This paper presents an autonomous system that determines optimal sensor placements for 3D digital-twins construction, leveraging multiple robots equipped with Light Detection and Ranging (LiDAR) sensors and employing reinforcement learning. The objective function is designed to encourage exploration by rewarding lower similarity between current- and initial-environmental maps and higher entropy, which indicate different environmental coverage and increased information content in the reconstructed digital-twin, respectively. Reinforcement learning with Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) ensures optimal sensor placement by identifying robot configurations that maximize the reward of the objective function. The presented system provides an adaptive and coordinated sensing solution, overcoming limitations of fixed or heuristic sensor placement methods in various real-world environments.

We propose a framework for managing machine-learning (ML) models for three dimensional image data in a blockchain network. ML models are registered to the blockchain with their associated datasets in a relevant state. The key enabler is an aggregation of the hash values calculated by ML models and their associated datasets. This allows third parties to prove the authenticity of these data for them, even if only part of the data is disclosed to third parties as raw data.

This paper presents a face-direction estimation system using a multi-LiDAR sensor network. The system estimates a human’s face-direction from 3D point cloud data, allowing for a rough gaze estimation without relying on color information. Experimental results validate the system’s feasibility, demonstrating its potential for privacy-preserving monitoring applications.

This paper presents an automated classification system for object detection in multi-LiDAR sensor networks. The system leverages a deep learning model for an initial coarse classification and subsequently employs feature-based extraction of secondary classes from point-cloud data. Additional labeling and deep learning-based processing on these secondary classes facilitate a more fine-grained classification. The efficacy of the presented approach is demonstrated with real-world data.

This paper presents a system for detecting and estimating the size of small objects using a multi-LiDAR sensor network. Based on the extracted point clouds, our system calculates object density and estimates object size. Our system shows that using two LiDARs generally improves point density, although size estimation accuracy varies with the conditions.

Light-detection-and-ranging (LiDAR) sensors are essential for the automated operation of network-connected vehicles in smart cities but are vulnerable to cyber-attacks. This paper introduces a system for anomaly detection that verifies the consistency of point cloud data across multiple LiDAR sensors. By comparing metrics derived from point cloud distributions under normal and potentially compromised conditions, the system effectively identifies anomalies, enhancing the security and reliability of network-connected LiDAR systems.

This paper proposes a system to improve the visibility of real-time Light-Detection-and-Ranging (LIDAR) data by enabling calibration with pre-created three-dimensional (3D) images. The proposed system automatically selects suitable frames to periodically maintain calibration, and we demonstrate its effectiveness through experiments.

Smart traffic monitoring at intersections which exploits three-dimensional light detection and ranging (LiDAR) sensor networks is a promising technique for achieving a safe and secure society. One challenge for widely spreading these systems is to effectively aggregate massive point cloud data generated by multiple LiDAR sensors installed at every corner of intersections with a limited cost and a limited communication bandwidth. To this end, this paper proposes a lightweight point cloud compression method for real-time smart traffic monitoring. The proposed method enables tiny low-cost processors installed at every LiDAR sensor to detect moving parts of a point cloud from point could data in real time. By sending compressed data of moving parts of a point cloud only to edge servers, the communication bandwidth is saved, which helps edge servers to analyze them for preventing traffic accidents in real time. Experimental results using the KoPER intersection dataset show that the average detection rate over 1,200 frames of data is around 95%. The processing time per frame is about 5.4 ms with a commercial edge-oriented processor, which is less than typical frame rates of modern LiDAR sensors. In addition, point could compression ratio of the proposed method is approximately 3.5 times better than that without the moving part detection technique.

Smart cities work as a platform that utilize the information and communication technology to provide public services efficiently. Smart monitoring is one of the important components for providing public services in smart cities. 3D-image sensing technology such as the light detection and ranging (LIDAR) is a promising means of capturing a public space for smart monitoring. However, 3D-image data may be sensitive as it is related to public safety and law enforcement. Blockchain technology may be a solution to this issue. Prior work presented a framework for hash value aggregation in a 3D sensor network to avoid overflow that occurs when handling 3D-image data generated by LIDAR devices in real time. However, the prior work presented only a theoretical result even though it was based on actual measurements of processing delay; the feasibility of the framework needs to be evaluated with a real implementation using real LIDAR sensor units. On the basis of the prior work, we propose a real system that actually measures the time taken to register the hash values at an edge computer and determines the optimal number of aggregated hash values to avoid overflow in registration on a blockchain network. We demonstrate the effectiveness of the proposed system through an experiment using an actual edge computer and multiple LIDAR sensor units.

This paper proposes an edge computing system that enables estimating physical activities of people in offices from time-series point-cloud data, obtained by using a light-detection-and-ranging (LIDAR) sensor network. The paper presents that the proposed system successfully constructs the model for estimating the number of typed characters from time-series point-cloud data, through an experiment using real LIDAR sensors.

This paper proposes an edge system with a sensor network formed by multiple light-detection-and-ranging (LI-DAR) sensors to identify the location of micro-mobility vehicles. The main contribution of this work is that we train machine learning (ML) models for detecting micro-mobility vehicles from point-cloud data acquired by multiple LIDAR sensors. An experiment verifies that the ML models work well for detecting a micro-mobility vehicle accurately.

This paper proposes an edge system that extracts blind spots from any viewpoint of vehicles. This process happens anywhere in the field, from spatial data acquired using a multi-LIDAR network, and provides the blind-spot information to micro-mobility vehicles. The proposed system converts the acquired spatial data into voxels and determines blind spots from every voxel in the space. This paper shows the prototype of the proposed system and the verification that the proposed system successfully extracts blind spots from any viewpoint in the field.

This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance. This paper reports a simulation of UAV dispatching using reinforcement learning and distributed ML inference.

Using multiple Light-Detection-and-Ranging (LIDAR) sensors in the robot vision system can provide visual information to the robot that covers blind spots. However, the data acquired by LIDAR sensors may be sparse, making difficult for robots to capture the size and the shape of a target object. This paper proposes a method for robot vision systems with multiple LIDAR sensors to construct a spatial model of a target object. The proposed method introduces a concept of ‘likelihood,’ which indicates how likely a part of the target object exists at each position in the space. This paper demonstrates the accuracy of the proposed method through an experiment using real LIDAR sensors against a benchmark method.

In this paper, we propose a system for automating spatial calibration for heterogeneous multi-LIDAR networks. The proposed system decides the order of light detection and ranging (LIDAR) sensors to be calibrated by using a predefined algorithm and calibrates the sensors in accordance with the order. We evaluated the accuracy of spatial calibration when calibrating LIDAR sensors in the order of the number of points acquired by the sensors and the field of view (FoV) of the sensors, and we show the effectiveness of the proposed system.

Smart monitoring plays an important role in securing people's safety in smart cities. In smart monitoring, machine-learning (ML) models are used for detecting objects in the environment, such as pedestrians and vehicles. Such ML models need to be securely managed against malicious attacks, such as tampering. This paper proposes a blockchain framework for securely managing ML models. It demonstrates the performance of a prototype of the proposed framework.

This demonstration proposes an edge system that enables sensing and computing capacities to be offloaded from vehicles to the system. We developed a prototype of the proposed system, in which a sensor network using multiple light-detection-and-ranging (LIDAR) units works for sensing and computing, while autonomous vehicles are equipped only with lightweight sensing and computation capabilities. This demon-stration presents the performance of the prototype in terms of movement accuracy to verify the feasibility of the proposed system.

Sparse mobile crowdsensing is a crowdsensing paradigm that reduces the sensing cost while ensuring data quality by collecting data sparsely and reconstructing desired data using inference algorithms including machine learning algorithms. However, real-time inference of spatial information with sparse mobile crowdsensing has not sufficiently considered the change of temporal characteristics of data. As a result, the accuracy of the reconstructed data can deteriorate over time. Therefore, this paper proposes a framework that periodically updates a machine learning model used for reconstructing data by evaluating the importance of the data in terms of both inference and re-training and giving priority to collecting important data.

To mitigate cyberattacks, detecting anomalies in network traffic is of key importance. In this paper, we propose a model training method for detection of Internet of Things (IoT) anomalous traffic that is robust against the contamination of anomalous samples in the training set. The key idea is to focus on the nature of IoT malware infections (i.e., only a limited number of IoT networks contain infected devices) and employ federated learning (FL) to mitigate the impact of anomalous samples on model training. The simulation evaluation using IoT traffic data obtained from residences and malware traffic data collected from sandbox experiments demonstrates that the proposed method does not cause accuracy degradation even when the anomalous samples are contaminated, in contrast with the detection accuracy of baseline methods, which does degrade.

This demonstration proposes a scheme that suppresses the data size of a 3D-image sensing network by adaptively filtering low-importance points, such as the points of floors and ceilings when the task is to track pedestrians. The adaptive filter can be dynamically changed to further reduce the amount of data if it is difficult for packets to reach the edge computer. We evaluate the proposed scheme through experiments and demonstrate that it performs better than benchmark schemes in terms of prompt arrival of the data.

A content recommendation system based on human brain activity has become a reality. However, the cost of collecting the information from people is problematic. This article proposes a scheme that resolves the tradeoff between the inference performance from a profile model to a brain model and the cost of collecting profile information. In the proposed scheme, a machine learning model infers the brain model from the profile model and a feature selection method is applied to reduce the cost, i.e., the number of questionnaire items, of collecting profile information. Since only the top questionnaire items with the highest importance scores are used, we can maintain the inference performance as high as possible while limiting the number of questionnaire items. We demonstrate the effectiveness of the proposed scheme with a performance evaluation using an experimentally obtained brain model and a profile model created from real profile information. The results over different experimental parameters, video lengths, and feature selection methods demonstrate that the proposed scheme successfully identifies the top questionnaire items that contribute most significantly to the inference of brain models.

Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting (EH) and have limited energy available to analyse acquired data. When edge infrastructure is available, a device has a choice: to perform analysis locally or offload the task to other resource-rich devices such as cloudlet servers. However, such a choice carries a price in terms of consumed energy and accuracy. On the one hand, transmitting raw data can result in a higher energy cost in comparison to the required energy to process data locally. On the other hand, performing data analytics on servers can improve the task's accuracy. Additionally, due to the correlation between information sent by multiple devices, accuracy might not be affected if some edge devices decide to neither process nor send data and preserve energy instead. For such a scenario, we propose a Deep Reinforcement Learning (DRL) based solution capable of learning and adapting the policy to the time-varying energy arrival due to EH patterns. We leverage two datasets, one to model energy an EH device can collect and the other to model the correlation between cameras. Furthermore, we compare the proposed solution performance to three baseline policies. Our results show that we can increase accuracy by 15% in comparison to conventional approaches while preventing outages.

Visual surveillance of dynamic objects on roads has been developed to ensure road safety for people. Particularly, vehicle tracking is considered as a key technology for the road safety; studies on multi-object tracking (MOT) are being actively pursued. However, when MOT is performed, raw vision data are not always available because of the technical limitation or the privacy concern of the system; MOT needs to be performed only using the coordinates obtained from the object detector without using features extracted from raw image data such as color of vehicles, which degrades the accuracy of MOT to the unsatisfactory level for road safety. This paper proposes an MOT scheme for moving vehicles that is inspired by cell tracking using the Viterbi algorithm. The proposed scheme extends the Brownian motion model, which was used in the base scheme of cell tracking, by weighting probability transitions in accordance with the direction of travel of vehicles on the road. We evaluate the proposed scheme using simulated vehicle-traffic data and verify that the proposed scheme performs better than benchmark schemes in terms of the accuracy of MOT. We also demonstrate an example of how the proposed scheme works well for real vehicle-traffic data.

This paper proposes a two-level processing scheme for three-dimension-image sensing. The first level processing selects only spatial regions needed for a smart monitoring task to reduce the total volume of data traffic. The second level processing integrates multiple (physical) image sensors into a virtual one to improve the delay and jitter performance in the realtime transmission of data from sensors to the cloud server. We develop a prototype system to implement the proposed scheme. Our demonstration validates that the proposed processing scheme works better than the benchmarks which do not adopt the two-level processing.

Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs) equipped with wireless transceivers (access points (APs)) are increasingly being touted as being able to provide a flexible "on-the-fly" communications infrastructure that can collect and transmit sensor data from sensors in remote, wilderness, or disaster-hit areas. Recent advances in the mechanical automation of UAVs have resulted in separable APs and replaceable batteries that can be carried by UAVs and placed at arbitrary locations in the field. These advanced mechanized UAV mesh networks pose interesting questions in terms of the design of the network model and the optimal UAV scheduling algorithms. This paper proposes the design of wireless mesh networks that depend on the mechanized automation (AP separation and battery replacement) capabilities of UAVs, which includes mathematical formulations and heuristic UAV scheduling algorithms for each network model. Through performance evaluation, the proposed design is benchmarked against the theoretical lower bound.

Service chaining is a method for providing desired network services to users by concatenating virtualized network functions (VNFs) in the network. There have been studies on service chain provisioning models that relax the visit order of VNFs and routing constraints. These models make it difficult to obtain an optimal solution in a practical time due to the huge number of decision variables associated with the problem. A heuristic approach that obtains a nearly-optimal solution within a practical time is needed. This paper proposes a column generation based heuristic algorithm for the service chain provisioning problem that relaxes the VNF visit order and routing constraints. The proposed algorithm divides the problem into a VNF placement problem and a routing problem and applies the column generation technique to solve the latter. Numerical results show that the proposed algorithm shortens the computation time compared to directly solving the original integer linear programming problem in exchange for some increase in the cost for VNF placement and link utilization.

This paper proposes a virtual network function (VNF) placement model for service chaining that minimizes the cost of using computation resources when no failure occurs while guaranteeing recovery against any single facility node failure within the recovery time objective (RTO) defined for each service. The proposed model adaptively allocates computation resources to each service under its RTO constraint. The proposed model introduces two sharing methods of computation resources among multiple service chains. The first method allows sharing a virtual machine (VM) where a VNF is scheduled to run after a failure, which contributes to suppressing the number of VMs reserved in preparation for a failure. The second method allows sharing computation capability used for VMs, which prevents unnecessary VNF scale-up that requires additional computation resources. A simulation study verifies that the proposed model reduces the cost of using computation resources compared to comparative models.

Real-time prediction of communications (or road) traffic by using cloud computing and sensor data collected by Internet-of-Things (IoT) devices would be very useful application of big-data analytics. However, upstream data flow from IoT devices to the cloud server could be problematic, even in fifth generation (5G) networks, because networks have mainly been designed for downstream data flows like for video delivery. This paper proposes a framework in which a software defined network (SDN), edge server, and cloud server cooperate with each other to control the upstream flow to maintain the accuracy of the real-time predictions under the condition of a limited network bandwidth. The framework consists of a system model, methods of prediction and determining the importance of data using machine learning, and a mathematical optimization. Our key idea is that the SDN controller optimizes data flows in the SDN on the basis of feature importance scores, which indicate the importance of the data in terms of the prediction accuracy. The feature importance scores are extracted from the prediction model by a machine-learning feature selection method that has traditionally been used to suppress effects of noise or irrelevant input variables. Our framework is examined in a simulation study using a real dataset consisting of mobile traffic logs. The results validate the framework; it maintains prediction accuracy under the constraint of limited available network bandwidth. Potential applications are also discussed.

Software-defined network (SDN) is a network that the centralized SDN controller stores flow entries in the flow table of each SDN switch and controls packet flows as instructed by the stored flow entries. When a multicast service is provided in an SDN, the SDN controller stores a multicast entry dedicated for a multicast group in each SDN switch. It is necessary to suppress the number of flow entries required to set up a multicast tree due to the limited capacity of the flow table. In a conventional research, a multicast routing model that suppresses the number of multicast entries in one multicast request by replacing a part of them with unicast entries has been devised. However, since this conventional model individually determines a multicast tree route for each request, unicast entries configured for the same receiver are distributed in various SDN switches when multiple multicast services are requested. As a result, there is still the possibility of improving the reduction of the number of flow entries. In this paper, we propose a multicast routing model for multiple multicast requests that minimizes the number of flow entries. This proposed model determines multiple multicast tree routes simultaneously so that a unicast entry configured for the same receiver and stored in the same SDN switch is shared by multicast trees. We formulate the proposed model as an Integer Linear Programming (ILP) problem. Numerical results show that the proposed model reduces the required number of flow entries compared to the conventional model.

Real-time prediction of spatial information has attracted a lot of attention. Machine learning enables us to provide real-time prediction of spatial information such as road traffic by using aggregated sensor data. The amount of mobile traffic is forecasted to increase exponentially, thereby causing serious transmission delays when traffic loads are heavy. If a part of the data used for predicting spatial information in real time does not arrive on time, the prediction accuracy degrades because the prediction is done without the missing data. A utility-based scheduling technique has been suggested as a way of prioritizing such delay-sensitive data. However, no study has not addressed the utility-based scheduling for the real-time prediction of spatial information. Therefore, this paper proposes a scheme that enables modeling the utility function for real- time prediction of spatial information. The scheme is roughly composed of two steps: the first creates training data from original time-series data and a machine learning model using the data, while the second models the utility function using the feature selection method in the learning model. Feature selection method enables extracting the importance of data in terms of how much the data contributes to the prediction accuracy. This paper assumes the road traffic prediction as a scenario and shows the utility function modeled by the proposed scheme using real spatial datasets. A numerical study demonstrates how the model of the utility function works effectively in prioritizing data for real-time prediction in terms of accuracy.

In this paper, we propose a virtual network function placement and routing model for multicast service chaining based on merging multiple service paths (MSC-M). The multicast service chaining (MSC) provides a multicast path, which connects a source node and multiple destination nodes, and virtual network functions (VNFs) are placed on the path so that users on the destination nodes receive their desired services. The conventional MSC model configures multicast paths for services, each of which has the same source data and the same set of VNFs in a predefined order. In the MSC-M model, if paths of different services carry the same data on the same link, these paths are allowed to be merged into one path at that link, which improves the utilization of network resources. The MSC-M model determines the placement of VNFs and the route of paths so that the total cost associated with VNF placement and link usage is minimized. The MSC-M model is formulated as an integer linear programming (ILP) problem. In the ILP problem, data flows whose source data is the same and which already passed the same subset of VNFs belong to the same group. A part of paths of different services which carry data flows belonging to the same group are allowed to be merged into one path. Numerical results show that the MSC-M model reduces the total cost by 28.7% at a maximum compared to the conventional MSC model.

Over the last decade, interpersonal communication has attracted more attention from researchers than before. Although the volume of data generated through various communication devices and tools could be enormous, the recent decrease in storage cost enables us to record and store it. The analysis of interpersonal communication is useful to estimate influence in social relationships among people, to detect communities, and to recommend potential friends for users on social networking services. A network graph, which is a mathematical model that represents people as nodes and past opportunities of interpersonal communication as edges, works in such analysis. However, when the capacity of the number of edges recordable in a graph database is limited, or when only a limited number of edges is used for high-speed analysis, it is still unclear which edges should be prioritized and utilized in the analysis. Previous studies suggested that edges in network graphs can be weighted on the basis of the aggregated duration of connections, the number of connections, or the connection time . However, temporal regularity in interpersonal communication has not been well considered in the previous studies. Therefore, in this paper, we propose an edge weighting method for network graphs from interpersonal communication that determines edge weighs on the basis of the scores obtained from the spectral analysis technique. The spectral analysis technique is utilized to numerically deal with temporal regularity and frequency of interpersonal communication. An examination using real records verifies that by using our edge weighting method, link prediction works better under a condition of the limited number of edges usable for the analysis. We also deeply analyze and present the distributions of the frequencies that characterize interpersonal communication.

This paper presents a novel system architecture for predictive road-traffic information delivery in which computing resources at the network edge and the central cloud are cooperatively used to analyze sensing data collected by vehicles on the road. In this paper, we also present the mathematical problem formulation of the proposed system architecture for ensuring that the system could successfully deliver road-traffic information at realtime without overflowed computational and network loads. The numerical examination using a real dataset and a realistic network emulator validates our system.1

Recently, the demand for realtime and accurate prediction of mobile traffic has been growing in traffic engineering and dynamic resource allocation that work to handle increased mobile data traffic. However, most conventional prediction techniques assumed that traffic logs at every unit time at every base station are perfectly available. This assumption is critical in realtime mobile traffic prediction because the volume of traffic log data collected at base stations is huge and they compete bandwidth with normal user application traffic when they are sent from base stations to the server that performs prediction. Therefore, in realtime mobile traffic prediction, we should consider the condition in which the bandwidth ensured for forwarding traffic log data is limited. In this paper, we propose a method that prioritizes traffic log data in the basis of the contribution to prediction accuracy; each base station sends more important traffic log data to the server with higher priority. The importance of each data entry of traffic log data means how much prediction accuracy would degrade if the entry is missing. The proposed method enables us to reduce the volume of traffic log data sent from base stations to the server while maintaining prediction accuracy at the sufficient level. Our simulation study using a real dataset of mobile-traffic measurement validates our method in terms of prediction accuracy under the limitation of available traffic log data.

Recently, some companies, such as Airbnb and Uber, have successfully provided services that utilize spare personal assets by sharing them with others. This idea, which is called the sharing economy, is predicted to have a major impact on society. In such a society, various devices will be shared with others. To share and utilize those devices efficiently, the permission level should be able to be controlled flexibly. Device owners generally do not want to share their devices with strangers, while they want to grant stronger permission to their close friends. However, none of the conventional authentication methods have provided such a functionality yet. Although device owners can manually configure the permission level for each user, it is a great burden. Therefore, this paper proposes a system that exploits online social relationships as a solution to the authentication problem when sharing devices. By acquiring and evaluating online social relationships between a device owner and a user, the proposed system automatically determines whether the user can access the device and how strong the permission for the user should be. The owner can make efficient use of the shared device without worrying about the access configuration of users. To examine the feasibility and effectiveness of the proposed system, this paper presents a prototype system and measures the performance of the prototype system. Furthermore, by simulating a specific scenario, this paper shows that the proposed system is capable of controlling the permission level for each user effectively based on the user's online social relationships.

Rapid increase of elderly population in the world is motivating innovative technologies for healthcare services that are both high-quality and cost-effective. Wireless sensor networks (WSNs) based smart home for assisted living is one of the most prosperous solutions to this area, since it removes the requirement of deploying wired devices and is easy to be implemented in existing home environments. However, limited battery capacity of sensor nodes constraints the lifetime of WSNs and prevents this solution to be widely accepted by the industry. Therefore, this paper proposes a logical correlation-based sleep scheduling mechanism (LCSSM) to achieve energy-efficient WSNs in smart homes. LCSSM first automatically generates logical correlations between sensors by learning from living patterns of different home residents. It then activates/deactivates sensors according to their logical correlations to reduce energy consumption. Numerical evaluation results based on real datasets have validated that the proposed LCSSM not only reduces energy consumption of WSNs significantly, but also retains the quality of sensing successfully. Specifically speaking, sensors with LCSSM successfully sense more than 94% valuable events occurred in the smart home with only 23%-57% active time of the whole evaluation period.

These days, people have shifted from global services to social services. However, it is common that privacy-sensitive data is exchanged in such social services and the conventional privacy control function is built just on the application level. Therefore, to consider privacy control in the network level, this paper proposes a framework for community associated networks enabled by the network virtualization technique. A community associated network is defined as a logical information space in which people who are socially connected with each other exchange and share their data including privacy sensitive data. In the proposed framework, community networks are created for each community and physical network resources are assigned to each community network. However, as the number of communities increases, the more physical network resources are needed. Therefore, this paper discusses the tradeoff between the number of community and network resource and shows numerical results obtained from the model.

Modern mobile devices (smart phones, wearable devices, and smart vehicles) have greater resources (communication, computation, and sensing) than before, and these resources are not always fully utilized by device users. Therefore, mobile devices, from time to time, encounter other devices that could provide resources to them. Because the amount of such resources has increased with the number of mobile devices, researchers have begun to consider making use of these resources, located at the “edge” of mobile networks, to increase the scalability of future information networks. This has led to a cooperation based architecture of mobile cloud computing (MCC). This paper reports the concept and design of a resource sharing mechanism that utilizes resources in mobile devices through opportunistic contacts between them. Theoretical models and formal definitions of problems are presented. The efficiency of the proposed mechanism is validated through simulation.

Bandwidth barter is an effective way of satisfying throughput requirements in wireless networks; we could expect a station (STA) allows another STA to borrow its bandwidth as long as it is also beneficial for the STA. Our previous work proved bandwidth barter between two STAs is optimized based on Nash bargaining solution (NBS), which brings the Pareto efficiency and the proportional fairness in the bartering game. However, it still remains an open issue how to solve the bartering game when the number of STAs is N (N >; 2), which is discussed in this paper.

This paper discusses a new paradigm of network service and in-network resource control: relational metric based control. The relational metrics indicate the closeness relationship between objects in the real world, and these objects could be people, locations, things, and content. Closeness is measured by using a fusion of online and physical sensing. We will describe the system model and discuss possible service applications of this technology.

In this paper, we discuss problems in densely deployed wireless networks. Particularly, we focus on wireless local area networks (WLANs) because they have enabled us to provide seamless and high capacity wireless access easily and inexpensively. However, recently, channel interference between different services has become a serious problem because access points (APs) of WLANs are located too densely. In the carrier sense multiple access with collision avoidance used in WLANs, the hidden terminal (HT) and the exposed terminal (ET) problems occur depending on the distance between stations and the carrier sensing range. In the higher dense deployment mentioned above, the HT and ET problems occur complicatedly. Therefore, we propose an AP cooperation system that detects the HT and ET problems between stations (STAs). In our system, APs are operated with time synchronization and obtain the information of connected STAs from received frames. The HT and ET problems are identified from the integration of the information obtained at different APs. The effectiveness is verified by simulations.

Mobile users want to be connected to a network at any time for mobile computing applications, such as thin-client communications and network games, even while they are moving toward their destinations. They could take an alternate route (called a “longcut” route) if the route is geographically longer than a shortcut route to their destinations but gives more wireless network resources, such as bandwidth and throughput. In the case of a single user, the longcut routes are shown to be effective when they are optimally selected within a specified cost restriction. In the case of multiple users, users should be diversified in their route to avoid competing with each other. Optimization should be achieved by the appropriate arrangement of routes an d, in addition, by incentives to relax users' cost restrictions.

We propose a TCP window-size delegation method for TXOP Exchange applicable to the downlink in wireless access networks. In TXOP Exchange, the compliant stations (STAs) cooperatively use their available bandwidth in accordance with their required QoSs. TXOP Exchange was previously validated for the uplink. The proposed delegation method enables STAs to delegate their bandwidth for the downlink as well without requiring any modifications to the legacy access point or the STAs. Simulation demonstrated that this method works well.

This paper discusses the modeling, the observation, and the analysis of a social experiment, in which we built an online social network to observe interactions between people. We try to model the problem from the micro-economics aspect; we use the concept of utility to model people's behaviors in this experiment. Furthermore, we consider incentive reward as an external factor and observed how people's behavior would change based on how we give incentive reward. We assumed the scenario of content recommendation in an online social network and compared the two reward assignment rules: i) a user gets reward if she or he informs her/his friend of the pointer to the content and ii) a user gets reward if her/his friend she or he informed of the content pointer reacts to the content. We expect that, in the former, the social network is not activated because the information flow is unidirectional, while, in the latter, the social network can be activated because it stimulates people to react to the information they have received.

It can be essential in the new-generation content services to predict the potential demands of people, which they themselves have not recognized or cannot express precisely. Social graphs representing the relationships between people are used for predicting demand in current Internet-based services. However, these graphs cannot represent the relationships of two users residing in common communities or common places. We propose representing not only a person but also things like communities and social events together as a single node in a social graph. This representation allows us to estimate who shares potential interests with a given person. We evaluated the estimation accuracy of our representation using an actual relational dataset from an academic database. Results show that our representation can estimate if two people share common interests that cannot be found with conventional methods that only use human nodes for estimation, and it can estimate the relations without using human nodes.

The enhancements of the transmission speed in wireless access networks and mobile-device capacity enable us to use data/audio streaming/video streaming multicast service in large scale networks via their personal mobile devices. Peer-to-peer (P2P) architecture ensures scalability and robustness more easily and more economically than server-client architecture; as the number of nodes in a P2P network increases, the amount of workload per node decreases and lessens the impact of node failure. However, mobile users feel much larger psychological cost due to strict limitations on bandwidth, processing power, memory capacity, and battery life, and they want to minimize their contributions to these services. Therefore, the issue of how we can reduce this psychological cost remains. In this paper, we consider how effective a social networking service is as a platform for mobile P2P multicast. We model users' cooperative behaviors in mobile P2P multicast streaming, and propose a social-network based P2P streaming architecture for mobile networks. We also measured the psychological forwarding cost of real users in mobile P2P multicast streaming through an emulation experiment, and verify that our social-network based mobile P2P multicast streaming improves service quality by reducing the psychological forwarding cost using multi-agent simulation.

Wireless local area networks (WLANs) have allowed us to deploy seamless and high capacity wireless systems easily and inexpensively. However, recently, channel interference between different services has become a serious problem because access points (APs) of WLANs are located too densely. In the carrier sense multiple access with collision avoidance used in WLANs, the hidden terminal (HT) problems occur depending on the distance between stations and the carrier sensing range. In the higher dense deployment mentioned above, the HT problems occur complicatedly. In this paper, we propose an AP cooperation system that detects the HT problems between stations (STAs). In our system, APs obtain the information of connected STAs from received frames and the HT problems are identified from the integration of the information obtained at different APs. The effectiveness is verified by simulations.

Enhancements of transmission speed and mobile node capability in wireless access networks enable people to watch movies and share large files with friends via their personal mobile devices. Considering the increased demand for such services in the next five to ten years, we will need to introduce P2P architectures to mobile networks to handle the large number of requests from mobile nodes. However, in mobile P2P networks, since negative effects caused by instable links and high leave rates are propagated to down-stream nodes, it is difficult to ensure service quality and long service lifetime. To solve this problem, we describe a novel node-allocation framework using multi-dimensional context parameters of mobile nodes, which includes available bandwidth, disconnection rate, and remaining battery capacity. Taking into account the significance of each parameter, our framework integrates the context parameters into a single parameter called Relay Ability. Each node is allocated to the P2P topology in accordance with its relay ability. To test our method, we applied our architecture to overlay multicast and several of results from comparative evaluations through computer simulation.

Cooperative forwarding in wireless networks has shown to yield rate and diversity gains, but it incurs energy costs borne by the cooperating nodes. In this paper we consider an incentive mechanism called Bandwidth Exchange (BE) where the nodes flexibly exchange the transmission bandwidth as a means of providing incentive for forwarding data, without increasing either the total bandwidth required or the total transmit power. The advent of cognitive radios and multicarrier systems such as Orthogonal Frequency Division Multiple Access (OFDMA) with the ability to flexibly delegate and employ a number of subcarriers makes this approach particularly appealing compared to other incentive mechanisms that are often based on abstract notions of credit and shared understanding of worth.We consider a N-node wireless network over a fading channel and use a Nash Bargaining Solution (NBS) mechanism to study the benefits of BE in terms of rate and coverage gains.We also propose two heuristic algorithms based on simple probabilistic rules for forwarding and study the tradeoffs in terms of performance among these approaches. Our results reveal that bandwidth exchange based forwarding can provide transmit power savings in OFDMA networks of at least 3dB compared to noncooperation.

The growth in broadband access and increases in terminal capability have enabled us to provide large-scale multimedia services. The traditional unicast architecture cannot support such a service because the transportation from a content server to many end hosts causes the overload to a content server. Overlay multicast techniques have been proposed to solve this problem. This high-layer technology allows us to provide largescale multicast services even for mobile networks. Also, the recent rapid growth of mobile broadband brought the large-scale mobile multicast services into reality. However, stream relays by users are indispensable in overlay multicast. Moreover, in mobile environments, users' willingness to relay depends on user states such as remaining battery charge, movement conditions, and the relationship with the relay requestor. In this paper, we investigate user actions and states in mobile overlay networks and model user actions as agents. Then, we discuss how user actions affect the performance of the mobile overlay network and which user states we should pay attention to in order to build effective networks through multiagent simulations.

With the rapid growth of the wireless network and the function of mobile terminal, the services which need the wide bandwidth like video streaming are expected to be used extensively over wireless networks. Overlay multicast in which a terminal relays the content to other terminals is able to deliver it to enormous terminals. However, due to instantaneous disconnection and rapid bandwidth decrease in wireless channels, we need a different architecture from the ones used for fixed environments. In this paper, we propose a new overlay-topology design, where we use the instability information of mobile terminals such as the disconnection rate and the changing range of physical transmission rate determined by the physical conditions including radio signal strength and handover. Moreover, to reduce the performance degradation caused by instantaneous disconnection and rapid bandwidth decrease and to obtain the route-diversity gain, the proposed method assigns more logical links to the terminals that likely suffer from the impact, which results in the improvement of the received stream qualities of the terminals. This paper also shows its superiority to the conventional methods in terms of service quality and robustness from the simulation results using a wireless channel-quality model we newly propose.

In wireless local area networks (WLANs), the demand of the multimedia applications such as voice telephony and peer-to-peer content distribution has followed the necessity of quality-of-service (QoS) control for uplink flows. However, in uplink WLANs, the hidden-station problem causes difficulties in the QoS control because of unfair collision probability. In this paper, we point out this hidden-station problem and clarify the following unfairness between user datagram protocol (UDP) and transmission control protocol (TCP) uplink flows: 1) the effect of collision caused by hidden-station relationship on throughput and 2) the instability of the throughput depending on the number of hidden stations. To solve these problems, we propose a mechanism that first groups stations according to the hidden-station relationship and type of transport protocol they use then assigns a transmission permitted period to each group. Our proposed mechanism eliminates the collisions between hidden stations and provides a flexible control over bandwidth allocation to UDP and TCP. Its performance is shown through simulation.

This paper presents an evaluation of in-network caching strategies for efficient delivery of content to mobile devices that are intermittently connected to the network. Placement of content into in-network caches is formulated as an optimization problem that minimizes access latency under certain cost constraints. Several heuristic solutions (longest lifetime, split & longest lifetime and proportional probability to lifetime) are investigated via numerical examples and simulations. The results show that the proposed methods offer significant performance improvement over random caching and can approach the performance of exhaustive caching at every node with reduced storage cost.

Cooperative forwarding in wireless networks has shown to yield benefits of rate and diversity gains, but it needs to be incentivized due to the energy and delay costs incurred by individual nodes in such cooperation. In this paper we consider an incentive mechanism called Bandwidth Exchange (BE) where the cooperating nodes flexibly exchange the transmission bandwidth (spectrum) as a means of providing incentive for forwarding data. The advent of cognitive radios with the ability to flexibly change their carrier frequency as well as their transmission bandwidth makes this form of incentive particularly attractive compared to other incentive mechanisms that are often based on abstract notions of credit and shared understanding of worth. Specifically, we consider a N-node wireless network and use a Nash Bargaining Solution (NBS) mechanism to study the benefits of BE in terms of rate and coverage gains.

In conventional road-vehicle communication systems, user terminals in the vehicles have to directly connect with wireless access points (APs). However, the speed of vehicles is so fast that the channel condition between the terminals and the APs constantly changes because of changing path-loss and time-varying fading. In this paper, to compensate for such deterioration, we propose to reduce relative speed between user terminals and APs by using an inter-vehicle packet relay technique. If a terminal can send or receive data via other vehicles running at lower speeds, the relative speed will decrease, which suppresses the dynamic range of path loss and deterioration by fading. We evaluate our method by computer simulations using a geometric propagation model. In the simulations, phase difference between multiple paths and path-loss fluctuation within one frame duration affect the performance. From the results of the simulations, we validate our method. Moreover, we evaluate interference in the overlapped zone between two AP areas. From the evaluation, we show that our packet relays do not cause a problem in interference between areas.

Users in peer-to-peer (P2P) content sharing can share their content by contributing their own resources to one another. However, since there is no incentive for contributing contents or resources to others, users may attempt to obtain content without any contribution. To motivate users to contribute their resources to the service, incentive-rewarding mechanisms have been proposed. On the other hand, emerging wireless technologies, such as IEEE 802.11 wireless local area networks, beyond third generation (B3G) cellular networks and mobile WiMAX, provide high-speed Internet access for wireless users. Using these high-speed wireless access, wireless users can use P2P services and share their content with other wireless users and with fixed users. However, this diversification of access networks makes it difficult to appropriately assign rewards to each user according to their contributions. This is because the cost necessary for contribution is different in different access networks. In this paper, we propose a novel incentive-rewarding mechanism called EMOTIVER that can assign rewards to users appropriately. The proposed mechanism uses an external evaluator and interactive learning agents. We also investigate a way of appropriately controlling rewards based on the system service's quality and managing policy.

When the number of users of mobile services increases past a certain point in a service area, all of users cannot consistently obtain satisfactory radio resources such as bandwidth and signal power because the resources are limited and shared. A solution for such a problem is user-position control. In the user-position control, the operator informs users of better communication areas (or spots) and navigates them to these positions. However, because of subjective costs caused by subjects moving from their original to a new position, they do not always attempt to move. To motivate users to contribute their resources in network services that require resource contributions for users, incentive-rewarding mechanisms have been proposed. However, there are no mechanisms that distribute rewards appropriately according to subjective factors involving users. Furthermore, since the conventional mechanisms limit how rewards are paid, they are applicable only for the network service they targeted. In this paper, we propose a novel incentive-rewarding mechanism to solve these problems, using an external evaluator and interactive learning agents. We also investigated ways of distributing rewards based on user states and user contributions. We applied the proposed mechanism and reward control to the user-position control, and demonstrated its validity.

The increase in users of wireless local area networks (WLAN) has raised the need for bandwidth management that prevents bandwidth monopolization and that meets users' requirements. In this paper, we propose and design a distributed channel access mechanism for bandwidth management in WLANs. Our mechanism can flexibly assign bandwidth based on a theoretical estimation, even in poor network conditions, where hidden stations exist and the physical transmission rates vary. The usefulness of our mechanism is validated through evaluations using computer simulations.

The demand for Internet access in vehicles has been growing. To access the Internet from vehicles, a wireless road- vehicle communication system is necessary. In conventional road-vehicle communication systems, user terminals in the vehicles have to directly connect to wireless access points (APs). However, the speed of vehicles is so fast that the channel condition between the terminals and the APs constantly changes due to changing path-loss and time-varying fading. In this paper, we used an inter-vehicle packet relay technique to improve channel quality in road-vehicle communication systems. If a terminal can send data via other vehicles moving at lower speed, the relative speed will decrease, which suppresses the dynamic range of path loss and deterioration by fading. We evaluated this method using numerical analysis to verify the availability of the method.

To satisfy the recent increase in demand for uplink data in code division multiple access systems, opportunistic scheduling methods for uplinks have been investigated. In this paper, we discuss an optimum scheduling for voice/data packet systems. When it is implemented in practical systems, limitations such as selectable SF, computation and control costs will reduce the performance. We evaluated three approaches to apply the optimum scheduling to practical systems and compared them to our proposed heuristic scheduling, where minimum computation and control costs are required. The computer simulation results showed that our proposed method is reasonable and the optimum scheduling provides high throughput by expanding the computation and control interval while reducing the costs

Cooperation between different mobile networks linked by multiple-interface mobile stations (MMSs) can yield many benefits, including coverage expansion, load balancing, and throughput improvement. We developed a network control mechanism, cooperative networking, which controls the cooperative benefits in heterogeneous infra-multihop networks. The proposed mechanism assigns a cooperation rule to MSs. By following the rule, every MS chooses a path to a base station, such as direct connection to 3G networks or infra-multihop connection to wireless local area networks. Cooperation rules are designed according to the cooperative benefits. In our proposed cooperative networking mechanism, network operators can adaptively select a networking policy appropriate for network conditions and the needs of users. Computer simulation results validated our proposed mechanism