Prolonged loneliness in older adults increases the risk of dementia, cardiovascular diseases, and premature death. However, timely detection and behavioral pattern analysis are challenging due to the slow progression of loneliness and diverse individual lifestyles. To address these barriers, we propose SMILE, an end-to-end framework for sensor-driven detection and counterfactual pattern analysis of loneliness. Our approach uses non-intrusive sensors to passively observe behavior and build a personalized profile over time. When SMILE detects meaningful changes in activity patterns, it employs generative models to produce counterfactual sensor profiles—synthetic representations of behavior predicted to result in lower loneliness scores. Suggested pattern changes are identified as the difference between observed and counterfactual profiles, highlighting the behaviors most associated with loneliness. We conducted a 6-month feasibility study with 18 participants from the United States and 15 from Japan to develop and evaluate SMILE. Our framework integrates a Time-series Transformer (TST) model for loneliness detection, which achieved 96.25% accuracy, and a diffusion model for generating behavioral explanations, which outperformed a baseline by up to 76.69%. These findings highlight the potential of SMILE to support sensor-driven, personalized loneliness detection and behavioral explanation in aging populations.

Due to the widespread of dashcams, we will have more videos that capture roads/streets in driving. Consequently, a vast amount of the videos will be available and can be utilized for analyzing road safety and similar purposes. For example, suppose different dashcams can take vehicle/pedestrian traffic at a risky intersection at different timings. In that case, the collection of such videos will effectively recognize the cause of dangerous situations without surveillance camera infrastructure. However, identifying a Road Segment of Interest (RSI) in the video, such as near the intersection region, is challenging as the video frames do not usually include location tags. In this paper, we present a unique approach to attack this challenge. Assume that a video segment, called reference video, captures an RSI. We re-identify the RSI taken in another video (called test video) that captures the roads containing that RSI. By this approach, we can automatically extract the video segment corresponding to RSI from a given test video, using the reference video. We introduce AKAZE features to assess frame-level similarity and develop an algorithm to find frame-by-frame matching between reference and test videos. We have evaluated our method using 10 reference videos that correspond to 10 RSIs, each with 5 test videos. The result has shown that the average frame error distance was only 3.03 in daytime and 4.93 in nighttime, which are sufficiently low to re-identify RSI in the newly obtained test videos.

Location-based services have undergone massive improvements over the last decade. Despite intense efforts in industry and academia, a pervasive infrastructure-free localization is still elusive. Towards making this possible, cellular-based systems have recently been proposed due to the wide-spread availability of the cellular networks and their support by commodity cellphones. However, these systems only consider locating the user in a 2D single floor environment, which reduces their value when used in multi-story buildings.

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Towards achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort; especially when done at high density; and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy.

This paper takes a Computer-Vision (CV) and RFID fusion approach to tracking RFID-tagged objects in a room environment. Unlike similar CV-RFID fusion techniques that fuse two different estimates from CV and RFID systems to increase the certainty of locations, our system does not directly localize RFID tags. Instead, each tag-attached object's usage time by a human is detected by observation of the tag's phase variance. The time is then used to identify the pinpoint locations where the object is handed and released by the human, using the corresponding human trajectory observed by 3D depth cameras. RSSI variance caused by human movement is also leveraged to estimate rough locations of such objects that are not used by humans. Finally, the correspondence between the objects and RFID tags is automatically recognized by clustering the feature vectors of objects. Consequently, once the type of one object, such as pen, book, and cup, the other objects in the cluster can be annotated with the same object type to facilitate object management in the system. The experimental results have shown that object usage is detected with 0.984 accuracy, objects are localized with 58.3cm median error in severe NLoS environment, and ten types of objects are identified with 0.842 accuracy in a laboratory room.

In this paper, we propose a low-cost, non-invasive home activity recognition method using low-resolution power consumption data. Notably, we tackle the following two challenges. Firstly, we use only the time series of power consumption data aggregated per house and measured every few tens of seconds, which is usually used for demand monitoring by smart meters. We design a set of activities that can be recognized by such low-resolution data, and find out an appropriate feature set to train and test balanced random forest classifiers. Secondly, we consider the divergence of activity patterns seen in different households. Since supervised learning dedicated to each household is not a realistic solution, we arrange different classifiers trained by different household data in supervised learning, and present a method to automatically choose the best-fit classifier for the household of interest in the online phase. The experiment was conducted to collect aggregated power consumption data from eight real homes for 191 days. The result of activity recognition using the dataset shows that the proposed method achieved 70% recognition accuracy in identifying activities like cooking and sleeping, which is significant for non-invasive remote monitoring.

Situation awareness in the transport system has been significant for understanding the cause of traffic congestion, potential risks of accidents, and so on, and many efforts have been made to recognize surrounding situations by vehicle onboard cameras for low-cost sensing. These methods use computer vision technologies to recognize objects in the vicinity of the vehicle in the video image. However, to correctly recognize the positional relationship between the surrounding objects and the vehicle, lane detection, particularly multiple lane detection, should robustly be performed. In this paper, we propose a method for detecting multiple lanes from video images taken by onboard cameras. Since frame-by-frame spatial lane detection does not often work in a severe environment on multi-lane roads, the proposed method leverages temporal change detection, which enables complementing such lanes that are not detected in a frame and eliminating wrongly-detected ones for robust detection and tracking. In the experimental using the video images, we have achieved an accuracy of more than 90% in multi-lane environments with different conditions such as lane boundary occlusion by other vehicles, nighttime, rain, side road junctions. Besides, we achieved 96.47% accuracy to detect lanes on which vehicles are driving, while 88.70% in the comparative method.

In this paper, we propose a daily activity recognition Method using multiple low-cost infrared portable sensors and power consumption monitors, which are easy to deploy with less risk of privacy violation. It is significant to monitor daily life of elderly living alone to prevent them from having less exercise at home and from being at home with a depression tendency. Taking those sensor values as the input, our method utilizes Markov Logic Network (MLN) to express the association rules of sensors and activities as soft logical expressions. Then using the estimated activities from MLN and user feedbacks, the method trains MLN for better accuracy of recognition. We have implemented our system as a monitoring system that summarizes the activities of daily living as a timetable, which facilitates for remote family to monitor target elderly. We have deployed the system to two real houses of elderly people, and have shown the average accuracy was more than 85%.

Understanding humans and its environment is a key enabler of smart, intelligent applications and services for a future smart society. To deploy such services in our ambient environment, it is expected to fully utilize battery-less and maintenance-free IoT devices and technologies for more ambient, distributed computing. In recent years, Wi-Fi-based communications are becoming more energy-efficient, and channel state information (CSI) has the potential to sense more detailed information about the things in the real world. Besides, ambient backscatter has appeared as a promising technology for zero-energy sensing and communications. Leveraging those state-of-the-art technologies, energy harvested IoT devices for context recognition of humans and objects will be in reality. A significant challenge is how to make use of inferior, less-powerful zero-energy IoT devices to achieve processing of interest, i.e., accurate recognition of humans and objects, while a single device does not work. Therefore, we consider orchestrating distributed tiny IoT devices for both sensing and communications. Particularly, distributed machine learning in the local environment will achieve highly promising sensing in our ambient environment. In this paper, we survey the state-of-the-art technologies for zero-energy sensing and communications in the context of humans and objects sensing and recognition. Then, we address the challenges to be tackled in terms of such distributed, intelligent sensing using zero-energy devices. Finally, we introduce the concept of utilizing distributed IoT devices, followed by the statement about our ongoing work toward future zero-energy sensing and processing.

We have been working on automatic location estimation of HVACs utilizing the RSSI of BLE (Bluetooth Low Energy) to improve the time-consuming process in the HVAC system network setting where technicians need to identify the location of each HVAC one by one to assign a network address to the HVAC. This short paper particularly reports an initial attempt of training a BLE propagation prediction model adaptive to "above-ceiling" environment, which is a key enabler for automation of the assignment process. Based on the 1.3 million RSSI sample data collected in a real building for one week, we trained the model with different parameters and discovered that RMSE of RSSI prediction has the following trend: 1) it decreases with the introduction of the obstacle features -the number of beams and the number of HVAC machines, and 2) it decreases with the increase of polynomial term degree. The best model so far has an RMSE of 4.832 dBm tested by 10-fold cross-validation. The methodology and quantified result of this initial attempt provide reference not only for the network assignment process but also for building BLE-mesh networks in the above-the-ceiling environment, which can be exploited for HVACs management or sensor data aggregation.

Autonomous vehicles will bring tremendous benefits to society. However, it is expected to take a considerable period of time for their spread, and during the transition period, autonomous and manually-driven vehicles will share the same roads. Under such situations, the driving behavior of autonomous vehicles will influence manual drivers, for example, manually-driven vehicles may be stuck behind the autonomous vehicles and overtake them frequently if autonomous vehicles drive slowly for safety. In this paper, we investigate how the traffic flow and driving stress vary with autonomous vehicles by microscopic traffic simulation. We develop a microscopic traffic simulator that can reproduce traffic flow with autonomous vehicles and manually-driven vehicles. The behavior of these vehicles can be modeled by the combination of Intelligent-Driver Model (IDM) and Lane change Model with Relaxation and Synchronization (LMRS). These models can express various driver characteristics through simulation parameters such as driving speed and distance between vehicles (net distance), and we are able to create realistic scenarios like overtaking autonomous vehicles by manually-driven vehicles with faster speed than the legal speed. From the simulation results, we found that there is a desirable combination of speed and time headway that achieves both smooth traffic and less stress of drivers with a given percentage of autonomous vehicles.

By introducing the state of the art sensing technologies to the home, monitoring the daily activities of elders living alone or with husband/wife only, will be common soon to support their life from remote sites. We have developed a portable monitoring system to estimate daily activities just using simple, low-cost binary sensors and power monitors using MLN (Markov Logic Network), and installed in real elders' homes for monitoring. However, such monitoring systems usually require the Internet connection, which is not often available in depopulated areas due to the blind zone of 3G/LTE or installation cost which incurs extra investment for those people who have no interest in Internet services. In this paper, we propose a method of collecting daily activity monitoring data, which is obtained by our monitoring system working standalone at home, via LPWA (Low Power Wide Area). The idea is to utilize vehicles such as community buses and mail trucks which drive daily along almost fixed routes. An LPWA onboard receiver is mounted to collect data from each LPWA transmitter embedded into our monitoring system at home. Since not all the houses may be covered by the LPWA, LPWA-based "house clusters" are automatically formed to deliver the data in the cluster via a home contacted by the receiver vehicle. We have tested the system prototype in our university campus and installed it to the five real houses in the mountainous areas in cooperation with the Toyooka city municipal government, which has to manage broad depopulated areas.

Abstract Several attempts have been made to grasp three‐dimensional (3D) ground shape from a 3D point cloud generated by aerial vehicles, which help fast situation recognition. However, identifying such objects on the ground from a 3D point cloud, which consists of 3D coordinates and color information, is not straightforward due to the gap between the low‐level point information (coordinates and colors) and high‐level context information (objects). In this paper, we propose a ground object recognition and segmentation method from a geo‐referenced point cloud. Basically, we rely on some existing tools to generate such a point cloud from aerial images, and our method tries to give semantics to each set of clustered points. In our method, firstly, such points that correspond to the ground surface are removed using the elevation data from the Geographical Survey Institute. Next, we apply an interpoint distance‐based clustering and color‐based clustering. Then, such clusters that share some regions are merged to correctly identify a cluster that corresponds to a single object. We have evaluated our method in several experiments in real fields. We have confirmed that our method can remove the ground surface within 20 cm error and can recognize most of the objects.

While heading to a destination, we usually rely on our cognitive map constructed by audiovisual information from maps and our sight. However, errors or gaps between the real and our cognitive map often confuse us and lead to "wayfinding". In such a wayfinding state, we tend to take actions like wandering for perceiving errors and gathering information about surrounding environment. If such behavior can be detected by smartphones, we may design new applications on the smartphones, for instance, virtual "concierge" that timely helps us when we lose our ways. Also grasping spots where people are likely to lose their ways in large museums and theme parks would be useful to install or improve the signs and directions to support visitors. In this paper, we propose a method to detect individuals' wayfinding behavior from walking features by smartphone sensors. Based on the preliminary experiment, we extract sensor data features that can be collected through Android OS without privacy concerns, and build a binary classifier of user states, "normal" and "wayfinding". Through the two field experiments with 17 and 104 subjects, we have confirmed that our classifier achieved the F-measure of 0.93 and 0.85, respectively.

Building Energy Management System (BEMS) is a vital approach in constructing a global energy-efficient environment. It can be operated by analyzing data collected from sensors located in designated indoor areas. The key is to improve the data processing results while reducing the total data processing/communication volume required in the whole Internet of Things (IoT) networks as much as possible. In this work, a novel in-network self-learning algorithm for BEMS through a collaborative Fog platform is proposed. In particular, we devise an emerging Fog computing enabled IoT network architecture, where most of data can be processed in the Sensor-to-Fog and Fog-to-Fog layers. Data processing on Cloud is only required if anomalous sensor data are detected, and thus, the energy consumption due to heavy data processing on Cloud will be significantly reduced. The proposed algorithm makes the best use of Fog node capability to realize distributed data collection and processing. Via Fog-to-Fog connections, it can examine the sensor data by collecting them from different search ranges, whose values are meanwhile optimized. Numerical experiments conducted in a real indoor environment demonstrate that our algorithm achieve a high prediction accuracy for anomaly detection even with relatively small sensor data for processing. The effectiveness of Fog node placement is also verified. The overall scheme is expected to be a feasible solution to construct a cost-effective IoT network to minimize energy consumption while maximizing the indoor user's comfort, from the perspective of achieving a high prediction accuracy in BEMS data monitoring.

Recently, several problems concerning smart and connected communities (S&CC) have been studied. In many S&CC applications such as autonomous driving, mobile crowdsourcing and crowd sensing, the mobility of vehicles and pedestrians has large impact on their performance and reliability. Many research works have used mobility generators to simulate realistic mobility, and evaluated the performance and reliability of the proposed applications and protocols. Although such mobility generators might be useful for producing typical mobility patterns, those mobility patterns are just snapshots and they cover only some part in the possible mobility patterns. Since many S&CC applications are used as social systems, their reliability and efficiency are very important. In order to improve the reliability of such mobility aware applications and accurately evaluate their performance, we need to collect many mobility patterns via simulation and/or observation from the real world, analyze their mobility influence statistically and provide adequate design platforms. In this paper, we first show the fact that many research works adopt snapshot-based mobility analyses. Then we propose a technique to reproduce a large part of possible mobility patterns, and provide a design platform to analyze their features and develop high-reliable mobility aware applications for S&CC. Some experimental results are also given.

Bluetooth Low Energy (BLE) is a promising technique for creating a cost-effective network to manage and control the BLE equipped equipment of buildings such as HVACs and lighting equipment, and the BLE network addresses can be used to identify the equipment. In order to enable the management (actuation and control) of equipment via BLE, each BLE network address must be linked with the physical location of equipment. However, the manual location identification effort is labour-intensive and time-consuming. In this paper, we introduce our basic idea to handle this issue and show a preliminary experimental result. The idea is to design semi-automatic position estimation technique by leveraging the change in RSSI of BLE devices during walking. Our method requires only one tester to carry a smartphone and walk around the building to collect the RSSI. We deployed 10 BLE devices to conduct the preliminary experiment in a single room. The result shows that the locations of 80% BLE devices are correctly identified.

Due to the nature of smartphones' portability and mobility, many mobile apps are usually utilized in the real field environment using GPS, Wi-Fi and embedded sensors. For example, any navigation app uses GPS and Wi-Fi to locate the user in the map, and streaming apps may be used in cafeteria or even outside to satisfy the users' demand to watch soccer games anywhere and anytime. To test the usability and performance of such mobile apps in in-situ environment, we need to bring the apps to such physical world and run (a number of) test scenarios, which is often cost-inefficient depending on the size, apps and situations assumed in those scenarios. In this paper, we design and develop a testbed to test mobile apps in VR space. The system allows developers to use a real smartphone in VR and to test and evaluate their apps at the interested locations, with various network environment. The system builds and reproduces the real world environment of 3D space and real networks in the VR environment, using the existing 3D city models and our original Wi-Fi database. Then it enables to real-timely integrate the screen of the VR user's smartphone in the VR space. The user can operate the app via the VR view, and test the usability and performance of the app in such an emulated environment. The experimental result shows our architecture could achieve such cyber-physical integration with 695.5 ms delay, which is negligible in many semi-realtime services such as navigations.

We present CrowdMeter: a participatory system that leverages the sensed data collected from users' phones during their daily train commutes to gauge the real-time congestion level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her context (e.g., waiting for a train, buying a ticket) along her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding congestion level along the passenger's route in a railway station. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three congestion levels (low, medium, high).Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the congestion levels accurately, highlighting its promise as a ubiquitous travel-support service.

The conventional wireless sensor networks are designed to gather information from sensors, and pervasive computing systems involve such wireless sensor networks that consist of a number of tiny-/micro-sensors to sense the real-world objects and phenomena. In addition, they also need to conduct training tasks for highly-intelligent processing like pattern recognition and anomaly detection, but doing such tasks in cloud servers may often be impossible or expensive due to lack of communication facility, data transfer overhead, privacy concerns and so on. In this paper, we propose a unique approach to carry out deep learning (both training/testing) among wireless sensors nodes for in-situ, edge-heavy processing. The idea is to appropriately assign neurons of CNN (Convolutional Neural Network) to wireless nodes, each of which has limited processing capability but can have some power when they are united. For this purpose, we have designed a distributed, restricted version of CNNs. We conducted two experiments using real data; one is for anomaly detection of temperature in an over-1,400m2lounge space using 50 temperature sensors to confirm the learning capability as well as communication overhead, and another is for activity recognition using a 6x6 array of thin-, energy-efficient film-type infra-red sensors with micro-processors to demonstrate our concept.

This paper presents a new algorithm for building an indoor map by integrating point clouds of 2D light detection and ranging (LIDAR) scanners in indoor environments. Iterative closest point (ICP) algorithm is one of the well-known methods for such purpose and often used for mobile robot SLAM. However, the algorithm is designed based on dense (or continuous) measurement of the same space with known relative positions of measurement points and angles, and it does not often work efficiently if the measurement is sparse and/or LIDAR locations are unknown. Such situations are seen when some installed LIDARs in a room are used to build a background indoor map for object tracking, or mobile LIDARs are used by technicians to build a digital indoor map, where each space is captured only at a few locations with different angles. To tackle this issue, our method extracts line segments and edge points as features from given LIDAR point clouds and finds shape coincidences commonly contained in a pair of given point clouds to identify positional relationships between LIDARs. By this information, these point clouds can be integrated into common 2D coordinates. Indoor map generation is realized by sequentially applying this integration procedure to every pair of point clouds. Also, the experiments on real data show that our method can identify the relative positions of LIDARs with 10cm-order errors in average, and by sequentially applying the point-cloud integration, the generated maps have only 3% errors.

Delay-tolerant networking (DTN) architecture has emerged to cope up with loss of continuous connectivity issue that may occur under some environmental conditions such as infrastructure down due to disaster. Bundle protocols generally enhance network delay and reliability by storing and forwarding data-packed bundles on multiple nodes in the same time through flooding algorithm. On the other hand, the flooding mechanism usually incurs a consumption of resources. In this paper, we propose a robust routing protocol called BALANCE for self-organized civilian DTN. In civilian contexts, a resource constraint is a primary issue. So, our BALANCE sets an objective function to minimize the number of replications over networks by using a regression model for predicting future spreading effect considering the delivering probability. Since the device owners (civilian) could be selfish and decide not to store or forward any bundles, we further introduce a selfish factor applied together with our newly formulated SINR-based function to achieve the delivering probability. We conduct evaluations in stationary and mobile scenarios regarding disaster situations. The results show the highest traded-off values of delivering performance and resource consumption of the proposed protocol compared to widely-used protocols and present our superior in restricted conditions like limited buffer size or selfish participants.

In this paper, we propose a general complex event processing (CEP) engine aiming for accomplishing at smart IoT edge devices in a fully distributed manner. We introduce a pseudo-source mechanism to cover a wide range of processing and obsolete prerequisite of source-specification at the same time, along with a brand-new event specification language defined to support relation-based processing. Against cloud-based approaches, our behind-edge approach can prevent data overflow and privacy issues, and fully distributed processing can draw the power of the edge devices. To achieve that in a resource-limited edge environment, we formulate an optimization problem of processing task assignment and stream delivery, and propose a fully-autonomous workload distribution mechanism. A large-scale simulation with a realistic smart-building scenario shows that our proposed method achieves about 6.6 times smaller flow volume and 2 times lower loss rate compared to centralization and is relatively superior to a hop-based distribution approach. Notably, a prototype engine is successfully deployed over an ad-hoc wireless sensor and actuator network through Intel Edison modules in the real environment.

Recently, several researches concerning with smart and connected communities have been studied. Soon the 4G / 5G technology becomes popular, and cellular base stations will be located densely in the urban space. They may offer intelligent services for autonomous driving, urban environment improvement, disaster mitigation, elderly/disabled people support and so on. Such infrastructure might function as edge servers for disaster support base. In this paper, we enumerate several research issues to be developed in the ICDCS community in the next decade in order for building safe, smart cities resistant to disasters. In particular, we focus on (A) up-to-date urban crowd mobility prediction and (B) resilient disaster information gathering mechanisms based on the edge computing paradigm. We investigate recent related works and projects, and introduce our on-going research work and insight for disaster mitigation.

Passive infra-red (PIR) motion detection binary sensors are reasonable for human tracking in household as they can be portable operated by small batteries and be attached onto walls, shelves, floors or ceilings. However, such flexibility of sensor location causes difficulty since the location information of each sensor should be managed and given to the tracking system by the residents. To mitigate human errors, we propose a sensor localization method to automatically identify the location of multiple binary motion sensors in a house from the observed sequence by binary sensors. The experimental results show that the accuracy of our approach is above 80% after 5 days observation.

Water accounts for about 60% of the human body, and when the body loses it (e.g., through urine, sweat, etc.) in higher rate than its intake rate (through drinking), dehydration symptoms occur. The dehydration causes many severe health problems like organ and cognitive impairment. Therefore, it is critical for the human to drink water in a sustained manner to avoid dehydration. To prevent humans from dehydration, continuous day-scale tracking of the water intake is needed. In this paper, we propose an unobtrusive method to recognize the drinking activity as well as estimate the water intake amount in milliliter scale by leveraging smartwatches. Our basic idea is to track the arm motion and discriminate the drinking activities from the similar hand-based motions like food intake, phone calls, etc. Thereafter, we estimate the water intake amount from the drinking duration.

In surface mine environment, the size of mining equipment is typically very huge. Therefore, the sight of equip- ment operators in high- mounted cockpits is severely limited and those operators are often unaware of the approaching or nearby vehicles. It is reported that this unawareness causes serious accidents, which motivates us for proximity detection of those vehicles. Inter-Vehicle Communication (IVC) is a promising approach in the road transport environment, but is not directly applicable to mining vehicles due to different features of radio propagation affected by huge bodies and terrain. In this paper, we propose a method that leverages IVC to support drivers' awareness in surface mine. The idea is that we build a radio propagation model for haul trucks, which are the most typical vehicles in surface mine, by accurate ray-tracing. The model is validated in the field experiment, where actual radio signal propagation is measured and compared with the model. Then a mining environment, where steep cliffs due to excavation are often seen, is modeled and we estimate the received signal strength based on the locations of haul trucks and antenna locations. As a result, we point out several blind spots in such environment, which is significant for V2V proximity detection in surface mine.

No abstract available.

Increasing availability and usability enhancement of Wi-Fi in public areas has become more active. However, due to the dense deployment of Wi-Fi access points (APs), there is a chaotic and disorderly environment in urban areas. In our previous work, we have designed a function that predicts the network performance at each Wi-Fi AP according to the measurement of IEEE802.11 MAC frames sensed in each Wi-Fi channel. However, it was not examined in such scenarios assuming urban environment. We should understand the situations of current Wi-Fi AP deployment and traffic conditions, and should confirm the effectiveness of channel migration in such realistic environment. In this study, we proposed urban Wi-Fi channel utilization model based on real urban Wi-Fi measurement. We show that our method can predict the best channels and APs can migrate to them in the urban scenario.

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.

In recent years, several advanced safety techniques have been put to practical use for supporting safe driving, ASV (Advanced Safety Vehicle) can detect the presence of surrounding vehicles and pedestrians autonomously. On the other hand, cooperative techniques such as cooperative vehicle-to-vehicle awareness have attracted a lot of attentions in the field of ITS. In this paper, we propose a method for identifying positional relationship of vehicle groups. In the proposed method, connected vehicles share surrounding vehicles' positions detected by installed range sensors via inter-vehicle communication. By composing those pieces of information into a single view, each connected vehicle can recognize the vehicle group ahead, which cannot be recognized standalone. By utilizing the distance information from two different information sources and executing the matching process, the correct positional relationship among neighboring vehicles can obtain. We have conducted a simulation-based experiment using two types of data to evaluate the effectiveness of the approach, and we have confirmed to achieve the correct identification with high probability by using shared velocity information.

We present PassActiv, a mobile sensing system for the automatic activity recognition of railway passengers. Our key observations show that certain passengers' activities (e.g., purchasing tickets, etc) present identifiable signatures on one or more cell-phone sensors which can be leveraged to automatically recognize those activities. Evaluation of PassActiv through a field experiment in major train and subway stations in Japan shows that PassActiv can detect different activities accurately with at most 3% false positive rate and 4% false negative rate for all types of passengers' activities.

A system to improve the quality of human life is developed and proposed. A model-based approach is used where smart home residents, appliances, energy sources and correlations among them are comprehensively modeled. The model was integrated with activity recognition information that enables the system to suggest smart life tips that provide advice to residents in a non-intrusive way. A crowd-sourced large-scale survey of 1,000 subjects was conducted that enabled important tips for improving the quality of human life to be quantified. On the basis of the survey results, quantitative metrics and strategies were designed for presenting suitable tips in a timely manner depending on the lifestyle of subjects. The system was evaluated by (1) 34 actual subjects in virtual smart homes and (2) family members in an actual house in an experiment lasting more than one month in which actual sensors were deployed.

This paper proposes a method of aggregating tempo-spatial data generated by sensors deployed in buildings or houses. The size of each sensor data such as temperature is usually small, but it often involves many additional data to represent its attribute values like time, location, data type and data precision. This would often increase the traffic volume between sensor gateway at building/home side and service providers at server side. In our method, such sensor data are packed into multidimensional matrices indexed by those attribute values for more compact representation, and the compressed sensing technique is adaptively applied to further reduce the data size. The method was applied to a field trial with KDDI corporation to collect data from 29 community facilities, and the traffic volume was reduced to 50% with reasonable precision of data restoration.

In this paper, we design a mobile augmented reality (AR) system called TweetGlue, which overlays text messages (i.e., tweets) that are posted to a local social networking service onto live view images from cameras in mobile/wearable devices. By displaying the tweets at the current positions of the users who posted them, it supports social interaction between the users. Accurate pose tracking of mobile devices is an essential building block of such mobile AR applications. While visual features that are extracted from scenes are commonly used for vision-based pose estimation, it would not be suitable for such a mobile social AR application because the features may be often occluded by human bodies nearby. To cope with the problem, we leverage an external pedestrian tracking system using a small number of laser-based distance measurement sensors (i.e., LRS sensors) to utilize the surrounding human bodies as virtual markers for pose estimation. The mobile devices periodically analyze images from the embedded camera sensor to estimate relative positions of pedestrians in the images. By matching the estimated relative positions with accurate human location measurements by the LRS sensors, the system robustly identifies location and horizontal orientation of the devices. Through simulation experiments, we show that the TweetGlue system can accurately identify pose of mobile devices in 83% of the simulated scenarios.

We propose a method to estimate car-level train congestion using Bluetooth RSSI observed by passengers' mobile phones. Our approach employs a two-stage algorithm where car-level location of passengers is estimated to infer car-level train congestion. We have learned Bluetooth signals attenuate due to passengers' bodies, distance and doors between cars through the analysis of over 50,000 Bluetooth real samples. Based on this prior knowledge, our algorithm is designed as a Bayesian-based likelihood estimator, and is robust to the change of both passengers and congestion at stations. The car-level positions are useful for passengers' personal navigation inside stations and car-level train congestion information helps determine better strategies of taking trains. Through a field experiment, we have confirmed the algorithm can estimate the location of 16 passengers with 83% accuracy and also estimate train congestion with 0.82 F-measure value in average.

Data collection from a crowd of mobile devices is an essential building block of emerging mobile sensing systems. In this paper, we propose an efficient information diffusion protocol for sensor data collection via an opportunistic network. The proposed method detects groups of pedestrians based on the history of radio connectivity between the nodes and maintains a local network (i.e., a cluster) among the detected group members. By collaboratively performing neighbor discovery and link management with the cluster members, it enhances energy-efficiency of the neighbor discovery and minimizes the information delivery delay. Simulation results show that the proposed method can improve the message delivery performance by 16%–83% with equivalent contact probing intervals.

This paper presents a strategy to choose WiFi channels in urban areas. We consider (i) inter-channel interference where adjacent channels interfere with each other in WiFi systems and (ii) urban situations where many APs in different systems are deployed in an uncoordinated way. As it is often hard to identify the channel with less interference in such a situation, we present a channel scoring function that estimates the performance level of each channel. To build the scoring function, we have conducted exhaustive simulations with a large number of scenarios, and multiple regression analysis has been applied where channel occupancy patterns, traffic volumes and RSS in those channels are used as explanatory variables. To evaluate our method, this scoring function was examined in a realistic scenario where several APs interfere with the AP of interest. We have confirmed that the scores and the actual performance are well-matched where the Spearman's rank correlation coefficient was sufficiently high and can identify the top-ranked channel as well.

Despite recent advances in localization technology for mobile devices, to provide real-time position information to people indoors is still a big challenge; usually there is a trade-off between localization accuracy and infrastructural costs (e.g., dense anchor deployment). A possible solution would be employing cooperative approaches which utilize estimated positions of surrounding mobile nodes to complement a small number of anchors. However, it often results in poor estimation accuracy since a temporary large position error due to node mobility easily propagates to neighbor nodes. This paper presents a novel cooperative localization algorithm that addresses this problem by focusing on “stop-and-go behavior” of indoor pedestrians. The key idea is to collaboratively find movement state (moving or static) of each node based on peer-to-peer distance measurement which is inherently necessary for cooperative localization, and use only static nodes as reference points for localization to avoid potential accuracy deterioration. Also, nodes in static state can reduce localization frequency to conserve battery power, keeping the tracking quality. Through extensive simulations, we have demonstrated the performance of our method in terms of accuracy and energy efficiency. The effectiveness in a real application scenario has been also confirmed using a measurement-based sensor model and real mobility traces.

This paper presents a scheduling algorithm for a set of wireless stations such as road-side access points for vehicular networks and outdoor WiFi stations, which are deployed in wide urban areas and may compete with each other for limited wireless resources. Different from a number of conventional approaches most of which consider detailed information on individual stations and signal interference among them, we focus more on geography of the areas of interest, and provide a novel algorithm that pursues the best balance among (i) optimality of resource utilization, (ii) robustness to new station installation and traffic demand changes, and (iii) scalability to the population of stations and area size. We have confirmed the performance by experimental simulations with several scenarios, and the applicability of approach has been testified by a case study on a scheduling problem for roadside access points of vehicular networks in cooperation with a manufacturing corporation.

In this paper, we propose a novel approach to positioning mobile phone users in a crowd of people. We assume such a situation that people in specific space (e.g., an exhibition hall) have mobile phones and they periodically probe proximity between the surrounding users via short-range wireless communication (e.g., Bluetooth). We also assume that laser range scanners (LRSs) are deployed in the space to track positions of those mobile phone users. By fusing the accurate, but anonymous trace information obtained by LRSs and the identified phone-to-phone proximity information, our method derives accurate identified traces of each mobile phone user, which are essential for pedestrian navigation and other emerging location/situation-aware applications. Through extensive simulations, we show that the tracking error quickly converges below 1m in most cases.

We propose an opportunistic ad hoc localization algorithm called Urban Pedestrians Localization (UPL), for estimating locations of mobile nodes in urban districts. The design principles of UPL are twofold. First, we assume that location landmarks are deployed sparsely due to deployment-cost constraints. Thus, most mobile nodes cannot expect to meet these location landmarks frequently. Each mobile node in UPL relies on location information received from its neighboring mobile nodes instead in order to estimate its area of presence in which the node is expected to exist. Although the area of presence of each mobile node becomes inexact as it moves, it can be used to reduce the areas of presence of the others. Second, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles for predicting the area of presence of mobile nodes accurately under mobility. This also helps to reduce each node's area of presence. The experimental results have shown that UPL could be limited to 0.7r positioning error in average, where r denotes the radio range by the above two ideas.

In this paper, we design an efficient method of simulating wireless networks that use CSMA/CA-based protocols in the MAC layer. In the method, a stochastic model to estimate the CSMA/CA frame transmission delay is naturally incorporated into the conventional fully event-based model. The stochastic model can simplify the interactions between a frame transmitter and its surrounding nodes, which alleviates the event scheduling overhead in simulation. The important feature is that the stochastic model can be applied in “per-node” and “time” basis, i.e. we may simulate the behavior of some intended nodes precisely while the others are simplified by the stochastic mode to save computational resources. To the best of our knowledge, this is the first approach to coexistence of the stochastic and event-based models in wireless multi-hop network simulation. We have implemented this scheme in a commercial network simulator and conducted several experiments. From the results, it is confirmed that the proposed method could perform simulation of frame transmission much faster than the fully event-based simulation achieving the same accuracy as the conventional model.

As an inexpensive and scalable access network to the Internet, we have extensively studied the Wireless Internet-access Mesh Network (WIMNET) that is composed of multiple access points (APs) connected through wireless links. Because most traffic in WIMNET passes through the gateway (GW) to the Internet, the links around the GW are much crowded so that they should be activated with higher priorities than others. In this paper, we propose the Fixed Back off-time Switching (CSMA-FBS) method for WIMNET using the CSMA/CA protocol to improve the performance by giving necessary activation chances to such links. Before starting communications, the target link activation rate and the active/passive back off-times for collision avoidance are calculated from the expected traffic load for each link. During communications, these back off-times are switched by comparing the actual link activation rate with the target one. By assigning different fixed values to these back off-times, the conflicts among the interfered links can be avoided. Through extensive simulations using two types of network topologies, we verify the effectiveness of our proposal.

The electronic triage system developed by our research group is a wireless sensor network, constructed by electronic triage tags composed of vital sign sensors and ZigBee modules. The system assumes a network of hundreds of patients, simultaneously transmitting data to a remote sink. We have investigated the performance of a simple data collection mechanism in real scenarios, based on IEEE 802.15.4 in a ZigBee device like SunSPOT, and have examined some challenges in such scenarios. The data delivery ratio of 98% has been recorded while sending data equivalent to that of 100 devices in a triage tent scenario, where every device is one hop away from the sink. Adjustment of hello packet interval according to the scenarios has been observed to be an important factor. The results are found to be useful in adjusting parameters in our future work.

In order to analyze important properties of network protocols, such as robustness and applicability, we may need exhaustive tests to observe effects of various factors under different settings. For recent protocols on dynamic, large-scale and environment-aware networks such as wireless sensor networks and mobile ad-hoc networks, we should consider many factors due to diversity of hardware profiles, upper/lower layer protocols and physical environments. In this paper, we propose a comprehensive test method to analyze the effect of (usually 10 or more) factors on such network systems. Our method takes a set of factors to be considered and their (representative) domain values as inputs, analyzes their effects on the systems, and determines dominant factors that have impact on the performance and their interactions. Instead of applying exhaustive tests that require all the combinations of domain values, we take a step-wise approach that examines step-by-step suspected sets of factors, which requires fewer combinations. We also justify this approach based on a reasonable fault model. The approach also contains an analytical method to identify the performance characteristics. Through realistic case studies, we show that we could find sets of dominant factors in wireless networks systematically.

In mobile wireless networks such as WSNs, WMNs and MANETs, movement of sensor nodes, clients and relay nodes has a great impact on the performance. Nevertheless, geography is too simplified in random-based mobility models such as RWP, while it is unrealistic to prepare trace-based mobility patterns for potential combinations of geography and mobility. To fulfill the gap, this paper provides a new method to automatically generate natural mobility patterns realizing designated node distributions. The goal of this work is to synthesize the movement patterns that can capture real (or intentional) node distributions. The method determines the probabilities of choosing waypoints from the subregions, satisfying the given node distributions. For this purpose, the relationship between the probabilities and node distributions is analyzed. Based on the analysis, the problem is formulated as an optimization problem of minimizing the error from the designated node distribution. Since the problem has non-linear constraints, a heuristic algorithm is designed to derive the near-optimal solutions. Several experiments have been conducted to show that a variety of node distributions could be realized in the proposed mobility model where the maximum error from the given node distributions was around 0.5%. Additionally, a case study has been conducted to show the applicability of the method.

This paper presents a cooperative localization approach for mobile nodes using wireless and ranging devices. We consider scenarios where node mobility follows stop-and-go behavior; we can then utilize the different movement states of nodes as an input to our localization approach. In the proposed method, each node autonomously finds among its surrounding nodes the ones that do not seem to move, and treats them as static nodes. Only nodes that are deemed static are then used as reference points for position estimation. Furthermore, each node adjusts its localization frequency automatically according to its estimated velocity. Performance evaluation results based on a realistic sensor model and actual mobility traces show that our method could achieve sufficient accuracy and efficiency for an exhibition scenario where people need to be tracked.

This paper presents a vehicular positioning system in which multiple vehicles cooperatively calibrate their positions and recognize surrounding vehicles with their GPS receivers and ranging sensors. The proposed system operates in a distributed manner and works even if all vehicles nearby do not or cannot participate in the system. Each vehicle acquires various pieces of positioning information with different degrees of accuracies depending on the sources and recency of information, and compiles them based on likelihood derived from estimated accuracies to minimize estimation errors. A simulation based performance evaluation given in the paper shows that the proposed system improves the estimation accuracy by 85% on average with respect to the standalone GPS receiver, and recognizes about 70% surrounding vehicles with an error of 1m.

Welcome to ICNP 2010, the eighteenth IEEE International Conference on Network Protocols, and welcome to Kyoto, Japan! ICNP is the premier conference covering all aspects of network protocols, including design, analysis, specification, verification, implementation and performance. This is our third organization of ICNP in Japan. In 1995, 15 years ago, we first organized ICNP at Tokyo. Tokyo is the capital of Japan, and the center of politics and economics. In 2000, we organized ICNP at Osaka, which is a commercial city. This time, we organize ICNP at Kyoto. Kyoto is the old capital of Japan, and so many historical sightseeing places are located, and is a center of Japanese culture. We hope that you will enjoy Japanese beautiful culture and autumn scenery.

In this paper, we propose an algorithm to estimate 2D shapes and positions of obstacles such as buildings using GPS and wireless communication history of mobile nodes. Our algorithm enables quick recognition of geography, which is required in broader types of activities such as rescue activities in emergency situations. Nevertheless, detailed building maps might not be immediately available in private regions such as large factories, warehouses and universities, or prepared maps might not be effective due to collapse of buildings or roads in disaster situations. Some methodologies adopt range measurement sensors like infra-red and laser sensors or cameras. However, they require dedicated hardware and actions for the measurement. Meanwhile, the proposed method can create a rough 2D view of buildings and roads using only wireless communication history between mobile nodes and position history from GPS receivers. The results from the experiment conducted in 150m×190m region on our university campus assuming rescue and treatment actions by 15 members have shown that our method could generate a local map with 85% accuracy within 350 seconds. We have also validated the performance of our algorithm by simulations with various settings.

In this paper, we propose a new trajectory estimation method named TRADE (TRAjectory estimation in DEcentralized way). TRADE is a range-free localization algorithm in fully decentralized mobile ad hoc networks. In TRADE, each mobile node periodically transmits messages containing its estimated trajectory information, and re-computes its own trajectory using those from its neighbors. This information exchange considerably contributes to improvement of the position accuracy. Furthermore, we give the optimal design of the protocol based on the analysis of the algorithm property. Through the analysis, we consider how much trajectory information should be exchanged among nodes to estimate the position within a certain error range in the protocol design. We have evaluated the position accuracy under various settings, and have shown the effectiveness of the protocol in the real world through two realistic application examples.

In this paper, we focus on the problem of implementing a periodic concurrent system with timing constraints into multi-context dynamically reconfigurable processors (DRP). A concurrent system has multiple tasks that can be executed in parallel. Moreover, some tasks in a specific set of processes might be required to synchronize each other. We propose a method for assigning tasks into a multi-context DRP such that timing constraints of the system are satisfied and the size of the program area required on each context for implementing the given system is minimized. We formulate the problem as an ILP problem and propose a heuristic algorithm for solving the ILP problem efficiently. Experimental results and a case study using ubiquitous sensor devices are given.

In this paper, we propose a new overlay multicast protocol designed for inter-active multimedia streaming applications. The protocol considers the heterogeneity of end-hosts and tries to minimize the negative impact (data outage) of end-hosts' unannounced departures. For this purpose, it concentrates on end- hosts' reliability (lifetime for instance) and constructs a shared tree called ms-DDBMSST (multiple-source Degree and Delay Bounded Maximum Stability Spanning Tree) as an overlay network that involves all the participants of the application, in a distributed manner. For a given set of nodes where some of them are senders, ms-DDBMSST is a spanning tree where the receive path stability of the entire tree is maximized while satisfying the delay-from-source constraint and degree constraint for each node. We believe that this is the first approach that defines ms-DDBMSST construction problem and presents a distributed protocol for the purpose. Our performance evaluation is based on experiments in both simulated networks and PlanetLab that strongly shows the efficiency and usefulness of the proposed protocol.

In this paper, we propose an off-line algorithm called TRACKIE to estimate trajectories of mobile nodes based on encounter information. This method only assumes reasonable number of landmarks and ad-hoc wireless communication facility of mobile nodes, and does not rely on multi-hop ad-hoc networks nor global positioning system. The method achieves low-cost estimation of trajectories and provides accurate solution (the average estimation error was less than 40% of the wireless range in simulations). We have evaluated TRACKIE with Micaz Mote and shown that estimation error is about 2 m in real environments where wireless range is about 3 m.

In this paper, we propose a method which efficiently collects, retains and propagates traffic information using inter- vehicle communication with "message ferrying" technique used in vehicular ad hoc network (VANET). In the proposed method, we use buses as message ferries which travel along regular routes. In order to improve information propagation efficiency in low- density areas, buses collect as much traffic information as possible from cars in their proximity, and periodically disseminate the collected information to neighboring cars. We have implemented the proposed system on the traffic simulator NETSTREAM and compared information propagation efficiency between our proposed method and a method which uses inter-vehicle communication among only ordinary cars. In the simulation, the proposed method improved the efficiency up to 50%.

In this paper, we propose a method for realistic mobility aware information gathering in disaster areas. In the proposed method, a disaster area is divided into a grid, and each node's safety information is held in its pre-defined home grid cells, and it can be referred by sending a query to mobile nodes in its home cells. In this paper, we propose an autonomous adaptable protocol so that it can adapt to a variety of realistic network environments. In order to achieve speedy and high propagation, we combine the notions of store/forward in opportunistic networks and geographical routing in MANETs. In the proposed protocol, if intermediate nodes cannot relay safety information to its home cells by multi-hop communication, they hold it until they meet preceding nodes and re-transmit it as proxies. If a shortest path is not available, a detour is autonomously found. We have evaluated the proposed method under realistic pedestrian flows using our network simulator MobiREAL, and shown that it can work well.

In this paper, we present a range-free ad-hoc localization algorithm called UPL (Urban Pedestrians Localization), for positioning mobile nodes in urban district. The design principle of UPL is two-fold. (1) We assume that location seeds are deployed sparsely due to deployment-cost constraints. Thus most mobile nodes cannot expect to meet these location seeds frequently. Therefore, each mobile node in UPL relies on location information received from its neighboring mobile nodes in order to estimate its area of presence. The area of presence of each mobile node becomes inexact as it moves, but it is helpful to reduce the areas of presence of the other mobile nodes. (2) To predict the area of presence of mobile nodes accurately under mobility, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles. This also helps to reduce each node's area of presence. The experimental results have shown that by the above two ideas UPL could achieve 8mpositioning error in average with 10mof radio range.

We design a mobility-aware data management (MoDA) scheme for mobile ad hoc networks (MANETs) composed by mobile nodes such as urban pedestrians and vehicles. By fully utilizing the knowledge about the trajectories of mobile nodes, MoDA determines how replicas of data are copied and transferred among mobile nodes to provide the required data accessibility. Experimental results have shown that MoDA could achieve the small number of data transfers among mobile nodes while keeping reasonable accessibility.

In this paper, we propose an intervehicle information dissemination protocol called received message-dependent protocol (RMDP) that propagates the preceding traffic information to the following vehicles and discuss its performance. The proposed protocol autonomously changes the dissemination interval, depending on the number of reception messages and detected reception errors, in order to avoid message collision among vehicles. We have constructed a realistic simulation environment of intervehicle communication by combining a traffic-flow simulator and an ad hoc network simulator. Our simulation results show that, by using RMDP, a lot of vehicles can acquire their preceding traffic information within short periods at both the conditions with light and heavy traffic. In addition, we have also shown that selection policy of disseminated data affects preceding-traffic-information acquisition time. Each vehicle holds other vehicles' traces for some period. Depending on the number of preserved other vehicles' traces and their preserved periods, the propagation ratio of the preceding traffic information varies. Some simulation results are explained.

In designing bus based systems with parallel and pipelined architecture, it is important to derive a real time budget (a specified execution time limit) for each task of a bus based system while satisfying given end-to-end real-time constraints of the entire system such as throughput and latency constraints. In this paper, we define a bus scenario representing a set of possible execution sequences of tasks and bus transfers executed in a bus based system. Then we propose a method for deriving real time budgets of all the tasks running in parallel and pipelined fashion from the pair of a system configuration (such as bus topology) and a bus scenario. In deriving such real time budgets, we consider computational complexity of each task, the amount of bus transfers and bus arbitration policies(e.g. fixed priority or time divided round robin based arbitration). We show that the proposed method is effective for designing several bus based systems such as MPEG decoders.

In urban areas including shopping malls and stations with many people, it is important to utilize various information which those people have obtained. In this paper, we propose a method for information registration and retrieval in MANET which achieves small communication cost and short response time. In our method, we divide the whole application field into multiple sub-areas and classify records into several categories so that mobile terminals in an area holds records with a category. Each area is associated with a category so that the number of queries for the category becomes the largest in the area. Thus, mobile users search records with a certain category by sending a query to nodes in the particular area using existing protocol such as LBM (Location-Based Multicast). Through simulations supposing actual urban area near Osaka station, we have confirmed that our method achieves practical communication cost and performance for information retrieval in MANET.

We have proposed realistic mobility models and developed a network simulator called MobiREAL for performance evaluation of MANET applications. Simulator users can easily generate a mobility scenario on any city section with the support tool of MobiREAL and can easily analyze the impact of mobility through the simulation visualization tool of MobiREAL. In this demonstration, we will show the usefulness of MobiREAL and importance to simulate MANET applications in realistic environments.

In this paper, we propose an application layer multicast protocol called Emma/QoS. Emma/QoS manages multiple multicast video streaming flows in a fully distributed manner so that the user gain in receiving those flows can be maximized. Emma/QoS assumes that each end-host is capable of filtering streaming flows when relaying them to the other end-hosts. Using this functionality, Emma/QoS (a) accommodates a request of a new video source by degrading the bit-rates of the existing video streams, and (b) adjusts their transmission rates according to given user benefit functions, which can be application-specific. Simulation results have shown the efficiency of Emma/QoS.

In this demonstration, we present cellular phone applications developed on top of our middleware, and show the usefulness of the proposed group formation mechanism based on user’s context and group communication. Our middleware allows application programmers to efficiently develop cooperative applications consisting of a large number of cell phone terminals. To cope with several restrictions of cellular phones, our middleware allows application software to be executed separately on a server and user terminals. Group communication is implemented as interprocess communication on the server, and only the userinterface parts are executed on the cellular phones.

In this paper, we propose a method to realize traffic information sharing among cars using inter-vehicle communication. When traffic information on a target area is retained by ordinary cars near the area, the information may be lost when the density of cars becomes low. In our method, we use the message ferrying technique together with the neighboring broadcast to mitigate this problem. We use buses which travel through regular routes as ferries. We let buses maintain the traffic information statistics in each area received from its neighboring cars. We implemented the proposed system, and conducted performance evaluation using traffic simulator NETSTREAM. As a result, we have confirmed that the proposed method can achieve better performance than using only neighboring broadcast.

This paper presents a study of the gateway access-point selection problem for the wireless infrastructure mesh network (WIMNET). WIMNET is a wireless mesh network composed of multiple access-points (APs) that communicate mutually using radio transmissions, and all the traffics to/from the Internet go through the limited number of gateway APs. Thus, the selection of these gateway APs often determines the whole performance of WIMNET, because heavy congestions at gateway APs and their peripheries can drastically increase the delay and the link failure due to radio interferences. In this paper, we define a cost function to minimize the total traffic in WIMNET in order to reduce congestions and present a simple algorithm. Through simulations using the newly developed WIMNET simulator, we show the effectiveness of our approach.

In this paper, we propose a method for cars to autonomously and cooperatively collect traffic jam statistics to estimate arrival time to destination for each car using inter-vehicle communication. In the method, the target geographical region is divided into areas, and each car measures time to pass through each area. Traffic information is collected by exchanging information between cars using inter-vehicle communication. In order to improve accuracy of estimation, we introduce several mechanisms to avoid same data to be repeatedly counted. Since wireless bandwidth usable for exchanging statistics information is limited, the proposed method includes a mechanism to categorize data, and send important data prior to other data. In order to evaluate effectiveness of the proposed method, we implemented the method on a traffic simulator NETSTREAM developed by Toyota Central R&D Labs conducted some experiments and confirmed that the method achieves practical performance in sharing traffic jam information using inter-vehicle communication

In this paper, we propose middleware for efficient and robust distributed network monitoring. In our technique, we specify network monitoring items, detection methods of viruses and DDoS attacks and the corresponding reactive actions as a management scenario. At the same time, we specify logical neighboring relations between separate network segments so that we can efficiently detect problems occurring in multiple segments. When a problem occurs in multiple network segments, the corresponding monitoring nodes form a group dynamically using logical relations. Based on the pre-defined management scenario, the nodes in the group carry out the reactive actions autonomously. We have designed and implemented our middleware consisting of several useful APIs. We have confirmed effectiveness of our technique through ns-2 simulation

This paper presents a formulation of the optical-drop wavelength assignment problem (ODWAP) and its heuristic algorithm for WDM ring networks. The wavelength-division multiplexing (WDM) technology has been popular in communication societies for providing very large communication bands by multiple lightpaths with different wavelengths on a single optical fiber. Particularly, a double-ring optical network architecture based on the packet-over-WDM technology such as the HORNET architecture has been studied as a next generation platform for metropolitan area networks (MANs). Each node in this architecture is equipped with a wavelength-fixed optical-drop and a tunable transmitter so that a lightpath can be established between any pair of nodes without wavelength conversions. In this paper, we formulate ODWAP for efficient wavelength reuse under heterogeneous traffic in this network. Then, we propose a simple heuristic algorithm for ODWAP. Through extensive simulations, we demonstrate the effectiveness of our approach in reducing waiting times for packet transmissions when a small number of wavelengths are available to retain the network cost for MANs.

In this paper, we propose an inter-vehicle ad-hoc communication protocol called RMDP (received message dependent protocol) which disseminates and propagates the preceding traffic information to the following vehicles, and discuss its performance. Our proposed protocol dynamically changes the dissemination interval depending on the number of reception messages for avoiding message collision in heavy traffic jam conditions. We design the parameters of the protocol based on the simulation results. Our simulation results show that by using RMDP, a lot of vehicles can acquire the preceding traffic information within very short periods. In addition, we have also shown that selection of disseminate data affects preceding traffic information acquiring time. Each vehicle holds other vehicles' tracks for some period. Depending on the number of preserved other vehicles' tracks and their preserved period, propagation ratio of the preceding traffic information varies. Some simulation results are explained.

In this paper, we propose a method for partitioning a given application program that exceeds resource limits of mobile terminals into two module sets. Only a part of modules of the given application is assigned on a mobile terminal and the rest of modules are running on its proxy server, and that the mobile terminal invokes the modules on the server using remote method invocation. It is desirable that we can minimize the total amount of communication, delay time and power consumption between the mobile terminal and its server (here, we call the total amount as the total cost). In the proposed technique, first, a given Java program is repeatedly simulated on a single machine, and we collect the statistics information to estimate the total cost. Then, we give the resource limitation of the mobile terminal such as the memory size and an objective function that shows what total cost should be minimized. Under those constraints, our tool divides optimized division by using simulated annealing (SA). We have applied our technique to some application programs and examined its usefulness by evaluating their total costs.

Due to the progress of portable computing devices such as PDAs, cellular phones and small sized PCs, many personal navigation systems have been developed which navigate their users to display routes to given destinations. Those navigation systems mainly focus on the guidance for personal use. In this paper, we have developed a group navigation system, which provides facilities for (1) personally customizable route navigation to a given destination, (2) management of group movement and (3) rehearsal usage when we make the personally customized route navigation. In our system, using wireless ad-hoc communication a few leaders of a group can collect and distribute the information about its members' current positions and give each member a suitable suggestion when the user is losing his/her way. The personalized route navigation scenario (program) running on portable devices can be obtained automatically only by clicking intersections sequentially on a given map and giving pictures and comments. A rehearsal mode is also prepared when we make the personalized route navigation.

In this paper, we propose a new protocol called shared tree streaming (or STS in short) protocol that is designed for interactive multimedia streaming applications. STS is a decentralized protocol that constructs a shared tree called s-DBMDT (sender-dependent degree-bounded minimum diameter tree) as an overlay network that involves all the participants of the application. For a given set of nodes where some of them are senders, s-DBMDT is a spanning tree where the maximum delay on the tree from those senders is minimized and the degree constraint on each node is held. We believe that this is the first approach that defines s-DBMDT construction problem and presents a distributed protocol for the purpose. Our performance evaluation is based on experiments in both simulated networks and real networks that strongly shows the efficiency and usefulness of STS protocol.

In this paper, we propose a probabilistic rule-based model to describe behavior of mobile nodes for accurately evaluating performance of MANET applications. The proposed model allows us to describe how mobile nodes change their destinations, routes and speeds/directions based on their positions, surroundings (e.g. neighboring nodes), information obtained from applications, and so on. We have designed and developed a network simulator called MobiREAL based on the proposed methodology. Through experiments, we show importance to simulate MANET applications in realistic environments.

In order for precise evaluation of MANET applications, more realistic mobility models are needed in wireless network simulations. In this paper, we focus on the behavior of pedestrians in urban areas and propose a new method to generate a mobility scenario called Urban Pedestrian Flows (UPF). In the proposed method, we classify pedestrians in a simulation field into multiple groups by their similar behavior patterns (simply called flows hereafter, which indicate how they move around geographic points). Given the observed road density in the target field, we derive using linear programming techniques how many pedestrians per minute follow each flow. Using the derived flows, we generate a UPF scenario which can be used in network simulators. In particular, we have enhanced a network simulator called MobiREAL, which has been developed in our research group, so that we can generate and use the UPF scenario. MobiREAL simulator has three main facilities: the behavior simulator, network simulator and animator. The behavior simulator can generate/delete mobile nodes according to the UPF scenario. The network simulator can simulate MANET protocols and applications. The animator offers elegant visualization of simulation traces as well as graphical user interfaces for facilitating derivation of UPF scenarios. Through several case studies, we show similarity of the derived flows to the observed ones, as well as the metrics that characterize the mobility of the scenario.

Service overlay networks have recently attracted tremendous interests. In this paper, we propose a new integrated framework for specifying services composed of service components running on different service nodes and for executing the services considering efficient utilization of overlay network resources. For a given service description written in an extended Petri net model, our method automatically derives a set of descriptions of service nodes' behavior which specifies how service nodes on an overlay network collaborate to provide the specified services. The derived descriptions minimize channel utilization, total response time or load of service nodes based on a given cost criterion. The experimental results show that a multimedia service for decorating and transcoding video contents can be well specified and implemented.

Here, we present a protocol for dynamically maintaining a degree-bounded delay sensitive spanning tree in a decentralized way on overlay networks. The protocol aims at repairing the spanning tree autonomously even if multiple node's leave operations or failures (disappearances) occur simultaneously or continuously in a specified period. It also aims at maintaining the diameter (maximum delay) of the tree as small as possible. The simulation results using ns-2 have shown that the protocol could keep reasonable diameters compared with the existing centralized static algorithm even if many node's participations and disappearances occur frequently.

In this paper, we propose a middleware library for efficiently developing distributed cooperative applications consisting of a large number of cellular phone users. Our middleware provides: (1) a dynamic group formation mechanism depending on users' locations and preferred subjects; and (2) a group communication mechanism called multiway synchronization for multicasting, synchronization and mutual exclusion. Most of Java executors on cellular phones do not support direct communication among user programs. Usable resources are also restricted. Therefore, in our middleware, most of user programs are executed on their servers as agents. Group communication is implemented as inter-process communication on the server, and only the user-interface parts are executed on the cellular phones.

In this paper, we propose a flexible and high-reliable HW/SW co-design method for real-time systems consisting of multiple functional modules using general purpose components such as DSP, CPU and memory. In our method, we specify a system as a parallel composition of concurrent periodic EFSMs with timing constraints. As communication primitives among EFSMs, multi-way synchronization mechanism can be specified. Here, we propose a technique for efficient development of real-time embedded systems considering both reliability and cost-performance. For the purpose, using a parametric model checking technique, we derive a parameter condition which must hold for the system to proceed without deadlocks and satisfy given timing constraints. Based on the derived parameter condition and cost-performance characteristic of available components, an appropriate combination of components is automatically selected so that the total cost is minimized. We have developed a design support tool based on the proposed technique. By applying our method to development of a basic functionality of a cellular phone, we could decide which functional modules should be implemented as dedicated HW units or on-chip-CPUs' software, and select suitable DSPs and memories with low costs.

The IEEE 802.6 MAC standard protocol defines the distributed-queue dual bus (DQDB) for metropolitan area networks (MANs). The isochronous channel reuse problem (ICRP) has been studied for the efficient use of DQDB. Given a set of established connections and a set of connection requests, the goal of ICRP is to maximize the number of satisfied requests by finding a proper channel assignment, such that no established connection is not only reassigned a channel, but also any pair of active connections does not interfere each other. We propose a minimal-state processing search algorithm for ICRP (MIPS/sub -/ICRP). The simulation results show that MIPS/sub -/ICRP always provides near-optimum solutions.

In this paper, we present a new application level multicast protocol called Emma (end-user multicast for multi-party applications) suitable for communication systems where multiple video sources are exchanged in real-time among end-hosts, such as video-conferencing. The primal feature of Emma is that video sources with the higher priority given by users are prioritized among others for the provision of quality of service at the user level and that all the operations in Emma are done in a distributed manner. Our experimental results have shown that Emma achieves reasonable performance on overlay networks with high user satisfaction.

In this paper, we propose a selection technique for replicated multicast video servers. We assume that each replicated video server transmits the same video source as different quality levels of multicast streams. Using an IGMP facility like m-trace, each receiver monitors packet count information of those streams on routers and periodically selects the one which is expected to provide low loss rate and to be suitable for the current available bandwidth of receivers. Moreover, collection of packet count information is done in a scalable and efficient manner by sharing the collected information across receivers. Our experimental results using the network simulator have shown that our method could achieve much higher quality satisfaction of receivers, under the reasonable amount of tracing traffic.

The satisfiability problem (SAT) is a typical NP-complete problem where a wide range of applications has been studied. Given a set of variables U and a set of clauses C, the goal of SAT is to find a truth assignment to variables in U such that every clause in C is satisfied if it exits, or to derive the infeasibility otherwise. This paper presents an approximation algorithm, called a minimal-state processing search algorithm for SAT (MIPS-SAT). MIPS-SAT repeatedly transits minimal states in terms of the cost function for searching a solution through a construction stage and a refinement stage. The first stage greedily generates an initial state composed of as many satisfied clauses as possible. The second stage iteratively seeks a solution while keeping state minimality. The performance of MIPS-SAT is verified through solving DIMACS benchmark instances.

In this paper, we propose a technique to implement communication protocols as hardware circuits using a model of concurrent EFSMs with multi-way synchronization. Since use of multi-way synchronization enables simple and comprehensible specifications of recent communication protocols which frequently use complicated mechanisms such as mutual exclusion and dynamic job assignment, the proposed model is expected to reduce development cost in designing/developing such protocols. We implement specifications described in the model so that EFSMs work synchronously with the same clock, and that the synchronization mechanism for checking executability of each tuple of synchronizing transitions is implemented as a combinational logic circuit. Through some experiments, we have confirmed that the proposed technique can synthesize hardware circuits with relatively good performances for practical use.

In this paper, we present a method for the synthesis of extended Petri net-based distributed specifications. Our method finds an optimal allocation of resources (computational data) that optimizes the derived distributed specification, based on some reasonable communication-cost criteria.

In this paper, we propose a verification method for pipelined microprocessors with out-of-order execution. We define a class of pipelined microprocessors with out-of-order execution and give a sufficient condition that guarantees the correctness of implementation. Each microprocessor in this class has a pipeline stg/sub 1/,...,stg/sub n/ such that the stages stg/sub c/,...,stg/sub n/ are so-called "in-order pipeline" and changes the execution order of instructions within the stages of stg/sub 1/,...,stg/sub c-1/. Using our method, we carried out the correctness proof of a practical 6-stage pipelined microprocessor that has a so-called scoreboard. We used a verifier having a decision procedure for Presburger sentences. The total CPU time spent in the proof was about 8 hours.

In this paper, we propose a receiver-cooperative bandwidth management method for layered multicast streams, considering not only bandwidth requirements but also receivers' preference. In the method, we assume that each receiver has a preference value for each layer of streams. When a receiver requests an additional layer and required bandwidth is not available on links, it can let other receivers release a part of the layers of streams which they receive if it increases the sum of the satisfied preference values of all receivers as a total. We give an algorithm to calculate an optimal way of releasing layers in a polynomial time under some assumption. We did an experiment to transmit JPEG video over a private IP network and confirmed that the method could control bandwidth among the video streams within a second.

A gradual neural network (GNN) approach with the improved gradual expansion scheme is proposed for broadcast scheduling in packet radio (PR) networks. A PR network provides data communications services to geographically distributed nodes through a radio channel. A time division multiple access (TDMA) protocol is adopted for the network. Packets are transmitted in repetition of a TDMA cycle, where the delay time in packet broadcasting should be minimized. The proposed gradual expansion scheme resolves the constraints of the problem using neuron inputs and outputs to reduce the computation time. Besides, an additional slot to a TDMA cycle is considered for slot assignments when a valid solution is not obtained within a current cycle. The performance comparison with a conventional GNN and a greedy algorithm shows the effectiveness of the proposed GNN approach.

In this paper, we propose a new bandwidth allocation technique where a new stream can preempt an appropriate amount of bandwidth from other existing streams, considering both quality and priority requirements of users. The existing preemption-based technique has focused on the preemption among the streams with the same user. However, in multi-party multimedia applications, there would be a case where we would like to raise the quality of a specific stream (e.g. in a video conference, the video stream of a new chair may become more important than those of the existing participants). Therefore the preemption should be allowed among streams with different users and the best preemption in terms of their requirements should be provided. In our technique, using the algorithm for solving the minimum flow cost problem, the preemption is calculated so that the total loss of quality and priority of the existing streams can be minimized. We have implemented the proposed technique and evaluated it through the experiment using MPEG1 video streams, and have confirmed that our technique can keep a high frame rate for each existing MPEG1 video stream even when accommodating a lot of new streams.

Some methods for deriving protocol specifications from given service specifications with time constraints have been proposed. However, existing methods cannot treat the class of service specifications with both parallel synchronization and data values. They also assume that all clocks in the distributed system are synchronized. We propose an algorithm to derive a correct protocol specification automatically from a given service specification described in an extended model of time Petri nets where the above restrictions are eliminated. Using our method, we will be free from considering the details of communication delays on the design of real-time distributed systems.

In general, the services of a distributed system are provided by some cooperative protocol entities. The protocol entities must exchange some data values and synchronization messages in order to ensure the temporal ordering of the events which are described in a service specification of the distributed system. It is desirable that a correct protocol entity specification for each node can be derived automatically from a given service specification. In this paper, we propose an algorithm which synthesizes a correct protocol entity specification automatically from a service specification in a Petri Net model with Registers called PNR model. In our model, parallel events and selective operations can be described naturally. The control flow of a service specification must be described as a free-choice net in order to simplify the derivation algorithm, however, many practical systems can be described in this class. In our approach, since each protocol entity specification is also described in our PNR model, we can easily understand what events can be executed in parallel at each protocol entity.

In this paper, we propose a method to synthesize protocol specifications automatically from service specifications written in a time-extended LOTOS called LOTOS/T+. In LOTOS/T+, structured descriptions, such as parallelism and interruption are allowed to describe service specifications, and time-constraints among non-adjacent actions can be described using Presburger formulas. Here we assume that there is a reliable communication channel between any two nodes and the maximum communication delay for each channel is bounded by a constant. Moreover we assume service specifications have no deadlocks. Under our simulation policy, a specification S' is derived from a given service specification S and a given maximum communication delay of each channel. In S', time-constraints necessary for exchanging synchronization messages are added. If S and S' can carry out the same behaviour, i.e., if S and S' are bisimulation equivalent when time is ignored, then a correct protocol specification for simulating S is derived from S' automatically.

In this paper, we propose a method for visualizing LOTOS specifications using multi-rendezvous mechanism. For visualization, we have extended LOTOS by introducing some primitive animation events. Using the extended LOTOS, we describe a visualization scenario for events where we would like to visualize their execution. Then, we execute the original specification and its visualization scenario in parallel under LOTOS multi-rendezvous mechanism so that the corresponding animation is activated when each event is executed. The pair of the original specification and its visualization scenario is converted into the multi-threaded object code using our LOTOS compiler. In our visualization method we can specify the visualization scenario without modifying the original specification, and we can derive an object code which animates the original specification in real time. We have tried to visualize a LOTOS specification of "Dijkstra's dining philosophers", and evaluated the usefulness of our approach.

We propose an automatic analysis and test case derivation method for LOTOS expressions with data values. We introduce the class of P-LOTOS expressions where the data types are restricted to Presburger arithmetic. That is, only the integer and Boolean types are used, and the operators of the integers are restricted to addition, subtraction, and comparison. For this class, we give an algorithm for deriving a set of test cases (a test suite). The algorithm is carried out by using a decision procedure for integer linear programming problems. We also give solutions for the deadlock detection problem, the detection of nonexecutable branches, and the detection of nondeterministic behaviors. We have implemented a tool for the analysis and test selection based on our techniques. The derivation of a test suite for a simplified Session protocol is described as an example.>

The authors propose a synthetic technique to derive a correct protocol specification from a given service specification modeled as a nondeterministic extended finite state machine (EFSM). Each EFSM has a finite state control and a finite number of registers. In the model, the next state and the next values of the registers are determined depending on not only the current state and input but also the current values of the registers. The registers correspond to the system resources and they are allocated to some of the protocol entities in a distributed system. The derived protocol entities' specifications satisfy the resource allocation specified by the designer. A procedure solving 0-1 integer linear programming problems is used to reduce the number of the messages exchanged among the protocol entities.>

It is well known that algebraic specification methods are promising for specifying programs and for verifying their various properties formally. In this paper, an algebraic specification of information transfer procedures of high-level data link control (HDLC) procedures is presented and some of the main properties of the specification are shown. First, we introduce abstract states, state transition functions, and output functions corresponding to elementary notions extracted from the description of HDLC procedures in ISO 3309-1979 (E) and ISO 4335-1979 (E). Second, we show axioms which represent the relations between the values of functions before and after the state transitions. Then, it is proved that the specification is ``consistent,'' ``sufficiently complete,'' and ``nonredundant.'' Also it is shown that an implementation which realizes the specification is naturally derived. In the last section, verification of various properties of HDLC procedures is formulated in the same framework as the algebraic specification, and some verification examples are presented.