Covert communication can reduce the security overhead of messages in New Radio (NR) Vehicle-to-Everything (V2X) and provide a higher level of privacy assurance. However, it also introduces a tradeoff between covertness and transmission. In this paper, we explore the tradeoff between covertness and transmission in NR V2X communications and propose a covert scheme, Covert Semi-Persistent Scheduling (C-SPS). To elucidate this tradeoff in the traditional case, we derive the theoretical results based on stochastic geometry, which accounts for the fundamental processes of semi-persistent scheduling, including transport block re-selection and resource collision. Based on our derivation, the optimal setting of C-SPS is obtained to maximize the covertness of message delivery while satisfying the transmission requirements of NR V2X. More importantly, we assess the covert threat posed by intelligent adversaries equipped with the Large Language Model (LLM) capable of reasoning based on the tailored chain of thought. Finally, we evaluate the effectiveness of C-SPS in balancing the tradeoff under both traditional case and LLM-assisted case.

In 6G vehicle-to-everything (V2X) scenary, precise millimeter-wave beam prediction under dynamic environmental disturbances faces critical challenges of latency-accuracy trade-offs and cross-scenario generalization. It not only requires accurate and prompt determination of the vehicle’s position, but also needs to overcome external environmental disturbances. However, most existing schemes suffer from critical limitations such as poor adaptability in extreme scenarios, prolonged decision latency, and restricted cross-scenario generalization capability. Towards this end, we propose LLM-MM: an end-to-end robust multimodal beam prediction framework for 6G V2X networks via MoE-LoRA Adaptation. Specifically, we first construct a distinctive beam prediction architecture to leverage the powerful inference capabilities of the Large Language Model (LLM), thereby shortening the inference time while ensuring the inference accuracy. Secondly, by integrating the Mixture-of-Experts with Low-Rank Adaptation model, LLM-MM not only ensures its generalization ability across multiple scenarios but also reduces the training cost. Our framework integrates a rigorously evaluated open-source LLM, selected through systematic comparison of multiple candidates to achieve optimal performance. Extensive numerical results demonstrate the advantages of proposed framework from multiple perspectives.

Mars exploration holds the potential to uncover the origin of life, which requires a reliable and sustainable electrical power supply to support long-duration missions. Wireless Power Transfer (WPT) based on Space Solar Power Satellites (SSPSs) has emerged as a promising option due to its continuous 24 × 7 availability and renewable nature. In particular, Laser-based WPT (LWPT) is especially suitable for space energy transmission due to its narrow beam width and high directivity. However, the extremely long propagation distance, high dynamics, and harsh conditions such as atmospheric attenuation, dust storms, and plasma effects degrade the transmission efficiency. To improve the robustness of energy delivery under such uncertain conditions, we incorporate Intelligent Reflecting Surfaces (IRS) to reconfigure the wireless propagation environment. Specifically, IRSs are exploited to provide alternative reflective transmission paths and enable adaptive wavefront control at the Mars rover, thereby enhancing the reliability and efficiency of power transfer when the direct link is partially blocked or severely attenuated. Additionally, we consider the presence of a malicious rover attempting to steal energy or disrupt legitimate charging. To ensure secure charging, we propose an IRS-assisted Challenge-Response Physical-Layer Authentication (CR-PLA) scheme. The False Alarm (FA) and Miss Detection (MD) probabilities are adopted as key performance metrics to quantify the authentication reliability. Finally, we formulate an optimization problem to maximize the harvested energy at the legitimate rover by jointly optimizing satellite selection, active transmit beamforming, and the passive IRS reflection, subject to the FA and MD probabilities. To address this intractable non-convex problem, we propose an Alternating Optimization (AO) algorithm to solve it iteratively. Numerical results demonstrate that, compared with benchmark methods such as Successive Convex Approximation (SCA) and greedy approach, the proposed method significantly enhances the harvested energy while effectively reducing MD probability.

With the rapid development of the internet of things (IoT), IoT devices are now capable of real-time monitoring and collecting environmental and production data through integrated sensors. However, these devices often face challenges related to limited storage capabilities and transmission range. Furthermore, the widespread deployment of IoT devices has raised significant concerns regarding privacy security. To enhance data collection efficiency and ensure the security of location privacy, this study proposes a high altitude platform (HAP) data collection and trajectory design scheme that is aware of location privacy. Firstly, our scheme utilizes HAPs to quickly cover the collection area and transmit data in real time via satellites. Secondly, a differential privacy-based perturbation mechanism is applied to reduce the risk of location information leakage. Finally, the trajectory optimization problem, incorporating privacy awareness, is modeled as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL) techniques to determine the movement decisions of the HAPs. Experimental results demonstrate that this scheme effectively protects location privacy while enhancing the efficiency and security of data collection.

As global demand for efficient network services increases, the limitations of traditional terrestrial wireless networks are becoming more apparent. Space-air-ground integrated networks (SAGIN) have emerged as a promising solution to advance next-generation network infrastructure. However, SAGIN faces significant security challenges—including the lack of a robust security architecture, trust and data reliability issues in multi-hop transmissions, and the need to enhance network performance without compromising security—rendering traditional boundary-based defenses inadequate. Therefore, we propose SECURELINK, a decentralized zero-trust architecture tailored for SAGIN, which replaces traditional perimeter defenses with a ”never trust, always verify” approach. This approach strengthens network security and flexibility through continuous verification and adherence to the principle of least privilege. SECURELINK integrates blockchain technology to establish a multi-layered security verification and data processing scheme, addressing the dynamic and decentralized features of SAGIN. Additionally, we introduce DFRIO, a zero-trust traffic offloading method based on decentralized federated reinforcement learning and blockchain, designed to enhance network performance within maintaining security. Simulation results demonstrate that our solution significantly enhances SAGIN’s defense capability without compromising network stability, outperforming the two baseline schemes by 18.3% and 42.6%, respectively.

Edge Learning environments, characterized by limited wireless resources, encounter significant bottlenecks in network performance, particularly in Federated Learning (FL) tasks. Current resource allocation strategies are primarily classified into “AI for Networking” and “Networking for AI”. However, both approaches fail to adequately address the interaction between network states and AI task requirements, thereby limiting their overall effectiveness. To address this, we propose a novel bidirectional dynamic collaborative optimization mechanism that enables real-time interaction between AI task performance and network states. This mechanism adjusts both AI task resource requirements and network configurations based on performance feedback, breaking away from traditional unidirectional optimization approaches. We introduce the AI-network unified algorithm, which incorporates data-driven dynamic sensing and enhances system adaptability and robustness, achieving self-optimization in edge learning. Theoretical analysis and simulation results demonstrate the significant advantages of our approach in simultaneously improving network resource utilization and AI task performance, providing an effective solution for the future wireless network.

Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the heterogeneous networks (HNs) environment. Moreover, current state-of-the-art distributed fault diagnosis methods, which utilize specific machine learning techniques, lack multi-scale adaptivity for heterogeneous device information, resulting in unsatisfactory diagnostic accuracy for HNs. In this paper, we develop an LLM-assisted end-to-end intelligent network health management framework. The framework first proposes a multi-scale data scaling method based on unsupervised learning to address the multi-scale data problem in HNs. Secondly, we combine the semantic rule tree with the attention mechanism to propose a Multi-Scale Semanticized Anomaly Detection Model (MSADM) that generates network semantic information while detecting anomalies. Finally, we embed a chain-of-thought-based large-scale language model downstream to adaptively analyze the fault diagnosis results and create an analysis report containing detailed fault information and optimization strategies. We compare our scheme with other fault diagnosis models and demonstrate that it performs well on several metrics of network fault diagnosis.

Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration parameters affect network key performance indicators (KPIs). Traditional performance evaluation methods can evaluate KPIs based on configuration parameters, but they are unable to explain how configuration parameters affect KPIs. Moreover, static causal discovery and inference methods are not directly applicable to dynamic networks. To address these challenges, we propose a self-iterative performance optimization method based on causal inference and interpretable network evaluation (CiiNet). CiiNet constructs causal graphs through change-point detection and hierarchical incremental causal discovery. Then, CiiNet introduces causal inference for critical parameter analysis (CPA). Using intervention analysis and regression-based parameter learning, CiiNet infers and evaluates the impact of critical parameters on KPIs. Based on the interpretable evaluation, CiiNet can further self-iteratively optimize the critical parameters for optimal network performance and dynamically obtain the optimal configurations. Our extensive experiments show that CiiNet outperforms other baseline methods regarding causal discovery, network performance evaluation, and CPA.

The integration of satellite, aerial, and terrestrial networks in Satellite–Air–Ground Integrated Networks (SAGIN) enhances connectivity but also introduces new vulnerabilities to multi-layer jamming attacks. These attacks—originating from space-based, air-based, and ground-based sources—exhibit diverse signal characteristics, resource constraints, and durations, posing significant threats to communication performance and system security. A single mitigation technique is often insufficient to address these varied challenges effectively. In this paper, we propose a multi-layer adaptive jamming mitigation framework that dynamically adapts to the type of jamming encountered, with a particular focus on threats targeting Low Earth orbit (LEO) satellites within SAGIN. We evaluate a range of mitigation techniques and analyze their performance across different jamming scenarios. Our results show that tailored mitigation strategies are essential in SAGIN to achieve higher Signal-to-Noise Ratio (SNR) and lower Bit Error Rate (BER), highlighting the importance of jamming-aware defenses for enhancing the resilience and security of SAGIN systems.

We propose using the Poisson point process (PPP) for accurate modeling in Space-air-ground integrated networks (SAGIN). SAGIN is experiencing significant growth, aiming for ultra-fast speeds, low latency, and integration of AI, IoT, and other emerging technologies. To achieve better performance than existing hybrid bands in SAGIN, we utilize three distinct frequency bands—Radio frequency (RF), Terahertz (THz), and Free-space optical communication (FSO)—to compensate for their shortcomings effectively. Then, we derive not only the coverage probability but also the rate coverage probability formula of the network and utilize Newton's method to deduce the most optimal channel allocation scheme. Moreover, we use comparison experiments between different frequency bands, altitudes, and other conditions to affirm its reliability and effectiveness in advancing SAGIN coverage and rate coverage probability. The results show that our proposed scheme performs up to 3 times better than a single-spectrum scheme and 2 times better than the existing mixed method.

The integration of Time-Sensitive Networking (TSN) with 5G technology provides Industrial IoT (IIoT) systems with essential low latency, high flexibility, and reliability. However, a key challenge in combining 5G and TSN is the deterministic scheduling of cross-domain flows, which requires precise time synchronisation and the ability to handle unpredictable changes in wireless channels. To address this challenge, we propose an online asynchronous scheduling mechanism. This mechanism is implemented at the 5G-TSN gateway, dynamically allocating TSN network time slot resources to enhance the network's deterministic scheduling capability in the presence of time asynchrony and network fluctuations. Extensive simulations on the OMNeT++ platform demonstrate that our online asynchronous algorithm effectively utilises network resources, reduces delays caused by wireless fluctuations and time asynchrony, and improves network throughput.

The evolution of 6G wireless networks requires robust and adaptive communication systems that can handle dynamic indoor environments. Accurate prediction of the terahertz (THz) channel is a key enabler of this adaptability, enabling proactive decisions such as beamforming and handover. Because traffic levels (i.e., user densities) fluctuate, the underlying channel statistics change over time, resulting in concept drift that can deteriorate the performance and generalization ability of deep learning (DL) models if they are tested against mismatched conditions (i.e., on a traffic level other than the one used for training). This paper introduces a novel framework for generalizable THz channel prediction, enabled by fusing environmental sensing with AI-assisted wireless communication to mitigate concept drift and maintain generalization. First, we investigate the use of DL-based channel prediction models tailored to specific traffic levels—light, moderate, and dense—and evaluate their performance under mismatched conditions. Results show up to 51% performance deterioration when models are exposed to mismatched traffic scenarios. To mitigate this issue, a traffic-aware channel prediction framework is proposed, comprising three stages: people counting using sensing technologies; quantization of the user count into traffic levels; and dynamic selection of the corresponding DL model. Simulation results demonstrate that integrating accurate sensing technologies, particularly vision-based systems, significantly reduces prediction deterioration to as low as 4%. The proposed framework’s adaptability ensures reliable channel prediction by aligning model selection with real-time traffic conditions, which highlights the potential of fusing environmental sensing with AI-assisted wireless communication to enhance the robustness of future 6G 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.

For future network applications, ubiquitous connections and real-time cloud offloading are important paradigms for enabling important services. To achieve these goals, satellite networks, Free-Space Optical (FSO) communications, and Mobile Edge Computing (MEC) are key technologies. This paper proposes an efficient latency based task offloading strategy in a multi-tier Space-Air-Ground Integrated Network (SAGIN) with MEC and FSO communications. We consider a static deployment of ground users in Yamagata prefecture, Japan, offloading computational tasks to High Altitude Platforms (HAPs) and a Low Earth Orbit (LEO) satellite constellation. In this system, elevation-based FSO visibility and atmospheric attenuation can affect transmission latency, while server workload can impact computation latency. We design a hierarchical clustering-based framework and evaluate it alongside two other baseline approaches in terms of latency performance. Results show that our clustering-based task assignment achieves lower average latency and better load balancing, highlighting its potential for real-time edge-enabled FSO systems.

6G networks are expected to significantly benefit from advanced wireless local area technologies such as Wireless Gigabit (WiGig), which operates in the 60 GHz frequency band. This band supports extremely high data rates and low latency, making it ideal for next-generation wireless applications such as the metaverse and holograms. However, WiGig signals are highly susceptible to attenuation from physical obstructions, resulting in frequent handovers and connectivity disruptions. Traditional reactive handover mechanisms are often slow due to latency in decision-making and processing overhead. However, proactive handover strategies that leverage channel prediction can enhance network reliability and improve the quality of service. This paper investigates the feasibility of using statistical methods, specifically the auto-regressive integrated moving average (ARIMA) model, to predict the received signal strength indicator (RSSI) in real-world indoor WiGig environments. Our results indicate that ARIMA exhibits poor predictive accuracy, with a root mean square error (RMSE) of 15 dBm, which may trigger inaccurate handover decisions by initiating handovers under strong signal conditions or failing to respond under weak ones. To overcome this shortcoming, we propose an ensemble learning-based channel prediction approach utilizing the random forest (RF) algorithm. Our results show that the RF model significantly outperforms ARIMA by effectively capturing the nonlinear dynamics of real-world indoor WiGig channels. Specifically, the RF model achieves a 90% reduction in both mean absolute error and RMSE, and a 99% reduction in mean squared error, offering a promising solution for robust proactive handover management in 6G networks.

The dynamic nature of user mobility and density in indoor WiGig networks poses a significant challenge to seamless handover, particularly in the 60 GHz band, where small and closely clustered channel gain values hinder effective decision-making. To address this, we propose a generalizable deep reinforcement learning (DRL)-based handover that integrates a novel reward function designed to amplify channel gain differentials, thereby improving the convergence speed and decision accuracy of learning agents. We investigate the performance of state-of-the-art DRL algorithms—Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C)—enhanced through advanced hyperparameter-tuning techniques, including grid search, random search, optuna, and hyperopt. Among these, DQN combined with grid search yields the best overall performance, surpassing A2C by 48% in average rolling reward and achieving 32% faster convergence than PPO.To assess generalization, we evaluate the merged DQN agent trained across varying user density scenarios (1–8 users) against expert agents specialized for individual densities and a high-density-trained agent tested across all scenarios. Our results reveal that the merged agent exhibits robust and consistent performance across all densities, indicating strong generalization capability. In contrast, the high-density agent suffers performance degradation of up to 13% when exposed to unseen scenarios, underscoring its limited adaptability. While expert agents perform optimally within their specific environments, their deployment complexity renders them impractical for real-time systems. These findings highlight the importance of training DRL agents across diverse scenarios to achieve scalable and generalizable handover solutions in dense and dynamic WiGig networks.

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.

Emerging wireless local area networks, such as WiGig that operate in the extremely high-frequency band (60 GHz) hold significant potential for the development of next-generation 6G networks by offering high throughput and low latency. However, the 60 GHz band is prone to severe signal degradation due to channel blockages, leading to frequent handovers and challenges in maintaining seamless connectivity. Reactive handover strategies can result in service delays due to overhead and decision-making latency. To tackle these issues, proactive approaches that utilize machine learning (ML) and deep learning (DL) are becoming increasingly popular for network optimization in WiGig networks. However, existing ML/DL models are often tailored to specific network environments, making them susceptible to concept drift — a phenomenon where even minor environmental changes can significantly degrade network performance due to incorrect decision-making. This paper investigates scenarios and environmental changes that can trigger concept drift in WiGig networks. We conduct real-world experiments to analyze the statistical behavior of received signal strength, highlighting the potential for concept drift. Based on our findings, we propose a direction for identifying concept drift in WiGig networks.

With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.

The development of 6G should enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.

Existing anti-jamming strategies for unmanned aerial vehicle (UAV) networks largely assume homogeneity among UAVs, neglecting the differences in hardware configurations, task requirements, and environmental adaptability. In the face of such heterogeneity, these strategies often fail to effectively counter intelligent jamming and co-channel interference. To address this issue, this paper proposes an intelligent anti-jamming framework designed specifically for the heterogeneous UAV network, allowing each UAV to autonomously adjust its transmission channel and power based on its hardware capabilities and task requirements in a distributed environment. This aims to optimize communication efficiency and reduce energy consumption. We formulate the anti-jamming problem as an adversarial game and confirm the existence of a unique equilibrium point within this model. Moreover, we introduce the novel Personalized Federated Soft Actor-Critic (PFSAC) algorithm, which combines the global model with local models to customize personalized anti-jamming strategies for each UAV, significantly enhancing network performance in complex jamming environments. Simulation results indicate that compared to other methods, our proposed algorithm significantly enhances the anti-jamming capability of heterogeneous UAV networks and performs better than them.

Highly dynamic heterogeneous networks (HDHNs), characterized by high node mobility and heterogeneity, frequently experience complex and recurrent network faults. Conventional centralized fault diagnosis methods demand real-time collection of extensive network-wide data, while distributed approaches often exhibit limited fault detection capabilities. Additionally, machine learning-based fault diagnosis methods are challenged by the scarcity of labeled fault samples required for training. To address these limitations, this study proposes a semi-distributed network fault diagnosis architecture based on a digital twin network (DTN). The proposed architecture facilitates the extraction of a comprehensive labeled fault dataset that closely replicates real-world network conditions. Using this dataset, we perform centralized training of an enhanced anomaly detection model, FTS-LSTM, to infer fault types at the node level. To overcome the drawbacks of both centralized and distributed approaches, we further introduce a semi-distributed fault diagnosis algorithm (SDFD) that integrates fault types and severity levels identified by nodes to infer overall network faults. The proposed fault diagnosis scheme is validated on a semi-physical DTN simulation platform, demonstrating its effectiveness in realistic scenarios.

Industry 4.0 generates a huge volume of data, where Federated Learning (FL) can be utilized to mine the data in a privacy-preserving manner. However, traditional FL in privacy-preserving is not sufficient, the uploaded local model gradients can be intercepted by external Eavesdroppers (Eve), with more enough of which the users’ raw data can be inferred, leading to privacy leakage. At the same time, the future 6G accommodating more devices, makes privacy concerns sharper. To tackle privacy issues in FL, in this paper, we propose a Model-Safeguarded FL framework based on Intelligent Reflecting Surface (IRS) (MSFL) where Non-Orthogonal Multiple Access (NOMA) is introduced to enable multiple devices access. Specifically, IRS is deployed between Base Station (BS) and participants, improving the wireless environment and preventing Eve from eavesdropping. The Deep Deterministic Policy Gradient (DDPG)-Optimized Power and Phase (DOPP) algorithm is proposed to jointly optimize transmission power at participants and IRS phase shift to maximize the minimum confidentiality capacity. Extensive results demonstrate that the maximum confidentiality capacity of our MSFL scheme is up to 1.7 bps/Hz at a transmission rate of 30 dBW, which is approximately 300% more than that of the Block Coordinate Ascent Method (BCAM) and Artificial Noise (AN).

With the advancements in six-generation (6G) communication technology, there is a growing need for comprehensive and interpretable network performance evaluation for network optimization. Traditional evaluation methods often overlook uncertainties and are limited to a single time scale or performance dimension, while the recent machine learning-based method lacks interpretability. To address this issue, we propose a multidimensional performance evaluator for interpretable and traceable network performance evaluation (MPITE). MPITE, constructed with a three-layer evaluation model incorporating physical, logical, and causal topology structures, reflects the causal relationship of communication system configurations, the changing network states, and performance metrics. We introduce a multidimensional performance index that considers value, time, and certainty dimensions to evaluate network performance comprehensively. We propose interpretable Bayesian theory-based network inference algorithms to derive network certainty for interpretable network performance evaluation. Then, we intelligently derive optimal network configuration parameters through reverse inferencing for network tracing. Experimental results demonstrate the advantage, interpretability, and traceability of MPITE.

Unmanned Aerial Vehicle (UAV) equipped with a gateway shows great potential for data collection in many scenarios, especially for the areas lacking of public network infrastructures. However, our in-field experiments on UAV-assisted LoRa networks show that a large throughput gap exists between the ground-to-air and ground-to-ground transmissions. We find that the misalignment of the radiation direction of transceiver antennas with height difference leads to additional signal strength loss, which is ignored by existing ground-to-ground transmissions. In this paper, we propose a directivity-aware ground-to-air link model called annulus model to quantify the impact of directivity on the ground-to-air link quality. Based on our model, a new ground-to-air channel access scheme for UAV-assisted LoRa networks,PreLoRa, is proposed. By predicting the link quality variations,PreLoRaschedules the transmission periods and adopts optimal transmission configurations for ground nodes to improve the link throughput. We implementPreLoRaon commercial LoRa platforms and extensively evaluate its performance in the wild. Experimental results show thatPreLoRacan significantly improve data collection throughput by up to 65.5% compared to baseline methods.

The Space-Air-Ground integrated network (SAGIN) has gained significant attention due to the explosive growth in mobile data traffic. In this network, Unmanned Aerial Vehicles (UAVs) play a critical role as air relay nodes, bridging ground and space networks. However, challenges arise from the dynamic position angles between UAVs and satellites, as well as fixed UAV altitudes, limiting air-to-space transmission capacity. Moreover, the finite UAV battery capacity carries the risk of energy interruptions during SAGIN transmissions. To address these issues, we propose an integrated model that considers UAV channel fading, energy consumption, and harvesting. This model allows us to comprehensively analyze SAGIN transmission performance. Within this framework, we calculate the UAV energy outage probability and signal-to-noise ratio (SNR) outage probability for SAGIN uplink transmission. Based on our network performance analysis, we derive an expression for the optimal UAV altitude, ensuring uninterrupted energy supply and preventing SNR outage. To assess the fairness of SAGIN transmission performance, we compare the capabilities of Ground-to-Air-to-Space and Ground-to-Space transmissions. Additionally, we provide closed-form expressions for the transmission time gap in both scenarios. Our numerical results validate the accuracy of these derived expressions and evaluate how key parameters impact the optimal UAV altitude in the SAGIN uplink.

This paper explores secret key generation in 5G and beyond LiFi networks using visible light in the downlink and infrared in the uplink. Unlike the existing works, we focus on a realistic indoor environment with multi-user mobility. Given inaccuracies in high-frequency channel models, we introduce the first deep learning model that combines the channel probing and quantization phases to generate initial secret keys with a minimal key disagreement rate (KDR) of 16% between the uplink and downlink, leading to a key generation rate (KGR) of 79 bits/s after information reconciliation. We show that LiFi channel statistics suffer from concept drifts with user density changes in the room. This increases the KDR by 28% - 44% and the generated keys fail to pass the NIST randomness tests. As a countermeasure, we introduce a voting ensemble model that mitigates concept drifts, maintaining a stable 16% KDR, 79 bits/s KGR, and passing NIST tests, despite the varying user densities.

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.

Artificial Intelligence (AI) applications on Internet of Things (IoT) networks often involve relaying generated data to a server for deep learning training, which poses security risks to users' data. Federated Learning (FL) offers a distributed model training paradigm in which local data are kept at the edge and locally trained models are exchanged and aggregated by a server over several rounds to produce a global model. While successful, standard FL algorithms do not support heterogeneous local model design, an essential requirement, especially for resource-limited edge devices. Recently, Knowledge Distillation-based FL algorithms have provided model-agnostic FL to enable clients to independently design their local model and share soft labels instead of model parameters. KD-based FL algorithms are computationally expensive due to additional distillation training. We propose Federated Learning with Selective Knowledge Distillation (FedSKD) to address the limitations of system heterogeneity; and computation and communication demands. We evaluate different aspects of the proposed algorithm relative to baseline FL algorithms. Results show that FedSKD incurs significantly less per-round computation time and communication overhead relative to the considered model-based and KD-based FL algorithms.

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.

Accurate channel prediction using deep learning (DL) algorithms can address the challenges of terahertz (THz) propagation, such as atmospheric absorption and object scattering, by enabling proactive handover and beamforming. However, indoor environments are inherently dynamic, with factors like occupancy level variations causing the channel characteristics to change over time. This phenomenon, known as concept drift, can severely degrade the DL model performance used in channel prediction. This paper investigates the impact of indoor occupancy level variations on the generalization ability of state-of-the-art DL models for THz channel prediction. We identify three distinct occupancy levels (low, medium, and high) within the THz indoor channel. Our results demonstrate that the state-of-the-art DL models exhibit limited generalization capabilities, with performance deterioration in prediction accuracy ranging from 4−62%. We propose a robust two-stage framework to mitigate concept drift in THz channel prediction. The first stage predicts the indoor occupancy level from the THz wireless signal, which is a multi-class classification problem. Due to the reoccurring concept of occupancy levels, the second stage contains a pool of models in a sleeping mode based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) architecture. One of these DL expert models is activated for channel prediction based on the occupancy level predicted from the previous stage. Our framework demonstrates superior generalization by limiting the performance deterioration from 62% due to concept drift to ≤ 9%. This represents an 85% reduction in performance deterioration compared to the existing state-of-the-art DL models.

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.

As an essential 6G component, the Low Earth Orbit (LEO) satellite communication has aroused increasing attentions from academia and industry to provide seamless and highly-efficient networking services. However, existing routing strategies are primarily designed for terrestrial networks or small-scale satellite networks, making it inapplicable to future LEO satellite constellations of ultra density, high dynamics, and large scale. Moreover, since Free Space Optical (FSO) communications have been expected for Inter-satellite Links (ISLs) and the number of constructed FSO ISLs depends on the Acquisition, Pointing, and Tracking (APT) terminals and geometric visibilities, the routing algorithm needs to be adaptive. To address these issues, this paper considers the dual-layer network architecture composed of Medium Earth Orbit (MEO) satellites and LEO satellites, where the regional network division is adopted for the LEO satellite layer to alleviate the complexity and improve the routing efficiency. Then, a multi-objective reinforcement learning-based routing strategy with local information considered is proposed to meet the differentiated Quality of Service (QoS) requirements of diversified terrestrial applications. A cooperative mechanism is also designed to address the conflicts caused by the routing design for different applications. The simulation results demonstrate the proposal is applicable to varying numbers of APT terminals and outperforms benchmark algorithms in terms of diversified QoS metrics.

Behavior-oriented decision-makers are critical components in generating intelligent decisions for user virtual interactions in metaverse. In this work, we study the efficiency and security of behavior-oriented decision-makers in metaverse from perspective of autonomous driving (AD), where modeling human uncertain driving behaviors is the key factor of their performance. We first explore the ability of different deep-neural-network-based decision-makers used in deep reinforcement learning for efficient autonomous vehicle control, and then we propose a novel neural backdoor attack against them using spatiotemporal driving behaviors, rather than an immediate state. With our attack, the adversary acts as a normal driver and can trigger attacks by driving his vehicle following specific spatiotemporal behaviors. Extensive experiments show that our proposed backdoor attack can achieve high stealthiness and effectiveness (less than 1% clean performance variance rate and more than 98% attack success rate) on behavior-oriented decision-makers, and is sustainable against existing advanced defenses.

High accuracy and simultaneous positioning is an essential demand in future Intelligent Transportation Systems (ITS), while the mobility and dynamics of vehicles place great challenges. Single Base Station (BS) positioning has become popular for its fast speed, high convenience, and low cost. With the construction of 5G, the wide bandwidth and high separation capability of millimeter Wave (mmWave) bring more possibilities for vehicle positioning via single BS. However, mmWave signals have high distance attenuation and are easily blocked by obstacles. In urban scenarios, the prevalent None-Line-of-Sight (NLoS) situations have severe impacts on positioning accuracy. The multipath effects, Doppler effects, and tracking lags further degrade the performance. To address these issues, we introduce the Intelligent Reflecting Surface (IRS) to single BS vehicle positioning for beyond Line-of-Sight (LoS) communications. We study the advantages of IRS in urban ITS to alleviate the multipath effects, Doppler effects, and tracking delay. To realize the real-time target tracking for IRS, the Unscented Kalman Filter (UKF) is adopted, for which stable communications between the BS and moving vehicle can be maintained. Simulation results show that the utilization of IRS can significantly improve the positioning accuracy and the adoption of UKF further enhances the performance.

The Space-Air-Ground Integrated Network (SAGIN) plays a pivotal role as a comprehensive foundational network communication infrastructure, presenting opportunities for highly efficient global data transmission. Nonetheless, given SAGIN's unique characteristics as a dynamically heterogeneous network, conventional network optimization methodologies encounter challenges in satisfying the stringent requirements for network latency and stability inherent to data transmission within this network environment. Therefore, this paper proposes the use of differentiated federated reinforcement learning (DFRL) to solve the traffic offloading problem in SAGIN, i.e., using multiple agents to generate differentiated traffic offloading policies. Considering the differentiated characteristics of each region of SAGIN, DFRL models the traffic offloading policy optimization process as the process of solving the Decentralized Partially Observable Markov Decision Process (DEC-POMDP) problem. The paper proposes a novel Differentiated Federated Soft Actor-Critic (DFSAC) algorithm to solve the problem. The DFSAC algorithm takes the network packet delay as the joint reward value and introduces the global trend model as the joint target action-value function of each agent to guide the update of each agent's policy. The simulation results demonstrate that the traffic offloading policy based on the DFSAC algorithm achieves better performance in terms of network throughput, packet loss rate, and packet delay compared to the traditional federated reinforcement learning approach and other baseline approaches.

The integration of Roadside Units (RSUs) is vital for the development of autonomous driving technologies. Challenges arise from sinking computing capabilities into RSUs and vehicles in the paradigm of Vehicle Edge Computing (VEC), particularly due to heterogeneous computation and communication capacities of network nodes and multiple sources of computing tasks (node-mounted and offloading tasks). These challenges complicate network stability from the perspective of a long-term optimization evolving over time, considering unpredictable task distribution and environmental states. To tackle these challenges, we approach the problem of partial task offloading to minimize task delay while meeting the demand of system stability over time as a dynamic long-term optimization. Utilizing Lyapunov stochastic optimization tools, we successfully decouple the long-term delay minimization and stability constraint, transforming it into a per-slot scheduling problem. Since the per-slot scheduling problem with complicated Lyapunov drift functions can not be solved by numerical optimization at each time step, our solution leverages a proposed deep reinforcement learning algorithm, leading to extensive simulations that demonstrate the superior effectiveness and efficiency of our proposal compared to existing schemes.

Personalized federated learning (PFL) is a framework that targets individual models for optimization, providing better privacy and flexibility for clients. However, in challenging intelligent sensing applications, the heterogeneous client’s data distributions make the aggregation of local models in the server unstable or even hard to converge. To deal with the performance degradation caused by the preceding problem, existing PFL methods focus more on how to fine-tune the global model but ignore the impact of the global model fusion algorithm on the results. In this article, we propose a new explainable neural-aware decoupling fusion based PFL framework, p-FedADF , to address the preceding challenges. It contains two carefully designed modules. The local decoupling module, deployed on the client, utilizes the architecture disentangle technique to decouple the feature extractors in the client’s local model into sub-network according to data categories. It obtains the inference process of feature extraction for different categories of data by training. The global aggregation module, deployed on the server, aligns the sub-network positions for multiple clients and implements a fine-grained generic feature extractor aggregation. In addition, we provide a mask encoding scheme to reduce the communication overhead of transmitting the sub-network sets between the server and clients. Our p-FedADF obtains 1.6%, 0.2%, 2.3%, and 4.5% improvement on a real-world dataset and three benchmark datasets, compared to state-of-the-art methods.

Space-air-ground integrated networks (SAGIN) incorporating multi-band terahertz (THz) and radio frequency (RF) communication have gained increasing attention in the 6G era. However, the heterogeneity, self-organization, and time-variability of SAGIN pose challenges in accurately modeling, quantitatively analyzing, and optimizing these networks. Additionally, the dynamic topology and randomness of the nodes, including low-earth orbit satellites and high-altitude platforms, make the conventional THz/RF channel allocation method of terrestrial networks unsuitable for SAGIN. To address these challenges, we construct an accurate model of SAGIN based on the binomial point process (BPP) model in stochastic geometry. Subsequently, we analyze the network performance, specifically the joint coverage and transmission rate, through the proposed model. We then propose a simulated annealing algorithm-based optimization algorithm to achieve the optimal THz and RF channel allocation, effectively improving the joint coverage and transmission rate performance. Our simulation results demonstrate the effectiveness of the optimization algorithm and provide insights into the deployment rules of SAGIN.

Space-air-ground integrated network (SAGIN) is a promising network architecture for next-generation wireless networks, which combines satellite networks, aerial networks, and terrestrial networks to enable ubiquitous global network services to ground users and improve connectivity for wide deployment wireless applications. Also, free-space optical (FSO) communication with the advantages of low deployment cost, energy efficiency, and extremely high-speed data-delivering capability has attracted more attention recently. However, data transmission efficiency in SAGIN is still limited by the dynamic time-varying network topology and data transmission link connection. In this paper, we construct an FSO/radio frequency (RF) space-air-ground integrated network to enable large-scale and high-speed data transmission as well as degrade the burden of terrestrial networks. In addition, a deep-Q network-based reinforcement learning with an experience replay memory mechanism is proposed to execute dynamic hybrid routing by evaluated rewards. The simulation results show that the proposal achieves significant network performance compared with baseline methods.

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.

The Internet of Things (IoT) Network brings more possibilities and connections to indoor scenarios. With the growing number of mobile devices and online services, indoor wireless networks are required to achieve higher speed and capacity for carrying the exponentially increasing amount of data. The Tera-hertz (THz) communication, with its large spectrum resources, is considered to be the main technology to achieve ultra-high-speed wireless communications in the 6G era. However, THz signals are severely attenuated over distances and the transmission of narrow-beam signals in THz is severely affected by the None-Line-of-Sight (NLoS) environment. To tackle this indoor transmission issue, Intelligent Reflecting Surface (IRS) attracts attention with its beyond Line-of-Sight (LoS) ability and easy deployment. However, an IRS can only serve a limited number of users and requires proper allocation due to the constraints on hardware. The transmission performance is also severely affected by the interference among sub-channels. In this paper, we study IRS-aided data offloading for indoor THz networks. Considering the user interference, transmission conditions, and diversified tasks, we optimize the allocation of limited IRS resource as well as partition planning. A dynamic algorithm based on combina-torial optimization is proposed. Simulation results reveal that our proposed approach can significantly improve the offloading performance.

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.

The mmWave WiGig frequency band can support high throughput and low latency emerging applications. In this context, accurate prediction of channel gain enables seamless connectivity with user mobility via proactive handover and beamforming. Machine learning techniques have been widely adopted in literature for mmWave channel prediction. However, the existing techniques assume that the indoor mmWave channel follows a stationary stochastic process. This paper demonstrates that indoor WiGig mmWave channels are non-stationary where the channel’s cumulative distribution function (CDF) changes with the user’s spatio-temporal mobility. Specifically, we show significant differences in the empirical CDF of the channel gain based on the user’s mobility stage, namely, room entering, wandering, and exiting. Thus, the dynamic WiGig mmWave indoor channel suffers from concept drift that impedes the generalization ability of deep learning-based channel prediction models. Our results demonstrate that a state-of-the-art deep learning channel prediction model based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) recurrent neural network suffers from a deterioration in the prediction accuracy by 11–68% depending on the user’s mobility stage and the model’s training. To mitigate the negative effect of concept drift and improve the generalization ability of the channel prediction model, we develop a robust deep learning model based on an ensemble strategy. Our results show that the weight average ensemble-based model maintains a stable prediction that keeps the performance deterioration below 4%.

With the increasing demand for low-latency data processing and resource-intensive applications in vehicular networks, leveraging unmanned aerial vehicles (UAVs) for edge computing tasks has emerged as a promising solution. The lifetime of a UAV's power supply is a critical factor that can significantly impact the entire system, especially when the UAV is equipped with a server for computing user tasks. Understanding the effect of UAV failure in a cooperative scenario is crucial, particularly with regards to task latency. Thus, in this paper, we analyze the consequences of UAV failure on a cooperative UAV-enabled Multi-Access Edge Computing (MEC) system, specifically in terms of task latency. To ensure uninterrupted service delivery and maintain the quality of service, the system automatically redistributes tasks from the affected UAVs to their operational counterparts in the event of a failure. Furthermore, the paper delves into investigating the impact of severe weather condition attenuation on the millimeter wave (mmWave) and Terahertz (THz) communication channel. Specifically, it focuses on analyzing the effects on transmission time delay and energy consumption in the system. Understanding these factors is essential for optimizing mmWave and THz communication channels performance under adverse weather conditions.

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.

This article proposes a novel intelligent reflecting surface (IRS) backscatter enhancing strategy to secure multi-input multioutput (MIMO) transmission in the presence of an eavesdropper and a malicious jammer. To be specific, the IRS is employed to backscatter the jamming signal into the desired signal to enhance the reception of the user. Utilizing this strategy, we maximize the system secrecy rate by jointly designing the reflection coefficients of IRS and active beamforming at the base station (BS). To efficiently handle this nonconvex optimization problem, we adopt an iterative block coordinate descent (BCD)-based algorithm, where the active beamforming is optimized through the Lagrange multiplier method and the backscatter coefficient matrix of IRS is optimized via the majorization-minimization (MM) method. Then, we examine the robustness of the proposed scheme when considering channel estimation errors. Extensive simulations confirm the secrecy performance gains achieved by our proposed strategy and verify its superiority compared to conventional IRS-based physical layer security (PLS) strategy, IRS backscatter-aided anti-eavesdropping strategy, and other baselines.

With the explosive growth of mobile devices and communication facilities, electromagnetic interference (EMI) has become a common phenomenon affecting the communication band. Based on the shielding capability of electromagnetic bands in EMI, frequency selective surfaces (FSSs) are used to shield or suppress specific electromagnetic bands. Additionally, EMI can be negative control and may change the EMI band. Thus, a single FSS cannot effectively shield EMI due to its limited shielding capacity. To address this issue, we first construct a novel interference shielding model to guard the target area. The related shielding problem is modeled as the UAV-driven FSS (UFSS) configuration problem. Second, we propose an intelligent configuration method based on a stochastic game to solve the configuration optimization problem effectively. In the proposed method, we model the interaction between UFSSs and interferers as a stochastic game, where we provide each UFSS with two different options for updating its shielding configuration strategy. According to the shielding configuration strategy generated by the proposed stochastic game, we propose a square loop resource allocation model based on resource constraints to promote each UFSS to update its square loop. Finally, the numerical results and analysis show that our proposed method is more effective and feasible than other band shielding configuration schemes.

In the context of the 5G/6G mobile network, high levels of requirements such as ultra-high data transmission rate, support for the high mobility node and seamless connection need to be handled. Additionally, ensuring user quality of service (QoS) in high-density and high-traffic mobile networks presents a significant challenge. Unmanned aerial vehicles (UAVs) have emerged as key components in providing flexible assistance in aerial spaces. To further enhance the network performance in dynamic and heterogeneous environments, an intelligent resource allocation strategy with low communication overhead is essential. In this paper, we construct a UAV-assisted mobile network to provide efficient communication for all mobile users in high-density and high-traffic environments, at the same time, a digital twin-empowered dynamic resource allocation strategy based on online training with low communication overhead is proposed. Our proposal employs digital twin-empowered multi-task learning to meet various resource allocation requirements for different node types. Moreover, we propose a deep-Q network-based reinforcement learning mechanism with experience replay memory to execute resource allocation decisions based on evaluated rewards. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.

For future networks in the 6G, it will be important to maintain a ubiquitous connection, bring processing heavy applications to remote areas, and analyze big amounts of data to efficiently provide services. To achieve such goals, the literature has utilized satellite networks to reach areas far away from the network core, and there has even been research into equipping such satellites with edge cloud servers to provide computation offloading to remote devices. However, analyzing the big data created by these devices is still a problem. One could transfer the data to a central server, but this has a high transmission cost. One could process the data through distributed machine learning, but such a technique is not as efficient as centralized learning. Thus, in this paper, we analyze the learning costs behind centralized and distributed learning and propose a hybrid solution that adaptively uses the advantages of both in a cloud server-equipped satellite network. Our proposal can identify the best learning strategy for each device based on the current scenario. Results show that the proposal is not only efficient in solving machine learning tasks, but it is also dynamic to react to different configurations while maintaining top performance.

This paper investigates a novel framework of intelligent reflecting surface (IRS) backscatter enabled multi-tier computing system. In such a hierarchical network, the data bits of computational task requested by each user equipment (UE) are broken up into three parts, which are respectively computed at tier-1 UEs, tier-2 access points (APs) and a tier-3 central server. Distinguished from conventional active antennas, IRS backscatter at the UEs is leveraged to offload data bits to the APs. Based on the established network framework, an optimization problem is formulated, which aims at maximizing the sum computational bits of system during the considered time block by jointly optimizing the active beamforming at the power beacon, the passive beamforming at the UEs, the active beamforming at the APs, the bandwidth and power allocation among all the UEs, as well as the time of local computing. To seek the optimal solution, the optimization problem is decomposed into two, namely the maximization of stage-1 sum computational bits and the minimization of stage-2 delay. By the objective function conversion and alternative optimization methods, the two problems are addressed. Extensive simulations are performed to confirm the feasibility of the proposed system and show the achievable performance in processing computational bits.

The sixth generation (6G) is conceived to address the expected high level of requirements (such as ultra-high-data-transmission rate, support for the highest moving speed and seamless connection, etc.) in the next decade and beyond. In the context of 6G, a large number of Industrial Internet of Things (IoT) (IIoT) devices may access the network, and thanks to the rapid development of artificial intelligence make smart manufacturing has the opportunity to be realized. However, a large number of IoT devices, the tremendous volume of data, the heterogeneous nature of devices, and the increasing concerns of privacy challenge the efficient management and quality of services in IIoT. To address these problems, in this article, a device-to-device (D2D) communication-aided digital twin edge network is proposed, where edge computing is introduced to bring computing and storage resources near to the end devices, and digital twin is utilized to fill the gap between physical and virtual space and D2D communication is applied to assist resource limited IoT devices to achieve normal communication. Moreover, digital twin-empowered federated reinforcement learning is leveraged to provide privacy awareness and decentralized resource allocation strategy training on D2D communication links to further improve network performance. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.

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.

With the fast development of emerging Internet of Everything applications, human-type communications (HTC) and machine-type communications (MTC) will inevitably coexist in future 6G cellular networks. To support massive connectivity while fulfilling diverse requirements of both HTC and MTC, we present a device-to-device aided rate splitting multiple access (RSMA) scheme by encoding both the MTC devices’ messages and HTC users’ common messages into a general common data stream in each cell (or group). Nevertheless, such deploying strategy may bring challenges in complex resource allocation and transmission modes selection. In view of this, we introduce a simple received signal strength (RSS) based transmission modes selection scheme, through which the RSS-threshold selection problem is formulated to maximize the HTC and MTC sum rates. Considering the computational complexity and scalability, we employ a multi-agent reinforcement learning based algorithm for each small base station to choose the optimal RSS threshold thus to achieve maximum sum rate while maintaining massive connectivity. Simulation results reveal our proposed RSMA deploying strategy outperforms non-orthogonal multiple access (NOMA) in system coverage while maintaining high-level system rate. Besides, the proposed MARL based scheme can further improve the system sum rate and coverage for both the HTC and MTC.

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.

With 6G networks, we can expect more devices to start operating in remote areas, away from the conventional network infrastructure. With an increase in the number of devices, we should also see more data that needs to be processed and analyzed. An adequate response to this scenario is using satellite networks to reach remote devices and transfer the big data generated by them to be analyzed in cloud servers through Machine Learning models. However, while this is a good solution for data analysis, it can run into bottlenecks caused by long transmission in the limited channels of satellite networks. In this paper, we will model and analyze this service model, allowing us to identify the limitations of centralized learning over satellite networks. This study manages to determine when centralized learning with remote devices is a viable solution and when it needs to be complemented by other techniques due to poor performance caused by long transmission and overloaded communication channels.

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.

Unmanned aerial vehicles (UAVs) have shown great potential in benefiting industries due to their good features, such as the ease of deployment and low maintenance cost. However, the communication security issue remains a serious challenge before the large-scale application of industrial UAVs. The untrusted communication environment can cause the leakage of valuable industrial data or the losing of important cargos that carried by UAVs. Traditional authentication mechanisms for protecting communications include public-key infrastructure-based, ID-based, and certificateless authentication. These mechanisms rely on a central authority and some of them may introduce high-complexity computation that is not suitable for industrial drones. Therefore, aiming at these challenges, we design a blockchain-assisted distributed and lightweight authentication service for industrial UAVs. The blockchain technology supports the distributed and immutable storage of industrial UAVs’ authentication information, and smart contracts enable convenient operations for drones to acquire or update the corresponding information. Security evaluation demonstrates that our scheme is resistant to various attacks and can guarantee trustworthy communications for industrial drones. Extensive experiments also show that our designed authentication service can not only achieve low computation and communication cost for industrial UAVs but also remain robust even if a small proportion of drones are compromised.

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.

To the best of our knowledge, this article represents the first attempt toward robust beamforming design for multiuser downlink communications in intelligent reflecting surface (IRS)-enhanced satellite (SAT) and high altitude platform (HAP) integrated network. Such network configuration is mainly composed of a single-antenna SAT, a multiple-antenna HAP, multiple single-antenna SAT and HAP terminals and an IRS. Although sharing the same spectrum resource with the SAT, the HAP suspended in the near-space intends to offer temporary higher-speed and lower-delay multiuser communication connections than the SAT. Therefore, the power budget should be carefully tuned at the HAP. Toward this end, we first formally formulate the transmit power minimization problem at the HAP under the constraints of signal-to-interference-plus-noise-ratio-outage-probability (SINR-OP) at each SAT and HAP terminal by taking into account the imperfect channel state informations and their Gaussian channel estimation errors. Note that the complicated superposition of direct SAT/HAP links and cascaded IRS links, makes the optimization problem rather challenging. Specifically, we transform the probabilistic constraints into approximate deterministic ones, by performing rank relaxation. Based on this, we are able to solve the optimization problem by alternately optimizing the two ensuring subproblems. As verified by extensive simulation results, the proposed IRS-enhanced beamforming schemes can substantially diminish the transmit power at the HAP compared to the beamforming counterparts without IRS.

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.

Millimeter wave (MmWave) dual-functional radar-communication (DFRC) technology is believed to hold the ability to alleviate spectrum congestion and inter-radar interference in 5G vehicle-to-everything (V2X) systems. The radar target sizes in V2X system may not be ignored in views of the demands of short-range sensing and ultra-narrow beams supported by massive MIMO mmWave beamforming. Under such scenario, a novel single-target-multi-beams (STMB) radar beam alignment scheme is proposed to acquire more accurate information on estimated ranges and velocities by allocating multiple radar beams to a certain target. For instance, the relative velocity direction can be accurately estimated based on STMB scheme by using a weighted linear estimation methods. Then, the hybrid analog-digital beamforming under STMB scheme is formulated and optimized by maximizing transmission rate subject to radar signal-to-interference-and-noise (SINR) constraints, where a radar beam cancellation algorithm is proposed to adjust adaptively the radar beam number pointing to a certain target, which can guarantee strict radar SINR constraint under different transmission power levels. The numerical results verify the effectiveness and reliability of STMB scheme and show that the proposed beamformer outperforms the benchmark in both spectral efficiency and minimum radar SINR.

Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the next generation network. The space satellites and air nodes are the potential candidates to assist and offload the terrain transmissions. However, due to the high mobility of space and air nodes as well as the high dynamic of network traffic, the conventional traffic offloading strategy is not applicable for the high dynamic SAGIN. In this paper, we propose a reinforcement learning based traffic offloading for SAGIN by considering the high mobility of nodes as well as frequent changing network traffic and link state. In the proposal, a double Q-learning algorithm with improved delay-sensitive replay memory algorithm (DSRPM) is proposed to train the node to decide offloading strategy based on the local and neighboring historical information. Furthermore, a joint information collection with hello package and offline training mechanism is proposed to assist the proposed offloading algorithm. The simulation shows that the proposal outperforms conventional offloading algorithms in terms of signaling overhead, dynamic adaptivity, packet drop rate and transmission delay.

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security.

With the development of the industrial Internet of Things (IIoT), intelligent healthcare aims to build a platform to monitor users’ health-related information based on wearable devices remotely. The evolution of blockchain and artificial intelligence technology also promotes the progress of secure intelligent healthcare. However, since the data are stored in the cloud server, it still faces the risk of being attacked and privacy leakage. Note that little attention has been paid to the security issue of privacy information mixed in raw data collected from large number of distributed and heterogeneous wearable healthcare devices. To solve this problem, in this article, we design a deep learning-based privacy preservation and data analytics system for IoT enabled healthcare. At the user end, we collect raw data and separate the users’ privacy information in the privacy-isolation zone. At the cloud end, we analyze the health-related data without users’ privacy information and construct a delicate security module based on the convolutional neural network. We also deploy and evaluate the prototype system, where extensive experiments prove its effectiveness and robustness.

With the rapid development of machine-type communications (MTC), the future communication architecture needs to provide services for both human-type communications (HTC) and MTC with unique characteristics. The huge connections from MTC bring serious challenges to the existing wireless network. Ultra-dense network (UDN), a promising candidate technology, can support massive device access through dense deployment of small base stations (SBSs). Different from the resource management in traditional wireless network with single base station (BS), the resource allocation problem at BS level is more prominent in UDN, and the diversity of devices will make this problem more complicated. In view of this, we investigate the joint optimization of massive access and resource management in the UDN where HTC and MTC coexist. Considering the computational complexity and scalability, we propose a multi-agent deep reinforcement learning based SBS state selection scheme, in which each SBS acts as an agent and selects the optimal state between active and idle by continuously interacting with the environment. In addition, we adopt the power-domain non-orthogonal multiple access to further improve system throughput, and use grant-based and grant-free access manners for HTC and MTC respectively, so as to meet their unique characteristics. Extensive numerical results demonstrate the superior performances of proposed scheme in multiple perspectives.

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.

Multiaccess edge computing is an essential technology that academia and industry have recognized as fundamental for the future of the Internet of Things. Current research on the subject utilizes virtual machines as the intermediary between end devices and cloud servers. However, recently a new framework was proposed that utilizes Cybertwins instead of virtual machines for the same function. Such framework comes with a myriad of advantages but, most importantly, in this case, it includes a control plane capable of enabling cooperation between the Cybertwins. In this article, we present a mathematical model of the total service delay of a Cybertwin-based multiaccess edge computing system that includes user mobility, migration of virtual servers, multiple physical servers at different network tiers, fronthaul and backhaul communication, processing, and content request/caching. We also propose algorithms for guiding the operation of Cybertwins and the control plane in a multiaccess edge computing scenario. Finally, a performance analysis between Cybertwin and a virtual machine-based scheme is offered. Simulations show that Cybertwin brings significant improvement for the assumed scenario in the form of a faster overall service due to the higher cooperation. The models and simulations here were designed with the characteristics of future networks, beyond the current 5G, in mind, making them likely relevant for future networks, where multiaccess edge computing and the Internet of Things should play an even more important role.

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.

The explosive growth of machine-type communications (MTC) devices poses critical challenges to the existing cellular networks. Therefore, how to support massive MTC devices with limited resources is an urgent problem to be solved. Bursty traffic is an important characteristic of MTC devices, which makes it difficult for agents to learn useful experience and has a negative impact on model convergence. However, most existing reinforcement learning-based literatures assume that devices have saturate data. Towards this end, we propose two distributed Q-learning aided uplink grant-free non-orthogonal multiple access (NOMA) schemes (including all-devices distributed Q-learning (ADDQ) scheme and portion-devices distributed Q-learning (PDDQ) scheme) to maximize the number of accessible devices, where the bursty traffic of massive MTC devices is carefully considered. In order to reduce the dimension of scheduling space and mitigate the impact of bursty traffic, the idea of grouping devices as well as transmission resources and the intermittent learning mode are adopted in our schemes. Extensive numerical results demonstrate the advantages of proposed schemes from multiple perspectives.

The densification of base station (BS) deployments is driving the evolution of network structures towards heterogeneous ultra-dense networks (UDN), making coordinated multipoint (CoMP) a viable and promising transmission solution. However, the BS cooperation regions formed by applying CoMP in the UDN are small and irregular, which causes frequent handover for mobile users. Different from most existing work that focus on the trigger time of handover, we explore how to choose the appropriate BS cooperation set to reduce handover rate. In this paper, we consider movement aware CoMP handover (MACH). By estimating cell dwell time, a user would be intelligently assigned to macro cell or small cell according to its movement trend. To enhance reliability, we further proposed improved MACH (iMACH) to achieve a trade-off between BSs with long dwell time and the current best performed BS for multipoint cooperation while user moving. Using stochastic geometry method, expressions of coverage probability, handover probability and throughput that characterize performance of the proposed schemes are derived. The numerical results indicate that the theoretical analyses fit the simulation results well and the proposed schemes surpass the existing schemes in terms of the aforementioned metrics, and more intelligent and suitable for ultra-dense scenarios.

Unmanned aerial vehicle (UAV) is recognized as one of the best sensing tools for gathering data in the industrial Internet of things sector. Besides, the flying ad hoc network (FANET) with multiple drones shows significant advantages in complicated task performing of large area. However, as an important part of communication security, key management for FANET currently depends heavily on the base station or infrastructures, which may easily become the attack target or increase the communication overheads of drones. Therefore, we propose a blockchain-based distributed key management scheme for heterogeneous FANET in this article, based on which drones can autonomously distribute cluster keys, update their public/private key pairs, migrate between clusters, and revoke malicious UAVs in a secure way. Security analysis and performance evaluation prove that our scheme can resist against a variety of external and internal attacks, and guarantee lightweight energy consumption for ordinary drones in the network.

Large-scale connectivity support is a critical challenge in the massive machine-type communications scenario. Grant-free random access (RA) is a promising solution because it can reduce severe signaling overhead in contention-based RA procedure. However, there will still be collisions due to the random selection of spectrum resources by the devices. Therefore, we propose a distributed Q-learning-assisted grant-free RA scheme to alleviate the collisions between devices. Considering the characteristic of the machine-type communications devices with bursty traffic, the random packet arrival model is adopted in this paper. In order to cope with the difficulties brought by the random transmission of devices to Q-learning, an action reward based on the active probabilities of devices is designed. In addition, we introduce the power domain nor-orthogonal multiple access to further enhance the number of accessible devices. Numerical results demonstrate the advantages of the proposed scheme from the devices' successful access probability.

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.

Accurate network traffic load (TL) prediction is essential in many networking applications. However, the real TLs in practical networks may have high variability and are difficult to be predicted, which may severely affect users’ quality of experience (QoE). To address this problem, we first analyze the real-world network traffic dataset to investigate real TLs properties and find out the distance-correlation between regions in a spatial graph have the potential to improve the prediction result. Hence, we propose a time-series model based method to consider the distance-correlation in an efficient way. Empirically, experimental studies on real data demonstrate that our proposed method can effectively reduce at least 10% error value on regions with high-variability TLs. Finally, we further discuss the impact of the distance-correlation on the TL prediction.

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.

Map information is of crucial importance to ensure the safety and reliability of vehicle, no matter indoor or outdoor, it should reflect the real-time changes of environment. Existing indoor map update mechanisms have several common limitations such as small update range, long cycle, large amount of update data, high cost and poor currency. Therefore, we present a multi-vehicle collaborative indoor map update scheme based on edge-cloud architecture to realize real-time autonomous map updating. This scheme can be achieved through continuous monitoring, tagging, identification, and layering of the environment during driving process. Compared with traditional map update schemes, experimental results show that our scheme can effectively realize the collaborative map update in indoor environment, enhance the map update efficiency, reduce the update delay, and improve the adaptability of vehicles.

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.

With the development of Internet and communication technologies, various information can easily spread to children via applications (Apps) on Internet-of-Things (IoT) devices (e.g., emerging smart toys, watches, and phones), especially, the Apps on smart phones based on Android. While greatly bringing up convenience for children's lives and studies, these Apps also make illegal and inappropriate contents (such as violence, pornography, gambling, and drug) more accessible to kids, which is harmful to minors' growth. To keep children away from inappropriate contents in applications, previous researches mainly focused on detecting unsuitable videos and advertisements in children applications or designing App maturity rating methods and parental control software. There are few literature that specially investigate the inspection of inappropriate contents in children Android Apps. Toward this end, we propose a novel automatic content inspection and the forensics framework to identify children Android Apps which are not proper for kids under 12. In addition, this framework offers evidence to make users understand why the inspected App is judged as unsuitable. In experiments, we apply this framework on some specially chosen Android Apps which distinctly include inappropriate contents to verify its performance. The results show that it can successfully identify those applications with high precision that reaches 85.7%. Besides, by analyzing the collected children's Android Apps through our framework, we find that 40% of them are identified to be improper, which illustrates the serious issue of unsuitable children Android Apps.

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.

Smart grid delivers power with two-way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low-battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users' perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers' user experience being well considered. Extensive simulations demonstrate that our proposed DRL-based solution obtains near-optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging.

Recently, the 5G is widely deployed for supporting communications of high mobility nodes including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the main components for constructing the wireless heterogeneous network (HetNet). To further improve the radio utilization, the Time Division Duplex (TDD) is considered to be the potential full-duplex communication technology in the high mobility 5G network. However, the high mobility of users leads to the high dynamic network traffic and unpredicted link state change. A new method to predict the dynamic traffic and channel condition and schedule the TDD configuration in real-time is essential for the high mobility environment. In this paper, we investigate the channel model in the high mobility and heterogeneous network and proposed a novel deep reinforcement learning based intelligent TDD configuration algorithm to dynamically allocate radio resources in an online manner. In the proposal, the deep neural network is employed to extract the features of the complex network information, and the dynamic Q-value iteration based reinforcement learning with experience replay memory mechanism is proposed to adaptively change TDD Up/Down-link ratio by evaluated rewards. The simulation results show that the proposal achieves significant network performance improvement in terms of both network throughput and packet loss rate, comparing with conventional TDD resource allocation algorithms.

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.

Advanced technologies are constantly emerging in automobile industry, which not only provides drivers with a comfortable driving experience, but also enhances the safety of passengers. However, there are still some security issues need to be solved in automobiles, such as automobile driver fingerprinting. At present, identification technologies, such as fingerprint recognition and iris recognition, cannot monitor the driver's identity in real-time manner. Therefore, it is of great significance to design a real-time automobile driver fingerprinting scheme to ensure the safety of people's properties and even lives. Different from previous work concerning automobile driver fingerprinting, in this article, we conduct a comprehensive study on behavioral characteristics of drivers in two vehicles, namely Luxgen U5 SUV and Buick Regal. We exploit the actual data of the controller area network to construct a driver identity comparison library by extracting and processing the feature data. Then, we construct a combined model based on convolutional neural network and support vector domain description to achieve efficient automobile driver fingerprinting. Extensive experimental results show that the proposed driver fingerprinting scheme can dynamically match the driver's identity in real time without affecting the normal driving.

Deep learning has emerged as a popular Artificial Intelligence (AI) technique to make conventional cyber physical systems become intelligent and sustainable. Recently, deep learning has been widely used in the network domain. With the aid of powerful deep neural networks, the communication network can carry out packets forwarding actions intelligently to avoid possible failure and congestion. However, with the high computing cost and process limitation in only the static network scenario, the existing deep learning based network traffic control algorithms cannot satisfy the sustainable requirement of next generation large scale dynamic network. To conquer the existing problems, a novel spatial-temporal value network aided deep learning based intelligent traffic control algorithm referred as ST-DeLTA is proposed in this paper. In ST-DeLTA, the value matrix and spatial temporal training model (ST model) are employed to intelligently extract the spatial as well as temporal features of traffic patterns and make adaptive packets forwarding decision in large scale and dynamic networks. The mathematical analysis gives the computing cost reduction of our proposal, and the computer simulation demonstrates that our proposal has significantly better training and network performance compared with traditional algorithms in terms of training accuracy, transmission throughput, and average packets loss rate.

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.

In this paper, multiple machine learning-enabled solutions are adopted to tackle the challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms (K-Means clustering and Gaussian mixture model) as well as supervised learning algorithms (directed acyclic graph-support vector machine, K-nearest-neighbor and back-propagation neural network). In these solutions, multiple secondary users (SUs) collaborate to perceive the presence of primary users (PUs), and the state of each PU need to be detected precisely. Furthermore, the sensing accuracy is analyzed in detail from the aspects of the number of SUs, the training data volume, the average signal-to-noise ratio of receivers, the ratio of PUs' power coefficients, as well as the training time and test time. Numerical results illustrate the effectiveness of our proposed solutions.

The densification of small cells, ultimately towards ultra-dense networks (UDN), makes coordinated multipoint (CoMP) a feasible transmission solution for mobile users. However, CoMP may increase the handover rate, as users move across small and irregular BS cooperation regions. In this paper, we consider movement aware CoMP handover (MACH) in heterogeneous UDNs. Unlike most prior works, which focus on the handover trigger time, we explore the appropriate selection of BS cooperation set to reduce handover rate. By estimating cell dwell time, a user would be intelligently assigned to macro cell or small cell according to its movement trend. Moreover, we achieve a balance between BSs with long dwell time and the current best performed BS for multipoint cooperation while user moving. The performance of the proposed MACH is analyzed in terms of coverage probability and handover probability using stochastic geometry. Through extensive simulations, we show that the analytical results fit well with simulations, and the proposed MACH outperforms the existing works in both handover probability and coverage probability.

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 conventional terrestrial cellular networks, mobile devices at the cell edge often suffer from poor channel conditions, and thus unmanned aerial vehicles (UAVs) are introduced in recent years to improve the reliability of communication links. However, with the rapid development of Internet of Things (IoT) technology, the emerging IoT applications have blooming demands for high computation capacity from the resource-constrained IoT mobile devices (IMDs), motivated by which, mobile edge computing has been envisioned as an appealing solution to the resource bottleneck problem of IMDs. In order to cope with poor communication performance and high computation demands of cell-edge IMDs, we in this paper leverage UAV-aided edge computing to collaboratively assist computation offloading, taking account of the limited battery life of both IMDs and the UAV. We investigate a joint optimization problem of collaborative computation offloading, bandwidth portion, bit allocation, and UAV trajectory design, aiming to minimize the weighted energy consumption of IMDs and the UAV. Extensive numerical results validate the necessity of introducing UAV-aided edge computing to cellular networks, and the advantages of our proposed scheme on energy savings.

Disasters, such as earthquakes, typhoons, and tsunamis, usually cause extreme damages to the communication infrastructures, which results in a heavy recovery workload and seriously affects people's life. The disaster recovery networks play a critical role to reduce the loss caused by the disasters. However, the suddenly varying traffic demand and limited resources after disasters may lead to the repetitive reconfigurations for running the existing packet forwarding strategies, such as the shortest path algorithms. To handle this problem, it is necessary to adopt the deep learning technique to develop a disaster-resilient solution. In this paper, we utilize the deep reinforcement learning technique to propose a self-adaptive routing method for the Movable and Deployable Resource Unit (MDRU) based backbone network. Compared with existing deep learning based routing strategy, our proposal can adapt to the sudden network errors. Moreover, we also analyze the deployment manner and consider a centralized control structure to significantly balance the traffic.

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.

Edge Cloud Computing is a key technology for enhancing mobile functionalities and real-time applications in devices with limited resources. This is done by sharing the resources of edge servers and offloading jobs to the edge cloud. In order to ensure a high-quality service and more efficient usage of resources, it is important not only to correctly configure the edge servers but also to carefully select where to deploy them. However, in Edge Cloud Computing there is a high amount of servers and, with the advent of 5G and Internet of Things, there will be a massive number of client devices as well. This would make the edge server deployment too complex to solve through convex techniques. In this situation, Machine Learning is the most appropriate approach. In this paper, we provide a deep analysis of the usage of k-Means Clustering and Particle Swarm Optimization in the edge cloud deployment problem. Our results show that the hyperparameters for these algorithms can significantly impact their running time as well as the efficiency of their results. Finally, we also provide how to best configure these algorithms for this specific problem.

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.

With the proliferation of Internet of Things (IoT) devices, the load caused by network demands has been increasing. This has direct consequences in terms of increased network requests and thus significantly higher energy consumption. To this end, this paper focuses on network power saving. In a cloud-radio access network with fiber-wireless, we propose a power saving method based on the optical line terminals (OLTs) and optical network units (ONUs), the equipment responsible for data transmission to and from the devices. Our proposed method is based on the novel idea of dividing the sleep phase of the ONUs into time slots, which facilitates synchronization and coordination in the whole system, thus increasing resource efficiency and bringing power economy not only to the ONUs, but also to the OLTs and the network as a whole. This is in contrast to the existing literature, which considers only ONUs or OLTs separately, without focusing on the cooperation within the system. Additionally, our proposed method was created with IoT traffic in mind. and it was designed to guarantee a maximum delay in order to make it applicable to mobile and real-time IoT. Finally, our proposed method allows the network to operate without external power sources for the ONUs by using power over fiber in conjunction with the OLT. Mathematical analyses show that our proposed method can successfully decrease the system’s energy consumption.

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.

Recently, the rapid advancement of high computing platforms has accelerated the development and applications of artificial intelligence techniques. Deep learning, which has been regarded as the next paradigm to revolutionize users' experiences, has attracted networking researchers' interests to relieve the burden due to the exponentially growing traffic and increasing complexities. Various intelligent packet transmission strategies have been proposed to tackle different network problems. However, most of the existing research just focuses on the network related improvements and neglects the analysis about the computation consumptions. In this paper, we propose a Value Iteration Architecture based Deep Learning (VIADL) method to conduct routing design to address the limitations of existing deep learning based routing algorithms in dynamic networks. Besides the network performance analysis, we also study the complexity of our proposal as well as the resource consumptions in different deployment manners. Moreover, we adopt the Heterogeneous Computing Platform (HCP) to conduct the training and running of the proposed VIADL since the theoretical analysis demonstrates the significant reduction of the time complexity with the multiple GPUs in HCPs. Furthermore, simulation results demonstrate that compared with the existing deep learning based method, our proposal can guarantee more stable network performance when network topology changes.

The fast increasing chip processing capacities driven by the Moore's Law have encouraged the academia and industry to consider more about general hardware architectures since they allow the repeated use for multiple purposes through the installations of applications. Some techniques utilizing the general hardware architectures have been developed to improve the flexibility of computer networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualization (NFV). For these networks, the applications are required to be computation/communication-efficient since the installed applications share the hardware. In this paper, we study the resource-limited disaster recovery networks constructed by the Movable and Deployable Resource Units (MDRUs) which consist of various general computation platforms. We propose an efficient safety confirmation method through the photo sharing by the survivors. In the proposal, the Absorbing Markov Chain is utilized to model the safety confirmation process, transition matrix of which can be adopted to choose the suitable photo size for optimizing the traffic overhead and buffer consumption. Through periodical update of the photo database, unnecessary packet transmissions can be further avoided with reasonable sacrifice of the computation overhead. To expedite the computation, the GPU-accelerated MDRU is considered to conduct the matrix calculations in a parallel fashion.

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 high-speed transmission has become extremely important with the rapid growth of network traffic in wireless networks. Because the available bandwidth of wireless channels are limited, Partially Overlapping Channels (POCs) are widely used in wireless networks to maximize the utilization of channel resources. However, with the traffic patterns of wireless networks becoming huge and dynamic, conventional POC assignment algorithms only designed for constantly generated network traffic are not suitable for the new generation wireless networks. Therefore, in this article, a joint deep Covolutional Neural Network (CNN) and activity vector based intelligent channel assignment algorithm is proposed, which is referred to as CNNAV. With the proposed CNNV approach, the network can learn from the historical traffic patterns and intelligently assign POCs to wireless links. The simulation result shows that, the network performance of our proposal in terms of both packets loss rate and network throughput are better than conventional POC assignment algorithms.

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.

The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By offloading the IoT MDs’ computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge computation offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and computation offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting computation offloading among multiple edge servers in ultradense IoT networks.

Due to the fast increase of sensing data and quick response requirement in the Internet of Things (IoT) delivery network, the high speed transmission has emerged as an important issue. Assigning suitable channels in the wireless IoT delivery network is a basic guarantee of high speed transmission. However, the high dynamics of traffic load (TL) make the conventional fixed channel assignment algorithm ineffective. Recently, the software defined networking-based IoT (SDN-IoT) is proposed to improve the transmission quality. Besides this, the intelligent technique of deep learning is widely researched in high computational SDN. Hence, we first propose a novel deep learning-based TL prediction algorithm to forecast future TL and congestion in network. Then, a deep learning-based partially channel assignment algorithm is proposed to intelligently allocate channels to each link in the SDN-IoT network. Finally, we consider a deep learning-based prediction and partially overlapping channel assignment to propose a novel intelligent channel assignment algorithm, which can intelligently avoid potential congestion and quickly assign suitable channels in SDN-IoT. The simulation result demonstrates that our proposal significantly outperforms conventional channel assignment algorithms.

Leveraging the concept of software-defined network (SDN), the integration of terrestrial 5G and satellite networks brings us lots of benefits. The placement problem of controllers and satellite gateways is of fundamental importance for design of such SDN-enabled integrated network, especially, for the network reliability and latency, since different placement schemes would produce various network performances. To the best of our knowledge, it is an entirely new problem. Toward this end, in this paper, we first explore the satellite gateway placement problem to obtain the minimum average latency. A simulated annealing based approximate solution (SAA), is developed for this problem, which is able to achieve a near-optimal latency. Based on the analysis of latency, we further investigate a more challenging problem, i.e., the joint placement of controllers and gateways, for the maximum network reliability while satisfying the latency constraint. A simulated annealing and clustering hybrid algorithm (SACA) is proposed to solve this problem. Extensive experiments based on real world online network topologies have been conducted and as validated by our numerical results, enumeration algorithms are able to produce optimal results but having extremely long running time, while SAA and SACA can achieve approximate optimal performances with much lower computational complexity.

Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to the cost of the physical servers. The best choice is to optimize the resources of the cloudlets through an intelligent choice of configuration that lowers delay and raises scalability. Thus, in this paper we propose an algorithm that utilizes Virtual Machine Migration and Transmission Power Control, together with a mathematical model of delay in Mobile Edge Computing and a heuristic algorithm called Particle Swarm Optimization, to balance the workload between cloudlets and consequently maximize cost-effectiveness. Our proposal is the first to consider simultaneously communication, computation, and migration in our assumed scale and, due to that, manages to outperform other conventional methods in terms of number of serviced users.

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.

Recently, network operators are confronting the challenge of exploding traffic and more complex network environments due to the increasing number of access terminals having various requirements for delay and package loss rate. However, traditional routing methods based on the maximum or minimum single metric value aim at improving the network quality of only one aspect, which makes them become incapable to deal with the increasingly complicated network traffic. Considering the improvement of deep learning techniques in recent years, in this paper, we propose a smart packet routing strategy with Tensor-based Deep Belief Architectures (TDBAs) that considers multiple parameters of network traffic. For better modeling the data in TDBAs, we use the tensors to represent the units in every layer as well as the weights and biases. The proposed TDBAs can be trained to predict the whole paths for every edge router. Simulation results demonstrate that our proposal outperforms the conventional Open Shortest Path First (OSPF) protocol in terms of overall packet loss rate and average delay per hop.

Near real time monitoring system utilizing earth observation satellite constellation is expected for disaster management. However, these satellites may not have sufficient time to transmit all of their observed data to a ground station because low earth orbit (LEO) satellites pass over a ground station for a few minutes. Therefore, if the satellite stores more data than the size of transmission data to a ground station, it needs to transmit residual data after approaching other ground stations. As a result, it causes lack of timeliness due to the overhead time for fine tracking between a satellite and a new target of ground station. To deal with above mention, this paper proposes novel routing method to improve transfer ratio of whole observed data in a contact time. Relative to the conventional method which transmits data to a ground station, the proposed method divides observed data and modulates them as multi-carrier signals so as to distribute to two or more ground stations by multi-hop relaying. Furthermore, the satellite of this system switches inter-satellite link and satellite-to-ground link for own data and other satellite''''s data utilizing channelizer dynamically. The effectiveness of our proposal is verified through simulation results.

High Throughput Satellite (HTS), which has large communication capacity, is being developed to meet the rising demand for satellite communication system at the time of disaster. However, HTS adopts static frequency resource allocation, hence it is difficult to effectively utilize frequency resources. Therefore, many technologies for effectively utilizing frequency resources have been developed. However, the effectiveness of such technologies was not revealed clearly, because there was no quantitative evaluation method to measure their effectiveness. To create such method of evaluation, it is essential to consider the flexibility of frequency resource allocation. Thus, the construction of an evaluation method for flexibility is an important research issue. Therefore, in this research, we aim to construct a system analysis model to measure the effectiveness of frequency flexibility of satellite communication systems. Furthermore, we evaluate the effectiveness of frequency flexibilization technologies and quantitatively show the validity of such technologies.

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.

Mobile devices are naturally limited due to their portable sizes and will therefore never be equal to their desktop counterparts. To overcome this, Edge Cloud Computing can be utilized to execute tasks on behalf of the devices, allowing them to run applications that would normally be too demanding. In this service model, it is important to maintain a low Service Delay to keep the service transparent to the user. This can be achieved by focusing on lowering the Transmission Delay and Processing Delay. While existing approaches in the literature focus on one of those two, we postulate that only when considering both delays you can efficiently lower Service Delay and provide quality to all applications. In order to do that while being feasible, we propose a method based on Particle Swarm Optimization for lowering Service Delay in Edge Cloud Computing. Our proposal is shown to be close to optimality while still maintaining a low execution time for multiple cloudlets scenarios. Moreover, our proposal outperforms existing approaches from the literature with single focus on computation or communication, even in situations with high processing and transmission burdens, proving the superiority of a dual focus approach.

This paper analyzes the information theoretic secrecy performance in finite-area wireless networks based on a stochastic geometry framework. Unlike most prior works, which explored the physical layer security with a large number of transmitters, legitimate receivers and eavesdroppers in infinite regions, we consider a finite downlink wireless network composing of a transmitter, a legitimate receiver and several eavesdroppers. The legitimate receiver attempts to receive confidential data from the transmitter in the presence of the eavesdroppers. We present the probabilistic characteristics of the achievable secrecy rates and average secrecy rates in both disk regions and regular L-sided convex polygon regions. As shown by extensive numerical results, the proposed framework could be leveraged to efficiently analyze the secrecy performance of finite-area networks, and give insights for network designers on how to achieve good secrecy performance in finite-area networks.

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 technological evolution of the Internet of Things (IoT) and of the related devices, and their increasing diffusion, give mobile network providers the opportunity to come up with more advanced and innovative services. Among these are the so-called context-aware services: highly customizable services tailored to the user’s preferences and needs, which rely on real-time knowledge of the user’s surroundings, without requiring complex configuration on the user’s part. Examples of context-aware services are user profile changes that result from context changes, user proximity-based advertising, or media content tagging, etc. Applications based on the information acquired, processed and distributed by the IoT can answer the following questions about the objects’ surroundings: what, who, where, when, why, and how.

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.

Recent years, Software Defined Routers (SDRs) (programmable routers) have emerged as a viable solution to provide a cost-effective packet processing platform with easy extensibility and programmability. Multi-core platforms significantly promote SDRs' parallel computing capacities, enabling them to adopt artificial intelligent techniques, i.e., deep learning, to manage routing paths. In this paper, we explore new opportunities in packet processing with deep learning to inexpensively shift the computing needs from rule-based route computation to deep learning based route estimation for high-throughput packet processing. Even though deep learning techniques have been extensively exploited in various computing areas, researchers have, to date, not been able to effectively utilize deep learning based route computation for high-speed core networks. We envision a supervised deep learning system to construct the routing tables and show how the proposed method can be integrated with programmable routers using both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). We demonstrate how our uniquely characterized input and output traffic patterns can enhance the route computation of the deep learning based SDRs through both analysis and extensive computer simulations. In particular, the simulation results demonstrate that our proposal outperforms the benchmark method in terms of delay, throughput, and signaling overhead.

Due to physical limitations, mobile devices are restricted in memory, battery, processing, among other characteristics. This results in many applications that cannot be run in such devices. This problem is fixed by Edge Cloud Computing, where the users offload tasks they cannot run to cloudlet servers in the edge of the network. The main requirement of such a system is having a low Service Delay, which would correspond to a high Quality of Service. This paper presents a method for minimizing Service Delay in a scenario with two cloudlet servers. The method has a dual focus on computation and communication elements, controlling Processing Delay through virtual machine migration and improving Transmission Delay with Transmission Power Control. The foundation of the proposal is a mathematical model of the scenario, whose analysis is used on a comparison between the proposed approach and two other conventional methods; these methods have single focus and only make an effort to improve either Transmission Delay or Processing Delay, but not both. As expected, the proposal presents the lowest Service Delay in all study cases, corroborating our conclusion that a dual focus approach is the best way to tackle the Service Delay problem in Edge Cloud Computing.

With the increase of social networking websites and the interaction frequency among users, the prediction of information diffusion is required to support effective generalization and efficient inference in the context of social big data era. However, the existing models either rely on expensive probabilistic modeling of information diffusion based on partially known network structures, or discover the implicit structures of diffusion from users' behaviors without considering the impacts of different diffused contents. To address the issues, in this paper, we propose a novel information-dependent embedding-based diffusion prediction (IEDP) model to map the users in observed diffusion process into a latent embedding space, then the temporal order of users with the timestamps in the cascade can be preserved by the embedding distance of users. Our proposed model further learns the propagation probability of information in the cascade as a function of the relative positions of information-specific user embeddings in the information-dependent subspace. Then, the problem of temporal propagation prediction can be converted into the task of spatial probability learning in the embedding space. Moreover, we present an efficient margin-based optimization algorithm with a fast computation to make the inference of the information diffusion in the latent embedding space. When applying our proposed method to several social network datasets, the experimental results show the effectiveness of our proposed approach for the information diffusion prediction and the efficiency with respect to the inference speed compared with the state-of-the-art methods.

Recently, numerous real-time, data-rich, and differentiated applications and services have appeared in the next-generation Heterogeneous Networks. As a result, the number of potentially “untrusted” connections to the mobile operator's core network is expected to dramatically increase. Therefore, the operators must provide adequate security, without significantly affecting the quality of service (QoS). Hence, joint consideration of QoS and security is a critical research issue. However, due to their difficult-to-model conflicting objectives, existing research works have often dealt with them separately. In this paper, we address this problem, formally formulate it, and envision GT-QoSec, a game-theoretic joint optimization of QoS and security. Using GT-QoSec, the mobile user equipment (UE) and their servicing base stations (eNBs) play games with each other. Thus, the UE obtains a balanced set of QoS and security levels while the eNBs maximize their bandwidth utilization. Extensive analysis and simulation results are presented to evaluate the performance of GT-QoSec in contrast with several conventional methods.

In this paper, we present a stochastic geometry based framework to analyze the coverage probability and ergodic rate with different channel allocations for device-to-device (D2D) communications. Different from existing works, we assume there are two different kinds of users, cellular users and D2D users, in the muti-channel uplink cellular network. Specifically, cellular users can upload data to the nearest base station (BS) directly through cellular channels. However, D2D users must upload data to their own D2D relays through D2D channels and then the D2D relays communicate with the nearest BS through cellular channels. There is no overlapping between cellular channels and D2D channels. Each cellular user and D2D relay adopt the channel inversion power control with maximum transmit power limit. Our results indicate that the framework can help to find the optimal channel allocation to achieve the optimal system performance in terms of coverage probability and average rate.

A concept of Cloud Radio Access Networks (C-RANs) with Power over Fiber (PoF) is expected to work as a post- disaster access network architecture. In this architecture, since external power is supplied to Remote Radio Heads (RRHs) through the optical-fiber cable for data communication, RRHs can operate even when power cables are disrupted due to disasters. This network, however, needs to reduce the power consumption for RRH operation and consider Quality of Experience (QoE) to provide sustainable service with enough user satisfaction. To this end, we propose user position guidance approaches which give advices of the best position to users. Our proposed approaches are able to achieve high sustainability while satisfying the QoE constraint. Furthermore, the effectiveness of our proposed approaches is evaluated by numerical calculation.

Recently, unmanned aerial vehicles (UAVs) have attracted attention as a means to observe a terrain due to their ability to fly above the place to be observed and collect information (e.g., picture, video and sensor data) easily. Moreover, UAVs can transmit data through equipped wireless transceivers. However, because the payload of UAVs is limited, the performance of the transceiver is limited as well. Therefore, UAVs need to cooperate with other UAVs for high speed and long distance data transmission. In this article, we suppose a network model for transmitting in real time observed data in a multihop relay communication; then we take into account parameters related to the observation area (e.g., area size, distance between a base station and observation area) and signal attenuation by obstacles (e.g., buildings, trees). Finally, we propose a way to maximize the network performances affected by those parameters in the network.

This paper investigates cooperative jamming for secure connection in wireless networks. A cooperative jamming strategy is proposed to thwart eavesdroppers anywhere in the network, even if they are located close to the source or the destination. The basic idea is to defeat eavesdroppers by a divide and conquer strategy, and exploit the helpful interference from the source and the destination to circumvent the nearby eavesdropper problem. Analysis and simulation results reveal that this strategy can enhance the secure connection probability and can tolerate any number of independent eavesdroppers provided that the number of legitimate nodes satisfies certain condition.

Although the importance of Disaster Response Networks (DRNs) has been highlighted in many researches, the requirement of spectrum agility has not been well addressed. In this paper, we focus on using all-spectrum cognitive radio for DRNs to fulfill this requirement. We consider a DRN constructed by Cognitive Radio Base Stations (CRBSs), which are deployed in the disaster affected area. Each CRBS is equipped with multiple antennas to support different frequency bands available in the area. Based on the considered DRN, we propose a Graph-based Hybrid Adaptive Routing scheme, which we refer to as GHAR. There are two phases in GHAR, centralized phase for topology formation and distributed phase for adaptive routing. In the centralized phase, we propose an algorithm that unites k non-overlapping minimum spanning trees to construct the topology for the next phase. We provide an analysis on the relationship between k and the adaptability with cognitive radio as well as the complexity of routing process. We also provide an analysis on the optimality of k. Furthermore, extensive simulations are conducted to validate our analysis. Simulation results confirm the effectiveness of our proposal and the existence of the optimal value of k.

The earth observation missions have improved its sensor performance, it results in a huge amount of data to be stored and transmitted to a ground station. Although, satellite to ground transmitter had been usually used X band, migration to Ka band has been studied in recent years for broadband transmission. Relative to X band, higher frequency has severe atmospheric effects, site diversity is one of the popular technique to mitigate rain attenuation. However, if Signal to Noise power Ratio (SNR) is different between receivers because of receiving antenna gain and rain attenuation, diversity gain decreases. This paper proposes novel beam controlling method to improve SNR performance for satellite to ground transmission. The proposed method estimates SNR in each of candidate transmitting antenna boresight, and update boresight in order to be equal SNR between receivers. Simulation results show that, proposed method improves SNR especially at a low elevation angle.

This paper investigates divide-and-conquer based cooperative jamming for physical-layer security enhancement in the presence of multiple eavesdroppers. Different from previous works, we consider a scenario where the eavesdroppers can be located anywhere inside the communication region of the source, no location information of the eavesdroppers is available and no constraint on the number of eavesdroppers is presupposed. The basic idea is to transmit the message in multiple rounds and exploit the helpful interference from the source and the destination to jam the eavesdroppers in close proximity. Stochastic geometry based analytic results as well as Monte Carlo simulations are presented to illustrate the achievable secrecy performances.

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.

Deployment of portable access points (APs) in disaster affected areas has been heralded by many contemporary researchers as a key technique to formulate disaster recovery networks. However, existing research works do not effectively address one of its key problems, i.e., the low capacity of the backbone network (constructed by the APs) which is unable to satisfy the high user demands emanating from the users in the local network of each AP. We consider cooperative communications to be a promising candidate to alleviate this problem, and formulate the trade-off relationship between the gained throughput and the network complexity. Also, we propose a novel graph-based topology control algorithm to solve the problem by exploiting cooperative communications to increase the inter-AP throughput gain. We first model the network by using a logical graph, where any two nodes are connected by a logical link if they are within the transmission range of each other. After that, k best paths, in terms of throughput gain, via mobile terminals, are found to connect any pair of APs. The constructed topology based on the resulted paths is used for cooperative communications. An in-depth analysis of the effect of the value of k on the network complexity and throughput gain is presented. Also, by introducing cooperative throughput gain speed as the utility of our proposal, we prove that there is an optimal value of k that maximizes the utility. Furthermore, extensive simulations are conducted to validate the analytical findings and demonstrate the effectiveness of our proposal.

There are many applications which cannot be executed by mobile devices due to their limitations in memory, processing, battery, among others. One solution to this would be offloading heavy tasks to cloud servers in the edge of the network, in a service model called Edge Cloud Computing. The main Quality of Service requirement of this model is a low Service Delay, which can be achieved by lowering Transmission Delay and Processing Delay. Works in literature focus on either one of those two types of delay. This paper, however, argues that an approach which combines transmission and processing technologies to lower Service Delay would be more efficient. This idea is defended by an analysis of the service model and existing stochastic modeling of the Edge Cloud Computing system. We conclude that a dual focus approach would be the only way of truly minimizing the Service Delay, therefore being the desired method to improve Quality of Service. We conclude by laying the foundation for a future model that follows such concept.

Both the variety of communication devices and their options in connecting to the Internet are continuously increasing due to the advances in numerous attractive network applications. These trends make designing the network much more complex. Legacy network design only had to consider predefined applications using common end-point devices such as plain old telephone system. On the other hand, the current and future network, especially in the access part, is expected to be universal so as to cover not only residential users, but also mobile users, and the huge number Internet of Things devices for the sake of cost efficiency. The key issue is the difficulty of designing a complex network that is also extremely energy efficient, particularly the future access system. Although many studies have attempted to improve the bandwidth utilization or energy efficiency in multiple-wavelength systems, little is known about how it will be impacted by the divergence among these multiple concrete services. In order to solve this energy issue, we propose a service multiplexing method with a profile-based network architecture, where each device declares its traffic profile. Our concept provides the feasible and practical control of complex network. It differs markedly from the methods that control the traffic at the packet or session level as it is easy to apply in actual networks.

In the era of the Internet of Things (IoT), the access network should provide Internet connectivity to huge numbers of wireless sensor nodes that are collecting data. For most sensor nodes, the macro cells of cellular networks provide cost-effective connectivity. However, covering the remaining nodes, which are either in the out-of-service state or have short-range wireless interfaces only, is a difficult issue. For example, when deploying small cells densely to provide fuller node coverage, the cost of the small cells should be well considered. From the perspective of economy, small cells that use the passive optical network (PON) and moderately priced wireless interfaces, called fiber-wireless (Fi-Wi) in this paper, seem the most promising candidates. However, the flexibility in Fi-Wi system deployment is strictly constrained by the need for a stable power source. Because most of the PON systems have been developed for fiber to the home (FTTH), the gateway node on the customer premise continuously consumes power and thus requires a stable power source. Therefore, the key issue is how to reduce the power consumption of the Fi-Wi gateway node drastically enough to run continuously with only battery and/or solar power. Additionally, the power-saving approach must meet the requirements of the IoT applications at the same time. We focus on the deep sleep approach, which turns OFF almost all the components, with significant power savings at the cost of a drop in quality of service (QoS). This paper proposes methods to control the deep sleep while assuring successful packet reception.

In this paper, we study the outage probability of device-to-device (D2D)-communication-enabled cellular networks from a general threshold-based perspective. Specifically, a mobile user equipment (TIE) transmits in D2D mode if the received signal strength (RSS) from the nearest base station (BS) is less than a specified threshold β ≥ 0; otherwise, it connects to the nearest BS and transmits in cellular mode. The RSS-threshold-based setting is general in the sense that by varying β from β = 0 to β = ∞, the network accordingly evolves from a traditional cellular network (including only cellular mode) toward a wireless ad hoc network (including only D2D mode). We provide a unified framework to analyze the downlink outage probability in a multichannel environment with Rayleigh fading, where the spatial distributions of BSs and TIEs are well explicitly accounted for by utilizing stochastic geometry. We derive closed-form expressions for the outage probability of a generic TIE and that in both cellular mode and D2D mode and quantify the performance gains in outage probability that can be obtained by allowing such RSS-thresholdbased D2D communications. We show that increasing the number of channels, although able to support more cellular TIEs, may result in an increase of outage probability in the D2D-enabled cellular network. The corresponding condition and reason are also identified by applying our framework.

Device-to-device (D2D) communication, which utilizes mobile devices located within close proximity for direct connection and data exchange, holds great promise for improving energy and spectrum efficiency of mobile multimedia in 5G networks. It has been observed that most available D2D-based works—considered only the single-cell scenario with a single BS. Such scenario-based schemes, although tractable and able to illustrate the relationship between D2D links and cellular links, failed to take into account the distribution of surrounding base stations and user equipments (UEs), as well as the accumulated interference from ongoing transmissions in other cells. Furthermore, the single-tier network with one BS considered in available works is far from the real 5G scenario in which multi-tier BSs are heterogeneously distributed among the whole network area. In light of such observations, we present in this article a model for network coverage probability and average rate analysis in a D2D communication overlaying a two-tier downlink cellular network, where nineteen macro base stations (MBSs) with pico base stations (PBSs) placed at the end point of macro cell (hexagons) borders are employed according to the 3GPP specifications, and mobile users are spatially distributed according to the homogeneous Poisson Point Process model. Each mobile UE is able to establish a D2D link with adjacent UEs or connect to a nearby macro or pico base station. Stochastic geometric analysis is adopted to characterize the intratier interference distribution within the MBS-tier, PBS-tier, and D2D-tier based on which network coverage probability and per-user average rate are derived with a careful consideration of important issues such as threshold value, SINR value, user density, content hit rate, spectrum allocation, and cell coverage range. Our results show that, even for the overlaying case, D2D communication can significantly improve network coverage probability and per-user average downlink rate. Another finding is that the frequency allocation for D2D communications should be carefully tuned according to network settings, which may result in totally different varying behaviors for the per-user average rate.

There has been surging research interest in utilizing mobile phones for information relaying in postdisaster areas lacking infrastructure support. A common complication for such postdisaster ad hoc communication is how to efficiently control the forwarding behaviors of relay nodes so as to save their energy consumption and buffer usage while simultaneously guaranteeing the desired delivery performance. Different from previous studies, we consider in this paper an explicit probabilistic stopping mechanism, where a relay node that is actively disseminating a message will stop spreading the message with a certain probability, after meeting another node having already received the message. Besides developing a two-dimensional Markov chain framework to characterize the message propagation process, we also derive the average time required for completion of message propagation, the probability distribution, the expectation, the variance of the fraction of nodes finally receiving the message, etc. Our results reveal that the explicit probabilistic stopping mechanism is very desirable for postdisaster communication, even being able to guarantee a majority of nodes in final message reception. What is more, the developed framework provides us a deeper understanding on how network parameters may affect these important performance metrics, which further enables network designers to accordingly tune controllable parameters.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range, and therefore, accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the information propagation speed (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

Recently, many large-scale natural disasters, such as earthquake and tsunami occur all over the world. One of the major problems after a disaster is the damage caused to the communication and power infrastructure, such as damaged base station and power grid. As a result, disaster victims are unable to communicate with outside area for an extended period of time. Therefore, it is essential to deploy a communication network, which can operate even without power supply or infrastructure. In this paper, we focus on Wireless Mesh Networks (WMNs), which consists of Solar Powered Base Station (SPBS) equipped with air interfaces. These WMNs can be promptly setup. However, because the power generated from solar panel is easily affected by weather condition, it is insufficient and unstable. Additionally, because the power consumption is affected by the distance between the SPBSs and the number of wireless links in each SPBS, it is difficult to maintain network connectivity in the WMNs that are consisted of SPBSs. Therefore, to address the network connectivity problem in the assumed network, we aim to reduce the power consumption of the wireless links by controlling the on-off cycle of air interfaces. We first analyze the network connectivity issue in disaster area and formulate this problem, and propose the on-off scheme of controlling the wireless links of air interfaces based on graph theory. Simulation results of our proposal show that our proposed scheme can ensure the network connectivity.

Advertising among vehicles has become popular with the proliferation of vehicular ad-hoc networks (VANETs). Since the price of the advertisements broadcast in such networks decay over time, distributing advertisements with a high price value to more private vehicles can generate more revenues to the sellers. In this paper, we consider a bus-based priced advertising scenario in a VANET, in which the buses act as the sources of advertisements and broadcast advertisements to private vehicles running within their communication range. Meanwhile, in the area where no bus exists, private vehicles share their advertising segments. The manner in which the buses and the private vehicles distribute and share advertisements in the network so as to draw the largest benefit is addressed in our formulated problem. To solve this problem, a bus-based priced advertisement dissemination scheme dubbed Bus-Ads is proposed by using coalition formation game. First, a bus-broadcast method is presented to enable each bus to distribute the priced advertising segments with the largest potential benefit to surrounding private vehicles. Second, we apply coalition formation game to guide private vehicles to construct broadcast coalitions for efficient advertisement sharing. Simulation results demonstrate that our proposed Bus-Ads method can achieve about twice the total benefits compared with that of the non-coalition-based approach.

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.

After a disaster strikes, the disaster victims usually become isolated and unable to utilize communication services for an extended period of time. Therefore, it is essential to establish a communication network that can operate when there is no power or infrastructure. In this paper, we focus on Satellite/Solar-powered Mesh Integrated Networks (SMIN), which are composed of a communication satellite, Earth Stations (ESs) and solar-powered Mesh Routers (MRs). A SMIN can connect to external networks via satellite and provide communication services in a large area through the wireless mesh network (WMN). To maximize the amount of communication traffic from the WMN, we aim to optimize the number of ESs and its deployment. When the number of ESs increases, the hop count between a MR and its closest ES decreases, thus resulting in an improved connectivity in the route. However, since the ESs share the bandwidth of satellite, allocated bandwidth to each ES decreases as the number of ESs increases. Therefore, we aim to optimize the number and deployment pattern of ESs. Additionally, we validate the amount of aggregated traffic that can be sent to the satellite through numerical analysis.

Data collected from the mobile Internet have the potential knowledge to provide important human mobility patterns. Understanding human mobility patterns is important to many location-based services, and could be used to predict users' behavior. In this paper, we concentrate on the issue of discovering human mobility patterns on both global and individual levels based on hotspots. We study the human mobility trajectories during 22 days for 3474 individuals collected at the core of a metropolitan Long Term Evolution (LTE) network in China. We employ a parameter-free method to detect hotspots, and demonstrate the effectiveness of our mobility pattern discovery algorithm by using the hotspots identified on both global and individual levels. We analyze the occurrence time distribution of these patterns and find that the global mobility patterns have higher occurrence probability in the morning, which indicates that people in a city tend to share the common commuting routes. For individual mobility patterns, there exists a strong spatiotemporal correlation property, implying that the individual mobility patterns have their own typical occurrence time depending on the pattern's context.

In this paper, we present a model for average rate analysis in a D2D communication overlaying two-tier downlink cellular network. Each mobile UE is able to establish D2D link with adjacent UEs or connect to a nearby macro or pico base station. Stochastic geometry analysis is adopted to characterize the medium contentions within macro and pico cells, as well as the D2D pair distributions, based on which closed-form per user average rate is derived with a careful consideration of the important issues such as frequency allocation, UE density, content hit rate, and cell coverage radius. Our results show that even for the overlaying case, D2D communication can significantly improve the per user average rate. Another finding is that the frequency allocation for D2D pairs should be carefully tuned according to network settings, which may result in totally different varying behaviors for the per user average rate.

Based on the tool of stochastic geometry, we present in this paper a framework for analyzing the coverage probability and ergodic rate in a D2D overlaying multi-channel downlink cellular network. Different from previous works, 1) we consider a flexible new scheme for mobile UEs to select operation mode individually, under which a mobile UE decides to establish a cellular link (with a BS) or a D2D link (with a neighboring UE) based on the pilot signal strength received from its nearest BS; 2) we allow a mobile UE which is located far from BSs to connect to a nearby BS via another intermediate UE in a two-hop manner. Our results indicate that the developed framework is very helpful for network designers to efficiently determine the optimal network parameters at which the optimum system performance can be achieved. Furthermore, as corroborated by extensive numerical results, enabling the D2D link based two-hop connection can significantly improve the network coverage performance, especially for the low SIR regime.

Different from previous works, in this paper, we adopt D2D communication as a routing extension to traditional cellular uplinks thus enabling a two-hop route between a user and the serving BS via a D2D relay. Specifically, a BS establishes a cellular link with a mobile user only if the pilot signal strength received from the user is above a specified threshold; otherwise, the user may establish a D2D link with a neighboring user and connect to a nearby BS in a two-hop manner. We present a stochastic geometry based framework to analyze the coverage probability and average rate in such a two-hop multi-channel uplink cellular network where mobile users adopt the fractional channel inversion power control with maximum transmit power limit. As validated by extensive numerical results, the developed framework enables network designers to efficiently determine the optimal control parameters so as to achieve the optimum system performance. Our results show that employing D2D link based two-hop connection can significantly improve both the network coverage and average rate for uplink traffic.

Wireless Data Center Network (Wi-DCN) is considered one of the most promising future data center architectures due to its low installation and management cost and high flexibility of network design. However, the existing Wi-DCN is, still, not capable of providing an efficient big data mining service such as MapReduce because its topology (i.e., Cayley graph with same degree) cannot achieve enough connectivity on the breakdown of servers and spectrum efficiency, which are important factors to improve the performance of big data mining. Therefore, in order to modify the existing Wi-DCN for big data mining, this paper proposes a spherical rack architecture based on a bimodal degree distribution that improves both failure tolerance and spectrum efficiency. Extensive computer simulations demonstrate the effectiveness of our proposed rack architecture in terms of data transmission time required for MapReduce under a failure-prone environment.

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.

Recently, mobile Internet gained a strong momentum of development, which has led to increasing demand on mobile network traffic characterization and modeling. A good model of mobile network traffic can be used to make accurate prediction regarding various performance metrics. Based on the network trace collected from network backbone, our paper studies mobile network traffic characteristics in terms of the flow arrival numbers and flow connection duration. Basically, we employ the Poisson regression from Generalized Linear Model with time window clustering so as to approximate a time-dependent Poisson Process to the flow arrival process. Our analytical results demonstrate the accuracy of the adopted approach. In addition, through approximating the Phase Type distribution to the heavy-tailed distribution, our paper also models the flow connection duration. The obtained results can help us get a comprehensive understanding of the network performance, in accordance with which the resource usage may be optimized, e.g., we can expand network bandwidth or increase the buffer size when the network arrival is high.

A concept of cloud radio access networks (C-RANs) is becoming a popular solution to support the required communication quality for new emerging service in the future network environment, i.e., more than 10 Gbps capacity, less than 1 ms latency, and connectivity for numerous devices. In this paper, we envision a C-RAN based on passive optical network (PON) exploiting power over fiber (PoF), which achieves low installation and operation costs since it is capable of providing communication services without external power supply for large amount of remote radio heads (RRHs). This network, however, needs to reduce the optical transmission power of PoF due to the fiber fuse issue. Additionally, the diversification of services, devices, and personality indicates the need to improve user satisfaction, i.e., quality of experience (QoE), based on the user's perspective, which is different from previous approaches that aim to guarantee quality of services (QoS). Therefore, we propose a QoE-guaranteed and power-efficient network operation strategy. Our proposed operation is able to reduce the transmission power while satisfying the QoE constraint by controlling both the schedule of RRH's sleep and optical transmission power of PoF. Furthermore, the effectiveness of our proposed operation scheme is evaluated through extensive computer simulations.

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.

Delay-tolerant networks (DTNs) are a promising network architecture which can provide reliable multi-hop message transmission between participating mobile nodes in an unfavorable environment that is prone to link disruption and disconnection by replicating and relaying messages without any need of physical infrastructure. Additionally, DTNs can also operate as cooperative DTNs to extend the coverage of other type of networks by carrying the messages that originate from farther away base station in a multi-hop fashion to the base station. In this paper, we focus on these cooperative DTNs and propose a novel routing scheme, ring distribution routing (RDR), that controls the replication and forwarding based on the source node surroundings. This paper also analyzes the reliability and buffer efficiency in RDR. Furthermore, we show that RDR provides the reliable and immediate message delivery in any environment through extensive computer simulations.

Recent technological advances in electronics, sensors, and communications have accelerated the widespread deployment of Unmanned Aircraft System (UAS)-aided applications. Nevertheless, networks composed of multiple UAS and ground stations, referred to as UAS-aided communications networks, have yet to receive sufficient research attention. In this paper, we address a fundamental research challenge stunting such networks, which is how to fairly maximize the energy efficiency (throughput per energy) in networks comprising adaptive modulation-capable ground nodes. For the mobility pattern intrinsic to the UASs, we demonstrate how adaptive modulation is affected. Furthermore, we formulate the problem of maximizing fair energy efficiency as a potential game that is played between the multiple ground nodes and substantiate its stability, optimality, and convergence. Based on the formulated potential game, a data collection method is proposed to maximize the energy efficiency with a fairness constraint. Additionally, we analyze the Price of Anarchy of our proposed game-theoretic data collection method. Extensive simulations exhibit the effectiveness of our proposal under varying environments.

In this paper, we explore the throughput-delay tradeoff in a mobile ad hoc network (MANET) operating under the practical reference point group mobility model and also a general setting of node moving speed. In particular, we consider a MANET with unit area and n nodes being divided evenly into Θ(nα) groups, α ∈ [0, 1], where the center of each group moves according to a random direction model with speed of no more than υ ∈ [0, 1]. We determine the regions of per-node throughput and average delay and their tradeoffs that can be achieved (in order sense) in such a network. For the regime of v = 0, we first prove that the per-node throughput capacity is Θ(n-α/2) and then develop a routing scheme to achieve this capacity, resulting in an average delay of Θ(max{n1/2, n1-α}) for any α ∈ [0, 1]. Regarding the regime of v > 0, we prove that the per-node throughput capacity can be improved to Θ(1), which is achievable by adopting a new routing scheme with an average delay of Θ(max{n1-α, nα/2/v}) for υ = o(1) and Θ(n) for v = Θ(1). The results in this paper help us to have a deep understanding on the fundamental performance scaling laws and also enable an efficient throughput-delay tradeoff to be achieved in MANETs with correlated mobility.

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.

The design of a wireless network is often critically affected by issues such as determining the optimal density of Access Points (APs) and the optimal channel assignment by exploiting partially overlapped channels (POCs) for significantly improving the network performance in terms of maximizing the overall network capacity. Contemporary research works have traditionally dealt with these two problems in an isolated manner though they should be considered within the same problem formulation. Furthermore, even though deployment of additional APs can improve the network capacity in case there are a few APs in a given area, the APs cannot be indefinitely added to the wireless network. This means that there is an upper bound to the network capacity maximization with respect to the number of APs. In fact, the network capacity starts to dramatically decrease when the number of deployed APs becomes excessive. This performance decrease can be accredited to the substantial interference among the high number of deployed APs. In order to address this challenge, in this paper, we propose an approach to jointly optimize the number of APs and POCs assignment. Our proposal derives the existence of the optimal density of APs with POCs, and models the POC assignment to the deployed APs from a novel perspective. Computer-based simulations are conducted to demonstrate the effectiveness of our proposal.

Recent technological advances in electronics, sensors, and communications devices have facilitated the proliferation of Unmanned Aircraft System (UAS)-aided applications. However, the UAS-aided communications networks are yet to receive sufficient research endeavor. In this paper, we address one of the most important research challenges pertaining to UAS-aided networks comprising adaptive modulation-capable nodes, namely how to fairly maximize the energy efficiency (throughput per energy). For the mobility pattern innate to the UAS, we demonstrate how the adaptive modulation behaves. Furthermore, we formulate the problem as a potential game that is played between the UAS and the network-nodes, and prove its stability, optimality, and convergence. Based upon the potential game, a data collection method is envisioned to maximize the energy efficiency with the fairness constraint. Additionally, we analyze the Price of Anarchy (PoA) of our proposed game. Extensive simulations exhibit the effectiveness of our proposal under varying environments.

Reference Point Group Mobility (RPGM) has been a practical mobility model used to efficiently capture the potential correlation among mobile nodes in many important applications. In this paper, we explore the throughput-delay tradeoff in a mobile ad hoc network (MANET) operating under the RPGM model and also a general setting of node moving speed. In particular, we consider a MANET with unit area and n nodes being divided evenly into Θ(nα) groups, a Є [0,1], where the center of each group moves according to a random direction model with speed no more than v e [0,1]. We determine the regions of per node throughput, average delay and their tradeoffs that can be achieved (in order sense) in such a network. For the regime of v =0, we first prove that the per node throughput capacity is Θ(n−α/2), and then develop a routing scheme to achieve this capacity, resulting an average delay of Θ (max1/2, n1-α) for any α Є [0,1]. Regarding the regime of v > 0, we prove that the per node throughput capacity there can be improved to Θ(1), which is achievable by adopting a new routing scheme with an average delay of Θ(max{n1-α, na/2/v}) for v = o(l) and Θ(n) for v = Θ(1). The results in this paper help us to have a deep understanding on the fundamental performance scaling laws and also enable an efficient throughput-delay tradeoff to be achieved in MANETs with correlated mobility.

MapReduce architecture has been considered as one of the most promising candidates for efficient and reliable big data mining. While current MapReduce is basically designed for data center and enterprise networks, in which a number of servers are interconnected with optical fiber cables, prospective MapReduce would be applied in optical-wireless environment such as optical-wireless data center network, fiber-wireless (FiWi) access network, and so forth. To modify MapReduce for opticalwireless hybrid network, we need to answer the fundamental research problem, “How does MapReduce architecture use optical and wireless resources for task allocation?” To answer this question, this paper reveals some challenging issues and proposes a context-aware task allocation scheme that is designed by considering characteristics of both optical and wireless communications. Our proposed task allocation scheme can minimize the completion time of big data processing. Numerical results are presented to demonstrate the effectiveness of our proposed method compared with existing task allocation schemes.

Since communications services become much more demanded after disaster strikes, it is necessary to promptly set up a temporary communications infrastructure to provide services to those in need. The Movable and Deployable Resource Unit (MDRU) based Wireless Mesh Network (WMN) is an attractive candidate to achieving this goal. In MDRU based WMN, the MDRU is transported to the disaster affected area by either ground or air transportation like truck or helicopter. After arriving at the disaster area, it configures any remaining wireless Access Points (AP) in the area to provide connectivity services. This work provides an insight on the performance of MDRU based WMN under the situation where multiple MDRUs are deployed within close region to increase the overall coverage and performance of the network. A simulation is conducted to estimate the performance of the network under both scenarios where mesh tier operates under a single channel and where a channel assignment scheme is applied. We show that the performance of MDRU based WMN can be greatly enhanced by deploying multiple MDRUs to the area. However, since the number of available MDRUs is limited, using more MDRUs than necessary is not efficient. We discuss some of the factors that should be taken into account when selecting an appropriate number of MDRUs for a single area.

By combining optical networks and wireless networks, Fiber Wireless (FiWi) networks are able to provide broadband and flexible communication. In order to reduce energy consumption of STAtions (STAs) and Optical Network Units (ONUs), there are several power saving mechanisms in optical networks and wireless networks. Generally, power saving mechanisms lead to delay. The delay problem becomes more critical in FiWi networks where multiple power saving mechanisms are used at the same time to reduce energy consumption. In this paper, we analyze the delay by considering the relationship between the power saving mechanisms, which independently control the STAs and the ONUs in FiWi networks, and point out the problem that two delays can occur at the same time. In order to address the problem, we propose a novel power saving mechanism of STAs, which controls the STAs by synchronizing two power saving mechanisms for ONUs and STAs. Through mathematical analysis and numerical evaluation, we confirm that the proposed method can significantly reduce energy consumption without any increase in latency.

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.

Discusses the inaugural issue of IEEE Internet of Things Journal and reports on its scope and content.

Smart micro-grids can produce “renewable” energy and store them in power storage devices. Power loss, however, is a significant problem in power exchange among the micro-grids and between the macro-station and individual micro-grids. To optimally reduce the total power losses in such a power grid system, in this paper, a greedy coalition formation algorithm is proposed, which allows the macro-station to coordinate mutual power exchange among the micro-grids and between each micro-grid and the macro-station. Our algorithm optimizes the total power losses across the entire power grid, including the cost of charging and discharging power storage devices and power losses due to power transfers. The algorithm creates exchange pairs among the micro-grids, giving priority to pairs with higher power loss reduction per exchanged power unit. Through computer-based simulations, we demonstrate that the proposed approach significantly reduces the average power loss compared with the conventional noncooperative method. The simulations also demonstrate that the communications overhead of our proposal (due to negotiations aimed at forming coalitions) does not significantly affect the available communication resource.

Due to the increasing popularity of multimedia streaming applications and services in recent years, the issue of trusted video delivery to prevent undesirable content-leakage has, indeed, become critical. While preserving user privacy, conventional systems have addressed this issue by proposing methods based on the observation of streamed traffic throughout the network. These conventional systems maintain a high detection accuracy while coping with some of the traffic variation in the network (e.g., network delay and packet loss), however, their detection performance substantially degrades owing to the significant variation of video lengths. In this paper, we focus on overcoming this issue by proposing a novel content-leakage detection scheme that is robust to the variation of the video length. By comparing videos of different lengths, we determine a relation between the length of videos to be compared and the similarity between the compared videos. Therefore, we enhance the detection performance of the proposed scheme even in an environment subjected to variation in length of video. Through a testbed experiment, the effectiveness of our proposed scheme is evaluated in terms of variation of video length, delay variation, and packet loss.

In recent years, research attention on smart grid comprising distributed power generators has increased. To produce electricity in the smart grid, many micro grids (MGs) may exploit various renewable energy resources. Because the production capacity of renewable resources cannot be controlled, the MGs often require the power plants to provide power for them. However, the power loss between each MG and the power plant is larger than that among the MGs. To alleviate this power loss, we propose a game theoretic coalition formulation strategy for the MGs dubbed GT-CFS. Our proposed GT-CFS allows the MGs (belonging to the same macro station (MS)) to autonomously cooperate and self-organize into a partition composed of disjoint MG coalitions. Also, GT-CFS enables the MGs, in a distributed manner, to decide whether they will remain in the coalitions or not upon environmental changes, e.g., the variation of the power demand of the MGs. Within every coalition, MGs coordinate the power transfer among themselves as well as with the MS, in a fashion to optimize a utility function, which captures the total losses over the power distribution lines. MGs in the same coalition will distribute the extra profits (i.e., payoff) produced from forming coalitions by their “Shapley value.” Through computer simulations, we demonstrate that the proposed GT-CFS reduces the average power loss per MG significantly in contrast with the conventional noncooperative approach.

Mobile Ad-hoc Network (MANET) has drawn attention of many researchers due to its ability to construct a network without any infrastructure. In MANET, mobile nodes can transmit packets by using multi-hop paradigm. However, with the high probability of link disruption, the performance of this network decreases with the increase of hop count between the source and destination. On the other hand, Delay- and Disruption-Tolerant Network (DTN) is more tolerant to the link disruption. These two types of networks can have different advantages depending on communication environments. Therefore, we focus on a system which is able to switch the routing method, i.e., DTN or MANET, in conformity with the change of communication environment. In this paper, we provide an adequate comparison between the performances of two transmission methods of MANET and DTN. With the aim of reducing the number of transmissions, we propose a routing method which combines both transmission methods. Simulation results show that our proposed method can significantly reduce the number of transmissions and it leads to lowering message delivery delay.

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.

In the last decade, there has been a tremendous increase in both the number of mobile devices and the consumer demand for mobile data communication. As a general network architecture, ad hoc mobile networks are expected to offload a large amount of mobile traffic in lots of promising application scenarios. However, how to achieve a good balance between delivery performances (like delivery delay and delivery probability) and network resource consumptions (like power energy and buffer storage) remains an extremely challenging problem. In this paper, we focus on the general k-hop relay routing, which covers a lot of popular routing schemes as special cases, such as the direct transmission (k = 1), the two-hop relay algorithm (k = 2), and the epidemic routing (k = n - 1). We first develop absorbing continuous-time Markov chain models to characterize the complicated message delivery process under the general k-hop relay routing, and then conduct Markovian analysis to derive all the above important performance metrics. Finally, extensive numerical results are presented to illustrate the achievable delivery performances under the general k-hop relay and the possible performance trade-offs there.

Wireless Mesh Network (WMN) is a promising networking architecture because of its useful characteristics such as low deployment cost, ease of maintenance, network robustness and reliable coverage. Each node in the network is referred to either as Mesh Router (MR), Mesh Client (MC), or Mesh Gateway (MG) depending on its role in the network. MRs are interconnected to form a mesh backbone network, which can relay communications service from MCs to the MG. In many situations, MRs deployment are uncontrollable, and thus deployed MRs may not have ideal locations. In addition, in a dense network, using all available MRs that are deployed randomly to form mesh backbone network would results in a lower performance than what could be achieved. Therefore, our goal aims to select a set of working MRs that would yield an improved upper bound throughput, while still preserving connectivity. Our contributions include using graphs to represent multi-tier WMN and utilizing them to determine the set of MRs that can be safely removed from the network without severing any connectivity of the network. Furthermore, we proposed algorithm that goes through those set of MRs to determine the MRs which should be removed from the network to improve the overall performance, and we demonstrate capacity improvement brought by our scheme through simulations.

Available works either explore the order sense capacity scaling laws or derive closed-form throughput results for mobile ad hoc networks (MANETs) where a transmitter randomly probes only once a neighboring node for possible transmission. Obviously, such single probing strategy may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs since the randomly selected node may already get the packets that the transmitter hopes to deliver. In this paper, we consider a two-hop relay MANET where each transmitter may conduct multiple rounds of probing so as to identify a possible receiver. We first develop closed-form expressions for per node throughput capacity in such probing-based network, with a careful consideration of the time cost taken to probe for an eligible receiver in each time slot. Extensive numerical results are further presented to explore the possible maximum per node throughput capacity, the corresponding optimum setting of probing round limit, and also their relationships with the network control parameters, like the probing time limit, the redundancy limit and the number of users, etc.

In order to realize environmentally friendly networks, energy-efficient technologies are essential. In Fiber Wireless (FiWi) networks that consists of optical and wireless networks, there are two main power saving mechanisms, namely, Power Saving Mode (PSM) and Optical Network Unit (ONU) sleep. PSM works in wireless networks, and ONU sleep works in optical networks. These two mechanisms work independently in FiWi networks. Since both PSM and ONU sleep turn off the node's transmission device to save energy, the throughput of the network decreases. Thus, a trade-off relationship between throughput and energy consumption exists. Therefore, taking account of this relationship is essential when discussing energy efficiency in these networks. In this paper, we focus on the effect of PSM and ONU sleep on throughput and energy consumption. We analyze both the energy consumption and the throughput in FiWi networks. Through analysis, we point out energy and throughput inefficiencies in FiWi networks and propose a novel method that determines the optimal ONU sleep period and behavior in order to increase throughput and decrease the energy consumption. Furthermore, we validate our proposed method through numerical analysis, and confirm that it improves both throughput and energy consumption.

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.

A common complication for routing in mobile sensor networks is how to efficiently control the forwarding behaviors of relay nodes so as to save their energy consumption and buffer usage while simultaneously satisfy the specified delivery performance requirement. Available works either assign each message with a lifetime, a maximum number of copies, or a sequence number, or flush special feedback information among the whole network after the message reception. In the former case, a relay node has no idea of the message reception status and will carry and forward the message until meeting the destination, while the latter could efficiently notify all relay nodes but demands extra communication resources. Different from previous studies, we consider in this paper an explicit probabilistic stopping mechanism for relay nodes. Under such mechanism, a relay node that is actively disseminating a message will stop spreading the message with a certain probability, after meeting another node having already received the message. We first develop a two-dimensional Markov chain framework to characterize the highly complicated dynamics until the end of message propagation, then conduct Markovian analysis to derive the associated important performance metrics, including the average time required for the completion of message propagation, the expectation and variance of the fraction of nodes finally receiving the message, and the probability that a given number of nodes end up with the message, etc. Finally, extensive numerical results are provided to analytically explore how the network parameter settings may affect these performance metrics.

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.

We study a novel distributed storage system integrated Data Centers (DCs) and satellite networks. This integrated system is expected as distributed storage system that can keep the storage service even if disasters strike because satellite is tolerant to link disruption caused by disasters. In this paper, we focus on data distribution method in the integrated system, and assume an erasure coding and a simple replication as data distribution method. We evaluate the storage volume and transmission time on each method which are required to restore lost data when some DCs are damaged by disasters. The storage volume of the erasure coding becomes lower than that of the replication while the transmission time becomes higher. A data transfer method is proposed in this paper to shorten the transmission time of the erasure coding. The proposed method can reduce the transmission volume on downlink communication by using network coding technologies. The numerical results show that the proposed method can restore the lost data in less time.

In wireless sensor networks with mobile sink, mobile sink traverse the sensing area and aggregate the data from nodes near the mobile sink. In this scheme, mobile sink can reduce the total energy consumption by dividing the network to the multiple clusters. Since, energy consumption is proportional to the sum of square of communication distance, dividing the network into smaller clusters can reduce the amount of energy required for transmission. Previous researches concluded that increasing number of the cluster reduces energy required for data transmission. However, these ideas do not take into account the energy consumption due to data request message flooding in dense networks. In this paper, we focus on the data request flooding problem which is the massive energy consumption for data request message in dense network. Moreover, we point out that energy consumption of data transmission and data request message are controlled by the number of clusters in the network.

In order to optimize energy consumption in smart grid, demand side management has gained a lot of attention recently. While existing research works attempt to optimize energy consumption either from the view point of the power company or that of users, we investigate whether it is possible to consider both parties' interests at the same time. In this paper, we propose a novel energy price model, which is a function of the total energy consumption in the considered system. In addition, a new objective function, to optimize the difference between the value and cost of energy, is proposed. The power company sends the energy price parameter and the latest consumption summary vector information to the users sequentially. Upon receiving these information, a user can optimize his own schedule and report it to the company. The company then updates its energy price parameter before communicating with the next customers. A two-step centralized game is proposed that models this interaction between the power company and its consumers. The game aims at reducing the system peak-to-average power ratio by simultaneously optimizing users' energy schedules and lowering the overall energy consumption in the system. Through simulation results the performance of the proposed game-based demand side management technique is evaluated.

A significant amount of works has been done to model the delivery performances in Intermittently Connected Mobile Networks (ICMNs). However, available works considered either the two-hop relay routing or the epidemic routing, which actually represent two extreme cases of the message delivery process in ICMNs. In this paper, we take one step ahead and focus on the three-hop relay routing where each message travels at most three hops to reach the destination. Under such a scheme, besides that the source can send a message copy to each node it meets, a relay which receives the message directly from the source can also replicate the message to other nodes, while a relay node which receives the message from another relay can only forward the message to the destination. In order to characterize the complicated message delivery process under the three-hop relay routing, a multidimensional Markov chain theoretical framework is developed. Based on the Markov chain framework and block matrix theory, closed-form expressions are further derived for the important message delivery delay and delivery cost. Extensive numerical results are also provided to explore the achievable delivery performances under the three-hop relay.

Hierarchical unstructured peer-to-peer (P2P) networks for file sharing systems such as Gnutella and Kazaa have made a tremendous achievement in the last decade. However, while these P2P networks can be tolerant to churn, i.e., the dynamics of peer participation and departure (or fault), there still remains the issue of vulnerability to Denial of Service (DoS) attacks, i.e., when the highest degree peers are removed. In order to overcome this shortcoming, we focus on a bimodal degree distribution, which is tolerant to both churn and DoS attacks. However, the network topology affects the network stability that was not taken into considered in the previous works. Therefore, we analyze the optimal network topology for DoS attack tolerance, and accordingly develop the peer joining procedure to construct and maintain the proposed network topology. Our proposed scheme is dubbed THUP (churn/DoS Tolerant, Hierarchical, Unstructured, P2P network). Performance evaluation conducted through computer simulations shows that THUP substantially improves the stability and communication efficiency compared with other existing P2P networking structures.

Extensive studies have shown that the peer-to-peer (P2P) traffic has already become the dominant traffic in the current Internet. The current P2P streaming user base is still undergoing stunning growth in China although its user scale already reached 158 million in 2010, 68% of Chinese web users. Hence, a comprehensive understanding of the P2P streaming network traffic characterization is essential to Internet Service Providers (ISPs) in terms of network planning and resource allocation. In this paper, based on the massive data collected with a passive network monitoring equipment placed in the Internet backbone, we provide an in-depth view of the current P2P streaming traffic in the current Internet of China. In particular, we statistically study the P2P streaming traffic in both wired (ADSL in this paper) and wireless (CDMA) networks, and characterize the traffic from both flow-level and packet-level aspects. Our study uncovers the significant impact of the P2P streaming traffic on the underlying network due to its unique characteristics and the bandwidth intensive nature of the corresponding applications. In addition, the result reveals the significant difference between the characterizations of the P2P streaming traffic in wired and wireless networks due to their respective intrinsic environmental characteristics.

This paper focuses on the delivery probability performance in a two-hop relay mobile ad hoc network (MANET) with erasure coding. Available works in this line either considered a simple extreme case of achieving the delivery probability 1, or assumed a simple traffic pattern with only one source-destination pair, or studied a very special MANET scenario (i.e., the sparsely distributed MANET) by assuming that whenever two nodes meet together they can transmit to each other. Obviously, such models cannot be applied for an accurate delivery probability analysis in the general MANETs where the interference, medium contention and traffic contention issues are of significant importance. In this paper, a general finite-state absorbing Markov chain theoretical framework is first developed to model the complicated message spreading process in the challenging MANETs. Based on the theoretical framework, closed-form expressions are further derived for the corresponding message delivery probability under any given message lifetime and message size, where all the above important issues in MANETs are carefully incorporated into analysis. As verified through extensive simulation studies, the new framework can be used to accurately predict the message delivery probability behavior and characterize its relationship with the message size, replication factor and node density there.

Mobile ad hoc networks (MANETs) have attracted much attention due to their mobility and ease of deployment. However, the wireless and dynamic natures render them more vulnerable to various types of security attacks than the wired networks. The major challenge is to guarantee secure network services. To meet this challenge, certificate revocation is an important integral component to secure network communications. In this paper, we focus on the issue of certificate revocation to isolate attackers from further participating in network activities. For quick and accurate certificate revocation, we propose the Cluster-based Certificate Revocation with Vindication Capability (CCRVC) scheme. In particular, to improve the reliability of the scheme, we recover the warned nodes to take part in the certificate revocation process; to enhance the accuracy, we propose the threshold-based mechanism to assess and vindicate warned nodes as legitimate nodes or not, before recovering them. The performances of our scheme are evaluated by both numerical and simulation analysis. Extensive results demonstrate that the proposed certificate revocation scheme is effective and efficient to guarantee secure communications in mobile ad hoc networks.

This paper studies the exact per node throughput capacity of a MANET, where the transmission power of each node can be controlled to adapt to a specified transmission range υ and a generalized two-hop relay with limited packet redundancy f is adopted for packet routing. Based on the concept of automatic feedback control and the Markov chain model, we first develop a general theoretical framework to fully depict the complicated packet delivery process in the challenging MANET. With the help of the framework, we are then able to derive the exact per node throughput capacity for a fixed setting of both υ and f. Based on the new throughput result, we further explore the optimal throughput capacity for any f but a fixed υ and also determine the corresponding optimum setting of f to achieve it. This result helps us to understand how such optimal capacity varies with υ (and thus transmission power) and to find the maximum possible throughput capacity of such a network for any f and υ. Interestingly, our results show that increasing the transmission power of the nodes improves the capacity, which is the same as that proved in fixed networks.

Smart grid has emerged as a promising technology for enabling bi-directional communication between the power company and its users to facilitate intelligent, robust, and resilient next generation power grid systems. Through this technology, both the power company and its subscribers can be equally benefited, not only from economic point of view, but also in terms of environment-friendly quality of power usage. One important challenge for the smart grid designers is the demand side management, which can lead to avoiding the peak hours and reducing the cost for the consumers. In this paper, we address the power balancing challenge for the smart grid and discuss different solutions including game theoretic methods and demand control policies. Also, we present our novel demand control policy for achieving an effective management of the power consumption. Computer simulations demonstrate the effectiveness of the proposed policy compared to existing ones.

This paper focuses on the per node throughput capacity in mobile ad hoc networks (MANETs) with the general group-based two-hop relay algorithm. Under such an algorithm with packet redundancy limit f and group size g (2HR-(f, g) for short), each packet is delivered to at most f distinct relay nodes and can be accepted by its destination if it is a fresh packet to the destination and also it is among g packets of the group the destination is currently requesting. A general Markov chain-based theoretical framework is first developed to characterize the complicated packet delivery process in the challenging MANET environment. With the help of the new theoretical framework, closed-form expressions are further derived for the throughput capacity of the 2HR-(f, g) algorithm, from which one can easily recover the available throughput capacity results by proper settings of the redundancy limit f and group size g.

Due to their simplicity and efficiency, the two-hop relay algorithm and its variants serve as a class of attractive routing schemes for mobile ad hoc networks (MANETs). With the available two-hop relay schemes, a node, whenever getting an opportunity for transmission, randomly probes only once a neighbor node for the possible transmission. It is notable that such single probing strategy, although simple, may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs. To alleviate such limitation for a more efficient utilization of limited wireless bandwidth, this paper explores a more general probing-based two-hop relay algorithm with limited packet redundancy. In such an algorithm with probing round limit τ and packet redundancy limit f, each transmitter node is allowed to conduct up to τ rounds of probing for identifying a possible receiver and each packet can be delivered to at most f distinct relays. A general theoretical framework is further developed to help us understand that under different setting of τ and f, how we can benefit from multiple probings in terms of the per node throughput capacity.

The concept of Quality of Service (QoS) offers different service levels to the network users. Through Service Level Specifications (SLSs), the users in a wireless network, which supports QoS, are able to express, at run-time, their expected service requirements through well defined parameters. Conventional QoS parameters, such as throughput, delay, jitter, packet loss rates, and so forth, are used for reliably ensuring a certain service level with respect to reliability and/or performance. However, most existing researches have ignored tunable security as a Quality of Service (QoS) parameter. The biggest challenge of integrating QoS and security parameters consists in their contrasting goals. This paper presents an idea to permit the users of an IEEE 802.11 Wireless Local Area Network (WLAN) to specify their security and QoS requirements in their Service Level Specifications (SLSs). Then, a game theoretic approach is presented so that the system can reach Service Level Agreement (SLA) with the users to ascertain a balanced set of security and QoS parameters for the users. The effectiveness of the proposed approach is verified through computer simulations.

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.

A lot of works have been dedicated towards understanding the relationship between throughput capacity and packet delay in mobile ad hoc networks (MANETs). However, nearly all these works either assume a localized transmission range, or report the relationship between throughput capacity and packet delay only in terms of the number of users. It remains largely unknown for such a fundamental relationship in terms of other network parameters, like the packet redundancy and node transmission range. As a first step towards this end, in this paper we derive closed-from expressions for throughput capacity and delivery delay under a general setting of node transmission range and also a generalized two-hop relay with limited packet redundancy. Extensive numerical results are further provided to explore how throughput capacity varies with delivery delay in terms of various network parameters, such as the number of users, the packet redundancy limit, and the node transmission range, etc.

A lot of work has been done to model and analyze the performances of two-hop relay algorithm and its variants. However, the delivery ratio, especially under limited message lifetime, has been largely neglected in literature, which is not only of significant importance for delay sensitive applications (where a message beyond some delay limit will typically be dropped) but also of practical interests for general MANET scenarios (where mobile nodes are usually both energy-constrained and buffer storage-limited). In this paper, we study the delivery ratio of a generalized two-hop relay with limited message lifetime and redundancy. In particular, a finite-state absorbing Markov chain-based theoretical framework is first developed to model the complicated message delivery process under the considered relay algorithm. Closed-form expressions are then derived for the message delivery ratio under any given message lifetime, where the important interference, medium contention and traffic contention issues are carefully incorporated into analysis. Finally, extensive simulations are conducted to validate the theoretical framework and corresponding delivery ratio results.

Topology control is a powerful solution to reduce power consumption and the number of collisions by minimizing the transmission range of each node by maintaining a certain level of network connectivity. Although many topology control algorithms have been developed for static networks, e.g., sensor and ad hoc networks with less topology change, where nodes are fixed and the network topology never changes, the topology control technologies can also be adopted for dynamic networks such as Mobile Ad hoc NETworks (MANETs) with an aim to efficiently construct reliable networks. However, in order to apply topology control technologies into MANETs, it is essential to address the issue of performance degradation due to node mobility. Topology information in each node needs to be frequently and appropriately updated according to its moving speed so as to maintain the connectivity with neighbors. In our proposed mechanism, each node determines an appropriate value of topology control update interval according to the mobility information of its neighbors. The proposed mechanism with an adopted topology control technique, based on a localized algorithm, can maintain local connectivity which results in keeping global network connectivity although the network is dynamic. This is a significant advantage of our approach. Simulation results demonstrate that our scheme can ensure a certain level of network connectivity even in MANETs.

The lack of a general capacity theory on mobile ad hoc networks (MANETs) is still a challenging roadblock stunting the application of such networks. The available works on this line mainly focus on deriving order sense results, which are helpful for us to explore the general scaling laws of throughput capacity but tell us little about the exact achievable throughput. This paper studies the exact per node throughput capacity of a MANET, where the transmission power of each node can be controlled to adapt to a specified transmission range v and a generalized two-hop relay with limited packet redundancy f is adopted for packet routing. Based on the concept of automatic feedback control and the Markov chain model, we first develop a general theoretical framework to fully depict the complicated packet delivery process in the challenging MANET environment. With the help of the framework, we are then able to derive the exact per node throughput capacity for a fixed setting of both v and f. Based on the new throughput result, we further explore the optimal throughput capacity for any f but a fixed v and also determine the corresponding optimum setting of f to achieve it. This result helps us to understand how such optimal capacity varies with v (and thus transmission power) and to find the maximum possible throughput capacity of such a network for any f and v. Surprisingly, our results here indicate that usually such maximum throughput capacity can not be achieved through the local transmission, a fact different from what is generally believed in literature.

Many important real networks can be modeled as intermittently connected mobile networks (ICMNs), like the vehicular ad hoc networks, wildlife tracking and habitat monitoring sensor networks, military networks, etc. However, the fundamental performance limits of ICMNs are still largely unknown so far. This paper explores the capability of these networks to support multicast traffic, where each source node desires to send packets to k distinct destinations and all nodes move according to the generalized hybrid random walk mobility model. We show how the network capacity and related delay/delay jitter for supporting multicast in such ICMNs are scaling with the basic network parameters under three transmission protocols: one-hop relay, two-hop relay without packet redundancy and two-hop relay with packet redundancy.

Since Wireless Mesh Networks (WMNs) can be easily deployed without wirelines among wireless mesh routers, they allow us to quickly recover network access services in disaster areas even if the existing network infrastructures have been enormously destroyed by terrible earthquake, tsunami, and so on. However, the performance of wireless mesh networks is largely affected by many factors, e.g., wireless mesh routers' locations, channel assignment, transmission scheduling, etc. In particular, the method of selecting gateways which has a connection to external networks significantly impacts on the network performance when the topology and routing have been fixed in the wireless mesh network. In this paper, we suppose a wireless mesh network which consists of wireless mesh routers and a base station directly connected to external networks. The base station is located at the center of the wireless mesh network chooses a certain number of wireless mesh routers as gateways, and establishes a connection with each of them. Our goal is to easily and quickly find the candidate gateways that maximize the system throughput without solving a complex optimization problem which includes a large number of parameters and involves heavy computation load. The performance of the proposed scheme is evaluated by numerical analysis, and demonstrated through computer simulations. The results show that our proposed scheme can determine the appropriate candidate gateway with high accuracy when there is a certain variance in the amount of traffic generated by users at each wireless mesh router.

In this paper, we focus on the bandwidth allocation issue in wireless access networks, which are made up of Ethernet Passive Optical Network (EPON) and Worldwide Interoperability for Microwave Access (WiMAX) networks, i.e., Fiber-Wire (FiWi) networks. Since the bandwidth allocation scheme largely determines the performance of the entire wireless access network, in the past decades, researchers have dedicated much effort to design bandwidth allocation algorithms based on different criteria in order to satisfy various performance requirements. Various types of bandwidth allocation scheme based on Max-Min Fairness (MMF) or Proportional Fairness (PF) criteria have been developed to increase not only system throughput but also user fairness. However, in general, there is a tradeoff relationship between maximizing system throughput and increasing the fairness among users in throughput, and the users satisfaction in their Quality of Service (QoS) cannot always be maximized by adopting fair bandwidth allocation methods. To cope with this issue, we propose a bandwidth allocation method which improves the QoS satisfaction of all users while maintaining the system throughput similar to standard schemes, such as MMF and PF. In our method, users satisfaction is quantified by using utility functions which can be different among users according to their applications and services. By transferring portions of bandwidth from fully filled users to others so as not to decrease the system throughput, the proposed scheme is able to eventually converge to a compromised point. The results of performance evaluation through computer simulations have demonstrated that our proposed scheme can successfully enhance the performance of wireless access networks.

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.

In Rayleigh block fading channels which represent fast-varying channels, long-term rate adaptation is required instead of instantaneous rate adaptation because the channel information fed back may be outdated. We maximize the long-term average transmission rate (LATR) in a two-hop relay network which adopts Chase combining (CC) type Hybrid Automatic-Repeat-reQuest (HARQ). The round transmission rate, i.e. the transmission rate of each HARQ round in each hop, is optimally selected based on the channel statistics of two hops. Two constraints are considered: the outage probability and the maximum number of HARQ rounds, L. In an infinite L case, we show that the optimal round transmission rate of one hop is determined only by the channel statistics of that hop, and can be expressed as a Lambert W function. In a finite L case, we propose a numerical search algorithm to find the optimal round transmission rate. If HARQ is not adopted, the LATR performance becomes very poor. As L increases in the two-hop relay with CC-based HARQ, the LATR performance becomes close to the LATR performance in the infinite L case. We also show the benefits of the proposed rate selection method compared to a non-optimal rate selection method in terms of the LATR.

One of the challenging roadblocks stunting the development and commercialization of mobile ad hoc networks (MANETs), is the lack of a thorough understanding of the fundamental performance limits in MANETs. Distinguished from available works which mainly focused on deriving order sense scaling laws of the delay performance in MANETs and usually assumed a localized transmission range, this paper examines the MANET packet delay from a much more detailed perspective. Specifically, we assume for each node a general transmission power control such that the transmission range can be flexibly adapted and adopt a generalized two-hop relay with limited packet redundancy for packet routing. For a tagged traffic flow in the MANET, we first develop a theoretical framework based on two correlated FIFO queues to fully characterize the complicated packet delivery process. Then for any feasible traffic input rate there, we derive closed-form expressions for the corresponding expected end-to-end packet delay. Extensive simulations are further conducted to validate our theoretical results.

The Wireless Mesh Network (WMN) has already been recognized as a promising broadband access network technology from both academic and commercial perspective. In order to improve the performance of WMNs, extensive research efforts have been dedicated towards finding means to increase the number of simultaneous transmissions in the network while avoiding signal interference among radios. In case of WMNs based on IEEE 802.11 b/g standards, most recent research works have relied upon the usage of orthogonal channels for solving the Channel Assignment (CA) problem. In this paper, we explore the possibility of exploiting Partially Overlapped Channels (POCs) by introducing a novel game theoretic distributed CA algorithm. Our proposed algorithm outperforms both the conventional orthogonal channel approach and the recent heuristic CA algorithms using POC. The proposed algorithm is shown to achieve near-optimal performance in the average case. In addition, the upper bound Price of Anarchy for Multi-Radio Multi-Channel (MRMC) networks is derived to evaluate the effectiveness of the proposed approach.

This paper examines the optimal forwarding problem in mobile ad hoc networks (MANETs) based on a generalized two-hop relay with limited packet redundancy f (f-cast) for packet routing. We formulate such problem as a forwarding game, where each node i individually decides a probability τi(i.e., a strategy) to deliver out its own traffic and helps to forward other traffic with probability 1-τi, τi∈[0,1], while its payoff is the achievable throughput capacity of its own traffic. We derive closed-form result for the per node throughput capacity (i.e., payoff function) when all nodes play the symmetric strategy profiles, identify all the possible Nash equilibria of the forwarding game, and prove that there exists a Nash equilibrium strategy profile that is strictly Pareto optimal. Finally, for any symmetric profile, we explore the possible maximum per node throughput capacity and determine the corresponding optimal setting of f to achieve it.

Recently, the demand for content delivery in wired/wireless heterogeneous networks is increasing at a rapid pace. Content Distribution/Delivery Networks (CDNs) are considered to be one of the best solutions for dealing with this increasing demand. In this paper, we point out that the performance of a CDN typically degrades in such heterogeneous environments due to the changes in not only user demand but also wireless mobility, which triggers unexpected fluctuations in traffic. Wireless users, roaming between different access networks, may contribute to sudden and unexpected demand spikes in certain parts of the content delivery system. To address this issue, we develop a cooperative server selection scheme, which is designed to maximize robustness to such changes with the cooperation between the content delivery system and its users. The performance of our proposal is evaluated by extensive computer simulations. The evaluation results demonstrate that our proposed scheme effectively makes the considered content delivery system resilient against request fluctuations while minimizing system overloading.

The available two-hop relay protocols with out-of-order or strictly in-order reception cannot provide a flexible control for the packet delivery delay, which may significantly limit their applications to the future mobile ad hoc networks (MANETs) with different delay requirements. This paper extends the conventional two-hop relay and proposes a general group-based two-hop relay algorithm with packet redundancy. In such an algorithm with packet redundancy limit$f$and group size$g$(2HR-$(f,g)$for short), each packet is delivered to at most$f$distinct relay nodes and can be accepted by its destination if it is a fresh packet to the destination and also it is among$g$packets of the group the destination is currently requesting. The 2HR-$(f,g)$covers the available two-hop relay protocols as special cases, like the in-order reception ones$(f\geq 1,g=1)$, the out-of-order reception ones with redundancy$(f>1,g=\infty)$, or without redundancy$(f=1,g=\infty)$. A Markov chain-based theoretical framework is further developed to analyze how the mean value and variance of packet delivery delay vary with the parameters$f$and$g$, where the important medium contention, interference, and traffic contention issues are carefully incorporated into the analysis. Extensive simulation and theoretical results are provided to illustrate the performance of the 2HR-$(f,g)$algorithm and the corresponding theoretical framework, which indicate that the theoretical framework is efficient in delay analysis and the new 2HR-$(f,g)$algorithm actually enables both the mean value and variance of packet delivery delay to be flexibly controlled in a large region.

Power-aware routing in Wireless Sensor Networks (WSNs) is designed to adequately prolong the lifetime of severely resource-constrained ad hoc wireless sensor nodes}. Recent research has identified the energy hole problem in single sink-based WSNs, a characteristic of the many-to-one (convergecast) traffic patterns. In this paper, we propose HYbrid Multi-hop routiNg (HYMN) algorithm, which is a hybrid of the two contemporary multi-hop routing algorithm architectures, namely, flat multi-hop routing that utilizes efficient transmission distances, and hierarchical multi-hop routing algorithms that capitalizes on data aggregation. We provide rigorous mathematical analysis for HYMN-optimize it and model its power consumption. In addition, through extensive simulations, we demonstrate the effective performance of HYMN in terms of superior connectivity.

Due to their simplicity and efficiency, the two-hop relay algorithm and its variants serve as a class of attractive routing schemes for mobile ad hoc networks (MANETs). With the available two-hop relay schemes, a node, whenever getting an opportunity for transmission, randomly probes only once a neighbor node for the possible transmission. It is notable that such single probing strategy, although simple, may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs. To alleviate such limitation for a more efficient utilization of limited wireless bandwidth, this paper proposes a more general probing-based two-hop relay algorithm with limited packet redundancy. In such an algorithm with probing round limit τ and packet redundancy limit f, each transmitter is allowed to conduct up to τ rounds of probing for identifying a possible receiver and each packet can be delivered to at most f distinct relays. A general theoretical framework is further developed to help us understand that under different setting of τ and f, how we can benefit from multiple probings in terms of the per node throughput capacity and the expected end-to-end packet delay.

Although topology control has received much attention in stationary sensor networks by effectively minimizing energy consumption, reducing interference, and shortening end-to-end delay, the transience of mobile nodes in Mobile Ad hoc Networks (MANETs) renders topology control a great challenge. To circumvent the transitory nature of mobile nodes, k-edge connected topology control algorithms have been proposed to construct robust topologies for mobile networks. However, uniformly using the value of k for localized topology control algorithms in any local graph is not effective because nodes move at different speeds. Moreover, the existing k-edge connected topology control algorithms need to determine the value of k a priori, but moving speeds of nodes are unpredictable, and therefore, these algorithms are not practical in MANETs. A dynamic method is proposed in this paper to effectively employ k-edge connected topology control algorithms in MANETs. The proposed method automatically determines the appropriate value of k for each local graph based on local information while ensuring the required connectivity ratio of the whole network. The results show that the dynamic method can enhance the practicality and scalability of existing k-edge connected topology control algorithms while guaranteeing the network connectivity.

Among the "store-carry-forward" kind of protocols, the two-hop relay and its variants have become a class of attractive routing protocols for the mobile ad hoc networks (MANETs) due to its efficiency and simplicity. This paper focuses on the performance modeling for two-hop relay with erasure coding, a promising technique for improving the delay performance of conventional two-hop relay with simple replication. A general Markov chain-based theoretical framework is first developed to model the complicated message delivery process in such a network, based on which not only the mean value but also the variance of message delivery delay are derived analytically. The important medium contention, interference and traffic contention issues are carefully incorporated into our analysis, so the new theoretical framework can be used to precisely predicate the message delivery delay performance of two-hop relay with erasure coding, as verified by extensive simulation results.

Two-hop relay is a class of attractive routing protocols for mobile ad hoc networks (MANETs) due to its efficiency and simplicity. This paper extends the conventional two-hop relay and proposes a more general group-based two-hop relay algorithm with redundancy. In such an algorithm with redundancy f and group size g (2HR-(f, g) for short), each packet is delivered to at most f distinct relay nodes and can be accepted by its destination if it is among the group of g packets the destination is currently requesting. The 2HR-(f, g) covers the available two-hop relay protocols as special cases, like the in-order protocols (f ≥ 1, g = 1), the out-of-order protocols with redundancy (f >; 1, g = ∞) or without redundancy (f = 1, g = ∞), and it enables a more flexible control of packet delivery process to be made in the challenging MANET environment. A general theoretical framework is further developed to explore how the control parameters f and g affect the expected packet delivery delay in an 2HR-(f, g) MANET, where the important medium contention, interference and traffic contention issues are carefully incorporated into the analysis. Finally, extensive simulation and theoretical results are provided to demonstrate the efficiency of the 2HR-(f, g) scheme and the corresponding theoretical framework.

The 2-hop relay algorithm and its variants have been attractive for ad hoc mobile networks, because they are simple yet efficient, and more importantly, they enable the capacity and delay to be studied analytically. This paper considers the 2-hop relay with f-cast (2HR-f) under i.i.d. mobility model, a general 2-hop relay algorithm that allows one packet to be delivered to at most f distinct relay nodes. The 2HR-f algorithm covers the available 2-hop relay algorithms (f = 1,√n) as special cases. Closed-form analytical models rather than order sense ones are developed for the 2HR-f algorithm with a careful consideration of important medium contention and queuing delay issues, which enable an accurate delay and capacity analysis to be performed for ad hoc mobile networks employing 2HR-f. Based on our models and some typical settings of f (say, f = 1,√n), one can easily derive the corresponding order sense results.

Certificate revocation is an important security component in mobile ad hoc networks (MANETs). Owing to their wireless and dynamic nature, MANETs are vulnerable to security attacks from malicious nodes. Certificate revocation mechanisms play an important role in securing a network. When the certificate of a malicious node is revoked, it is denied from all activities and isolated from the network. The main challenge for certificate revocation is to revoke the certificates of malicious nodes promptly and accurately. In this paper, we build upon our previously proposed scheme, a clustering-based certificate revocation scheme, which outperforms other techniques in terms of being able to quickly revoke attackers' certificates and recover falsely accused certificates. However, owing to a limitation in the scheme's certificate accusation and recovery mechanism, the number of nodes capable of accusing malicious nodes decreases over time. This can eventually lead to the case where malicious nodes can no longer be revoked in a timely manner. To solve this problem, we propose a new method to enhance the effectiveness and efficiency of the scheme by employing a threshold based approach to restore a node's accusation ability and to ensure sufficient normal nodes to accuse malicious nodes in MANETs. Extensive simulations show that the new method can effectively improve the performance of certificate revocation.

Fiber-Wireless (FiWi) access networks, have rapidly matured as a last mile Internet access network solution due to their novel combination of Ethernet Passive Optical Networks (EPON) as a backhaul and Wireless Mesh Networks (WMN) as an access network. The high bandwidth provided by the optical lines, as well as the flexibility offered by the wireless network, offers a great degree of cost-efficiency in terms of sharing an optical line with a number of simultaneous users. In a FiWi network, Gateways (GWs) located between the EPON and WMN serve both the function of an Optical Network Unit (ONU) in the EPON and a mesh router in WMN. Since all of the downstream from the EPON to the WMN and all of the upstream from the WMN to the EPON must be exchanged at GWs, traffic distribution technique between GWs is necessary to achieve efficient utilization of the network resources. Controlling the downstream traffic is a significant issue in preventing performance degradation due to network congestion at the GWs, because the bandwidth of WMN is generally narrower than that of the EPON. In addition, the number of hops from a GW to an end-user in the WMN needs to be taken into account in the traffic distribution process, because the increased number of hops results in lower communication efficiency due to mutual interferences between adjacent links and effects of cross traffic. Therefore, in this paper, we focus on the downstream controlling of FiWi networks, and propose a traffic distribution scheme which utilizes an aspect of EPON to properly distribute traffic load among GWs. A hop count limitation mechanism is adopted to avoid throughput degradation caused by increased wireless interference and effects of cross traffic in the WMN. Simulation results show a trade-off relationship between fair load balancing among GWs and high throughput for end-users, and the proposed scheme can accommodate it by regulating hop count limitation.

While GEostationary Orbit (GEO) satellite systems provide us with a wide coverage area, their long delay serves as a significant constraint for real-time applications. On the other hand, Low Earth Orbit (LEO) satellite systems are best suited to delay sensitive applications. However, the coverage and mobility issues of LEO satellites lead to relatively high management costs. In this paper, we devise a new load balancing and quality of service (QoS) provisioning scheme to accommodate both real-time and non-real-time traffic based on a new congestion-prediction scheme. The effect of this new scheme is expected to improve the efficiency of the GEO/LEO hybrid satellite networks and the QoS satisfaction of end users.

Owing to the heterogeneity and high degree of connectivity of various networks, there likely exist multiple available paths between a source and a destination. An effective model of delay-controlled load distribution becomes essential to efficiently utilize such parallel paths for multimedia data transmission and real-time applications, which are commonly known to be sensitive to packet delay, packet delay variation, and packet reordering. Recent research on load distribution has focused on load balancing efficiency, bandwidth utilization, and packet order preservation; however, a majority of the solutions do not address delay-related issues. This paper proposes a new load distribution model aiming to minimize the difference among end-to-end delays, thereby reducing packet delay variation and risk of packet reordering without additional network overhead. In general, the lower the risk of packet reordering, the smaller the delay induced by the packet reordering recovery process, i.e., extra delay induced by the packet reordering recovery process is expected to decrease. Therefore, our model can reduce not only the end-to-end delay but also the packet reordering recovery time. Finally, our proposed model is shown to outperform other existing models, via analysis and simulations.

The two-hop relay algorithm and its variants have been attractive for ad hoc mobile networks, because they are simple yet efficient, and more importantly, they enable the capacity and delay to be studied analytically. This paper considers a general two-hop relay with f-cast (2HR-f), where each packet is delivered to at most f distinct relay nodes and should be received in order at its destination. We derive the closed-form theoretical models rather than order sense ones for the 2HR-f algorithm with a careful consideration of the important interference, medium contention, traffic contention and queuing delay issues, which enable an accurate delay and capacity analysis to be performed for an ad hoc mobile network employing the 2HR-f. Based on our models, one can directly get the corresponding order sense results. Extensive simulation studies are also conducted to demonstrate the efficiency of these new models.

The Wireless Mesh Network (WMN) has already been recognized as a promising technology as broadband access network from both academic and industry points of view. In order to improve its performance, research has been carried on how to increase the number of simultaneous transmissions in the network while avoiding signal interference among radios. Considering WMNs based upon IEEE 802.11 b/g standards, lately most of researchers have been relying on the usage of orthogonal channels for solving the Channel Assignment (CA) problem. However, in this paper, we introduce a novel CA algorithm exploiting partially overlapped channels (POC) that overcome the common orthogonal channel approach and also a recent proposed CA algorithm using POC.

In recent years, along with the increasing popularity of multi-hop wireless networks, there has been a growing demand in the coupling of these networks to external ones such as the Internet. As traffic destined for external networks increases, special attention is required not only in gateway selection, but also in optimized routing and scheduling in order to maximize the network performance. In this paper we introduce the Ideally Scheduled Route Optimization (ISRO) method to address this concern. ISRO is the combination of three separate optimization problems: optimal routing of gateway traffic under ideal conditions, interference-free scheduling to determine link capacity, and route adjustment in light of the new link capacities. The performance of ISRO is evaluated by experiment which shows significant potential in maximizing the throughput and capacity of the network.

Power aware routing in Wireless Sensor Networks (WSNs) focuses on the crucial issue of extending the network lifetime of WSNs, which are limited by low capacity batteries. However, most of the previously proposed power aware routing algorithms have an inherent problem, which is the isolation of the sink node due to the quick power exhaustion of nodes that are close to the sink. In this paper, we propose a solution, referred to as HYbrid Multi-hop routiNg (HYMN), which addresses this problem by combining two routing strategies, namely flat multi-hop routing and hierarchical multi-hop routing. The former method aims at minimizing the total power consumption in the network while the latter attempts to decrease the amount of transferred data traffic by utilizing data compression. We present a mathematical analysis on the effect of the hybrid location on the performance of HYMN, and also demonstrate the effectiveness of HYMN through extensive simulations.

Recently, much effort aiming at achieving ubiquitous networks has been made. A ubiquitous network refers to a network environment, which enables anytime and anywhere access, by possibly any given device or by any user. In a ubiquitous network, applications require many types of information such as temperature and so forth. A great deal of attention has been paid to aggregate this information in Wireless Sensor Networks (WSNs). A WSN consists of tiny nodes comprising sensing and communication devices. The information sensed by each node is relayed via other nodes in the WSN to the destination node called the ``sink''. One of the most significant challenges pertaining to any WSN is to reduce the energy consumption of its nodes, which run on scarce battery resources. An effective scheme to reduce this energy consumption is to exploit the sink node's mobility, which however presents new challenges to the sink node's routing and information aggregation. In this paper, we propose a new routing and data aggregation scheme based on clustering. Simulation results demonstrate that our scheme can provide better energy efficient data aggregation as compared to the KAT (K-means And Traveling salesman path) mobility.

Along with the rapid deployment of video streaming applications and services over the networks, prevention of undesirable leakage of streaming contents have become a serious issue, which needs substantial attention. Our research group had previously proposed streaming content leakage detection technology based on the fact that different contents exhibit different traffic patterns when they are being delivered over the networks. By comparing traffic patterns obtained near the content server with those measured at egress nodes, these conventional systems are able to detect the stream leakage to the external network. Although our earlier works demonstrated that the performance of our detection method in delayed and lossy networks can be enhanced by adopting an advanced traffic pattern generation algorithm, their robustness to reordered or encrypted streams requires further investigation. In this paper, we first verify, through experiments, that the packet reordering does not significantly degrade the performance of the detection scheme while the packet encryption technologies substantially affect the conventional systems regardless of the employed traffic pattern generators. In order to address this problem, we envision an enhanced mechanism for dealing with encrypted traffic. We also empirically evaluate the performance of the proposed scheme and validate its effectiveness.

In Mobile Ad hoc NETworks (MANETs), certification systems play an important role in maintaining network security because attackers can freely move and repeatedly launch attacks against different nodes. By adopting certification systems, it becomes possible to exclude identified attackers from the network permanently by revoking the certifications of the attackers. A simple way to identify attackers is to collect information on attackers from nodes in the network. However, in this approach, it is difficult to differentiate valid accusations made by legitimate nodes from false accusations made by malicious nodes. In addition, the amount of traffic in order to exchange information on attackers and the necessary time to gather the information increases as the network size becomes larger. In this paper, we propose a certificate revocation scheme which can revoke the certification of attackers in a short time with a small amount of operating traffic. By clustering nodes and introducing multi-level node reliability, the proposed scheme can mitigate the improper certificate revocation due to false accusations by malicious users.

Energy consumption and network connectivity are two of the important research issues that are yet to be resolved in mobile ad hoc networks (MANETs). As taken advantage of in static networks, reliable topology control algorithms are also considered to be a good approach for mobile networks. However, a more adequate evaluation of these algorithms regarding mobility is still needed. In this paper, we evaluate the performance of some well-known topology control algorithms with various scenarios and measurements. The results show that Local Tree-based Reliable Topology (LTRT), a recently proposed algorithm, is the most scalable method and provides the most benefit in terms of redundant connectivity.

While Transmission Control Protocol (TCP) is the most popular transport protocol used in terrestrial networks, its performance is not adequate in wireless networks with long delay, e.g., satellite networks. Though some improvements of TCP and new transport protocols have been proposed, we focus on Variable-structure congestion Control Protocol (VCP) designed for high Bandwidth Delay Product (BDP) networks. In VCP, Explicit Congestion Notification (ECN) is used to generate the feedback signal from routers to sources in order to notify the utilization ratio of a bottleneck link. By adjusting its congestion window according to the network traffic conditions, VCP is able to achieve high link utilization even in high BDP networks. However, VCP requires a long time to fill the link capacity due to its more conservative window control mechanism than the slow start phase in TCP. In addition, throughput is unnecessarily degraded in VCP due to packet losses in wireless environments. In this paper, to address these issues, we propose two extensions of VCP, namely Bandwidth-Independent Start-up Extension (BISE) and Wireless Loss Tolerant Extension (WLTE). BISE can quickly increase its congestion window in the start-up phase. WLTE can maintain high throughput in wireless environments. The performance of the proposed schemes is evaluated through computer simulations. The results demonstrate that the proposed schemes dramatically improve the performance of VCP in the initial phase and also in wireless environments.

Today, with the rapid advance in broadband technology, streaming technology is applied to many applications, such as content delivery systems and web conference systems. On the other hand, we must implement digital rights management (DRM) to control content spreading and to avoid unintended content use. Traitor tracing is one of the key technologies that constructs DRM systems, and enables content distributors to observe and control content reception. General methods make use of watermarking to provide users' individual information unique to each user. However, these methods need to produce many individual contents. Especially, this is not realistic for real-time streaming systems. Furthermore, watermarking, which is a key technology adopted by contemporary methods, has known limitations and attacks against it. This is why the authors have proposed a method to monitor the content stream using traffic patterns constructed from only traffic volume information obtained from routers. The proposed method can determine who is watching the streaming content and whether or not a secondary content delivery exists. This information can be also used with general methods to construct a more practical traitor-tracing system. A method to cope with random errors and burst errors has also been investigated. Finally, the results of simulation and practical experiment are provided demonstrating the effectiveness of the proposed approach.

The unbridled growth of the Internet and the network-based applications has contributed to enormous security leaks. Even the cryptographic protocols, which are used to provide secure communication, are often targeted by diverse attacks. Intrusion detection systems (IDSs) are often employed to monitor network traffic and host activities that may lead to unauthorized accesses and attacks against vulnerable services. Most of the conventional misuse-based and anomaly-based IDSs are ineffective against attacks targeted at encrypted protocols since they heavily rely on inspecting the payload contents. To combat against attacks on encrypted protocols, we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs). We have categorized various attacks against cryptographic protocols. The MSs, by sniffing the encrypted traffic, extract features for detecting these attacks and construct normal usage behavior profiles. Upon detecting suspicious activities due to the deviations from these normal profiles, the MSs notify the victim servers, which may then take necessary actions. In addition to detecting attacks, the MSs can also trace back the originating network of the attack. We call our unique approach DTRAB since it focuses on both Detection and TRAceBack in the MS level. The effectiveness of the proposed detection and traceback methods are verified through extensive simulations and Internet datasets.

DNS is one of the internet's fundamental building blocks, used by various applications such as web and mail transfer. Therefore, monitoring DNS traffic has potential to detect host anomalies such as spammers and infected hosts in a network. However, previous works assume a small number of hosts or target on domain name anomalies, so that they cannot be applied to a large-scale networks due to performance issues. A large number of hosts and long-term tracing consume computational resources and make real-time analysis difficult. In this paper, we propose anomaly detection for DNS servers using frequent host selection, which selects only potential hosts and does not depend on the number of hosts. We evaluate the proposed system using DNS traffic for 6 months of tracing, and show that the system can feasibly handle hosts in the dataset and detect anomalies, such as mail servers suffering from spam and DNS servers are configured incorrectly.

Due to the dynamic topology and non infrastructure, network participants cooperate with their neighbors to route packets. The lack of centralized services allows mobile ad hoc networks to be easily and swiftly deployed, but make it difficult to check others' identities on the other hand. Cryptographic tools have been introduced to secure group communications, such as private and public key infrastructure. The autonomous and distributed nature of mobile ad hoc network demands a decentralized authentication service, where public key infrastructure is considered a better solution. Public key infrastructure can ensure both confidentiality and authenticity, but it is impractical to provide an online trusted third party as certificate authority (CA) for mobile ad hoc network. In this paper, we proposed a new key management protocol which utilizes certificate graphs and distributed certificate authorities. Certificate graph maintained by each user represents the trust among his neighbors, then the maximum clique of certificate graph is selected to be CAs. Based on the assumption that initial certificate graph building is secure, good users have more friends while bad ones have less, thus a reliable group can be constructed. The most trustful subset of these good users -the maximum clique - is elected as the governor of this group, which takes the responsibility of certificate authentication.

Due to their ability to overcome many shortcomings associated with the contemporary client-server paradigm, Peer-to-Peer (P2P) networks have attracted phenomenal interests from researchers in both academia and industry. Interactive and multimedia streaming applications using P2P networks are, however, often prone to long startup delays, which disrupt the smooth playback and undermine users' perceived quality of service. In addition, P2P networks must be able to support a potential number of users while ensuring that the resources are efficiently utilized. In this paper, by addressing these shortcomings in the traditional P2P framework, we envision a novel scheme to effectively provide a Video-on-Demand (VoD) using P2P-based mesh overlay networks. The proposed scheme covers two main phases, namely requesting and scheduling modes. The former aims at dynamically selecting the required contents from the available peers. On the other hand, in the scheduling mode, the incoming requests are scheduled in a priority-based manner for minimizing the startup latency and sustaining the playback rate to an acceptable level. Computer simulations have been conducted to verify the effectiveness of the proposed scheme. The obtained results demonstrate the scalability of our envisioned scheme in addition to its capability to reduce the startup delay and provide a sustainable playback rate.

In IEEE 802.11 Distributed Coordination Function (DCF), the multi-rate Basic Service Set (BSS) suffers from the performance anomaly issue, which brings unfairness in terms of channel occupancy time. To solve the issue, this paper presents a rate control scheme for TCP Friendly Rate Control (TFRC) in IEEE 802.11 DCF mode. The proposed scheme controls the sending rate so that each station can use the wireless channel for equal duration. This is based on the channel occupancy period used by each station in BSS, which is monitored at the Media Access Control (MAC) layer. The performance of the proposed scheme over multi-rate IEEE 802.11 DCF is evaluated and compared with that of normal TFRC through several simulations. The simulation results show that the proposed scheme exhibits fairness in terms of channel occupancy time among the competing stations, which accordingly mitigates the performance anomaly. The proposed scheme improves the aggregated throughput in BSS.

Owing to the diverse geographical distributions of users, multi-layered satellite networks tend to exhibit high variances causing traffic concentrations at particular satellites to increase drastically. This results in high packet drop rates and severe degradation of Quality of Service (QoS). The Explicit Load Balancing (ELB) scheme was developed to address these issues in Low Earth Orbit (LEO) satellite networks by having the satellites, which experience heavy traffic, redirect a portion of the traffic via alternative paths. To cope with network congestion (over a single layer) and for better traffic distribution, multi layer satellites were proposed. In this paper, we propose an efficient traffic distribution scheme for multi-layered satellite networks based on ELB in which we extend the range for exchanging the traffic-load information for achieving further reductions in packet drop rates. We also present an enhanced technique for efficiently computing the detouring ratio. The effectiveness of the envisioned approach is validated via simulations.

In the modern era of Internet, providing Quality of Service (QoS) is a challenging issue, particularly in resource-constrained wireless networks with delay-sensitive multimedia traffic. Real-time and multimedia services are now available to end-users over wired networks, Wireless Local Area Networks (WLANs), and Wireless Personal Area Networks (WPANs). While the usual trend is to provide the best possible QoS for these services, it is also imperative to deploy security requirements along with the QoS parameters. In this paper, we argue that the existing approaches for including security parameters (such as encryption/decryption key lengths) with QoS parameters (e.g., end-to-end delay requirements) lead to further security risks and consequently fail to provide an adequate solution. Through simulations, we point out the pitfalls of integrating delay and security support in the contemporary approaches. We also envision QoS2, a framework integrating both quality of security and QoS, in order to provide possible solutions for solving these problems. We also demonstrate via simulation the effectiveness and strength of our adopted approach.

The eHealth system is envisioned as a promising approach to improving health care through information technology, where security and privacy are crucial for its success and largescale deployment. In this paper, we propose a strong privacy-preserving Scheme against Global Eavesdropping, named SAGE, for eHealth systems. The proposed SAGE can achieve not only the content oriented privacy but also the contextual privacy against a strong global adversary. Extensive analysis demonstrates the effectiveness and practicability of the proposed scheme.

In next generation wireless networks, Internet service providers (ISPs) are expected to offer services through several wireless technologies (e.g., WLAN, 3G, WiFi, and WiMAX). Thus, mobile computers equipped with multiple interfaces will be able to maintain simultaneous connections with different networks and increase their data communication rates by aggregating the bandwidth available at these networks. To guarantee quality-of-service (QoS) for these applications, this paper proposes a dynamic QoS negotiation scheme that allows users to dynamically negotiate the service levels required for their traffic and to reach them through one or more wireless interfaces. Such bandwidth aggregation (BAG) scheme implies transmission of data belonging to a single application via multiple paths with different characteristics, which may result in an out-of-order delivery of data packets to the receiver and introduce additional delays for packets reordering. The proposed QoS negotiation system aims to ensure the continuity of QoS perceived by mobile users while they are on the move between different access points, and also, a fair use of the network resources. The performance of the proposed dynamic QoS negotiation system is investigated and compared against other schemes. The obtained results demonstrate the outstanding performance of the proposed scheme as it enhances the scalability of the system and minimizes the reordering delay and the associated packet loss rate.

Application layer multicast (ALM) is highly expected to replace IP multicasting as the new technological choice for content delivery. Depending on the streaming application, ALM nodes will construct a multicast tree and deliver the stream through this tree. However, if a node resides in the tree leaves, it cannot deliver the stream to its descendant nodes. In this case, quality of service (QoS) will be compromised dramatically. To overcome this problem, topology-aware hierarchical arrangement graph (THAG) was proposed. By employing multiple description coding (MDC), THAG first splits the stream into a number of descriptions, and then uses arrangement graph (AG) to construct node-disjoint multicast trees for each description. However, using a constant AG size in THAG creates difficulty in delivering descriptions appropriately across a heterogeneous network. In this paper, we propose a method, referred to as network-aware hierarchical arrangement graph (NHAG), to change the AG size dynamically to enhance THAG performance, even in heterogeneous networks. Finally, we evaluate the proposed scheme by experiments using the network simulator ns-2. By comparing our proposed method to THAG and SplitStream, we show that our method provides better performance in terms of throughput and QoS. The results indicate that our approach is more reliable than other methods in heterogeneous networks.

During the last several years, the Internet has evolved from a wired infrastructure to a hybrid of wired and wireless domains by spreading worldwide interoperability for microwave access (WiMAX), Wi-Fi, and cellular networks. Therefore, there is a growing need to facilitate reliable content delivery over such heterogeneous networks. On the other hand, application layer multicast (ALM) has become a promising approach for streaming media content from a server to a large number of interested nodes. ALM nodes construct a multicast tree and deliver the stream through this tree. However, if a node leaves, it cannot deliver the stream to its descendant nodes. In this case, quality-of-service (QoS) is compromised dramatically. Especially, this problem is exacerbated in wireless networks because of packet errors and handovers. In order to cope with this problem, multiple-tree multicasts have been proposed. However, existing methods fail to deliver contents reliably in combined wired and wireless networks. In this paper, we propose a method to ensure the robustness of node departure, while meeting various bandwidth constraints by using layered multiple description coding (LMDC). Finally, we evaluate the proposed method via extensive simulations by using the network simulator (ns-2). By comparing our proposed method with the existing ones, we demonstrate that our method provides better performance in terms of total throughput, relative delay penalty (RDP), and relative delay variation (RDV). The results indicate that our approach is a more reliable content delivery system when compared with contemporary methods in the context of heterogeneous networks containing wired and wireless environments.

Broadcasting, in the context of ad-hoc networks, is a costly operation, and thus topology control has been proposed to achieve efficient broadcasting with low interference and low energy consumption. By topology control, each node optimizes its transmission power by maintaining network connectivity in a localized manner. Local Minimum Spanning Tree (LMST) is the state-of-the-art topology control algorithm, which has been proven to provide satisfactory performance. However, LMST almost always results in a 1-connected network, without redundancy to tolerate external factors. In this paper, we propose Local Tree-based Reliable Topology (LTRT), which is mathematically proven to guarantee k-edge connectivity while preserving the features of LMST. LTRT can be easily constructed with a low computational complexity of O(k(m + n log n)), where k is the connectivity of the resulting topology, n is the number of neighboring nodes, and m is the number of edges. Simulation results have demonstrated the efficiency of LTRT and its superiority over other localized algorithms.

For the management of early stage atherosclerosis, the concept of metabolic syndrome is important. An inexpensive, ultra-mobile ultrasound imaging device is proposed for the accurate diagnosis of the atherosclerosis. A linear probe capable of transmitting and receiving 64 ultrasound beams with the central frequency of 7.5 MHz is made. Echo images are displayed on a 5.7 inch LCD screen. The dimension of the device is 270 times 184 times 62 mm and the total weight including the probe is 1.9kg. For the popularization of the echo images for the patients' education and motivation, ultrasound movie client systems were proposed using a mobile phone or PlayStation Portabletrade. The entire procedure proposed in the paper consists of the development of electronic devices and configuration of this system including to the training of community health nurses to use this device.

Recent advances in solid state and packaging technologies have enabled production of more efficient and reasonably small devices such as micro electro mechanical systems (MEMS). Wireless sensor networks (WSNs) can gather data from sensor nodes and are now at the practical stage of realizations because of the above advances. Conventional researches have mainly focused on extending the lifetime of WSNs because sensor nodes are only equipped with small-capacity batteries. Mobile ubiquitous LAN extension (MULE) is one of the approaches to meet such demand, and it can gather data from isolated nodes. The KAT mobility scheme is one of the mobility schemes on MULE focusing on the efficiency. Therefore, this scheme is expected to prolong the lifetime of the network. However, this scheme cannot ensure that the mobile sinks can gather the data from all of the nodes. In this paper, we focus on the fairness issue of data gathered by the mobile sinks while also considering the efficiency of data gathering. We propose a new mobility scheme based on a new clustering method and the set cover algorithm to ensure that the mobile sinks can gather data from all of the nodes, and simulation results show that fairness of data gathering by the proposed mobility scheme is greatly improved as compared to conventional KAT mobility scheme.

In recent years, information leakage through the Internet has become a new social problem. Many information leakage incidents are caused by illegal applications such as peer-to-peer (P2P) file sharing software. To prevent information leakage, early detection and blocking of the traffic exchanged by illegal applications is strongly required. In this paper, we propose a method for application discrimination of monitored traffic based on the transition pattern of payload length during start up phase of the communication. The proposed method does not need port numbers, which can be spoofed easily. Through experiments using real network traffic, we show that the proposed method can quickly and accurately discriminate applications.

The coupling of mobile ad-hoc networks (MANETs) and the Internet is gaining attention by researchers working towards future ubiquitous computing environments. In this work, we focus on the situation that occurs when specialized, sensitive data are sent to the Internet from MANET nodes. These special data types are especially susceptible to security risks such as information leak and data falsification. Therefore, it is necessary for such special data to be forwarded by a secure/trusted gateway which is under control of a trusted network administrator. However, we assume there can be multiple gateways deployed in a MANET, where the cost ineffectiveness makes it difficult for a network administrator to simultaneously manage every gateway. Because of the risk of forwarding special data through an unmaintained gateway, we propose a routing protocol which allows a source node to have all data forwarded to the Internet through a trusted gateway. To achieve desirable performance, we improve upon one of the newest routing protocols, Dynamic MANET On-demand (DYMO). Through simulations, we evaluated our proposal in comparison with the conventional DYMO protocol. The results show that our proposal achieves performance allowing MANET source nodes to choose gateways for specific data.

Data confidentiality is one of the most important concerns in security of ad-hoc networks which have been widely studied in recent years. In this paper, we consider the public-key cryptography which is one of the simplest and viable means to maintain data confidentiality. There are several ways to distribute a public key. Flooding is an intuitive approach to distribute each node’s public key. However, the normal flooding approach is costly, and can cause MAC-level contention in a dense region of nodes. Tree based topology flooding can be appied to mitigate these problems. The construction algorithm should use ideally only local information. In this paper, we propose a completely localized algorithm called the Local Tree-based Reliable Topology (LTRT) algorithm, which achieves both reliability and efficiency. LTRT is a localized version of TRT that has 2-edge connectivity. Each node can distribute its public key to all other nodes in the network by LTRT. Simulation results show the efficiency of LTRT and its superiority over other localized algorithms.

In multi-hop sensor networks, information obtained by the monitoring nodes need to be routed to the sinks. If we assume that the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver, the energy consumption rate per unit information transmission depends on the choice of the next hop node. In a power-aware routing approach, most proposed algorithms aim at minimizing the total energy consumption or maximizing network lifetime. In this paper, we propose a new routing algorithm with two goals: minimizing the total energy consumption and ensuring fairness of energy consumption between nodes. We formulate this as a nonlinear programming problem and use a sub-gradient algorithm to solve the problem. We also evaluate the proposed algorithm via simulations at the end of this paper.

Application Layer Multicast (ALM) is highly expected to be the new technological choice contents delivery in lieu of IP multicast. Depending on each node's streaming application, ALM constructs multicast trees and delivers the stream through those trees. The problem of ALM is that when a node resides in tree leaves, the stream cannot be delivered to descendant nodes. To overcome this problem, Topology-aware Hierarchical Arrangement Graph (THAG) was proposed. By employing Multiple Description Coding (MDC), THAG first splits the stream into a number of sub-streams, and then uses Arrangement Graph (AG) to construct an independent tree for each sub-stream. However, using the same size of AG in THAG has a difficulty delivering a stream appropriately across a heterogeneous network. In this paper, we propose a method to change the size of AG dynamically in enhancing THAG performance well even in a heterogeneous network. Finally, we evaluate the proposed scheme by experiments in ns -2. By comparing with THAG, we show that our proposal scheme provides a better performance in throughput and Bandwidth Satisfaction Rate (BSR).

The transmission of high quality video requires high bandwidth. Ensuring constantly high bandwidth in wireless environments is a challenging task given constraints in the current wireless network resources. Current mobile computers are equipped with multiples wireless interfaces that can be used to improve the video quality by aggregating the bandwidth of these interfaces. Such Bandwidth Aggregation (BAG) approach involves multiple paths in communication and gives rise to a number of issues related to the management of the Service Level Agreement (SLA) and packet reordering. To guarantee an efficient and fair management of SLA, this paper presents a bandwidth aggregation-aware QoS negotiation mechanism that enables users to dynamically negotiate their desired service levels and to reach them through the use of bandwidth aggregation. This operation is performed while ensuring a fair use of the network resources among all competing users. To cope with packet reordering, a new scheduling strategy is presented. The performance evaluation of the proposed bandwidth aggregation-aware QoS negotiation scheme and the proposed scheduling algorithm are conducted via simulations and the results are discussed.

Attacks against encrypted protocols are becoming increasingly popular. They pose a serious challenge to the conventional intrusion detection systems (IDSs) which heavily rely on inspecting the network packet fields and are consequently unable to monitor encrypted sessions. IDSs can be broadly categorized into two types: signature-based and anomaly-based IDSs. The signature-based IDSs rely on previous attack signatures but are often ineffective against new attacks. On the other hand, anomaly-based detection systems depend on detecting the change in the protocol behavior caused by an attack. The latter can be employed to detect novel attacks, and therefore are often preferred over their signature-based counterpart. In this paper, we envision an anomaly-based IDS which can detect attacks against popular encrypted protocols, such as SSH and SSL. The proposed system creates a normal behavior profile and uses non-parametric Cusum algorithm to detect deviation from the normal profile. Upon detecting an anomaly, the proposed mechanism generates an alert, sets a delay to the protocol response, and traces back the attacker. The effectiveness of the proposed detection scheme is verified via simulations.

Satellite communication systems exhibit important and unique features that qualify them to be an integral part of a global ubiquitous information system. Given the universality of the Internet protocol (IP), traffic over satellite network is expected to be all IP. Success of these all-IP satellite systems depends on their abilities to guarantee QoS. QoS provisioning has been a hot topic in terrestrial wired networks. It has been, however, highly overlooked in wireless networks. An efficient QoS provisioning in wireless networks in general, and in satellite networks in particular, can be possible only with the development of new schemes that are able to dynamically (re)negotiate service levels in an adaptive manner to changes in network conditions upon handoff occurrences. This paper surveys major dynamic service level negotiation schemes proposed for terrestrial wireless networks and discusses their limitations when applied to satellite networks. As a solution, a dynamic service level negotiation scheme specifically tailored to satellite networks is portrayed. Comparison of the proposed scheme to other dynamic negotiation approaches, via a qualitative and quantitative analysis, is also presented.

Mobile users are expected to be highly dynamic in next generation mobile networks. Additionally they will be served a wide variety of services with different transmission rates and expect high quality of service (QoS). Since the number of mobile subscribers is rapidly increasing and given the limited resources of any robust network, guarantee of high QoS is possible only by the deployment of network elements that optimally allocate network resources and instantly adapt to network conditions. In attempt to support mobility in IP networks, the hierarchical mobile IPv6 (HMIPv6) has been proposed. An important issue that has been highly overlooked in the design of HMIPv6 consists in its lack of a mechanism that can efficiently control and distribute traffic among multiple mobility anchor points (MAPs). In the absence of such mechanism, some MAPs may get congested while others remain underutilized. In such scenario, mobile users connecting to congested MAPs may experience significant packet drops and excessive queuing delays. This ultimately affects QoS. In this vein, this paper proposes an application-driven mechanism for selection of MAPs. The key idea behind the proposed scheme consists in the reference of access points to the transmission rate of the users' applications to decide which MAP visiting users should be registering with. The decision of MAPs is performed in a way that the load variance of all MAPs, serving the access point in question, is minimized. Issues related to the frequency of binding update messages are also considered in the selection of MAPs. The performance of the proposed scheme is evaluated via computer simulations. In terms of QoS, encouraging results are obtained: better traffic distribution among MAPs and lower handoff delays.

To distinguish similar characters, it is preferable to construct a classifier using a projective feature space which differentiates two similar categories. The classifier CMF has been proposed for a discriminant function, in similar characters recognition. In the CMF, a subspace is constructed by some eigenvectors, that corresponds to the smallest eigenvalues, is applied as projective feature space. A difference vector of two class-mean feature vectors are assumed as the difference between two similar categories, the CMF is constructed by projecting a feature vector onto this difference vector. In this paper, we propose new discriminant function expanding the CMF. In proposed method, we treat the Difference Subspace, which is difference between two subspaces as difference between two similar categories. The efficiency of the proposed new discriminant function has been demonstrated in similar characters recognition through extensive experiments on hand-written Japanese characters derived from the ETL9B database.

Attacks against encrypted protocols have become increasingly popular and sophisticated. Such attacks are often undetectable by the traditional Intrusion Detection Systems (IDSs). Additionally, the encrypted attack-traffic makes tracing the source of the attack substantially more difficult. In this paper, we address these issues and devise a mechanism to trace back attackers against encrypted protocols. In our efforts to combat attacks against cryptographic protocols, we have integrated a traceback mechanism at the monitoring stubs (MSs), which were introduced in one of our previous works. While we previously focused on strategically placing monitoring stubs to detect attacks against encrypted protocols, in this work we aim at equipping MSs with a traceback feature. In our approach, when a given MS detects an attack, it starts tracing back to the root of the attack. The traceback mechanism relies on monitoring the extracted features at different MSs, i.e., in different points of the target network. At each MS, the monitored features over time provide a pattern which is compared or correlated with the monitored patterns at the neighboring MSs. A high correlation value in the patterns observed by two adjacent MSs indicates that the attack traffic propagated through the network elements covered by these MSs. Based on these correlation values and a prior knowledge of the network topology, the system can then construct a path back to the attacking hosts. The effectiveness of the proposed traceback scheme is verified by simulations.

Users in next generation wireless networks are expected to be highly dynamic while maintaining connectivity through different devices with different processing and communication capabilities. In wireless environments, bandwidth is scarce and channel conditions are time-varying. To guarantee quality of service (QoS) to users roaming between heterogeneous wireless networks, a dynamic QoS negotiation mechanism, which allows users to dynamically negotiate their service-levels with the network, is required. Several protocols for dynamic service level negotiation have been proposed, each focusing on a particular mode. This paper presents an overview of these protocols and discusses their limitations. To alleviate these shortcomings, a dynamic QoS negotiation scheme to allow users to change their service levels in response to changes in both network conditions and their own resource requirements is proposed. In the proposed scheme, upon an intra-domain handoff of a mobile node, the visited access point consults the previously used access point to confirm the legitimacy of the service negotiation request issued by the mobile node. The performance of the proposed scheme has been investigated and compared with other dynamic negotiation approaches. It was demonstrated that the proposed scheme outperforms the state-of-the-art method, in terms of the signaling overhead and data storage, at the expense of a slight increase in the overall negotiation delay.

Flooding-based approaches are incorporated in reactive routing protocols as the fundamental strategy for route discovery. They overtly affect traffic as the frequency of route discovery increases along with the mobility of users in a mobile ad hoc network (MANET). This paper presents a scheme for reducing overall traffic and end-to-end delay in highly MANET networks. Firstly a new routing algorithm is proposed to reduce the frequency of flood requests by elongating the link duration of the selected paths. In order to increase the path duration, non-disjoint paths are also considered. This concept is a novel approach in route discovery as previous reactive routing protocols seek only disjoint paths. Secondly another novel approach is presented to estimate the link expiration time without the need for global positioning system (GPS) devices. To prevent broadcast storms that may be intrigued during the path discovery operation, another scheme is also introduced. The basic concept behind the proposed scheme is to broadcast only specific and well-defined packets, referred to as "best packets" in the paper. The new protocol is simulated with regard to traffic overhead. Although our main aim in this paper is to reduce the net control traffic in a MANET network, there are other benefits arising from the proposed schemes, namely the increase in link duration, reduction in the end-to-end communication delay, less disruption in data flow, and fewer path setups.

Today, with the rapid advance in broadband technology, digital contents delivery applications have been used widely and the streaming technology has made the contents delivery more popular. Nowadays, there is high expectation on Digital Rights Management (DRM). Traitor Tracing is one of the DRM technologies and enables us to observe user's contents streaming and detect illegal contents streaming. However, malicious users can interrupt tracing with illegal processes at user-side computers. To prevent all illegal processes at the user- side, routers should analyze information embedded into packets, which is unrealistic. In this article, we propose a system to detect illegal contents streaming by using only traffic patterns which are constructed from the amount of traffic traversing routers. We also investigate a method to cope with random errors and burst errors which occur frequently in wireless environment and show the satisfactory result which we have obtained in a practical testing environment.

Rapid advances in wireless networking technologies have made it possible to construct a mobile ad hoc network (MANET) which can be applied in infrastructureless situations. However, due to their inherent characteristics, MANETs are vulnerable to various kinds of attacks which aim at disrupting their routing operations. To develop a strong security scheme to protect against these attacks it is necessary to understand the possible form of attacks that may be launched. Recently, researchers have proposed and investigated several possible attacks against MANET. However, there are still unanticipated or sophisticated attacks that have not been well studied. In this paper, we present a collusion attack model against optimized link state routing (OLSR) protocol which is one of the four standard routing protocols for MANETs. After analyzed the attack in detail and demonstrated the feasibility of the attack through simulations, we present a technique to detect the attack by utilizing information of two hops neighbors.

In light of the fast propagation of recent Internet worms, human intervention in securing the Internet during worm outbreaks is of little significance. In order to reduce the damage worms may cause, existing intrusion detection systems (IDSs) need to be adaptive to the security-related requirements of their monitoring networks. This paper presents a Multilevel security based Autonomic Parameter Selector (MAPS) that can be implemented over any existing IDSs. The deployment architecture consists of a number of hierarchically placed local security managers, metropolitan security managers, and a global security manager. These security managers report events to a worm advisory system (WAS). WAS accordingly sets the threat level of the network. Based on this level, MAPS selects the most optimum parameters for the entire IDS to combat against the propagating worm. The MAPS architecture maintains the system performance by constantly evaluating three metrics, namely False Negative Avoidance, False Positive Avoidance, and performance overhead. Extensive experiments, using real network traffic and a recently proposed worm detection system, demonstrate that MAPS is capable of advising an IDS with optimum parameter values to effectively and promptly hinder further propagation of worms.

Due to geographical and/or climatic constraints, the community of future satellite users will exhibit a significant variance in its density over the Globe. This density variance will yield a scenario where some satellite links are congested while others are underutilized. To ensure an intelligent engineering of traffic over satellite networks, this paper proposes a routing protocol that enables neighboring satellites to explicitly exchange information on their congestion status. A "soon-to-be-congested" satellite requests its neighboring satellites to decrease their data forwarding rates. In response, the neighboring satellites search for less congested paths that do not include the satellite in question and communicate a portion of data, primarily destined to the satellite, via the retrieved paths. By so doing, congestion, and the resulting packet drops, can be avoided. A better distribution of traffic among satellites can be guaranteed as well. The proposed scheme is dubbed "Explicit Load Balancing" (ELB) scheme. A set of simulations is conducted to evaluate the performance of the ELB scheme using the Network Simulator. In terms of Quality of Service, encouraging results are obtained: better traffic distribution, higher throughput, and lower packet drops.

This paper describes the architecture of a global sensor network based on a constellation of LEO satellites. The considered sensor network is heterogeneous: two types of sensor nodes are envisioned. One type does the sensing and relays the gathered data to the other type that performs data aggregation and communicates it directly to the satellites. The main challenging tasks in the design of the architecture are explored and adequate solutions are provided. A set of data dissemination techniques is then presented. Following this, a mathematical model is developed to evaluate the energy use of the sensors. Open research issues for the realization of such architecture are finally discussed.

In this paper, we propose the method for reducing out-of-order packets at handover event in LEO satellite networks. In LEO satellite networks, every communicating terminal handovers independently. Therefore, delay between terminals varies drastically within a short period. This drastic delay variation causes out-of-order packets and unnecessary fast retransmission of TCP. To avoid such delay variation, the proposed method makes a satellite to predict and control handover timing of connected user terminals. In the proposed method, two communicating terminals handover in a synchronized manner. By doing this, out-of-order packets at a handover can be reduced and this contributes to avoid occurrence of TCP's false retransmissions.

Sustaining long link durations in highly mobile ad hoc networks presents a great challenge, mostly untreated in recent literature. In this paper we introduce a new routing algorithm based on the relative velocity of mobile nodes, which also incorporates Quality of Service (QoS), termed QoS Multipath Doppler Routing (QaS-MUDOR). The primary aim of QoS-MUBOR is to maintain long link durations, whilst meeting QoS constraints. The routing protocol proposed is based on data retrieval from nodes, where nodes act as content providers. This simulates scenarios such as downloading a file, a web page, or any form of data from other nodes which can provide it. We will show how utilizing the relative velocity of nodes using the Doppler shift subjected to packets assists in selecting stable paths, whilst maintaining the QoS requirements in highly mobile pseudo-linear systems such as an aeronautical ad hoc network.

Internet Worms pose a serious threat to today's Internet. Signature matching is an important approach to detect worms. However, as most signature development processes are manual, they require significant time. They are thus not efficient in reducing the damage worms may cause. In this paper, an efficient signature-based method is proposed for automatic detection of worms over large-scale networks. In the proposed system, detection is performed in a hierarchical manner. Security managers of local networks collect worm-like or suspicious flows and handle these flows to high-hierarchy metropolitan managers. In response, the latter use this information to generate robust signature. The global manager which lies on top of the hierarchy, multicasts the signature to local managers via metropolitan managers. This enables local managers to detect worms that try to penetrate into their networks. The proposed system is evaluated using an off-line real network traffic that contains traces of worms. Experimental results indicate that the proposed system exhibits high detection rates with low false alarm rates.

Over the past few years, a number of private networks have emerged. In these private networks, a server provides its subscribed clients with Internet services, forming a one-to-many network topology. Given the fact that users are located at different distances from the server, usage of the Transmission Control Protocol (TCP) for communication results in drastically unfair bandwidth allocations among the users. In this regard, this paper addresses the fairness and efficiency issues of TCP in such one-to-many IP (Internet Protocol) networks. The efficiency of TCP is controlled by matching the aggregate traffic rate of all TCP connections to the sum of the link capacity and total buffer size. On the other hand, its unfairness issue is mitigated by allocating bandwidth among individual flows in relative proportion with their RTTs. Simulation results elucidate that the proposed method makes better utilization of the network resources, reduces the number of packet drops, and provides a fair service to users.

Low Earth Orbit (LEO) satellite networks are well characterized by frequent and bursty handover occurrences, and these handovers largely affect the cost of mobility management in LEO satellite networks. Although geographical location of a mobile node is useful information to make the mobility management independent from handovers, it is difficult to decide a last-hop satellite of the node based only on geographical location information. This last-hop ambiguity problem needs additional cost to find the real last-hop satellite. To reduce lasthop ambiguity, we propose to exploit orbital information of the satellite connected with a destination node in addition to geographical location information. Simulation results shows that the number of last-hop candidates and hop counts between candidates are reduced by introducing orbital information. Through a mathematical analysis, we evaluate the cost required for mobility management and show the effectiveness of the proposed method.

In the near future, RTP/RTCP-based multimedia streaming will become the norm not only in wired networks but also in mobile environments. Presently when a handoff occurs between heterogeneous networks (with different available bandwidths), a RTP sender cannot stream media at a suitable rate over the new network. Furthermore, RTCP fails to precisely adapt to sudden changes in network resources due to handoff. In order to solve these issues, 1) senders should be aware when mobile nodes are about to perform a handoff; 2) senders should then efficiently probe the available bandwidth in the new network and accordingly adjust their streaming rates. In this paper, we propose a scheme that allows mobile nodes to explicitly notify their handoff timing by using newly-defined RTCP packets. In the proposed scheme, senders probe the available bandwidth in the new network using low-priority RTP dummy packets. The performance of the proposed scheme is evaluated and compared with conventional schemes through extensive simulations. The simulation results show that the proposed scheme achieves appropriate bandwidth utilization immediately after a handoff occurrence and lowers packet losses during the handoff. The proposed scheme exhibits also high TCP-friendliness

There has been an enormous growth in mobile ad hoc networks (MANETs) in land based small to medium size networks with relatively strict power and resources. In this paper the concept of ad hoc networking between aircraft is introduced, which can be considered as a novel approach in increasing the data rate and practicality of future in-flight broadband Internet access. This method also reduces the Internet traffic load on satellite nodes and also propagation delay for real-time traffic transmissions, by effectively bypassing the satellite link for nonreal time data. A dynamic routing algorithm is also proposed for efficient routing in this kind of system. A new cost metric for increasing path duration is introduced to assist routing in the proposed ad hoc network

Internetworking over vehicle ad-hoc networks (VANETs) is getting increasing attention from all major car manufacturers. The design of effective vehicular communications poses a series of technical challenges. Guaranteeing a stable and reliable routing mechanism over VANETs is an important step towards the realization of effective vehicular communications. In current ad-hoc routing protocols, the control messages in reactive protocols and route update timers in proactive protocols are not used to anticipate link breakage. They solely indicate presence or absence of a route to a given node. Consequently, the route maintenance process at both protocol types is initiated only after a link breakage event takes place. This paper argues the use of information on vehicle headings to predict a possible link breakage event prior to its occurrence. Vehicles are grouped according to their velocity vectors. When a vehicle shifts to a different group and a route, involving the vehicle, is to be broken, the proposed protocol searches for a more stable and "more durable" route that includes vehicles from the same group. The proposed scheme is dubbed velocity-heading based routing protocol (VHRP). Whilst the proposed scheme can be implemented on any existing routing protocol, the paper considers the case of VHRP over destination-sequenced distance vector (DSDV) routing protocol. The performance of the scheme is evaluated through computer simulations. Simulation results indicate that knowledge on the vehicles' heading adds major benefits to routing in terms of reducing the number of link breakage events and increasing the end-to-end throughput

Protection of mobile IP networks from denial-of-service (DoS) attacks, a serious security threat in today's Internet, is a one major step toward making this paradigm a reality. The paper proposes a method to detect DoS attacks, issued from mobile users, in the vicinity of flooding sources and in early stages before they cripple the targeted system. The fundamental challenge in attack detection consists in distinguishing between simple flash events and DoS attacks so as not to deprive innocent users from having legitimate accesses. In the proposed mechanism, this distinction is based on the fact that legitimate TCP flows obey the congestion control protocol, whereas misbehaving sources remain unresponsive. Suspicious flows are sent a test feedback and are required to decrease their sending rates. Legitimacy of such flows is decided based on their responsiveness. The scheme performance is evaluated through a set of simulations and encouraging results are obtained: short detection latency and high detection accuracy

While mobile communication systems provide certainly more flexibility to end-users, they present complex mobility management issues. To tackle mobility management issues, the concept of mobility anchor points (MAPs) was introduced and its use was proposed within the framework of the hierarchical mobile IPv6 (HMIPv6) protocol. However, due to traffic dynamics, the protocol performance remains critically affected by the selection of MAPs. This paper proposes a dynamic and efficient mobility management strategy for the selection of the most appropriate MAP with the lightest traffic load. The MAP selection is based on an estimation of MAP load transition using the exponential moving average (EMA) method. The proposed selection scheme is referred to as dynamic and efficient MAP selection (DEMAPS). The scheme performance is evaluated through simulations. Simulation results show that the DEMAPS scheme substantially reduces the number of packet drops, guarantees shorter service delays, makes better utilization of the network resources, avoids redundant transmissions, and maintains a fair and efficient distribution of the network load.

In order to realize the dream of global broadband coverage, the need for satellite communication systems has grown rapidly during the last few years. Several low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary (GEO) satellite constellations have been thus proposed in the recent literature. However, these constellations either require a potential number of satellites or are unable to provide data transmission with high elevation angles. This paper proposes a new satellite constellation composed of quasi geostationary satellites. The main advantage of the constellation is in its ability to provide global coverage with a significantly small number of satellites while, at the same time, maintaining high elevation angles. Since end-terminals can be simultaneously covered by plural satellites in the proposed constellation, a scheme is proposed to select the most appropriate satellite for communication. The selection is based on the geographical location information of end-terminals. The efficiency of the proposed scheme is verified through a set of simulations. Simulation results reveal the good performance of the proposed method in reducing the delay, the delay variation, and ultimately improving the overall quality of service.

Mobile ad hoc networks (MANET) are usually formed without any major infrastructure. As a result, they are relatively vulnerable to malicious network attacks and therefore the security is a more significant issue than in infrastructure-type wireless networks. In these networks, it is difficult to identify malicious hosts, as the topology of the network changes dynamically. A malicious host can easily interrupt a route for which the malicious host is one of the forming nodes in the communication path. In the literature, there are several proposals to detect such malicious host inside the network. In those methods usually a baseline profile is defined in accordance to static training data and then they are used to verify the identity and the topology of the network, thus avoiding any malicious host to be joined in the network. Since the topology of a MANET is dynamically changing, use of a static profile is not efficient. In this paper, we propose a new intrusion detection scheme based on a learning process, so that the training data can be updated at particular time intervals. The simulation results show the effectiveness of the proposed technique compared to conventional schemes

In mobile environments, the fundamental challenge upon a handoff phenomenon consists in an efficient probing of the availability of the new network resources and an appropriate rate adjustment in the new network cell. This paper proposes the usage of low-priority dummy packets to probe the availability of the new network resources. Indeed, when a mobile node enters a cell overlapping area and is about to change its point-of-attachment to the network, two connections are simultaneously set between the mobile node and the sender: one through the old point-of-attachment and another through the new one. The sender transmits actual data through the old connection. Meanwhile, it sends dummy segments through the new connection to verify the bandwidth availability of the new network. The proposed scheme is dubbed dummy segment based bandwidth probing (DSBP). The performance of the DSBP scheme is evaluated and compared with existing schemes through extensive simulations. The results show that DSBP substantially improves the system efficiency, reduces the number of packet drops, and makes better utilization of the network bandwidth.

As originally specified, TCP did not perform well over satellite network systems, systems known with their rapidly time-varying network topologies. This paper addresses some vexing attributes that impair TCP performance in LEO satellite networks. The paper proposes a scheme that allows satellite systems to automatically adapt to the number of active TCP flows, the free buffer size and the bandwidth-delay product of the network. The proposed scheme controls the efficiency of the system by matching the aggregate traffic rate to the sum of the link capacity and total buffer size. This attribute helps to adjust TCP's aggressiveness and to prevent persistent queues from forming. The system min-max fairness is achieved by allocating bandwidth among individual flows in proportion with their RTTs. Simulation results elucidate that the proposed scheme substantially improves the system fairness, reduces the number of packet drops and makes better utilization of the bottleneck link. The results demonstrate also that the proposed scheme works properly in more complicated environments where connections traverse multiple bottlenecks and the available bandwidth may change over data transmission time.

Transmission control protocol (TCP) is the most widely used transport protocol in today's Internet. Despite the fact that several mechanisms have been presented in recent literature to improve TCP, there remain some vexing attributes that impair TCPs performance. This paper addresses the issue of the efficiency and fairness of TCP in multihops satellite constellations. It mainly focuses on the effect of the change in flows count on TCP behavior. In case of a handover occurrence, a TCP sender may be forced to be sharing a new set of satellites with other users resulting in a change of flows count. This paper argues that the TCP rate of each flow should be dynamically adjusted to the available bandwidth when the number of flows that are competing for a single link, changes over time. An explicit and fair scheme is developed. The scheme matches the aggregate window size of all active TCP flows to the network pipe. At the same time, it provides all the active connections with feedbacks proportional to their round-trip time values so that the system converges to optimal efficiency and fairness. Feedbacks are signaled to TCP sources through the receiver's advertised window field in the TCP header of acknowledgments. Senders should accordingly regulate their sending rates. The proposed scheme is referred to as explicit and fair window adjustment (XFWA). Extensive simulation results show that the XFWA scheme substantially improves the system fairness, reduces the number of packet drops, and makes better utilization of the bottleneck link.

Low earth orbit (LEO) satellite networks are capable of providing wireless connectivity to any part of the world while guaranteeing short propagation delays. There is a huge need for developing Internet protocol (IP) friendly networking technologies that aim to integrate emerging LEO satellite networks with the already existing terrestrial IP networks. LEO satellite networks are well characterized by frequent handover occurrences. These handovers largely affect mobility management in LEO satellite networks. Existing IP mobility management protocols, such as mobile IP, manage the location of mobile nodes on the basis of the network topology. Applying such mechanisms in LEO satellite networks will cause a binding update of mobile nodes upon every handover occurrence. Given the frequent occurrence of handovers in LEO satellite networks, a potentially large number of binding update requests will be generated and ultimately affects the scalability of mobility management. This paper argues a handover-independent mobility management scheme for LEO satellite networks. The proposed scheme purposes to exploit geographical location information to make the mobility management independent from handovers. This handover-independent management method reduces the number of update requests and eventually increases the system scalability. A detailed description of the actual implementation of the scheme is given. Through a mathematical analysis, the paper evaluates the required management cost and accordingly verifies the scalability of the proposed scheme.

For many pattern recognition methods, high recognition accuracy is obtained at very high expense of computational cost. In this paper, a new algorithm that reduces the computational cost for calculating discriminant function is proposed. This algorithm consists of two stages which are feature vector. Division and dimensional reduction. The processing of feature division is based on characteristic of covariance matrix. The dimensional reduction in the second stage is done by an approximation of the Mahalanobis distance. Compared with the well-known dimensional reduction method of K-L expansion, experimental results show the proposed algorithm not only reduces the computational cost but also improves the recognition accuracy.

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

Today, high accuracy of character recognition is attainable using a neural network for problems with a relatively small number of categories. But for large categories, like Chinese characters, it is difficult to reach the neural network convergence because of the "local minima problem" and a large number of calculations. Studies are being done to solve the problem by splitting the neural network into some small modules. The effectiveness of the combination of learning vector quantization (LVQ) and back propagation (BP) has been reported. LVQ is used for rough classification and BP is used for fine recognition. It is difficult to obtain high accuracy for rough classification by LVQ itself. To deal with this problem, we propose hierarchical learning vector quantization (HLVQ). HLVQ divides categories in feature space hierarchically in the learning procedure. The adjacent feature spaces overlap each other near the borders. HLVQ possesses both classification speed and accuracy due to the hierarchical architecture and the overlapping technique. In the experiment using ETL9B, the largest database of handwritten characters in Japan, (includes 3036 categories, 607,200 samples), the effectiveness of HLVQ was verified.