Efficient tactile communication will pave the way for future augmented reality services. Tactile coding solutions for vibrotactile signals have been proposed as one of the enabling technologies. However, due to the all-or-nothing behavior, such coding solutions cause destructive quality degradation, known as cliff and leveling-off effects, over unstable wireless channels. We propose a novel joint source-channel coding scheme for tactile communication. The proposed scheme transforms the tactile signals into frequency domain representations and transmits them to mitigate cliff and leveling-off effects. Moreover, the proposed scheme unequally scales each frequency domain representation based on human tactile perception to simultaneously realize error resilience against channel noise and maximize the quality of vibrotactile signals with respect to human tactile perception. Evaluations show that the proposed scheme mitigates the cliff and leveling-off effects even when the wireless channel quality fluctuates during vibrotactile communication.

This study aims to enhance the transmission efficiency of microwave power transfer, which has garnered significant attention as a method of wireless power supply for IoT devices and drones, by using Intelligent Reflecting Surface (IRS). IRS is a plate-shaped device that can control the reflection characteristics of radio waves, and it is expected to improve the transmission efficiency when placed between a wireless transmitter and a wireless receiver. However, existing methods of reflection phase optimization for IRS are predominantly based on sequential feedback or a small number of discrete reflection phases, both of which have problems, including feedback overhead and beam shape limitations. In this paper, we propose a novel reflection phase optimization method based on curve fitting. This approach is capable of continuous reflection phase control and optimization of the reflection phase of all reflective elements simultaneously. Through numerical simulation and actual experiments using an implemented IRS prototype, it was observed that the proposed method enhanced the received power by up to 4 dB compared to a conventional sequential optimization method. However, it was also observed that the range limitation of the reflection phase in the implemented IRS hindered the effectiveness of the proposed method.

Non-terrestrial networks (NTNs) contribute significantly to offering connectivity services for the upcoming sixth generation (6G). However, many services demand real-time uplink data transmission. Owing to the dynamic topology, instability of NTN channel status, resource constraints, and tremendous mobile users on ground networks; resilient and effective resource management and channel accessibility pose substantial challenges. This work investigates the uplink sum-rate maximization and latency minimization problems with a channel allocation scheme for user equipments (UEs) to high-altitude platforms (HAPs) of a particular NTN when UEs simultaneously transmit data to HAPs and HAPs forward it to low-earth orbit satellite (LEO-sat) via multiple subcarriers. We formulate a fairness resource allocation optimization problem reflecting the system utility and load of the HAP-LEO-sat link for aerial-to-space uplinks. To tackle the high-dimensional non-convex optimization problems and short visibility of LEO-sat in the learning, we advocate a novel deep reward-based multi-agent reinforcement learning (DeepRMARL) with prioritized experience replay (PER) and priority sampling approach (PSA). Furthermore, we present a long-short-term memory (LSTM) model for future environment channel prediction due to time-varying channels between UEs and HAPs. The results explicitly unveil the feasibility of our method in optimizing total uplink rate, achieving a higher fairness index, minimizing latency by approx. 12.2%, and predicting future environment states with approx. 92.8% accuracy.

Wireless sensing research is underway to generate 2D images and 2D videos corresponding to an object or space using the measured amplitude and phase changes during RF signal propagation. The obtained 2D images and 2D videos can be used for object recognition and distance measurement based on image processing techniques. However, 2D images only contain visual information about the sensing target from a specific viewpoint. This paper proposes Channel State Information to Point Cloud (CSI2PC) to enable the observation of a sensing target from multiple viewpoints. CSI2PC generates a 3D point cloud corresponding to the 3D structure of the sensing target from Channel State Information (CSI), which stores the variation of amplitude and phase. CSI2PC generates a 3D point cloud from the measured CSI using a neural network (NN) architecture based on Generative Adversarial Networks (GAN) and Graph Neural Networks (GNN). To ensure that the generated point clouds accurately represent the sensing target, the proposed scheme designs 1) a two-stage learning of the proposed NN architecture and 2) a loss function considering the 3D point cloud reconstruction. Experimental results using consumer Wi-Fi devices show that the proposed CSI2PC can reconstruct a clean point cloud from the measured CSI and accurately classify the object using the point cloud-based classification model.

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellite-based image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem’s nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.

This paper shares the challenges facing today's augmented reality (AR) smartphone applications, particularly in the realm of localization and tracking failure. Our research identifies limitations in current vision-based landmarks such as QR codes and AprilTags, commonly used to aid in localization, and the drawbacks of LiDAR integration in variable lighting conditions, compromising AR's accuracy and functionality. We also examine the constraints of Inertial Measurement Units (IMU) on movement speed, highlighting its impact on the dynamic performance of AR applications. Based on our extensive 316 experimental cases for 113 hours, including 34 case studies with 17 subjects in 2 sites, this paper presents the field with a nuanced analysis of the failure modes inherent in smartphone-based AR localization. We further explore a prototype solution which fuses ultra-wideband (UWB)-based sensing with the vision-based systems to alleviate these failure modes. Our approach addresses the immediate challenges of AR localization and opens avenues for future research and development in creating more spatially aware and interactive digital worlds. All of our demonstration videos, code, and datasets are available here1.

Intelligent reflecting surface (IRS) has emerged as a promising communication technology to increase throughput and facilitate communication in non-line-of-sight scenarios. While existing studies primarily focus on the application of IRS in communication tasks, its unique capability to precisely control radio frequency ($\mathbf{R F}$) signal reflections offers untapped potential for non-communication purposes. This paper ventures into an innovative domain and explores the application of IRS properties for image processing tasks conducted over the air. We introduce a novel scheme called “single antenna imaging”, a proof-of-concept scheme for over-the-air image processing. This approach leverages analog modulation and compressed sensing techniques to reconstruct high-quality images from a limited number of transmissions between multiple transmit antennas and a single receive antenna. Evaluations using the Kodak image dataset show that the proposed scheme can retrieve the same quality images with over 40% reduction in traffic compared to the simple minimum norm solution (MNS) and linear minimum mean square error (LMMSE) schemes.

Conventional point cloud delivery schemes employ tree-based or graph-based digital compression techniques to stream three-dimensional (3D) points and their associated attributes over wireless multiple-input multiple-output (MIMO) channels for 3D scene reconstruction. However, these digital-based delivery methods suffer from the cliff and leveling effects. Moreover, in MIMO communication environments, poor quality in some spatial layers, i.e., subchannels, can lead to point cloud quality degradation. We propose a novel point cloud delivery scheme that addresses both effects simultaneously. Our approach leverages a k-dimensional tree (k-d tree)-based graph Fourier transform (GFT) for energy compaction, a subchannel assignment mechanism to fully utilize subchannel diversity in MIMO links, and analog modulation with non-uniform power allocation for error-resilient and fluctuation-resilient transmission. Evaluation results demonstrate that the proposed scheme delivers superior 3D reconstruction quality compared to conventional digital-based point cloud delivery schemes, such as geometry-based point cloud compression (G-PCC), in noisy MIMO channels.

A long network delay leads to decreased player performance in online games and an unfair game experience between the players. In this study, we propose a novel delay compensation method to reduce the impact of network delay on player performance in online 3D shooting games. The key contributions of the proposed scheme are two-fold. The first contribution utilizes two deep reinforcement learning (DRL) networks, timing and direction DRL, to predict each player's sudden movement under the network delay. Specifically, the timing DRL outputs when the player will change the movement direction while the direction DRL outputs the indenting direction of the player. The second contribution of this study is to send each player's trained model to other players at the beginning of the game match. This research is the first to discuss how to transfer the trained model, as well as the effect of the model transmissions on the delay compensation performance in the band-limited paths. We evaluated the effectiveness of the proposed delay compensation using an online 3D shooting game implemented by Unity 3D. The obtained evaluation results indicate that the proposed delay compensation accurately reproduces the player's position, even under long network delays. In addition, the transmitted model can predict the player's position when the available rate for the model transmission is approximately 60 KB.

Viewport-based 360-degree video delivery is a typical method to reduce video traffic for virtual reality (VR) applications. However, viewport-based solutions cause some key issues in multi-user VR applications, including large video traffic volumes owing to redundant video transmission across multiple headset users and quality degradation owing to view-port transition. In this study, we propose a 360-degree video delivery scheme for multi-user VR applications. To overcome the abovementioned issues, the proposed approach includes JPEG-XS-based video recompression at the edge server to follow the viewport transition and hybrid unicast and multicast tile delivery to prevent redundant transmissions. Moreover, we present the results of evaluations using 360-degree video and corresponding fixation points of multiple users to show that the proposed scheme prevents redundant transmissions across multiple headset users and provides better video quality for each user than existing solutions for the same video traffic.

The evolution of a cell phone network and a wireless local area network (LAN) has enabled various devices to be connected to wireless networks anytime, anywhere. This has led to the emergence of various applications, such as automatic transportation of parts and cargo by automated guided vehicles, remote control of unmanned vehicles, and drones. It will be a challenge to maintain high-quality connections, which satisfy the requirements of such emerging applications, for a large number of intelligent connected devices. To improve the network performance, we must control the wireless network setting and the behavior of the connected vehicles, i.e., robots. From this perspective, this proposes a novel framework, CoRein, that achieves simultaneous optimization of robot behavior and wireless network setting. In contrast to the existing studies, CoRein provides optimal actions for robots and wireless networks using deep reinforcement learning (DRL) architecture. To the best of our knowledge, our study firstly attempts to solve different tasks corresponding to robots and networking with a unique DRL architecture. To evaluate the effectiveness of CoRein, we considered a scenario where two robots were moving back and forth while communicating with one of two access points. CoRein calculates the robot velocities and AP selection. We carried out simulations and indoor experiments with our robot testbeds to evaluate the network performances. Evaluation results confirmed that the two robots adjusted their positions and the access points to increase the wireless network's performance, i.e., throughput and round trip time while changing the speed of their movements.

A key challenge in holographic-type communication is transmitting point cloud signals to users across diverse channel quality and bandwidths. Digital based point cloud coding efficiently reduces point cloud traffic. In contrast, the quantization and entropy coding in digital-based schemes causes quality degradation owing to channel quality fluctuations and diversity. This study proposes a novel scheme for point cloud delivery over wireless channels. The proposed scheme consists of a graph auto-encoder (GAE) architecture to compress the point cloud into coded symbols and restore the point cloud from the received symbols. The proposed scheme addresses the quality degradation due to channel quality fluctuations and bandwidth diversity via, the following two steps. First, the coded symbols are directly mapped onto transmission symbols, analog modulation, to ensure that the point cloud quality follows the instantaneous channel quality of each user. Second, a non-uniform dropout is introduced to realize a rateless property in the GAE architecture for gradually improving the point cloud quality according to the available bandwidth. Evaluation results demonstrate that the proposed GAE architecture can yield better point cloud quality than digital-based and analog-based schemes, even when users have varying available bandwidths.

Sending tactile information over wireless channels will pave the way for the realization of immersive Extended Reality (XR) applications. Existing vibrotactile communication solutions have integrated traditional source and channel coding to provide high-quality vibrotactile information over band-limited and error-prone wireless channels. However, due to the bit-error vulnerability of source and channel coding and the unrecoverable distortion of source coding, the existing solutions suffer from catastrophic quality degradation. This paper proposes a novel tactile communication scheme inspired by semantic communication. The main contribution to the existing solutions is to replace the source and channel coding with the semantic vibrotactile coder and transceiver. Specifically, the semantic encoder compresses the vibrotactile information using a two-dimensional discrete cosine transform (2D-DCT) without binarization, and the transmitter directly maps the DCT coefficients to the transmission symbols with the optimized power allocation to avoid the catastrophic quality degradation due to the traditional source and channel coding and to maximize the vibrotactile quality under the wireless channel condition. In addition, we design an overhead-less semantic receiver to reduce the communication overhead required for the optimized power allocation. We implement a vibrotactile acquisition system for empirical evaluations, and the results showed that the proposed scheme provides better vibrotactile quality even in low-quality and high-quality wireless channels compared to the tactile communication schemes with the traditional source and channel coding.

Collaborative learning has been qualitatively analyzed by learning science researchers to enhance learning performance. A quantitative analysis system supports the existing qualitative analysis of collaborative learning. We propose an Internet of Things (IoT) system comprising business-card-type badges, radio-over-fiber (RoF)-based synchronization, and a collaboration analysis algorithm to support collaborative learning analytics in different venues (e.g., offline, online, and hybrid). We showed that our proposed system quantitatively supports qualitative analysis of collaborative learning in different environments.

Holographic-type communication, i.e., three-dimensional (3D) content delivery, will be a crucial application for modern wireless and mobile networks. In this paper, we propose a novel soft delivery scheme to realize efficient 3D content delivery. Specifically, the proposed scheme sends a single 2D image over error-prone wireless channels using discrete cosine transform followed by near-analog modulation. At the receiver, a 2D-to-3D decoder based on graph neural networks (GNN) reconstructs the corresponding 3D point cloud and mesh from the received 2D image. We verify that the proposed soft 2D-to-3D delivery scheme can reconstruct clean 3D data gracefully from the soft-delivered 2D image even in the presence of fading and noise distortion. In addition, the proposed scheme can generate higher-quality 3D data compared with direct 3D content delivery schemes.

Backscatter technology reduces power consumption during data transmission to 1/1000 that of existing wireless transmitters. However, backscatter communication faces two challenges related to medium access control (MAC). First, the carrier wave is not always supplied by an external device when the backscatter tag attempts to transmit data. In addition, if multiple backscatter tags communicate simultaneously, their outgoing frames collide with each other. This study proposes an asynchronous MAC protocol for backscatter tags without a receiving function. The proposed MAC protocol consists of a pseudo-synchronization system and a devised transmission interval between the carrier wave and each backscatter tag. The success rate of data transmission is improved by devising a carrier supply interval and backscatter tag operation. In addition, frame collisions decrease when using a pseudo-random number series to set the transmission interval for each backscatter tag. In a pseudo-synchronization system, the receiver feedbacks time information to the carrier transmitter when the receivers receive or detect a frame. The carrier transmitter adjusts the timing of the carrier supply to match the frame transmission from the backscatter tag, based on the information fed back. This improves the success rate of the communication without time synchronization. The simulation evaluation reveals that the proposed method improves the communication success rate by approximately 1.43 times in the assumed environment.

This study proposes mainly 2 different backscatter medium access control (MAC) protocols. Both proposed protocols work for half-duplex and full-duplex transmission modes in a bistatic backscatter system. The first one, namely express backscatter MAC (XBM), allows higher transmission rates in a bistatic backscatter network, aiming to increase network capacity. It takes advantage of carrier signals for faster transmissions whenever they are generated. The second protocol, reliable backscatter MAC (RBM), provides more reliability to the backscatter transmission, especially when hidden nodes exist. It brings more reliability by introducing a two-step CTS_to_Self mechanism to separate backscatter communication from Wi-Fi communication and avoid hidden node problem. In addition, our MAC protocols enable simultaneous backscatter transmissions at the MAC level by letting several tags carry their data on the same carrier signal. We evaluated and compared our proposals through numerical analyses that highlighted agreeable performance levels.

3D point cloud data formats are used to express three-dimensional (3D) information using numerous points in a 3D space. A key challenge is the delivery of high-quality 3D point cloud for the users under a diverse channel quality and available bandwidth to share the same 3D space across multiple untethered extended reality (XR) users. The existing digital-based schemes suffer from two issues owing to the diversity: cliff and leveling-off effects. This paper proposes a novel soft multicasting scheme of point cloud data for untethered XR users. The key ideas of the proposed scheme are three-fold: 1) integration of graph signal processing and analog modulation to adaptively improve the 3D reconstruction quality according to the channel quality for all individual XR users, 2) integration of Givens rotation and non-uniform adaptive quantization to reduce metadata overhead for the graph Fourier transform, and 3) prioritized transmission of the metadata to realize adaptive quality improvement based on the bandwidth available for each XR user. This paper reveals that the proposed scheme prevents cliff and leveling-off effects even when the XR users experience different channel qualities. Furthermore, the proposed transmission exhibits better 3D reconstruction quality compared with the state-of-the-art graph-based delivery scheme in band-limited environments.

In backscatter communications, data is transmitted with up to 1/1000 of the power consumption, instead of the typical several tens of milliwatts. However, if there are multiple nodes for backscatter communication in the same space, harmonics and sidebands limit the number of multiplexing nodes. Both are suppressed by switching the impedance in multiple steps or generating the signal with opposite phases; however, these methods increase the hardware cost of the backscatter tag. This paper proposes harmonics-controlled frequency division multiple access (HC-FDMA), which allows multiple access interference in backscatter communications without hardware modification. HC-FDMA consists of two modules: a modulation scheme and a channel selection algorithm. The modulation scheme controls the harmonics to an arbitrary channel using ∆Σ modulation. The channel selection algorithm optimizes the frequencies of the signal component, harmonics, and sidebands. Targeting IEEE 802.15.4-compatible backscatter, backscatter communication using HC-FDMA achieves 1.45 times multiplexing compared to the case without HC-FDMA. In addition, the analysis results characterize the capability of HC-FDMA.

Multicast-based multimedia streaming is expected to be a key application in the next generation of wireless and mobile networks. For such applications, we utilize hierarchical non-terrestrial networks (NTNs) consisting of low earth orbit (LEO) satellites, high-altitude platforms (HAPs), and user equipment (UE). However, existing multicast protocols for NTN cause a long delay and jitter owing to packet loss, thus being the cause of low-quality streaming services. We propose a reliable multi-cast protocol, namely, overhearing and non-orthogonal multiple access (NOMA) repairing reliable multicast (ONRM), for delay reduction. In ONRM, the HAP overhears packets from the LEO to UE and retransmits the lost packets using NOMA on behalf of the LEO to prevent delay increments. We carried out evaluations based on the release of Third Generation Partnership Project (3GPP) in terms of delay, throughput, and jitter. We prepared existing multicast protocols, pragmatic general multi-cast (PGM), reliable and efficient multicast protocol (REMP), and extended bit-indexed explicit replication (extended BIER) for comparison. Results show that ONRM outperforms other multicast protocols. Especially, the proposed ONRM can realize a low jitter and enables high-quality multimedia streaming over NTN.

Point cloud is an emerging data format useful for various applications such has holographic display, autonomous vehicle, and augmented reality. Conventionally, communications of point cloud data have relied on digital compression and digital modulation for three-dimensional (3D) data streaming. However, such digital-based delivery schemes have fundamental issues called cliff and leveling effects, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast+, to overcome cliff and leveling effects. Specifically, our method utilizes hybrid digital-analog coding, integrating digital compression and analog coding based on graph Fourier transform (GFT), to gracefully improve 3D reconstruction quality with the improvement of channel quality. We demonstrate that HoloCast+ offers better 3D reconstruction quality in terms of the symmetric mean square error (sMSE) by up to 18.3 dB and 10.5 dB, respectively, compared to conventional digital-based and analog-based delivery methods in wireless fading environments.

In typical point cloud delivery, a sender uses octree-based and graph-based digital video compression to send three-dimensional (3D) points and color attributes. However, the digital-based schemes have an issue called the cliff effect, where the 3D reconstruction quality will be a step function in terms of wireless channel quality. To prevent the cliff effect subject to channel quality fluctuation, we have proposed a wireless point cloud delivery called HoloCast inspired by soft delivery. Although the HoloCast realizes graceful quality improvement according to instantaneous wireless channel quality, it requires large communication overheads. In this paper, we propose a novel scheme for soft point cloud delivery to simultaneously realize better 3D reconstruction quality and lower communication overheads. The proposed scheme introduces an end-to-end deep learning framework based on graph neural network (GNN) to reconstruct high-quality point clouds from its distorted observation under wireless fading channels. We demonstrate that the proposed GNN-based scheme can reconstruct a clean 3D point cloud with low overheads by removing fading and noise effects.

In this paper, we present an experimental evaluation of the several phase optimization algorithms for distributed cooperative microwave power transfer. The proposed evaluation uses multiple Tx antennas distributed in a space. When the Tx antennas are in the far field from each other, appropriate phase control can generate constructive interference at the target location. The phase optimization algorithm optimizes the phase of each Tx antenna based on the received-signal-strength indication (RSSI) feedback from the target. The experimental results indicate that the RSSI measurement resolution and the controllable phase resolution at each Tx antenna affect the performance of the phase optimization.

Screen-camera communications, using a liquid crystal display (LCD) screen and camera image sensors, have been attractive variants of visible light communications (VLC) since any external light-emitting modules and photo detectors are required for recent mobile devices, which are usually equipped with display and camera. A major issue in screen-camera communications is a performance loss in transmission rate due to nonlinear channel impairments with ambient noise. To improve transmission rates, we investigate the impact of nonlinear channel equalization, nonbinary channel coding, probabilistic shaping, and nonlinear precoding for high-order modulation schemes. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.8-3.3 times higher transmission rates compared to existing schemes for a communication distance of 60-160 cm.

In recent years, LTE system with unlicensed bands is proposed. It can achieve not only to boost the capacity of cellular networks but also to use unlicensed bands effectively since LTE can achieve better spectrum efficiency than Wi-Fi. However, when LTE is introduced into an environment where Wi-Fi access points are already deployed, significant interference may be caused to Wi-Fi, Therefore, the coexistence of LTE and Wi-Fi with unlicensed bands is the problem. In this paper, we assume an environment where LTE base stations and Wi-Fi access points are deployed high-densely with unlicensed bands and co-channel interference frequently is caused. Then, we aim to enhance the average user throughput and its fairness through a centralized control of LTE and Wi-Fi, Specifically, we propose a channel assignment method and a users' access system selection method using GA. The proposed method enhances both the average user throughput and the fairness of user throughput compared with conventional static channel assignment methods.

Wireless communications for wireless devices require a significant power consumption for signal amplification. Such a significant power consumption may induce short lifetime of battery-operated wireless devices, especially, sensor devices and Internet of Things (IoT) devices. Backscatter communications have been proposed in recent years for ultra-low-power wireless communications, e.g., Wi-Fi. In this study, we propose and implement a novel backscatter communication scheme for sensor devices, i.e., IEEE 802.15.4-compatible backscatter. The proposed backscatter scheme consists of three devices: RF signal generator, backscatter transmitter, and standard IEEE 802.15.4 receiver. Specifically, the backscatter transmitter receives the RF signals with/without frequency hopping emitted from the RF signal generator and modulates the received signals into IEEE 802.15.4 packets by switching the impedance of the transmission antenna. Since the backscatter transmitter does not need signal amplification, instead, only needs to switch on/off state of the transmission antenna for packet transmissions, the power consumption of the proposed scheme is significantly lower than the conventional IEEE 802.15.4-based schemes. From the experimental evaluations, we demonstrated that the proposed backscatter scheme can realize IEEE 802.15.4 packet transmissions with low power at a certain angle and communication distance. In addition, we also clarified the frequency hopping at the RF signal generator can increase the number of the received packets since it may reduce the dead spots due to multi-path fading.

Soft image delivery uses pseudo-analog modulation for wireless image transmissions to prevent cliff and leveling effects subject to channel quality fluctuation and to realize graceful quality improvement according to wireless channel quality. Despite its attractive feature of graceful performance, the conventional soft image delivery suffers from low image quality when the analog-modulated symbols are severely impaired by fading and strong channel noise. In this paper, we propose a novel scheme of soft image delivery to reconstruct high-quality images from its low-quality observations. Specifically, the proposed scheme integrates deep convolutional neural network (DCNN)-based image restoration, i.e., deep image prior, into soft image delivery. The deep image prior learns a mapping function from the noisy image to the clean image based on user's perception-aware loss function using multiple training images in prior to soft delivery. The mapping function can restore a clean image even when the received image is distorted by strong fading and additive noise. From the evaluation results, the proposed scheme can remove fading and noise effects from the received images by using DCNN-based image restoration. For example, the proposed scheme achieves up to 0.44 improvement compared with the conventional soft image delivery in terms of structural similarity (SSIM) index at a deep fading channel.

Conventional point cloud delivery schemes use graph-based compression to stream three-dimensional (3D) points and the corresponding color attributes over wireless channels for 3D scene reconstructions. However, the graph-based compression requires a significant communication overhead for graph signal decoding, i.e., inverse graph Fourier transform (IGFT), and such large overhead causes a low 3D reconstruction quality due to power and rate losses. We propose a novel scheme of point cloud delivery to significantly reduce the amount of overhead while allowing a small degradation in the 3D reconstruction quality. Specifically, the proposed scheme exploits Givens rotation for the graph-based transform basis matrix to compress the basis matrix into quantized angle parameters. Even when the angle parameters are strongly quantized for compression, the receiver can reconstruct a clean point cloud by using the basis matrix obtained from the quantized angle parameters. Evaluation results show the Givens rotation in the proposed scheme achieves overhead reduction with a slight quality degradation. For example, the proposed scheme achieves 89.8% overhead reduction with 1.3 dB quality degradation compared with the conventional point cloud delivery scheme.

To supply energy for Internet of Things (IoT) devices, this paper proposes phase-controlled wireless power transfer by generating constructive standing waves, on multiple IoT devices, using distributed multiple antennas. The proposed approach realizes equitable power supply distribution among all the IoT devices by switching the phase sets for TX antennas in a time-division manner, and each phase set is optimized for individual IoT devices. In particular, our approach uses received signal strength indicator (RSSI) feedback packets to optimize the phase set of each TX antenna and applies a heuristic algorithm to reduce the number of RSSI feedback packets. We evaluated our approach using computer simulations and experimental micro benchmarks. The computer simulation assumes 64 antennas installed at 2 m intervals on the ceiling of a 15 m by 15 m by 2.5 m room and, each antenna emits 100 mW continuous nonmodulated sine waves. The simulation results show that each of the 10 IoT devices placed on the floor is able to receive an average of approximately 53.6 uW, which is approximately 5.1 times the existing method. The experimental micro benchmarks show the feasibility of the proposed method.

Wireless 360-degree video delivery provides virtual reality (VR) immersive experience where each user can freely switch his/her viewing orientation. The existing schemes of the wireless 360-degree video streaming use digital-based compression and transmission. However, they have many disadvantages in terms of video traffic and quality: cliff effect due to unstable wireless channels, large video traffic due to the extremely high resolution of the 360-degree video, and the perceptual redundancy within the transmitted video. To solve the above problems, this paper proposes a novel wireless 360-degree video transmission scheme called 360Cast. 360Cast adopts the analog-based transmission, including power allocation and analog modulation, to achieve graceful video quality improvement even in time-varying wireless channels. Here, the power allocation considers the distortion of human perception and sphere-to-plane projection to maximize the human perceptual quality at the 360-degree video playback. In addition, 360Cast predicts and transmits the user's viewport based on the recent HMD user's orientation by using dynamic linear regression (DLR) and only transmits the viewport for traffic reduction. Evaluation results show that the proposed 360Cast provides better human perceptual quality via HMD compared with the existing analog transmission schemes, i.e., SoftCast and FoveaCast, by using the integration of viewport prediction, power allocation, and analog modulation.

Backscatter communication is attracting attention for its ultra-low-power consumption ability. The said capability will enhance IoT which aims to enable a large number of novel applications for object-to-object communication. In such a network, where numerous tags may transmit simultaneously, it is important to make sure that the number of simultaneous transmissions allows reliable decoding operations on a large scale. Here we introduce QuadScatter, a series of algorithms that make profit of the maximum number of simultaneous backscatter transmissions to enable a network of a significant number of transmitters, tags, and readers. The maximum number of simultaneous transmissions is verified by simulations and the overall capacity of the network allows successful communications above 3dB of signal-to-noise ratio (SNR). QuadScatter shows agreeable results compared to the exhaustive search algorithm. The simulation results highlight computational time and simultaneous transmission improvements of at least 250x and 2x. Furthermore, while the exhaustive search is limited to a few nodes (<0), our proposal uses a larger number of nodes.

Simultaneous screen-to-camera and screen-to-eye communications, i.e., watermarking, have been proposed in visible light communications. The main purpose of such communications is to provide many data bits for camera devices and visual information for human eyes by using a common displayed image. To this end, the existing studies leverage the capability discrepancy and distinctive features between the human vision system and camera devices. However, the existing techniques mainly require high refresh rates in both screen and camera devices to achieve better throughput while keeping high visual quality. In this paper, we propose a novel transmission scheme for efficient simultaneous screen-to- camera and screen-to-eye communications without a need of high refresh rates. Specifically, we use deep convolutional neural networks (DCNN)-based watermark encoder and decoder to embed many bits into high-quality images, and then to maximize throughput from the bit-embedded image. With end-to-end adversarial learning, the encoder networks learn a mapping function to embed digital data into an original image based on a perceptual loss function while the decoder networks also learn a mapping function from the bit-embedded image to the data bits based on a cross-entropy loss function. From the evaluations, we show that the proposed watermark encoding and decoding networks yield high throughput from the bit-embedded images compared with a simple DCNN-based watermarking. In addition, the bit-embedded images on the screen achieve high quality for human perception.

Soft delivery, i.e., analog transmission, has been proposed to provide graceful video/image quality even in unstable wireless channels. However, existing analog schemes require a significant amount of metadata for power allocation and decoding operations. It causes large overheads and quality degradation due to rate and power losses. Although the amount of overheads can be reduced by introducing Gaussian Markov random field (GMRF) model, the model mismatch can degrade reconstruction quality. In this paper, we propose a novel analog transmission scheme to simultaneously reduce the overheads and yield better reconstruction quality. The proposed scheme uses a deep neural network (DNN) for metadata compression and decompression. Specifically, the metadata is compressed into few variables using the proposed DNN-based metadata encoder before transmission. The variables are then transmitted and decompressed at the receiver for high-quality video/image reconstruction. Evaluations using test images demonstrate that our proposed scheme reduces overheads by 80.0% with 11.2 dB improvement of reconstruction quality compared to the existing analog transmission schemes.

In conventional point cloud delivery, a sender uses octree-based digital video compression to stream three-dimensional (3D) points and the corresponding color attributes over band-limited links, e.g., wireless channels, for 3D scene reconstructions. However, the digital-based delivery schemes have an issue called cliff effect, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast, to gracefully improve the reconstruction quality with the improvement of wireless channel quality. HoloCast regards the 3D points and color components as graph signals and directly transmits linear-transformed signals based on graph Fourier transform (GFT), without digital quantization and entropy coding operations. One of main contributions in HoloCast is that the use of GFT can deal with non-ordered and non-uniformly distributed multidimensional signals such as holographic data unlike conventional delivery schemes. Performance results with point cloud data show that HoloCast yields better 3D reconstruction quality compared to digital-based methods in noisy wireless environment.

Multi-homed wireless video streaming systems concurrently send video frames of one video content from multiple wireless access networks. The coexistence of Long Term Evolution (LTE) and Wi-Fi systems is one of the typical multi-homed wireless environments. To utilize such multiple wireless access links for high-quality streaming, we need to overcome two issues: rate decrement in the LTE channel due to cellular congestion and channel quality fluctuation in Wi-Fi channels due to noise, interference, and channel fading. To this end, we propose a hybrid digital-analog video transmission scheme, which integrates digital video coding, near-analog modulation, and compressive sensing, considering Wi-Fi offloading. To avoid an effect of the cellular congestion, the proposed scheme offloads a part of digital-encoded video data, which are originally transmitted in the LTE channel, to available Wi-Fi channels if LTE's data rate becomes insufficient during the streaming session. In addition, the proposed scheme uses a combination of compressive sensing and near-analog modulation for video delivery in Wi-Fi channels to ensure the received video quality can be proportionally improved according to the instantaneous quality of the Wi-Fi channels. From evaluations, the proposed Wi-Fi offloading technique keeps better video quality even when the capacity of LTE channel becomes limited within a streaming session. In addition, it was found that the received video quality can be gracefully improved according to the number of receivable Wi-Fi access points and each Wi-Fi channel quality.

In this paper, a traffic reduction scheme for videobased call and chat applications that uses deep neural network (DNN) based super resolution is proposed. Specifically, a sender transmits low-quality, low-resolution video frames containing face information in order to reduce the amount of video traffic. The receiver uses DNN-based super resolution to reconstruct highquality, high-resolution video frames from the low-quality video frames. The proposed scheme makes two contributions. First, face features are adopted for parameter optimization of DNNbased super resolution for high-quality image reconstruction, and second, the scheme includes a newly designed loss function that considers face features that allow high-quality face-containing video frames to be reconstructed at the receiver. According to our evaluation results using real video frames of video calls, the proposed scheme reduces the amount of video traffic by more than 90% as compared with conventional schemes that implement the standard video encoder. In this case, the proposed scheme achieves a reconstructed image quality up to 0.85 in terms of structural similarity (SSIM).

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

There is a demand for device-free user location estimation with high accuracy in order to realize various indoor applications. This paper proposes an IEEE 802.11ac explicit feedback-based channel state information (CSI) learning system which can be used for device-free user location estimation. The proposed CSI learning system captures CSI feedback from off-the-shelf Wi-Fi devices and extracts 624 features from a CSI feedback frame defined in IEEE 802.11ac. We evaluated the proposed system using location estimation with six patterns: different combinations of device-free user movement and access point antenna orientation. The evaluation results show that the machine learning based localization achieves approximately 96% accuracy for seven positions of the user, and the divergence of CSI improves localization performance.

Inter-cell interference coordination (ICIC) has been widely studied for mitigating the effects of severe inter-cell interference (ICI) in cell- edge users. However, based on the scarcity of frequency resources in orthogonal multiple access systems, the ICIC methods proposed in the previous papers have difficulty in maintaining the overall performance and fairness of a system. Non-orthogonal multiple access (NOMA) is a promising radio access technology that can serve multiple users simultaneously with the same frequency resources. However, most previous work has not considered the ICI problem in NOMA systems. We propose a centralized adaptive ICIC framework for downlink NOMA systems, including a distributed clustering algorithm, a distributed power allocation algorithm, and a centralized frequency allocation algorithm. Simulation results demonstrate that the proposed framework outperforms all benchmark frameworks and can improve both the overall performance of a system and fairness among users.

This paper proposes a system that estimates crowdedness, such as the numbers of people and wireless communication devices, from the received signal strength indication (RSSI), which is measured using sensor nodes already-deployed for a different purpose. For example, the system estimates crowdedness using sensor nodes of structural health monitoring. Our system consists of two parts: an RSSI synchronous measurement and a crowdedness estimation algorithm. The RSSI synchronous measurement enables us to measure and collect two kinds of RSSI by synchronized RSSI sampling: the inter-node RSSI and surrounding RSSI. The crowdedness estimation algorithm estimates the number of people from the collected inter-node RSSI and the number of wireless communication devices from the collected surrounding RSSI in a location. We evaluated the proposed system in the laboratory at Osaka University. The evaluation results demonstrate that the system estimates the presence or absence of people in the laboratory with approximately 92% accuracy and the number of people with approximately 79% accuracy, with errors of up to two people.

Wireless multi-view plus depth (MVD) video streaming enables free viewpoint video playback on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. Existing schemes of wireless MVD streaming use digital-based compression to achieve better coding efficiency. However, the digital-based schemes have an issue called the cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to assign quantization levels and transmission power across MVD frames are cumbersome. To realize high-quality wireless MVD video streaming, we propose a novel graceful video delivery scheme, called FreeCast. FreeCast directly transmits linear-transformed signals based on 5-D discrete cosine transform, without digital quantization and entropy coding operations. In addition, we exploit a fitting function based on a multidimensional Gaussian Markov random field model for overhead reduction to mitigate rate and power loss due to large overhead. The proposed FreeCast achieves graceful video quality with the improvement of wireless channel quality under a low overhead requirement. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling a transmission power assignment. Performance results with several test MVD video sequences show that FreeCast yields better video quality in band-limited environments by significantly decreasing the amount of overhead. For instance, structural similarity (SSIM) performance of FreeCast is approximately 0.127 higher than the existing graceful video delivery schemes across wireless channel quality, i.e., signal-to-noise ratio, of 0-25 dB at a transmission symbol rate of 37.5 Msymbols/s.

In 360-degree video streaming, a user may watch a part of video frames, namely, viewport, through an interactive display, e.g., head-mounted display, for the immersive experience. One of major issues for high-quality 360-degree video delivery is how to reduce perceptual redundancy in video frames according to user's viewing viewport. To this end, an existing scheme divides video frames into multiple tiles and adaptively encodes each tile based on user's viewing viewport using digital-based video compression. However, the video compression causes an issue in wireless channel called cliff effect, which causes sudden quality degradation in user's viewport and poor immersion in immersive applications. To reduce perceptual redundancy without cliff effect in wireless 360-degree video delivery, we propose a novel transmission scheme. Our scheme skips quantization and entropy coding, instead, directly transmits linear-transformed signals based on three-dimensional discrete cosine transform (3D-DeT) or the combination of one-dimensional DeT (1D-DeT) and spherical wavelet transform (SWT). By skipping both operations, the quality of viewport can be simply enhanced by its power allocation since it is directly applied at the pixel level instead of the bit level. It is demonstrated with 360-degree video sequences that the proposed scheme offers a great advantage over the conventional digital-based schemes.

In conventional multi-view video systems using progressive download, each user downloads only a desired viewpoint for traffic reduction; however, playback stalls occur after every view switching. The playback stalls induce low user satisfaction for such multi-view applications. A progressive download-based multi-view video scheme assuming successive view switching has been proposed to reduce video traffic and prevent playback stalls. However, when the user switches to a viewpoint other than adjacent viewpoints, this still causes playback stalls when watching the desired viewpoint. In particular, when a user randomly switches an observed viewpoint to find the preferred viewpoint, i.e., zapping, it needs to send videos of all viewpoints to accept zapping. This also causes playback stalls owing to sharp traffic growth. In this paper, we propose a novel progressive download-based multi-view video delivery scheme to decrease the number of playback stalls even when the user switches to an observed viewpoint other than adjacent viewpoints, including zapping. The main idea of the proposed scheme is to execute segment download scheduling by taking into account the view switching probability and viewpoint popularity of past users. Specifically, a user node foresees a user's view switching behavior in future video frames based on those information and schedules a download order of priority segments, which will be highly likely to be switched by the user, for smooth view switching. Evaluations using Joint Multi-view Video Coding (JMVC) encoder and multiview video sequences show that our scheme decreases the number of playback stalls.

Exploiting the inter-camera correlation among the crowdsourced mobile video contributors is desirable as it can improve the coding efficiency and/or video quality. This can be achieved by using a digital video encoder such as H.264/Multi-view Video Coding (MVC). However, highly-complex nature of digitalbased video encoding requires a large amount of power, which is a limited resource of the consumer-grade mobile devices. In addition to video encoding, transmission power is also consumed for video uploading based on the amount of uploading video traffic to satisfy video quality requirement. In this paper, we propose a powerefficient video uploading scheme for crowdsourced multi- view video streaming considering power-saving in both video encoding and video transmission. To this end, we design a cluster-based redirect transmission scheme. In this scheme, video frames of multiple contributors are only encoded by four-dimensional discrete cosine transform (4D-DCT) and modulated by near-analog modulation at a cluster head. By removing digital-based coding and modulation, our scheme achieves the same video quality with the existing scheme under low power requirements. Evaluation results show that the proposed scheme outperforms the digital-based video uploading schemes in terms of video quality and the amount of video traffic to achieve a targeted video quality.

With the development of communication services, backbone networks need to deal with various types of traffic by utilizing appropriate switching systems in a flexible manner. Optical packet and circuit integrated (OPCI) networks offer both optical packet-switching (OPS) and optical circuit-switching (OCS) and have been studied for this purpose. A space division multiplexing-based OPCI network provides significant communication capacity and shares spectrum resources for OPS and OCS traffic demands on the same physical infrastructure to efficiently accommodate future services. For appropriate resource utilization, the ratio of spectrum resources between OPS and OCS must be flexibly managed based on the dynamically changing traffic. One of important issues of OPCI networks is sharing spectrum resources under a dynamic traffic model. In this paper, we propose a dynamic spectrum-sharing method for both OPS and OCS data transmissions to enhance resource utilization. The spectrum-sharing method consists of dynamic resource boundary management and load-balancing by exploiting integer linear programming periodically in operation. We evaluate the proposed method with computer simulation and verify its effectiveness in terms of both OPS loss probability and OCS blocking probability.

A major challenge of screen-camera visual multiinput multi-output (MIMO) communications is to increase the achievable throughput by reducing nonlinear channel effects including perspective distortion, ambient lights, and color mixing. To mitigate such nonlinear effects, an existing transmission method uses linear or simple nonlinear equalizations in decoding operations. However, the throughput improvement from the equalization techniques is often limited because the effects are composed of a combination of various nonlinear distortions. In addition to the above issue, the existing studies consider specific environments, such as indoor and static communications, although screen-camera communications can be used for a variety of applications including outdoor and mobile scenarios. In this study, we propose 1) deep neural network (DNN)- based decoding for screen-camera communications to increase the achievable throughput and 2) Unity 3D-based evaluation methodology to synthetically learn the DNN for being robust against many different screen-camera environments. The DNN finds the best nonlinear kernels for equalization from numerously captured images, and then decodes original bits from newly captured images based on the trained nonlinear kernels. In the Unity-based evaluation tool, we can easily capture numerous photo-realistic images in different screen-camera scenarios to learn the impact of perspective distortion, screen- to-camera distance, motion blur, and ambient lights on the throughput since Unity-based environment can freely set programmable screens, cameras, and ambient lights on a 3D space. As an initial proof of concept, we demonstrate that the proposed DNN-based decoder scheme improves the achievable throughput by up to 148% compared to existing methods by equalizing nonlinear effects.

Wireless video delivery using multiple transmission paths has been studied in recent years to improve the received video quality. Hybrid WiFi-VLC is one of the promising multi- path techniques for indoor communications. Hybrid WiFi- VLC uses radio and visible light bands for transmissions. When a sender simply transmits videos over the hybrid path, the received video quality will be low due to two features of the visible light band. First one is the quality of visible light path will be significantly changed depending on the position and angle of devices, unlike the conventional radio communication. The second one is visible light path is typically one-way link. It means the conventional schemes have not been possible to provide video contents of adequate quality for each user. In order to address the above-mentioned issues, this paper proposes a video transmission scheme for hybrid WiFi-VLC environments. The proposed scheme integrates two-dimensional discrete wavelet transform (2D-DWT), digital video coding, and pseudo-analog modulation to provide baseline quality over radio paths and realize graceful quality enhancement according to the quality of visible light paths without the channel state information. Evaluations show that the proposed method improves PSNR performance by 11 dB compared to a simple method of dividing and transmitting video data over the radio and visible light paths across signal-to-noise ratios of 0 to 25 dB.

Cognitive radio (CR) has emerged as a promising technology to solve the tremendous spectrum scarcity problem. Although CR has shown very interesting results, it is still dealing with some drawbacks such as blocking or forced termination events in opportunistic mode and capacity limitation in underlay mode. In this paper carrier aggregation is proposed for wireless mesh networks to enhance the network throughput in underlay CR condition. In the proposed scenario, the primary user uses traditional channels whereas the secondary user aggregates carriers in PU's channels for its transmission. An analytical performance study is carried out which shows the effectiveness of our proposal. With relatively few aggregated carriers per channel, carrier aggregation outperforms single channel and channel aggregation due to its higher detection probability. These results can be used to extend the already well known properties of wireless mesh networks and their applications.

Soft video delivery, i.e., analog video transmission, has been proposed to provide high video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations causing large overhead and quality degradation due to rate and power losses. To reduce the overhead while keeping the video quality high, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that the proposed scheme reduces overhead by 99.7% with 1.2-dB improvement of video quality (in terms of peak signal-to-noise ratio) compared to the existing analog video transmission scheme. We also investigate the impact of bandwidth limitation, showing a significant gain up to 2.7 dB for narrow-band systems.

Existing studies on multiview video streaming are proposed for terrestrial environments. They can be classified into request-reply and all camera transmission models, while both models suffer from a long switching delay and low video quality in an underwater acoustic path. The implementation of multiview video transmission in an underwater acoustic path requires reduction of the switching delay and the maintenance of high video quality. To this end, we propose a video transmission scheme, namely, Dolphin. Dolphin comprises time-shifted slot assignment, stochastic transmission, correlation-based compensation, and greedy transmission. Dolphin assigns asymmetric and time-shifted transmission slots to an encoder node and a user node according to a propagation delay. Stochastic transmission sends a potential camera's video that will be the most likely to be played back by the user before playback. If the stochastic transmission fails, the video frames of the user's desired camera are encoded with the mispredicted video frames to prevent switching delay from increasing. Moreover, greedy transmission sends all potential video frames, which are potentially displayed by a user, and determines rate allocation of the video frames based on the display probability. Evaluations using Mitsubishi Electric Research Laboratories' benchmark test sequences demonstrated that Dolphin achieved a short switching delay with a slight degradation in video quality irrespective of communication distance and acoustic path quality. For example, Dolphin decreases the switching delay by 92.7% compared with an existing request-reply model with 0.4-dB quality degradation at a communication distance of 300 m when a user tends to gaze at one camera.

The recent widespread use of high-performance terminal equipment has resulted in a rapid increase in the mobile data traffic. Therefore, public wireless local area networks (WLANs) are being used often to supplement the cellular networks. However, the effective throughput degrades significantly when many users connect to a single access point (AP). To overcome this problem, the GBR service should be provided in public WLANs. Therefore, we proposed AP virtualization method to provide the guaranteed bit rate (GBR) service and best effort (BE) service. In this paper, we propose a backhaul network model for virtualized access area. Moreover, we propose the dynamic bandwidth assignment to each service in a virtualized backhaul, for reducing the call-blocking probability of GBR users and improving the satisfaction degree of BE users. Finally, we evaluate the performance of the proposed model through simulation experiments and discuss its feasibility.

In recent years, as higher performance terminal equipments are spread, the amount of mobile data traffic is growing rapidly by using popularization of streaming services in wireless networks. To overcome this situation, it is considered that Access Points (APs) are deployed densely for capacity improvement. In such an environment, virtualization is a possible technique to use multiple APs efficiently. In this paper, we assume the environment where macro cells and small cells are co-existing, and BE (Best Effort) users and GBR (Guaranteed Bit Rate) users arrive. Then, we propose a virtual AP configuration method. Specifically, BE and GBR virtual APs are configured with multiple physical APs respectively and the configuration of both virtual APs are changed dynamically at the constant interval. The proposed method achieves reducing the call blocking rate of GBR users and improving satisfaction of BE users.

Coexistence between ZigBee and WiFi technologies, which operate within the same frequency band, is increasing with the widespread use of the IoT (Internet of Things). ZigBee devices suffer significant decrease in the sink arrival rate of packets in the presence of WiFi interference. To overcome this problem, many channel control methods have been proposed. The existing methods switch only ZigBee channels to avoid interference with WiFi. Here, we propose a method for improving ZigBee packet arrival rate by controlling both the WiFi and ZigBee channels. In this paper, we propose a cooperative channel control method designed to improve ZigBee's packet arrival rate. We propose a method that controls not only ZigBee devices and channels but also requests a temporary pause in the use of specific WiFi channels. Finally, we show the effectiveness of the proposed method using computer simulations.

Real-time multi-view video streaming plays an important role in new interactive and augmented video applications such as telepresence, remote surgery, and entertainment. Multiview video streaming approaches exploit time and inter-camera domain correlations in video frames for compression, thereby obtaining two benefits: video traffic reduction and video quality maintenance. However, excessive video encoding requires long periods of time for encoding/decoding computation in real-time multi-view video streaming and this process is vulnerable to frame loss, both of which may induce stalling and skipping during video playback at the user node. To address these problems, we propose Adaptive Multi-view Video Streaming (AMVS) system for real-time streaming. AMVS employs a greedy algorithm to adaptively select a prediction structure with low encoding/decoding computation based on the available network bandwidth. AMVS encodes and transmits multi-view video by considering the similarity of the user's request and the efficiency of transmission. Evaluations using Joint Multiview Video Coding (JMVC) demonstrated that AMVS achieves low encoding/decoding computation and high video quality while satisfying bandwidth limitations.

In current medical information networks, there is a problem because the cost of core switches is increasing owing to the need to meet demands in terms of throughput and robustness. To address this problem, we propose MediFlow as a medical information network based on OpenFlow. MediFlow replaces expensive, high-performance core switches with a MediFlow core network comprising multiple inexpensive and fully connected OpenFlow switches. The MediFlow core network adaptively allocates the communication capacity to time-varying traffic in a medical information network. If a link fails, MediFlow dynamically assigns a path to ensure that the communication system remains robust. We implemented MediFlow using commercial OpenFlow switches. The experimental evaluation showed that MediFlow could improve the throughput by allocating individual paths to each flow about 7 s after the occurrence of congestion by converging multiple flows. In addition, even if a link failure occurred in the path used by the flow, MediFlow re-established a path with a disconnection time of about 0.7 s.

Wireless video streaming for multi-view plus depth (MVD) frames enables free viewpoint video on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. To transmit MVD frames over wireless links, existing schemes use digital-based compression to achieve better compression efficiency. However, the digital-based schemes have an issue called cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to allocate quantization levels and transmission power across MVD frames is cumbersome. To facilitate the MVD video streaming, we propose an analog-based transmission scheme. Our scheme directly transmits linear-transformed signals based on five-dimensional discrete cosine transform (5D-DCT), without relying on quantization and entropy coding. The proposed scheme achieves graceful video quality with the improvement of wireless channel quality. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling transmission power assignment. It is demonstrated with test MVD video sequences that the analog-based scheme offers a great advantage over the conventional digital-based scheme. For instance, the video quality of one intermediate virtual viewpoint is approximately 2.1 dB higher than digital-based schemes at a wireless channel quality, i.e., signal-to-noise ratio (SNR), of 15 dB.

With the increasing popularity of smartphones and Internet of Things (IoT) devices, there has been an increase in the amount of mobile traffic on networks. In this paper, we discuss an access strategy for downlink/uplink decoupling (DUDe) with multiple frequency channels to enable the efficient use of frequencies in wireless networks. DUDe is a wireless communications scheme that effectively utilizes frequencies when communicating by connecting the uplink and downlink to different base stations. We propose two base station selection methods, i.e., signal-to-interference-plus-noise ratio (SINR)-based decoupling with re-association (SBD-RA) and SINR-based decoupling with first-come first-association (SBD-FCFA). Based on the evaluation results obtained by event driven simulations, we show that both SBD-FCFA and SBD-RA provide higher throughput by utilizing frequencies more efficiently than DUDe when using the same channel, or without using DUDe. We also show that SBD-RA achieves a high throughput although SBD-RA has a larger computational complexity than SBD-FCFA.

Backscatter wireless communication (BWC) is a promising research area for the low power Internet of Things (IoT) devices. A substantial number of research works have been conducted in this area to increase the range of BWC. Along with physical layer achievements in BWC, an efficient medium access control (MAC) protocol is mandatory to attain the optimal benefit. In this paper, a novel MAC protocol is proposed to support BWC in a Wi-Fi network, where the AP and the Wi-Fi clients are capable of in-band full-duplex (IBFD) transmission, but the wireless tags (WTags) are half-duplex capable. In this MAC protocol, the AP suppresses the uplink transmission of the corresponding Wi-Fi client while transmitting the downlink data to support the BWC. Some new control frames have been introduced to facilitate this task. A mathematical analysis of this proposed MAC is presented here together with an evaluation of its performance. The mean overhead time is observed as 121 μs with the downlink utilization factor of 80%. The average throughput of the network decreases as the number of WTag increases. This average throughput is observed as 63.5 Mbps and 59 Mbps (with 30 WTags and 30 Wi-Fi clients) for the data delivery frequency of two times/WTag and eight times/WTag respectively.

Recent works on enhancing comfortability and convenience in vehicles reveal that information collections by various wireless sensor nodes have the potential due to their easiness of install and their small size. However, the conventional wireless network standards achieve insufficient reliability for practical in-vehicle communications. In this paper, through implementing 2.4GHz devices in an actual vehicle with crews in the cabin. Based on the results, we propose a method to provide reliable multi-hop communications in a vehicle using overhearing. In the proposed method, the neighbor nodes overhear a packet sent from a source node. If the destination node does not receive the packet, neighbor nodes send the overheard packet as proxies of the source node. We evaluate the performance of the proposed method by computer simulation and experimental implementation. One of the results shows that the proposed method achieves 99.9% packet arrival ratio within 20ms.

Screen-camera communication, which uses a liquid crystal display (LCD) screen and camera image sensors, has been an attractive variant of visible light communications (VLC) since display and camera have been equipped with in various mobile devices. To improve transmission rates, we investigate the impact of nonlinear channel equalization and nonbinary channel coding as well as high-order modulation schemes. Equalization techniques can reduce the effect of channel impairments, such as color mixing. Nonbinary coding improves reliability of high-order modulation and thus increases the transmission rate. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.6-2.4 times higher transmission rates compared to existing schemes for a communication distance of 40-100 cm.

Progressive download for multi-view video delivery is one of promising techniques to provide immersive and interactive experiences to users. In conventional multi-view video systems, a user downloads videos of all viewpoints of one content to realize smooth view switching. However, it causes increase of video traffic, and thus low video quality within an available download rate. To download only a desired viewpoint is another approach for reduction on volume of video traffic. However, playback stalls occur after view switching. The stalls induce low user's satisfaction for applications. In this paper, we aim at two objectives: 1) to achieve reduction on video traffic and 2) to achieve reduction on the number of playback stalls. To this end, we propose a new multi-view video delivery scheme for progressive download. The main idea of the proposed scheme is that the user only downloads potential videos, which are potentially played back by the user, to realize both traffic reduction and smooth view switching. In addition, we propose two download scheduling algorithms to prevent playback stalls even in a low download rate. The first algorithm prevents stalls in frequent view switching cases while the other prevents stalls in gazing cases. Evaluations using Joint Multi-view Video Coding (JMVC) encoder and multi-view video sequences show that the proposed method reduces video traffic by 55.1% compared with a simulcast scheme. In addition, the proposed method decreases the number of playback stalls during video playback by 86.0% compared with a request and response scheme.

Soft video delivery, i.e., analog video transmission, has been proposed to provide graceful video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations. It causes large overheads and quality degradation because of rate and power losses. To reduce the overheads while keeping high video quality, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that our proposed scheme reduces overheads by 97 % with 3.4 dB improvement of video quality compared to the existing analog video transmission scheme.

The multi-view video streaming is one of promising technologies for emerging video services. For those applications of multi-view video streaming, video frames of all viewpoints, i.e., cameras, are needed to be transmitted to viewers because the demands of all the viewers' view-switching are unpredictable. However, existing transmission schemes are highly vulnerable to frame loss. Specifically, the frame loss in one viewpoint induces a collapse of decoding for other viewpoint videos. To improve loss-resilience, we propose a multi-path based multi-view video transmission. Our scheme encodes video frames into multiple descriptions that are mutually independent of each other, by using inter-view prediction. It then transmits each description using multiple transmission paths. Our scheme makes three contributions:1) it reduces video traffic even for a large number of cameras, 2) it prevents an increase in the number of undecoded video frames caused by one frame loss, and 3) it conceals frame loss by using the video frames in the other paths. Our scheme generalizes to an arbitrary number of transmission paths and discusses the detailed performance at the number of the paths of 2. Evaluations show that our proposed scheme improves video quality by 3 dB compared to existing transmission schemes in loss-prone environments.

In recent years, the number of requests to transfer large files via large high-speed computer networks has been increasing rapidly. Typically, these requests are handled in the “best effort” manner and resulting in unpredictable completion times. In this paper, we consider a model where a transfer request either must be completed by a user-specified deadline or must be rejected if its deadline cannot be satisfied. We propose a bandwidth scheduling method and a routing method for reducing the call-blocking probability in a bandwidth-guaranteed network. Finally, we show their excellent performance by simulation experiments.

With the development of various radio communication systems in recent years, spectrum sharing in heterogeneous networks has received a considerable amount of research attention for the efficient use of spectral resources, as the lack of these has emerged as an important problem. Prevalent spectral sharing methods focus on improving total throughput, and ignore lower throughput along the cell edges. Hence, users receive unfair throughput that is dependent on their place. To solve this problem, in this paper, we propose improving fairness in spectrum sharing by considering Coordinated Multi-Point transmission /reception (CoMP). In order to promote cell edge performance in particular, we propose a method to allocate parts of spectral resources to cell edges to use CoMP and wireless systems therein using spectrum sharing. We used simulations to confirm that the proposed method improves cell edge performance and fairness in terms of user throughput.

Recent advancements in antenna design and signal processing have made it feasible to transmit and receive at the same time by using same carrier frequency, which is called in-band full-duplex (IBFD) wireless communications. Along with physical layer advancements, efficient medium access control (MAC) design is required to achieve optimum benefit from this latest technology. In this paper, a full-duplex MAC (FD-MAC) is proposed for a WLAN to support asymmetric length of traffic for uplink and downlink, which is named as Asym- MAC. The main feature of this MAC is that multiple users can transmit data to the access point (AP) during the transmission of a single downlink frame from AP. Moreover, there is no contention period and hence, there is no collision among the user terminals and AP for channel access. In Asym-MAC, AP always initiates the transmission. The performance of Asym-MAC is compared with the traditional half-duplex MAC (HD-MAC) and another proposed FD-MAC. Asym-MAC outperforms traditional HD-MAC by about 100% throughput gain. Moreover, this MAC shows higher throughput gain as compared to another existing FD-MAC and the throughput gain is about 67%. The results suggest that a significant throughput gain can be achieved by using this MAC, which can be used for future FD WLANs.

As growing the size of digital contents, it is harder to predict the completion time of file transfer. This is an important problem particularly in a data migration at cloud computing. Therefore, some bandwidth assignment methods have been proposed in a model where a transfer request either must be completed by a specified deadline or must be rejected if the deadline cannot be satisfied. However, existing methods are not practical in a large scale network since they assume a centralized control. To overcome this problem, in this paper, we propose a distributed bandwidth assignment method without using global network information. Simulation results show that the proposed method achieves almost equal call-blocking probability to the existing centralized control method.

The quality of wireless video delivery is susceptible to wireless channel instability, including channel fading, interference, noise, and packet loss. To improve the video quality in such wireless channels, analog transmission schemes have been proposed recently. However, the existing analog schemes have two drawbacks in high energy of video signals and loss resilience. To overcome the issues, we propose a new hybrid digital-analog transmission scheme, which jointly uses digital coding and analog coding based on compressive sensing. Digital coding generates the residual signals between the original and the encoded signals to decrease the energy of data for analog coding. Compressive sensing redistributes the energy across whole video packets to increase the resistance to packet loss. We show that our proposed hybrid scheme improves video quality by up to 11.6 dB compared to the existing analog scheme in an erasure wireless channel. In addition, our proposed scheme also improves video quality by 1.5 dB compared to the existing hybrid scheme.

Current one-to-one wireless communications is reaching the Shannon limit. Previous works have studied space multiplexing schemes, e.g., superposition coding and successive interference cancellation, to overcome the limitation. In this paper, we focus on a network multiple-input multiple- output (MIMO) architecture, which is one such space multiplexing scheme, where multiple access points in the network MIMO system cooperate with each other to improve wireless communications capacity. However, channel sounding overhead for estimating the channel state information of every path between multiple access points and multiple clients is a significant problem. Likewise, the computational overhead for deciding which paths to use for the network MIMO transmission is also high. We propose Throughput-Aware Random Clustering (TARC) of access points to reduce the network MIMO overhead. TARC takes a cross-layer approach for choosing access points to participate in network MIMO transmission on the physical layer based on throughput in the data link layer. From our validation using simulations, we show that the proposed method is able to achieve approximately 2.5 times higher throughput than using all access points in the network and 1.4 times better throughput than adopting static cluster sizes.

Full-duplex wireless information and power transfer allows the implementation of wireless sensor networks without batteries. In a full-duplex wireless network, an access point and sensor nodes transmit information and power signals simultaneously. However, this simultaneous transmission leads to a collision problem. To solve this collision problem, we propose a media access control (MAC) protocol, namely the Full-Duplex Simultaneous Wireless Information and Power Transfer Medium Access Control (FD-SWIPT MAC) protocol, for full-duplex wireless information and power transfer. The evaluation results show that the proposed FD-SWIPT MAC protocol outperforms other MAC protocols.

Combining wireless transmission of data and power signals enables us to use wireless devices without charging batteries. To improve the utilization of wireless resources, a sender could simultaneously transmit data and power signals in the same frequency channel. A disadvantage of simultaneous transmission is that it induces interference between data and power signals. To minimize the effect of interference, we propose a new frequency-sharing system. The proposed system makes two contributions. The first contribution is interference cancellation of the power signal to receive data from the collided signal using a combination of digital and analog interference cancellation techniques. The second contribution is a media access control protocol to receive the transmitted power effectively by varying the sleep time. To evaluate the performance of the proposed system, we built an experimental apparatus using software-defined radio. Evaluations show that it is feasible to transmit data and power simultaneously using the proposed system.

To guarantee the quality of service is one of the most important problems in ICT (Information Communication Technology) area. Therefore, we had proposed a service platform which integrates service control and network control by componentization of distributed objects. On the other hand, with advances of wireless communication technologies, users eager to have network services with a mobile terminal. However, this platform does not consider any wireless access networks. In this paper, we extend the platform to support mobile terminals with heterogeneous wireless access networks and also propose a traffic load balancing method on them for bandwidth guaranteed services. Finally, the effectiveness of the proposed method is evaluated by simulation experiments.

Multi-view video system includes three sections: acquisition, transmission, and display. This paper focuses on the acquisition of multi-view video. Existing multi-view video acquisition studies exploit multi-camera arrays mutually connected by wires. However, this imposes the limitations of places and objects. To overcome the limitations, we exploit multiple mobile cameras and wireless networks for multi-view video acquisition. The acquisition of the multi-view video needs to achieve a reduction in video traffic while maintaining high video quality for communication between mobile cameras and an access point. This paper proposes Multi-view Video Streaming with Mobile Cameras (MVS/MC) to satisfy these requirements. MVS/MC has two features: packet overhearing and transmission order control. First, each mobile camera overhears other cameras' video packets, and encodes its own video frames using the overheard video packets. Second, the access point controls the transmission order of the mobile cameras, thus realizing bidirectional interview prediction. Bidirectional inter-view prediction exploits the inter-camera domain correlation among the mobile cameras to further remove the redundant information. Evaluations using multi-view video sequences show that, compared with existing methods, MVS/MC reduces the volume of traffic with only a slight degradation in video quality. For example, MVS/MC reduces traffic by 52 % compared to existing methods when PSNR is 36 dB.

In a wireless sensor network (WSN), one of the most important challenges is extending network lifetime because it is difficult to replace sensor nodes and recharge their batteries. In a WSN, nodes around a sink tend to die earlier than other nodes because these nodes have to transmit and relay more packets. To overcome this problem, many load-balancing methods were proposed. By the way, in recent years, two or more overlapping WSNs have been constructed in the same geographic area. In this situation, the overlapping WSNs are expected to extend their lifetime by cooperative packet forwarding. In this paper, we introduce shared nodes with multichannel communication capability as interchange points among respective WSN planes. With these shared nodes, we propose a novel cooperative routing method for WSNs to balance each other's load. Finally, we show the effectiveness of the proposed method by simulation experiments.

Multi-view video consists of multiple video sequences captured simultaneously by multiple closely spaced cameras and enables users to freely change their viewpoints by playing different video sequences. Therefore, the transmission bitrate of multi-view video requires more bandwidth than conventional multimedia. In order to reduce the bandwidth requirement, User Dependent Multi-view Video Transmission (UDMVT), which is based on Multi-view Video Coding (MVC), has been proposed for single users. In UDMVT, the same frames are encoded into different versions for each user, which increases the transmission bitrate for multiple users because of the redundant transmission of overlapping frames. In order to address this problem, we proposed User dependent Multi-view video Streaming for Multi-users (UMSM). UMSM possesses two characteristics: overlapping frames required by multiple users are transmitted only once by multicast while un-overlapping frames required by each user are transmitted by unicast and offset of video requests between multiple users is aligned so as to maximize the area of overlapping frames. UMSM achieves a low transmission bitrate of multi-view video for multiple users by means of combining these characteristics. To investigate the effect of user's view switching on traffic reduction, we design three types of view-switching models, namely, scanning model, random model, and watching model. Simulation results obtained using benchmark test sequences provided by MERL reveal that UMSM decreases the transmission bitrate by 80.6% on average for five users, as compared to MVC, when users gaze a viewpoint for a while after switching views in order to search an attractive viewpoint.

Wireless full-duplexing enables a transmission and a reception on the same frequency channel at the same time, and has the potential to improve the end-to-end throughput of wireless multi-hop networks. In the present paper, we propose a media access control (MAC) protocol for wireless full- duplex and multi-hop networks called Relay Full- Duplex MAC (RFD-MAC). The RFD-MAC is an asynchronous full-duplex MAC protocol, which consists of a primary transmission and a secondary transmission. The RFD-MAC increases the full-duplex links by overhearing frames, which include 1-bit information concerning the existence of a successive frame, and selecting a secondary transmission node using the gathered information. The gathered information is also used to avoid a collision between the primary and secondary transmission. Simulation results reveal that the proposed RFD-MAC improves up to 68%, 49% and 56% of end-to-end throughput compared to CSMA/CA, FD-MAC and MFD-MAC, respectively.

Optimizing overall performance of Wireless Sensor Networks (WSNs) is important due to the limited resources available to nodes. Several aspects of this optimization problem have been studied (e.g. improving Medium Access Control (MAC) protocols, routing, energy management) mostly separately, although there is a strong inter-connection between them. In this paper an Artificial Intelligence (AI) based framework is presented to address this problem. Mixed-Observability Markov Decision Processes (MOMDPs) are used to effectively model multiple aspects of WSNs in stochastic environments including MAC in data link layer, routing in network layer, data aggregation, power management, etc. MOMDPs distinguish between full and partial observability, hence they are more efficient than other similar AI methods. The proposed framework provides global optimization of user-defined performance metrics, e.g. minimization of time delay, energy consumption and data inaccuracy. Near-optimal joint network policies are obtained via offline approximation of optimal MOMDP solutions and they are distributed among the individual nodes. Resulting node-policies place effectively no additional computational overhead on nodes in runtime. Experiments evaluate the framework by demonstrating near-optimal solutions for a small-scale WSN in detail in case of given tradeoff criteria. The proposed approach produces better joint network behavior in 5 out of 6 cases compared to other two standard methods in simulation by increasing overall network performance by more than 20% in average.

Multi-view video consists of multiple video sequences captured simultaneously from different angles by multiple closely spaced cameras. It enables the users to freely change their viewpoints by playing different video sequences. Transmission of multi-view video requires more bandwidth than conventional multimedia. To reduce the bandwidth, UDMVT (User Dependent Multi-view Video Transmission) based on MVC (Multi-view Video Coding) has been proposed for single user. In UDMVT, for multiple users the same frames are encoded into different versions for each user, which increases the redundant transmission. For this problem, this paper proposes UMSM (User dependent Multi-view video Streaming for Multi-users). UMSM possesses two characteristics. The first characteristic is that the overlapped frames that are required by multiple users are transmitted only once using the multicast to avoid unnecessary duplication of transmission. The second characteristic is that a time lag of the video request by multiple users is adjusted to coincide with the next request. Simulation results using benchmark test sequences provided by MERL show that UMSM decreases the transmission bit-rate 55.3% on average for 5 users watching the same multi-view video as compared with UDMVT.

Multi-view video consists of multiple video sequences captured simultaneously by multiple closely spaced cameras from different angles. It allows the users to change their viewpoints by playing different video sequence. However, the transmission of multi-view video is much larger than conventional media and increases with increasing number of views, which brings much more increment in the bandwidth requirement. In our previous works, we have proposed a live-encoding scheme call UDMVT [1][2] to reduce the transmission bitrate for multi-view video transmission. In UDMVT, the periodic feedback containing the position and switching speed of user is used to predict a triangle area. Frames in this triangle area are possible to be displayed in next period of time. Only these specific frames are transmitted instead of all the frames to reduce the transmission bitrate. Even though, UDMVT hasn't effectively solve the “out of triangle” problem in which the user may switch to the outside of the triangle during the playback in the triangle area. In this paper, we propose a layered scheme called Layered User dependent Multi-view video Streaming (LUMS) to solve the “out of triangle” problem for UDMVT. In LUMS each triangle is layered into several layers according to the minimum and maximum switching speed. The corresponding layers are transmitted according to the current switching speed each time received from feedback. If the “out of triangle” occurs, the higher layers will be retransmitted. All the frames in high layers are encoded into B-frame predicted from the basic and lower layers, so the retransmission bitrate is low. Performance evaluation proves that LUMS reduce transmission bitrate by 44.9% compared to UDMVT.

Multi-view video consists of multiple views which are taken by multiple cameras from different positions and angles. It allows the users to freely change their viewpoints. Typical applications of multi-view video include Free Viewpoint TV, remote medical surgery, wireless multimedia sensor networks and so on. However, the traffic of multi-view video is also several times larger than traditional multimedia, which brings much more increment in the bandwidth requirement. To overcome this problem, Simulcast and MVC (Multi-view Video Coding) can be used to decrease the traffic for multi-view video. However, although Simulcast removes redundant information within each view, it still contains a large amount of inter-view redundant information. MVC further removes the correlation between the views, but all the views should be encoded and transmitted. So the bit-rates are still too high in these schemes. In this paper, in order to reduce the bit-rate for multi-view video transmission, we analyze the user's motion which is classified into random access model and successive motion model. We find that in the successive motion model only part of frames are possible to be displayed in a period of time. According to the analytical result, we propose a user dependent system to decrease the bit-rate of multi-view video transmission. The proposed system only encodes and transmits those frames that are possible to be displayed according to the user's motion. Further, in order to support the proposed system, we also propose a prediction structure as a substitute of the prediction structure in MVC. Performance evaluation proves that the proposed system reduces the average bit-rate for the transmission of multi-view video and is to be more effective when the number of views is large.

The traffic of multi-view video is several times larger than traditional multimedia, which brings much more increment in the bandwidth requirement. To overcome this problem, Simulcast and MVC (Multi-view Video Coding) can be used to decrease the traffic for multi-view video. However, although Simulcast removes redundant information within each view, it still contains a large amount of inter-view redundant information. MVC further removes the correlation between the views, but all the views should be encoded and transmitted. So the bit-rates are still too high in these schemes. In this paper, in order to reduce the bit-rate for multi-view video transmission, we analyze the user's motion which is classified into random access model and successive motion model. We find that in the successive motion model only part of frames are possible to be displayed in a period of time. According to the analytical result, we propose a user dependent scheme to decrease the bit-rate of multi-view video transmission. The proposed scheme only encodes and transmits those frames that are possible to be displayed according to the user's motion. Further, in order to support the proposed scheme, we also propose a prediction structure as a substitute of the prediction structure in MVC. Performance evaluation proves that this proposed scheme reduces the average bit-rate for the transmission of multi-view video.

Energy harvesting has been studied as a candidate for powering next generation wireless sensor networks. The technologies that can harvest electric power from ambient energy sources include solar, vibration, heat and wind. However, sensor nodes powered by energy harvesting devices cannot always communicate with other nodes because the energy harvesting devices cannot provide a stable supply power. A node cannot know whether its neighboring nodes have enough energy to receive a data packet that it has transmitted. During the process of relaying the packet, each additional hop increases the overall probability of losing the packet. In this paper, we propose two data collection protocols for the energy harvesting wireless sensor networks called Probabilistic ReTransmission protocol (PRT) and PRT with Collision Consideration (PRT-CC). The idea is to derive the number of times to retransmit a packet based on the reception probability and the active intervals computed by the receivers themselves. In PRT-CC, each node computes the reception probability with packet collision consideration. The simulation results show that the proposed protocols achieve higher delivery ratio than the previous works (GR-DD and GR DD-RT).

Opportunistic spectrum access (OSA) is a suitable approach to increase spectrum efficiency for the current spectrum management. This paper considers the temporal aspect of 802.11-based OSA scenario. Firstly, we investigate the secondary users (SU) multi-rate transmission and number of hops effects to the SU performance when exploiting primary users (PU) idle duration. Numerical analysis and simulation results show that, during limited PU idle duration, higher data rate transmission is essential even though it results in more hops but as the SU source traffic is getting busier, the balance between the data rate and number of hops is required. Secondly, according to the multi-rate multi-hop investigation, we propose a rate adaption scheme with PU activity consideration, called Auto Rate Increase (ARI). ARI is based on the Auto Rate Fallback (ARF) scheme with the difference that ARI aims to increase the SU data rate whenever it detects PU interruption failure. ARI also adopts ARF simplicity, hence, there is no need for any prior nor additional knowledge of the networks environment. Simulation results show, under tight OSA environment, ARI scheme can increase the SU performance up to 56% compared with the original ARF scheme.

Due to the Recent development in wireless technology, wireless sensor networks attract researchers' attention because of their applicability in many fields for effective collection of sensing data with low cost. Wireless sensor networks have many applications; some of the applications are military application, environmental application and flood detection. For example, in an environmental application for forest fire detection, the sensor nodes sense the fire information, then transmit or relay the information to base station in a multi-hop way. In wireless sensor networks, energy saving is critical issue as sensor nodes are battery-powered. Here we propose, partial data aggregation as one of the energy saving technique. In this paper, we analyze the tradeoffs among communication delay, energy consumption, and data accuracy of the partial data aggregation technique and discuss the results. First, we analyze the partial data aggregation with Markovian chain; analytical result shows that, non-aggregation method suffers large energy consumption while full aggregation suffers long transmission delay. From the analysis results, we find that the proposed partial aggregation method WRP (Waterfalls Random partial aggregation) can trade off energy consumption and transmission delay. Thus, we discuss the tradeoffs among data accuracy, transmission delay and energy consumption with different criteria and parameters. The results show that we could control the significance of transmission delay, energy consumption and data accuracy by tradeoffs index (TOI). We also analyze the several applications of wireless sensor networks with different significance based on the TOI. From the observed results, we found that we could set the significance of transmission delay, energy consumption and data accuracy for different applications based on different criteria TOI. Thus, by evaluating and comparing the criteria with different data generation rate as well as aggregation factor, we get the least TOI value, which denotes the desired tradeoffs among them.

In this paper, a novel medium access control (MAC) protocol with transmission power and transmission rate control in multi-rate ad hoc networks is proposed to realize high energy efficient data transmission. In the proposed protocol, each node prepares a table that includes energy efficiency in all combinations of transmission power and rate. By exchanging of control frames and looking up the transmission power and rate table, direct and relay transmission sequences are used arbitrarily. When relay transmission by intermediate node between sender and receiver is more effective in terms of power consumption, relay sequence is adopted instead of direct transmission. We show that the proposed protocol can realize high energy efficient data transmission via computer simulations.

Directional antennas are expected to provide significant improvements over omni-directional antennas in wireless ad hoc networks. Directional MAC protocols, however, introduce new kinds of problems arising from directivity. One major problem is deafness, caused by a lack of state information from neighbor nodes (i.e., idle or busy). This paper proposes DMAC/DA (Directional MAC with Deafness Avoidance) to overcome the deafness problem. In DMAC/DA, WTS (Wait To Send) frames are transmitted by the transmitter and the receiver after the successful exchange of directional RTS (Request To Send) and CTS (Clear To Send) to notify the on-going communication to potential transmitters that may experience deafness. Furthermore, DMAC/DA is enhanced by the next packet notification to distinguish transmitters from neighbors. We evaluate our protocol through extensive simulation study with different values of parameters such as the number of flows, data size and beamwidth. The experimental results show that DMAC/DA outperforms existing directional MAC protocols, such as DMAC (Directional MAC) and MDA (MAC protocol for Directional Antennas), in terms of throughput, RTS failure ratio, and control overhead.

Procedures that provide a virtual link service aggregating bandwidths of multiple physical links are called a multilink protocol. The multilink protocol enables to utilize bandwidths of multiple links flexibly and to improve reliability of communications. This paper focuses on the resequencing function needed to preserve packet sequence integrity. The resequencing is generally performed by both each link layer protocol as well as the multilink protocol. This paper shows that a problem of the head of line (HOL) blocking occurs due to the resequencing if packets of multiple IP flows are transferred over the multilink connection. To solve this problem, this paper proposes the Per- Flow ReSequencing (PFRS) scheme for the multilink protocol and evaluates its effectiveness through analysis and simulations.

Recent studies on directional media access control (MAC) protocols using smart antennas for wireless ad hoc networks have shown that directional MAC protocols outperform traditional omni-directional MAC protocols. Those studies evaluate performance primarily based on a simulations, where antenna beam is assumed to be ideal, i.e., with neither side-lobes nor back-lobes. Propagation conditions are also assumed to be based on a mathematical model without realistic fading. However, for the real application of ad hoc networks those optimistic assumptions do not hold anytime. In this paper, we develop at first a testbed for directional MAC protocols which enables investigation of the performance of MAC protocols in a real environment. It incorporates ESPAR as a practical smart antenna, IEEE802.15.4/ZigBee, GPS and gyro modules to allow easy installation of different MAC protocols. To our knowledge, it is the first compact testbed with a practical smart antenna for directional MAC protocols. We implement a directional MAC protocol called SWAMP to evaluate it in a real environment. The empirical discussion based on the experimental results shows the degradation of the protocol with ideal antennas, and it shows that the protocol still achieves the SDMA effect of spatial reuse and the effect of communication range extension.

This paper studies the case where multiple IP flows are aggregated over a single satellite channel and an error recovery by retransmissions is performed by Selective- Repeat (SR) ARQ. We propose MQ-PFRS (Multi-QoS Per-Flow Resequencing) ARQ that provides a differentiated service for an IP flow depending on its traffic class, such as real-time traffic (ex. UDP) or non-real-time traffic (ex. TCP). MQ-PFRS ARQ eliminates interferences among IP flows by resequencing received packets independently for each IP flow. It also controls the maximum packet delay by limiting the persistency of retransmissions and the maximum resequencing time for each packet. This paper also presents the analysis of the probability distribution of real-time packet delays. Simulation results show that the delay variation of real-time traffic is effectively controlled by proposed MQ-PFRS ARQ and the results of the analysis well capture the characteristics of the packet delay distribution. This means that MQ-PFRS is effective for handling multiple classes of IP flows and quality of real-time traffic transferred by the scheme can be predicted by the analysis.

Recently, several MAC protocols using directional antennas, typically referred to as directional MAC protocols, have been proposed for wireless ad hoc networks. However, the MAC protocols in the previous studies were evaluated using simulation with ideal antenna forms. When using practical antenna beam forms, side lobes and back lobes exist which may cause new problems. In this paper, we evaluate the performance of the directional MAC protocol in the previous studies with practical antenna beam forms. In addition, we propose a directional MAC protocol that assumes practical antenna beam forms. The proposed MAC protocol mitigates data collisions due to the effects of minor lobes by the following two schemes. First, the nodes rotate directional receiving antenna beams in an idle state. Second, nodes transmit NAV (Network Allocation Vector) request frames to avoid collisions from neighboring nodes. By controlling the transmitting power, our protocol allows nodes to communicate with nodes beyond the omni-directional transmission range. The simulation results show that the proposed directional MAC protocol improves throughput performance compared to existing directional MAC protocol when using practical antenna beam forms.

Recently, several MAC protocols using directional antennas, typically referred to as directional MAC protocols, have been proposed for wireless ad hoc networks. Although directional transmissions are expected to provide significant improvements, directional MAC protocols introduce new kinds of problems. One such problem is deafness. Deafness is caused when a transmitter repeatedly attempts to communicate with its intended receiver, but it fails because the receiver has its beam pointed towards a direction away from the transmitter. This paper proposes RI-DMAC (Receiver-Initiated Directional MAC) to address the issue of deafness in directional MAC protocols. RI-DMAC is a combination of sender-initiated and receiver-initiated operations. The sender-initiated mode is the default mode and the receiverinitiated mode is triggered when the transmitter experiences deafness. In RI-DMAC, each node maintains a polling table and polls a potential deafness node using the RTR (Ready To Receive) frame after the completion of every dialog. The experimental results show that RI-DMAC improves throughput and fairness performance compared to existing directional MAC protocols.

This paper proposes two schemes for wireless sensor networks with multiple sinks in order to save battery energy and to improve network lifetime performance. In these schemes, each sensor node transmits sensing data to randomly chosen sink according to the sending rates. These schemes are referred to as DispersiveCast. The first decentralized scheme called B-DOP scheme (basic DispersiveCast of packet), enables each sensor node to derive locally the sending rate by the number of hops to sinks. The second one is O-DOP scheme (optimal DispersiveCast of packet) that is partially centralized than B-DOP. In O-DOP, sinks send the information to nodes so as for them to calculate the optimal sending rates. DispersiveCast is effective when the generate rate of sensing data at every sensor node geographical nonuniformity. In such a situation, batteries of nodes in the specific area deplete fast and others late, which causes partial depletion of the entire network. As a result, network lifetime can not be extended sufficiently. We evaluate the DispersiveCast schemes by simulations for several network topologies and show that DispersiveCast scheme prolongs the network lifetime compared with the conventional nearest sink scheme, especially O-DOP can achieve 1.5 times longer

In this paper, we propose a novel energy efficient route construction scheme with transmission power control in ad hoc sensor networks. The proposed scheme is very simple and can improve energy consumption performance without any information of neighbor nodes. In the proposed scheme, when a node receives a route request (RREQ), the node calculates the standby time inversely proportional to the received power of the RREQ. The node relays the RREQ with full power when the standby time expires in order to construct the least energy consumption route. After the route construction, source and intermediate nodes transmit packets with power controlled medium access control (MAC) protocol. In addition, we propose the extended version of the proposed scheme to construct energy efficient route in discrete power control environment. Simulation results show the proposed scheme can construct more energy efficient route than the conventional schemes in both continuous and discrete power control environment.

In this paper, in order to realize the consecutive long-term operations of sensor networks, we propose a novel sustainable sensor network routing protocol considering easy node exchangeability. In the proposed routing protocol, in order to exchange nodes easily, power consumption of nodes is biased intentionally. We focus on a routing protocolfor the sustainable sensor networks. In the proposed routing protocol, the power consumption of nodes in a specific zone becomes larger than that in other zones. Battery of Nodes in the specific zone is emptied earlier. As restricting the geographical area of the node exchange, node exchangeability improves. We evaluate the proposed routing protocol by means of computer simulations to show the effectiveness of the proposed routing protocol.

Smart antennas are expected to enhance scalability in ad hoc networks. This paper describes the evaluations of three directional MAC protocols, DMAC, MMAC, and SWAMP, as well as the IEEE 802.11 DCF omni-directional protocol in a multi-hop transmission environment. These evaluations address the problem that performance strongly depends on the route topology between the source and destination, referred to as the directional hidden terminal problem. After analyzing this problem, we propose three MAC level solutions. The MAC solutions are NAV indicators, i.e. HCTS, BRTS, and RCTS, which indicate on-going communications to a directional hidden terminal to set NAV. Based on simulated results, we show that all the proposed MAC solutions could improve the throughput performance

An SR (selective-repeat) ARQ (automatic repeat request) protocol is used for effective packet error recovery on a satellite channel. This SR ARQ has a problem of large delay due to the resequencing of received packets. To mitigate this problem, the PFRS (per flow resequencing) scheme was proposed, where resequencing is performed independently for each upper layer flow, while the detection of lost packets and associated retransmissions are performed based on the whole flows multiplexed over the SR ARQ. The paper models the SR ARQ protocol, where the maximum number of retransmissions is limited, by a collection of simple stop-and-wait protocols; it shows calculation results on the delay distribution of retransmission and resequencing and proves the effectiveness of the PFRS scheme.

The next generation (NG) wireless system, called beyond 3G, is envisioned as a seamless worldwide wireless system. Handover latency is an important metric for the NG wireless system. This paper analyzes the handover latency of mobility support schemes including mobile Ethernet. We compare various mobility support schemes from multiple viewpoints. For the quantitative evaluation, we introduce an analytical network model through a Markovian model. The sequence of location update frames in four schemes, i.e., mobile Ethernet, a scheme using MPLS, a scheme using VLAN tag, and mobile IPv6, formulates the handover latency and handover costs. The combination of the latency of each switch, router, and link defines handover latency. The feature of this analysis is that it evaluates the latency of four schemes within the same model. Numerical results show mobile Ethernet achieves small handover latency. By means of analytical results concerning handover latency and handover costs, we consider the benefit of each scheme.

A number of mobile hosts (MHs) might be densely grouped in an area caused by traffic congestion. In such a situation, the greater part of the MHs will require useful data, such as traffic information, parking information, and other driving-related information. Simultaneous data-transmission broadcasts using a common link are regarded as a suitable medium to distribute this location-dependent information. However, there is no guarantee that MHs can finish receiving the information completely within a limited time. In this paper, we propose a reliable multicast system that consists of a multicast-group-management method called "Advanced-Join (AJ) system", a data-retransmission method, and a data-recovery-processing method. A "base station (BS) multicast group" is defined as the set of BSs in a certain area to which MHs belonging to the same multicast group connect. The AJ system allows a BS that is a member of a BS multicast group to make consolidated join requests to a multicast group on behalf of its MHs. The data-retransmission method determines and processes retransmission data by coordinating retransmission requests from two or more MHs that are members of the same multicast group. The data-recovery-processing method performs interactive data recovery between BSs belonging to the same BS multicast group. By controlling the members of the BS multicast group according to the movement of MHs and deciding the retransmission packets, it is expected that message traffic for the multicast-group management will be reduced, and packet loss during an MHs' movement between BSs will be eliminated. Evaluation results show that about 86.8% of MHs complete data reception with the proposed system, while only two MHs complete reception with the simple broadcast system. In addition, the proposed system with the data-retransmission method needs only two-thirds of BSs compared with that without retransmissions.

There are many paths where users can communicate with wired and/or wireless links. Multiple links are used at the same time and in the same place. In these situations, users can select a better path by measuring the path characteristics. This is also issue in mobile IP triangle routing [K. Ogawa et al., (2001)] [A. Sawai et al., (2001)]. Upload and download traffic is significantly different at short period. For path selection, a one-way measurement is needed, thus we propose a one-way differential delay, which can be used easily to select a better path. We develop a prototype and simulation system.

In most of the previous research on wireless ad hoc networks, omni-directional antennas are usually used. Smart antennas, however, may offer some benefits, i.e., enhancement of spatial reuse of the wireless channel and extension of the transmission range. This paper proposes a MAC protocol called SWAMP for ad hoc networks using smart antennas based on IEEE 802.11 DCF. SWAMP consists of dual access mode which takes advantage of the two benefits. Simulation studies show SWAMP has high throughput, low end-to-end delay and low overhead compared to the IEEE 802.11, DMAC and MMAC.

Omni-directional antennas are usually used in most of the previous researches on wireless ad hoc networks. Smart antennas, however, may offer some benefits compared with them, i.e., enhancement of spatial reuse of the wireless channel and extension of the transmission range. We have proposed a MAC protocol called SWAMP, which consists of two access modes and uses four beamform patterns effectively, one mode mainly designed for spatial reuse of the wireless channel and the other mode mainly for extension of the communication area. This paper evaluates SWAMP through a random topology model with mobility and a grid topology model. The results show the protocol offers an improvement compared to the IEEE 802.11 DCF in terms of success ratio, throughput, delay and number of simultaneous communications.

It is difficult to retain accessibility to data on other hosts in ad hoc networks because of disconnections caused by movements of hosts. This fact affects the practical use of ad hoc networks. When important data are operated on ad hoc networks, it is demanded to retain accessibility to the data from users even if there are disconnections between mobile hosts. Hara (2001) has proposed some methods for allocating replicas on ad hoc networks for achieving high accessibility to data objects shared by multiple hosts. The methods are based on an assumption that all hosts on the network know the probabilities of data access to each object and the probabilities do not change. In this paper, we handle a case that the probability of data access from each host change with time and its location. We propose a method to enhance the accessibility to data that are associated with locations and used frequently by hosts that are close to the location. We assume data object is accessed by geocast. Such kind of data will be used in personal navigation systems, systems handling virtual objects overlaid on to the real world like Space Tag and cooperative work for disaster relief etc.

We propose a multimedia data transmission method, Multitrack, that achieves high-speed and efficient data transmission between multiple mobile hosts and the Internet. This method enables terminals that have a low-speed link to the Internet to receive and play a high quality streaming video, and mitigates the load on the server. Multitrack is used in a cluster-type network where multiple mobile hosts are connected to each other with short-range, high-speed links. High-speed communication with hosts outside the cluster is realized by using multiple wireless links from each mobile host in the cluster and the Internet in parallel and dispersing data traffic to them. By using Multitrack, users of the hosts in the cluster can see a high quality streaming video by sharing the received multimedia data. However, because of the differences in the bandwidth and the delay, it is important to disperse traffic according to the quality of multiple links in order to offer stable multimedia data transmission. We propose an effective traffic dispersion method for this environment, and evaluate this method and other methods by simulations.

An optimistic consistency scheme has been established with respect to data consistency and availability in distributed systems. Wireless networks are becoming popular in data communication and suitable for data sharing among users using multicast capability, but have hidden node problems. This paper proposes a simple data coherency protocol for mobile devices that has less data traffic in data collaboration using a multicast communication feature and is capable of automatic recovery from unpredictable disconnection, such as hidden node problems, by a data versioning scheme using a version vector and update log management. We evaluated the efficiency of data sharing over multicast communication and the recovery cost from failure caused by hidden nodes and confirmed the efficiency of the protocol.

When distributed object technology is used in a wide area network, large delay causes inefficient access to an object. For this problem we propose a replicated object relocation protocol called RORP, where the replicated object is relocated to an intermediate site considering communication delay, processing ability of servers, reference frequency of objects, time of object transmission, domain and limited number of replications. The paper describes the protocol and discusses its performance through an implementation of RORP prototype. We evaluate the client oriented replication scheme by processing amount distributed to clients, intermediate sites and servers. In addition the paper shows how RORP enables reduction of a response time and total network traffic including control messages and object retrieval messages.

Mobile agent technologies are becoming popular as an efficient way to access network resources. Because an application using mobile agents has some unique problems caused by frequent creation, migration and disappearance of mobile agents, a mobile agent platform has to provide not only agents and their execution engines but some functions and mechanisms which are specialized to mobile agent systems. We discuss the practical problems occurring in mobile agent environments, that is, agent controllability, resource restrictions and security. To solve these problems, we propose a mobile agent platform, called SFM (Secured Floating Market) model. This model fulfils the agent controllability by agent control parameters and has the measures against the resource restrictions such as location of resources and agents, processing capability and condition of load. Besides, this model guarantees some suitable security strength levels for flexible execution of various services and user requirements. We implement the prototype of this model using Aglets.

Summary form only given. We present an implementation of a VOD (video on demand) system that addresses the problem of increasing video stream supply capabilities. To obtain fundamental video stream transfer characteristics on Ethernet, we first performed an experiment in which it became clear that bottlenecks of the CPU of a video server were the most serious. To solve this problem we implemented a distributed RAID4 (Redundant Arrays of Inexpensive Disks) method video server. Distributed RAID means extending the RAID method usually applied to disks to the servers, so as to achieve improvements in performance and reliability. The great advantage of this architecture is that the linear performance improvement can be achieved by adding inexpensive servers of a diffusion model.

In a distributed cooperative system, each agent has to observe its environment in order to create an adequate subgoal. The paper discusses tradeoff between cost of observation and achievement of the shared final goal of agents moving in a lattice world. The model is referred to as the restricted Tower of Babel. A cooperation mechanism called LM-DMax is evaluated through simulation against observation interval, the number of agents and moving area site. We find that the model has an optimal observation interval, an optimal area size to minimize the total cost. Then three adaptive policies which modify the observation interval of each agent are proposed. They are geometrical policy, arithmetical policy and slow-start policy. Slow-start policy shows the best result among them. Though the discussion is restricted to the specific model, it is directly applicable to a vehicle scheduling problem and a robot planning problem.

Closed queuing network models representing computer systems with programs that have variation in concurrency level are discussed. For such models computation by existing approximation methods results in an explosion in the size of the state transition matrix. An innovative approximate analytic method based on the overall average concurrent level of programs is proposed. It is found that use of the proposed method considerably reduces the number of states and thus requires less memory. This average concurrency method is a fast algorithm for predicting performance levels of computer systems. Various examples were studied and the accuracy of the method was confirmed determined by comparison to the results of more exact simulations.>