The Sixth-generation mobile communication system is expected to serve as a foundation for supporting large-scale connections of IoT devices across various fields, such as smart cities, healthcare, and transportation. Meanwhile, control plane congestion caused by the numerous connection management (CM) procedures for user plane release/re-establishment has emerged as a significant challenge. This paper proposes a novel CM mechanism that loosens the structural coupling between the UE and the U-plane.

Extreme Edge Computing (EEC) leverages nearby resources, such as smartphones and smart IoT devices, to offer reduced latency and enhanced privacy through local data processing. However, the variety of devices and unstable network connections present significant challenges. WebAssembly enables a web-based container platform suitable for EEC; however, it is challenging to host applications continuously on a single node. This paper presents a lightweight solution for migrating Wasm workloads between heterogeneous devices on the platform. Our approach utilizes the Asyncify mechanism to access and preserve the access-restricted CPU and stack states for immediate transfers without altering the web browser or Wasm runtime. Our evaluation shows migration times under 5 milliseconds, an improvement of over 80 times compared to the existing Linux container system, with minimal performance degradation of just 0.4%.

Reliable Moon-Earth communication is essential for long-term human presence on the Moon. Ensuring reliable communication requires careful consideration of various factors, including the choice of communication protocols. For high-latency deep-space links, the Bundle Protocol (BP) is designed to handle disruptions. At the same time, the Artemis program envisions IP-based wireless technologies, such as Wi-Fi and mobile networks, on the lunar surface. However, since these two approaches rely on different protocol stacks, a gateway is needed to bridge IP and BP, allowing end devices to communicate seamlessly without BP awareness. This paper presents a gateway bridging IP and BP networks and evaluates three implementations based on their termination layer. Through emulated Moon-Earth communication experiments, we show that application performance varies significantly with termination layer selection, highlighting the need for mission-specific gateway design.

Sixth-generation (6G) mobile communication systems have been studied to deal with the exponential increase in mobile traffic. Improving the processing load and power consumption will be a challenge for the 6G Radio Access Network (RAN). Simultaneously, RAN virtualization, where the RAN functions are implemented in commodity servers as software, has been developed and commercialized from the viewpoint of reducing cost, scalability, and simplifying operations. Central Processing Units (CPUs) generally execute the virtualized RAN functions. In contrast, hardware offload schemes have been considered, where the implemented GPUs (Graphics Processing Units) and FPGAs (Field Programmable Gate Arrays) run a part of the functions instead of CPUs according to the characteristics of each hardware. For instance, GPU can perform parallel processing faster and more efficiently than CPU, while FPGA can perform routine operations excellently. In addition to these proper selections of execution hardware for each RAN function, we propose an architecture that switches with or without hardware offloading depending on the traffic loads to decrease power consumption. Through experimental evaluation, we measured the energy efficiency of the offloading for each RAN function and overall 5G RAN. Finally, we discuss the threshold of dynamic offloading and estimate the power consumption of a 5G base station from statistical data.

Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellite, independently handling DNN tasks generated by diverse user equipments (UEs) becomes a significant challenge. One viable solution is dividing a DNN task into multiple subtasks and subsequently distributing them across multiple satellites for collaborative computing. However, it is challenging to partition DNN appropriately and allocate subtasks into suitable satellites while ensuring load balancing. To this end, we propose a collaborative satellite computing system designed to improve task processing efficiency in satellite networks. Based on this system, a workload-balanced adaptive task splitting scheme is developed to equitably distribute the workload of DNN slices for collaborative inference, consequently enhancing the utilization of satellite computing resources. Additionally, a self-adaptive task offloading scheme based on a genetic algorithm (GA) is introduced to determine optimal offloading decisions within dynamic network environments. The numerical results illustrate that our proposal can outperform comparable methods in terms of task completion rate, delay, and resource utilization.

IoT devices are now used in many different fields and applications, and they generate a large amount of traffic when sending sensor data to the cloud for analysis. By offloading these computing tasks to edge computing, the network traffic on the core network and latency can be reduced. However, the resources at the edge are limited, making it difficult to respond to the rapidly expanding number of IoT devices. To solve the problem, extreme edge computing, which utilizes the computing resources of end devices for task offloading, is emerging. The conventional task execution methods in edge computing are unsuitable for extreme edge computing because of the nonuniformity of end devices and connectivity. To meet the requirements of extreme edge computing, we propose a novel WebAssembly container system that is portable, lightweight, and manageable. Our proposed system has two types of container engines working as native applications and Web applications. This enables the execution of general-purpose computing tasks on various devices with different CPU architectures and OSs. We evaluate our prototype implementation working on these devices to confirm the benefits and effects of our proposed system for extreme edge computing.

Large-scale failures in mobile networks cause congestion due to User Equipment (UE) reconnection. Though the 3rd Generation Partnership Project (3GPP) defines congestion control functions in Network Functions (NFs), such as the Access and Mobility Management Function (AMF), the control parameters are expected to be calculated in AMFs with depleted computational resources under congestion conditions. This paper proposes a congestion control scheme based on coordinating the Radio Access Network (RAN) and core networks. In this scheme, the orchestration platform calculates the traffic reduction rate at the base stations based on the statistics of RAN and core networks instead of the AMF during congestion. Simulation results show that the proposed method increases the time to convergence by only 8.9 percent compared to the theoretical limit. The study also discusses the issues to be considered in the 3GPP and O-RAN specifications for implementing the proposed method.

This paper proposes an architecture and route control scheme of the layer 1 repeater-based IAB system, which can expand a high-frequency radio access area with high-quality communication service. Although repeaters just amplify and retransmit radio signals, the proposed system enables building a multi-hop network using beamforming and scanning. It introduces an external route control server to calculate the route tree under the limited hop count requirement and minimize the total route tree cost. This paper also shows its effectiveness through simulations using the NetworkX package.

In Named Data Networking (NDN), consumers can retrieve content from anywhere in the network, including from the server and routers' cache. Therefore, the Round-Trip Time (RTT) of packets can vary when downloading content in NDN compared to TCP, which establishes a session between a client and a specific server. The current transport layer protocol of NDN is mainly designed with reference to TCP. Its timer-based loss detection is not well designed for the nature of NDN, where the RTT of each packet can vary significantly, and leads to false loss detection. In this paper, we attempt to revisit the loss detection mechanism in NDN. We clarify the potential performance degradation of NDN due to the spurious timeouts and propose an NDN-PTO mechanism that improves the accuracy of loss detection by identifying spurious timeouts with a probe Interest. We evaluate the performance of NDN-PTO through a comprehensive simulation and describe its benefits.

This paper proposes an IP forwarding information base (FIB) encoding leveraging an emerging data structure called a learned index , which uses machine learning to associate key-position pairs in a key-value store. A learned index for FIB lookups is expected to yield a more compact representation and faster lookups compared to existing FIBs based on tries or hash tables, at the cost of efficient FIB updates, which is difficult to support with a learned index. We optimize our implementation for lookup speed, exploiting that for efficient FIB lookups it is enough to approximate the key-position pairs with a piece-wise linear function, instead of having to learn the key-position pairs. The experiments using real BGP routing information snapshots suggest that the size of the proposed FIB is compact and lookup speed is sufficiently fast regardless of the length of matched prefixes.

The emergence of fog computing has brought unprecedented opportunities to the Internet-of-Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this article, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality, and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.

With the rapid development of IoT (Internet of Things) technology, numerous sensors are being deployed to the smart society with the integration of IoT services. Since the collected sensor data reflect the current status of the surrounding environment, determining accurate positions for sensors is crucial from the stage of setting the sensors to realize meaningful sensor data analysis. In this paper, we propose LESAR, a localization system for environmental sensing using augmented reality. LESAR uses a smartphone camera with the AR (Augmented Reality) function to measure the distances between sensors, while the ID of each sensor is identified simultaneously by analyzing the collected Bluetooth signals. The vision-based approach used can enable three-dimensional localization through the simple use of a smartphone.

Named Data Networking (NDN) is a promising future Internet architecture which can overcome challenges in Multi-Access Edge Computing (MEC). In this paper, we focus on the forwarding strategy which is an essential part for realizing an NDN-based MEC architecture. Through extensive experiments on a testbed, we evaluate each default strategy in NDN with regard to three criteria: reliability, throughput, and overhead. We point out pros and cons of each strategy, and find two missing features, in-network recovery and load balancing, for improving the performance of MEC communication. Finally, we design a forwarding strategy with the above features which achieves higher throughput and reliability for end users than existing strategies while also reducing network congestion.

Motivated by ubiquitous surveillance cameras in a smart city, a monitoring service can be provided to citizens. However, the rise of privacy concerns may disrupt this advanced service. Yet, the existing cloud-based services have not clearly proven that they can preserve Wth-privacy in which the relationship of three types of information, i.e., who requests the service, what the target is and where the camera is, does not leak. We address this problem by proposing a content-centric privacy platform (C2P2) that enables the construction of a Wth-privacy-preserving monitoring service without cloud dependency. C2P2 uses an image classification model of a target serving as the key to access the monitoring service specific to the target. In C2P2, communication is based on information-centric networking (ICN) that enables privacy preservation to be centered on the content itself rather than relying on a centralized system. Moreover, to preserve the privacy of bystanders, C2P2 separates the sensitive information (e.g., human faces) from the non-sensitive information (e.g., image background), while the privacy-aware forwarding strategies in C2P2 enable data aggregation and prevent privacy leakage resulting from false positive of image recognition. We evaluate the privacy leakage of C2P2 compared to that of the cloud-based system. The privacy analysis shows that, compared to the cloud-based system, C2P2 achieves a lower privacy loss ratio while reducing the communication cost significantly.

A global content distribution is one of the important roles of the Internet. Information-Centric Networking (ICN) is a future internet architecture which attempts to redesign the Internet with a focus on content distribution. However, it has the potential performance degradation in the global content distribution. In this paper, we propose an ICN performance enhancing proxy (ICN-PEP) to mitigate this performance degradation. The key idea is to prefetch Data packets and to serve them to the consumer with the shortened round trip time. By utilizing ICN features, it is developed as offline and state-less proxy which has an advantage of scalability. We evaluate the performance of ICN-PEP in both simulation and real network and show that ICN-PEP improves the performance of global content distribution.

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.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

In this paper, we reveal the potential performance degradation in the global content distribution over Information-Centric Networking (ICN), and propose an ICN performance enhancing proxy (PEP) to mitigate this degradation. ICN follows a pull-based communication model, and congestion control is done by the consumer. Due to the fact that the consumer increases its congestion window only with the reception of data packets, it requires some round-trip time (RTT) to send large amount of interests. In the global network, which has a large RTT values, consumer's congestion window is increased slowly, and this leads to low throughput. Here we propose the ICN-PEP that can accelerate the growth of the consumer's congestion window by prefetching future data packets. We evaluated the ICN-PEP performance from the video streaming over the global ICN testbed, and our ICN-PEP could reduce the startup time of video streaming.

Information Centric Networking (ICN), the security, privacy and access control mechanisms are deployed directly in the network layer. However, the gap between security in the network and application layers creates a vulnerable space for cyber attacks from inside a device. We adopt a proxy reencryption technique to facilitate a flexible ICN access control specific to applications. Unlike the existing work in which the proxy re-encryption is done in the network, we propose an In-device Proxy Re-Encryption Service (IPRES) for filling this security gap and for efficiency in both access control management and resource usage. We present the architecture and protocol of IPRES incorporating principles from security management specific to each application. Our evaluation shows that IPRES improves the state of the art by closing the security gap while it introduces acceptable operational and memory overheads at the device level.

Fog computing is an emerging architecture for bringing processing, storage, and control from the Cloud closer to the Things/Users. Fog has mostly been studied in the vertical continuum between the Things/Users and the Cloud to provide resources traditionally existing in the remote Cloud to the applications. This paper introduces ICN-Fog, a novel horizontal Fog-to-Fog layer enabled by Information-Centric Networking. ICN-Fog enriches applications with horizontal data transfer in the Fog layer, distributed processing among Fog nodes, and built-in mobility support thanks to the smart connectionless name-based Fog-to-Fog data communications. We explain the rationale behind our design and demonstrate the advantages of the proposed Fog architecture through two representative case studies.

Crowdsourcing in cellular networks, wherein smartphone users upload sensing data pieces using their smartphones to a service provider, has become popular. However, a shortage of cellular uplink frequency resources prevents a service provider from collecting large data with reasonable costs. Incorporating collaborative upload, wherein representatives upload their sensing data pieces as well as those of others, is a promising solution. A key to make a business based on such crowdsourcing, which we call collaborative crowdsourcing, successful is how to motivate smartphone users to become representatives. In this paper, we propose a novel rewarding scheme for the users to advocate collaborative crowdsourcing.

Many video streaming services employ the dynamic adaptive streaming over HTTP (DASH) technique to achieve better user's QoE. Named-data Networking (NDN) can bring many benefits to the video distribution with in-network cache and stateful forwarding. However, simply applying DASH technique to NDN causes problems. First, in-network cache may affect download time which makes bitrate adaptation a difficult job. Second, various representations of the same video segments can consume cache storage and make caching less efficient. To overcome these problems, cache-friendly bitrate adaptation was proposed in [1] which adopts the bitrate based on the congestion feedback from intermediate routers. In this demonstration, we show the congestion feedback-aware DASH video application. Our contributions are twofold: (1) we customize the NFD to input congestion feedback into a data packet, and (2) we implemented the NDN-plugin for VLC player whose adaptation logic is based on the congestion feedback, and thus it can follow the current network status.

Content-Centric Networking (CCN) is an attractive future Internet architecture that enables rapid data retrieval due to its native in-network caching mechanism. However, existing caching policies in CCN do not always perform effectively in terms of the user-centric performance that is typically measured by the content download time. This is because they have been designed to improve network-centric performances, such as cache hit ratio and hop count. In this paper, we focus on the Round-Trip Time (RTT) computed at routers, and present two caching policies based on the RTT in order to achieve the high user centric performance. Our policies have the advantage that they can be deployed with no modification of the CCN packet structure in contrast to the existing ones. This paper demonstrates that our policies achieve much shorter download time than the existing ones over various network topologies, and provides deep insights on caching policies to improve the user-centric performance.

Since the radio frequency is a limited resource and mobile traffic is expected to continue increasing, traffic offloading to other networks is promising solution to accommodate large traffic. Especially, 60 GHz band has recently attracted attentions due to its high-speed communication. However, its short communication distance provides only a narrow coverage area. In order to overcome the disadvantage of its narrow coverage area, this paper presents a novel communication scheme for content delivery. Our demonstration shows that a user can use both 60 GHz and lower frequency bands seamlessly and unconsciously.

Information-centric networking (ICN) can bring many benefits to network operators, e.g., in-network request resolution via router's cache, and name-based forwarding. However, creating a smooth migration path from the current IP network to the ICN is a challenging task that must be investigated. Network function virtualization (NFV) is one of the key technologies to achieve this migration because of its flexibility in supporting new network services as software. Nevertheless, when we consider the deployment of ICN services over NFV, two performance issues are found: processing delay of name look-up and computational overhead of the virtual machine. In this paper we propose NFV infrastructure-assisted name-based forwarding by integrating the name look-up into the Open vSwitch. The contributions are twofold: 1) First, we provide a novel look-up scheme that can forward ICN packets without costly longest prefix match searching. 2) Second, we integrate this forwarding scheme into the virtualization infrastructure to mitigate computational overhead.

In the aftermath of natural disasters (e.g., earthquakes and hurricanes), information dissemination to and from citizens and authorities often involves the use of mobile network services. They may do so to seek help, distribute critical information, and possibly to confirm the safety of relatives and friends. The traffic on the cellular network typically increases substantially in the aftermath of such disasters. The situation is further compounded by disruptions caused by damage to the network infrastructure and the non-availability of power. Networks are fragmented, and communication is intermittent and disruption prone. In this paper, we propose a name-based many-to-many communication service for such fragmented networks. The key feature of this service is an integration of several communication methods, such as push-based and pull-based. The service effectively supports a variety of network situations by leveraging the features of information-centric networks.

Interest aggregation is one effective technique used in content-centric networking to speed up content delivery and reduce load on the network and the content sources. Interests, i.e. requests, for one content chunk are grouped together so that only the first Interest is forwarded toward the content source, and consequently, only one reply, i.e. Data, is responded for the whole Interest group. Ordinary Interest aggregation, however, is limited to Interests for the same content chunk which means Interests for a content should closely synchronize in time to be able to aggregate. In this paper, using network coding, we remove such limitation: An Interest for an encoded chunk of a content can be aggregated with a previous Interest for virtually any other encoded chunk of that content which contains new information. The crucial point is how to quickly verify the novelty of information contained in an encoded chunk. Our proposed network coding-based Interest aggregation is lightweight and effectively increases the number of Interests which can be aggregated. As a result, it reduces content delivery time as well as the load on network and content sources compared with the ordinary Interest aggregation without network coding. Simulation results confirm the effectiveness of our proposed method.

In the latest version of content-centric networking (CCN 1.0), a number of interests are issued when a large content is retrieved, and this could be a serious problem due to routers' heavy workload. In order to solve this problem, this paper proposes a novel strategy of "grouping" multiple interests with the common information and "packing" them to a special interest called the list interest. Our list interest is designed to cooperate with the manifest of CCN 1.0. This paper demonstrates that by using the common information in the list interest, the router can search its FIB/PIT/CS for the list interest-based request with dramatically smaller complexity than the case of the standard interest-based request. Furthermore, we also give a novel TCP-like congestion control method for list interests.

In the research of content-centric networking (CCN), there have been investigated several fragmentation methods of CCN messages in order to implement CCN as an L3 protocol. The latest version of the CCN protocol (CCN 1.0) adopts the end-to-end fragmentation method that fragments CCN messages only at the content publisher. This allows intermediate routers to simply forward fragmentation packets, and hence this can solve the problem of the processing delay at routers in the existing fragmentation methods for the CCN. However, the end-to-end fragmentation has not been well-analyzed and well-evaluated yet. In this paper, we analyze the end-to-end fragmentation and clarify its important parameter for the efficient transmission. Moreover, we evaluate fragmentation methods for the cache-hit ratio and the header overhead, and we clarify advantages and disadvantages of the methods in terms of the transmission efficiency.

In this paper, we propose a novel incentive mechanism for message delivery services based on delay tolerant networks (DTNs). In recent years, the amount of mobile data traffic has grown rapidly due to the increase in global smartphone shipments. Some research has focused on offloading the mobile data traffic through other types of networks, such as DTNs, Wi-Fi access points, and femtocells. From the viewpoint of mobile operators, smartphone users need to be involved in order to reduce the amount of mobile data traffic via 3G/LTE networks. As a way of supporting the delivery of messages by providing surplus resources from users' smartphones DTNs look like a promising solution. However, in a DTN-based message delivery service, smartphone users who do not want to obtain the messages may also join such a store-carry-forward scheme. Therefore, it follows that smartphone users need to be motivated to transmit the messages to other users. In this paper, we propose a reward-based incentive mechanism for such a service, where the mobile operator announces a total reward to be shared among smartphone users, and smartphone users provide surplus resources for disseminating messages to other users. Consequently, we show how a win-win relationship between mobile operator and smartphone users can be created with our incentive mechanism.

The Locator/ID Separation Protocol (LISP) is being standardized in IETF, which separates the IP address functionality into routing locators (RLOC) and endpoint identifiers (EID). LISP is applicable for instant Virtual Private Network (VPN) services because it has IP tunneling features. However, there are two main issues with application to VPN. One is a Map server/Map resolver (MS/MR) basically allows any third party to know a mapping table even if it is irrelevant to the VPN. The other is an MS/MR should identify the VPN to which the requested site belongs because the requested EIDs can be private addresses and may overlap with other VPN sites. A simple solution is to deploy an independent MS/MR for every VPN. However, this is undesirable from a cost perspective. We present a new LISP-MS/MR that provides a large number of VPN services using a single set. This MS/MR creates a logically separated EID-to-RLOC mapping table for each VPN and selects an appropriate type according to the requesting VPN site. To ensure this, our scheme uses Authentication data included in the Map register for an initial VPN identification of the registering site and associates the identified VPN ID with the registered RLOC. In following Map requests, the appropriate mapping table for the requesting VPN site is selected based on its RLOC. We confirmed the basic network functions of VPN using the prototype system. The results of scalability tests show that our MS/MR completes the transaction for nearly-simultaneous 10,000 Map registers or requests within three seconds.

We propose a scheme for application service providers that will allow them to provide a secure and traceable online image sharing service. To this end, a service provider needs to achieve security against unauthorized read access and traceability in the event of unauthorized write access. Digital watermarking is a promising approach for achieving traceability. However, if an image is encrypted using a cryptographic key shared between authorized users for security, it is difficult for the service provider to flexibly embed a digital watermark into the encrypted image. To overcome this encryption limitation, we propose a novel solution based on the secure multiparty computation (SMC) protocol and secret sharing scheme (SSS). Our goal is to design functions that prevent unauthorized read access, and allow the image copy path to be traced back to the user who performs unauthorized write access. The preliminary analysis of the security for our solution shows that it is significantly more secure than the straightforward operation, especially when images are shared among many users. We also implement our solution and evaluate its experimental performance. Consequently, we show that the latency overhead of our solution is reasonably small (i.e., sub-100 milliseconds) in practice.

This paper proposes a new type of range-free localization method based on affine transformation. Nodes extract subgraphs with a grid topology from a sensor network and assign x-y coordinates to themselves in a decentralized manner. The nodes estimate their positions using an affine transformation based on the mapping of the physical positions and the x-y coordinates of three anchors in an extracted graph. In contrast with multilateration-based localization methods, the proposed method works well even in a non-convex hull deployment, such as a terrain with big regions without sensors. We provide a theoretical analysis and simulation results. We also present a strategy for minimizing the position estimation error and maximizing the coverage of the proposed method. In the simulation results, the position estimation error is 0.18 (normalized by the radio communication range) and the coverage is almost 100% in a non-convex hull deployment.

The importance of context-aware services has grown significantly and it is hoped to provide these services to mobile cellular phones. In this paper, we propose a platform that enables the provision of location-aware services to mobile phones. The number of mobile phones is huge and people mobile phones are always on the move. Thus the platform constructs an overlay network between clients for the mitigation server load and introduces the concept of peers moving to existing P2P network architecture. From a simulation analysis, we evaluate the effectiveness of the proposed platform and present the optimized policies for the overlay network.

The growth of the Internet has facilitated data collection on a large scale. In Internet research using questionnaires, privacy is an important issue. To ensure privacy, we propose an answering method using probability conversion for Internet research. In this paper, we develop noval evaluated value, anonymity. And from the evaluation of two conversion functions, the applicable scope of the proposed method is clarified.

This paper proposes a novel approach for improving channel zapping delay on IPTV broadcasting services. Channel zapping delay is a crucial issue for digital TV broadcasting, which entails audio/video data buffering before reproduction. Such delay could be mitigated in an IP environment if a receiver accelerated this buffering using additional burst transmissions from dedicated servers when a channel zap occurs. However, most conventional solutions are based on the unicast burst, which may cause an impulsive server/network load because channel zaps tend to happen simultaneously when programs are finished or suspended by commercial messages. To reduce this load, we propose a new multicast-based solution that takes into consideration the timing variation in channel zap on each receiver. We have confirmed a maximum 1-second reduction in the zapping delay using commercial multicast streams.

Network tomography enables network operators to detect and mitigate congestion by inferring internal states from multiple end-to-end active performance measurements. In previous studies, network tomography was applied to either small networks, e.g., a network which allows measurements in a full-mesh manner, or a reasonably large network, e.g., overlay-network with 100 end nodes. In contrast, our study was conducted to develop a large-scale tomography-based measurement system that scales with the nation-wide Internet. In this paper, we propose a novel architecture which consists of a probability measurement path selection method and a distributed database building method. The architecture provides network operators with look up services for measurement results so that they can achieve network-tomography applications.

As a result of widespread of peer-to-peer (P2P) file sharing applications, it is a serious problem that inter-domain traffic between Internet service provider (ISP) networks increases. We present a novel inter-domain traffic flow model focusing on the presence of a passive peer, and proposes a new P2P traffic control method based on the model. This method uses a filter considering P2P flow characteristics and can be applied to the P2P networks whose protocol is closed. We also evaluate both validity and effectiveness of our proposals by performing more than 20 days experiments using real P2P network environment. The results prove that our model is reasonable enough and the proposed method is promising for decreasing inter-domain P2P traffic.

High-speed links such as OC-48c and OC-192c links need to be used as backbone links of the Internet in order to accommodate rapidly increasing Internet traffic. Network experiments over a test bed are important to check new services and new applications perform well over high-speed Internet backbones. In order to obtain the correct experiment results, it is important to use suitable background traffic at the network experiments. The existing traffic testers use MMPP (Markov modulated Poisson process) and MAP (Markov arrival process) as traffic models to generate background traffic. However, MMPP and MAP are so simplistic that the network experiments may produce misleading results. In order to solve the problem, we have developed an OC-48c traffic tester for generating and analyzing long-range dependence traffic because the traffic model is considered one of the most realistic traffic models. The traffic tester achieves the OC-48 rate traffic generation using hardware logic, and enables the long-range dependence traffic generation using time series data of packet numbers or byte numbers.