The spread of the Internet of Things (IoT) has led to the generation of large amounts of data, requiring massive communication, computing, and storage resources. Cloud computing plays an important role in realizing most IoT applications classified as massive machine type communication and cyber-physical control applications in vertical domains. To handle the increasing amount of IoT data, it is important to reduce the traffic concentrated in the cloud by distributing the computing and storage resources to the network edge side and to suppress the latency of the IoT applications. In this paper, we first present a recent literature review on fog/edge computing and data aggregation as representative traffic reduction technologies for efficiently utilizing communication, computing, and storage resources in IoT systems, and then focus on data aggregation control minimizing the latency in an IoT gateway. We then present a unified modeling for statistical and nonstatistical data aggregation and analyze its latency. We analytically derive the Laplace-Stieltjes transform and average of the stationary distribution of the latency and approximate the average latency; we subsequently apply it to an adaptive aggregation number control for the time-variant data arrival. The transient traffic characteristics, that is, the absorption of traffic fluctuations realizing a stable optimal latency, were clarified through a simulation with a time-variant Poisson input and non-Poisson inputs, such as a Beta input, which is a typical IoT traffic model.

With the spread of the broadband Internet and high-performance devices, various services have become available anytime, anywhere. As a result, attention is focused on the service quality and Quality of Experience (QoE) is emphasized as an evaluation index from the user's viewpoint. Since QoE is a subjective evaluation metric and deeply involved with user perception and expectation, quantitative and comparative research was difficult because the QoE study is still in its infancy. At present, after tremendous devoted efforts have contributed to this research area, a shape of the QoE management architecture has become clear. Moreover, not only for research but also for business, video streaming services are expected as a promising Internet service incorporating QoE. This paper reviews the present state of QoE studies with the above background and describes the future prospect of QoE. Firstly, the historical aspects of QoE is reviewed starting with QoS (Quality of Service). Secondly, a QoE management architecture is proposed in this paper, which consists of QoE measurement, QoE assessment, QoS-QoE mapping, QoE modeling, and QoE adaptation. Thirdly, QoE studies related with video streaming services are introduced, and finally individual QoE and physiology-based QoE measurement methodologies are explained as future prospect in the field of QoE studies.

Since the quality of video streaming services is degraded due to the encoding, network congestion, and lack of a playout buffer, the normality of services needs to be monitored by gathering the quality measured at the end clients. When measuring quality at the end clients, the computational power should be sufficiently low, the bitstream information cannot be accessed for the quality estimation due to the encryption, and reference video cannot be used at the end clients. Therefore, metadata-based models have been developed and standardized that take metadata such as the resolution, framerate, and bitrate, and stalling information as input and calculate the quality. However, calculated quality for linear TV and video on demand (VoD) services cannot be compared because metadata-based models cannot calculate the impacts of codec strategies (e.g., H.264/AVC, H.265/HEVC, and AV1) and configurations (e.g., 1-pass encoding for linear TV or 2-pass encoding for VoD) on the quality. To take into account the impact of codec strategies and configurations, coefficients of metadata-based model need to be optimized per codec strategy and configuration using subjective quality. However, extensive subjective assessment tests are difficult to frequently conduct because they are expensive and time consuming and need to be conducted by video quality experts. Therefore, to monitor the quality of several types of video streaming services (e.g., linear TV and VoD) and to compare these qualities, a derivation procedure is proposed for obtaining coefficients of metadata-based models using a full-reference model. To validate the procedure, extensive subjective assessment tests were conducted. Finally, it is shown that three metadata-based models (i.e., the P.1203.1 mode 0 model, extended P.1203.1 mode 0 model, and model proposed by Yamagishi et al.) based on the proposed procedure using the video multimethod assessment fusion (VMAF) algorithm estimate quality accurately in terms of root mean squared error.

In wireless links between ground stations and UAVs (Unmanned Aerial Vehicles), wireless signals may be attenuated by obstructions such as buildings. A three-dimensional RSS (Received Signal Strength) map (3D-RSS map), which represents a set of RSSs at various reception points in a three-dimensional area, is a promising geographical database that can be used to design reliable ground-to-air wireless links. The construction of a 3D-RSS map requires higher computational complexity, especially for a large 3D area. In order to sequentially estimate a 3D-RSS map from partial observations of RSS values in the 3D area, we propose a graph Laplacian-based sequential smooth estimator. In the proposed estimator, the 3D area is divided into voxels, and a UAV observes the RSS values at the voxels along a predetermined path. By considering the voxels as vertices in an undirected graph, a measurement graph is dynamically constructed using vertices from which recent observations were obtained and their neighboring vertices, and the 3D-RSS map is sequentially estimated by performing graph Laplacian regularized least square estimation.

This paper describes a novel 4K 120fps (frames per second) real-time HEVC (High Efficiency Video Coding) encoder for high-frame-rate video encoding and transmission. Motion portrayal problems such as motion blur and jerkiness may occur in video scenes containing fast-moving objects or quick camera panning. A high-frame-rate solves such problems and provides a more immersive viewing experience that can express even the fast-moving scenes without discomfort. It can also be used in remote operation for scenes with high motion, such as VAR (Video Assistant Referee) systems in sports. Real-time encoding of high-frame-rate videos with low latency and temporal scalability is required for providing such high-frame-rate video services. The proposed encoder achieves full 4K/120fps real-time encoding, which is twice the current 4K service frame rate of 60fps, by multichip configuration with two encoder LSI. Exchange of reference picture data near a spatially divided slice boundary provides cross-chip motion estimation, and maintains the coding efficiency. The encoder supports temporal-scalable coding mode, in which it output stream with temporal scalability transmitted over one or two transmission paths. The encoder also supports the other mode, low-delay coding mode, in which it achieves 21.8msec low-latency processing through motion vector restriction. Evaluation of the proposed encoder's multichip configuration shows that the BD-bitrate (the average rate of bitrate increase), compared to simple slice division without inter-chip transfer, is -2.86% at minimum and -2.41% on average in temporal-scalable coding mode. The proposed encoder system will open the door to the next generation of high-frame-rate UHDTV (ultra-high-definition television) services.

While online communities are important platforms for various social activities, many online communities fail to survive, which motivates researchers to investigate factors affecting the growth and survival of online communities. We comprehensively examine the effects of a wide variety of social network features on the growth and survival of communities in Reddit. We show that several social network features, including clique ratio, density, clustering coefficient, reciprocity and centralization, have significant effects on the survival of communities. In contrast, we also show that social network features examined in this paper only have weak effects on the growth of communities. Moreover, we conducted experiments predicting future growth and survival of online communities utilizing social network features as well as contents and activity features in the communities. The results show that prediction models utilizing social network features as well as contents and activity features achieve approximately 30% higher F₁ measure, which evaluates the prediction accuracy, than the models only using contents and activity features. In contrast, it is also shown that social network features are not effective for predicting the growth of communities.

On the Internet, there are lots of unused domain names that are not used for any actual services. Domain parking is a monetization mechanism for displaying online advertisements in such unused domain names. Some domain names used in cyber attacks are known to leverage domain parking services after the attack. However, the temporal relationships between domain parking services and malicious domain names have not been studied well. In this study, we investigated how malicious domain names using domain parking services change over time. We conducted a large-scale measurement study of more than 66.8 million domain names that have used domain parking services in the past 19 months. We reveal the existence of 3,964 domain names that have been malicious after using domain parking. We further identify what types of malicious activities (e.g., phishing and malware) such malicious domain names tend to be used for. We also reveal the existence of 3.02 million domain names that utilized multiple parking services simultaneously or while switching between them. Our study can contribute to the efficient analysis of malicious domain names using domain parking services.

This paper introduces a method to estimate latent traffic from its origin to destination from the link packet loss rate and traffic volume. In addition, we propose a method for the joint optimization of routing and link provisioning based on the estimated latent traffic. Observed traffic could deviate from the original traffic demand and become latent when the traffic passes through congested links because of changes in user behavioral and/or applications as a result of degraded quality of experience (QoE). The latent traffic is actualized by improving congested link capacity. When link provisioning is based on observed traffic, actual traffic might cause new congestion at other links. Thus, network providers need to estimate the origin-destination (OD) original traffic demand for network planning. Although the estimation of original traffic has been well studied, the estimation was only applicable for links. In this paper, we propose a method to estimate latent OD traffic by combining and expanding techniques. The method consists of three steps. The first step is to estimate the actual OD traffic and loss rate from the actual traffic and packet loss rate of the links. The second step is to estimate the latent traffic demand. Finally, using this estimated demand, the link capacity and routing matrix are optimized. We evaluate our method by simulation and confirm that congestion could be avoided by capacity provisioning based on estimated latent traffic, while provisioning based on observed traffic retains the congestion. The combined method can avoid congestion with an increment of 23% compared with capacity provisioning only. We also evaluated our method's adaptability, i.e., the ability to estimate the required parameter for the estimations using fewer given values, but values obtained in the environment.

Network service providers need to appropriately design systems and carefully configuring the settings and parameters to ensure that the systems keep running consistently and deliver the desired services. This can be a heavy and error-prone task. Intent-based system design methods have been developed to help with such tasks. These methods receive service-level requirements and generate service configurations to fulfill the given requirements. One such method is search-based system design, which can flexibly generate systems of various architectures. However, it has difficulty dealing with constraints on the quantitative parameters of systems, e.g., disk volume, RAM size, and QoS. To deal with practical cases, intent-based system design engines need to be able to handle quantitative parameters and constraints. In this work, we propose a new intent-based system design method based on search-based design that augments search states with quantitative constraints. Our method can generate a system that meets both functional and quantitative service requirements by combining a search-based design method with constraint checking. Experimental results show that our method can automatically generate a system that fulfills all given requirements within a reasonable computation time.

With the development and spread of Internet of Things technologies, various types of data for IoT applications can be generated anywhere and at any time. Among such data, there are data that depend heavily on generation time and location. We define these data as spatio-temporal data (STD). In previous studies, we proposed a STD retention system using vehicular networks to achieve the “Local production and consumption of STD” paradigm. The system can quickly provide STD for users within a specific location by retaining the STD within the area. However, this system does not take into account that each type of STD has different requirements for STD retention. In particular, the lifetime of STD and the diffusion time to the entire area directly influence the performance of STD retention. Therefore, we propose an efficient diffusion and elimination control method for retention based on the requirements of STD. The results of simulation evaluation demonstrated that the proposed method can satisfy the requirements of STD, while maintaining a high coverage rate in the area.

In network function virtualization (NFV) environments, service chaining is an emerging technology that enables network operators to provide network service dynamically and flexibly by using virtual network function (VNF). In the service chaining, a service chain is expected to be constructed based on VNF performances such as dependences among VNFs and traffic changing effects in VNFs. For achieving optimal data transmission services in NFV environments, we focus on the optimal service chain construction based on VNF performances so that both the maximum amount of traffic on links and the total number of VNF instances are decreased. In this paper, at first, an optimization problem is formulated for determining placements of VNFs and a route for each service chain. The service chains can be constructed by solving this optimization problem with an optimization software or meta-heuristic algorithm. Then, for the optimization problem, we propose a heuristic service chain construction algorithm. By using our proposed algorithm, the service chains can be constructed appropriately more quickly. We evaluate the performance of the proposed heuristic algorithm with simulation, and we investigate the effectiveness of the heuristic algorithm from the performance comparison. From some numerical examples, we show that the proposed heuristic algorithm is effective to decrease the amount of traffic and the number of VNF instances. Moreover, it is shown that our proposed heuristic algorithm can construct service chains quickly.

This paper reports the results of a new test on what types of failure cause falls in the stock prices of telecommunication service providers. This analysis of stock price is complementary to our previous one on market share. A clear result of our new test is that the type of failure causing falls in stock price is different from the type causing decline in market share. Specifically, the previous study identified frequent failures as causes of decline in market share, while the current study indicates large failures affecting many users as causes of falls in stock price. Together, these analyses give important information for reliability designs of telecommunications networks.

The increased performance of mobile terminals has made it feasible to collect data using users' terminals. By making the best use of the network performance data widely collected in this way, network operators should deeply understand the current network conditions, identify the performance-degraded components in the network, and estimate the degree of their performance degradation. For their demands, one powerful solution with such end-to-end data measured by users' terminals is network tomography. Meanwhile, with the advance of network virtualization by software-defined networking, routing is dynamically changed due to congestion or other factors, and each end-to-end measurement flow collected from users may pass through different paths between even the same origin-destination node pair. Therefore, it is difficult and costly to identify through which path each measurement flow has passed, so it is also difficult to naively apply conventional network tomography to such networks where the measurement paths cannot be uniquely determined. We propose a novel network tomography for the networks with undeterministic routing where the measurement flows pass through multiple paths in spite of the origin-destination node pair being the same. The basic idea of our method is to introduce routing probability in accordance with the aggregated information of measurement flows. We present two algorithms and evaluate their performances by comparing them with algorithms of conventional tomography using determined routing information. Moreover, we verify that the proposed algorithms are applicable to a more practical network.

Path loss in high frequency bands above 6GHz is the most fundamental and significant propagation characteristic of IMT-2020. To develop and evaluate such high frequency bands, ITU-R SG5 WP5D recently released channel models applicable up to 100GHz. The channel models include path loss models applicable to 0.5-100GHz. A path loss model is used for cell design and the evaluation of the radio technologies, which is the main purpose of WP5D. Prediction accuracy in various locations, Tx positions, frequency bands, and other parameters are significant in cell design. This article presents the prediction accuracy of UMa path loss models which are detailed in Report ITU-R M.2412 for IMT-2020. We also propose UMa_A' as an extension model of UMa_A. While UMa_A applies different equations to the bands below and above 6GHz to predict path loss, UMa_A' covers all bands by using the equations of UMa_A below 6GHz. By using the UMa_A' model, we can predict path loss by taking various parameters (such as BS antenna height) into account over a wide frequency range (0.5-100GHz). This is useful for considering the deployment of BS antennas at various positions with a wide frequency band. We verify model accuracy by extensive measurements in the frequency bands from 2 to 66GHz, distances up to 1600 m, and an UMa environment with three Tx antenna heights. The UMa_A' extension model can predict path loss with the low RMSE of about 7dB at 2-26.4GHz, which is more accurate than the UMa_A and UMa_B models. Although the applicability of the UMa_A' model at 66GHz is unclear and needs further verification, the evaluation results for 66GHz demonstrate that the antenna height may affect the prediction accuracy at 66GHz.

This paper presents the design and measurement of wideband multi-ring microstrip antennas fed by an L-probe for single- and dual-band operation. The proposed antennas consist of one or two square ring patches and an L-probe arranged in a multi-layered dielectric substrate. By using a thick substrate for the L-probe and optimizing the distances between the L-probe and the patches, wideband performance is successfully achieved. The optimal substrate thickness of the L-probe and patches to obtain good wideband performance were determined, and prototype antennas for single- and dual-band operation were fabricated and tested. The measured fractional bandwidths corresponding to reflection coefficients below -10dB were 46.1% for the single-band antenna and 20.3% and 15.7% for the dual-band antenna. The measured gains of the test antennas in the above bandwidths were 0-6.9dBi for the single-band antenna and 3.0-8.6dBi for the dual-band antenna. Although the E-plane radiation patterns were slightly tilted against the frequency, stable broadside radiation was confirmed. The proposed antennas exhibited excellent performance as wideband planar antennas for single- and dual-band operation. The proposed wideband antennas can be easily extended to a dual linearly polarized antenna by using another L-probe in the orthogonal position.

We propose two simple weight calculation methods (primary method and enhanced method), that estimate approximated phase plane from a few antenna phase and calculate weights of all antenna elements, for wireless backhaul systems that utilize millimeter wave band massive antenna arrays. Such systems are expected to be used instead of optical fiber for connecting many small cell base stations (SCBSs) to the core network, and supporting the rapid deployment of SCBSs. However, beamforming with massive antenna arrays requires many analog-digital converters (ADCs) and incurs the issue of implementation complexity. The proposed methods overcome the problem by reducing the number of ADCs. Computer simulations clarify the appropriate layout and the number of ADCs connected to antenna elements; the effectiveness of the proposed methods is confirmed by evaluations with measured channel state information (CSI) in propagation experiments on a wireless backhaul system. Experimental verifications on the case of calculating the weight of 200 elements from the phases of just 9 elements show that the array gain degradation from ideal (the case in which the phases of all elements are used estimation) with both methods is less than 0.4 dB in the direct wave dominant environment. In addition, the enhanced method holds the array gain degradation to under 0.8dB in an environment existing reflected waves. These results show that the proposed methods can attain high accuracy beamforming while reducing ADC number.

Wireless Mesh Networks (WMNs) are often designed on IEEE 802.11 standards and are being widely studied due to their adaptability in practical network scenarios, where the overall performance has been improved by the use of the Multi-Radio and Multi-Channel (MRMC) configuration. However, because of the limitation on the number of available orthogonal channels and radios on each router, the network still suffers from low throughput due to packet collisions. Many studies have demonstrated that the optimized channel assignment to radio interfaces so as to avoid interference among wireless links is an effective solution. However, no existing channel assignment scheme can achieve hidden-terminal-free transmission and thus avoid communication performance degradation given the limited number of orthogonal channels. In this paper, we propose a new static channel assignment scheme CASCA (CSMA-aware Static Channel Assignment) based on a Partial MAX-SAT formulation of the channel assignment problem that incorporates a CSMA-aware interference model. The evaluation results show that CASCA achieves hidden-terminal-freedom in both grid and random topology networks with 3-4 orthogonal channels with preservation of network connectivity. In addition, the network simulation results show that CASCA presents good communication performance with low MAC-layer collision rate.

Distributed detection techniques of multiple-input multiple-output (MIMO) spatially multiplexed signals are studied in this paper. This system considered employs multiple mobile stations (MSs) to receive signals from a base station, and then share their received signal waveforms with collaborating MSs. In order to reduce the amount of traffic over the collaborating wireless links, distributed detection techniques are proposed, in which multiple MSs are in charge of detection by making use of both the shared signal waveforms and its own received waveform. Selection combining schemes of detected bit sequences are studied to finalize the decisions. Residual error coefficients in iterative MIMO equalization and detection are utilized in this selection. The error-ratio performance is elucidated not only by computer simulations, but also by offline processing using experimental signals recorded in a measurement campaign.

This paper studies a harvested power-oriented simultaneous wireless information and power transfer (SWIPT) scheme over multiple-input multiple-output (MIMO) interference channels in which energy harvesting (EH) circuits exhibit nonlinearity. To maximize the power harvested by all receivers, we propose an algorithm to jointly optimize the transmit beamforming vectors, power splitting (PS) ratios and the receive decoding vectors. As all variables are coupled to some extent, the problem is non-convex and hard to solve. To deal with this non-convex problem, an iterative optimization method is proposed. When two variables are fixed, the third variable is optimized. Specifically, when the transmit beamforming vectors are optimized, the transferred objective function is the sum of several fractional functions. Non-linear sum-of-ratios programming is used to solve the transferred objective function. The convergence and advantage of our proposed scheme compared with traditional EH circuits are validated by simulation results.

In this paper, we propose a robust parameters estimation algorithm for channel coded systems based on the low-density parity-check (LDPC) code over fading channels with impulse noise. The estimated parameters are then used to generate bit log-likelihood ratios (LLRs) for a soft-inputLDPC decoder. The expectation-maximization (EM) algorithm is used to estimate the parameters, including the channel gain and the parameters of the Bernoulli-Gaussian (B-G) impulse noise model. The parameters can be estimated accurately and the average number of iterations of the proposed algorithm is acceptable. Simulation results show that over a wide range of impulse noise power, the proposed algorithm approaches the optimal performance under different Rician channel factors and even under Middleton class-A (M-CA) impulse noise models.

With the development of the IEEE 802.11 standard for wireless LANs, there has been an enormous increase in the usage of wireless LANs in factories, plants, and other industrial environments. In industrial applications, wireless LAN systems require high reliability for stable real-time communications. In this paper, we propose a multi-access-point (AP) diversity method that contributes to the realization of robust data transmissions toward realization of ultra-reliable low-latency communications (URLLC) in wireless LANs. The proposed method can obtain a diversity effect of multipaths with independent transmission errors and collisions without modification of the IEEE 802.11 standard or increasing overhead of communication resources. We evaluate the effects of the proposed method by numerical analysis, develop a prototype to demonstrate its feasibility, and perform experiments using the prototype in a factory wireless environment. These numerical evaluations and experiments show that the proposed method increases reliability and decreases transmission delay.