This paper introduces a novel approach for coordinated evacuation route planning during disasters, focusing on guiding groups of individuals-such as families or neigh-bors-who are spatially separated but socially connected. Recognizing the importance of group cohesion during evacuations, we designed and implemented a prototype system, which was validated through a real-world evacuation drill conducted in Kobe City during the 30th memorial year of the Great HanshinEarthquake. We introduce and formulate a new problem, termed the ‘Evacuation Routing Problem’ (ERP), using integer linear programming (ILP). Our system recommends that group members rendezvous at specific points along their evacuation routes. The core idea is to model each evacuation route as a tree on a predefined road network, where the leaves correspond to evacuees (group members) and the root corresponds to the shelter. This problem can be viewed as a variant of the Steiner Tree Problem, augmented with an additional constraint to ensure that members of a group meet at an optimal rendezvous node while en route to the shelter. To validate our method, we conducted a pilot experiment in Kobe City, Japan. We developed a prototype system featuring a web-based interface that generates high-quality evacuation routes derived from ILP solutions. The experiment involved two family groups, and the developed system navigated a real road network that included various points of interest such as parks, shopping malls, offices, and schools. The results showed that one family successfully gathered at the proposed rendezvous point and evacuated together, while the other group was unable to meet due to a suggested detour. Drawing from these experimental outcomes and user feedback, we analyze system limitations and discuss potential improvements from a user-centric perspective.

This study proposes an adaptive message delivery mechanism for a broker in the WMO Information System 2.0 (WIS2.0), a next-generation world weather loT data exchange system, to satisfy the time constraints (deadlines) of the messages requested by subscribers in the system. Since WIS2.0 is a worldwide, heterogeneous system, each broker has to handle a variety of requests by each subscriber to receive messages (data) with different time constraints and sizes. Therefore, it needs to control the timing of message delivery to subscribers, considering publishers' delivery timing, subscribers' processing performance, network bandwidth, message types, data volume, and so on. To this end, the proposed method takes a reinforcement learning-based approach where the feasible timing to transmit messages is learned by monitoring the message arrival patterns from the publishers, message ACK delay from the subscriber, and the sensing window size, all of which are easily observable at the broker. Experiments using a network simulator showed that under the best-case scenario, the proposed method could achieve 10% higher message goodput compared to DWEDF-RL, the state-of-the-art RL-based packet scheduling method tailored to the pub/sub model.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

Considering user behaviors in the performance evaluation of mobile networks is crucial as traffic generation in such networks is highly dependent on mobility and communication activities of mobile users. This paper presents a simulator that can faithfully simulate user behaviors together with the target network system. The following three components have been implemented in a network simulator in order to model realistic user behaviors: (i) a function of loading geographical data for the target area, (ii) a pedestrian mobility model in which users walk along streets based on the given geographical data, and (iii) a communication behavioral model, which accounts for correlations between user profiles and their communication activities. Simulation of a mobile network system is performed and shown that the simulator replicates realistic behaviors of individual users, and also that the user behaviors do influence the predicted overall system performance. This indicates the importance of accounting for user behaviors in mobile network system evaluation.

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.

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 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.