To enable a globally integrated non-terrestrial network (NTN), the interconnection of heterogeneous low-earth orbit (LEO) satellite constellations via geostationary (GEO) relays is paramount. A significant challenge in this architecture lies in the dynamic allocation of hybrid free-space optical (FSO) and radio frequency (RF) links for the GEO-LEO interconnection. Conventional heuristic approaches that prioritize either instantaneous traffic demand or link visibility would be inadequate, as they fail to balance these conflicting metrics, resulting in performance bottlenecks and inefficient resource utilization. To resolve this allocation challenge, we propose a novel two-stage quasi-optimal control framework that maximizes the network-wide traffic accommodation rate. Our approach employs Binary Particle Swarm Optimization (BPSO) to solve the combinatorial FSO/RF link assignment problem, followed by convex optimization for RF bandwidth allocation. Simulation results demonstrate that our method consistently outperforms conventional approaches across various network loads, significantly improving the traffic accommodation rate and overall data throughput, thereby validating its effectiveness for dynamic resource management in hybrid satellite networks.

This study investigates a scenario where multiple mobile network operators (MNOs) share an intelligent reflecting surface (IRS), a technology that efficiently manipulates electromagnetic waves. Although IRSs are energy-efficient, constraints on their installation lead to competition among MNOs, increasing redundancy and energy consumption. In a multi-MNO environment, achieving fairness and optimizing performance is crucial. To address these challenges, this study proposes a cooperative passive beamforming strategy for maximizing the minimum achievable rate among users of different MNOs. An algorithm based on the projected gradient ascent (PGA) method is introduced to resolve the inherent complexity of this max-min fairness problem. Numerical evaluations demonstrate that the proposed IRS-sharing approach outperforms traditional single MNO-specific and sequential IRS control schemes. These findings highlight the potential benefits of IRS sharing, underscoring its practical value in enhancing network efficiency and fairness in multi-operator scenarios.

The use cases of satellite communications are rapidly expanding towards 6 G technology. Long propagation distances and dynamic changes in the network topology create an unstable environment with greater delays and disruptions than those of terrestrial networks. Delay/disruption-tolerant networks (DTN) have been developed to address these challenges. Since the nearearth satellite network is a scheduled network, contact plans can be created beforhand. Contact graph routing (CGR) is a contact-plan-based method used to determine the shortest path to deliver a bundle, a data unit of a DTN, to its destination. However, the conventional contact graph-based bundle transmission control methods do not consider the transfer order or bundle generation from multiple sources, such as observation satellites, ground, and marine users. Therefore, the bundle delivery ratio will decrease in future satellite networks, in which traffic of various sizes and acceptable delays are expected to flow in. In this study, we propose a bundle transmission control method for a multi-source DTN that considers the bundle variety. The simulation evaluation results demonstrated that the proposed method achieved a higher bundle delivery ratio than the conventional methods.

The scale of data centers has increased significantly in recent years, with their energy demands adversely impacting the environment. Consequently, distributed green data centers, equipped powered renewable energy, have gained considerable attention. However, conventional optical fibers installations for such data centers incur high costs and face locations constraints. Therefore, this study explores the use of optical satellite communications for distributed green data centers. Efficient task allocation is crucial to minimize service delays. However, task transmission time depends on satellite-ground link performance, while processing time is affected by the energy at data centers. Additionally, the non-linear relationship between the server per-formance and power consumption highlights the inefficiency, task allocation without future-aware considerations. To address this, we propose a method for task allocation and processing performance control that minimizes service delays through predictions of task generation, link performance, and power generation. In the proposed method, we formulate an optimization problem based on prediction data and find its solution by exploration. Simulations demonstrate that the proposed method significantly reduces tasks transmission and processing times.

Intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflecting elements. It is expected to solve the problems of high-frequency band communications, such as vulnerability to obstacles, and to realize super-multiplex connections in the high-frequency band. Since the reflective elements of IRS can only be time-division controlled and can basically support only one user per time slot, it is highly likely that a large number of resource blocks will be allocated to a single user to perform communications. However, in such a case, the frequency efficiency is reduced due to the effect of beam squint. In this paper, we show the effectiveness of a method to increase frequency efficiency by optimizing the reflection direction through resource allocation and IRS phase control.

Free-space optical (FSO) communication is a promising technology in satellite communications, offering numerous advantages, such as high capacity utilizing wide bandwidth, license-free operation, and high security. Despite these advantages, FSO communications are faced with challenges owing to significant attenuation in the atmosphere. Consequently, hybrid FSO/radio frequency (RF) space-air-ground integrated networks (SAGINs) with stable RF communications have garnered considerable attention. One key area that remains underdeveloped within SAGIN is the cooperative data transmission between satellites and high-altitude platform stations. To address this gap, this study proposed a coverage area control scheme for multiple downlink routing in a hybrid FSO/RF SAGIN to effectively utilize limited RF resources. Furthermore, we developed an algorithm to streamline the search for optimal control parameters within the network. Based on simulation results, integrating flexible bandwidth control with coverage area control improves spectral efficiency. Furthermore, our findings highlight the critical role of accurate traffic prediction in enhancing the performance of coverage area control mechanisms.

Recently, electric vertical takeoff and landing (eVTOL) aircraft has attracted attention and are being developed worldwide. For social implementation, eVTOLs need to be continuously provided with high-speed, high-capacity communications, as applications such as dynamic map updates are required to ensure sufficient safety. Additionally, the use of cellular phone networks is currently being considered for eVTOLs, as is the case with Unmanned Aerial Vehicles (UAVs). As such, eVTOLs travelling in free space - the airspace above - are significantly affected by interference from their surroundings. However, existing handover methods only affect a single eVTOL, so it is necessary to develop a handover method that takes into account the impact of interference on the surrounding mobility. Therefore, this study proposes a handover method that considers the impact of interference on eVTOLs traveling over the surrounding area to maximize the total communication capacity of the entire eVTOL group. Results show that the proposed model improves the total communication capacity of the eVTOL group.

In the fifth-generation mobile communication systems, the millimeter-wave band was introduced to meet the increasing traffic demand in the world. However, a millimeter-wave coverage area is small owing to the high straightness of radio waves and the high propagation loss. Expanding the coverage area by installing more base stations(BSs) and repeaters is not cost-effective due to the high cost of millimeter wave band equipment. To tackle this issue, an intelligent reflecting surface (IRS) has been proposed. However, the IRS cannot output beams by itself, it requires a feed beam from the base station (BS), and the beams are managed by the BS with a back-hole IRS controller, leading to an increase in beam control overhead and a decrease in spectral efficiency in a frame. To address this, we propose the IRS beam sweeping with dedicated frequency bands. The proposed scheme can produce high spectral efficiency while accommodating a realistic number of IRS beams.

Low Earth Orbit (LEO) satellite communication systems are gaining prominence for providing extensive coverage in 6G networks. LEO satellites face challenges owing to the high path loss caused by long distances, thereby necessitating high-power or highly directional antennas. Traditional solutions, such as phased and reflective arrays, fail to meet the size, weight, and price/power (SWaP) requirements, as well as beam flexibility. To address these limitations, this study explores the potential of integrating intelligent reflecting surface (IRS) technology with LEO satellites, offering better SWaP compliance and beam control. Multi-beamforming technology is crucial for LEO satellite communications, and we propose a frequency prism technique to address this requirement. We introduce the delay adjustable-IRS (DA-IRS) as a solution to realize the frequency prism by controlling the time delay for incident wave reflections. Additionally, we model a control strategy to optimize the frequency utilization efficiency to address uneven throughput demands within the coverage area. The proposed approach is assessed through simulations. These findings demonstrate the viability of our model for meeting the key requirements of LEO satellite communication systems.

In the context of a network with multiple access points (APs) utilizing coordinated orthogonal frequency division multiple access (C-OFDMA), a channel unfairness among APs arises when the APs participating in the coordination decrease the available channels accessible to non-coordinated nodes. This study introduces a solution in the form of C-OFDMA, in which coordinated sides prioritize channels that may not be used by the non-coordinated sides for resource allocation to STAs that are overlapped within the range of the non-coordinated sides. Consequently, conflicts between non-coordinated and coordinated APs are minimized and non-coordinated nodes cannot have their number of available channels decreased through carrier sense. This enhances channel fairness among all APs, thus facilitates the effective implementation of C-OFDMA. Moreover it maintains system throughput in C-OFDMA without RU reuse. Based on simulations, in systems where non-coordinated and coordinated nodes are in close proximity, the proposed method enhances these throughput and fairness compared with conventional C-OFDMA approaches. This allows C-OFDMA to be efficiently utilized even in dense wireless local area networks, which are increasing in popularity.

In recent wireless local area networks (WLANs), high-density access points (APs) have been considered for high-speed communications. However, networks with a high-density of APs can cause interference between basic service sets (BSSs). To address this issue, the Institution of Electrical and Electronic Engineers (IEEE) 802.11bn Task Group is considering to introduce multi-AP coordination (MAPC), in which multiple APs work together. Each coordination function has different performance in terms of instantaneous throughput and transmission overhead, hence coordination function selection may improve performance. In this work, we evaluate the performance of coordination function selection. We propose models for estimating the instantaneous throughput and transmission overhead of each coordination function. Simulations show that coordination function selection can improve performance compared with the case in which all APs use a single coordination function. Furthermore, we show the optimal coordination function pattern is considered to be related to the distance between APs and stations in the BSSs. Coordination function selection contributes to the improvement of MAPC performance in various environments.

Recently, the utilization of non-terrestrial networks (NTN), including satellite communications, has expanded substantially. This expansion has resulted in heightened demands for improved communication performance from NTNs. Among the various solutions, inter-satellite optical wireless communication has emerged as a promising technology for enhancing communication capabilities. However, the complexity of optical pointing technology poses a significant challenge. Existing research often assumes limitations in establishing connections between satellites in intersecting orbits with high relative velocities, instead relying on links in the same and adjacent orbits that are comparatively easier to establish. This study bridges the gap in the existing research by conducting a quantitative comparison of the same, adjacent, and intersecting orbital connections. The primary objective of this comparison is to investigate the potential for enhancing communication stability and efficiency by transitioning from multi-hop information transmission using satellites on adjacent orbits to connections within the same orbit by utilizing intersecting-orbit satellites. Verification results show that, under certain conditions, communication stability and efficiency can be improved by using intersecting-orbit satellites.

Satellite communication is increasingly essential and widely used, especially with the rapid development of the Internet of Things (IoT) and networks beyond fifth-generation (B5G), providing ubiquitous coverage. However, the current reactive approaches to optimize resources have become inadequate due to the massive rise in IoT traffic with varying patterns and limited resources of satellite networks. These approaches fail to predict dynamic traffic and its requirements. To efficiently allocate satellite communication resources as necessary, there is a need to proactively predict traffic demand. We propose utilizing mobile edge computing-enabled high altitude platform stations (HAPS) to predict traffic from ground users to satellite networks at HAPS. However, resource control based on traffic prediction in satellite networks faces challenges such as the wastage of resources or insufficient resource availability due to misaligned traffic variations and resource control timing. To overcome these challenges, we propose a dynamic scheduling strategy for resource control based on traffic demand prediction. This strategy aims to reduce resource wastage in satellite communication systems. Our proposed approach can predict traffic with high accuracy and allocate resources with minimal difference between achievable throughput and required throughput, demonstrating high resource utilization. We evaluated the effectiveness of our scheduling strategy through simulation analysis by comparing it with periodic resource control.

This study proposes a passive beamforming strategy among different communication systems (CSs) that share an intelligent reflecting surface (IRS). The conventional beamforming strategy based on channel state information is unfeasible in a shared IRS scenario owing to the challenges in handling physical layer information among multiple CSse Thus, we propose a machine learning-based framework for IRS sharing systems to address this problem; the programmable radio environment is treated with IRS as an application programming interface that can be called from each CS. The effectiveness of the proposed machine learning-based passive beamforming design was validated through experiments. The number of transmission sites and the diversity of transmitting site positions influence the effectiveness of the IRS sharing scenario.

In recent years, demand for V2X (Vehicle-to-Everything) communication, which connects vehicles and objects, has been increasing rapidly with the aim of realizing use cases such as automated driving. It is difficult to guarantee the V2X functionality in the event of disaster; however, this study examines alternatives that employ UAVs (Unmanned Aerial Vehicles). V2X uses direct communication between UEs (User Equipments), known as NR (New Radio) Sidelink. The method of communication resource allocation using NR Sidelink has two modes: Mode 1, in which a base station provides centralized control, and Mode 2, in which each UE makes autonomous selection. Because the time from traffic arrival to the completion of transmission has different dependencies depending on the number of UEs in each mode, it is necessary to consider the correlation between the mode of operation, number of UEs, and latency. Therefore, in this study, we proposed a method of controlling the operating mode of each UE through UAV flight direction according to UE distribution to efficiently allocate communication resources and reduce latency when using NR Sidelink for V2X communication, using a performance evaluation to demonstrate its efficacy.

An intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflective elements. The IRS is expected to both solve the problems of high-frequency band communication, such as vulnerability to obstacles and large distance attenuation, and realize ultra-multiple connections in the high-frequency band. However, the reflective elements of the IRS can only be controlled by time division, which means that the IRS can support only one user per time slot, whereas frequency resource division communication can be used by base stations to support multiple users. Therefore, in this study, we investigated a method that enables an IRS to support the communication of multiple users simultaneously. The proposed method determines the frequency allocated to each user and expands the reflected beamwidth of the IRS to maximize the effect of the frequency shift. This approach takes advantage of the misalignment in the reflection direction for each frequency that occurs when multiple frequencies with the same phase shift are incident on the IRS. Simulation experiments show the effect of allocation considering the misalignment in the reflection directions and the effect of expanding the beamwidth in a certain environment at 28 GHz. The results show the potential of this approach in IRS-based multi-user communication systems.

Currently, the volume of communication by mobile terminals are increasing owing to 5G and other technologies. A robust network and appropriate routing control methods are requied to transmit information in unstable wireless communication environments and avoid congestion. Therefore, in recent years, numerous studies have been conducted on wireless mesh networks (WMNs), which provide a fault-tolerant communication environment by securing multiple communication paths and whose topology can be freely configured and extended. Additionally, machine learning routing is attracting attention as a new routing method for wireless communication environments. However, when performing machine learning on a large WMN, the learning time increases and rapid routing control may be impossible. In this study, we apply federated learning to machine learning and propose a machine-learning-based routing method that can be applied to large-scale WMNs. Furthermore, experimental results demonstrate the effectiveness of the proposed method in various environments: congestion avoidance is achieved in a large-scale WMN by machine-learning routing using federated learning. This study is expected to serve as a basis for significant progress in the realization of large-scale WMNs as wireless communication infrastructure.

An intelligent reflecting surface (IRS), which comprises numerous passive elements, is considered a promising technology for smart wireless communication. However, the passive characteristics of an IRS render the explicit estimation of the channel state information to appropriately adjust its reflection coefficient challenging. This study proposes a deep reinforcement learning-based algorithm that learns the precoding vector of the base station (BS) and the IRS phase shift from the wireless environment to address this problem. We develop a beam-pattern-based learning framework for this algorithm that indirectly maps the wireless environment to the phase shift to manage the large state-action space caused by the multiple elements of the BS and IRS. Based on the simulation results, the proposed algorithm can learn from the environment and establish a transmission strategy that improves the user's transmission rate. Furthermore, the results validate that the proposed algorithm based on the beam pattern learning framework is more efficient and scalable to the number of IRS elements compared to the method that directly builds a mapping to the phase shift.

Intelligent Reflecting Surfaces (IRS) have attracted attention in recent years as devices that can improve communication quality by controlling the propagation environment that was originally given to them, and their use in future communications is highly anticipated. This study focuses on Standalone-IRS (SA- IRS), a device that can autonomously estimate and control its environment. SA-IRS is independent of existing communication systems and has the ability to collect the necessary information to exert control by itself. Therefore, it does not need to be connected to other devices for control, and is expected to be used as a low- installation-cost device for improving the propagation environment for small-scale communications, such as in factories using local 5G. Basically, the control of IRSs requires Channel State Information (CSI) to understand the propagation environment. However, because an IRS is a passive device that cannot transmit or receive information, CSI estimated from the difference of transmitted and received signals is not available for SA-IRS. This motivated us to propose an SA-IRS codebook that links IRS control to user location information to control IRS without CSI. In addition, because of the overhead involved in calculating the linked IRS control, which is an element of the SA-IRS codebook, we propose an efficient SA-IRS codebook construction method. The objective of this research is to efficiently control SA-IRS, which is an IRS with low installation cost, and to ensure high communication quality by using the above-mentioned proposed method. Simulations are also conducted to evaluate the effectiveness of the proposed method.

Space-air-ground integrated networks (SAGINs) represent a promising framework to expand the coverage, as necessitated by sixth-generation frameworks (6G). Herein, we assume that SAGINs use a high-altitude platform station (HAPS) as the airborne layer component. In future technologies, more stringent frequency constraints are expected to be encountered owing to the increase in communication demands. Therefore, we focus on effectively using the frequency resources through frequency sharing in SAGINs. Specifically, we attempt to implement frequency sharing for ground-to-HAPS and HAPS-to-satellite communications. Notably, when using the same frequency, the backlobes of the satellite-facing antennas of HAPS may interfere with the reception of the ground terminal, and this interference must be suppressed. To this end, we apply the transmit-side directivity control technique to the SAGIN, which is used in terrestrial multi-input multi-output systems for interference suppression. The number of users that can be accommodated by zero forcing and zero forcing-dirty paper coding control methods is compared with that in the uncontrolled case, and the effectiveness of the transmit-side directivity control in SAGIN is quantified.

Wireless local area networks (WLANs) have become an indispensable part of day-to-day living. In the future, as the number of Internet of Things (IoT) devices increases, the amount of communication per terminal is expected to increase. To cope with this increase in demand, it is necessary to introduce a multi-access point (AP) communication environment, which is currently being discussed in the IEEE 802.11be Standardization Committee, to perform joint transmission (JT). Furthermore, the use of high-frequency bands is necessary to increase transmission capacity. However, when using high-frequency bands in a multi-AP communication environment, communication may become difficult owing to blockages, or interference may occur in an environment where there is high density deployment of APs. In this study, we first consider the introduction of an intelligent reflecting surface (IRS) in a multi-AP network to manage the blockage of radio waves due to shielding in high-frequency bands. When IRSs are introduced, it becomes possible to generate transmission paths that bypass blockages even in an environment where shielding exists, and it becomes possible to use multiple APs for a single communication. However, JT communication with IRSs may be prone to interference instead of increasing the paths. This study demonstrates that the effect of interfering signals can be decreased by the optimal allocation of stations by the AP and IRS.

The Internet of Things (IoT) has created acute network security concerns owing to their weak protocols and limited system resources. Vulnerable IoT devices increase the risk of compromising other devices connected to the network. Hence, vulnerability and risk assessments are necessary for IoT devices. Correspondingly, the Internet-wide port scan (IWPS) technique has garnered significant attention for its ability to discover and probe Internet-wide connected IoT devices. However, IWPS performance depends on the end wireless local area network (WLAN) state (e.g., congestion and signal-to-interference-plus-noise ratio). Scans oblivious to such dynamic WLAN factors can cause probe packet loss while increasing port-scan delays, which reduces the discovery rate, which misses the point of network security. Therefore, in this study, we propose a novel holistic approach to identifying WLAN environmental states based on round-trip time and probe-packet responses. To demonstrate the effectiveness of the proposed approach, we perform extensive experiments on real WLAN environments with various devices. The accuracy of the estimated states was validated at greater than 90% by analyzing the captured probe data at each WLAN.

In recent years, intelligent reflecting surfaces (IRSs) for large-capacity and highly reliable wireless communication have attracted widespread attention. However, a multiuser access system with multiple IRSs poses limitations in reducing the large signaling overhead of channel estimation for numerous links between the IRSs and users. One approach to reduce the exhaustive channel estimation involves associating the IRS with a user and performing beam tracking for a certain period. However, as the IRS–user association is fixed during the tracking period, the dynamic variations in their link status caused by user mobility degrade the system performance if the association is decided without prior planning. Therefore, this paper proposes an IRS–user association strategy considering user mobility for IRS-aided multibeam transmission systems. Contrary to prior works, our association strategy aims to optimize the long-term performance of systems in terms of capacity and reliability. The proposed strategy minimized performance degradation even under drastic fluctuations of link conditions, thereby reducing channel estimation overhead because both the IRS and user can be associated for long periods with low performance degradation.

The demand for satellite communication increases with digitalization and globalization in the era of 5G and beyond; therefore, research and development of high-throughput satellites (HTSs) to increase the communication capacity and improve the flexibility of satellite communication systems is underway. Additionally, digital beamforming (DBF), which can control the directivity of multiple beams through high-speed digital signal processing, has received significant attention as a technology to improve the flexibility of resource allocation with HTSs. The principal control parameters in the DBF-based HTS, including the transmission power, beam gain, and placement location of each beam, cause alterations in the positional characteristics of the total throughput in the coverage area of an HTS. However, there are no power resource allocation models with dual control of these control parameters, based on the above positional characteristic. This study clarifies the effect of such control parameters on the total throughput in the coverage area of an HTS. We demonstrate a method for improving the flexibility of resource allocation to satisfy the geographic distribution of traffic requirements by constructing a power resource allocation model with a DBF-based fusion control in the HTS communication system. We evaluated the effectiveness of our proposed method through a simulation analysis using exponential annealing.

Despite unprecedented advancements, wireless local area network (WLAN) technologies for the Internet of Things (IoT), such as IEEE 802.11ah (i.e., WiFi-HaLow), are prone to serious security threats, owing to their constrained computational and memory resources, which limit the use of heavyweight intrusion protection and security protocols. To address this problem, security administrators (sec-admins) must perform regular and comprehensive vulnerability assessments of IoT devices. An Internet-wide port scan (IWPS) is the initial step. However, the medium access control mechanism of IEEE 802.11ah, designed specifically for heterogeneous IoT traffic and low-power operations, can degrade network performance in the case of traditional port-scan traffic. Moreover, Internet-security (IPSec) protocol support is mandatory for IPv6-enabled IoT devices to ensure data confidentiality, integrity, and availability. Although the objective of a port scan is to improve IoT security, the resultant network performance can adversely affect IPSec services. Therefore, in this study, we optimize the IWPS to maximize the IoT security over IEEE 802.11ah WLAN. To this end, we propose novel mathematical models to evaluate IoT security based on port-scan network performance and IPsec services, which derives an optimal scan rate for sec-admins. The effectiveness of the proposed framework is verified by comprehensive numerical analysis, which shows that our approach minimizes the risk to IoT devices while probing them at an optimal scan rate.

With the emergence of Internet of Things (IoT), network security has become an area of acute concern owing to susceptibilities of IoT security that can be exploited to attack other devices and network infrastructures. Internet-Wide Port Scan (IWPS), a well-established network sifting mechanism that identifies threats and defensive mechanisms, is gaining attention to probe IoT networks and identify vulnerable IoT devices. A key enabler for IoT networks is the Wireless Local Area Network (WLAN) that comprises of numerous heterogeneous devices such as smartphones, computers, IoT devices, and so on. Hence, efficient probing of such networks can be challenging; since networks can easily experience congestion, poor signal-to-noise-ratio (SINR), etc. that can result in loss of probe packets and subsequently low discovery rate. In this paper these issues have been addressed and a holistic classification algorithm has been proposed to identify the states of heterogeneous WLAN environment based on real-time and historical measurements. Such a classification can assist in choosing scan strategies for improved IWPS performance. The experimental results presented in this paper reveal that the proposed algorithm is very effective to classify such a complex and heterogeneous environment.

Internet of Things (IoT) has become an integral and pervasive part of everyday life, e.g. the infrastructure of intelligent traffic systems and smart cities. Such next-generation IoT applications require intelligent data processing that is performed via edge cloud computing (ECC) within a short period. In ECC, the edge server executes data processing; this is expected to reduce service delay. However, in the existing ECC research, infrastructure such as radio towers for communication and base stations for installing edge servers is indispensable; thus, it cannot respond to the demand for computing resources in the event of large-scale disasters and in infrastructureless areas. Therefore, researchers are studying the provision of computing resources using unmanned aerial vehicles (UAVs). On account of these studies, computation resources may be provided in several situations. However, in the face of this technology, to provide services with the delay time that the application allows, it is necessary to consider the workload balance and communication range of the UAV. In this paper, we propose a handover solution method considering workload balance and create a mathematical model with a transparent system. Moreover, numerical analyses show the effectiveness of the proposed method. With this contribution, it is possible to provide communication and computational resources in infrastructureless areas.

In recent years, the expectations for high throughput satellite (HTS) have diversified based on rapid increase in traffic demands. However, the Ku-band and Ka-band utilized by HTS are growing tighter. It is necessary to utilize the limited frequency ranges efficiently and share resources with other communication systems. The digital beam forming (DBF), which has a high area flexibility for allocating power resources, is being developed to adapt to the diversification of communication applications. However, it remains unclear how multi-spot beam placement is related to throughput in an HTS communication system equipped with DBF. In this study, we attempted to determine how the distances between spot beams in the same frequency band and the distances between adjacent spot beams in different frequency bands are related to overall system throughput and to derive a multi-spot beam arrangement to improve overall system throughput. The main contributions of this study are the clarification of relationships between the positions of multi-spot beams and overall system throughput and the construction of a novel mathematical model to derive multi-spot beam arrangements to enhance overall throughput. The effectiveness of our proposal is evaluated through numerical analysis.

In recent years, vulnerable Intenet of Things (IoT) devices have engendered several distributed denial of service (DDoS) attacks owing to the generation of massive IoT botnets by various IoT malware such as Mirai, Persirai and among others. IoT devices are vulnerable owing to constrained memory and computation resources that restrict the implementation of complex security protocols and anti-malware programs. In recent times, there have been attempts to implement periodic Internet-Wide port scan (IWPS) to identify vulnerable IoT devices at each wireless local area network (WLAN). IoT WLAN standard such as IEEE 802.11ah has been designed with the objective of low power consumption and massive connectivity, but is constrained by its low network performance particularly on traditional scan traffic; thus it can result in security degradation. Hence, in this paper, we propose novel models for security analyses of IoT based on the network performance of IWPS over IEEE 802.11ah. The results verify that the network-Oblivious IWPS can degrade IoT security and increase risk.

Recently, Internet-wide scanning has emerged as an important element of detecting the security vulnerabilities of the Internet of Things (IoT) devices. However, Internet-wide scanning induces network congestion when sending a huge number of port scanning packets in a short time. The wireless networks are particularly subject to congestion from scanning traffic. Therefore, the scan rate should be low to reduce network congestion. However, as the number of IoT devices connected to a WLAN increases, a higher scan rate is required due to the excessive time consumption of low rate scanning. In this article, we propose a method for setting an optimal scan rate which is as high as possible without imposing congestion on a WLAN. To address this problem, we construct a model for quantifying the impact of scanning traffic on IoT data communication in a target network and demonstrate how scanning packet delay elicits the congestion of the target network. The validity of our model is evaluated and is corroborated by numerical analysis. In addition, numerical analysis results demonstrate the effectiveness of the proposed scan rate optimizing method in terms of low network congestion and a high scanning rate.

The data privacy and confidentiality in Internet-of-Things (IoT) networks have been one of the most concerned problems due to increasing threats. The commonly utilized IoT chips adopt a fixed authentication and encryption scheme in the link layer even though multiple options are usually supported. As different authentication and encryption operations mean dissimilar protections and various energy consumption, the fixed security strategy neglects the remaining energy, dynamic threats, and diverse service requirements, leading to low energy efficiency. Moreover, fixed high-level security protections consume too much energy even though the security requirement may be low, which results in a short working time. To address this problem, we propose an artificial intelligence (AI)-based adaptive security specification method for 6G IoT networks where the IoT devices are connected to cellular networks via different frequency bands, including terahertz (THz) and millimeter wave (mmWave). The IoT sensing devices are assumed to support the energy harvesting technique which is expected to be widely adopted in 6G. In our proposal, the extended Kalman filtering (EKF) method is first adopted to predict future harvesting power. Then, in each energy-aware cycle, we design a mathematical model to calculate the required energy of different security strategies and choose the supported highest level protection which can meet service requirement and avoid energy exhaustion. The simulation results illustrate that the proposal can not only provide satisfied security protection for different services but also adjust the security protection to avoid the energy exhaustion, leading to a significant improvement of throughput and working time.

As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Satellite communication systems have attracted considerable attention recently because they can communicate in various conditions, including terrestrial, airspace, and marine terrain. In addition, high-throughput satellites (HTSs) that allow high-speed and large-capacity communication are currently being launched. However, the allocation of communication resources to each beam is fixed in conventional HTSs, which exhibit low flexibility under state changes. This includes requests to the satellite communication systems or environmental changes around the systems. Therefore, the use of a digital channelizer in a satellite communication system can allocate frequency resources to each beam. However, even if a digital channelizer is used, conventional frequency resource allocation methods that consider inter-beam interference do not use the same frequency resource as the adjacent beam. As a result, the frequency resources cannot be used effectively. To alleviate this issue, we propose a frequency resource allocation method with inter-beam interference so that the satellite communication system can allocate frequency resources more flexibly. In addition, we extend the conventional flexibility analysis model such that it quantifies the flexibility more accurately. Finally, the effectiveness of the proposed method is demonstrated under state changes of the satellite communication system using the extended flexibility analysis model.

The use of intelligent transportation systems (ITSs) is expanding, and vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure communication (V2I) have attracted considerable attention. During disasters, it becomes difficult to share information with vehicles in sparse areas and distant places. Therefore, we considered using unmanned aerial vehicle (UAVs) in ITSs during disasters. We developed a method to control the flight speed and modulation and coding scheme (MCS) of a UAV and maximize the number of vehicles that receive data in one transmission. A model to derive the optimal UAV's flight speed and MCS was constructed. Then, we confirmed that an optimal UAV's flight speed and MCS exist that maximize the number of vehicles receiving data in one transmission by the UAV by numerical analysis. In the evaluation using the flight speed and MCS derived from the model, the expected number of vehicles that received data in one transmission at each data size held by the UAV was derived and compared with other methods. From the evaluation, we confirmed that the proposed method maximizes the number of vehicles receiving data in one transmission by the UAV for each data size maintained by the UAV.

In recent years, data transmission by wireless communication using unmanned aerial vehicles (UAVs) has attracted attention for video transmission applications such as environmental observation. Relay UAV networks have been developed in which the observation UAV transmits data to the ground station via a relay UAV, thus allowing the observation range to be expanded beyond the range of data transmission from the observation UAV directly to the ground station. However, as the number of transmitting UAVs increases, due to interference from other UAVs, the number of UAVs in a standby state that cannot transmit data also increases. Combined with this is the existing problem in UAV data transmission caused by the limited energy storage capabilities of UAV batteries. Therefore, the energy necessary for flight must be efficiently utilized to transmit data effectively. In this paper, we consider the energy utilization efficiency of peripheral UAVs by accounting for the energy consumed by peripheral UAV flight during the time spent in a standby state due to interference from the target UAV. Then, we propose a method to maximize the utilization efficiency of the energy by controlling the transmission power and the relay UAV position in a UAV relay transmission system.

The Internet of Things (IoT) technology has attracted widespread attention since it can connect a huge number of heterogeneous devices to construct an intelligent environment to offer various services. Driven by the decreasing expense and significant convenience, it is expected that the total number of IoT devices will reach as high as 50 billion by 2020. And, these devices will provide diversified Internet services, including smart home, elder care, and vehicle-to-everything (V2X) communications. For the IoT technology, the network performance as well as the energy efficiency are both important since most end devices are battery limited. In this article, we focus on the IoT network based on the long-range wide-area network (LoRaWAN) protocol since its chirp modulation technology can adopt different spreading factors (SFs) to realize flexible communications. We propose a novel resource allocation strategy which utilizes multilayer virtual cell-based spatial time division multiple access (STDMA) scheme. This new method can not only simplify the calculation of the interference but also enables the cell radius to be adjusted to fit the communication distance. Moreover, the relationship between the power consumption and the network data rate is also analyzed in this article. We numerically analyze the optimal power consumption to achieve the highest data rate. The final performance evaluation demonstrates the significant improvement of our proposal compared with the conventional method.

The latest evolution of cellular technologies, i.e., 5G including long term evolution-advanced (LTE-A) Pro and 5G new radio promises enhancement to mobile technologies for the Internet of Things (IoT). Despite 5G's vision to cater to IoT, yet some of the aspects are still optimized for human-to-human (H2H) communication. More specifically, the existing group paging mechanism in LTE-A Pro has not yet clearly defined approaches to group, mobile IoT devices (MIDs) having diverse characteristics, such as discontinuous reception (DRX) and data transmission frequency (DTF) with various mobility patterns. Inappropriate grouping of MIDs may lead to increased energy consumption and degraded quality of service, especially in terms of packet arrival delay (PAD) and packet loss rate (PLR). Therefore, in this paper, we devise novel models to estimate PAD, PLR, and energy consumption for MIDs, specifically for the group paging mechanism. Based on the proposed models, we formulate an optimization problem with the objective to minimize energy consumption of MIDs, while providing required PAD and PLR. The nonlinear convex optimization problem addressed herein is solved using the Lagrangian approach, and the Karush-Kuhn-Tucker conditions have been applied to derive optimal characteristics for MIDs to join the group, namely, DRX and DTF. The extensive numerical results presented verify the effectiveness of the proposed method, and the mathematical models demonstrate the superiority of our proposed approach over random grouping approach concerning significant energy consumption of MIDs.

Unmanned aircraft systems (UASs) have attracted considerable attention in various industries. Furthermore, the use of unmanned aircrafts (UAs) is expected to spread rapidly and change our daily lives, in the future. However, while the number of UAs is increasing, the available frequency allocated to the UASs remains limited. Thus, when numerous UAs are used at high densities, the wireless communication between the UAs and the ground stations (GSs) may fail because of interference. To solve this issue, a time-division multiple access scheme can be adopted in the UASs. For efficient use of the allocated frequency, it is desirable that the UAs free from interference communicate simultaneously. Therefore, in this paper, we propose an efficient resource allocation scheme that increases the number of UAs that communicate simultaneously and improves the throughput. Our proposed scheme is based on the concept of multilayer virtual cells, and can increase the number of UAs that communicate simultaneously by considering the distance between the UAs and the GSs. The effectiveness of our proposed resource allocation scheme is evaluated through simulations. Our proposed scheme is found to be more efficient than other existing schemes, in terms of the number of UAs that can communicate simultaneously.

The evolution of cellular networks under Long Term Evolution (LTE) has paved the path for LTE-Advanced (LTE-A) Pro that proposes forward LTE enhancements for Machine Type Communications (MTC) and meets the stringent requirements for realization of the Internet-of-Things (IoT). This paper identifies possible improvements in LTE-A Pro's existing group paging scheme, which is more expedient for human-to-human communications and inadequate for IoT applications. We propose a novel energy efficient group paging scheme by considering diverse IoT characteristics including Quality of Service considerations. Simulation results reveal that our proposed approach can significantly reduce energy consumption of IoT devices over existing group paging schemes.

Recently, Wi-Fi Direct (WFD) enabled mobile Device-to-Device (D2D) communication emerged as a promising technique to carry out communication in disaster affected areas by exploiting its ability to operate without existing communication infrastructures. However, existing methods utilizing WFD randomly select communication partners, and therefore, are not effective in diffusing safety information pertaining to disaster victims over a wide coverage area. We address this issue in this paper, and propose a novel and efficient information diffusion method exploiting WFD by designing special virtual cells comprising the location information of the mobile devices. Our designed virtual cell enables information diffusion by allocating the same time to multiple areas without interference. We also evaluate the effectiveness of our proposed method through computer-based simulations. The simulation results demonstrate that the scheduling control of our proposal outperforms those of the conventional methods.

Unmanned aircraft systems (UASs) have attracted worldwide attention in recent years because of their potential applicability in a multitude of fields such as environmental monitoring and the communication provisioning environment. These applications enable data to be remotely collected from distant places through unmanned aerial vehicles (UAVs) equipped with communication devices. However, the communication range of a single UAV is limited because of the restrictions of the wireless transceiver as a result of the payload limitation. Therefore, UASs consisting of relay-based UAVs in which data are transferred via multiple UAVs have been studied to achieve long-distance communication with a high throughput. The time division long-term evolution advanced (TD-LTE-A) relaying technique may be applicable to UASs as it offers a reasonable solution. However, this technique is currently ineffective for UASs due to the limitation caused by its frame configuration. To alleviate this issue, in this paper, we propose a method to optimize radio resources based on the TD-LTE-A frame structure, which is capable of realizing efficient relay communication. Our proposed method considers a flexible frame configuration and updates cycle of control information to overcome the shortcoming of the conventional scheme. The effectiveness of the proposed radio resource optimization is demonstrated through simulation under varying environments.

Unmanned aircraft systems are expected to open pathways towards the development of many new services. Owing to their airborne flexible mobility, systems using unmanned aircraft (UA) have a significant potential to change our daily lives. However, several issues with such systems need to be addressed. In particular, wireless transmission of data such as high quality video from a UA to Ground Stations (GSs) is a major challenge affecting how airborne UAs are controlled. In this paper, we attempt to construct a base model of data transmission from UA to GSs in order to most efficiently use communication resources. To do this, we propose a method to allocate resources for individual UA missions that takes differences in requirements and surroundings into account. In this method, time division multiple access (TDMA)-based resource allocation is performed by anticipating the effective throughput according to the surrounding radio wave propagation environment. The effectiveness of this proposed resource allocation method is evaluated through simulation.

As computing system continues to play an increasing role in daily life, user authentication is now an important component. One of the most widely accepted methods for user authentication is through proof of knowledge of a piece of secret information, such as password. However, entering this non-mutable secret for authentication in public space often allows attackers to steal the secret by shoulder surfing or video recording. We observe that it is possible to block attacker's access to user input using augmented reality (AR) display, which is only available to the user. Based on this intuition, we present AugAuth, an authentication scheme in AR using commercial off-the-shelf(COTS) gesture control sensors as an input device. AugAuth can resist against shoulder surfing by presenting user input interface that is only visible to the user and is unique every time. To enable user input with finger movement using the gesture control armband, Myo, we have solved several challenges in electromyogram signal processing, such as annotating the start of signal and finger classification. The experiment results for our input system of a group of volunteers show that our finger classification function has high accuracy and AugAuth is practical for use in real life authentication scenarios.

Unmanned aerial vehicles (UAVs) can be used for a wide variety of applications, including agriculture, infrastructure maintenance, and disaster response. In this paper, we focus on the use of UAVs for disaster response. Multiple UAVs equipped with communication terminals can be deployed to construct an airborne network connecting isolated areas. Another use is for real-time video transmission from a UAV to a ground station, using multiple UAVs operating simultaneously by different organizations, e.g., rescue teams, fire departments, broadcasting companies, and so forth. In both cases, frequency resources must be shared efficiently among adjacent UAVs. Thus, we describe a radio resource management system for UAVs. The focus of this paper is data communications, rather than the broader issue of command and control communications. First, we present experimental results from field experiments using WiFi communication terminals that do not have centralized radio resource management functionality. Then, we propose a centralized resource allocation technique that assumes an orthogonal frequency division multiplexing (OFDM)-based communication system, using resource blocks consistent with the long-term evolution (LTE) standard.

Unmanned aerial vehicles (UAVs) have attracted attention as a means of monitoring the environment because of their ability to remotely collect data from distant places. However, due to the payload limitations, the communication range of UAVs is limited. Therefore, UAVs need to cooperate with other UAVs in order to achieve high speed and long distance communication. In this paper, we construct a base model of data transmission over multi-hop UAV networks. Time Division, Long Term Evolution Advanced (TD-LTE-A) relaying is a technique that may be applicable to such networks. However, it is currently not suitable for UAV networks due to the limitation caused by its frame structure. Therefore, in this study, we propose a TD- LTE-A based radio access scheme that can realize efficient relay communication. The effectiveness of this proposed radio access scheme is evaluated through simulation.

The development of technologies related to the Internet of Things (IoT) provides a new perspective on applications pertaining to smart cities. Smart city applications focus on resolving issues facing people in everyday life, and have attracted a considerable amount of research interest. The typical issue encountered in such places of daily use, such as stations, shopping malls, and stadiums is crowd dynamics management. Therefore, we focus on crowd dynamics management to resolve the problem of congestion using IoT technologies. Real-time crowd dynamics management can be achieved by gathering information relating to congestion and propose less crowded places. Although many crowd dynamics management applications have been proposed in various scenarios and many models have been devised to this end, a general model for evaluating the control effectiveness of crowd dynamics management has not yet been developed in IoT research. Therefore, in this paper, we propose a model to evaluate the performance of crowd dynamics management applications. In other words, the objective of this paper is to present the proof-of-concept of control effectiveness of crowd dynamics management. Our model uses feedback control theory, and enables an integrated evaluation of the control effectiveness of crowd dynamics management methods under various scenarios. We also provide extensive numerical results to verify the effectiveness of the model.

With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attention in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to their unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable devices. Specifically, the coarse access control on current AR systems could lead to possible abuse of sensor data. Although the always-on gesture sensors are frequently quoted as a privacy concern, there has not been any study on information leakage of these devices. In this article, we present our study on side-channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric currents of muscle contractions. We design novel algorithms based on dynamic cumulative sum and wavelet transform to determine the exact time of finger movements. Furthermore, we adopt the Hudgins feature set in a support vector machine to classify recorded signal segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor in keystroke recovery. We evaluated the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lowercase letters, uppercase letters, numbers, and symbols with a mean success rate of 91%.

In this paper, a new framework based on the tool of stochastic geometry is proposed to analyze the coverage probability and average rate with different channel allocations in a D2D multi-channel downlink cellular network. We consider a network with two types of users: cellular users and D2D users, where each D2D user has its own D2D relay and can only receive data from the relay. Cellular users and D2D relays can establish cellular links with the nearest base station through cellular channels only if the SINR is above a threshold. D2D users communicate with their own D2D relays to form D2D links through specific D2D channels. As validated by extensive numerical results, we are able to find the optimal channel allocation for D2D communications, to achieve the optimal system performance in terms of coverage probability and average rate.

With the development of wireless communication technology, the utilization of the ambient information that the network users observe has attracted much attention. In this paper, we focus especially on the utilization of observed environmental signals for authentication systems. Ambient information taken as unique data at a particular time and place can be utilized to construct stronger authentication systems. However, since the environmental condition of the network is different for each location, which has a huge effect on the observed ambient information in the area, required data are also different for each place. Thus, in this paper, we propose an efficient data collection method which dynamically changes the way data is collected according to the requirements and the network condition. More specifically, our proposal aims to collect uniform amount of fresh data from areas with varying population density. Additionally, an algorithm to improve the efficiency of our proposal regarding the accommodation of the environmental condition of the network is introduced. Moreover, numerical results verify the effectiveness of our proposal.

Machine-to-Machine (M2M) communication is the emerging technology for automation applications without human intervention. The 3GPP organization defines Machine Type Communications (MTC) to enhance support of M2M devices. Due to a potentially massive number of coexisting MTC devices, achieving efficient device identifier (ID) management and signaling is challenging. In this paper, a scheduled device grouping scheme is proposed to minimize the number of required device IDs and signaling overhead. The grouped devices share one ID while sequentially attaching to the network to execute application specific tasks. Applications with various goals are fulfilled considering expiration time and task dependency. Beyond formulating an integer programming problem, the issue is mapped into a variation of bin packing problem. The solution reaches 30% to 50% ID reduction by adopting a classic fitting algorithm.

The development of technologies for realizing “Internet of Things” inspire many applications which utilize the things' network. Additionally, the ambient information of the things including various information collected via the network has attracted much attention as useful data for novel applications. By collecting the ambient information from things and constructing databases of them, it is expected to make our lives smarter and more convenient. On the other hand, to maximize the advantage of such a database, it is important to keep the information in the database as fresh as possible. However, in order to keep the freshness of the database, periodically collecting data with short interval is needed, which causes heavy traffic congestion when we use the traditional access control schemes in the existing communication network technologies. Thus, in this paper, we propose a novel access control scheme to keep the freshness of the database while avoiding traffic congestion. Additionally, an optimization to improve the efficiency of our proposed method is provided with mathematical expressions. Moreover, numerical results verify the effectiveness of our proposal.

Fiber-Wireless (FiWi) network is a classification of network that combines the massive bandwidth of the optical network and the reach of the wireless network. FiWi networks are usually composed of an optical and a wireless component. Since both components are designed to work independently, some mechanisms, such as the different power saving methods in both components, may not cooperate with each other and this may result in an undesirable performance. In this paper, we identify that the conflicting power saving mechanisms cause unnecessary energy consumption and introduce additional delay to the overall FiWi network. To cope with this problem, we propose a novel ONU sleep method, which dynamically control the ONU sleep period based on the STAs energy control mechanism. Finally, we demonstrate that our proposed method has shorter latency and is more efficient in term of energy consumption than the existing method.

In recent years, tremendous amount of traffic is delivered by the Internet. However, ground networks cannot provide communication environment to disaster areas and isolated areas such as mountain and sea. Thus, as the next generation networks, optical satellite networks have attracted much attention because of many advantages such as high capacity, disaster resistance, and large coverage. Since optical communication can increase traffic rate in comparison with radio wave, the optical satellite networks can provide high speed communication. On the other hand, optical communication is greatly influenced by the atmospheric condition, which can lead to traffic congestion. Thus, we focus on utilizing satellites with embedded mass storage device to manage large amount of traffic in the network. Since satellites embedded mass storage device can store the traffic temporary, it is possible to deliver the data when the downlink condition is more favorable. However, there are no traffic control method that effectively use mass storage device embedded in satellite while taking into account optical link between satellite and optical ground station. Therefore, in this article, we propose a new traffic control method to effectively use mass storage device embedded in satellite according to optical downlink condition between satellite and optical ground station.

In recent years, HETerogeneous NETworks (HET-NET) arises as a promising network technique to manage a large number of mobile devices. By using the networks having different coverage size in the HETNET, it enables to increase the network capacity drastically. However, sometimes variations in user distribution causes inhomogeneous traffic load among the networks having different coverage size in the HETNET. On the other hand, Device-to-Device (D2D) communication technologies have attracted much attention as another solution to increase the network capacity. The direct communication between user devices creates flexible networks. Thus, we focus on utilizing D2D communication technologies in HETNET to avoid the inhomogeneous load among the networks having different coverage size. In this paper, a traffic detouring method is proposed and the advantage of the proposed method is analyzed with some mathematical expressions. Additionally, numerical results demonstrate the effectiveness of our proposal.

Satellite-Routed Sensor System (SRSS) has attracted attentions as a next generation sensor network system to realize data gathering from a large scale sensors deployment. In this system, a large number of sensor terminals send sensed data to the monitoring stations which are located in the different area via a satellite. With the help of satellite, it is possible to collect data from sensor terminals that are located in an area that has no physical infrastructure. Thus, SRSS is expected to provide many services such as real-time traffic control system and disaster detection systems by utilizing gathered data from large area. However, an efficient access control method is required to accommodate a large number of sensor terminals trying to transmit their sensed data to the satellite. Therefore, this paper proposes a novel data gathering method that can efficiently allocate bandwidth to the sensor terminals in need to transmit their sensed data. Additionally, an optimization to improve the efficiency of our proposed method is provided with mathematical expressions. The effectiveness of our proposal is evaluated through numerical results.

Next generation satellite-routed sensor system (SRSS) is expected to provide disaster detection system with high real-time performance. By using satellite networks, SRSS realizes data collection from multiple sensor terminals deployed in a wide area. However, an efficient access control scheme is needed to achieve multiple access from numerous sensor terminals to the satellite with its limited bandwidth. Conventional research works propose a countermeasure to avoid data collisions at the satellite. However, they do not consider the situation that a significantly large number of sensor terminals communicate with the satellite anytime, which cause data collisions and decrease real-time performance. Therefore, in order to efficiently resolve these problems, we propose a new scheme which utilizes a divide and conquer approach for efficient bandwidth allocation. The effect of the scheme on the amount of time for allocating satellite bandwidth is also analyzed. The analysis clearly shows the advantage of our proposed scheme. Furthermore, numerical results demonstrate the effectiveness of our proposal.

Recently, since many kinds of wireless devices have been widely used and a large amount of contents is available on the Internet, a network system that provides adequate services anytime and anywhere is required. In this research, we focus on satellite networks using mass storage devices to provide the above mentioned services. In this kind of network, multiple satellites are used to cover the whole surface of the earth, and each satellite is equipped with a mass storage device. By using mass storage devices, the satellite network can manage a high buffer capacity to handle large amounts of data. However, no routing method has been developed for such kind of satellite network that can utilize the storage devices and manage the large amount of data in the network effectively. In this paper, we propose a novel routing scheme for the efficient utilization of the mass storage on satellites to mitigate network congestion. The proposed method is analyzed mathematically. The numerical results validate the effectiveness of our proposed method.

Multi-Layered Satellite Networks (MLSNs) have many advantages such as extensive coverage, lower delay performance, and disaster resistance. Moreover, the networks permit load distribution by bypassing traffic efficiently from lower layers to upper layers. In the future, the MLSNs should play an important role to provide global communication services. However, sometimes traffic congestion happens in these networks since the distribution of users is unbalanced heavily depending on geographical restrictions, which causes bad effects on the networks such as increasing delay. Therefore, we focus on network design to avoid traffic congestion. There are many constitution elements to design these networks. One of the most significant elements is the altitude of satellites because it affects propagation distance and number of links between layers in MLSNs, and thus the packet transfer delay of the networks. Therefore, we analyze the relationship between the altitude of satellites and the packet transfer delay with network-wide equalization. Furthermore, the existence of the optimal altitude of satellites is denoted in this paper. Our analyses are validated by simulation experiments.

Non-Geostationary satellite networks have many advantages to enable ubiquitous wireless environments such as, extensive coverage, disaster-resistance, and efficient power consumption. Furthermore, to use these networks more efficiently, multi-layered satellite networks are a promising approach, due to their ability to achieve increases in network capacity and to detour traffic efficiently, while maintaining the advantages of each layer. However, they suffer from high delay. In this paper, we focus on constellation design of two-layered satellite networks, in particular on the satellite altitude that minimizes the total packet delivery delay of the network. We express the relationship between the total packet delivery delay and the satellite altitude in mathematical form and develop an expression for determining the altitude to minimize total packet delivery delay. Simulation results validate our analyses.

Recently, Non-Geostationary Earth Orbit (NGEO) satellite networks have gained research attention. Since they offer many features, e.g., extensive coverage, disaster-resistance, and efficient power consumption, they are considered as a good candidate for providing global communication services. Moreover, Multi-Layered Satellite Networks (MLSNs), which consist of layered NGEO satellite networks, have attracted much attention since they achieve excellent load distribution through bypassing traffic from the lower layer to upper layer. However, there is a possibility that traffic congestion may exist at a satellite on the upper layer because each satellite on the upper layer usually covers more than one satellite on lower layers in MLSNs. In this paper, we focus on traffic control in two-layered networks, especially on distributing the packet flow between the two layers in order to minimize the transfer delay of the network. Simulation results demonstrate the correctness of our analyses about delay in the network.