Wi-Fi access points have been widely deployed in homes, offices, and public spaces. Some APs allow users to adjust the antenna orientation to improve communication performance by optimizing antenna polarization. However, it is difficult for non-expert users to determine the optimal orientation, and users often leave the antenna orientation in ineffective positions. To address this issue, we developed a mechanical Wi-Fi antenna device capable of automatically tuning its orientation. Experimental results show that antenna orientation could cause a throughput variation of approximately 70 Mbps under line-of-sight conditions. Furthermore, Bayesian optimization identified better configurations than random search, demonstrating its effectiveness for orientation tuning.

Wireless sensing is an emerging technology with a wide range of applications, but most existing systems capture only the motion of a single point, such as in respiration monitoring. This limitation is critical for tasks requiring multi-point data, such as respiratory volume measurement, where different body points provide distinct information, and a single point cannot represent them all. In this paper, we propose LiSen, a smartphone-integrated LiDAR system for multi-point wireless sensing, and demonstrate its contact-free capability for measuring respiratory volume. LiSen uses smartphone LiDAR to track multiple chest and abdominal points, enabling the first ranging-based spirometer system that captures the full volume curve without new-user calibration. We leverage the unique feature of multi-point sensing to address challenges such as body interference, diverse breathing patterns, and pressure differences. Tests with 35 examinees show that LiSen accurately estimates both instantaneous forced expiratory and inspiratory volume, achieving mean absolute errors below 0.24 L and 0.30 L, respectively, and an 8.93% error for four common pulmonary function indices.

Decentralized federated learning (FL) is a promising approach for training machine learning models on sensor networks, Internet of Things (IoT) devices, and other edge systems where no central server exists. While federated learning offers advantages such as preserving data privacy, it often suffers from non-independent and identically distributed (IID) data distributions across devices, which cause significant performance degradation. This issue is particularly severe when directly optimizing model parameters, because neural network training is inherently non-convex and standard convergence guarantees for convex optimization do not apply. Unlike existing decentralized FL methods that primarily operate in parameter space, we propose federated function-space alternating direction method of multipliers (FedF-ADMM). FedF-ADMM exploits the convexity of loss functionals within function space to derive alternating direction method of multipliers (ADMM)-based update directions, which are subsequently projected onto the parameter space via knowledge distillation. We further introduce a stabilization coefficient to enhance robustness under severe non-IID settings and analyze its behavior from a control-theoretic perspective by interpreting it as a proportional-integral (PI) term. Experiments under challenging non-IID scenarios, including settings where each device has data from only a single label, demonstrate that FedF-ADMM achieves faster and more stable convergence than existing decentralized FL methods, while attaining higher accuracy and better consensus among devices.

Micro-mobility has become a vital means of transportation in recent years, however, it has also resulted in a rise in traffic incidents. Timely tracking and predicting riders' maneuvers hold the potential to ensure active protection and allow for sufficient time to avert accidents by issuing timely warnings and interventions. We contend that the rider's head dynamics can provide valuable information regarding their subsequent maneuvers. Riders' traveling habits, however diverse, not to mention the rapidly varying riding environment. The above factors contribute to significant disruptions in the data source, and various micro-mobility forms further exacerbate the issue. We accordingly present HeadMon+, which predicts the rider's subsequent maneuver by examining their head dynamics, and it can effectively adapt to various riding conditions and individuals. The system incorporates a deep learning framework with an advanced domain adversarial network. By single-time pre-training, HeadMon+ is capable of adapting to new data domains, including human subjects, and riding conditions for robust maneuver prediction. Based on our evaluation, we have found that the maneuver prediction of HeadMon+ has an overall precision of 94% with a prediction time gap of 4 seconds. HeadMon+'s low cost and rapid response capability make it easily deployed and then contribute to enhancing safe riding.

There will be an increase in situations where images taken in specific locations are transmitted through networks for various services. However, this trend can lead to significant communication loads due to simultaneous transmission of images from multiple locations. Therefore, it is important to reduce the amount of network traffic in image transmission. While traditional compression methods focus on minimizing information loss in images, some applications only require the retention of semantic information, suggesting potential improvements in communication efficiency. This paper proposes an image generation-based transmission method for highly-efficient communications exploiting composition and descriptive information. The proposed method extracts specific information from an image to decrease the amount of data transmission, and reconstructs the image using an image-generative model by a receiver. In addition, image compression and reconstruction in the proposed method are demonstrated through an experiment. The experimental results indicate a need for a method to evaluate the output and a method for image reconstruction based on this evaluation.

We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.

Silent speech interaction (SSI) allows users to discreetly input text without using their hands. Existing wearable SSI systems typically require custom devices and are limited to a small lexicon, limiting their utility to a small set of command words. This work proposes ReHEarSSE, an earbud-based ultrasonic SSI system capable of generalizing to words that do not appear in its training dataset, providing support for nearly an entire dictionary’s worth of words. As a user silently spells words, ReHEarSSE uses autoregressive features to identify subtle changes in ear canal shape. ReHEarSSE infers words using a deep learning model trained to optimize connectionist temporal classification (CTC) loss with an intermediate embedding that accounts for different letters and transitions between them. We find that ReHEarSSE recognizes 100 unseen words with an accuracy of 89.3%.

As the capabilities of smart sensing and mobile technologies continue to evolve and expand, storing diverse sensor data on smartphones and cloud servers becomes increasingly challenging. Effective data compression is crucial to alleviate these storage pressures. Compressed sensing (CS) offers a promising approach, but traditional CS methods often struggle with the unique characteristics of sensor data-like variability, dynamic changes, and different sampling rates-leading to slow processing and poor reconstruction quality. To address these issues, we developed Mob-ISTA-1DNet, an innovative CS framework that integrates deep learning with the iterative shrinkage-thresholding algorithm (ISTA) to adaptively compress and reconstruct smartphone sensor data. This framework is designed to manage the complexities of smartphone sensor data, ensuring high-quality reconstruction across diverse conditions. We developed a mobile application to collect data from 30 volunteers over one month, including accelerometer, gyroscope, barometer, and other sensor measurements. Comparative analysis reveals that Mob-ISTA-1DNet not only enhances reconstruction accuracy but also significantly reduces processing time, consistently outperforming other methods in various scenarios.

Child-rearing depression, triggered by the chronic stress of parenting, can lead to serious mental health issues if not detected early. This study uses passive mobile sensing to analyze the behavioral and psychological patterns of households with preschool children. By collecting data from 131 participants (including 18 parents of preschoolers), we aim to differentiate child-rearing anxiety and behavior patterns. Our focus includes step counts, location data, call frequency, and psychological states. Results indicate that parents of preschoolers have fewer steps, visit fewer locations, and have higher call activity. They also show higher stress and anxiety but lower depression levels, suggesting that family support may mitigate depressive symptoms. These insights could aid in developing early detection and intervention strategies for child-rearing depression.

Although micro-mobility has become a popular and indispensable mode of transportation in recent years, it has also introduced a large number of traffic accidents. Timely tracking and predicting the maneuvers hold the potential to prevent accidents through prompt warnings and interventions. However, the open and simple structure of micro-mobility makes it hard to install sophisticated infrastructures for maneuver prediction. In this paper, we argue that the micro-mobility body dynamics provide sufficient information for maneuver prediction. Our preliminary study suggests that micro-mobility body dynamic patterns appear beforehand and exhibit the correlation with steering maneuvers. We accordingly present RideGuard, which leverages a built-in Inertial Measurement Unit on smartphones to achieve the prediction of steering maneuvers. Through a dual-stream CNN deep learning architecture, RideGuard effectively captures complex patterns and feature relationships from the time and frequency domain. Our extensive real-traffic experiments involving 20 participants demonstrate the superiority of RideGuard: employing a 3s detection window, RideGuard attains a minimum of 94% precision in maneuver prediction with a 5s prediction time gap. The low-cost and rapid response feature of RideGuard enables feasible deployment and promotes safer riding practices. Additionally, we open-source our well-labeled dataset to facilitate further research.

Monitoring and controlling the exposure of an individual to ultraviolet (UV) radiation is crucial for personal health. The use of the global navigation satellite system (GNSS) signals received by a personal off-the-shelf smartphone has been studied as a novel estimation method. In the existing method, satellites are grouped based on their positions and the signal information is represented by group statistics, leading to a coarse estimation. We propose a new UV index estimation method that directly utilizes satellite-wise information and their spatial relationships with a point-cloud neural network, considering the similarity between GNSS signals and point clouds. We collected GNSS signals and UV index data from two locations within the same area and demonstrated that the proposed method enhances the estimation accuracy and smoothness.

Public awareness of automated external defibrillator (AED) locations is crucial for prompt retrieval in cardiac emergencies. We propose a simulation-based approach as a preliminary step towards developing gamified mobile apps to enhance this awareness. By simulating AED retrieval in real-world pedestrian networks under various scenarios, we identify key elements that can improve retrieval efficiency. Our findings confirm the viability of the framework and highlight crucial aspects for improvement towards efficient future applications.

With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning. This involves predicting a user's next POI (point-of-interest) visit using their past visit history. However, the uneven distribution of visitations over time and space, namely the long-tail problem in spatial distribution, makes it difficult for AI models to predict those POIs that are less visited by humans. In light of this issue, we propose the $\underline{\bf{Lo}}$ng-$\underline{\bf{T}}$ail Adjusted $\underline{\bf{Next}}$ POI Prediction (LoTNext) framework for mobility prediction, combining a Long-Tailed Graph Adjustment module to reduce the impact of the long-tailed nodes in the user-POI interaction graph and a novel Long-Tailed Loss Adjustment module to adjust loss by logit score and sample weight adjustment strategy. Also, we employ the auxiliary prediction task to enhance generalization and accuracy. Our experiments with two real-world trajectory datasets demonstrate that LoTNext significantly surpasses existing state-of-the-art works.

Free-space trajectory similarity calculation, e.g., DTW, Hausdorff, and Fréchet, often incur quadratic time complexity, thus learning-based methods have been proposed to accelerate the computation. The core idea is to train an encoder to transform trajectories into representation vectors and then compute vector similarity to approximate the ground truth. However, existing methods face dual challenges of effectiveness and efficiency: 1) they all utilize Euclidean distance to compute representation similarity, which leads to the severe curse of dimensionality issue - reducing the distinguishability among representations and significantly affecting the accuracy of subsequent similarity search tasks; 2) most of them are trained in triplets manner and often necessitate additional information which downgrades the efficiency; 3) previous studies, while emphasizing the scalability in terms of efficiency, overlooked the deterioration of effectiveness when the dataset size grows. To cope with these issues, we propose a simple, yet accurate, fast, scalable model that only uses a single-layer vanilla transformer encoder as the feature extractor and employs tailored representation similarity functions to approximate various ground truth similarity measures. Extensive experiments demonstrate our model significantly mitigates the curse of dimensionality issue and outperforms the state-of-the-arts in effectiveness, efficiency, and scalability.

Ahstract-Various methods focusing on either spatial or temporal utilization, such as path diversity or link utilization, have been studied to realize efficient ad hoc networks. Furthermore, a method that integrates these methods has been proposed; however, it has only been evaluated theoretically and partially. Hence, achieving efficiency from both viewpoints as a protocol is challenging. This study proposes and defines block transmission-enabled opportunistic routing, which can achieve spatiotemporal efficiency as a protocol. The proposed method was evaluated by comparing its performance with that of two conventional methods that focus on either spatial or temporal efficiency, through computer simulations.

Machine learning (ML) is predominantly performed in the cloud or other computing facilities. While this computing method allows for the benefits of ML to be leveraged in urban settings, it may also lead to unfair sharing of the environment owing to the heat generated by servers in areas housing computing infrastructure. In this study, we developed a green distributed machine leaning (DML) concept-CoopL-to calculate the local consumption of computational resources based on DML, thereby mitigating the environmental burden. Moreover, we analyzed the impact of long-distance communication by incrementally raising the communication latency. Furthermore, the power consumption was examined by considering the hop count of the router.

Federated learning (FL) achieves collaborative learning without the need for data sharing, thus preventing privacy leakage. To extend FL into a fully decentralized algorithm, researchers have applied distributed optimization algorithms to FL by considering machine learning (ML) tasks as parameter optimization problems. Conversely, the consensus-based multi-hop federated distillation (CMFD) proposed in the authors' previous work makes neural network (NN) models get close with others in a function space rather than in a parameter space. Hence, this study solves two unresolved challenges of CMFD: (1) communication cost reduction and (2) visualization of model convergence. Based on a proposed dynamic communication cost reduction method (DCCR), the amount of data transferred in a network is reduced; however, with a slight degradation in the prediction accuracy. In addition, a technique for visualizing the distance between the NN models in a function space is also proposed. The technique applies a dimensionality reduction technique by approximating infinite-dimensional functions as numerical vectors to visualize the trajectory of how the models change by the distributed learning algorithm.

Although micro-mobility brings convenience to modern cities, they also cause various social problems, such as traffic accidents, casualties, and substantial economic losses. Wearing protective equipment has become the primary recommendation for safe riding. However, passive protection cannot prevent the occurrence of accidents. Thus, timely predicting the rider's maneuver is essential for active protection and providing more time to avoid potential accidents from happening. Through the qualitative study, we argue that we can use the rider's head dynamic as an information source to predict the rider's following maneuvers. We accordingly present HeadMon, a riding maneuver prediction system for safe riding. HeadMon utilizes the head dynamics of a rider by installing an inertial measurement unit on the helmet. It uses the extracted head dynamics features as the input of the deep learning architecture to achieve prediction. We implemented the HeadMon prototype on Android smartphone as a proof of concept. Through comprehensive experiments with 20 participants, the result demonstrates the excellent performance of HeadMon: not only could it achieve an overall precision of at least 85% for maneuver prediction under a 4s prediction time gap, but it also could keep a high accuracy under a low sampling rate. The low-cost feature of HeadMon allows it to be readily deployable and towards more safety riding.

Distracted riding behavior is one of the main causes of bicycle-related traffic accidents, resulting in a large number of casualties and economic losses every year. There is an urgent need to address this problem by accurately detecting distracted riding behaviors. Inspired by the observation that distracted riding behaviors induce unique head motion features that respond to the rider’s attention, we present the HeadSense, a helmet-based system that not only monitors the visual search episode of the rider but also detects distracted riding behaviors. Specifically, HeadSense leverages the inertial motion unit (IMU) to recognize distracted behaviors such as using smartphones, attracting to the roadside element, and abreast riding. We designed, implemented, and evaluated HeadSense through extensive experiments. We conducted experiments with 19 participants inside the university’s campus. The experimental results show that HeadSense can achieve an overall accuracy of 86.14% while monitoring visual search episodes. Moreover, HeadSense can detect the occurrence of distracted riding behaviors with an average precision of up to 85.04%.

In this letter, we investigate the problem of detecting compressed stochastic sparse signals with unknown sparsity degree under Bernoulli–Gaussian model. In addition to the generalized likelihood ratio test (GLRT) proposed in [1], the corresponding Rao test and Wald test are derived in this letter. By observing that obtaining their analytical performance is challenging, we further propose a new probability constraint estimator (PCE) of the unknown sparsity degree. Interestingly, by adopting the PCE, the GLRT, Rao and Wald tests are shown to be statistically equivalent and reduce to a new detector (i.e., the detector with PCE) with a simple structure. The analytical performance of the detector with PCE is thus derived, which is verified by Monte Carlo simulations. Finally, numerical experiments illustrate that the proposed Rao test and the detector with PCE outperform the original GLRT.

Trip purpose plays a critical role in reflecting human mobility behavior. However, it is relatively difficult to determine. With the rapid growth of urban mobility and big mobile data, utilizing these data for trip purpose classification has been a long-term objective to enhance travel demand and behavior models used in urban planning. Although studies on this topic have been extensively conducted, most past research preferred relying on traveler attributes or long-term travel histories to achieve accurate results. These data could be privacy sensitive and often do not satisfy real-world scenarios. This study addresses the problem of classifying trip purpose by only space activity information to avoid privacy conflict. 1) External memories are collected from factorized components based on the non-negative Tucker decomposition scheme. 2) These memories are extended by the cross-attention mechanism to achieve feature augmentation. 3) Subsequently, a novel concept called "latent mode alignment" is proposed. By leveraging the linear characteristics of external memories, geographic contextual latent modes are represented and matched with travel activities; this procedure is called "alignment." 4) The gate mechanism controls the eventual outputs for update. The proposed plug-in memory network (PMN), combined with baseline models, effectively outperforms the original settings. Moreover, combination models are validated with strong tolerance through missing data tests, which are common and problematic in real-world scenarios. The proposed PMN is a plug-and-play design that is easy to combine with newly developed classification models, and other memory collection methods can be expected.

Wearing a mask considerably mitigates the risk of infection from droplets. Automatic detection of whether a person wears a mask in his/her daily life and the type of masks the person wears can provide useful information for various services such as infection risk assessment, just-in-time alerts, and lifelogging. However, such automatic detection is difficult without the use of video processing or specialized equipment. In this study, the motion sensor of a commercially available smartwatch was used to detect the mask-wearing status. An investigation of the acceleration characteristic and an evaluation experiment of the mask-wearing state detection model revealed an accuracy of approximately 90% when specific motions were classified using motion sensors and machine learning. Furthermore, 98% accuracy was achieved when classifying sitting and walking activities.

Users and operators of shared spaces must ensure safety in such areas to prevent the spread of COVID-19. Although each organization has operated a variety of safety-related systems, including contact tracing, congestion monitoring, and check-in services, it is unclear what elements, such as privacy protection level, benefits, and permission procedures, have promoted the usage of these systems. In this study, we created MOCHA, a platform for sharing and tracking room-level locations. This platform automatically detects visited places by scanning Bluetooth beacons in each room using smartphones and shares location data according to predefined user settings. The collected data is used for room-level contact tracing, congestion monitoring, and reservation services. According to >6,500 users' usage data for a year in a university, outlining the advantages of utilizing the app encouraged people to install the app, and reinforced connections in small private groups are encouraged to use the app continuously.

While micro-mobility brings convenience to the modern city, they also cause various social problems such as traffic accidents, casualties, and huge economic losses. Wearing protective equipment has become the primary recommendation for safe riding, but passive protection cannot prevent accidents from happening after all. Thus, timely predicting the rider's maneuver is essential for more active protection and buying more time to avoid potential accidents from happening. In this poster, we explore the feasibility of using riders' head dynamics to predict their riding maneuvers. Through ten participants' preliminary study, we observed that not only do riders' head movements appear ahead of their maneuvers but also head movement patterns are distinct with different maneuver intentions. We then construct a deep learning network using Long Short Term Memory, achieving 89% of accuracy on maneuver prediction.

Monitoring the amount of UV irradiance to which individuals are exposed and ensuring that every individual receives the optimal amount has been the subject of extensive research. In previous research, the UV index was estimated using cell phone cameras, light sensors on smartphones, or wearable UV sensors. We propose a method for estimating the UV index using the widespread global navigation satellite system (GNSS) sensors available on smart-phones. In contrast to approaches that require the sensor to be exposed continuously to the irradiance, this method, which leverages GNSS sensors, has the potential advantage of enabling UV index measurement simply by carrying the phone as usual. As a first step in measuring the index using GNSS sensors, GNSS data were collected from cell phones placed at three locations in a single area; the OpenUV API was utilized as a baseline. The proposed method achieved a mean absolute error of 0.1523, which significantly outperformed the baseline.

Indoor localization technology is one of the most important topics in the fields of Internet of Things (IoT) and ubiquitous computing, and it has attracted much attention in recent years due to its ability to enable a variety of services. Localization methods using Bluetooth or Wi-Fi signal strength can introduce a low-cost location estimation system. However, due to the instability of the received signal strength and signals leaking from adjacent rooms, a simple method based on signal strength alone frequently results in misjudgment, depending on the signal propagation characteristics. In this paper, we propose a method to suppress misjudgment by considering the characteristics of stay time in different rooms. Our proposed method estimates the user state by fitting the distribution of time spent in each room to a Weibull distribution and applying survival analysis. The experimental results suggest that the method will provide more accurate information about the rooms in which users stay.

As intelligent sensing and smartphone technologies have progressed, a huge amount of highly heterogeneous data have come to be stored in smartphones and uploaded to servers for analysis on a daily basis. This has led to vast storage overheads for users and companies. Hence, data compression becomes the most efficient strategy for suppressing the increase in storage overhead. Compressed sensing (CS) technology is one approach to compressing data, but traditional CS-based algorithms are significantly time-consuming and have low reconstruction performance. In light of these drawbacks, this paper proposes a compressed sensing framework that instead takes advantage of the low time cost and adaptive learning capability of deep learning methods, wherein a convolutional neural network (CNN) is used for compressing and reconstructing acceleration data. Our experiments with actual smartphone acceleration data show that the proposed method dramatically improves the reconstruction performance with very little reconstruction time compared with traditional compressed sensing methods.

The childcare environment has significantly changed, owing to accelerating women's social advancement and the increasing number of nuclear families. Improving and supporting childcare have become major challenges in current society. Automatically recording and subsequently observing childcare activities can be used for various purposes to support childcare. However, methods to detect childcare activities using off-the-shelf devices have not yet been proposed. This study develops a method to detect childcare activities that parents perform for their babies using an off-the-shelf wearable device. We define nine childcare activities and develop corresponding detection models based on motion-sensor data from a smartwatch. Our evaluation in a laboratory setting resulted in classification performances of 71% (F1: 0.66).

The frequency and duration of social contact, represented by conversation, is positively correlated with our physical and mental health. Therefore, a method that automatically measures social contact can provide insight into people's health conditions and risks. Even though off-the-shelf wristwatch-type wearable devices are widely used in our daily lives and have rich computational resources, they have not been used as a social-contact monitoring tool in everyday conditions. In this study, we propose a system, called Ohanashi, for continuously monitoring conversational events as a means of social contact in daily life, by edge processing on an off-the-shelf smartwatch. To monitor the conversational event, we developed an audio classification model and implemented it as a smartwatch application, which can classify conversation and noise from an audio stream. Our performance evaluation shows that the classification model can classify conversation and noise with more than 86% accuracy in both silent and noisy environments, and the system can monitor conversation events for more than 15 hours on a smartwatch.

Riding bicycles with only one hand on the handlebar can severely undermine the operator’s steering capability and threaten road and transportation safety. Prior studies have exploited motion sensors to detect riding contexts and recognize related behaviors. Nevertheless, they fail to integrate a scheme to account for single-handed riding with elements crucial to danger prevention: awareness of the surroundings, response to danger, and convenient adoption. In this work, we proposed, designed, and implemented DoubleCheck: a smartphone-based real-time framework for cycling hand detection and distraction recognition. The method monitors handlebar holding on different road surfaces and recognizes hazardous distraction activities related to single-handed cycling using motion signals captured by a built-in inertial measurement unit in a handlebar-borne smartphone. It was designed on the premise that single-handed cycling enabled operators to adapt their body movements to different (often distracting) activities. We conducted an evaluation experiment using 22 participants on asphalt and pavement. The results indicate that DoubleCheck achieves an F1-score of 0.96 for hand detection and 0.69 for distraction recognition, demonstrating its efficacy as a candidate rider-safety precautionary measure.

Owing to lifestyle changes, urbanization, and the COVID-19 pandemic, many people spend more time indoors and tend to receive less direct sunlight than before. Although excessive or inadequate ultraviolet (UV) exposure can be harmful to our physical and mental health, moderate UV exposure is essential for vitamin D (VD) production in the body. In this study, we estimate the UV exposure using an off-the-shelf smartphone, and explore the relationship between the UV values and GPS signal strength (C/N0). The results demonstrate that a strong correlation (R2= 0.73) between the UV values and carrier to noise density (C/N0) even if the smartphone and UV sensor are moved. Therefore, it is possible to estimate the UV exposure to some extent from a person's location, even while carrying a smartphone.

In vehicular edge computing (VEC), offloading the tasks to the nearby resource-rich edge servers helps each vehicle enhance computational capabilities and improve in-vehicle applications' performance. However, the concentration of travel at specific spaces and times poses significant challenges on the load-balancing and scheduling of computation tasks at the edge servers. This paper studies a low-complexity dynamic online offloading strategy that efficiently reduces task delay and computing resource consumption in the multi-user, multiserver vehicular edge computing scenarios. Our design addresses issues of computation task placement and execution order of the tasks on each server. We use a realistic approach that vehicles generate tasks over time, and the set of the tasks is unknown in advance so that the offloading decisions are made in runtime. Extensive simulations are conducted on a real mobility trace of Luxembourg city, and the results show that the proposed algorithm effectively improves the offloading utility of the system.

Large-scale events attracting many participants might have a strong negative impact on productivity, mobility, comfort, and safety in a city. Within the few years, serious accidents led by congestion have occurred, especially during sports events, religious ceremonies, festivals, and so on. To alleviate these serious accidents, predicting the occurrence of a large-scale event is much significant. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this paper, we present an early event detection technique named BusBeat. BusBeat uses GPS trajectory data collected from periodic-cars that are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. BusBeat interpolates the missing GPS data by using the features of the periodic-cars. In addition, BusBeat uses the network-based analysis with a Time-dependent Congestion Network (TCN) in order to detect geo-spatial events. BusBeat achieves early event detection without incurring any privacy invasion, by using the continuous trajectories of the periodic-cars that provide the real-time traffic flow and speed. Since traffic towards an event venue would be slow before the event starts, BusBeat detects the geo-spatial events before the attendees gather. We evaluate our BusBeat using over 7,000-bus data collected in Beijing for 5 months and compare with the check-in data collected from a social network service.

Contagious diseases like COVID-19 spread periodically and threaten our lives. Self-protection, such as washing hands, wearing a mask, and staying home, are simple and practical solutions to safeguard against these diseases. Most governments and health departments recommend that people maintain self-protection. Although continuous self-protection effectively prevents the spread of infection, only the intent to self-protect is unsustainable in the long term. In this study, we design, develop, and deploy an application to track users' daily activities semi-automatically and enhance self-protection behavior using mobile sensing and gamified feedback techniques. Currently, more than 324 people have installed the app via AppStore, and 52 users have shared their activity data to our research group.

In mobile ad hoc networks, radio interference and mobility of nodes may degrade the packet arrival rate due to the dynamic topological change. Then, traditional reactive routing protocols may cause huge network resource consumption due to the route request flooding for discovering the destination. To solve the above issue, flooding area restriction methods can reduce the unnecessary control messages to narrow the flooding area based on the location information. However, each node must share location information before sending route requests via control messages. Besides, it is also difficult to share the correct location information of nodes due to the mobility of nodes. This paper proposes a flooding area restriction method to reduce the unnecessary control messages to determine the flooding area by sharing the location information via a mobile network. In addition, computer simulations reveal the effectiveness of the proposed method in comparison with a traditional routing protocol.

In this paper, we present a diffusion-based molecular communication using DNA molecules as data packets. In particular, we design a scheme utilizing the two types of nucleobase pairs to represent binary bits so that a bit sequence can be encoded onto a single DNA molecule. Therefore, the DNA molecule can be used as a data packet. Because of the stochastic character of the diffusion process and the variation of DNA diffusion coefficient correlated to the size of the molecule, namely number of base pairs composing the DNA fragment, the uncertainty of arrival order is a crucial factor that influences the reliable transmission. By modeling the observation window and focus slot, we concentrate on the order of the arriving DNA sequences. Based on the probability of each state, we derive the channel capacity of the proposed diffusive DNA based molecular communication. We also testify the effect of some parameters on the channel capacity, including base pair number, communication distance, and time slot width. Furthermore, the analysis shows that due to the high-density information encoded onto the DNA molecule, the proposed diffusive DNA based molecular communication has a higher channel capacity than other diffusion-based molecular communication.

In light of the recognized need to collect and analyze data to maintain urban development, the “smart city” concept has gained much attention recently. The development of sensing and information techniques has facilitated the analysis of urban mobility to better understand the characteristics of cities. Of the information and data that can be used to characterize cities, transportation data are among the most useful because transportation is so closely related to human and other aspects of urban mobility. In extracting features from automatically collected data, the greatest difficulty comes from the size or complexity of the data set, as these often have too many attributes or indices to analyze. This paper discusses the results of analyses of smart card ticketing authentication logs using nonnegative matrix factorization (NMF). The results present extracted features applicable to assessing various user and station characteristics and dynamics.

The analysis and study of the relationship between a geo-spatial event and human mobility in an urban area is very significant for improving productivity, mobility, and safety. In particular, in order to alleviate serious road congestions, traffic jams, and stampedes, it is essential to predict and be informed about the occurrence of an event as soon as possible. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this context, this paper presents an early event detection technique using GPS trajectories collected from periodic-cars, which are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. Using these trajectories, which provide the real-time and continuous traffic flow and speed, our technique detects large-scale events in advance, without incurring any privacy invasion. The behavior of periodic-cars shows a certain sign of a large-scale event before attendees gather around a venue because traffic can be slowed around the venue before the event occurrence. We evaluated our method using over 7,000-bus data from January to May in 2015 in Beijing, which we compared with the check-in data collected from a social network service.

Metropolitan areas witness significant fluctuations in mobile traffic due to patterns of human mobility. This fluctuation drastically deteriorates the efficiency and financial viability of conventional maximum-based network design. If networks are deployed to deal with the peak traffic rate at each site, their capacities are underutilized for most of time. To improve the efficiency of deploying base stations (BSs), this paper proposes a concept of an Autonomous Base Station with Optical Reflex Backhaul (ABSORB) architecture that can adapt to fluctuations in mobile traffic. In the ABSORB architecture, traffic at demand nodes is forwarded to and from an ABS with an arbitrary radio access technology (RAT). An ABS is connected to a gateway node through ORB, which consists of fiber optic networks. ABSs move to new locations following the demand movement, according to a relocation schedule that is periodically rearranged by an ABSORB controller. The network is flexibly reconstructed according to the demand distribution. The ABSORB architecture can be employed in various networks, and can coexist with traditional static architectures. It will drastically reduce the number of BSs, total deployment cost, and power consumption in comparison with the traditional design.

Knowing the location of a train is necessary for the development of useful services for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate, especially on a subway. This paper proposes an online algorithm for localization on a subway using only a barometer. We estimate the motion state from the change of elevation, then estimate the last station stopped at using the similarity of a series of elevations recorded when the train stopped to the actual elevations of the stations. We evaluated the proposed method using data from the subway in Tokyo. We also developed a mobile application to demonstrate the proposed method.

Molecular communication (MC) is a promising technique to enable the communication among nanomachines for various applications in healthcare industry such as targeted drug delivery. In this paper, we focus on the intersymbol interference (ISI) problem in diffusion-based MC. In this kind of communication, the ISI is notably influenced by communication distance and code width. On this basis, we propose an adaptive code width (ACW) protocol to mitigate the intersymbol interference. In this protocol, a 'distance feedback' is used to measure the communication distance. We adopt a signal attenuation model to accomplish this task. According to the measured distance, the transmitter can adapt the modulation using an appropriate code width in order to mitigate the ISI. Moreover, the ACW protocol improves the transmission efficiency when communication distance is short and ISI does not affect bit error rate. Finally it is verified that this protocol is feasible to control the ISI at a low level even when the channel varies due to the mobility of the transceivers.

Privacy issue has become serious in the area of mobile participatory sensing, smart grids, location-based services, and intelligent transportation systems. In response, many researchers are tackling the issue with technical approaches. In particular, data perturbation schemes, where all sensor data are processed on the user-side, are prospective techniques for utilizing personal data collected by embedded systems and sensor-equipped devices. However, existing data perturbation schemes require sharing the protocol between the user-side and the server-side, even though this can allow malicious attackers to estimate the original sensor data from the perturbed data. In this context, the paper presents a protocol-hidden perturbation framework, called Democratic Privacy, with which a service provider can acquire the original data probability without sharing the perturbation protocol with each user. In Democratic Privacy, the perturbation protocol is selected dramatically by each user, and the original data are reconstructed based on the selection of the crowd. Consequently, malicious attackers cannot distinguish the individual settings by eavesdropping. In addition, this paper presents Three-level Perturbation as one of the methods for Democratic Privacy, and evaluates it by means of simulations. The results of our evaluation demonstrate that Democratic Privacy provides a secure platform for pervasive computing environments.

This paper proposes a wired and wireless network cooperation (NeCo) system to quickly recover civilian telecommunication services in the aftermath of a catastrophic disaster. The proposed NeCo system achieves both rapid recovery and high throughput using wireless bypass routes backhauled by wired networks. With the NeCo system, active leaf nodes relay packets to and from dead leaf nodes whose wired communication channels have been disrupted. Thus, the dead leaf nodes can recover communication with root nodes outside the disaster area. In the this study, optimal bypass routes are computed to maximize the expected wireless throughput by solving a linear programming problem. Another issue is to overcome the limitation that the distribution of leaf nodes is determined by the demand distribution. We also introduce deploying additional recovery nodes to expand the application range of the NeCo system. Numerical simulations showed that the proposed NeCo achieved a higher throughput than an existing method, irrespective of the wired network's topology, and that our NeCo is suitable in cases where leaf nodes are widely distributed around a disaster area.

In recent years, cost reduction and capacity enlargement of memories have resulted in more and more buffers in switches and routers. Consequently, today's network suffers from bufferbloat, in which excess frame buffering causes high latency and jitter and reduces throughput. N rate N+1 color marking (NRN+1CM) was proposed to achieve per-flow fairness in a ring aggregation network. With NRN+1CM, colors are assigned to frames based on input rate and frames are discarded based on their color and frame-drop threshold. Although bufferbloat is avoided by its frame-drop threshold notification process, it was not clarified with a queuing model. This paper demonstrates how bufferbloat is avoided using M(n)/M/1/K queuing model and the formulated model is verified with a computer simulation.

Tele Echo Tube (TET) is a speaking tube installation that acoustically interacts with a deep mountain echo through the slightly vibrating lampshade like interface. TET allows users to interact with the mountain echo living at 1,200 meter elevation in The University of Tokyo Forests (35°94-N,138°80-E) in real time through an augmented echo sounding experience with the vibration over a satellite data network through the position of Von Uexkull's "Umwelt". This novel interactive system can create an imaginable presence of the mythological creature in the undeveloped natural locations beyond our cultural and imaginable boundaries. In doing so, TET discovers the cultural cognitive processes of our imagination mechanism. Such a discovery would help us design an interactive system that leverages the boundary of the real and virtual worlds by engaging a culturally cognition to perform a nonhuman-centric interaction with a culturally imaginable creature.

Community sensing is an emerging paradigm that enables the increasing number of mobile device users to share the minute statistics collected by themselves. In particular, this system is expected to be used for medical and public health research studies, as these mobile devices are in close proximity of the users almost at all times. However, since the mobile devices collect users' sensitive information, a number of privacy concerns will hinder the spread of community sensing applications for medical research. Therefore, we require an environment that enables general users to join community mobile sensing. A widely known technique for preserving privacy in mobile sensing is data perturbation, which adds noises on the user side and allows the central server to reconstruct the statistics of the original data. In this paper, we review a critical vulnerability of state-of-the-art perturbation schemes in which a malicious attacker may restore users' sensitive information from the perturbed data through long-term monitoring attacks. To overcome such vulnerability, we propose privacy-preserving community sensing with multidimensional randomized response, in which all sensed data are processed twice. Using our scheme, we are able to collect users' medical information with security. We evaluate how our scheme can preserve privacy while maintaining the data integrity of aggregated information.

In this paper, the user cooperation behaviors under eavesdropping attack are analyzed through game theory. Considering the physical layer security, we prove that the conventional cooperation scheme actually deteriorates the secrecy performance compared to the direct transmission, given that the eavesdropper has a better channel condition to the users than the destination. In this case, the necessary condition of the cooperation that the users should obtain additional utilities from the cooperation is not satisfied, which makes the users have no incentive to participate in the cooperation game. In order to motivate users, an adaptive cooperation scheme is designed to improve the secrecy performance even if the eavesdropping channel is superior to the destination's channel, and it is also observed that the mutual cooperation is one of the Nash equilibriums. We further exploit the Stackelberg game with a punishment mechanism to make the mutual cooperation as the unique Nash equilibrium.

Currently, human-computer interaction (HCI) is primarily focused on human-centric interactions; however, people experience many nonhuman-centric interactions during the course of a day. Interactions with nature, such as experiencing the sounds of birds or trickling water, can imprint the beauty of nature in our memories. In this context, this paper presents an interface of such nonhuman interactions to observe people's reaction to the interactions through an imaginable interaction with a mythological creature. Tele Echo Tube (TET) is a speaking tube interface that acoustically interacts with a deep mountain echo through the slightly vibrating lampshade-like interface. TET allows users to interact with the mountain echo in real time through an augmented echo-sounding experience with the vibration over a satellite data network. This novel interactive system can create an imaginable presence of the mythological creature in the undeveloped natural locations beyond our cultural and imaginable boundaries. The results indicate that users take the reflection of the sound as a cue that triggers the nonlinguistic believability in the form of the mythological metaphor of the mountain echo. This echo-like experience of believable interaction in an augmented reality between a human and nature gave the users an imaginable presence of the mountain echo with a high degree of excitement. This paper describes the development and integration of nonhuman-centric design protocols, requirements, methods, and context evaluation.

Location estimation with artificial infrastructures for mobile computing has been actively studied, but originally, people get a lot of information from nature during a course of a day. Nature including animals and plants supplies various information acoustically and visually. In this context, we present the concept of Cicada Fingerprinting System, which is a future localization scheme that will enable us to make the most of the information from nature. In our system, users collect the chirp of cicadas as acoustic data via smartphone embedded with microphone. Using the chirp of cicadas such as Wi-Fi fingerprinting, we can specify the location of users regardless of the existence of a roof. That is to say, cicada fingerprinting system applies cicada's instinctive behaviour to a localization. Furthermore, by our system, users are able to feel a sense of belonging to a nature even in urban area, where we spend much time in daily life. This novel system is designed for making general users conscious of presence of nature around.

We demonstrate interoperable mobile agents for language- and platform-independent wireless sensor network programming, with low-power resource-constrained embedded devices as static nodes and Android-based smartphones as mobile nodes, over disparate networks: 6LoWPAN and Wi-Fi. Representational state transfer architectural principles are applied in agent composition, control and migration and exposing the system resources to the Web: the devices, agents, sensor data, tasks and data processing results. The adaptable agent composition includes the task code in any programming language, the agent migrates according to a resource list and the state, i.e. the intermediate task result, represents the agent as a system resource. Mobile agents are then utilized in two tasks: first to collect light sensor data cooperatively in location, saving the mobile node battery whenever possible, and secondly the magnetometer sensor data with the scanned Wi-Fi access points' signal strength is used to detect groups of mobile nodes moving in the same direction in real-time.

Pervasive smartphones that embed a variety of sensors enable us to sense and learn about the physical environment around us, even the society we live in. However, the sheer volume of data collected through participatory sensing can deeply hamper the performance of various applications (e.g., data processing time and transmitting cost). In this paper, we proposed a method to reduce the volume of sensor data while preserving the information content of the original data. Our proposed method REPresentative Sense (REPSense) borrows the idea from electoral system. Hence, after data reduction, output data (target) can represent the original data (source) as parliament members are elected to delegate their constituencies. This method can compress multi-dimensional data with arbitrary distribution. We evaluated our method using real-world datasets collected by 12 users over a period of 4 months. The results show that our method outperforms state-of-the-art by comparing baseline methods in terms of data divergence and data processing performance.

Early detection of onset and outbreak of infectious diseases has paramount importance in containing such diseases before they turn into epidemics. The incredible growth in popularity and spatial resolution of coverage have made micro-blogging sites like Twitter a promising source of information for assessing the evolution of intensity of such diseases within a locality. However, identifying tweets with self-reported illness from other `disease related' tweets is important for avoiding false alarms. In this research, our endeavor is to segregate the tweets all of which fall under the general category of `disease related'. By using relatively very small training set and modifying the conventional n-gram feature selection method, we could isolate tweets reporting individual's illness with around 88.7% precision.

In this paper, we present an integrated framework, named NaviComf, which constructs pedestrian navigation systems for comfort in varying environments by using multi-modal sensing technologies. With NaviComf we aim to systematically provide solutions to three key problems: (1) how to build the environmental data warehouse (EDW) which works as an infrastructure providing comprehensive and predictive environmental information, (2) how to integrate heterogeneous environmental information from multi-modal sensors into an aggregate value which facilitates further processing, and (3) how to determine the optimal path plans in environments which are varying continuously. In NaviComf the multidimensional data model and data prediction method are applied to build the EDW. Then a novel multi-factor cost (MFC) model is proposed as the fundamental concept to integrate the multi-modal sensor data. Based on the former two solutions, the optimal path planning (PP) problem is solved in a time-dependent network by applying a dynamic programming method. In the evaluations of NaviComf, sensor data for temperature, humidity, and pedestrian traffic flow have been gathered in real environments and a prototype system has been implemented with the data. Evaluations are conducted by using the prototype system and the results show that NaviComf can efficiently navigate pedestrians through more comfortable paths as compared to the traditional navigation method.

We consider the problem of distributed particle filtering, where a set of nodes are required to collectively estimate the state of a nonlinear dynamic system from their individual measurements. An efficient distributed particle filters based on diffusion strategy is proposed. In diffusion strategy, nodes communicate with their one-hop neighbor nodes only, and the information is diffused across the network. The performance analysis for this distributed target tracking algorithm is given in the paper. Simulation results demonstrate the effectiveness of our algorithm.

For the problem of low altitude target tracking with acoustic wireless network, the signal propagation time delay effect must be considered. The target has been far away from its emitting position when the signal is received by sensors. This effect leads to synchronous sensors in the measurement space sample asynchronously in the state space and the sample frequency becomes unknown and time varying. In this paper, a batch type distribution fusion algorithm is proposed which consists of two steps. First, a linear search method is used for estimating the time-varying state transition time which is the parameter of least square solution for the initial state. This initial state is used again to optimize the state transition time until the iteration termination condition is satisfied. Second, a time register procedure and a distribution fusion technique are presented to obtain the global track. Simulation results verify the efficiency of the proposed method.

Due to hardware and computational constraints, wireless sensor networks (WSNs) normally do not take measurements of time-of-arrival or time-difference-of-arrival for range-based localization. Instead, WSNs in some applications use range-free localization for simple but less accurate determination of sensor positions. A well-known algorithm for this purpose is the centroid algorithm. This paper presents a range-free localization technique based on the radical line of intersecting circles. This technique provides greater accuracy than the centroid algorithm, at the expense of a slight increase in computational load. Simulation results show that for the scenarios studied, the radical line method can give an approximately 2 to 30% increase in accuracy over the centroid algorithm, depending on whether or not the anchors have identical ranges, and on the value of DOI.

To seek and collect useful sensor data in a participatory sensing environment, participants should be able to coordinate their activities in a timely manner. However, existing systems deal separately with the "preparation time" to define the goal and method of sensing, and the "sensing time" in the field. Therefore, participants cannot easily modify the sensing method, including the collaboration setting, once they are out in the field. In this demonstration, we will present a system called Kitokito, which allows participants to easily create small sensing tasks, and iteratively modify the collaboration and sensing method during the "sensing time."

Key distribution plays an important role in wireless sensor networks (WSNs), for information must be kept secure, even in an environment with limited storage ability and low data processing speed. However, it is a challenge to work in such a harsh requirement environment, the traditional key management scheme such as key distribution center (KDC) and public key cannot be used. Key pre-distribution is a good way to address this problem in WSNs, and while several schemes have been presented before, they cannot perform well in a large scale network. In this paper, we first use node's deployment knowledge to propose a hexagon scheme, and then combine it with the bivariate-polynomial to realize a new key agreement scheme. The simulation results show that our new scheme can be used in a large scale network, is attack resistant with low memory overhead, and achieves good network connectivity. We also introduce the measure how to effectively use key pre-distribution skills in the routing protocols.

In this paper, a cooperative localization algorithm is proposed that considers the existence of obstacles in mobility-assisted wireless sensor networks (WSNs). An optimal movement scheduling method with mobile elements (MEs) is proposed to address limitations of static WSNs in node localization. In this scheme, a mobile anchor node cooperates with static sensor nodes and moves actively to refine location performance. It takes advantage of cooperation between MEs and static sensors while, at the same time, taking into account the relay node availability to make the best use of beacon signals. For achieving high localization accuracy and coverage, a novel convex position estimation algorithm is proposed, which can effectively solve the problem when infeasible points occur because of the effects of radio irregularity and obstacles. This method is the only rangefree based convex method to solve the localization problem when the feasible set of localization inequalities is empty. Simulation results demonstrate the effectiveness of this algorithm.

In this paper, a cooperative localization algorithm is proposed that considers the existence of obstacles in mobility-assisted wireless sensor networks (WSNs). In this scheme, a mobile anchor (MA) node cooperates with static sensor nodes and moves actively to refine location performance. The localization accuracy of the proposed algorithm can be improved further by changing the transmission range of mobile anchor node. The algorithm takes advantage of cooperation between MAs and static sensors while, at the same time, taking into account the relay node availability to make the best use of beacon signals. For achieving high localization accuracy and coverage, a novel convex position estimation algorithm is proposed, which can effectively solve the localization problem when infeasible points occur because of the effects of radio irregularity and obstacles. This method is the only range-free based convex method to solve the localization problem when the feasible set of localization inequalities is empty. Simulation results demonstrate the effectiveness of this algorithm.

In this paper, we propose a range-free cooperative localization algorithm for mobile sensor networks by combining hop distance measurements and particle filtering. In the hop distance measurement step, a differential error correction scheme is devised to reduce the positioning error accumulated over multiple hops. A backoff-based broadcast mechanism is also introduced in our localization algorithm. It efficiently suppresses redundant broadcasts and reduces message overhead. The proposed localization method has fast converges with small location estimation error. We verify your algorithm in various scenarios and compare it with conventional localization methods. Simulation results show that our proposal is superior to the state-of-the-art localization algorithms for mobile sensor networks.

Wireless sensor networks (WSNs) have been proposed for a multitude of location-dependent applications. To stamp the collected data and facilitate communication protocols, it is necessary to identify the location of each sensor. In this paper, we discuss the performance of a novel received signal strength indicator (RSSI) positioning scheme, which uses a generalized geometrical location algorithm to achieve an accurate estimation based on mean received signal strength measurements. In order to improve the network performance and address limitations of static WSNs position estimation, mobile sensors are utilized effectively and an attractive movement strategy with mobile elements is designed. The effectiveness of our approach is validated and compared with the traditional RSSI method by extensive simulations.

Ad hoc networks enable application services in various environments including indoor/underground spaces and urban canyons; however conventional positioning infrastructures such as the GPS generally do not work well in these environments. We propose a lightweight, RFED-based positioning system that can be installed quickly and easily at various sites of ad hoc application deployment. The system includes a novel device called RFID Tape, which allows for efficient deployment and maintenance of a series of RFID location reference points. Pedestrian devices obtain location information from RFID reference points, and use motion sensors and a P2P-based technique to allow for continuous positioning even when the reference points are sparse. The device and the mechanism together facilitate the provision of location-aware features in ad hoc applications.

Sensor networks using mobile robots have recently been proposed to provide more flexible and efficient sensing. However, mobile robots in a practical world have many accidents with actuator or communication devices especially in their traveling to the destination. Moreover, in the practical world, there are many obstacles and bad conditioned area for both wireless and movement of mobile robots. In this paper, we introduce a practical scheme for delivering data with concerning a practical world issues.

Sensor networks using mobile robots have been proposed to provide more flexible and efficient sensing. One of the key challenges under this paradigm is a management of transferring a huge amount of data. In this paper, we propose a model and protocol for session control exploiting controlled mobility in multi-robot sensor networks (MRSNs) to transfer the huge amount of data efficiently. Since a communication in MRSNs is mainly provided by a physical movement of the mobile node, the performance of the session heavily depends on this movement. However, if a node initiating the session can utilize sufficient amount of nodes, the performance of the session significantly depends on the wireless communication. In this paper, we propose a suitable method of session control which is determined the balance between movement and wireless communications based on local information such as the location, number, maximum-velocity and buffer-size of surrounding nodes. WISER/s also takes a system policy of either delay minimization or energy efficiency into consideration. In this paper, we describe a method of how to initiate, terminate and manage the session including dynamic selection of optimal nodes' formation. We evaluate the performance of WISER/s using mobile robots which are equipped with MICA2 mote and comparing with non-optimized method. The experimental results demonstrate that WISER/s achieves better performance than non-optimized method.

In this paper, we analyze security and privacy issues in RFID-based reference point systems, which seamlessly provide high resolution location information so as to enable innovative mobile applications. Our preliminary analysis revealed the significance of the labor cost in deploying RFID reference points; therefore, we carefully analyze several system architecture candidates in terms of deployment cost and security/privacy threats. Based on the analysis, we select a scalable architecture to avoid the bottleneck in deployment cost even though it can be less secure. Finally, we briefly discuss potential solutions for critical security and privacy issues in the selected architecture.

In ad hoc networks, unidirectional links between nodes exist due to differing propagation patterns or wireless interferences. Routing protocols for bidirectional links must cause the decline of connectivity. In recent years, some routing protocols considering unidirectional links have been proposed. However, these protocols have a problem: the broadcast traffic for backward path discovery increases. This paper proposes a novel routing protocol for ad hoc networks that positively uses unidirectional links and that drastically reduces the number of control packets. Our protocol improves network connectivity and reduces the number of control traffic.

In this paper we propose a design of application programming interfaces (APIs) for configuration of multi-robot sensor networks (MRSNs). Such networks have unique aspects: intentional mobility and delay tolerance. These aspects limit the applicability of conventional approaches proposed for mobile ad-hoc networks (MANETs). APIs for configuration of MRSNs must take these aspects into account. In addition, an application program should be transparent from changing of physical devices such as wireless devices. In this paper we describe the design and prototype implementation of APIs for configuration of MRSNs.

Supporting range query over peer-to-peer systems has attracted many research efforts in recent years. In this paper, we propose a new multidimensional indexing structure for P2P systems called distributed Hilbert R-trees (DHR-trees). DHR-trees enables multidimensional range query to be executed similarly as in overlapping regions tree in P2P systems. Its distributed structure makes it fault-tolerant and scalable to dynamic network environment with a large number of peers as well. Our experiments shows that it performs well on multidimensional range query while the maintenance cost is reasonably low

IEEE802.11 WLAN (Wireless LAN) has been widely used in enterprise and public space such as air port. In these large WLAN networks, RRM (Radio Resource Management) is necessary for the efficient use of radio resource and also for load balancing among APs (Access Point) Since, in IEEE802.11, STA (Station) has the right to select an AP with which it will associate, AP selection mechanism implemented in STA is important for RRM. This paper proposes an AP selection mechanism, called HRFA (High-Rate First Association) in order to achieve load balancing and the efficient use of radio resource. Furthermore, IEEE802.11e is currently standardizing a MAC protocol to provide QoS (Quality of Service) in MAC layer and support real-time traffic over WLAN. In order for WLAN to be more widely used and to provide QoS in WLAN networks, the functionalities of IEEE802.11e have to be provided in WLAN devices. Our proposed HRFA can be applied to IEEE802.11e WLAN and can be implemented without any modifications in IEEE802.11 and 802.11e standard. Therefore it is useful from implementation-cost and compatibility point of views. Simulation results show that HRFA can efficiently ultilize radio resource and also achieve load balancing in IEEE802.11e WLAN networks.

The IEEE 802.11e defines a MAC protocol, which provides EDCA (enhanced distributed channel access) and HCCA (HCF controlled channel access) to support differentiation service over WLAN (wireless LAN). In IEE E802.11e WLAN, real-time application such as VoIP (voice over IP) can have more chance to access the WM (wireless medium) than non real-time application. In addition to QoS support in WLAN, power consumption is a critical issue when WLAN is used in handheld devices. For power saving in the use of real-time applications like VoIP under EDCA, U-APSD (unscheduled automatic power save delivery) was proposed in Y. Chen et al. (2004). In fact, it can save power consumption and works well when it is used for bi-directional voice connections generated at constant bit rate. However, when it is used for real-time applications like ON-OFF traffic, buffering delay at AP (access point) increases. To reduce the buffering delay, this paper proposes two mechanisms. Simulation results show that they can alleviate buffering delay generated at AP

Two-point lossless rotation transformations are used on realizing lossless DCTs or lossless orthogonal wavelet transforms. However, the two-point lossless rotation transformations based on the ladder network do not have high compatibility with their non-lossless versions. Therefore, in this paper, we propose two-point lossless rotation transformations with periodic structure and compare them with those based on the ladder network. We design corresponding tables by making use of periodic structure after supplementary rotation transformations. Since we can make the corresponding tables in consideration of compatibility with the non-lossless transformations, we can design the lossless rotation transformations that have high compatibility with them. A computer simulation shows that a lossless orthogonal wavelet transform based on the proposed method has high compatibility with its non-lossless version.

No abstract available.

The advance of ISM-band radio-based tracking systems (for example, wireless LAN-based tracking system) extends the application of location-based services (LBS), but it also threatens to allow the movement of users to be tracked when they are transmitting frames. Several protection methods based on periodic address updates have already been proposed. However, new correlation attacks, which utilize the correlation between the old and new addresses of the same node, can defeat current protection methods. To combat such attacks, we propose the concept of a silent period. A silent period is defined as a transition period between the use of new and old pseudonyms, when a node is not allowed to disclose either the old or the new address. Through analysis, we find that a silent period should contain a constant period and a variable period. The effect of the constant period is to mix the spatial relation between the node's disappearing points and emerging points. The variable period mixes the temporal relation between the node's disappearing times and emerging times. We evaluate the performance of the silent period through simulation. The results show that the silent period proposal significantly reduces the duration of time a node can be tracked continuously. There are still many open research problems before random address can be implemented to protect wireless location privacy, but silent period protocol is the first step to realizing it.

Ad hoc networks have recently become a hot topic. In ad hoc networks, battery power is an important resource, since most terminals are battery powered. Terminals consume extra energy when their network interfaces are in the idle state or when they overhear packets not destined for them. They should, therefore, switch off their radio when they do not have to send or receive packets. IEEE802.11 features a power saving mechanism (PSM) in the distributed coordination function (DCF). In PSM for DCF, nodes must stay awake for a fixed time, called the ATIM window (ad-hoc traffic indication map window). If nodes do not have data to send or receive, they enter the doze state except for during the ATIM window. However, ad hoc networks with PSM have larger end-to-end delays to deliver packets and suffer lower throughput than the standard IEEE802.11. To solve this problem, this paper proposes a protocol that reduces delay and achieves high throughput and energy efficiency. Simulation results show that our proposal outperforms other PSMs in terms of throughput, end-to-end delay and energy efficiency.

We present some observations and analysis on Bluetooth PAN's performance degradation in multi hop network based on our experiments. We highlight a situation in which control packets can be received properly but not data packets, as well as its effects on scatternet performance. Based on our analysis, we propose a cross-layer optimized routing protocol for Bluetooth (CORB), which outperforms AODV based routing protocols. CORB is a QoS-extended AODV routing protocol that is optimized for Bluetooth MAC. It has two unique characteristics. First, it uses a new load metric (LM) in QoS routing protocol instead of the number of hops as in conventional best effort routing. LM reflects nodes' link bandwidth with respect to Bluetooth nodes' role in the scatternet. This helps the CORB to bypass heavily the loaded nodes, and find routes with larger bandwidth. Second, LM and some MAC layer parameters are dynamically adjusted in response to the changes in radio conditions. These two characteristics of CORB contribute to its improved stability, and rapid response to changing radio conditions.

We investigate analysis/synthesis systems for progressive-to-lossless embedded wavelet image coding. We propose a system which does not use an inverse lossless wavelet transform (ILWT) but an inverse wavelet transform (IWT). In this system, we must correct the mean value of rounding errors of each band. We also investigate a mixed-type system which is obtained by applying repeatedly a 4-band non-separable forward lossless wavelet transform (FLWT) for the lowest frequency band. Our simulation shows that the PSNR of the reconstructed image of the non-separable FLWT followed by the IWT is 2.7 dB higher than that of the separable FLWT followed by the ILWT at a bit rate, and that the mixed-type system switching inverse transforms depending on the bit rate has good performance at all bit rates.

This paper endeavors to present a scheme to detect and estimate bottleneck bandwidth along the path in the Internet. We have participated in the RIPE NCC's TTM project to perform one-way delay (OWD) and loss measurement from a host in our laboratory to other hosts in Europe and USA. TTM is an active measurement system, which has implemented the IPPM one-way delay (RFC2679) and one-way loss metrics (RFC2680). From measured delay, loss, and traceroute's data, we can know the path properties such as bandwidth, path rerouting, congestion between each host, and so on. Based on measured delay, we propose an algorithm called Estimating Bottleneck Bandwidth using Packet-pair (EBBP), to estimate bottleneck bandwidth. Our algorithm is based on Bolot's (1993) equation, but we use OWD instead of round trip delay. Every participated host uses a GPS receiver to avoid the problem of clock difference. We make a phase plot graph from measured delay, extract useful samples, quantize extracted samples, and find the intercept of the phase plot graph by EBBP. Finally, we can estimate bottleneck bandwidth along the path.

What we are concerned with in this paper is a shared virtual environment (SVE) on a non-dedicated network like the Internet. Especially, we address haptics on an SVE for the new generation of network applications. The goal of our research Is to build an SVE system in which multiple participants can collaborate using haptics feedback, even though the participants are located around the world. One of the problems for this system is network impairment, and we have examined the effect of constant network delay and packet loss on the haptics collaboration system. In this paper we examine and evaluate the effect of delay jitter on the system when using media synchronization and dead reckoning.

The 64-point separable lossless two dimensional (2D) WHT is composed of the 8-point lossless one dimensional WHT. The latter is obtained by first decomposing the 8-point WHT into 2-point WHTs and then replacing every 2-point WHT by a ladder network. Since the coefficients in the ladder network then become real, the advantage of being multiplier-free vanishes. This paper therefore proposes a 64-point nonseparable lossless 2D WHT without multiplication as follows. First, the 64-point separable 2D WHT is decomposed into 4-point 2D WHTs. Second, every 4-point 2D WHT is replaced by a 2D ladder network which is multiplier-free. It is also shown that the transform coefficients of the proposed transform are closer to those of the 64-point lossy 2D WHT than those of the 64-point separable lossless 2D WHT.

Since the lossless DCT is compatible with JPEG or MPEG, it is expected to play an important role in unified lossless/lossy image coding. However, there is a problem that the difference between the transform coefficients of the lossless DCT and those of the (lossy) DCT is not very small. We present the design of a two dimensional lossless DCT based on a 4-point two dimensional lossless WHT and indicate that its number of rounding off becomes smaller than that of the one dimensional lossless DCT and, as a result, the difference between its transform coefficients and those of the DCT becomes small.

Currently network haptic applications become more effective than ever, various usages are being sought in order to improve efficiency and accuracy of tele-operation and communications among users in shared virtual environments (SVEs). The behavior of haptic communication system under realistic network conditions (delay, jitter, and bandwidth limitation) has recently been studied. Therefore the aim of our research is to develop the network-based haptic interaction system which adapts to realistic network environments including the Internet. In this work, we design and implement a prototype of the system with network tolerance in consideration of various network problems. Experiments using objective and subjective evaluations showed that the system works properly under realistic network conditions.

In lossless transforms, integer input signals are transformed into integer transform coefficients and losslessly reconstructed. Lossless versions of the discrete cosine transform and wavelet transforms have been proposed. In this paper, we design a lossless version of the lapped orthogonal transform (LOT). The fast LOT is decomposed into block transforms. Then the lossless LOT is obtained by replacing them by the corresponding lossless ladder networks. We investigate two cases of 31-band and 64-band decomposition in which the 4-point and 8-point lossless LOT are used, respectively. We compare them with the conventional lossless methods in terms of lossless and lossy compression efficiency. The proposed methods are found to have good performance.

In this paper, we present a method for designing lossless versions of two-channel FIR filter banks. We demonstrate that equal length PR FIR filter banks can be decomposed into 2-point transforms and unequal length filter banks into interpolative predictions. The lossless versions of the filter banks are obtained by replacing every constituent module by the corresponding lossless version. This method allows construction of the lossless versions of filter banks with arbitrary filter length. Lossless versions of several filter banks are designed and they are found to yield good performance for lossless image compression.

In this paper a reversible discrete cosine transform (RDCT) is presented. The N-point reversible transform is firstly presented, then the 8-point RDCT is obtained by substituting the 2 and 4-point reversible transforms for the 2 and 4-point transforms which compose the 8-point discrete cosine transform (DCT), respectively. The integer input signal can be losslessly recovered, although the transform coefficients are integer numbers. If the floor functions are ignored in RDCT, the transform is exactly the same as DCT with determinant=1. RDCT is also normalized so that we can avoid the problem that dynamic range is nonuniform. A simulation on continuous-tone still images shows that the lossless and lossy compression efficiencies of RDCT are comparable to those obtained with reversible wavelet transform.