Optical Camera Communication (OCC) uses light sources and Complementary Metal-Oxide-Semiconductor (CMOS) cameras for visible light communication. In this study, we propose a real-time system for self-localization and control in autonomous robots using OCC. This system enables precise localization and low-latency control without LiDAR, reducing costs and maintenance demands. Experimental results demonstrate localization accuracy comparable to advanced Simultaneous Localization and Mapping (SLAM) while ensuring responsive control with minimal latency. Optimizing LED placements further enhances localization accuracy. This method offers a cost-effective and reliable approach for autonomous robots navigation in various environments.

Optical camera communication (OCC) represents a promising visible light communication technology. Nonetheless, typical OCC systems utilizing frame-based cameras are encumbered by limitations, including low bit rate and high processing load. To address these issues, OCC system utilizing an event-based vision sensor (EVS) as receivers have been proposed. The EVS enables high-speed, low-latency, and robust communication due to its asynchronous operation and high dynamic range. In existing event-based OCC systems, conventional modulation schemes such as on-off keying (OOK) and pulse position modulation have been applied, however, to the best of our knowledge, no modulation method has been proposed that fully exploits the unique characteristics of the EVS. This paper proposes a novel modulation scheme, called the event interval modulation (EIM) scheme, specifically designed for event-based OCC. EIM enables improvement in transmission speed by modulating information using the intervals between events. This paper proposes a theoretical model of EIM and conducts a proof-of-concept experiment. First, the parameters of the EVS are tuned and customized to optimize the frequency response specifically for EIM. Then, the maximum modulation order usable in EIM is determined experimentally. We conduct transmission experiments based on the obtained parameters. Finally, we report successful transmission at 28 kbps over 10 meters and 8.4 kbps over 50 meters in an indoor environment. This sets a new benchmark for bit rate in event-based OCC systems.

Optical Camera Communication (OCC) is one of the types of visible light communication which enables low-cost and license-free communication by using general-purpose devices, such as LEDs or displays as transmitters and optical cameras as receivers. It is expected to have variety of applications. Its one of the challenges is constrained communication speed; limited by the frame rate of display and image sensors. Employing multi-level Color Shift Keying (CSK) is being considered as an effective approach to achieve higher data capacity. However, demodulation errors occur when the constellation of the received signal deviates from the reference point due to hue changes of the light source in the shooting environment or internal processing of the image sensor device. This paper proposes an equalization method that uses deep learning to compensate for received constellations and improve communication performance. A simple indoor experiment using LEDs as a light source demonstrates its fundamental effectiveness.

To cope with the increasing mobile traffic, a centralized radio access network (C-RAN: Centralized Radio Access Network) has been investigated. Small cells are possible to distribute the traffic and improve system capacity. However, the effect is limited since the traffic fluctuates depending on location and time of day. To overcome fluctuations in traffic demand, we have proposed an adaptive network architecture that uses crowdsourced radio units (CRUs). CRUs installed in vehicles can function as small cells, adapting to communication demand and enabling efficient traffic accommodation. This paper evaluates the throughput characteristics of the proposed scheme when using actual user distribution fluctuations. Computer simulation clarifies that using CRUs can improve throughput compared to fixed small cells when using user distribution of Tokyo station in Japan.

Optical Camera Communication (OCC) is visible light communication between an LED and a camera. OCC has attracted considerable attention as a license-free and low-cost wireless communication. A practical scenario for mobile OCC is to obtain information by scanning the digital signage with users' smartphones. However, to the best of our knowledge, there are no modulation schemes for embedding optical signals in human-perceivable content. Therefore, in this paper we propose a modulation scheme called Signage Cipher Modulation (SCM) for OCC. The aim of the SCM is to embed optical signals in a human-visible video content in digital signage. It integrates data communication with advertising and cinematic content. We have formulated the performance limit of the SCM, and then confirmed its performance with experimental results.

3D point cloud data has attracted growing attention in various fields. The identification of grounding surfaces from point cloud contributes for numerous practical applications including interactive systems and infection prevention. However, it is difficult to distinguish a target object from other objects with similar shapes. RGB cameras can recognize targets with similar shapes, but cannot accurately gauge the contour of the grounding surface. There have been no studies on seamless methods combining RGB and depth data to estimate the grounding surface of a target object. To address this problem, this paper proposes a grounding surface estimation method using RGB-D data. It first identifies the target object from RGB data. Then, the grounding surface of the target object is estimated from point cloud data. The grounding contour of the target object is accurately estimated even when several objects with similar shapes are on the physical surface. The performance of the proposed scheme was evaluated from experiments using Intel RealSense D455. We confirmed that the proposed scheme achieves identifying the grounding surface, even in the presence of non-target objects.

Optical camera communication (OCC), a type of visible light communication, is expected to have various applications such as sensor devices because it can be realized at low cost and license-free by using general-purpose devices. Although the communication speed is limited by the frame rate of the camera and other factors, Color Shift Keying (CSK) is being considered as an effective means of increasing capacity. However, in mobile environments, the hue of the captured light source changes depending on the angle of the transmitter and receiver, resulting in demodulation errors. This paper proposes a dual-camera diversity selection to stabilize received optical symbols. Experiments using a synchronized dual-camera sensor kit confirm the effectiveness of the proposed approach.

This paper proposes a simplified enhancement of deep learning-based Joint Source-Channel Coding (Deep JSCC) over multiple-input multiple-output (MIMO) channel. Deep JSCC utilizes trained encoder and decoder weights, allowing for the integrated implementation of source coding and channel coding for batch image transmission. During the training phase, the system is trained based on single-input single-output (SISO) with additive white Gaussian noise (AWGN) communication channel principles. In the testing phase, image signals are transmitted via MIMO channels after passing through the respective Deep JSCC encoder and precoding, and subsequently, these signals are input to the decoder after MIMO detection. We examine fundamental image transmission performance in terms of PSNR and SSIM under various MIMO weight designs.

Optical Camera Communication (OCC) is an emerging wireless communication technology. It enables data transfer between light sources such as LEDs and the CMOS image sensors of cameras. Existing work has focused on improving the communication performance as regards data rate and bit error rate. However, there has been no study on the simultaneous transmission of different information from a single transmitter to multiple receivers with different camera parameters. Therefore, this paper proposes a Camera Parameters Division Multiplexing (CPDM) signal transmission for OCC. The goal of the CPDM is to multiplex optical signals to different receiver cameras using a single transmitter. The feasibility of the proposed CPDM is demonstrated through first experimental results. We have also evaluated the phase shift tolerance to confirm the simultaneous multi-receiver communication OCC.

Sensor data acquisition has many benefits in agriculture, and research is actively being conducted into the communication of sensor data. 3D modeling of farmland using photogrammetry has also been reported in several studies. However, there has never been a system that can integrate sensor data acquisition and 3D model generation to create a farmland digital twin. This paper proposes an integrative creation of a farmland digital twin with photogrammetry and Optical Camera Communication (OCC) using a camera. Utilization of OCC as a means of communication allows for high-density sensing at a low cost. This system facilitates decision-making by easily comparing farmland situations in detail. We conducted a preliminary experiment at a haskap farm in Chitose City, Hokkaido to demonstrate the feasibility of the proposed system.

This paper investigated the impact of a quantizer in analog-to-digital and digital-to-analog converters in communication devices on image quality when using deep learning-based joint source-channel coding modulation (JSCCM) for image transmission. In recent years, JSCCM, which efficiently encodes images and videos with low information entropy, has attracted great attention. JSCCM has a structure based on an autoencoder and determines the compression ratios for the image input by adjusting the number of IQ symbol output. The IQ symbol output from the encoder are allocated to symbol constellations with higher degrees of arbitrariness than those in typical square quadrature amplitude modulation and are therefore expected to be strongly affected by the quantization noise. In this paper, we employed quantization to the IQ symbol sequence and investigated its effect. Adjusting the quantizer's clipping ratio and the number of quantization bits, we examined the images' tolerance of the peak signal-to-noise ratio (PSNR). The simulation results showed that by adequately adjusting the clipping ratio, the image quality can be guaranteed to be equivalent to ideal conditions without quantization noise, and the number of required quantization bits that do not degrade the PSNR, was calculated.

Human movement analysis is a significant topic for understanding and improving human activities. In this paper we propose a vision-based swing trajectory detection scheme. The goal of the proposed idea is to trace the trajectory of swung object using an RGBD camera. The coordinates of the tip of grasping object are estimated from hand joints in pose estimation. The feasibility of the proposed scheme was demonstrated via experimental results of cleaning activities using a mop. It will contribute for assessing users' progress by giving further training tips for swing motions.

Asynchronous Quick Link (A-QL) is the tri-color-band screen code specified as one of the transmission symbol formats in IEEE802.15.7. An edge detection algorithm is required to extract information from A-QL symbol. However, edge detection wastefully recognizes unwanted objects and it results in communication failure. To improve its detection performance, this paper proposes to apply YOLO as deep learning-based object detection and demonstrates its effectiveness.

Effective monitoring and support of cleaning activities is one of the significant challenges in maintaining a clean and healthy environment for infection prevention. Therefore, in this study, we focus on cleaning activities to propose a real-time activity support system. Users' activities are recognized with a machine learning technology based on accelerometer data acquired with mop-mounted sensors. We developed a real-time support system where a robot provides audio feedback for motivating the user. It was confirmed that the proposed system will contribute for maintaining clean environment by encouraging users to establish a lifestyle in the new normal era.

Deep joint source-channel coding (JSCC) has been attracting attention for achieving task-oriented communication. It replaces traditional information source coding and channel coding with a deep learning-based autoencoder, directly mapping information sources such as images to IQ symbols. For images, it is claimed to avoid the cliff effect and achieve a higher peak signal noise ratio (PSNR) even in low SNR regions. While related work has assumed various propagation channel models and validated the effectiveness of Deep JSCC, there are few reports confirming its principles through experiments. Specifically, to the best of our knowledge, there are no reported examples of experiments of Deep JSCC in 5G systems. In this paper, we present a proof-of-concept of Deep JSCC in a 5G system. We modified commercially available 5G base stations (gNB) and 5G terminals to enable input and output of IQ data from external devices. We connect the 5G devices using coaxial cables and attenuators, transmit and receive JSCC signals, and evaluate the PSNR. The results demonstrate that even when communicating at power levels lower than the minimum receiver sensitivity specified in the receiver’s datasheet, the image can be successfully restored with less than 1 dB degradation in PSNR compared with the simulation result.

Optical camera communication (OCC) can be realized by commercial LEDs or displays as a transmitter and image sensors as a receiver. One of the challenges to enhance the transmission capacity in OCC is a two-dimensional light source with a display at the transmitting side. So far, the optimization of the imaging process and the method of tracking and detecting the light source have not been studied in detail. This paper proposes a dynamic light source detection system based on the A-QL method specified in IEEE 802.15.7 as a transmission symbol format. It employs YOLO, a deep learning-based object detection algorithm, and optimizes the image capture process as a symbol detection system. The proposed system can detect two-dimensional symbols with adjusting its angle and orientation. Its effectiveness and feasibility are demonstrated through an experimental evaluation.

Underwater communication is expected to provide ubiquitous connectivity all over the world. The considerable practical issue of underwater optical communication (UWOC) is the directivity control due to oceanic turbulence. Underwater optical camera communication (UWOCC) is a promising option for UWOC that provides low-directivity visible light communication. OCC is a wireless communication technology between a light source such as LED and a complementary metal-oxide-semiconductor (CMOS) image sensor in a camera. Although OCC has attracted considerable attention, equalization and demodulation of received optical signals in underwater environments have not been well studied. Thus, in this paper we propose a predictive equalization technique for UWOCC assuming color shift keying (CSK) based on the different attenuation of light intensity depending on the wavelength. The received optical signals are identified by predicting color shift in the color space from link distance. This paper also reports the first demonstration of the proposed predictive equalization in seawater. The feasibility is confirmed via experimental results with 8 and 16 CSK transmission at a pier in Kobe city, Japan.

Underwater communication has attracted considerable attention for providing ubiquitous connectivity all over the world. The major constraint for laser diode (LD)-based underwater optical communication (UWOC) is the strong directivity of the transmitter and the receiver. The directivity control of laser beams is a fatal challenge due to oceanic turbulence. Underwater optical camera communication (UWOCC) is a promising option thanks to its low-directivity. However, the previous works on UWOCC have only focused on link-level propagation characteristics. Therefore, this paper proposes a drone-based underwater sensor network system with UWOCC. An underwater drone cruises around and receives optical signals from spatially distributed sensor nodes in underwater environments. The feasibility of the proposed system is confirmed via experimental results in Kobe city, Japan.

Numerous radio units (RUs) are required to densely compose small cells in the beyond 5G mobile networks. The concept of vehicle-mounted RUs is a promising solution for dynamically deploying small cells in accordance with demand distribution. Wireless relay fronthaul networking is the key enabler for this concept, where forwarding paths of fronthaul streams are computed in real-time by an edge server using the link-state information reported from RUs. Optimization of the reporting interval is a significant issue because of the tradeoff between the freshness of report messages and network load. However, existing works have not investigated the freshness of link information in highly dynamic environments. In this paper, we introduce a new reliability metric called the reliability of information (RoI). The RoI is defined as the joint probability that all nodes in the network maintain correct information. We establish a tractable lower bound of the RoI under a mild assumption on the dynamics of node connectivity, which enables us to design small cells guaranteeing information reliability. We further formulate and solve an optimization problem to find an optimal reporting interval. The usefulness of the proposed scheme is demonstrated through simulation experiments for a highly dynamic vehicular network.

Device-to-Device (D2D) communication is a promising solution for providing on-demand network connectivity to numerous devices. In particular, the autonomous D2D approach enables personal devices to flexibly communicate with each other with less operation. Despite all the benefits of D2D communication, security is a significant concern because of the broadcast nature of wireless communication. The biggest threats for the autonomous D2D are masquerading, impersonation, man-in-the-middle (MITM) attacks due to absence of a trusted third party. There have been many research efforts on this problem including physical layer based and the well-known Diffie-Hellman based approaches. However, they cannot be employed for authentication between physically distant devices. To address this problem, this paper proposes a multi-channel authentication for the autonomous D2D using optical camera communication (OCC). It executes the Diffie-Hellman key exchange in an optical link between a light source and a camera. The idea behind the proposed scheme is to leverage the limited reachability of OCC for ensuring security; a device can only communicate with a visible device. In this paper we introduce the security analysis for the proposed authentication and preliminary results using smartphones.

The world is often stricken by catastrophic disasters. On-demand drone-mounted visible light communication (VLC) networks are suitable for monitoring disaster-stricken areas for leveraging disaster-response operations. The concept of an image sensor-based VLC has also attracted attention in the recent past for establishing stable links using unstably moving drones. However, existing works did not sufficiently consider the one-to-many image sensor-based VLC system. Thus, this paper proposes the concept of a one-to-many image sensor-based VLC between a camera and multiple drone-mounted LED lights with a drone-positioning algorithm to avoid interference among VLC links. Multiple drones are deployed on-demand in a disaster-stricken area to monitor the ground and continuously send image data to a camera with image sensor-based visible light communication (VLC) links. The proposed idea is demonstrated with the proof-of-concept (PoC) implemented with drones that are equipped with LED panels and a 4K camera. As a result, we confirmed the feasibility of the proposed system.

The COVID-19 pandemic has presented social challenges to establish the new normal lifestyle in our daily lives. The goal of this paper is to enable easy and low-cost monitoring of cleaning activity to keep a clean environment for preventing infection. Although human activity recognition has been a hot research topic in pervasive computing, existing schemes have not been optimized for monitoring cleaning activities. To address this issue, this paper provides an initial concept and preliminary experimental results of cleaning activity recognition using accelerometer data and RFID tags. In the proposed scheme, machine learning technologies and short range wireless communication are employed for recognizing the time and place of wiping as an example of cleaning activities, because it is an important activity for shared places to avoid infection. This paper reports the evaluation results on the recognition accuracy using the proof-of-concept (PoC) implementation to clarify the required sampling rate and time-window size for further experiments. Also, a real-time feedback system is implemented to provide the monitoring results for users. The proposed scheme contributes for efficient monitoring of cleaning activities for creating the new normal era.

As one of the realization for real-time remote monitoring and object recognition using high-definition camera images that can support safe and automated driving, this paper proposes image size reduction by differentiation from the background, focusing on fixedly installed cameras at Road-Side Unit (RSU). The video images acquired by the camera installed on RSU are transferred to the edge server, and information related to the traffic situation is recognized by image processing. Since it requires high-capacity transmission, the use of the millimeter-wave band having large bandwidth available is essential. Meanwhile, a multi-hop relay is desirable due to its short coverage. In this case, a multi-hop relay should support video image traffic from multiple RSU nodes and hence it accumulates the transmission latency. The proposed scheme greatly reduces the size of the image that needs to be transmitted, while maintaining object (vehicle) detection accuracy.

The deep joint source-channel coding and modulation (JSCCM) is a promising technology to realize efficient communication over extreme environments such as underwater area. In previous works, it is shown that deep convolutional neural networks (CNN) can successfully learn JSCCM encoder and decoder, outperforming conventional separation-based coding and modulation schemes in low signal-to-noise ratio settings. This paper proposes a new architecture for deep JSCCM based on the self-attention mechanism. We show that the proposed architecture achieves significant performance improvement compared with the CNN-based schemes while requiring a smaller network size in terms of the number of weight parameters. Furthermore, we present efficient hardware implementation of the proposed JSCCM encoder on a field programmable gate array (FPGA). In particular, we demonstrate that a systolic-array-like structure is effective for FPGA implementation of the proposed JSCCM scheme based on the self-attention mechanism.

Optical Camera Communication (OCC) is a promising solution for long-range point-to-multipoint communication between drones and a ground camera. OCC requires line-of-sight (LoS) channels for a camera to receive optical signals transmitted from drone-mounted LED lights or panels. When multiple drones are deployed in a certain area to transmit optical signals simultaneously, inter-light interference avoidance is a significant issue. The inter-light interference has been modeled in the previous works. However, existing works have not sufficiently investigated the trajectory control of drones. To address this issue, in this paper, we propose a distributed trajectory control algorithm for drones to avoid inter-light interference. Based on the approximate interference model in the image plane, each drone controls its trajectory to ensure LoS communication links of other drones. The performance of the proposed algorithm is confirmed via intense multi-agent simulation. The proposed algorithm contributes to establishing stable point-to-multipoint OCC links between drones and a ground camera.

Distributed Denial-of-Service (DDoS) defense mechanisms have been a significant research issue in network security. A wide variety of DDoS defense strategies have been introduced such as machine learning based approaches. Among them, the cyclic queuing based approach is a promising solution for mitigating flooding attacks with resource-limited devices at a network edge. Attacking flows are detected via the cyclic queuing, i.e. repeated reconfiguration of queue mappings, with the variation of the current queue sizes as metrics. However, continuous reconfiguration during normal operation periods is computationally intensive and increases power consumption. To address this problem, this paper proposes a two-stage mitigation scheme with variational auto-encoder (VAE) and cyclic queuing. With the proposed scheme, an edge node such as a layer-2 switch first detects anomaly with VAE, and then high-rate malicious flows are identified with the cyclic queuing algorithm. The key idea for improving detection speed is to narrow down suspected flows with the history of queue sizes around the anomaly detection. The performance of anomaly detection with VAE was evaluated with open datasets. Then, the performance of the proposed algorithm was confirmed via theoretical analysis and computer simulation.

A hybrid virtual work model which combines on-site and remote work will contribute for increasing productivity during and after the pandemic. Many workers are interested in collaboration tools among on-site and remote workers for well-being. A significant problem that lies in a hybrid model is that uninterrupted work hours shrank during the work from home period. There have been many worker assistance systems based on focus monitoring technologies. However, existing schemes did not consider groups of workers in hybrid environments. To address this problem, in this paper we propose a real-time interruptive task mediation system among hybrid workers. The goal of the proposed scheme is to improve the total productivity of workers by optimally allocating interruptive tasks based on focus monitoring. The focus state of a worker is considered as a two-state stationary Markov process. The optimum monitoring interval to ensure the target error tolerance is determined using the Age of Information. The feasibility of the proposed scheme was demonstrated via computer simulations and preliminary experimental results.

Underwater networks are expected to be service platforms for broad-sea and deep-sea activities. The significant challenge of underwater communication has been achieving high-speed and long-distance data transmission due to the high-attenuation and time-varying channel state in underwater environments. It is reasonable to get the underwater data above the water surface for establishing underwater-ground networks. However, it is still an unsolved issue to efficiently establish underwater-ground communication channel. To address this problem, we propose an aquatic fronthaul for underwater-ground communication, where floating aquatic relay nodes relay data from underwater drones/sensors to a ground radio unit. We propose a relocation algorithm for aquatic relay nodes to efficiently reconstruct the network according to the distribution of underwater nodes. The advantage of the proposed algorithm is robustness for the uncertainty of underwater node locations due to the difficulty in underwater localization. The performance of the proposed algorithm was evaluated with multi-agent simulations. The feasibility of the aquatic fronthaul network was confirmed via the experimental results with a Wi-Fi mesh network above the water.

The spatio-temporal patterns of mobile traffic demand due to human mobility and lifestyles are significant issue for efficient deployment of 6G mobile networks. A drone cell is a promising solution for provide flexible and on-demand connectivity. The existing works have assumed wireless fronthaul/backhaul links between a drone cell and a ground node such as millimeter wave (mmWave) links. The unstable aerial environments may deteriorate the communication quality in wireless links. The concept of tethered communication for moving nodes is a solution for establishing stable and high-bandwidth fronthaul/backhaul for drone cells. However, experimental results with real drones have not been reported so far. Therefore, this paper introduces the concept of the tethered drone cell and the first experimental results for 6G communications. We also propose the routing algorithm for a drone cell to autonomously move to the next destination. The feasibility of the proposed scheme was demonstrated via computer simulations and preliminary experimental results using real drones.

Underwater communication is one of the biggest challenges for 6G communications to provide ubiquitous connectivity all over the world. Although optical communication is a strong option for underwater communication, the directivity control of laser beams has been a considerable practical issue due to oceanic turbulence. While optical camera communication (OCC) is an emerging technology for next generation wireless communication, there have been few works on underwater OCC (UWOCC). The propagation characteristics of the optical signals transmitted from LED lights in UWOCC have not been well studied. Therefore, This paper proposes a predictive equalization technique for UWOCC assuming color shift keying (CSK), where the optical signals are modulated by modifying the intensity of the three-color LED luminaires. The proposed technique predictively equalizes the received signals from the symbol color and link distance leveraging the different attenuation of light intensity depending on the wavelength. We demonstrate the feasibility of the proposed idea via experimental results at a depth of 3.5 meters. The proposed equalization improved symbol error rate (SER) so that the overall bit error rate (BER) was significantly suppressed.

Communication in extreme environments is an important research topic for various use cases including environmental monitoring. A typical example is underwater acoustic communication for 6G mobile networks. The major challenges in such environments are extremely high-latency and high-error rate. They make real-time image transmission difficult using existing communication protocols. This is partly because frequent retransmission in noisy networks increases latency and leads to serious deterioration of real-timeness. To address this problem, this paper proposes a stochastic image transmission with Constrained Application Protocol (CoAP) for extreme environments. The goal of the proposed idea is to achieve approximate real-time image transmission without retransmission using CoAP over UDP. To this end, an image is divided into blocks, and value is assigned for each block based on the requirement. By the stochastic transmission of blocks, the reception probability is guaranteed without retransmission even when packets are lost in networks. We implemented the proposed scheme using Raspberry Pi 4 to demonstrate the feasibility. The performance of the proposed image transmission was confirmed from the experimental results.

In recent years, the advances in deep learning (DL) technology have greatly improved artificial intelligence (AI)-related research and services. Among them, real-time object recognition using network cameras has become an important technology for various applications. A large number of network cameras are being deployed for real-time object detection using DL models at GPU-based edge servers. A significant issue for widely deploying this type of systems is low-cost network deployment and low-latency data transmission. A promising option for efficiently accommodating numerous network cameras is time- and wavelength-division multiplexed passive optical network (TWDM-PON), which has prevailed in optical access network systems. The key challenge in a GPU-based inference system via TWDM-PON is to optimally allocate upstream wavelengths and bandwidths to enable real-time inference. To address this problem, this article proposes the concept of an inference system in which many cameras upload image data to a GPU-based edge server via TWDM-PON. A real-time resource allocation scheme for TWDM-PON is also proposed to guarantee low latency and time-synchronized data arrival at the edge. We formulated the wavelength and bandwidth allocation problem as a Boolean satisfiability problem (SAT) for fast computation. The performance of the proposed method is verified by computer simulation. The proposed scheme contributes to the increase in the batch size of arriving data at the edge server while ensuring low-latency data transmission. As a consequence, the computational efficiency of the GPU-based inference server is greatly improved by the increase in the batch size of data.

A key aspect to realize Internet of things applications such as industry automation and smart agriculture is to enable realtime and networked automatic monitoring via cloud computing and computer vision. However, to design a networked monitoring system, it is necessary to realize a balance between the monitoring accuracy and monitoring cost, for instance, between the network traffic to transmit images and the computation load. Although the monitoring cost can be decreased by increasing the sampling interval of cameras, it becomes more likely that informative images cannot be obtained; in other words, the monitoring accuracy decreases with a reduction in the amount of data. Moreover, although on-device image processing can decrease the network traffic, a large computation delay may be incurred, limiting the sampling rate of the monitoring system. The objective of this study was to examine the balance between the monitoring accuracy and cost and to develop a joint optimization technique for the sampling interval and computation offloading to minimize the monitoring cost in a networked monitoring system while ensuring a high monitoring accuracy. The main contributions of this paper are that we prove the joint optimization problem can be solved explicitly and to develop an algorithm to obtain the solution of the joint optimization problem. The simulation results demonstrated that the proposed algorithm can reduce the monitoring cost by 24-48% while maximizing the number of nodes ensured to achieve high monitoring accuracy.

In recent years, advances in deep learning (DL) technology have greatly improved research and services related to artificial intelligence (AI). In particular, real-time object recognition has become an important technology in smart cities. To achieve this, low-cost network deployment and low-latency data transfer are the key technologies. In this paper, we focus on Time- and Wavelength-Division Multiplexed Passive Optical Network (TWDM-PON) based inference systems to deploy cost-efficient networks that accommodate many network cameras. A significant issue for a GPU-based inference system via TWDM-PON is optimally allocating upstream wavelength and bandwidth to enable real-time inference. However, it has not been considered to increase the batch size of arrival data at edge servers ensuring low-latency transmission. Therefore, this paper proposes a concept of an inference system in which a large number of cameras periodically upload image data to a GPU-based server via TWDM-PONe We also propose a cooperative wavelength and bandwidth allocation algorithm to ensure low-latency and time-synchronized data arrival at the edge. The performance of the proposed scheme is verified with computer simulation.

The Internet of Things (IoT) has been growing rapidly in recent years. With the appearance of 5G, it is expected to become even more indispensable to people's lives. In accordance with the increase of Distributed Denial-of-Service (DDoS) attacks from IoT devices, DDoS defense has become a hot research topic. DDoS detection mechanisms executed on routers and SDN environments have been intensely studied. However, these methods have the disadvantage of requiring the cost and performance of the devices. In addition, there is no existing DDoS mitigation algorithm on the network edge that can be performed with the low-cost and low-performance equipment. Therefore, this paper proposes a light-weight DDoS mitigation scheme at the network edge using limited resources of inexpensive devices such as home gateways. The goal of the proposed scheme is to detect and mitigate flooding attacks. It utilizes unused queue resources to detect malicious flows by random shuffling of queue allocation and discard the packets of the detected flows. The performance of the proposed scheme was confirmed via theoretical analysis and computer simulation. The simulation results match the theoretical results and the proposed algorithm can efficiently detect malicious flows using limited resources.

Underwater communication is a promising technology to provide ubiquitous network connectivity, where acoustic waves are used as the primary carrier for long-range communication. It has been a challenging research topic to efficiently transmit images with under-water acoustic communication (UAC), due to its inherently narrow bandwidth, strong signal attenuation, time-varying multipath propagation, and low propagation speed. In this paper, we present a new approach to addressing these limitations in UAC, namely the joint source-channel coding and modulation (JSCCM) based on a deep neural network (DNN). We develop a training method of DNN-based encoder and decoder, which directly encode/decode image-pixel values to modulated symbols, unlike conventional separation-based source and channel coding and modulation. Through numerical simulations, the deep JSCCM is confirmed to achieve significantly higher data-rate than conventional schemes.

Optical Camera Communication (OCC) is a promising solution for future wireless communication thanks to the advantages including security, license, and cost-efficiency. Widely available smart devices with em-bedded cameras such as smartphones, tablets, and digital cameras can be employed as receivers in OCC with-out modifying hardware. A complementary metal-oxide-semiconductor (CMOS) sensor in a camera can receive optical signals from multiple light sources at the same time. Since the light source occupies a certain area in the image plane, the received signal powers differ among the corresponding pixels. When the number of light sources increase, the signals from a light source can be blocked by another light source or affected by the blooming effect of other optical signals. However, there has never been a general model for avoiding such interference in OCC. Therefore, in this paper we propose a general model for avoiding inter-light sources interference. The proposed model formulates the constraints with perspective transformation based on the parameters of an image sensor and a camera. We also provide preliminary experimental results to validate the proposed model.

This paper proposes a retransmission control system based on image recognition accuracy as a traffic reduction technique for improving network bandwidth usage efficiency in image recognition service using wireless edge computing. For traffic reduction, image compression is useful. However, it is known to deteriorate the recognition accuracy. Our proposed system is applied to guarantee this deterioration. By retransmitting images according to the recognition accuracy, we aim to reduce traffic and to guarantee recognition accuracy. When compressing the image, PSNR is calculated to adaptively change the ratio of the image compression, to maintain the image quality. This paper uses down-sampler as an image compression method and demonstrates the effectiveness of the proposed system through a wireless network simulation. We confirm that our proposed retransmission system can reduce the network traffic congestion, and guarantee the recognition accuracy as same as the conventional method.

Optical Camera Communication (OCC) is an emerging technology for wireless communication between smart devices such as smartphones. A light source such as a LED light and a LED panel is employed as a transmitter in OCC. Among the various modulation schemes, color shift keying (CSK) has attracted considerable attention to improve throughput. CSK exploits the design of three-color LED luminaires. Although CSK is a promising modulation technique, the relationship between symbol colors and external environments has not been considered yet. Some colors become difficult to identify depending on environments including ambient light. To address this problem, this paper proposes an adaptive (N+1)-CSK to define one more color in addition to general CSK. The goal of the proposed scheme is to reduce bit errors by explicitly defining a base color, i.e. NULL color. One symbol is adaptively selected as the base color from the N+1 symbols according to external conditions, and data signals are transmitted using other symbols. We also provide preliminary experimental results to validate the proposed scheme. The bit error rate (BER) is significantly suppressed by appropriately setting the base color depending on the pilot signals of each symbol.

This paper proposes a space-time-domain successive interference cancellation-based adaptive equalizer (STD-SIC-AE) for underwater acoustic communication (UAC). The demand for high-capacity real-time video transmission underwater has increased for exploring ocean resources and marine research. UAC is capable of long-haul transmission and is the promising means for deep-sea exploration. However, the transmission capacity is limited because of the reflected wave from the sea surface and seafloor. It causes a multipath interference with a long propagation delay that is not easy to remove by the conventional space-time-domain equalizer. This is because that the finite impulse response (FIR) filter requires impractically huge taps. In this paper, by taking advantage of the fact that the interference (delay) wave is a direct wave that has already been received, a replica is generated by the received direct wave and the SIC is operated in the time domain. The numerical simulation verifies that our proposed STD-SIC-AE can significantly improve BER performance in terms of SNR and SIR even under higher-order modulation such as 16QAM.

This paper proposes the concept of a visible light communication (VLC) system with LED-mounted drones for post-disaster monitoring and reports the results of the experimental feasibility evaluation. Post-disaster monitoring is a critical social issue given that in the case of a large-scale disaster, communication failure can occur, and the power supply can be interrupted. A VLC system is useful for investigating the disaster situation, can assist in life-saving activity by lighting the area, and allows for communication from the disaster area to a base station. In this study, the VLC system consists of LED-mounted drones and an image-sensor-based receiver. The drones are videoed and subsequently detected by the camera with a YOLOv3-based convolutional neural network (CNN). After detecting the position of the drones, the light signal is demodulated. This paper reports the following feasibility evaluation results; 90% accuracy in drone recognition and a low bit error rate with the distance of up to 80 m between the drones and the camera.

The concept of user-provided networks (UPNs) have been investigated to utilize the power of citizens in the deployment of mobile networks. In addition to conventional fixed host models, mobile models have been proposed to utilize movable hosts including mobile phone users. When mobile UPNs are considered, it becomes difficult for clients to establish a stable connection to hosts because they can be in moving environments, such as public buses and taxis. To address this problem, this article focuses on Age of Information (AoI) which a measure of the freshness of a continually updated piece of information. Thus, this article proposes an AoI-based host-selection algorithm for mobile UPNs for establishing stable connection. It determines the host device to establish a wireless link based on the history of values and AoI of received signal strength indicator (RSSI). The performance of the proposed scheme is confirmed through theoretical analysis and computer simulations.

A mobile base station (MBS) comprises a central unit (CU), a distributed unit (DU), and a remote unit (RU) for efficient deployment in 5th-generation mobile communications system and beyond. The link between a DU and an RU is established using an optical fiber and is renowned as fronthaul (FH). Our study aims to reduce the cost of this FH link. Networking and wirelessly connecting the FH link have been studied to suppress the FH link cost and to flexibly deploy RU1s. Wireless fronthauling is an important solution because it can dispense with optical fiber laying. The location of MBSs is also an important issue. RUs are densely deployed to gain high wireless throughput per area. A DU should be located at a place where RUs can perform cooperative operation and the latency requirement is satisfied. To reduce the optical fiber deployment cost, we studied the DU placement design using wireless multihop links and point-to-point (PtP) optical links. It determines the DU locations to increase the wireless links for reducing the deployment cost. The problem of this scheme is that most of the RUs are still connected to the DU via PtP optical links. This paper proposes a novel FH designing scheme that employs a passive optical network (PON) and wireless multi-hop as well as PtP optical links. We propose a separate designing algorithm for the wireless and PON links. The optical fiber link cost is observed to reduce when compared with that of the previously proposed scheme using a simulation.

Crowdsensing has been intensely studied to leverage the power of citizens for completing large-scale sensing tasks at a lower cost. It is considered as an efficient and economical approach on crowdsensing to develop unified platforms for various sensing applications. However, existing frameworks were vertically integrated systems, and thus the installation cost cannot be shared among multiple organizers. Another important issue of crowdsensing is to preserve the security and privacy of an individual and to maintain the integrity of sensor data. To address these problems, this paper proposes the network-side task allocation (NeSTA) framework for crowdsensing. In the proposed framework, the mobile network mediates the organizers and the participants. A sensing task is requested by the organizer to the mobile network, and then allocated to the participants by mobile edges. The installation cost of applications is significantly reduced with the horizontal integration of multiple applications. The privacy is ensured by obscuring the participants from the organizer. The size of task-allocation problem is reduced by dividing the original problem into subproblems. The validity of the proposed approach was confirmed through computer simulations using open dataset.

Centralized radio access network (C-RAN) architecture prevails to efficiently forward the ever increasing mobile traffic towards beyond 5G era. Densely deployed radio units (RUs) compose small cells and an ultra high-density distributed antenna system (UHD-DAS). Distributed units (DUs) are placed close to RUs and linked to them via fronthaul to satisfy strict latency requirement. The DUs are connected to a central unit (CU) installed in a central office through optical midhaul links. Although it has been a hot research topic to compose a fronthaul network, there has been little research on the concept of midhaul networking. Therefore, this paper proposes the C-RAN architecture which consists of passive optical network (PON)-based midhaul links and wireless relay fronthaul networks. The goal of the proposed idea is to reduce fiber deployment cost by optical fiber reduction. We also propose a joint routing and dynamic wavelength and bandwidth allocation (DWBA) algorithm for optimally allocating resources considering the bandwidth utilization and the latency requirement. It is confirmed through computer simulations that the accommodation efficiency of optical midhaul can increase fourfold with the proposed scheme.

Many radio units (RUs) are densely deployed to compose small cells with high frequency bands in the Centralized Radio Access Network (C-RAN) architecture which prevails in the 5G mobile networks. More and more cells will be required in the era of beyond 5G and 6G to ensure wide coverage with high data rate. The concept of on-board cells, i.e. vehicle-mounted RUs, is a promising solution for future mobile networks because it enables dynamic and efficient network deployment to deal with the fluctuations in mobile traffic demand. Cell zooming has also been proposed to adaptively adjust the cell size according to fluctuating traffic load. However, it has not been considered to employ cell zooming for vehicle-mounted cells. The existing cell zooming schemes cannot be employed for vehicle-mounted cells because of their high mobility. Therefore, this paper proposes a cell zooming scheme for vehicle-mounted RUs. The goal of the proposed technique is to optimize the size of each cell according to the current distribution of RUs for reducing transmission power. It contributes for energy efficiency of mobile networks. The performance of the proposed approach was confirmed through computer simulations.

This paper proposes the blind interference power estimation via deep learning approach exploiting the visualized wireless signal information. Blind adaptive array (BAA) signal processing is the powerful solution to suppress various kinds of interference such as inter-cell interference (ICI) and intersystem interference (ISysI) for which receivers cannot obtain a priori information represented as channel state information (CSI). However, BAAs cannot always suppress interference due to its blind nature. Depending on signal-to-interference power ration (SIR), adequate BAA algorithms should be switched. In order to estimate SIR in a blind manner, we propose to apply a convolutional neural network (CNN) trained by IQ constellation images where contains the desired and interference signals. This paper presents its methodology and fundamental possibility.

The concept of vehicle-mounted crowdsourced radio units (CRUs) for a smart city has been proposed to utilize the power of citizens in the deployment of small cells of the centralized radio access network (C-RAN) architecture. Wireless relay fronthaul networking is a promising solution for efficient utilization of vehicle-mounted small cells. However, there have been no routing schemes that can satisfy the strict delay requirements of mobile fronthaul coping with the high dynamicity of vehicles. Thus, this paper proposes a real-time routing scheme for establishing wireless relay fronthaul with vehicle-mounted CRUs. The route optimization is formulated as a boolean satisfiability problem (SAT), and an FPGA-based SAT solver is employed for the fast computation. It can dynamically optimize the forwarding paths in real-time with the constraints of delay requirements. The performance of the proposed routing scheme is confirmed via computer simulations.

Device-to-Device (D2D) communication is expected to be a promising solution in next generation cellular technologies to efficiently provide connectivity over a wide area with low-cost. This paper proposes a gamified participatory D2D (GP-D2D) communication for encouraging mobile users to appropriately activate access point (AP) functions with their mobile devices. It is implied that gamification is an effective approach for encouraging autonomous D2D communication if the game-settings such as gain mechanisms are appropriately defined.

In metropolitan areas, spatio-temporal patterns of human mobility result in significant fluctuations of mobile traffic. Such fluctuations drastically deteriorate the efficiency and financial viability of conventional mobile networks. This is because mobile networks have been designed to cope with the peak traffic, and thus their capacities are underutilized for most of time. To make matters worse, this trend will be intensified with the increase in mobile traffic. To address this issue, this paper proposes a concept of adaptive mobile network architecture with moving nodes towards beyond 5G era. It consists of densely deployed radio units (RUs) and moving distributed units (DUs) in the centralized radio access network (C-RAN) architecture. The mobile traffic is forwarded through optical midhaul links and wireless relay fronthaul links satisfying the latency requirement. This paper also proposes an algorithm for optimizing the activation states of RUs, the relocation schedule of DUs, and forwarding paths of fronthaul streams according to the demand distribution. It was confirmed with computer simulations that the proposed architecture can efficiently activate RUs and DUs by optimizing the location of DUs and forwarding paths of fronthaul streams.

The centralized radio access network (C-RAN) architecture prevails in beyond 5G mobile networks. Along with the cell size reduction, the efficiency of C-RAN architecture is drastically deteriorated by the spatio-temporal fluctuations in mobile traffic demand. To address this problem, we proposed a concept of adaptive C-RAN architecture for smart cities with crowdsourced radio units (CRUs). The advantages of the proposed scheme are high flexibility and low cost, because the distribution of CRUs follows that of mobile users. This paper introduces the experimental results on the performance of radio units mounted on parked vehicles to show the efficacy of the proposed scheme.

Many small cells are densely deployed in 5G mobile networks. The efficiency of conventional centralized radio access network (C-RAN) architectures is drastically deteriorated by the spatio-temporal fluctuation in mobile traffic demand. To address this issue, this paper proposes a concept of adaptive C-RAN architecture for smart cities with crowdsourced radio units (CRUs) mounted on parked vehicles. The advantages of the proposed scheme are high flexibility and low cost, because the distribution of CRUs follows the distribution of mobile users. The performance of the proposed scheme is confirmed with numerical analysis and computer simulations.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) has attracted considerable attention for the next generation optical access systems. Among potential applications of TWDM-PON, a major application is the support of mobile fronthaul streams between radio units (RUs) and distributed units (DUs) in the centralized radio access network (C-RAN) architecture, which consists of central units (CUs), DUs, and RUs. The upstream fronthaul traffic that an optical line terminal (OLT) receives is expected to become highly bursty due to the variable data rate generated by employing new functional split options and the synchronization of data transmission between neighboring RUs caused by time-division duplex (TDD). However, there has been no wavelength and bandwidth allocation scheme for TWDM-PON that is designed to efficiently accommodate fronthaul streams satisfying the strict delay requirement. Therefore, in this paper we propose a novel wavelength and bandwidth allocation algorithm that can minimize the number of active wavelength channels considering the high burstiness and delay requirement of fronthaul data transmission. Through computer simulations it was confirmed that the number of active wavelength channels can be reduced by 50% with the proposed algorithm, and thus more RUs can be efficiently accommodated using TWDM-PON.

A new functional split mobile base station (MBS) has been getting attention for 5G and beyond 5G(BSG) to reduce an optical bandwidth. The MBS is split into three components: a central unit (CU), a distributed unit (DU), and a radio unit (RU). The link between a DU and a RU is connected by an optical fiber and well known as fronthaul. In particular, RUs will be densely deployed in antenna site to increase the mobile data rate. There is inefficiency in the bandwidth usage in the fronthaul link because all the RUs are not always in activated state. Therefore, a mobile operator needs a cost-effective fronthaul network. Employing the wireless multihop system has been studied to construct 5G/B5G fronthaul network. Both optical and wireless links should be employed since it is challenging to replace all of the optical link to the wireless link. However, the method of the deployment of the DU site to reduce the optical fiber cost has not been considered. This paper proposes a novel efficient deployment design of DU site incorporating wireless multihop connection in order to satisfactory reduce optical fiber deployment cost.

This paper proposes a novel concept of small cell called vehicle cell that utilizes the power of citizens for efficient deployment of mobile networks. Vehicle cell is an access point installed on a vehicle and provides network resources for mobile users. The vehicle cell is activated/deactivated by the driver on the basis of the cost and incentives from the mobile carrier. Mobile carriers control the network by optimizing the incentives for drivers in accordance with the mobile traffic dynamics.

Vehicle-installed access points (VAPs) based mobile network emerges as a flexible and efficient wireless access means for 5G and beyond. This paper reports analytical results on vehicle-to-pedestrian (V2P) connectivity on higher frequency band. Introducing 5G new radio (NR) numerology can expand applicability of orthogonal frequency division multiplexing (OFDM) on such intensive mobility environment where VAPs move past. Additional proposal is a coordinated multipoint (CoMP) based coverage expansion via multiple VAPs. Simulative evaluation also presents its effectiveness based on street canyon scenario.

In future radio access systems, since the number of mobile base stations will increase to cope with the increasing mobile traffic, the number of mobile fronthaul (MFH) links will also have to increase. To reduce the MFH link cost, MFH networking has been attracting attention. In particular, the use of a time-division multiplexed passive optical network (TDM-PON) makes the MFH link cost effective. On the other hand, a TDM-PON has a huge latency when forwarding uplink traffic. In a typical dynamic bandwidth allocation (DBA) scheme, an optical network unit (ONU) has a very long transmission waiting time, e.g., several milliseconds. This transmission waiting time in the ONU is a critical problem since the latency requirement for the MFH link is very strict, e.g., less than 250 μs defined by the Third Generation Partnership Project. In this paper, we propose a novel statistical DBA scheme taking high-speed traffic fluctuation to reduce the transmission waiting time. Our proposed scheme allocates bandwidth based on the only ratio of the data amount of each distributed unit and detects the pattern of the transmission interval of the burst signal. We show the feasibility with a numerical simulation and experiments.

A time sensitive converged network that can aggregate fronthaul and machine-to-machine (M2M) streams is required for the fifth generation mobile communication system (5G) era. Recently, a time sensitive network (TSN) for fronthaul in IEEE 802.1CM has been attracting attention. A time aware shaper (TAS), which is a component technology of a TSN, is an effective way to realize a converged network. However, lower priority streams suffer from a lack of usable bandwidth and a huge latency when employing a TAS because the broad bandwidth is reserved for high priority streams in TAS schemes. In this paper, we propose a gate shrunk (GS-)TAS, where a GS-frame is added at the end of high priority streams. We evaluate bandwidth use efficiency by using a numerical simulation. We also simulate the cumulative distribution function (CDF) for the latency performance in a bridged node. We report that the latency reduction rate for the M2M stream is 55.2% for a CDF of 99.7%. In addition, the GS-TAS use does not affect the fronthaul streams.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) have attracted attention as the next step in relation to optical access systems. A major application of TWDM-PON is expected to be fronthaul streams in the centralized radio access network (C-RAN) architecture of mobile networks. In the 5G era, the upstream traffic that OLT receives becomes highly bursty, because of the variable data rate generated by the functional split between baseband unit and remote radio heads, and the global synchronization of data transmission with time-division duplex (TDD). To flexibly allocate upstream bandwidth via the dynamic wavelength and bandwidth allocation (DWBA) scheme has been a hot research topic for TWDM-PON. However, there is no existing DWBA scheme for TWDM-PON that is designed to satisfy the strict delay requirement for fronthaul with minimum number of active wavelength channels. Therefore, in this paper we propose a novel efficient DWBA algorithm that minimizes active wavelength channels considering the high burstiness of fronthaul data transmission. With the proposed algorithm, the allowable delay and constraint for allocation timing for each ONU is formulated based on the different propagation delay between the OLT and ONUs. Then, the wavelength channel and bandwidth is allocated with the simple allocation algorithm based on the descending sort by propagation delay. It was confirmed through computer simulations that the proposed algorithm can reduce the number of active wavelength by 50% by considering propagation delay.

A fronthaul bridged network has attracted attention as a way of efficiently constructing the centralized radio access network (C-RAN) architecture. If we change the functional split of C-RAN and employ time-division duplex (TDD), the data rate in fronthaul will become variable and the global synchronization of fronthaul streams will occur. This feature results in an increase in the queuing delay in fronthaul bridges among fronthaul flows. This paper proposes a novel low-latency routing scheme designed to satisfy the latency requirements in fronthaul networks with path-control protocols. The proposed scheme formulates the maximum queuing delay by defining competitive links and flows. It selects the set of paths that satisfy the latency requirements with the Markov chain Monte Carlo method using machine learning (MCMC-ML). The initial paths are selected from candidate paths using the learned solutions, and path-reselection is performed with the MCMC method. We confirmed with computer simulations that the proposed scheme can compute routes for all flows that satisfy the delay requirements. We also confirmed that the route computation is accelerated with the learned solutions, even if the flow distribution changes.

Metropolitan areas witness significant fluctuations in mobile traffic due to patterns of human mobility. This fluctuation drastically deteriorates the efficiency and financial viability of conventional maximum-based network design. If networks are deployed to deal with the peak traffic rate at each site, their capacities are underutilized for most of time. To improve the efficiency of deploying base stations (BSs), this paper proposes a concept of an Autonomous Base Station with Optical Reflex Backhaul (ABSORB) architecture that can adapt to fluctuations in mobile traffic. In the ABSORB architecture, traffic at demand nodes is forwarded to and from an ABS with an arbitrary radio access technology (RAT). An ABS is connected to a gateway node through ORB, which consists of fiber optic networks. ABSs move to new locations following the demand movement, according to a relocation schedule that is periodically rearranged by an ABSORB controller. The network is flexibly reconstructed according to the demand distribution. The ABSORB architecture can be employed in various networks, and can coexist with traditional static architectures. It will drastically reduce the number of BSs, total deployment cost, and power consumption in comparison with the traditional design.

This paper proposes a wired and wireless network cooperation (NeCo) system to quickly recover civilian telecommunication services in the aftermath of a catastrophic disaster. The proposed NeCo system achieves both rapid recovery and high throughput using wireless bypass routes backhauled by wired networks. With the NeCo system, active leaf nodes relay packets to and from dead leaf nodes whose wired communication channels have been disrupted. Thus, the dead leaf nodes can recover communication with root nodes outside the disaster area. In the this study, optimal bypass routes are computed to maximize the expected wireless throughput by solving a linear programming problem. Another issue is to overcome the limitation that the distribution of leaf nodes is determined by the demand distribution. We also introduce deploying additional recovery nodes to expand the application range of the NeCo system. Numerical simulations showed that the proposed NeCo achieved a higher throughput than an existing method, irrespective of the wired network's topology, and that our NeCo is suitable in cases where leaf nodes are widely distributed around a disaster area.

In recent years, cost reduction and capacity enlargement of memories have resulted in more and more buffers in switches and routers. Consequently, today's network suffers from bufferbloat, in which excess frame buffering causes high latency and jitter and reduces throughput. N rate N+1 color marking (NRN+1CM) was proposed to achieve per-flow fairness in a ring aggregation network. With NRN+1CM, colors are assigned to frames based on input rate and frames are discarded based on their color and frame-drop threshold. Although bufferbloat is avoided by its frame-drop threshold notification process, it was not clarified with a queuing model. This paper demonstrates how bufferbloat is avoided using M(n)/M/1/K queuing model and the formulated model is verified with a computer simulation.

Software Defined Networking (SDN) makes load balancing efficient and intelligent. However, fully deploying an SDN presents economical, organizational and technical challenges. Therefore, load balancing in hybrid SDN networks where the routing protocol parameters are designed through an SDN channel is one of the hottest topics in the field. In addition, symmetrically routing is a practical requirement for network operators since asymmetrically routed flows make the network too complex to manage. In this paper, we propose a load balancing method for symmetrically routed hybrid SDN networks that can handle existing distributed routing parameters, link cost and path selection. The proposed method optimizes link cost and path selection simultaneously under a symmetrically routed condition, while traditional methods optimize them individually. In a numerical simulation, the load balancing performance of the proposed method is better and more stable than any of the traditional methods. Furthermore, the proposed method has high versatility, and provides the best performance even in networks without distributed routing protocols.

The populations of industrialized countries will decline over the coming decades. To ensure that we have efficient access networks in the coming age of depopulation, it is important to consider the reorganization of central offices over time. The cost for operating and maintaining central offices can be reduced by efficiently closing central offices. However, dynamic facility location models that have been developed largely focused on the problem of optimally expanding a network over time to meet increasing demand. The practical relevance of the dynamic models is limited because the complexity and the required amount of data are much greater than with static models. This paper proposes a dynamic access network reorganization model for the depopulation age. Although the proposed model is a dynamic model, it is simple because it assumes that the only option will be to close central offices. The model first searches for a set of optimal solutions for each planning period as a capacitated facility location problem. Then, it selects the optimal combination of optimal solutions that can minimize the total number of open offices with the limitation of network reorganization. The validity of the proposed model was evaluated with computer simulations. It was confirmed that although the proposed model is a dynamic model it is simple and practical.

With shortest path bridging MAC (SPBM) in an access network, shortest path transmission can be realized without a blocking port in any network topology. The approach is expected to use network resources efficiently and to simplify the operating procedure. Access networks can be flexibly constructed on demand with SPBM. However, if there is a deviation in the flow traffic rate, the paths of high rate flows can overlap on specific links and congestion occurs. It is important to avoid congestion by selecting the optimal path for each flow. This paper proposes a rate-based path selection algorithm for access networks with SPBM. The proposed algorithm assumes that a path with a low average rate will be congested because the rates of TCP flows decrease on a congested path. When a new flow arrives at an edge switch, it selects the path with the highest average rate on which it can be expected to realize a high rate. I confirmed with computer simulations that the proposed algorithm could efficiently utilize links and improve throughput fairness.

Our study is motivated by the need for per-flow fairness in a ring aggregation network. This paper proposes a multicolor marking and queuing delay suppression scheme called N rate N + 1 color marking (NRN + 1 CM) that is designed to achieve per-flow fairness. The key idea is to assign a color to a frame according to the flow input rate with high burst tolerance using a simple marker. The color indicates the dropping priority. When congestion occurs, frames are selectively discarded based on their color and the dropping threshold. The accumulation of queuing delay is suppressed with a dropping threshold notification process. The effect of NRN+1 CM was confirmed by a theoretical analysis and computer simulations.

A cascade aggregation can enable access networks to be deployed efficiently in areas with a low subscriber density. To achieve fairness for best effort (BE) traffic from subscribers, we proposed N rate N+1 color marking (NRN+1CM). However, it is important to realize weighted fairness. Network operators often provide different services that limit the maximum bandwidth or ensure that bandwidth is allocated with appropriate weights. This paper proposes weighted NRN+1CM which can set the maximum bandwidth and the weight for each subscriber and to allocate bandwidth based on the weights. The proposed algorithm modifies the color generation probability with the weight. If the input rate exceeds the maximum rate, frames are discarded to limit the output rate. We confirmed the effect of the proposed algorithm from computer simulations. The throughput ratio matched the weights and the throughput was limited to the maximum rate regardless of changes in traffic.

The populations of industrialized countries will decline over the coming decades. To ensure that we have efficient access networks in the coming age of depopulation, it is important to consider the reorganization of central offices over time. Dynamic facility location models have been developed. However, they largely focus on the problem of optimally expanding a network over time to meet increasing demand. Their practical relevance is limited because their complexity and the amount of data they require are much greater than with static models. This paper proposes a dynamic access network reorganization model for the depopulation age. Although the proposed model is a dynamic model, it is simple because it assumes that the only option will be to close central offices. The model first searches for a set of optimal solutions for each planning period as a capacitated facility location problem. Then, it selects the optimal combination of optimal solutions that can minimize the total number of open offices with the limitation of network reorganization. The validity of the proposed model was evaluated with computer simulations. They revealed that although the proposed model is a dynamic model it is simple and practical.

With cascade aggregation, which can enable access networks to be deployed efficiently in areas with a low subscriber density, the problem arises of unfairness of Best Effort (BE) traffic between subscribers. This paper proposes an N rate N+1 color marking (NRN+1CM) function for achieving bandwidth fairness on cascade aggregation. The basic idea behind NRN+1CM is to color subscribers' BE frames and discard BE frames based on queue length and frame color. Multicolor markers have been studied but existing multicolor markers cannot estimate burst traffic rates accurately. We propose a multicolor marker with a single token bucket. The proposed marker can select a color from N+1 colors according to the traffic rate, independent of burstiness. NRN+1CM is scalable because it works on a simple queue configuration without messaging between layer-2 switches. Numerical simulations show that NRN+1CM can select colors accurately and achieve approximately fair bandwidth sharing.