature extraction on detected objects cropped by YOLOv8, an object detection algorithm, and then integrates these features into a single feature vector using LSTM, enabling location estimation that takes into account multiple object features.

In this paper, we propose a MiRRoR(Mixed-Reality of Robust Rendering) that enables user localization in the mobility scenario. Mixed Reality (MR) is an emerging technology for interacting with spatial digital content. It merges virtual content and the actual world through the camera. Nowadays, many kinds of Head-Mount Display (HMD) devices for MR have been released, and they can be more popular in the future. However, MR is not available in mobility situations because of localization problems. This research aims to solve this problem using a sensor fusion approach. We developed an MR bus tour with visual-based localization and GPS, then conducted a preliminary experiment to evaluate localization accuracy and MR experience.

Sustained input of lifelog data is critical for conversational health applications, where algorithms and AI advise users based on recorded lifelog data such as meals, exercise, and sleep. However, an effective method for presenting information to encourage this continuity has not been identified. This study developed three intervention methods for prompting lifelog entries: (a) wording adjustment based on individual characteristics, (b) timing adjustment based on physical activity, and (c) a combination of these adjustments. An empirical experiment with 422 participants was conducted to evaluate the effects.

This study presents SatisFace, a novel approach to measuring customer satisfaction in amusement parks by analyzing facial expressions at entry and exit points. Recognizing satisfaction's subjectivity, SatisFace assesses facial Action Unit (AU) changes, tailoring assessments to individual user characteristics. Data were collected through an experiment with 200 participants at Soleil Hill, using questionnaires and OpenFace for facial analysis. A gradient-boosting machine learning model revealed a significant correlation between changes in facial expressions and satisfaction levels.

Class-incremental semantic segmentation (CISS) is a challenging task for operating image analysis from mobile devices in a changing real-world environment. In CISS, data-replay method is effective which selects and stores a part of past class data as the exemplar memory to learn increasing class objects sequentially without forgetting. We set SSUL-M as our baseline and propose the exemplar memory selection method considering the objects' classes.

This study aims to develop a heat stroke risk estimation system that takes into account not only environmental information but also internal factors of individuals in order to prevent the increasing number of heat stroke deaths in Japan. 26 outdoor workers were subjected to comprehensive measurements of environmental and biological data between August 15 and September 17, 2023, A prediction model was developed using a random forest classifier. While the model is able to predict with high accuracy cases with no risk, there is room for improvement in the detection of risky cases. The system aims to personalize heat stroke prevention measures to achieve more effective risk management.

The preparation of a large and diverse dataset is essential for training a robust deep learning model. However, there are instances where certain data are theoretically possible but challenging to observe in reality (e.g., traffic light violations). We refer to these unique instances as 'Scarce Realities', highlighting their rarity and the difficulties they present in data collection and model training. One effective and emerging approach involves using generative models to generate and augment such data. In this study, we demonstrate the promising potential of combining object detection models with simple image generation models as a way to generate fake videos. We achieve this by partially editing existing videos to artificially create 'Scarce Realities', using the generation of fake dashboard camera footage of traffic light violations as an example.

Many smart city applications utilize deep learning technologies to process the data generated by sensors and smart devices. However, current application hosting platforms are not suitable for deep learning applications, because of their special requirements, including GPU and large trained model data. We propose a smart city application hosting platform named MLess, which serves and scales deep-learning applications across servers. MLess adds an extra abstraction layer between applications and executing hosts, thus it allows developers to write applications in a serverless manner. We developed a Proof-of-Concept implementation of MLess and made preliminary evaluations against it. In future work, we plan to add QoS support like inference accuracy or throughput.

One major research problem regarding mobile sensing systems is how to accelerate the processing speed which is bottle-necked by the deep-learning-based object detection. The focus of this paper is directed towards a typical mobile sensing scenario wherein sequences of frames containing interested objects are sparsely dispersed throughout the video stream. In light of this, we propose a stochastic scheduling algorithm named JumpQ. In the case of consecutive negative detections, JumpQ reduces the probability of detection, while in the case of positive detections, JumpQ promptly returns to frame-by-frame detection and retraces the buffered frames to detect the objects. Our experiment reports that the JumpQ algorithm accelerates processing speed by over 100%, all while incurring a negligible impact on sensing accuracy.

In this paper, we propose a MeowSorter that identifies stray and pet cats using deep learning technology, to address the problem that lost cats with owners are mistakenly identified as stray cats and wrongfully euthanized. We made a dataset of 800 facial images and compared the accuracy of six image recognition algorithms. The optimal algorithm for classifying stray and pet cats is ResNet-50, achieving an accuracy of 85.44%. Our findings confirm that cat eyes and ears are key differentiators, rivaling cat expert judgments.

Deep learning models need a lot of labeled data, which is costly and time-consuming to collect. A method that learns a common knowledge (meta-knowledge) from similar tasks to train new tasks efficiently with fewer data is effective. We propose "Feature-adaptive Re-MAML" (FARe-MAML), a novel method for acquiring the most optimal learning method, taking into account the features of the new training data.

Data binding in front-end user interface web development allows for the UI to update automatically in sync with data, removing complexity from development and simplifying programming. However, data binding often causes website performance to degrade due to its increased complexity. In this paper, we propose "Visible Anchors" to solve the performance degradation caused by data binding. We present a novel web front-end compiler called FaST that builds upon this idea. We also compare the rendering speed of websites built by existing methods and the FaST compiler. The evaluation result revealed that the websites built by the FaST compiler are rendered at a minimum 17.1 times faster than the ones built by the existing methods. Most notably, FaST achieves all of this without any additional cost to the developer.

Road damage such as potholes may occur on roads due to aging, which may affect traffic. Periodic inspections of road damages are difficult due to the high cost of road surveys. The development of a system that automatically detects potholes and other road damages from dash cam images can allow inexpensive road inspections and can overall improve the problem of the long-term overlook of road damages. Last year, we conducted a demonstration experiment in Edogawa City, Tokyo, using an existing image-based road damage detection method. From that experiment, we found that the detection of potholes on actual roads often causes false positives in detecting shadows and manholes. In this study, we propose a method to reduce false positives in pothole detection, which was considered to be a problem through the demonstration experiment. Since the evaluation based on a pothole-only dataset is not practical, we constructed a dataset for evaluation by adding shadow and manhole images. Our method consists of two main components: data augmentation and an ensemble of classification mechanisms for object detection models. The result of the test on the reconstructed pothole dataset showed that the Average Precision (AP), which is a measure to evaluate the performance of object detection, and F1, which is the harmonic mean of precision and recall, were improved compared to the existing method. Our new method is expected to be an effective pipeline for tasks and situations where false positives are likely to occur and where false positives are more considered as an issue than false negatives, given that they are not dependent on the domain of potholes.

Crowdedness sensing of buses is playing an important role in the disease control of COVID-19 and bus resource scheduling. This research analyzes the relationship between carbon dioxide concentration, bus environment and the number of passengers by linear regression. Our prototype system collects the data of bus environment and carbon dioxide concentration to estimate the number of passengers in real time. By collecting the sensing data from a shuttle bus of university campus, we experimentally evaluate the feasibility and sensing performance of the crowdedness estimation model.

Emotions are essential for constructing social relationships between humans and interactive systems. Although emotional and empathetic dialogue generation methods have been proposed for dialogue systems, appropriate dialogue involves not only mirroring emotions and always being empathetic but also complex factors such as context. This paper proposes Emotion Regulation Chat (ER-Chat) as an end-to-end dialogue framework for emotion regulation. Emotion regulation is concerned with actions to approach appropriate emotional states. Learning appropriate emotion and intent when responding on the basis of the context of the dialogue enables the generation of more human-like dialogue. We conducted automatic and human evaluations to demonstrate the superiority of ER-Chat over the baseline system. The results show that inclusion of emotion and intent prediction mechanisms enable generation of dialogues with greater fluency, diversity, emotion awareness, and emotion appropriateness, which are greatly preferred by humans.

In this study, we construct an object detection model that targets billboard advertisements in images and detects them regardless of their content and models to extract multiple elements from billboards. In addition, we try multiple methods for each task to clarify an appropriate method. As a demonstration, we prepare a web application that extracts elements from images of billboards by using the model constructed in this study.

Many researchers and companies have engaged in estimating users' interests so that an online shopping system can tell what he/she wants now. This paper tackles the next challenge in online shopping, i.e., predicting the times that users go shopping online. To predict the timing of online shopping, we focus on "wandering behavior" in web search activities and propose a "search wandering score" (SWS). Online shopping behavior can be categorized into three states: "wandering shop-ping", "focused shopping", and others. Wandering shopping is a state in which users make purchases in high SWS situations; focused shopping is a state in which users buy things in low SWS situations. Unlike previous studies, our work is based on an analysis of large-scale data containing shopping and search logs produced by approximately 200,000 users of a real web portal site for over a year. The results of an extensive evaluation show that our methodology can predict user's future shopping behavior types with 86% accuracy. This research is the first step towards understanding the relationship between users' mental states and their online shopping behavior.

With an increase in the number of IoT devices, the amount of data transferred between the devices and applications is becoming huge. To ensure that these data are properly used, a new IoT data transfer system is needed to better control the timing and the content of the data to transmit. One of the promising means for such a large-scale city-data transfer is the publish/subscribe messaging model, which can separate data senders and receivers so that they can run independently. However, existing pub/sub systems cannot cope well with the mobility of senders and receivers, thereby limiting its applicability to real-world uses. In concrete, they don't consider Time-to-live (TTL) of data. Users can use the data anytime and within TTL of it. IoT platforms can improve controlling data transmission by used to this characteristic. In this paper, we focus on the Time-to-Live of data (data-TTL). Our system can control data transmission by using data-TTL and combine with the user's movement information. We have constructed a system, that is capable of control data transmission for mobility aware.

Conversational agents are increasingly becoming digital partners of our everyday computing experiences offering a variety of purposeful information and utility services. Although rich on competency, these agents are entirely oblivious to their users' situational and emotional context today and incapable of adjusting their interaction style and tone contextually. To this end, we present a mixed-method study that informs the design of a situation- and emotion-aware conversational agent for kinetic earables. We surveyed 280 users, and qualitatively interviewed 12 users to understand their expectation from a conversational agent in adapting the interaction style. Grounded on our findings, we develop a first-of-its-kind emotion regulator for a conversational agent on kinetic earable that dynamically adjusts its conversation style, tone, volume in response to users emotional, environmental, social and activity context gathered through speech prosody, motion signals and ambient sound. We describe these context models, the end-to-end system including a purpose-built kinetic earable and their real-world assessment. The experimental results demonstrate that our regulation mechanism invariably elicits better and affective user experience in comparison to baseline conditions in different real-world settings.

Conversational agents are increasingly becoming digital partners of our everyday computing experiences offering a variety of purposeful information and utility services. Although rich on competency, these agents are entirely oblivious to their users' situational and emotional context today and incapable of adjusting their interaction style and tone contextually. To this end, we present a first-of-its-kind situation-aware conversational agent on kinetic earable that dynamically adjusts its conversation style, tone, volume in response to users emotional, environmental, social and activity context gathered through speech prosody, ambient sound and motion signatures.

In correlation with the socio-economic development, changes in people's lifestyle brought about significant impact on dietary patterns. Though public concerns over healthy eating are increasing, many are still uncertain when choosing a well balanced meal amid welter of information. In this paper, we propose "KomaFLens'', a mobile system and application built for Microsoft HoloLens, which aims to motivate long term dietary habit modification through gamification. The primary purpose of this research is to enhance the users' nutritional knowledge and to guide them to make healthier choices in their diet. Our preliminary evaluation revealed interesting points for discussion regarding the procedure for capturing food labels. Streamlining the operational method to boost tractability will improve the accuracy when recording food intakes.

Shape changing robot is an approach towards locomotion on uncertain terrain due to its omni-directional features. However, the current locomotion method for such robots rely on discontinuous rolling. We propose a free form locomotion: an omni directional continuous crawling for deformable robots. This method introduce continuous shifting of contact surface similar to amoeba movement. A Mochibot that has thirty two telescopic legs is developed to verify the proposed locomotion method. Through the experiments, we have confirmed that the robot can track smooth paths: straight, smooth, and hand written curves. We also evaluate errors between desired and measured trajectories of the robot.

We propose a damaged traffic lane detection method ensuring high accuracy in spite of only a few number of supervised data which are labeled traffic lane images. In general, supervised machine learning approach is very powerful for image classification. However, preparing a large amount of supervised data is time-consuming task because labeling damaged or not damaged is usually done manually through visual inspection of images. Thus, lowering the cost of labeling data is a great concern. To this end, we adopt a semi-supervised machine learning approach which learns from both labeled and unlabeled data by constructing graph based on the image similarity. We captured a large amount of the road lane images. Then, we constructed graph structure whose nodes are the road lane images and whose edges are the similarity between the images. In several nodes, we assigned labels which denote "damaged" or "not damaged." Finally we utilized the label propagation, which made it possible to infer the labels of the unlabeled data from the labeled data. These estimation resulted in the accuracy rate over 85% from the supervised data, which accounted for only 1.8% of the total data.

The information of daily garbage diposal can be used to develop many appealing applications in smart cities. This poster introduces DeepCounter, an automotive sensing system to providing a finegrained spatio-temporal distribution on the amount of disposed garbage bags. In the system, deep learning based image processing is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck. A prototype system is implemented and experimental evaluation validates the feasibility of our proposal using realistic garbage collection videos in Fujisawa city Japan.

Can we inspect the road condition at a low cost? City infrastructures, such as roads are very important for citizens to their city lives. Roads require constant inspection and repair due to deterioration, but it is expensive to do so with manual labor. Meanwhile, there are official city vehicles, especially garbage trucks that run through the entire area of a city every day and have cameras to record their driving. When we use these cameras, we can watch roads conditions anytime, anywhere. In our study, we focus on these cameras and attempt detecting the road damage, such as road marking blur. To achieve our goal, we explore the new system in this paper. This system adopts the object detection approach that is end-to-end learning and based on deep neural networks, which propose the blur region candidate and detect whether the road markings are blurred or not all at once. In our experiment, first, we obtain the drive recorder video from sanitation engineer and then annotate them. After annotation, we trained our models and calculate the mean average precision to evaluate our models. As a result, our model performs on our collected dataset.

Analyzing and feeding back the results on real-world services are important missions in the Big Data era to realize smart city. However, analyzing real-world data is still challenging because of dirtiness of data and large variety of analytic requirements. To cope with the challenges, this paper proposes and develops an analytical toolbox for smart city applications. The analytical toolbox consists of three phases: preparation, analysis, and visualization. The preparation phase deals with the dirtiness of the data by including fundamental data cleansing techniques and data integration techniques. The analysis phase is responsible for ETL (extract, transform and load) process and analytical query processing from the next phase. The visualization phase deals with analytical requirements from users and visualization of analytical results. This paper showcases a real-world use case of the proposed analytical toolbox. The use case is now open in public with help of Fujisawa city, Japan, and this fact indicates that the proposed analytical toolbox is feasible for real-world data analysis and feeding back to citizens.

We propose "SmileWave", the first selfie social networking service to reveal the existence of emotional cognation through smiling selfies on the social network. We conducted multiple rounds of in-the-wild user studies with 86 cumulative total users for total duration of 5 weeks. Throughout the entire study, we confirmed the occurrence of smile-based emotional contagion over social network, not only in the momentary duration but in longer term period.

Collecting urban data in a citywide scale plays a fundamental role in the research, development and implementation of smart cities. This demo introduces Cruisers, an automotive sensing platform for smart cities, which is developed based on the following ideas. a) Garbage collecting trucks are used as host automobiles to accommodate sensors, b) 3G cellular communication network is used to wirelessly deliver sensed data directly to servers, and c) Proxy server(s) are adopted to convert the format of sensed data to required ones. This platform has been deployed to 24 garbage collecting trucks at Fujisawa city, i.e., nearly 1/4 of the total number of such trucks in the city. An iOS application is also developed to demonstrate the sensing process and the covered area.

This paper proposes ''SmileWave", a system for revealing smile-based emotional contagion, propagation effect of the similar emotion through smiley facial expression, on the social network where users interact each other through web-based user interface rather than in-person interaction. SmileWave is a picture-based networking service and detects the change of smile degree when the user looks at posted smile images of others. Our extensive user study with 50 participants for 30 days confirmed the emotional contagion effect on SmileWave. Users' smile degree improved by 27% when the user looked at posted smile images. The result also proved that there is a stronger effect on smile-based emotional contagion when the examinee and the person in the image are in close relationship.

Recent technological trends in mobile/wearable devices and sensors have been enabling an increasing number of people to collect and store their "lifelog" easily in their daily lives. Beyond exercise behavior change of individual users, our research focus is on the behavior change of teams, based on lifelogging technologies and lifelog sharing. In this paper, we propose and evaluate six different types of lifelog sharing models among team members for their exercise promotion, leveraging the concepts of "competition" and "collaboration." According to our experimental mobile web application for exercise promotion and an extensive user study conducted with a total of 64 participants over a period of three weeks, the model with a "competition" technique resulted in the most effective performance for competitive teams, such as sports teams.

In today's ubiquitous computing environment where users carry, manipulate, and interact with an increasing number of networked devices, applications and web services, human attention is the new bottleneck in computing. It is therefore important to minimize a user's mental effort due to notifications, especially in situations where users are mobile and using multiple wearable and mobile devices. To this end, we propose Attelia II, a novel middleware that identifies breakpoints in users' lives while using those devices, and delivers notifications at these moments. Attelia II works in real-time and uses only the mobile and wearable devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our in-the-wild evaluation in users' multi-device environment (smart phones and smart watches) with 41 participants for 1 month validated the effectiveness of Attelia. Our new physical activity-based breakpoint detection, in addition to the UI Event-based breakpoint detection, resulted in a 71.8% greater reduction of users' perception of workload, compared with our previous system that used UI events only. Adding this functionality to a smart watch reduced workload perception by 19.4% compared to random timing of notification deliveries. Our multi-device breakpoint detection across smart phones and watches resulted in about 3 times greater reduction in workload perception than our previous system.

In today's ubiquitous computing environment where the number of devices, applications and web services are ever increasing, human attention is the new bottleneck in computing. To minimize user cognitive load, we propose Attelia, a novel middleware that identifies breakpoints in user interaction and delivers notifications at these moments. Attelia works in realtime and uses only the mobile devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our evaluation proved the effectiveness of Attelia. A controlled user study showed that notifications at detected breakpoint timing resulted in 46% lower cognitive load compared to randomly-timed notifications. Furthermore, our “in-the-wild” user study with 30 participants for 16 days further validated Attelia's value, with a 33% decrease in cognitive load compared to randomly-timed notifications.

Operating Systems (OS) in wireless sensor nodes can be classified into event-driven systems or multithreaded systems. Most event-driven systems, such as TinyOS, drive down power consumption although context switching for real-time processing is not available. Among multithreaded systems, non-preemptive systems, such as Protothreads in Contiki, often have lack of real-time processing capability. In Protothreads, if a higher-priority task was posted while a lower-priority task has been running, the lower-priority task cannot be preempted. Thus, one challenge is that without changing the semantics of Protothreads, how the system can be preemptive as well as lowering the power consumption for real-time tasks such as target tracking. In this paper, we propose a dynamically switchable scheduling system for operating systems using Protothreads where events with time constraint have occurred. This system enables to trigger interruption, to process real-time tasks preferentially when real-time events occurred, and to save energy by executing tasks except real-time tasks as a standard event-driven system. Exeprimental results show that latency in Contiki is reduced by about 75% in the best case and is kept constant with power efficiency.

Recent technological trends on mobile/wearable devices and sensors have been enabling increasing number of people to collect and store their “life-logs” easily in their daily lives. Beyond exercise behavior change of individual user, our research focus is on the behavior change of teams, based on life-logging technologies and information sharing. In this paper, we propose and evaluate six different types of information sharing model among team members for their exercise promotion, leveraging concepts of “competition” and “collaboration”. According to our experimental mobile web application for exercise promotion and extensive user study among 64 total users for three weeks, the model with “external competition” technique resulted the most effective performance for competitive teams such as sport teams.

We propose a novel technique that aggregates multiple sensor streams generated by totally different types of sensors into a visually enhanced video stream. This paper shows major features of SENSeTREAM and demonstrates enhancement of user experience in an online live music event. Since SENSeTREAM is a video stream with sensor values encoded in a two-dimensional graphical code, it can transmit multiple sensor data streams while maintaining their synchronization. A SENSeTREAM can be transmitted via existing live streaming services, and can be saved into existing video archive services. We have implemented a prototype SENSeTREAM generator and deployed it to an online live music event. Through the pilot study, we confirmed that SENSeTREAM works with popular streaming services, and provide a new media experience for live performances. We also indicate future direction for establishing visual stream aggregation and its applications.

The global spread of mobile phones creates a new vision in the world. It is called mobile sensing, in which human beings are regarded as sensors to produce aggregated models and knowledge. In this setting, it is likely that user privacy is violated. Therefore, we investigate privacy-preserving metho

No abstract available.

Welcome to Taipei, Taiwan, and the IEEE 19th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2013). RTCSA has been a prestigious technical conference sponsored by the IEEE Technical Committee on Real-Time Systems for years. The objective of the conference is to bring together academic researchers and industry developers for intensive discussion of recent advances in the field of embedded systems, real-time systems, and cyber-physical systems.

Deploying Wireless Sensor Networks (WSN) securely still requires users to have certain skills and exert effort. In the near "sensor everywhere" future, a much simpler method for deploying WSN will be necessary for end-users. We propose LiDSN(Light Communication for Deploying Secure Wireless Sensor Networks) which enables users to achieve deployment tasks via simple interaction. LiDSN leverages light-based communication between an LED and a light sensor in order to add a new sensor node securely into existing WSN. Through touching interaction, a new sensor node ID and secret key can be transmitted to the WSN, and then the WSN is able to identify which node should be added while maintaining the security of the WSN.

Nowadays, since each sensor network is managed within a single organization, sensor data cannot be obtained externally. When these sensor networks are virtualized that means everyone is able to obtain data anywhere without minding which sensor network the data belongs, two features will be required. One of these is geographical range query. This research realizes it using Z-order in the same way with related works [1][2][3][4]. The other requirement is distributed sensor data management. Current systems adapt the way that stores the data in a (or some) centralized server(s), or that stores the data in many servers, having one centralized server to store indexes of the address of the data. This research proposes a method not relating real space geographical information and relative position of peer in ID space. By using this method, in the place where density of people and smart phones with many sensors increase suddenly such as Super Bowl and new year countdown in NY, by using DHT, sensor data don't concentrate on a specified peer on managing the data. This research simulates and evaluates this method.

Compressive Sensing (CS) is a novel approach for data representation, which can represent signals at a rate below the Nyquist rate with low computation costs on encoder. For these characteristics, CS is very suitable for low power sensor nodes to save power consumption that is a primary problem in Wireless Sensor Networks (WSN). But there are many problems when using CS in a real environment. One of these is that pattern of sensor values change dynamically. It decreases the efficiency of power consumption and accuracy of recovery. To solve the problem, we propose Pattern-based Matrix-size Optimization Algorithm (PMOA), which aims to improve the accuracy of exact recovery and power consumption.

Analyzing groups of people having a conversation enable to provide context-aware services, such as life log, group-wares, and the virtualization of social networks. We propose a novel method for extract chatting groups by leveraging Bluetooth RSSI and voice data acquired from smart phones. Neighboring people are detected from Bluetooth RSSI, and conversation groups are extract by talking states. The purpose of this paper is to define algorithm that works on efficiently on smart phones that are general and widespread mobile devices.

This paper proposes the information access method that is based on the distance between users and objects. In Addition, demonstrate Lupe system, which visualizes WSN status information utilizing our method. The evaluative experiment shows that our method is useful in where a number of sensors are setup. As a result our method and Lupe system enable to easily brows WSN status information for end-user.

A demonstration experiment called “Timely & Timely” was a evaluation of a system in which ubiquitous sensor's data and reporter's reports are displayed on digital signage and smart phones. This experiment was taken place at a shopping mall called “Lalaport Kashiwanoha” in Chiba, Japan. We have designed and implemented an information distribution system in which we can specify which information should reach which customers. Specifying the certain information is called “Channel” and the information receiving software running on Android smart phones is called “Ubiroid.” Each channel is defined by a USDL (Universal Service Description Language) based on XML. The developers and users can easily search and use channels thanks to this markup language. We rent 20 Android smart phones to the guests and received questionnaire results from those who participated in this experiment. This paper presents design, implementation, and evaluation of “Timely & Timely” and also shows the results from the experiment questionnaires.

Mobile nodes have limited computing resources, among which battery is one of the most important resources, since the lifetime of a node depends on the amount of battery and energy usage in the node. Application processes on mobile nodes include those are important for the users for certain duration of time, and those not. Therefore, exhausting whole battery for running non-important ones such as games, would be waste of energy. To maximize the user satisfactions with limited amount of energy, a sophisticated pacing mechanism is required for energy usage. We propose a process lifetime reservation system, called “pSurvive.” It enables users to reserve energy for running specific processes for a certain duration of time. Users are allowed to run any applications until the total energy consumption reaches the “deadline” to run the reserved processes. pSurvive enables this with the following three novel mechanisms. First, P-Monitor monitors running processes and devices (e.g. NIC, LCD, etc.) in a node for their energy consumption. Second, P-Analyzer estimates future energy consumption based on the energy usage information given by P-Monitor. Third, P-Reserver accepts energy reservation requests from users, and pace the energy consumption by shedding unnecessary tasks. This paper first discusses requirements for energy reservation on mobile nodes. It then reports the design and the implementation of pSurvive for Android mobile nodes, and shows that pSurvive achieves process lifetime reservation based on the fine-grained energy monitoring.

Computers are now present almost everywhere and connected into ever more complex networks. This means not only that embedded systems are more complicated, but also that communication among the diverse stakeholders of systems is much harder than before. This paper introduces the D-Case approach to a systematic explanation of embedded-systems dependability. A D-Case is a structured document that argues for the dependability of a system, supported by evidence. This extends the notion of safety cases commonly used in (European) safety-critical sectors. The goal is to develop the D-Case language for communication systems dependability among the stakeholders. The paper reports the experience in constructing a D-Case for the remote test surveillance system developed to demonstrate certain dependability system components. D-Case construction is shown to be an effective method in explaining how each system component contributes to the overall dependability of the system. Another experiment shows how the D-Case approach can promote dependability through the life cycle of a larger system. Finally, the paper presents some comments on the difficulties and insights for future work.

This paper presents u-Photo Mobile a clickable digital still image to visualize and operate ubiquitous services. U-Photo Mobile is designed and implemented to be executed on smart phones e.g. Apple's iPhone. Users shoot the ubiquitous services attached with a two-dimensional bar-code. The u-Photo Mobile system decodes the ID and finds functions of the service. The touch screen of u-Photo Mobile lets users check and control the service. The operation of ubiquitous services are realized by making a network connection between u-Photo Mobile and the services. With this intuitive method, the users can interact with ubiquitous service through the metaphor of “taking and tapping a photograph.” The u-Photo provides users with an instinctive technique, which realizes a seamless interaction between user's mobile phones and intelligent environments.

This paper introduces a new dependable operating system project, called DEOS, started in 2006, and scheduled to continue for six years. In this project, a safety extension mechanism called P-Bus is to be designed, and implemented in the Linux kernel so that a future dependability attribute is implemented with P-Bus. A hardware abstraction layer, called SPUMONE, is introduced so that a light-weight operating system, called ArcOS, and a monitoring service on top of ArcOS monitors the Linux kernel to provide a safety-net for the Linux kernel. New dependability metrics are being designed to enable developers and users to decide which hardware or software solution meets their dependability requirements, and thus can be used.

The number of elders who encounter falling accidents has been increasing in the past few decades. Falling accidents could cause major injuries, such as having bruise, breaking bone, and in the worst case, losing life. Therefore, preventing elders from falling accidents is important in order to ensu

Migration between different wireless access networks often involves disconnected period, which is caused by passing an area of bad wireless coverage and potential overhead to switch the network on the network interface to connect to. The disconnected period can cause extra transmission delay due to the timer-driven retransmission behavior in the transport protocols, such as TCP and SCTP. We propose a new retransmission scheme to achieve better migration performance in SCTP, which is a newer connection-oriented and reliable transport protocol that is becoming popular. Our scheme minimizes the extra transmission delay by leveraging address reconfiguration operation in SCTP without involving other layers. It decreases the delay more than 5 seconds compared to the original SCTP when migration involves approximately ten-second disconnected period. The implementation of our scheme is already imported in FreeBSD.

PR-SCTP provides a timeliness transport service. When the lifetime of the data specified by the application expires, the sender PR-SCTP gives up retransmitting the data, and transmits a Forward TSN chunk to make the receiver advance the Cumulative ACK point. However, on the mobile communication, connectivity of the nodes is intermittent. PRSCTP does not work properly in this case, thereby the timeliness transmission is impaired. In order to address this issue, we propose a new algorithm that transmits a Forward TSN triggered by readdressing events in SCTP associations. Our scheme allows PR-SCTP to restart transmission of data with lifetime smoothly regardless of duration of the connectivity disruption.

This paper proposes Spinning Sensors middleware that realizes a robotic sensor node mechanism comprised of a sensor node and a robotic actuator node such as a motor or a mobile robot. We can increase sensing space, time, and accuracy of a sensor node by attaching them onto robotic actuators. To realize a robotic sensor node, we need to achieve collaborative utilization of arbitrary sensors and actuators, and automated calculation of sensing area and time. We stated these problems as spontaneous coordination problem and sensing area calculation problem. The Spinning Sensors middleware provides the mechanism of device coordination, data processing, and management of spatiotemporal model of robotic sensor nodes. In this paper, we discuss a robotic sensor node model, and design and implementation of the middleware. We introduce three kinds of applications using the middleware such as environment monitoring, sensor controlled robot, and context-aware service. The experiments using the robotic sensor node and the middleware are conducted to evaluate and measure the possibility, performance, and practicality of a robotic sensor node mechanism.

We explore the design patterns and architectural tradeoffs for achieving interoperability across communication middleware platforms, and describe uMiddle, a bridging framework for universal interoperability that enables seamless device interaction over diverse platforms. The proliferation of middleware platforms that cater to specific devices has created isolated islands of devices with no uniform protocol for interoperability across these islands. This void makes it difficult to rapidly prototype pervasive computing applications spanning a wide variety of devices. We discuss the design space of architectural solutions that can address this void, and detail the trade-offs that must be faced when trying to achieve cross-platform interoperability. uMiddle is a framework for achieving such interoperability, and serves as a powerful platform for creating applications that are independent of specific underlying communication platforms.

We propose a novel tool, called objSampler, with which users can record and recall "encounters" with objects in ubiquitous computing environments. We encounter various things, individuals, and places in the real world either consciously, meaning encounters that we are aware of, or unconsciously, meaning those we are unaware of but physically close to them. While some of those encounters are particularly important or treasurable to us, the physical memory in our brain is often too volatile to remember them. In objSampler, we address this issue by providing a state-of-art hardware called objPipette that embeds a sensor node, an RF-ID reader, and a battery cell. Users can record conscious encounters with it by scanning RF-ID tags pasted on real world objects. In addition, the objPipette detects and records the places where the user is. Users can recall the recorded encounters by using a software support in objSampler, called objScope. This paper describes the design and implementation of objSampler. User study, which is also provided in this paper, shows that objSampler provides a unique and intuitive means to achieve the above goal

This paper presents an interactive analysis and visualization framework for behavior histories, called mPATH framework. In ubiquitous computing environment, it is possible to infer human activities through various sensors and accumulation of their data. Visualization of such human activities is one of the key issues in terms of memory and sharing our experiences, since it acts as a memory assist when we recall, talk about, and report what we did in the past. However, current approaches for analysis and visualization are designed for a specific use, and therefore can not be applied to diverse use. Our approach provides users with programmability by a visual language interface for analyzing and visualizing the behavior histories. The framework includes icons representing data sources of behavior histories, analysis filters, and viewers. By composing them, users can create their own analysis method of behavior histories. We also demonstrate several visualizations on the framework. The visualizations show the flexibility of creating behavior history viewers on the mPATH framework.

This paper proposes a service model based on the service hierarchical structure called Galaxy service model, and a service discovery framework called Galaxy service directory system. In a ubiquitous computing environment, software services are embedded into various devices. The application construction requires service discovery mechanism and a recursive definition of services. The service discovery mechanism should, therefore, provide applications with common interface to look up the hierarchically represented composite services. In existing service framework, applications cannot be combined into other applications, since they do not provide the recursive service representation facility. In contrast, a service in Galaxy has a hierarchical description by deploying nested services. This model makes other services and applications to deal with an application that is composed by services. Galaxy service directory system facilitates structure-bounded service registration and service discovery. The system enables applications to find different composition-levels of service through a common interface.

This paper proposes a middleware for home networks, called Virtual Networked Appliance (VNA) architecture, in which the service description method and the Service to Service (S2S) communication mechanism are separated in an orthogonal way. Through the separation, VNA architecture solved the following two problems of existing middleware technologies: aspect violation and middleware fragmentation. In this paper, we first clarify the two problems and their relationship. Then, we describe the proposed middleware architecture as a solution from the viewpoint of the overall configuration and the S2S communication mechanism.

This paper proposes a pluggable service-to-service (S2S) communication mechanism in a middleware for home networks, called Virtual Networked Appliance (VNA) architecture. In the architecture, service description method and the plug-gable S2S communication mechanism are separated in an orthogonal way. Through the separation, VNA architecture solved problems of home networks on which users have to operate multiple heterogeneous middleware technologies simultaneously: middleware fragmentation problem, due to complexity of realizing heterogeneous services on one middle-ware technology: aspect realization violation problem. The pluggable S2S communication mechanism provides service programmers with a simple aspect representation method to define a service-specific protocol concern apart from the service's implementation. It also provides off-the-shelf protocol modules of such well-known communication protocols as RTP, RTSP, HTTP, and SMTP for an inter-service communication, and dynamically loads them based on the aspects defined by the programmer. This reduces the complexity of implementing heterogeneous services on the VNA architecture, thereby addressing the problems. In this paper, we first clarify the two problems. Then, we describe the proposed mechanism with an overview of the middleware architecture referring to a composite service: "Follow-You-and-Me Video."

In this paper, we present a new mobility-support model for applications, Application Module Request Broker (AMRB). We focus on two types of mobility: host mobility and application code mobility. These two types of mobility dynamically change the binding between applications name and location in the network. AMRB conceals these changes of banding to reduce a complexity in development of applications. In AMRB, we deal with mobile application codes that communicate with each other as Application Modules (AMs). AMRB provides AM's service transparent communication for applications by using a specifier which does not need to include any network location information. Furthermore, applications can use AM's service transparently of migration by exploiting location management mechanism. In this paper, we describe the design and implementation of AMRB and some evaluations. Also, we demonstrate a sample application that AMRB is effective for developing mobile sensor type applications.