Large language models (LLMs) have revolutionized natural language processing, yet their high computational demands pose significant challenges for real-time inference, especially in multi-user server speculative decoding and resource-constrained environments. Speculative decoding has emerged as a promising technique to accelerate LLM inference by using lightweight draft models to generate candidate tokens, which are subsequently verified by a larger, more accurate model. However, ensuring both high goodput (the effective rate of accepted tokens) and fairness across multiple draft servers cooperating with a central verification server remains an open challenge. This paper introduces GOODSPEED, a novel distributed inference framework that optimizes goodput through adaptive speculative decoding. GOODSPEED employs a central verification server that coordinates a set of heterogeneous draft servers, each running a small language model to generate speculative tokens. To manage resource allocation effectively, GOODSPEED incorporates a gradient scheduling algorithm that dynamically assigns token verification tasks, maximizing a logarithmic utility function to ensure proportional fairness across servers. By processing speculative outputs from all draft servers in parallel, the framework enables efficient collaboration between the verification server and distributed draft generators, streamlining both latency and throughput. Through rigorous fluid sample path analysis, we show that GOODSPEED converges to the optimal goodput allocation in steady-state conditions and maintains near-optimal performance with provably bounded error under dynamic workloads. These results demonstrate that GOODSPEED provides a scalable, fair and efficient solution for multi-server speculative decoding in distributed LLM inference systems.

The rapid expansion of the Internet of Things (IoT) has led to unprecedented growth in multivariate time-series (MVTS) data, which are vital for real-world applications such as industrial monitoring, cyber-physical security, and smart city operations. These data streams are susceptible to anomalies that may indicate system malfunctions, security breaches, or environmental hazards. However, existing MVTS anomaly detection (MTAD) approaches, typically trained in centralized settings, struggle in IoT deployments due to data heterogeneity, resource constraints, and privacy concerns. We propose FEDKO, a novel federated learning (FL) framework that couples Reservoir Computing with Koopman operator theory for efficient, privacy-preserving MTAD in distributed IoT networks. At its core, ReKO, a lightweight spatio-temporal Reservoir-Koopman model, lifts nonlinear MVTS dynamics into a linear space for stable prediction and reconstruction. We formulate the FL training as a bi-level optimization procedure where the inner level learns locally stable Koopman dynamics, and the outer level refines lifted feature representations and reconstruction mappings. We further provide theoretical convergence guarantees, anomaly discriminability analysis, and a structural privacy characterization of the framework. Experiments on four IoT MVTS datasets and deployment on an NVIDIA Jetson edge device show that FEDKO achieves a balanced precision–recall profile with competitive F1-scores under heterogeneous federated settings, while substantially reducing communication and memory footprints compared with MTAD baselines.

Robust Federated Learning (RoFL) extends traditional federated learning, not only by enabling multiple clients to collaboratively train a shared model under the coordination of an edge server, but also by incorporating client-side defense mechanisms (e.g., adversarial training) to defend against adversarial attacks while preserving data privacy. However, recent studies have shown that RoFL also remains vulnerable to the challenges posed by non-independent and identically distributed (non-IID) data distributions across heterogeneous clients, which can degrade overall model generalization and robustness. To mitigate this challenge, in this paper, we propose a novel RoFL framework, called RoFLCCA, to address non-IID challenges while defending against adversarial attacks. In particular, we first introduce a local classifier calibration mechanism that utilizes feature-level augmentation to mitigate the effects of non-IID data. By incorporating global class-wise feature statistics, each client can adjust its classifier using synthetic features derived from these shared representations. Second, we propose a calibrated classifier-guided global adversarial alignment strategy, which enforces consistency between augmented and adversarial predictions to improve robustness. Simulation results demonstrate the effectiveness of the proposed RoFLCCA, which consistently outperforms existing robust federated baselines across different datasets and settings. On average, it achieves a 7.07% improvement in clean accuracy and a 4.71% gain in adversarial robustness, highlighting its ability to enhance both generalization and defense against adversarial threats.

The advent of the sixth-generation (6G) wireless networks, enhanced by artificial intelligence, promises ubiquitous connectivity through Low Earth Orbit (LEO) satellites. These satellites are capable of collecting vast amounts of geographically diverse and real-time data, which can be immensely valuable for training intelligent models. However, limited inter-satellite communication and data privacy constraints hinder data collection on a single server for training. Therefore, we propose SemSpaceFL, a novel hierarchical federated learning (HFL) framework for LEO satellite networks, with integrated semantic communication capabilities. Our framework introduces a two-tier aggregation architecture where satellite models are first aggregated at regional gateways before final consolidation at a cloud server, which explicitly accounts for satellite mobility patterns and energy constraints. The key innovation lies in our novel aggregation approach, which dynamically adjusts the contribution of each satellite based on its trajectory and association with different gateways, which ensures stable model convergence despite the highly dynamic nature of LEO constellations. To further enhance communication efficiency, we incorporate semantic encoding-decoding techniques trained through the proposed HFL framework, which enables intelligent data compression while maintaining signal integrity. Our experimental results demonstrate that the proposed aggregation strategy achieves superior performance and faster convergence compared to existing benchmarks, while effectively managing the challenges of satellite mobility and energy limitations in dynamic LEO networks.

The proliferation of data-intensive, low-latency applications has driven the adoption of multi-access edge computing (MEC) to meet the demand for high-performance computing at the network edge. However, ensuring reliable communication under non-line-of-sight (NLoS) conditions remains a significant challenge. While reconfigurable intelligent surfaces (RISs) and the more recent simultaneously transmitting and reflecting RISs (STAR-RISs) offer promising solutions, their passive nature and susceptibility to multiplicative fading limit performance gains. To address these challenges, we propose a novel active STAR-RIS-assisted MEC system that enhances signal strength and adaptability by enabling amplification and joint control over signal transmission and reflection. Our objective is to minimize the energy consumption of user devices, considering both local task computation and uplink task offloading, while maintaining task queue stability. We formulate a joint energy minimization problem with system constraints and long-term queue stability requirements. This problem is decomposed into subproblems: (i) sequential fractional programming is applied to optimize user transmit power, (ii) convex optimization is used to determine partial task offloading ratios, and (iii) a modified Lyapunov optimization combined with double deep Q-networks (DDQN) is proposed to iteratively solve the active STAR-RIS parameters (amplitude and phase shift), amplification control, and task admission at the user side. Numerical results indicate that our proposed system outperforms the conventional passive STAR-RIS-assisted system by 18.64% and the conventional passive RIS-assisted system by 30.43%, respectively.

The use of terahertz (THz) communications with massive multiple input multiple output (MIMO) systems in 6G can potentially provide high data rates and low latency communications. However, accurate channel estimation in THz frequencies presents significant challenges due to factors such as high propagation losses, sensitivity to environmental obstructions, and strong atmospheric absorption. These challenges are particularly pronounced in urban environments, where traditional channel estimation methods often fail to deliver reliable results, particularly in complex non-line-of-sight (NLoS) scenarios. This paper introduces a novel vision-based channel estimation technique that integrates causal reasoning into urban THz communication systems. The proposed method combines computer vision algorithms with variational causal dynamics (VCD) to analyze real-time images of the urban environment, allowing for a deeper understanding of the physical factors that influence THz signal propagation. By capturing the complex, dynamic interactions between physical objects (such as buildings, trees, and vehicles) and the transmitted signals, the model can predict the channel with up to twice the accuracy of conventional methods. This model improves estimation accuracy and demonstrates superior generalization performance. Hence, it can provide reliable predictions even in previously unseen urban environments. The effectiveness of the proposed method is particularly evident in NLoS conditions, where it significantly outperforms traditional methods such as by accounting for indirect signal paths, such as reflections and diffractions. Simulation results confirm that the proposed vision-based approach surpasses conventional artificial intelligence (AI)-based estimation techniques in accuracy and robustness, showing a substantial improvement across various dynamic urban scenarios. This framework provides a promising solution for enabling resilient THz communication, offering scalability and practicality for future 6G deployments in diverse urban landscapes.

This paper proposes an AI framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)-assisted base station (BS)-enabled wireless network. The AI-driven framework aims to achieve optimized power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO BS for serving the users. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the communication signal-to-interference-plus-noise ratio (SINRc) of the received signals and beam-pattern gains to improve the sensing SINR of reflected echo signals, which in turn maximizes the achievable rate of users. A novel AI-driven framework is presented to tackle the formulated NP-hard problem that divides it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the sensing information achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINRcimprovement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively. Therefore, our proposed AI-driven framework guarantees efficient power allocation for holographic beamforming through ISAC schemes leveraging AoS.

The emergence of unmanned aerial vehicles (UAVs) in wireless communication has opened up new prospects for improving network performance and user experience. This study aims to create a smart supply chain that enables collaborative communication, navigation, and caching in multi-UAV wireless networks. To achieve this, a user-UAV clustering strategy using the Whale Optimization Algorithm (WOA) optimizes resource distribution and network management. Additionally, Multi-Agent Deep Deterministic Policy Gradients (MADDPG) jointly optimize UAV trajectory planning, bandwidth allocation, and caching decisions, enabling UAVs to enhance trajectory, allocate resources, and manage cached content efficiently. Experimental results show a 10% reduction in average response time and a 12% decrease in system energy consumption, demonstrating the efficiency of the proposed approach in improving data delivery and extending UAV lifespan for sustainable network operations.

The rapid evolution towards the sixth-generation (6G) networks demands advanced beamforming techniques to address challenges in dynamic, high-mobility scenarios, such as vehicular communications. Vision-based beam prediction utilizing RGB camera images emerges as a promising solution for accurate and responsive beam selection. However, reliance on visual data introduces unique vulnerabilities, particularly susceptibility to adversarial attacks, thus potentially compromising beam accuracy and overall network reliability. In this paper, we conduct the first systematic exploration of adversarial threats specifically targeting vision-based mmWave beam selection systems. Traditional white-box attacks are impractical in this context because ground-truth beam indices are inaccessible and spatial dynamics are complex. To address this, we propose a novel black-box adversarial attack strategy, termed Spatial Proxy Attack (SPA), which leverages spatial correlations between user positions and beam indices to craft effective perturbations without requiring access to model parameters or labels. To counteract these adversarial vulnerabilities, we formulate an optimization framework aimed at simultaneously enhancing beam selection accuracy under clean conditions and robustness against adversarial perturbations. We introduce a hybrid deep learning architecture integrated with a dedicated Feature Refinement Module (FRM), designed to systematically reshaping irrelevant, noisy and adversarially perturbed visual features. Evaluations using standard backbone models such as ResNet-50 and MobileNetV2 demonstrate that our proposed method significantly improves performance, achieving up to an +21.07% gain in Top-K accuracy under clean conditions and up to a +37.32% increase in Top-1 adversarial robustness compared to different baseline models.

Vision-based beam prediction has emerged as a promising alternative to pilot-driven beamforming in millimeter-wave (mmWave) communications, particularly in highly dynamic 6G environments. By leveraging visual sensing modalities, these systems enable low-latency beam selection without relying on extensive pilot transmissions. However, existing approaches are fundamentally limited by the assumption of fixed user distributions and static codebooks, thus rendering them ineffective in scenarios involving continual user arrivals and evolving beam configurations. In this paper, we propose the first continual learning framework for vision-based beam prediction that adaptively incorporates new user types and expanding codebooks without full model retraining. To ensure scalable adaptation, the proposed method integrates task-driven learning with knowledge distillation and Replay Buffer. As new user types emerge, the model incrementally expands its output space to reflect the updated codebook while retaining a compact Buffer of past samples for stability. A dual-objective optimization governs model updates, thereby combining a supervised loss over new data with a temperature-scaled divergence term that aligns current and prior model predictions on Replayed instances. This mitigates catastrophic forgetting and ensures robust performance across tasks. Experimental results demonstrate that the proposed framework effectively maintains high beam prediction accuracy under continual adaptation, while also achieving lower power loss and enhanced normalized received power relative to baseline methods.

Extending wireless connectivity to remote areas is essential for delivering intelligent services in critical sectors, i.e., healthcare, agriculture, and disaster management. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their agile mobility, low deployment cost, and line-of-sight (LoS) communication capabilities. However, efficiently managing UAV resources while integrating sensing, communication, and computing (ISCC) functionalities presents significant challenges. In this paper, we propose a multi-UAV-assisted ISCC framework that simultaneously supports wireless communication links for computational task offloading, remote computing, and active target sensing. A comprehensive system model is developed, and a joint optimization problem is formulated to minimize the weighted sum energy consumption of UAVs and remote users, subject to constraints on latency, power budget, and UAV mobility. To solve the resulting non-convex problem, we design a decomposition-based solution that integrates a convex optimization technique with the deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate the effectiveness of the proposed framework in achieving energy-efficient operation under practical system constraints.

The Open Radio Access Network (O-RAN) paradigm offers a compelling solution to the constraints of traditional RAN by establishing an open framework that enables data-driven optimization at the individual user level, which is essential for the evolution of the next-generation (NextG) cellular networks. Accurate predictions of CPU demand for each user's equipment (UE) in the O-Cloud at the next step will enable more efficient CPU resource optimization. While AI is promising to optimize CPU utilization, it encounters two significant challenges. First, the varying number of UEs leads to shifts in feature dimensions, rendering the model unable to accept these inputs since the input dimension of the AI model remains fixed. Second, the ongoing introduction of new types of UEs over time with distinct CPU demands and dynamic combinations of various active devices adds further variability, thereby complicating predictive accuracy. To address the first challenge, in this research, we propose a novel dynamic number of devices management (DNDM) framework that effectively accommodates a dynamic number of devices in O-RAN, addressing the challenges associated with variable UE demands in future NextG O-RAN. We formulate an optimization problem for the second challenge, enabling the model to learn new demand scenarios while preserving knowledge from previously encountered configurations. To solve the optimization, we propose an exemplar replaybased continual adaptation (CA) framework designed to operate within the near real-time RAN Intelligence Controller (RT-RIC). The CA-DNDM actively prevents catastrophic forgetting and delivers continuous adaptability, seamlessly handling evolving UE types and quantities. Through extensive experimental results, we demonstrate that the proposed CA-DNDM framework effectively handles scenarios with varying UE counts and reliably predicts CPU demand for new situations while preserving the knowledge gained from prior scenarios.

Deep Joint Source-Channel Coding (Deep-JSCC) has emerged as a promising semantic communication approach for wireless image transmission by jointly optimizing source and channel coding using deep learning techniques. However, traditional Deep-JSCC architectures employ fixed encoder-decoder structures, limiting their adaptability to varying device capabilities, real-time performance optimization, power constraints and channel conditions. To address these limitations, we propose DD-JSCC: Dynamic Deep Joint Source-Channel Coding for Semantic Communications, a novel encoder-decoder architecture designed for semantic communication systems. Unlike traditional Deep-JSCC models, DD-JSCC is flexible for dynamically adjusting its layer structures in real-time based on transmitter and receiver capabilities, power constraints, compression ratios, and current channel conditions. This adaptability is achieved through a hierarchical layer activation mechanism combined with implicit regularization via sequential randomized training, effectively reducing combinatorial complexity, preventing overfitting, and ensuring consistent feature representations across varying configurations. Simulation results demonstrate that DDJSCC enhances the performance of image reconstruction in semantic communications, achieving up to 2 dB improvement in Peak Signal-to-Noise Ratio (PSNR) over fixed Deep-JSCC architectures, while reducing training costs by over 40%. The proposed unified framework eliminates the need for multiple specialized models, significantly reducing training complexity and deployment overhead.

Low Earth orbit (LEO) satellites nowadays play a pivotal role in collecting images for the Earth observation. However, the images collected by satellites are possibly tremendous, which causes challenges in dealing with the satellite images. Those challenges include: 1) the unrealistic of transmitting those massive image data to the ground station for centralized analysis because of restricted satellite communication bandwidth and the data privacy issue, and 2) satellite data may be non-independent and identically distributed (non-IID). In this paper, we propose a prototype-guided federated knowledge distillation (Pro-FedKD) approach in an LEO Satellite-high altitude platform (HAP) system, which is designed based on self-knowledge distillation (SKD), federated prototype learning (FedProto) and federated learning (FL). Owing to the adoption of FL, the first challenge can be handled since FL does not require data to leave the local side. To cope with the second challenge, SKD and FedProto are employed. In addition, both model aggregation and prototype aggregation are employed on a pre-defined HAP. To enhance the effectiveness, a top-$N$model aggregation mechanism is proposed, in which among all models,$N$local models that can achieve the top$N$maximum accuracies over the validation dataset of the pre-defined HAP will be selected for aggregation. Experiments demonstrate the error rate gained by the proposed Pro-FedKD method is separately 3.76×, 3.17×, 1.55×, and 1.18× smaller than FedExP, MOON, FedProto, and pFedSD over the EuroSAT dataset, demonstrating a significant reduction. The proposed method also exhibits preeminence in other datasets.

Do diverse perspectives help students learn better? Multi-teacher knowledge distillation, which is a more effective technique than traditional single-teacher methods, supervises the student from different perspectives (i.e., teacher). While effective, multi-teacher, teacher ensemble, or teaching assistant-based approaches are computationally expensive and resource-intensive, as they require training multiple teacher networks. These concerns raise a question: can we supervise the student with diverse perspectives using only a single teacher? We, as the pioneer, demonstrate TeKAP, a novel teacher knowledge augmentation technique that generates multiple synthetic teacher knowledge by perturbing the knowledge of a single pretrained teacher i.e., Teacher Knowledge Augmentation via Perturbation, at both the feature and logit levels. These multiple augmented teachers simulate an ensemble of models together. The student model is trained on both the actual and augmented teacher knowledge, benefiting from the diversity of an ensemble without the need to train multiple teachers. TeKAP significantly reduces training time and computational resources, making it feasible for large-scale applications and easily manageable. Experimental results demonstrate that our proposed method helps existing state-of-the-art knowledge distillation techniques achieve better performance, highlighting its potential as a cost-effective alternative. The source code can be found in the supplementary.

Federated Learning (FL) allows collaborative training while ensuring data privacy across distributed edge devices, making it a popular solution for privacy-sensitive applications. However, FL faces significant challenges due to statistical heterogeneity, particularly domain heterogeneity, which impedes the global mode’s convergence. In this study, we introduce a new framework to address this challenge by improving the generalization ability of the FL global model under domain heterogeneity, using prototype augmentation. Specifically, we introduce FedAPC (Federated Augmented Prototype Contrastive Learning), a prototype-based FL framework designed to enhance feature diversity and model robustness. FedAPC leverages prototypes derived from the mean features of augmented data to capture richer representations. By aligning local features with global prototypes, we enable the model to learn meaningful semantic features while reducing overfitting to any specific domain. Experimental results on the Office-10 and Digits datasets illustrate that our framework outperforms SOTA baselines, demonstrating superior performance.

Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate: 1) the challenge of processing the observed data without transmitting those large-size data to the ground because the connection between the satellites and the ground stations is intermittent, and 2) the challenge of processing the non-independent and identically distributed (non-IID) satellite data. In this paper, to cope with those challenges, we propose an orbit-based spectral clustering-assisted clustered federated self-knowledge distillation (OSC-FSKD) approach for each orbit of an LEO satellite constellation, which retains the advantage of FL that the observed data does not need to be sent to the ground. Specifically, we introduce normalized Laplacian-based spectral clustering (NLSC) into federated learning (FL) to create clustered FL in each round to address the challenge resulting from non-IID data. Particularly, NLSC is adopted to dynamically group clients into several clusters based on cosine similarities calculated by model updates. In addition, self-knowledge distillation is utilized to construct each local client, where the most recent updated local model is used to guide current local model training. Experiments demonstrate that the observation accuracy obtained by the proposed method is separately$1. 01\times, 2.15\times, 1.10\times$, and$1.03\times$higher than that of pFedSD, FedProx, FedAU, and FedALA approaches using the SAT4 dataset. The proposed method also shows superiority when using other datasets.

The proliferation of edge devices has dramatically increased the generation of multivariate time-series (MVTS) data, essential for applications from healthcare to smart cities. Such data streams, however, are vulnerable to anomalies that signal crucial problems like system failures or security incidents. Traditional MVTS anomaly detection methods, encompassing statistical and centralized machine learning approaches, struggle with the heterogeneity, variability, and privacy concerns of large-scale, distributed environments. In response, we introduce FedKO, a novel unsupervised Federated Learning framework that leverages the linear predictive capabilities of Koopman operator theory along with the dynamic adaptability of Reservoir Computing. This enables effective spatiotemporal processing and privacy-preserving for MVTS data. FedKO is formulated as a bi-level optimization problem, utilizing a specific federated algorithm to explore a shared Reservoir-Koopman model across diverse datasets. Such a model is then deployable on edge devices for efficient detection of anomalies in local MVTS streams. Experimental results across various datasets showcase FedKO’s superior performance against state-of-the-art methods in MVTS anomaly detection. Moreover, FedKO reduces up to 8x communication size and 2x memory usage, making it highly suitable for large-scale systems.

Integrated terrestrial-nonterrestrial networks have recently gained much attention because they can bridge the gap between the conventional terrestrial infrastructure and nonterrestrial networks. In addition to seamless connectivity, such networks can offer edge computing services to the users with real-time data processing demand. In this article, an integrated terrestrial-nonterrestrial network with multiaccess edge computing (ITNT-MEC) system is considered in which the aerial users (AUEs) share the resources of terrestrial base stations (TBSs) with their existing terrestrial users (TUEs) and that of low-Earth orbit (LEO) satellites with their neighboring satellites. The goal is to minimize the total energy consumption of AUEs, TUEs, and LEO satellites by jointly optimizing the AUE-TBS/LEO satellite association, AUEs’ trajectories, task allocation, as well as network resource allocation. Due to the dynamic nature of network environment and nonconvex characteristics, it is significantly challenging to solve the formulated optimization problem. Therefore, a block coordinate descent (BCD)-based algorithm that integrates deep reinforcement learning (DRL) methods, such as double deep Q-learning network (DDQN), deep deterministic policy gradient (DDPG), and convex optimization methods, is proposed. Simulation results show that the total energy consumption in the proposed approach is reduced by 14%, 26.9%, 34%, 35.8%, 45.5%, and 55.4%, respectively, when compared to the baselines, such as DDPG-based task offloading (DDPG-TO), DDQN-based task offloading (DDQN-TO), DQN-based task offloading (DQN-TO), equal resource allocation (ERA), random association (RA), and fixed trajectory (FT).

Federated learning (FL) enables privacy-preserving model training across decentralized data. However, significant data heterogeneity, common in domains like the Internet of Things (IoT), hinders generalization. Federated Domain Generalization (FDG) extends the FL paradigm by aiming to train models that generalize effectively to unseen domains, without requiring access to data from those domains during training. Current FDG methods primarily use ResNet backbones pre-trained on ImageNet-1K, limiting adaptability due to architectural constraints and limited pre-training diversity. This reliance has created a gap in leveraging advanced architectures and diverse pre-training datasets to address these challenges. To bridge this gap, we present the first comprehensive investigation into the efficacy of advanced pre-trained architectures such as Vision Transformers, ConvNeXt, and Swin Transformers, in enhancing FDG performance. Unlike ResNet, these architectures capture global context and long-range dependencies, making them well-suited for FDG. Beyond architectural evaluation, we systematically assess the impact of diverse pre-training datasets and compare self-supervised and supervised strategies. Our analysis rigorously investigates the influence of architectural depth, parameter efficiency, and the interplay between diverse model families and dataset characteristics on FDG performance. We find that advanced architectures pre-trained on large datasets significantly outperform ResNet models. Specifically, ConvNeXt architectures outperform all other candidates. We find self-supervised methods using masked image patch reconstruction via discrete token prediction outperform their supervised counterparts. We observe that certain advanced model variants with fewer parameters outperform larger ResNet models. This underscores the need for advanced architectures and scalable pretraining to enable efficient and generalizable FDG.

Combining different data modalities enables deep neural networks to tackle complex tasks more effectively, making multimodal learning increasingly popular. To harness multimodal data closer to end users, it is essential to integrate multimodal learning with privacy-preserving approaches like federated learning (FL). However, compared to conventional unimodal learning, multimodal setting requires dedicated encoders for each modality, resulting in larger and more complex models. Training these models requires significant resources, presenting a substantial challenge for FL clients operating with limited computation and communication resources. To address these challenges, we introduce LW-FedMML, a layer-wise federated multimodal learning (FedMML) approach which decomposes the training process into multiple stages. Each stage focuses on training only a portion of the model, thereby significantly reducing the memory and computational requirements. Moreover, FL clients only need to exchange the trained model portion with the central server, lowering the resulting communication cost. We conduct extensive experiments across various FL and multimodal learning settings to validate the effectiveness of our proposed method. The results demonstrate that LW-FedMML can compete with conventional end-to-end FedMML while significantly reducing the resource burden on FL clients. Specifically, LW-FedMML reduces memory usage by up to$2.7\times $, computational operations (FLOPs) by$2.4\times $, and total communication cost by$2.3\times $. We also explore a progressive training approach called Prog-FedMML. While it offers lesser resource efficiency than LW-FedMML, Prog-FedMML has the potential to surpass the performance of end-to-end FedMML, making it a viable option for scenarios with fewer resource constraints.

Recent state-of-the-art (SOTA) techniques have demonstrated substantial efficacy in 3D Human Pose Estimation (HPE) from videos. Despite strong progress, no prior work has used soft supervision to handle Hard-to-Estimate (H2E) samples in 3D pose estimation, which is inherently a regression task. Existing H2E-example mining solutions, based on logit distillation, progressive target refinement, and label smoothing, are confined to classification problems. Traditional regression-based problems are deprived of the benefits of these regularization techniques. A soft supervision-based H2E example mining technique is crucial for regression problems.To the best of the author’s knowledge, there are no soft supervision-based regularization techniques exist for regression problems. This paper proposes a novel training strategy, referred to as Progressive Soft-Supervision Training for Regression Problems (PSTR). PSTR introduces the concept of progressivesoft targetsto the 3D pose estimator, a regression-based task. Highly inaccurate predictions, representing the H2E poses, are focused more while preserving the representations for Easy-to-Estimate (E2E) poses. PSTR forces the network to learn an alternate optimum inductive bias in the solution spacevia dynamically shifting gradients. The proposed PSTR improves SOTA performances on the large-scale Human3.6m dataset by a large margin with an average MPJPE, and P-MPJPE of 1.2mmand 1.09mmfor Protocol 1 and 2, respectively, where improvements on PCK, AUC, and MPJPE for MPI_INF_3DHP dataset are, 1.53%, 1.75% and 1.40mmfor 2D-3D pose uplifting and 1.60%, 1.95% and 1.60mmfor RGB to 3D pose estimation tasks, respectively. PSTR can be effortlessly deployed on any regression-based task.

Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) provides 360-degree full-space coverage, optimizing both transmission and reflection for improved network performance and dynamic control of the indoor environment. However, deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. In this work, a novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication. To address these, we formulate an optimization problem involving user assignment, access point (AP) beamforming, and STAR-RIS phase control. A decomposition approach is used to solve the complex problem efficiently, employing a many-to-one matching algorithm for user-AP assignment and K-means clustering for resource management. Additionally, multi-agent deep reinforcement learning (MADRL) is leveraged to optimize the control of the STAR-RIS. Within the proposed MADRL framework, a novel approach is introduced in which each decision variable acts as an independent agent, enabling collaborative learning and decision making. The MADRL framework is enhanced by incorporating convex approximation (CA), which accelerates policy learning through suboptimal solutions from successive convex approximation (SCA), leading to faster adaptation and convergence. Simulations demonstrate significant improvements in network utility compared to baseline approaches.

Multi-access edge computing (MEC)-enabled integrated space-air-ground (SAG) networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). Leveraging the ample bandwidth in the terahertz (THz) spectrum, in this paper, we propose MEC-enabled integrated SAG networks with collaboration among unmanned aerial vehicles (UAVs). We then formulate the problem of minimizing the energy consumption of devices and UAVs in the proposed MEC-enabled integrated SAG networks by optimizing tasks offloading decisions, THz sub-bands assignment, transmit power control, and UAVs deployment. The formulated problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, which is challenging to solve. We thus propose a block coordinate descent (BCD) approach to decompose the problem into four sub-problems: 1) device task offloading decision problem, 2) THz sub-band assignment and power control problem, 3) UAV deployment problem, and 4) UAV task offloading decision problem. We then propose to use a matching game, concave-convex procedure (CCP) method, successive convex approximation (SCA), and block successive upper-bound minimization (BSUM) approaches for solving the individual subproblems. Finally, extensive simulations are performed to demonstrate the effectiveness of our proposed algorithm.

In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers’ network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for instance, the NT data per prosumer may be non-independent and identically distributed (non-IID), and the boundary between benign and malicious traffic becomes blurred. To this end, we propose an edge-assisted federated prototype knowledge distillation (E-FPKD) approach, where each client is deployed on a dedicated local edge server (DLES) and can report its availability for joining the federated learning (FL) process. Prior to the E-FPKD approach, to enhance accuracy, the Pearson Correlation Coefficient is adopted for feature selection. Regarding the proposed E-FPKD approach, we integrate the knowledge distillation and prototype aggregation technique into FL to deal with the non-IID challenge. To address the boundary issue, instead of directly calculating the distance between benign and malicious traffic, we consider maximizing the overall detection correctness of all prosumers (ODC), which can mitigate the computational cost compared with the former way. After detection, a rule-based method will be triggered at each DLES for cyber-attack intervention. Experimental analysis demonstrates that the proposed E-FPKD can achieve the largest ODC on NSL-KDD, UNSW-NB15, and IoTID20 datasets in both binary and multi-class classification, compared with baselines. For instance, the ODC for IoTID20 obtained via the proposed method is separately 0.3782% and 4.4471% greater than FedProto and FedAU in multi-class classification.

The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, rain attenuation, long propagation delays, and co-channel interference pose challenges to efficient satellite communication. Therefore, semantic communication (SC) has emerged as a promising solution to improve transmission efficiency and address these issues. In this paper, we explore the potential of SC as a bandwidth-efficient, latency-minimizing strategy specifically suited to 6G satellite communications. The existing SC methods have demonstrated efficacy in direct satellite-terrestrial transmissions; however, they still encounter certain limitations. Specifically, some ground users (GUs) experience poor signal-to-noise ratios (SNR), making direct satellite communication challenging. To address these issues, we propose a novel framework that optimizes gateway hop-relay selection for GUs with low SNR and integrates gateway-based denoising mechanisms to ensure high-quality-of-service (QoS) in satellite-based SC networks. This approach directly mitigates distortion, leading to significant improvements in satellite service performance by delivering customized services tailored to the unique signal conditions of each GU. Our findings represent a critical advancement in reliable and efficient data transmission from the Earth observation satellites, thereby enabling fast and effective responses to urgent events. Simulation results demonstrate that our proposed strategy significantly enhances overall network performance, outperforming conventional methods by offering tailored communication services based on specific GU conditions.

This paper investigates a novel network architecture – the 6G integrated terrestrial-non-terrestrial network (ITNTN) with multi-access edge computing (ITNT-MEC). This system aims to bridge the connectivity gap between terrestrial infrastructure and non-terrestrial networks while offering real-time data processing through edge computing. We consider a scenario where aerial user equipments (AUEs) share resources of terrestrial base stations (TBSs) with terrestrial UEs (TUEs). We formulate an optimization problem to minimize the total energy consumption of both AUEs and TUEs. This problem involves joint optimization of AUE association (i.e., TBS or low Earth orbit (LEO) satellite), AUE trajectories, and TBS bandwidth allocation. Due to the dynamic network environment and non-convex optimization characteristics, solving this problem presents a significant challenge. To address this, we propose a novel algorithm that combines block coordinate descent (BCD) with deep deterministic policy gradient (DDPG) and a convex optimization method. Simulation results demonstrate the significant reductions in total energy consumption compared to baseline approaches, achieving improvements of 23%, 36.6%, and 46.5% against DQN-TO, RA, and FT, respectively.

Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to traditional communication methods. The rationality for adopting SC is its inherent ability to reduce communication costs and make spectrum efficient for 6G non-terrestrial networks (6G-NTNs). We focus on the critical satellite imagery downlink communications latency optimization for Earth observation through SC techniques. We formulate the latency minimization problem with SC quality-of-service (SC-QoS) constraints and address this problem with a meta-heuristic discrete whale optimization algorithm (DWOA) and a one-to-one matching game. The proposed approach for captured image processing and transmission includes the integration of joint semantic and channel encoding to ensure downlink sum-rate optimization and latency minimization. Empirical results from experiments demonstrate the efficiency of the proposed framework for latency optimization while preserving high-quality data transmission when compared to baselines.

Federated learning (FL) makes it possible to advance towards edge intelligence by enabling collaborative and privacy-preserving model training across distributed edge devices. One of the main challenges in FL is non-IID (not Independent and Identically Distributed) nature of data distribution across edge devices, which results in inconsistent update directions of local and global models, thus hindering model convergence. Moreover, recent studies have shown that FL models can significantly degrade performance under adversarial attacks, which further poses challenges for deployment at edge sides. In this work, we attempt to improve the robustness of FL model under adversarial attacks in non-IID settings by sharing knowledge between a central server and edge devices via knowledge distillation. Specifically, we propose a new knowledge distillation-based federated adversarial training (FAT) framework, termed FedAdv (Federated Adversarial), which involves an edge server collecting global prototypes by aggregating local prototypes obtained from participating devices after adversarial training (AT). These global prototypes are subsequently distributed to the edge devices for regularization. This regularization mechanism aims to encourage each device to align its local representation with the corresponding global prototype. By doing so, it helps prevent significant deviations of local model updates from the global model. Experimental results on MNIST and Fashion-MNIST show that our strategy yields comparable or superior performance gains in both natural and robust accuracy compared to several baselines.

The 6G wireless communication networks need an intelligent networking system to meet the ever-increasing de-mands of various applications and mobile devices to ensure power savings, energy efficiency (EE), high integration of devices, and mass connection. To achieve these aims, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-aided cell-free (CF) network is suggested to allocate desired power for beamforming by activating the required number of grids from the serving HMIMOs for serving the users. An optimization problem is developed to ensure effective power allocation that maximizes the EE of the system. A Transformer-based AI framework is proposed to solve the formulated NP-hard problem that distributes desired power for serving the users by activating the required number of grids from the required number of serving HMIMOs in the CF network. Finally, simulation results represent that the proposed power allocation framework outperforms the gated recurrent unit and long short-term memory-based mechanisms, achieving a combined power savings of 12.5% and 4.06%, and a combined EE improvement of 14.68% and 8.93%, correspondingly. Therefore, our suggested AI-based framework guarantees effective power allocation for beamforming to serve the users.

The large communication and computation overhead of federated learning (FL) is one of the main challenges facing its practical deployment over resource-constrained clients and systems. In this work, SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. In SpaFL, a trainable threshold is defined for each filter/neuron to prune its all connected parameters, thereby leading to structured sparsity. To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters, thereby learning how to prune. Further, global thresholds are used to update model parameters by extracting aggregated parameter importance. The generalization bound of SpaFL is also derived, thereby proving key insights on the relation between sparsity and performance. Experimental results show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines. The code is available at https://github.com/news-vt/SpaFL_NeruIPS_2024

Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. However, recent studies have shown that FL is vulnerable to adversarial examples (AEs), leading to a significant drop in its performance. Meanwhile, the non-independent and identically distributed (non-IID) challenge of data distribution between edge devices can further degrade the performance of models. Consequently, both AEs and non-IID pose challenges to deploying robust learning models at the edge. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks, which can be termed as federated adversarial training (FAT). Moreover, we address the non-IID challenge by implementing a simple yet effective logits calibration strategy under the FAT framework, which can enhance the robustness of models when subjected to adversarial attacks. Specifically, we employ a direct strategy to adjust the logits output by assigning higher weights to classes with small samples during training. This approach effectively tackles the class imbalance in the training data, with the goal of mitigating biases between local and global models. Experimental results on three dataset benchmarks, MNIST, Fashion-MNIST, and CIFAR-10 show that our strategy achieves competitive results in natural and robust accuracy compared to several baselines.

The upcoming 6G wireless communication networks are anticipated to offer extensive mobile connectivity, faster data services with reduced power consumption, and seamless integration among different technologies for providing effective beamforming. To accomplish these aims, a transfer learning empowered AI framework is proposed to allocate the power for serving the users under the coverage areas of the corresponding holographic MIMOs (HMIMOs) by activating the required number of grids from the respective HMIMOs. An optimization problem is formulated with the goal of maximizing the utility function for achievable rate, which in turn maximizes the signal-to-interference-plus-noise ratio (SINR), and achievable rate of the users. The HMIMO that serves the highest number of users is considered as the parent HMIMO and the rest of the HMIMOs are regarded as the child HMIMOs. A Transformer-based AI framework is utilized for allocating the power to the users under the coverage areas of the parent HMIMO and transfers the knowledge of the trained model to the child HMIMOs which requires lower learning cost to allocate power to the corresponding users within the coverage areas of the child HMIMOs. Finally, simulation results show that the proposed AI framework empowered by transfer learning surpasses the baseline methods such as gated recurrent unit and long short-term memory, achieving power savings ranging from 28.14% to 38.92% and achievable rate enhancements from 16.58 bps/Hz to 16.84 bps/Hz.

The future cellular networks, known as Next Generation (NextG), are anticipated to employ infrastructure based on cloud computing, with programmable, virtualized, and disaggregated designs. The separation of control functions from physical infrastructure will be implemented, and the utilization of standardized interfaces will facilitate the establishment of tailored closed-control loops. The O-RAN (Open Radio Access Network) paradigm aims to resolve the issue of limited control over the RAN by proposing an open design framework that enables data-driven and intelligent optimization at the individual user level. The growing ubiquity of O-RAN networks has underscored the significance of network traffic categorization in effectively managing O-RAN networks and preserving cybersecurity. This article analyzes the O-RAN alliance’s disaggregated network architecture, focusing on its significant contribution to NextG networks. This paper proposes a novel approach to examine and assess traffic patterns inside the O-RAN architecture. The ongoing acquisition of knowledge regarding encrypted traffic is of utmost importance in light of the perpetual advancements in applications and the advent of encryption technologies. The ability to dynamically adjust in traffic analysis is crucial for effectively addressing the evolving network environment, specifically inside the O-RAN architecture. To address this, we propose a new method named Incremental O-RAN Traffic Categorization (IORTC), specially designed to categorize encrypted data within the O-RAN framework. The IORTC system manages the continuous collection of knowledge from encrypted traffic in the O-RAN framework. It is designed to adapt to the evolution of various encrypted traffic categories while retaining information about earlier encrypted traffic. Based on the experimental results, our method demonstrates a remarkable ability to assimilate new traffic patterns while managing the retention of previously acquired knowledge, and it performed well in all evaluation criteria with an average accuracy of 98%.

Wireless power transfer for charging electric vehicles (EVs) using the inductive charging mechanism enables EVs to recharge their batteries wirelessly while in motion or during halts at traffic signals. Additionally, resonant inductive charging (RIC) can wirelessly transfer energy with high efficiency and over longer distances. To achieve these objectives, we propose a RIC-enabled system for traffic signal scenarios capable of selecting EVs based on their required energy needs for travel and the remaining traffic signal time. We formulate an optimization problem that allows the selected EVs to maximize their battery energy during traffic signal halts, consequently optimizing the defined utility function. Given the dynamic nature of the problem, it falls under the category of NP-hard problems, and to address this, we propose a novel artificial intelligence framework. Our approach utilizes both the halt time and energy demand information, resulting in the development of the Traffic Signal-Aware Electric Vehicle Selection and Resource Allocation algorithm. We employ long short-term memory based deep learning model to predict battery energy needs and generate energy demand score information. The generated demand score, along with the remaining traffic signal time, serves as conditions for the final selection and allocation of charging resources to EVs. Finally, experimental results confirm the effectiveness of our proposed method, demonstrating superior performance compared to the deep neural network model. Furthermore, in terms of battery energy, our approach achieves a 55.1% increase compared to baseline-B and an 8.4% increase compared to baseline-C.

Urban air Mobility (UAM) has been conceived as a new form of transportation. UAM ultimately aims to operate unmanned, so it needs to select its trajectory and periodically send its status to the base station (BS). As an status indicator, the age of information (AoI) signifies the freshness of the information, and it is crucial for applications like real-time control systems. In this article, we address two main challenges: optimizing the UAM’s trajectory and updating the AoI between the UAM and the BS. We formulate an algorithm to maximize the energy efficiency of each UAM’s trajectory and jointly minimize the AoI cycle. As a complicated and non-convex problem, we approach proximal policy optimization (PPO) as our solution in this paper. Experiment results show that our proposed method outperformed the direct trajectory baseline in similar energy efficiency but achieved 46% increased efficiency in average AoI.

The semantic communication system has become one of the promising communication technologies to support high data-intensive artificial intelligence (AI) applications and services such as meta-verse, 3D maps, and so on for achieving low communication overhead. Unlike traditional communication system, accurate channel estimation is a vital issue in semantic wireless communication since a semantic transmitter is required to send the core meaning of a message rather than an entire bit streams for the receiver. Thus, designing a semantic communication framework is challenging due to the dependencies of the semantic encoder and decoder over the orthogonal frequency division multiplexing (OFDM) setting. Therefore, first, this work designs a holistic semantic communication system model that is composed of a semantic encoder, a 3GPP-defined cluster delay line (CDL) wireless channel model, and a semantic decoder for AI services. Second, this paper proposes a semantic communication framework for AI services, 1) a masked autoencoder (MAE)-based channel estimation, and 2) a hierarchical reinforcement learning (HRL)-based pilot allocation method. Third, the proposed semantic communication framework is trained in an end-to-end manner, combined with OFDM layers, considering the image reconstruction and the performance of vision AI applications. Finally, the proposed MAE-based channel estimation and HRL-based pilot allocation RL agent are integrated into the semantic communication framework. Finally, Experimental results show that the proposed semantic framework demonstrates up to a 21.25% performance improvement in image segmentation tasks. Furthermore, the proposed channel estimator and pilot allocator also show higher channel estimation accuracy compared to existing channel estimators and pilot allocation methods.

To increase the quality of the 6G / B5G network, conventional cellular networks based on terrestrial base stations are geographically and economically restricted. Meanwhile, Non-Orthogonal Multiple Access (NOMA) allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, Simultaneously Transmitting and Reflecting RISs (STAR-RISs) can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission, which makes existing terrestrial networks more complicated and is considered a major challenge. Motivated by the above, we study the joint user pairing for NOMA and the beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of mobile users (MUs) by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a mixed-integer non-linear programming (MINLP). To address this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems, namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose Multi-Agent Proximal Policy Optimization (MAPPO), which can make quick decisions in the given environment owing to its low complexity. Finally, simulation results prove that our proposed MAPPO algorithm is superior to Proximal Policy Optimization (PPO) and Advanced Actor-Critic (A2C) by a maximum of 1% and 6%, respectively. Furthermore, the proposed algorithm converges 1.5 times faster than the typical PPO algorithm.

In the rapidly advancing landscape of 6G wireless communication, beamforming plays a crucial role especially with the utilization of millimeter-wave and terahertz frequency bands being pivotal for achieving ultra-high data rates. Despite their promise, these bands present a significant challenge due to the beam training overheads required for precise beamforming, particularly in high-mobility applications like intelligent transportation systems and emerging virtual reality platforms such as the metaverse. While initial deep learning models mitigates the beam training overheads, their performance is compromised due to sensitivity to environmental and lighting conditions, revealing a critical gap in robustness. To address this limitation, this paper proposed a semantic-based method specifically designed to enhance the robustness of beamforming. Utilizing the cutting-edge You Only Look Once version 8 (YOLOv8) algorithm, semantic data from RGB camera images has been extracted to significantly improve the system’s adaptability across a range of environmental conditions. Further, to complement the beam management a novel approach is proposed for the identification of target vehicle by employing K-means clustering in conjunction with the GPS data of the target vehicle. For maintaining the ultra reliable low latency communication (URLLC) a lightweight model has been used to predict the optimal beamforming index. The efficacy of our proposed model is empirically substantiated through rigorous experimental trials in real-world 6G environment, demonstrating significant improvements in average received power ranging from 6.49% to 38.27%, compared to the baselines.

Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution among edge clients can impair the local model performance. The existing single prototype-based strategy represents a class by using the mean of the feature space. However, feature spaces are usually not clustered, and a single prototype may not represent a class well. Motivated by this, this article proposes a multiprototype federated contrastive learning approach (MP-FedCL) which demonstrates the effectiveness of using a multiprototype strategy over a single-prototype under non-IID settings, including both label and feature skewness. Specifically, a multiprototype computation strategy based on k-means is first proposed to capture different embedding representations for each class space, using multiple prototypes$(k$centroids) to represent a class in the embedding space. In each global round, the computed multiple prototypes and their respective model parameters are sent to the edge server for aggregation into a global prototype pool, which is then sent back to all clients to guide their local training. Finally, local training for each client minimizes their own supervised learning tasks and learns from shared prototypes in the global prototype pool through supervised contrastive learning, which encourages them to learn knowledge related to their own class from others and reduces the absorption of unrelated knowledge in each global iteration. Experimental results on MNIST, Digit-5, Office-10, and DomainNet show that our method outperforms multiple baselines, with an average test accuracy improvement of about 4.6% and 10.4% under feature and label non-IID distributions, respectively.

Sixth-generation wireless networks are required to satisfy the ever-increasing demands of diverse applications to guarantee power savings, energy efficiency (EE), and mass connectivity. To accomplish these goals, in this article, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-empowered cell-free (CF) network is proposed while leveraging integrated sensing and communication (ISAC). The proposed AI-based framework allocates the desired power for beamforming by activating the required number of grids from the serving HMIMO base stations (BSs) in the CF network to serve the users. An optimization problem is formulated that maximizes the sensing utility function, which in turn maximizes the signal-to-interference-plus-noise ratio (SINR) of the received signal, the sensing SINR of the reflected echo signal, and EE, ensuring efficient power allocation. To solve the optimization problem, an AI-based framework is proposed to enable a decomposition of the NP-hard problem into two subproblems: 1) a sensing subproblem and 2) a power allocation subproblem. Initially, a variational autoencoder (VAE)-based scheme is utilized to solve the sensing subproblem that identifies the current location of the users with the sensing information. Then, a transformer-based mechanism is devised to allocate the desired power to users by activating the required grids from the serving HMIMO BSs in the CF network based on the sensing information achieved with the VAE-based scheme. Simulation results demonstrate that the proposed AI-based framework outperforms the long short-term memory and gated recurrent unit-based mechanisms, with cumulative power savings of 8.64% and 16.02%, and cumulative EE of 14.49% and 16.61%, accordingly, considering the ground truth values.

Vehicle-to-everything (V2X) communications is pivotal for modern transportation systems, but the challenges arise in scenarios with buildings, leading to signal obstruction and limited coverage. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Researchers are exploring on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360° coverage while utilizing the advantages of RIS technology. In this article, an STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitude and phase shift values of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two subproblems. For the first subproblem, we model the control variables as a Markov Decision Process and propose a combined double deep$Q$-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Numerical results demonstrate that our solution approach outperforms the vanilla DDQN approach by 5.2%, and our proposed system outperforms the conventional RIS by 39%.

In contrast to methods relying on a centralized training, emerging Internet of Things (IoT) applications can employ federated learning (FL) to train a variety of models for performance improvement and improved privacy preservation. FL calls for the distributed training of local models at end-devices, which uses a lot of processing power (i.e., CPU cycles/sec). Most end-devices have computing power limitations, such as IoT temperature sensors. One solution for this problem is split FL. However, split FL has its problems, including a single point of failure, issues with fairness, and a poor convergence rate. We provide a novel framework, called hierarchical split FL (HSFL), to overcome these issues. On grouping, our HSFL framework is built. Partial models are constructed within each group at the devices, with the remaining work done at the edge servers. Each group then performs local aggregation at the edge following the computation of local models. End devices are given access to such an edge aggregated model so they can update their models. For each group, a unique edge aggregated HSFL model is produced by this procedure after a set number of rounds. Shared among edge servers, these edge aggregated HSFL models are then aggregated to produce a global model. Additionally, we propose an optimization problem that takes into account the relative local accuracy (RLA) of devices, transmission latency, transmission energy, and edge servers’ compute latency in order to reduce the cost of HSFL. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem and cannot be solved easily. To tackle this challenge, we perform decomposition of the formulated problem to yield subproblems. These subproblems are edge computing resource allocation problem and joint RLA minimization, wireless resource allocation, task offloading, and transmit power allocation subproblem. Due to the convex nature of edge computing, resource allocation is done so utilizing a convex optimizer, as opposed to a block successive upper-bound minimization (BSUM)-based approach for joint RLA minimization, resource allocation, job offloading, and transmit power allocation. Finally, we present the performance evaluation findings for the proposed HSFL scheme.

A combination of Industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realize Industry 4.0 and beyond. However, scheduling more IIoT devices engaged in FL contributes to accelerated learning speed, but resulting in increased learning cost in terms of energy consumption and model accuracy reduction. In this article, we investigate the tradeoff between learning speed and cost in a three-layer FL-enabled IIoT system. Particularly, a weighted learning utility function is designed by capturing such a tradeoff. We aim to maximize the weighted learning utility in an FL training round by jointly optimizing the edge association as well as the allocations of resource block, computation capacity, and transmit power of the IIoT device. The resulting problem is a nonconvex and mixed-integer optimization problem, and consequently, it is difficult to solve. We thereby decompose the original problem into three subproblems, and then propose an overall alternating optimization algorithm to solve the subproblems iteratively until convergence. Via experimental results, it is demonstrated that the proposed scheme significantly improves the system-wide learning utility as compared to other baseline schemes. It is also shown that the proposed scheme can achieve the optimized tradeoff between learning speed and learning cost.

Satellite systems face a significant challenge in effectively utilizing limited communication resources to meet the demands of ground network traffic, characterized by asymmetrical spatial distribution and time-varying characteristics. Moreover, the coverage range and signal transmission distance of low Earth orbit (LEO) satellites are restricted by notable propagation attenuation, molecular absorption, and space losses in sub-terahertz (THz) frequencies. This paper introduces a novel approach to maximize LEO satellite coverage by leveraging reconfigurable intelligent surface (RIS) within 6G sub-THz networks. Optimization objectives include improving end-to-end (E2E) data rate, optimizing satellite-remote user equipment (RUE) associations, data packet routing within satellite constellations, RIS phase shift, and ground base station (GBS) transmit power (i.e., active beamforming). The formulated joint optimization problem poses significant challenges because of its time-varying environment, non-convex characteristics, and NP-hard complexity. To address these challenges, we propose a block coordinate descent (BCD) algorithm that integrates balanced K-means clustering, multi-agent proximal policy optimization (MAPPO) deep reinforcement learning (DRL), and whale optimization algorithm (WOA) techniques. The performance of the proposed approach is demonstrated through comprehensive simulation results, demonstrating its superiority over existing baseline methods in the literature.

This article introduces a novelattentive drivingframework in intelligent transportation systems (ITS) to investigate the influence of cognitive behavior on distracting driving activities that lead to inattention while driving. Therefore, this work proposes a holistic computational and communication framework that can monitor on-compartment real-time multimodal sensory observation such as physiological, camera, and environmental inputs while capable of distraction detection and emotion recognition for driver's mood stabilization. In particular, this work develops a capsule network for distraction detection, a 1-D convolutional neural network for emotion recognition, an a priori algorithm for sequential context fusion, and a Bayesian network for recommending auditory stimulus content for driver mood stabilization and audio-visual safety messages for road safety. Further, an asynchronous client control scheme has developed to overcome the challenges of multitime scale sensory observations and communicate among the multimodel sensory hubs. Finally, a prototype is developed and tested in a simulation environment. The quantitative analysis results show that the proposed framework can successfully detect around 89% and 87% of distractive activities and the affective state of a driver, respectively. Finally, based on experimental results, the proposed system demonstrates the capability to sustain a driver's attention for approximately 97% of the time, with a confidence level of 95%.

The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively.

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensing-communication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.

With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activities. While traditional machine learning-based intrusion detection systems (ML-IDS) effectively employ supervised learning methods, they possess limitations such as the requirement for labeled data and challenges with high dimensionality. Recent unsupervised ML-IDS approaches such as AutoEncoders and Generative Adversarial Networks (GAN) offer alternative solutions but pose challenges in deployment onto resource-constrained IoT devices and in interpretability. To address these concerns, this paper proposes a novel federated unsupervised anomaly detection framework – FedPCA – that leverages Principal Component Analysis (PCA) and the Alternating Directions Method Multipliers (ADMM) to learn common representations of distributed non-i.i.d. datasets. Building on the FedPCA framework, we propose two algorithms, FedPE in Euclidean space and FedPG on Grassmann manifolds. Our approach enables real-time threat detection and mitigation at the device level, enhancing network resilience while ensuring privacy. Moreover, the proposed algorithms are accompanied by theoretical convergence rates even under a sub-sampling scheme, a novel result. Experimental results on the UNSW-NB15 and TON-IoT datasets show that our proposed methods offer performance in anomaly detection comparable to non-linear baselines, while providing significant improvements in communication and memory efficiency, underscoring their potential for securing IoT networks.

Binary neural networks (BNNs) have been widely adopted to reduce the computational cost and memory storage on edge-computing devices by using one-bit representation for activations and weights. However, as neural networks become wider/deeper to improve accuracy and meet practical requirements, the computational burden remains a significant challenge even on the binary version. To address these issues, this paper proposes a novel method called Minimum Spanning Tree (MST) compression that learns to compress and accelerate BNNs. The proposed architecture leverages an observation from previous works that an output channel in a binary convolution can be computed using another output channel and XNOR operations with weights that differ from the weights of the reused channel. We first construct a fully connected graph with vertices corresponding to output channels, where the distance between two vertices is the number of different values between the weight sets used for these outputs. Then, the MST of the graph with the minimum depth is proposed to reorder output calculations, aiming to reduce computational cost and latency. Moreover, we propose a new learning algorithm to reduce the total MST distance during training. Experimental results on benchmark models demonstrate that our method achieves significant compression ratios with negligible accuracy drops, making it a promising approach for resource-constrained edge-computing devices.

Transferable targeted adversarial attack against deep image classifiers has remained an open issue. Depending on the space to optimize the loss, the existing methods can be divided into two categories: (a) feature space attack and (b) output space attack. The feature space attack outperforms output space one by a large margin but at the cost of requiring the training of layer-wise auxiliary classifiers for each corresponding target class together with the greedy search for the optimal layers. In this work, we revisit the method of output space attack and improve it from two perspectives. First, we identify over-fitting as one major factor that hinders transferability, for which we propose to augment the network input and/or feature layers with noise. Second, we propose a new cross-entropy loss with two ends: one for pushing the sample far from the source class, i.e. ground-truth class, and the other for pulling it close to the target class. We demonstrate that simple techniques are sufficient enough for achieving very competitive performance.

The future sixth-generation (6G) wireless communication networks are expected to provide massive connectivity with lower power requirements for generating the desired beamforming. Therefore, holographic MIMO assisted integrated sensing and communication framework is proposed that ensures lower power requirements to activate the minimum number of grids from the holographic grid array (HGA) for the effective beamforming. An optimization problem is formulated that maximizes the signal to noise-interference ratio (SNIR) of the users which in turn maximizes the utility function for sensing (UFS) considering the beampattern gains, distances, and sensing-communication loss. A novel artificial intelligence (AI) framework is proposed to solve the formulated problem which is a NP-hard problem. First, a variational autoencoder (VAE) based scheme is developed to solve the challenges of determining the exact location of the users and complete data distribution. Then, a sequential neural network-based mechanism is devised to allocate the communication resources to the heterogeneous users for the desired beamforming based on the results obtained from VAE. Finally, simulation results demonstrate that the proposed algorithms confirm 23% power savings compared to long short-term memory (LSTM) method to perform effective beamforming for serving the users.

The significant growth in data consumption among mobile users necessitates the development of new architecture to meet the increasing demand. On the other hand, reconfigurable intelligent surface (RIS) has grown in popularity in 6G due to its improved spectral efficiency, simplicity of deployment, and low cost. However, with the constrained limitation of the coverage by conventional RIS, the research direction has turned towards simultaneously transmitting and reflecting RIS (STAR-RIS) to provide 360° coverage alongside the benefits of RIS. In this paper, a STAR-RIS-assisted downlink communication system for both indoor and outdoor users is investigated. Then, the optimization problem to maximize the spectral efficiency while jointly controlling the beamforming power for each user and phase shift values of the STAR-RIS is formulated. Since the formulated problem is NP-hard and challenging to solve in polynomial time, a policy gradient method for reinforcement learning named proximal policy optimization (PPO) is implemented to solve the problem. To demonstrate the effectiveness of our proposed algorithm, extensive simulation results are executed. Numerical results prove that our proposed algorithm outperforms several benchmark schemes in the literature.

We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device information by a hostile federated aggregator server in space. To address this, we propose using homomorphic-based encryption and decryption security techniques for federated aggregators and IoRTs. We evaluate the secure learning performance of our proposed framework using simulations on advanced datasets and aggregation approach. The results shoiv that compared to the benchmark scheme, the proposed secured computing networks improve communication overhead and latency performance.

Federated learning (FL) allows local clients to train a global model by cooperating with a server while ensuring that their raw data is not revealed. However, most existing works usually choose clients randomly, regardless of their capabilities and contributions to training. Additionally, FL client selection mechanisms concentrate on a significant challenge associated with system or statistical heterogeneity. This paper tries to manage both the system and statistical heterogeneity of distributed clients in the networks. First, to manage the system heterogeneity, an optimization objective is first proposed to maximize the number of clients with similar capabilities such as storage, computational, and communication capabilities. Then, a network framework with a logical layer is proposed to logically group similar clients by checking their capabilities. Finally, to manage the statistical heterogeneity among clients, a novel Contribution-based Dynamic Federated training strategy, called CDFed, is designed to dynamically adjust the probability of clients being chosen based on Shapley values in each global round. Experimental results on two baseline datasets: MNIST and FMNIST, demonstrate that our proposal has a faster convergence rate, about 50%, and a higher average test accuracy, at least 1%, than baselines in most cases.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air–sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge. The design of an SAS-NTN for energy-efficient resource allocation is investigated in this study. The goal is to maximize system energy efficiency (EE) by collaboratively optimizing user equipment (UE) association, power control, and unmanned aerial vehicle (UAV) deployment. Given the limited payloads of UAVs, this work focuses on minimizing the total energy cost of UAVs (trajectory and transmission) while meeting EE requirements. A mixed-integer nonlinear programming problem is proposed, followed by the development of an algorithm to decompose, and solve each problem distributedly. The binary (UE association) and continuous (power, deployment) variables are separated using the Bender decomposition (BD), and then the Dinkelbach algorithm (DA) is used to convert fractional programming into an equivalent solvable form in the subproblem. A standard optimization solver is utilized to deal with the complexity of the master problem for binary variables. The alternating direction method of multipliers (ADMM) algorithm is used to solve the subproblem for the continuous variables. Our proposed algorithm provides a suboptimal solution, and simulation results demonstrate that the algorithm achieves better EE and spectral efficiency (SE) than baselines.

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. Thus, a reliable XAI twin system becomes essential to discretizing the physical behavior of the Internet of Everything (IoE) and identifying the reasons behind that behavior for enabling ZSM. To address the challenges of extensible, modular, and stateless management functions in ZSM, a novel neuro-symbolic XAI twin framework is proposed that to enable trustworthy ZSM for a wireless IoE. The proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. The physical space of the XAI twin executes a neural-network-driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation, such as uplink, downlink, and service provisioning. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multiarm bandit-based learning problem is proposed for reducing the gap between the expected explained score and the current obtained score of the proposed neuro-symbolic XAI twin. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation.

Nowadays, unmanned aerial vehicles (UAVs)-assisted mobile-edge computing (MEC) systems have been exploited as a promising solution for providing computation services to mobile users outside of terrestrial networks. However, it remains challenging for standalone UAVs to meet the computation requirement of numerous users due to their limited computation capacity and battery lives. Therefore, we propose a collaborative scheme among UAVs to share the workload between them. Furthermore, this work is the first to consider the task topology of offloading in MEC-enabled UAVs networks while restricting their power consumption. We study the task topology, in which a task consists of a set of subtasks, and each subtask has dependencies upon other subtasks. In the real world, subtasks with dependencies must wait for their preceding subtasks to complete before being executed, and this affects the offloading strategy. Next, we formulate an optimization problem to minimize the average latency of users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem is NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two subproblems: 1) offloading decision problem and 2) communication resource allocation problem. Then, a metaheuristic method is proposed to find the suboptimal solution to the former problem, while the latter problem is solved by using convex optimization. Finally, we conduct simulation experiments to prove that our proposed offloading technique outperforms several benchmark schemes in minimizing the average latency of users for dependency tasks and achieving higher uplink transmission rates.

The proliferation of novel infotainment services such as Virtual Reality(VR)-based services has fundamentally changed the existing mobile networks. These bandwidth-hungry services expanded at a tremendously rapid pace, thus, generating a burden of data traffic in the mobile networks. To cope with this issue, one can use Multi-access Edge Computing (MEC) to bring the resource to the edge. By doing so, we can release the burden of the core network by taking the communication, computation, and caching resources nearby the end-users (UEs). Nevertheless, due to the vast adoption of VR-enabled devices, MEC resources might be insufficient in peak times or dense settings. To overcome these challenges, we propose a system model where the service provider (SP) might rent Unmanned Area Vehicles (UAVs) from UAV service providers (USPs) to serve as micro-based stations (UBSs) that expand the service area and improve the spectrum efficiency. In which, UAV can pre-cached certain sets of VR-based contents and serve UEs via air-to-ground (A2G) communication. Furthermore, future intelligent devices are capable of 5G and B5G communication interfaces, and thus, they can communicate with UAVs via A2G links. By doing so, we can significantly reduce a considerable amount of data traffic in mobile networks. In order to successfully enable such kinds of services, an attractive incentive mechanism is required. Therefore, we propose a contract theory-based incentive mechanism for UAV-assisted MEC in VR-based infotainment services, in which the MEC offers an amount reward to a UAV for serving as a UBS in a specific location for certain time slots. We then derive an optimal contract-based scheme with individual rationality and incentive compatibility conditions. The numerical findings show that our proposed approach outperforms the Linear Pricing (LP) technique and is close to the optimal solution in terms of social welfare. Additionally, our proposed scheme significantly enhanced the fairness of utility for UAVs in asymmetric information problems.

Unmanned aerial vehicles (UAVs) nowadays are developing rapidly for various applications such as UAV taxis and delivery drones. However, the limited battery energy restricts the flight distance of the UAVs. Thus, urban prosumers equipped with drone recharge stations are introduced to provide charging services for the UAVs. In this article, first, a day-ahead energy scheduling problem for UAV charging-enabled urban prosumers is studied, where the objective is to maximize the overall energy satisfaction of the prosumers with ensuring the Quality of Service (QoS) of the charged UAVs. Specifically, to deal with the considered problem, we decompose it into two stages: 1) the day-ahead energy requirement data prediction stage and 2) energy scheduling stage per prosumer. Thus, second, a joint method based on hierarchical federated learning (HFL) on long short-term memory (LSTM) architecture (HFL-LSTM) and stochastic game-based multi-agent double deep$Q$-learning (MADDQN) with community agent-independent approach is proposed. In particular, the HFL-LSTM approach is leveraged to forecast each prosumer’s energy requirement data without centralized collecting local prosumers’ data such that to protect data privacy. Then, the stochastic game is adopted to analyze the formulated problem, aiming to find the Nash equilibrium (NE) strategy. Afterward, MADDQN with a community agent-independent method is utilized to achieve the best energy scheduling strategy per prosumer. Finally, the experimental results demonstrate the superiority of the proposed joint method that can achieve the lowest mean squared error with the value of 0.0152 and the highest energy satisfaction$(36388)$achieved by the NE policy compared with the benchmarks.

Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for the local training process and allocating communication resources among mobile devices for exchanging learning information must be considered. Furthermore, the convergence performance of different learning services depends on the hyper-learning rate parameter that needs to be precisely decided. Towards this end, we propose a joint resource optimization and hyper-learning rate control problem, namely${{\sf MS-FEDL}}$, regarding the energy consumption of mobile devices and overall learning time. We design a centralized algorithm based on the block coordinate descent method and a decentralized JP-miADMM algorithm for solving the${{\sf MS-FEDL}}$problem. Different from the centralized approach, the decentralized approach requires many iterations to obtain but it allows each learning service to independently manage the local resource and learning process without revealing the learning service information. Our simulation results demonstrate the convergence performance of our proposed algorithms and the superior performance of our proposed algorithms compared to the heuristic strategy.

Mobile Network Operators (MNO) can reduce their Capital and Operational Expenditure (CAPEX) and (OPEX) with the help of tower sharing approach by utilizing the physical infrastructure equipped by a third party tower provider to expand their network coverage. Moreover, Computing Resource Providers (CRP) are also setting up their micro-datacenters at tower stations to provide the Multi-access Edge Computing (MEC) services by cooperating with tower providers. Since both the communication and computing services contribute to the task offloading in MEC, the resource allocation has become a challenging problem. In this paper, we formulate the joint uplink, downlink, and computing resources allocation problem in which the objectives of both MNOs and CRP are to minimize their OPEX. The task offloading is modeled as a network of queues where the end-to-end latency is calculated based on the performance of the queue network. Then, the formulated problem is transformed into a Generalized Nash Equilibrium Problem (GNEP) to capture the conflicting interests in the resource allocation among MNOs and CRP. To solve the formulated GNEP efficiently, two decentralized algorithms are proposed by introducing the penalty parameters to the coupling constraints. In addition, the convergence and performance of the algorithms on different parameters are analyzed.

Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that go beyond existing mechanisms such as federated learning (FL). Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this article. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn, is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithm demonstrates better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.

More than ever, autonomous systems that are critical to everyday life, social infrastructure, and social security are increasingly dependent on sophisticated hardware computer systems. As the computer hardware systems become increasingly complex and semiconductor manufacturing technology becomes continuously miniaturized (especially in several nanometer technologies), there is a corresponding increased vulnerability to system malfunction caused by runtime hardware defects. Detecting defects that will be generated in postdeployment time is impossible in the predeployment verification. This is the reason why some lifetime testabilities are necessary, especially for autonomous systems requiring extremely high-reliable functionalities. This article proposes a hardware architecture that extends the pre-deployment mass production verification (such as the scan-test) to the lifetime of the hardware systems,to guarantee the mass production verification level integrity for the hardware systems in their lifetime. In addition, this article explains how to recover hardware functionalities compromised by runtime defects.

Space-aerial-assisted multi-access edge computing (SA-MEC) has recently been a promising solution to offer the ubiquitous communication and computing services to the resource-constrained internet of things (IoT) devices. Particularly, low earth orbit satellites (LEOSats) and unmanned aerial vehicles (UAVs) having the computing resources onboard assist those devices to compute their generated tasks with the minimum delay under the energy budget. However, due to the existence of inter-cell interference among the devices, they may consume more energy or incur longer delay to offload the tasks to the UAVs. Therefore, the optimal task offloading and resource (channels) allocation should be determined without dissipating much energy and overloading the UAVs. In this work, BCD-based task offloading and resource allocation scheme is proposed to minimize the total task completion latency of the devices by considering the scarce communication resources and energy limitation of devices and UAVs.

In this era of sophisticated technology for smart cities, when communication between smart things is crucial, Internet of Everything (IoE) networks play a key role in merging the cyber and physical worlds. IoE networks are used in a range of applications, including smart agriculture, smart housing, and smart medical services, thanks to the implementation of smart sensor networks (SSN). However, if human administration of the IoE network is impossible due to unforeseen reasons, the installed IoE network's life cycle is critical. The ability to reduce the amount of energy consumed by an IoE network is crucial for extending the network's life cycle. The objective of this work is to tackle the tough task of reducing IoE network energy usage (EU) on a wide scale. This study proposed an energy-efficient IoE network deployment problem for SSN, unmanned aerial vehicles (UAVs), and low earth orbit (LEO) satellites to achieve the aim of energy-efficient utilization of the stored UAVs' energy. For managing the EU of the IoE network, the proposed problem is a mixed-integer linear programming (MILP) optimization problem which is NP-hard in nature. To address this challenge, we use genetic algorithms (GA) to solve the task of minimizing EU while maintaining a low level of complexity. The proposed solution to the EU problem is flexible and effective, and it contributes to the IoE network's goal of a low EU and a long system lifespan.

Accurate channel estimation is an essential factor in determining the efficiency of a wireless communication system. Moreover, in Reconfigurable Intelligent Surfaces(RIS)-Assisted wireless networks using millimeter wave (mmWave), it is crucial to optimize each RIS element's phase shift. Therefore, in this paper, we propose a channel estimation method in TDD-based wireless communication system using auto encoder in RIS-Assisted wireless networks. The trade-off relationship between channel estimation accuracy and the number of pilot signals is optimized when performing channel estimation. Through denoising autoencoder and Average Percentage of Zeros(APoZ), we find the optimal pilot pattern considering not only the number of pilots but also the location. As a result of the experiment, the proposed method has little difference in performance or outperforms the full neural network without pruning.

As connected devices diversified, the attack surfaces and types of network intrusion increased. The conventional intrusion detection methods, such as rule-based methods, cannot detect novel attack types due to their design. For deep learning method research, RNN or LSTM-based anomaly detection exists. However, this method requires high computational power, making it difficult to implement in environments where GPU or TPU cannot be utilized. This paper introduces a 2D anomaly detection method for network intrusion detection. The proposed 2D anomaly detection method requires less computational power than the LSTM or RNN model but performs comparably. Our methods can detect multiple packets at once. Provided methods require less computational power, they can be implemented in an environment with low computational power, i.e. IoT devices. The existing accuracy calculation methods cannot accurately evaluate the proposed methods' multiple packet detection. Therefore, this paper proposes a novel calculation method for multiple anomaly detection. The UNSW-NB15 Dataset was used for training and testing and achieved 99.51%, 97.84%, and 97.88% accuracy on each binary, gray, original method.

Federated learning is a distributed learning system that addresses the distributed difficulty such as communication overhead and private information in machine learning while maintaining high performance. However, the distributed learners have to dedicate their resources to improving the global model, which is not likely to happen voluntarily. This motivated us to design an incentive mechanism for users (data owners) to actively participate in the FL processes. In this paper, we consider multiple co-existing FL service providers (FLSPs) with the need to train their models and multiple data owners (DOs) that can offer that service. In the system, DO, and FLSP will submit their cost and valuation values to the cloud platform. Based on this information, we formulate an optimization problem that aims to maximize the social welfare under the nonnegative utility constraint and maximum gain of FLSPs. Then, we propose a heuristic algorithm, Binary Whale Optimization Algorithm (B-WOA), that can solve our formulated NP-hard problem in polynomial time. Finally, numerical results are shown to demonstrate the effectiveness of our proposed algorithm. Moreover, we also compare the performance of our proposed algorithm with Hungarian and greedy algorithms.

Fifth-generation (5G) networks use millimeter-wave (mmWave) technology to process high-speed and capacity data services. However, wireless communication losses occur due to mmWave limitations, i.e., penetration, rain attenuation, and coverage range. Furthermore, many base stations (BSs) are needed to support stable wireless communications and overcome coverage distances in rural and suburban areas. Therefore, a new wireless communication platform that supports communication services at the aerial level is required. Furthermore, this aerial platform enables line-of-sight (LoS) communications rather than non-LoS (NLoS), which is advantageous in overcoming ground-level losses. Thus, an unmanned aerial vehicle (UAV) or an unmanned aerial platform (UAP) that can be rapidly and dynamically deployed at the point of interest is considered. Despite these benefits, UAV-BSs (also known as aerial BSs) still have optimization problems to solve, i.e., resource allocation and trajectory optimization. Thus, this study considered resource-based multi-agent deep reinforcement learning (MADRL) to solve the resource allocation and trajectory optimization problems of UAV-BSs at the same time. However, our proposed optimization problem is non-convex. Thus we proposed an algorithm based on multi-agent proximal policy optimization (MAPPO) DRL. The proposed algorithm treats each agent as a resource variable to perform optimization more effectively. As a result, the proposed algorithm achieved faster convergence and higher rewards than the baselines.

Prosumers nowadays are capable of consuming and generating renewable energy along with providing charging services for public electric vehicles (EVs) through EV support equipment (EVSE). However, the energy demand of prosumers and EVs as well as the renewable energy generation of prosumers have uncertain nature, which causes difficulty for each prosumer to purchase the proper energy at a lower price in advance. Thus, it is paramount important to do energy procurement prediction (EPP) for each prosumer. Nevertheless, submitting data from each prosumer to a centralized server for EPP will result in communication delay and need to consume a huge amount of network bandwidth and energy. Therefore, in this paper, a clustering-based serverless edge computing-assisted federated learning (FL) approach is proposed for EPP, where the objective is to minimize the Huber loss between the predicted and the real value per prosumer. In particular, firstly, normalized Laplacian-based spectral clustering is leveraged to group the prosumers with a similar energy procurement pattern to solve the problem of biased energy procurement forecast caused by updating the model among all the clients. Secondly, long short-term memory (LSTM) in the federated learning setting is utilized to train the global model of each clustered group, where the model aggregation occurs in the serverless edge computing ability-enhanced local edge server with the best performance. The evaluation results demonstrate the proposed method can achieve the lowest Huber loss compared with the baseline methods.

Reconfigurable Intelligent Surfaces (RISs) have become an emerging paradigm to improve the average sum-rate, enhance energy efficiency and extend coverage areas in wireless communications. In this paper, a multiple RISs-enabled energy-efficient downlink communication system is investigated. Then, to maximize energy efficiency for the proposed system, the joint optimization problem of user-RIS association, reflective elements ON/OFF states, phase shift, and transmit power is formulated. However, as the formulated problem is mixed-integer, non-convex, and NP-hard, it is challenging to solve in polynomial time. To overcome the challenge, by using the Block Coordinate Descent (BCD) method, the formulated problem is decomposed into two sub-problems: 1) joint user-RIS association, reflective elements ON/OFF states, and phase shift problem, and 2) power control problem. Then, the deep reinforcement learning (DRL) algorithm and convex optimization technique are deployed in order to solve the decomposed sub-problems alternatively to find close optimal solutions. Finally, comprehensive simulation results are established to demonstrate the effectiveness of our proposed algorithms.

Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore, this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation (6G) communication networks. In the considered scenario, multiple UAVs must provide on-demand terabits per second (TB/s) services to an urban area along with existing terrestrial networks. However, THz-empowered UAVs pose some new constraints, e.g., dynamic THz-channel conditions for ground users (GUs) association and UAV trajectory optimization to fulfill GU’s throughput demands. Thus, a framework is proposed to address these challenges, where a joint UAVs-GUs association, transmit power, and the trajectory optimization problem is studied. The formulated problem is mixed-integer non-linear programming (MINLP), which is NP-hard to solve. Consequently, an iterative algorithm is proposed to solve three sub-problems iteratively, i.e., UAVs-GUs association, transmit power, and trajectory optimization. Simulation results demonstrate that the proposed algorithm increased the throughput by up to 10%, 68.9%, and 69.1% respectively compared to baseline algorithms.

One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the challenges incorporate when the AI model conceives a lake of interpretation and intuition to the network service provider. Therefore, this paper provides an explainable artificial intelligence (XAI) framework for quality-aware IoE service delivery that enables both intelligence and interpretation. First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user. Second, a regression problem is devised to solve the formulated problem, where explainable coefficients of the contextual matrices are estimated by Shapley value interpretation. Third, the XAI-enabled quality-aware IoE service delivery algorithm is implemented by employing ensemble-based regression models for ensuring the interpretation of contextual relationships among the matrices to reconfigure network parameters. Finally, the experiment results show that the uplink improvement rate becomes 42.43% and 16.32% for the AdaBoost and Extra Trees, respectively, while the downlink improvement rate reaches up to 28.57% and 14.29%. However, the AdaBoost-based approach cannot maintain the CQI of IoE service users. Therefore, the proposed Extra Trees-based regression model shows significant performance gain for mitigating the trade-off between accuracy and interpretability than other baselines.

Terahertz (THz) communication has the promise of enabling ultra-high data speeds in the sixth-generation (6G) wireless networks. Meanwhile, an intelligent reflecting surface (IRS) may influence incident electromagnetic wave propagation by changing the phase shifts with passive reflecting components. It can enhance spectrum efficiency and coverage capability, and minimize blockage vulnerability caused by severe THz wave propagation attenuation and poor diffraction. Recently, unmanned aerial vehicles (UAVs) have provided the services of aerial-based multi-access edge computing (MEC) ubiquitously. Motivated by above facts, this paper considers the IRS-assisted MEC-enabled UAV system for 6G THz communications networks. To that aim, the joint optimization of UAV computation power, IRS phase shift, and THz sub-band allocation are being explored to reduce total network latency. However, the designed problem is mixed-integer non-linear programming (MINLP), which is challenging to solve in polynomial time. Therefore, an iterative algorithm based on the Hungarian algorithm and the Whale-Optimization algorithm (WOA) is proposed to address this problem. The Hungarian algorithm optimizes the sub-band allocation while WOA optimizes the IRS phase shift. Finally, simulation results show that the proposed algorithm can reduce network latency by up to 50% compared to baseline algorithms.

In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and autonomous vehicles (AVs). To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multiagent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. The experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE’s active charging time, and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.

A recent take toward federated analytics (FA), which allows analytical insights of distributed data sets, reuses the federated learning (FL) infrastructure to evaluate the summary of model performances across the training devices. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, which often results in learning models with poor generalization, i.e., an ability to handle new/unseen data, for real-world applications. Moreover, a hierarchical FL structure with distributed computing platforms demonstrates incoherent model performances at different aggregation levels. Therefore, we need to design a robust learning mechanism than the FL that 1) unleashes a viable infrastructure for FA and 2) trains learning models with better generalization capability. In this work, we adopt the novel democratized learning (Dem-AI) principles and designs to meet these objectives. First, we show the hierarchical learning structure of the proposed edge-assisted Dem-AI mechanism, namelyEdge-DemLearn, as a practical framework to empower generalization capability in support of FA. Second, we validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions by leveraging the distributed computing infrastructure. The distributed edge computing servers construct regional models, minimize the communication loads, and ensure distributed data analytic application’s scalability. To that end, we adhere to a near-optimal two-sided many-to-one matching approach to handle the combinatorial constraints in Edge-DemLearn and solve it for fast knowledge acquisition with optimization of resource allocation and associations between multiple servers and devices. Extensive simulation results on real data sets demonstrate the effectiveness of the proposed methods.

Recently, unmanned aerial vehicles (UAVs)-assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates independently, however, it is challenging to meet the computation demands of the mobile users due to the limited computing capacity at the UAV’s MEC server as well as the UAV’s energy constraint. Therefore, collaboration among UAVs is needed. In this article, a collaborative multi-UAV-assisted MEC system integrated with an MEC-enabled terrestrial base station (BS) is proposed. Then, the problem of minimizing the total latency experienced by the mobile users in the proposed system is studied by optimizing the offloading decision as well as the allocation of communication and computing resources while satisfying the energy constraints of both mobile users and UAVs. The proposed problem is shown to be a nonconvex, mixed-integer nonlinear programming (MINLP) problem that is intractable. Therefore, the formulated problem is decomposed into three subproblems: 1) users tasks offloading decision problem; 2) communication resource allocation problem; and 3) UAV-assisted MEC decision problem. Then, the Lagrangian relaxation and alternating direction method of multipliers (ADMMs) methods are applied to solve the decomposed problems, alternatively. Simulation results show that the proposed approach reduces the average latency by up to 40.7% and 4.3% compared to the greedy and exhaustive search methods.

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to run the applications with high computation demand and stringent delay requirement in beyond 5G era. In this work, we study a multi-UAV-assisted two-stage MEC system in which UAVs provide the computing and relaying services to the mobile devices. Due to the limited computing resources, each UAV executes only a portion of the offloaded tasks from its associated MDs in the first stage. Hence, in the second stage, each UAV relays the portions of the tasks to the terrestrial base station (TBS) which has rich computing resources enough to handle all the tasks relayed to it. In this regard, we formulate a joint task offloading, communication and computation resource allocation problem to minimize the energy consumption of MDs and UAVs by considering the limited resources of UAVs and the tolerable latency of the tasks. The formulated problem is a mixed-integer non-convex problem which is NP hard. To solve the formulated optimization problem, we apply the Block Successive Upper-bound Minimization (BSUM) method which guarantees to obtain the stationary points of the non-convex objective function. Finally, the extensive evaluation results are conducted to show the superior performance of our proposed framework.

The proliferation of compute-intensive services in next-generation vehicular networks will impose an unprecedented computation demand to meet stringent latency and resource requirements. Vehicular edge or fog computing has been a widely adopted solution to enhance the computational capacity of vehicular networks; however, the computation requirements of these compute hungry applications will surpass the capabilities of such a solution. To address this challenge, the on-board resources of neighboring mobile vehicles can be utilized. However, such resource utilization requires an incentive mechanism to motivate privately owned neighboring vehicles to participate in sharing their resources. In this paper, we propose a contract theory-based incentive mechanism that maximizes the social welfare of the vehicular networks by motivating neighboring vehicles to participate in sharing their resources. The proposed approach enables the Road Side Units (RSUs) to provide appropriate rewards by offering a tailored contract to each resource sharing vehicle based on their contribution and unique characteristics. Moreover, we derive an optimal contract scheme for computational task offloading, taking into account the individual rationality and incentive-compatible constraints. Finally, we perform numerical evaluations to demonstrate the effectiveness of our proposed scheme. The proposed scheme achieves up to 28% higher computing resource utilization, 17.2% lower energy consumption per computing resource utilization, and 17.1% lesser energy consumption per task completed when compared to the linear pricing incentive baseline.

The number of devices connected to the Internet of Things (IoT) is growing at an enormous rate globally. In the next generation networks, distributed fog computing deployments at the network edge can provide computing resources to the users, especially for latency-sensitive applications. Further, the heterogeneous needs of the fifth generation (5G) networks demand the virtualization of network functions, termed as network function virtualization (NFV). Therefore, an integrated NFV and fog computing resource allocation framework for IoT is of prime importance. Accordingly, in this paper, we model the interactions between the data service operators (DSOs) and the authorized data service subscribers (ADSSs) as an equilibrium problem with equilibrium constraints (EPEC), and utilize the alternating direction method of multipliers (ADMM) as a large-scale optimization tool to obtain solutions. This results in the optimization of resource pricing for the DSOs and the amount of resources to be purchased by the ADSSs. Moreover, we propose a many-to-many matching based model to allocate the fog node (FN) resources according to the VNF resource requirements of the ADSSs. Simulation results show the effectiveness of our proposed approach in achieving efficient resource allocation in NFV enabled IoT fog computing.

Recently, the deployment of electric vehicles supply equipment (EVSE) and its market is expanding rapidly to support the massive penetration of electric vehicles (EVs). However, to accomplish an effective EV charging mechanism for urban prosumer communities, it is imperative to tackle the challenges of distinct energy generation among the communities, dependency of the total purchasable energy price of each EV based on the distance between EV and EVSE, and extreme uncertainty among the energy demand and generation. Therefore, in this paper, the problem of EV charging of urban prosumer communities is studied. In particular, a joint optimization problem is proposed to maximize both the social welfare and EV charging achieved rate of the considered urban prosumer communities. Consequently, the formulated problem is decomposed into 1) truthful double auction problem for determining the unit price and winners by maximizing social welfare, and 2) EV auction losers charging problem for improving EVs charging achieved rate by purchasing energy from the power grid. Then the breakeven-based double auction (BDA) mechanism is proposed to find the unit price and EV winners’ for charging. Sequentially, a multi-agent deep reinforcement learning-based asynchronous advantage actor-critic algorithm with a long short-term memory layer (A3C-LSTM) is adopted to achieve the optimal grid energy buying decision for ensuring the charging of the losers. Finally, the experimental results demonstrate the efficacy of the proposed model that can increase the number of EV charging up to 57.31%, and prosumer communities have gained 86.04% of their income compared to baseline methods.

With the emergence of sixth-generation (6G) mobile communication technologies, intelligent gadgets are expanding. Meanwhile, due to the fast rise of marine operations for trade, research, military, oil drilling, and recreational activities, the number of maritime internet-of-things (MIoT) devices is also expanding. On-demand deployment of multiaccess edge computing (MEC) empowered unmanned aerial vehicles (UAVs) to meet the network coverage demand for MIoT devices at the seaside is presented. The MEC-UAVs are considered as reliable, cost-effective, and efficient for deployment and service provision to MIoT devices. The network profit maximization on MEC-UAV deployment and effective service allocation to MIoT devices is investigated. A combinatorial optimization problem as an integer linear programming (ILP) is formulated, which is NP-hard. To deal with a complex problem, we propose a Bender decomposition (BD) algorithm. The BD decomposes the ILP into the master problem for MEC-UAVs deployment and subproblem for MIoT device association. Finally, numerical results demonstrate that the proposed algorithm provides the polynomial-time computational complexity and achieves a near-optimal solution.

Nowadays, Federated Learning has emerged as the prominent collaborative learning approach among multiple machine learning techniques. This framework enables communication-efficient and privacy-preserving solution that a group of users interacts with a server to collaboratively train a powerful global model without exchanging users' raw data. However, federated learning might face the significant challenge with high communication cost when exchanging the huge model parameters. Moreover, training such a large model on devices is an obstacle under the battery limitation of mobile devices. To address this hindrance, we propose the federated learning with bi-level distillation, namely FedBD. The key idea of this proposal is to exchange the soft targets instead of transferring the model parameters between server and clients. The exchange knowledge was constructed based on the prediction outcomes for the shared reference dataset. By interchanging the knowledge of the learning models, our algorithm obtains the benefits of reducing both communication and computation costs. The proposed mechanism allows the different model architectures between server and learning agents. The experiments show that our proposed method can achieve comparable or even slightly higher accuracy than FedAvg algorithm on the image classification task while using fewer communication resources and power.

The recent flourish of diversified distributed energy resources (DERs) such as generators, consumers, and prosumers brings indispensable cybersecurity challenges for the smart grid controller. Therefore, to assure a resilient smart grid operation, in this paper, we study the problem of continuous-time consensus policy-based DERs control mechanism for the smart grid controller. In particular, we propose an intelligent grid shepherd for the smart grid controller in the power grid framework. That can autonomously detect the abnormal behavior of the received status message from each DER and apply control decisions into the smart grid controller. To do this, first, we propose a continuous-time Markov decision process problem by formulating a resilient control system for the intelligent grid shepherd. Second, we design a data-informed policy-based model-free reinforcement learning framework to find the optimal consensus policy for each DER control decision (i.e., remain connected with the main grid or disconnected). Thus, we devise a distributed energy resources control algorithm for the intelligent grid shepherd. Particularly, we design an advantage actor-critic scheme under the continuous-time domain with the shared neural network mechanism. Finally, experimental results show the efficiency of the proposed intelligent grid shepherd in terms of accuracy and robustness towards a resilient DERs control.

Multi-access edge computing (MEC) is witnessed to be an integral part of emerging augmented reality (AR) / virtual reality (VR) applications. These applications require contents from the cloud, thus suffer from high latency that is not desirable. To address this issue, one can store the frequently requested content at the MEC server. However, MEC servers have limited computing capabilities. Additionally, there are significant variations in a number of requests from the MEC server. Therefore, we propose collaborative edge caching. Our collaborative edge caching will serve the end-users in collaboration to fully exploit the available caching resources at the network edge. We derive optimization scheme based on the alternating direction method of multipliers(ADMM). Finally, we perform numerical evaluations to demonstrate the effectiveness of our proposed scheme.

The network capacity is fastened by utilizing unmanned aerial vehicles (UAVs) and mobile users can get feasible services independent of the infrastructure coverage. Furthermore, with the help of network virtualization technology, mobile network operators (MNOs) can lease their cellular network infrastructures and wireless network resources to the service providers (SPs) who are providing specific services to their mobile users. Wireless resource leasing among SPs, on the other hand, is problematic because each aims to maximize its own profit whilst assuring the QoS requirement of their users. Thus, in this paper, we propose a wireless resource sharing problem in the UAVs-assisted virtualized wireless networks with the goal of maximizing the total profit of SPs whilst guaranteeing the QoS requirement of each mobile user and satisfying the resource constraint of the UAVs. Then, we deploy the Lagrangian relaxation-based solution approach in order to address our proposed problem. Finally, we provide detailed numerical results to show the effectiveness of our proposed algorithm.

Non-terrestrial networks (NTN), encompassing space and air platforms, are a key component of the upcoming sixth-generation (6G) cellular network. Meanwhile, maritime network traffic has grown significantly in recent years due to sea transportation used for national defense, research, recreational activities, domestic and international trade. In this paper, the seamless and reliable demand for communication and computation in maritime wireless networks is investigated. Two types of marine user equipment (UEs), i.e., low-antenna gain and high-antenna gain UEs, are considered. A joint task computation and time allocation problem for weighted sum-rate maximization is formulated as mixed-integer linear programming (MILP). The goal is to design an algorithm that enables the network to efficiently provide backhaul resources to an unmanned aerial vehicle (UAV) and offload HUEs tasks to LEO satellite for blue data (i.e., marine user's data). To solve this MILP, a solution based on the Bender and primal decomposition is proposed. The Bender decomposes MILP into the master problem for binary task decision and subproblem for continuous-time resource allocation. Moreover, primal decomposition deals with a coupling constraint in the subproblem. Finally, numerical results demonstrate that the proposed algorithm provides the maritime UEs coverage demand in polynomial time computational complexity and achieves a near-optimal solution.

Nowadays, there is an ever-increasing interests in federated learning, which allows end devices to collaboratively train a global machine learning model in a decentralized paradigm without sharing individual data. Despite the advantages of low communication cost and preserving data privacy, federated learning is also facing with new challenges to address. Practically, end devices will consider the resources cost and willingness caused by machine learning model training when they are invited to participate a federated learning task. So, how to assign the preferable tasks to the devices with high willingness has to be considered. Besides, the end devices have the property of high mobility, which means the time of devices localizing within the network is limited. Therefore, to reduce the task execution time is necessary. To address these problems, we first analyze and formulate the latency minimization problem for multitask federated learning in a multiaccess edge computing (MEC) network scenario. Then, we model the corresponding problem as a matching game to find the optimal task assignment solutions. Moreover, considering the large-scale Internet-of-Things (IoT) scenario, it is almost impossible for two sides to know the details of every individual of the other side so that the complete preference list (CPL) cannot be built in reality. Therefore, we propose an algorithm for large-scale matching with the incomplete preference list to address the problem. Finally, we conduct the numerical simulation in various cases to demonstrate the effectiveness of our proposed method. The results show that our approach can achieve similar performance with the CPL case.

The Internet of Things (IoT) is gaining ground as a pervasive presence around us by enabling miniaturized “things” with computation and communication capabilities to collect, process, analyze, and interpret information. Consequently, trustworthy data act as fuel for applications that rely on the data generated by these things, for critical decision-making processes, data debugging, risk assessment, forensic analysis, and performance tuning. Currently, secure and reliable data communication in IoT is based on public-key cryptosystems such as the elliptic curve cryptosystem (ECC). Nevertheless, the reliance on the security of de-facto cryptographic primitives is at risk of being broken by the impending quantum computers. Therefore, the transition from classical primitives to quantum-safe primitives is indispensable to ensure the overall security of data en route. In this article, we investigate applications of one of the postquantum signatures called hash-based signature (HBS) schemes for the security of IoT devices in the quantum era. We give a succinct overview of the evolution of HBS schemes with an emphasis on their construction parameters and associated strengths and weaknesses. Then, we outline the striking features of HBS schemes and their significance for IoT security in the quantum era. We also investigate the optimal selection of HBS in the IoT networks with respect to their performance-constrained requirements, resource-constrained nature, and design optimization objectives. In addition to ongoing standardization efforts, we also highlight current and future research and deployment challenges along with possible solutions. Finally, we outline the essential measures and recommendations that must be adopted by the IoT ecosystem while preparing for the quantum world.

Recently, we have witnessed an increasing demand in augmented reality (AR)-based fifth-generation (5G) and beyond applications, such as smart gaming, smart navigation, smart military wearable, and smart industries. These AR-based applications require on-demand computational and caching resources with low latency that can be provided via multiaccess edge computing (MEC) server. However, due to the massive growth of AR-enabled devices, the MEC server resources might be insufficient. To overcome this challenge, we can utilize the computational and caching resources of user equipment (UE) to serve the other UEs in its close vicinity. Successfully enabling such interaction among devices requires an attractive incentive mechanism. Therefore, we propose a contract theory-based incentive mechanism for enabling on-device caching for AR-based applications. In our approach, the MEC offers a reward to the UE for providing its resources (i.e., storage capacity, power, etc.). Furthermore, under the information asymmetry problem, we derive an optimal mechanism via the contract theory for enabling on-device caching subject to the individual rationality and incentive-compatible constraints. Finally, we perform numerical evaluations to validate the effectiveness of our proposed scheme.

In this article, the problem of minimizing energy and time consumption for task computation and transmission in mobile-edge computing-enabled balloon networks is investigated. In the considered network, high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational capabilities to process the computational tasks offloaded from their associated users. Since the data size of each user’s computational task varies over time, the HABs must dynamically adjust their resource allocation schemes to meet the users’ needs. This problem is posed as an optimization problem, whose goal is to minimize the energy and time consumption for task computation and transmission by adjusting the user association, service sequence, and task allocation schemes. To solve this problem, a support vector machine (SVM)-based federated learning (FL) algorithm is proposed to determine the user association proactively. The proposed SVM-based FL method enables HABs to cooperatively build an SVM model that can determine all user associations without any transmissions of either user historical associations or computational tasks to other HABs. Given the predictions of the optimal user association, the service sequence and task allocation of each user can be optimized so as to minimize the weighted sum of the energy and time consumption. Simulations with real-city cellular traffic data show that the proposed algorithm can reduce the weighted sum of the energy and time consumption of all users by up to 15.4% compared to a conventional centralized method.

Ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for the maximum data rates. Owing to the trade-off among latency, reliability and spectral efficiency, sharing of radio resources between eMBB and uRLLC services, heads to a challenging scheduling dilemma. In this paper, we study the co-scheduling problem of eMBB and uRLLC traffic based upon the puncturing technique. Precisely, we formulate an optimization problem aiming to maximize the minimum expected achieved rate (MEAR) of eMBB user equipment (UE) while fulfilling the provisions of the uRLLC traffic. We decompose the original problem into two sub-problems, namely scheduling problem of eMBB UEs and uRLLC UEs while prevailing objective unchanged. Radio resources are scheduled among the eMBB UEs on a time slot basis, whereas it is handled for uRLLC UEs on a mini-slot basis. Moreover, for resolving the scheduling issue of eMBB UEs, we use penalty successive upper bound minimization (PSUM) based algorithm, whereas the optimal transportation model (TM) is adopted for solving the same problem of uRLLC UEs. Furthermore, a heuristic algorithm is also provided to solve the first sub-problem with lower complexity. Finally, the significance of the proposed approach over other baseline approaches is established through numerical analysis in terms of the MEAR and fairness scores of the eMBB UEs.

Unmanned aerial vehicles (UAVs) can provide an effective solution for improving the coverage, capacity, and the overall performance of terrestrial wireless cellular networks. In particular, UAV-assisted cellular networks can meet the stringent performance requirements of the fifth generation new radio (5G NR) applications. In this article, the problem of energy-efficient resource allocation in UAV-assisted cellular networks is studied under the reliability and latency constraints of 5G NR applications. The framework of ruin theory is employed to allow solar-powered UAVs to capture the dynamics of harvested and consumed energies. First, the surplus power of every UAV is modeled, and then it is used to compute the probability of ruin of the UAVs. The probability of ruin denotes the vulnerability of draining out the power of a UAV. Next, the probability of ruin is used for efficient user association with each UAV. Then, power allocation for 5G NR applications is performed to maximize the achievable network rate using the water-filling approach. Simulation results demonstrate that the proposed ruin-based scheme can enhance the flight duration up to 61% and the number of served users in a UAV flight by up to 58%, compared to a baseline SINR-based scheme.

In this paper, a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs) is proposed. The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP) thereby improving coverage particularly for non-line-of-sight (NLoS) areas. However, due to the possibility of highly stochastic blockage over mmW links, designing an intelligent controller to jointly optimize the mmW AP beam and RIS phase shifts is a daunting task. In this regard, first, a parametric risk-sensitive episodic return is proposed to maximize the expected bitrate and mitigate the risk of mmW link blockage. Then, a closed-form approximation of the policy gradient of the risk-sensitive episodic return is analytically derived. Next, the problem of joint beamforming for mmW AP and phase shift control for mmW RISs is modeled as an identical payoff stochastic game within a cooperative multi-agent environment, in which the agents are the mmW AP and the RISs. Two centralized and distributed controllers are proposed to control the policies of the mmW AP and RISs. Todirectlyfind a near optimal solution, the parametric functional-form policies for the controllers are modeled using deep recurrent neural networks (RNNs). The deep RNN-based controllers are then trained based on the derived closed-form gradient of the risk-sensitive episodic return. It is proved that the gradient update algorithm converges to the same locally optimal parameters as the deep RNN-based centralized and distributed controllers. Simulation results show that the error between the policies of the optimal and the RNN-based controllers is less than 1.5%. Moreover, the variance of the achievable rates resulting from the deep RNN-based controllers is 60% less than the variance of the risk-averse baseline.

In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6% and 3.13% more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.

Without steering wheel and driver's seat, the self-driving cars will have new interior outlook and spaces that can be used for enhanced infotainment services. For traveling people, self-driving cars will be new places for engaging in infotainment services. Therefore, self-driving cars should determine themselves the infotainment contents that are likely to entertain their passengers. However, the choice of infotainment contents depends on passengers' features such as age, emotion, and gender. Also, retrieving infotainment contents at data center can hinder infotainment services due to high end-to-end delay. To address these challenges, we propose infotainment caching in self-driving cars, where caching decisions are based on passengers' features obtained using deep learning. First, we proposed deep learning models to predict the contents need to be cached in self-driving cars and close proximity of self-driving cars in multi-access edge computing servers attached to roadside units. Second, we proposed a communication model for retrieving infotainment contents to cache. Third, we proposed a caching model for retrieved contents. Fourth, we proposed a computation model for the cached contents, where cached contents can be served in different formats/qualities based on demands. Finally, we proposed an optimization problem whose goal is to link the proposed models into one optimization problem that minimizes the content downloading delay. To solve the formulated problem, a block successive majorization-minimization technique is applied. The simulation results show that the accuracy of prediction for the contents that need to be cached is 97.82% and our approach can minimize the delay.

Edge Intelligence based on federated learning (FL) can be considered to be a promising paradigm for many emerging, strict latency Internet of Things (IoT) applications. Furthermore, a rapid upsurge in the number of IoT devices is expected in the foreseeable future. Although FL enables privacy-preserving, on-device machine learning, it still exhibits a privacy leakage issue. A malicious aggregation server can infer the sensitive information of other end-devices using their local learning model updates. Furthermore, centralized FL aggregation server might stop working due to security attack or a physical damage. To address the aforementioned issues, we propose a novel concept of socially-aware-clustering-enabled dispersed FL. First, we present a novel framework for socially-aware-clustering-enabled dispersed FL. Second, we formulate a problem for minimizing the loss function of the proposed FL scheme. Third, we decompose the formulated problem into three sub-problems, such as local devices relative accuracy minimization (i.e., end-devices local accuracy maximization) sub-problem, clustering sub-problem, and resource allocation sub-problem, due to the NP-hard nature of the formulated problem. The clustering and resource allocation sub-problems are solved using low complexity schemes based on a matching theory. The end devices' relative accuracy minimization problem is solved by using a convex optimizer. Finally, numerical results are provided for validation of the proposed FL scheme. Furthermore, we show the convergence of the proposed FL scheme for image classification tasks using the MNIST dataset.

Sharding is a promising solution to achieving scalability within the blockchain network. A sharded blockchain network consists of a beacon chain and several committees powered by the participants (i.e., validators) through the Proof-of-Stake (PoS) consensus protocol. Efficient and scalable as it can be, the sharded blockchain based on PoS is vulnerable to discouragement attack. A discouragement attack occurs when malicious validators censor messages to discourage validators from participating in the network. Furthermore, no rate-limiting validator rotation (enter/exit quickly) makes it more challenging to detect such an attack. In this paper, considering the undetermined rotation and the discouragement attack, we render the beacon chain an intermediary, allowing the beacon chain to interact with validators and the cyber-insurer, aiming to encourage the validators' stable rotation through insurance compensation. Specifically, we utilize a two-stage hierarchical game-based model to formulate the complicated interactions under the cyber insurance framework. In the first stage, the beacon chain develops compensatory strategies according to the insurer's profile. In the second stage, the beacon chain designs a series of contracts for validators, including insurance items, compensatory strategies, and rotation requirements. Consequently, the proposed scheme incentivizes validators to remain online by transferring risk to the cyber insurer and enables the sharded blockchain network to weaken the attack's impact through validators' stable rotation. This paper presents closed-form solutions for the proposed model, in which the beacon chain and the cyber insurer can gain maximized profits. The simulations demonstrate the feasibility and superiority of the proposed model.

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this article, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.

The stringent requirements of mobile edge computing (MEC) applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. However, operating such high capacity MEC hosts can significantly increase energy consumption. Thus, a base station (BS) unit can act as a self-powered BS. In this article, an effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. First, a two-stage linear stochastic programming problem is formulated with the goal of minimizing the total energy consumption cost of the system while fulfilling the energy demand. Second, a semi-distributed data-driven solution is proposed by developing a novel multi-agent meta-reinforcement learning (MAMRL) framework to solve the formulated problem. In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent. Sequentially, the meta-agent optimizes (i.e., exploits) the energy dispatch decision by accepting only the observations from each local agent with its own state information. Meanwhile, each BS agent estimates its own energy dispatch policy by applying the learned parameters from meta-agent. Finally, the proposed MAMRL framework is benchmarked by analyzing deterministic, asymmetric, and stochastic environments in terms of non-renewable energy usages, energy cost, and accuracy. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost (with 95.8% prediction accuracy), compared to other baseline methods.

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advantages such as preserving data privacy, FL still has challenges of heterogeneity across UEs' data and physical resources. To address these challenges, we first propose FEDL, a FL algorithm which can handle heterogeneous UE data without further assumptions except strongly convex and smooth loss functions. We provide a convergence rate characterizing the trade-off between local computation rounds of each UE to update its local model and global communication rounds to update the FL global model. We then employ FEDL in wireless networks as a resource allocation optimization problem that captures the trade-off between FEDL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FEDL is non-convex, we exploit this problem's structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights into problem design. Finally, we empirically evaluate the convergence of FEDL with PyTorch experiments, and provide extensive numerical results for the wireless resource allocation sub-problems. Experimental results show that FEDL outperforms the vanilla FedAvg algorithm in terms of convergence rate and test accuracy in various settings.

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.

In this article, the performance of UAV-based mmW links is investigated when UAVs are equipped with square array antennas. The 3GPP antenna propagation patterns are used to model the square array antenna. It is shown that the square array antenna is sensitive to both horizontal and vertical angular vibrations of UAVs. In order to explore the relationship between the vibrations of UAVs and their antenna pattern, the UAV-based mmW channels are characterized by considering the large scale path loss, small scale fading along with antenna patterns as well as the random effect of UAVs' angular vibrations. To enable effective performance analysis, tractable and closed-form statistical channel models are derived for aerial-to-aerial (A2A), ground-to-aerial (G2A), and aerial-to-ground (A2G) channels. The accuracy of analytical models is verified by employing Monte Carlo simulations. Analytical results are then used to study the effect of antenna pattern gain under different conditions for the UAVs' angular vibrations for establishing reliable UAV-assisted mmW links in terms of achieving minimum outage probability. Simulation results show that the performance of UAV-based mmW links with directional antennas is largely dependent on the random fluctuations of hovering UAVs. Moreover, UAVs with higher antenna directivity gains achieve better performance at larger link length. However, for UAVs with lower stability, lower antenna directivity gains result in a more reliable communication link. Finally, based on the geometrical properties of a given region, we investigate the optimal antenna pattern along with the optimal aerial position for UAV relay to attain minimum outage probability.

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can achieve high spectrum and energy efficiency for future wireless networks by integrating a massive number of low-cost and passive reflecting elements. An RIS can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, and, then, reflect this manipulated wave to a desired destination, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under a limited RIS control link and practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Moreover, a new resource allocation algorithm that jointly considers user scheduling and power control is designed, under consideration of the proposed passive beamforming and modulation schemes. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collects local models and constructs the global model. Then, we formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers. In the proposed game, each mobile user submits its bids according to the minimal energy cost that the mobile users experiences in participating in FL. To decide winners in the auction and maximize social welfare, we propose the primal-dual greedy auction mechanism. The proposed mechanism can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency. Finally, numerical results are shown to demonstrate the performance effectiveness of our proposed mechanism.

In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model to a base station (BS) which aggregates the local FL model and broadcasts it back to all of the users. Since FL involves an exchange of a learning model between users and the BS, both computation and communication latencies are determined by the learning accuracy level. Meanwhile, due to the limited energy budget of the wireless users, both local computation energy and transmission energy must be considered during the FL process. This joint learning and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the system under a latency constraint. To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived. Since the iterative algorithm requires an initial feasible solution, we construct the completion time minimization problem and a bisection-based algorithm is proposed to obtain the optimal solution, which is a feasible solution to the original energy minimization problem. Numerical results show that the proposed algorithms can reduce up to 59.5% energy consumption compared to the conventional FL method.

Task offloading scheme provides opportunistic energy saving for computation-intensive on-vehicle applications. The evolution of the Vehicular Edge Computing (VEC) paradigm has contributed a vast potential that can enhance the performance of such vehicles with energy-hungry and delay-sensitive services. However, determining how much workload to compute locally or offload to the VEC server is still quite challenging. Moreover, when all the vehicles try to offload their computation tasks to the same VEC server, it leads to deterioration in the performance gain due to overburden. Recently, unmanned aerial vehicle (UAV) as the edge server has gained huge attraction due to its well maneuverability and cost efficiency. In this paper, we study the energy-efficient offloading as well as association of the vehicles between the road side unit (RSU) and UAV. First, we formulate the joint offloading and association problem. Next, we decompose the formulated mixed integer linear (MIL) problem into two subproblems and then solve them by using standard convex optimization. Finally, we compare our proposed algorithm with benchmark schemes and the numerical results demonstrate that our algorithm outperforms the benchmark solutions.

Recent years have shown a remarkable interest in federated learning from researchers to make several Internet of Things applications smart. Although, federated learning offers users' privacy preservation, it has communication resources optimization challenge. In this paper, we consider federated learning for cellular networks. We formulate an optimization problem to jointly minimizes latency and effect of loss in federated learning model accuracy due to channel uncertainties. We decompose the main optimization problem into two sub-problems: resource allocation and device association sub-problems, due to the NP-hard nature of the main optimization problem. To solve these sub-problems, we propose an iterative approach which further uses efficient heuristic algorithms for resource blocks allocation and device association. Finally, we provide numerical results for the validation of our proposed scheme.

Federated learning is a distributed learning method that trains a deep network on user devices without collecting data from central server. It is useful when the central server can't collect data. However, the absence of data on central server means that deep network compression using data is not possible. Deep network compression is very important because it enables inference even on device with low capacity. In this paper, we proposed a new quantization method that significantly reduces FPROPS(floating-point operations per second) in deep networks without leaking user data in federated learning. Quantization parameters are trained by general learning loss, and updated simultaneously with weight. We call this method as OQFL(Optimized Quantization in Federated Learning). OQFL is a method of learning deep networks and quantization while maintaining security in a distributed network environment including edge computing. We introduce the OQFL method and simulate it in various Convolutional deep neural networks. We shows that OQFL is possible in most representative convolutional deep neural network. Surprisingly, OQFL(4bits) can preserve the accuracy of conventional federated learning(32bits) in test dataset.

Many mission critical devices are increasing with upcoming 5G network to fulfill a low latency for a real time network service on smart factory, autonomous vehicle, etc. Distributed cloud computing system also has a key role to execute the various mobile devices, because, an edge computing is the nearest from the mobile devices to provide low latency and computation energy consumption. In this paper, we consider the autonomous vehicles with video live streaming services. Especially, the vehicles require a low transmission delay as within 10 ms. To reduce a latency with low energy consumption, we propose a service chaining offloading decision with a deep reinforcement learning. We split tasks of the vehicle per service function blocks which have their own role. So it can do partial offloading and user association in a On-Device Edge of the vehicle and in the SBS at the same time. We can get results that service chaining offloading decision gives more optimal energy consumption with low-latency to autonomous vehicle users.

Traffic classification (TC) has a principal function in autonomous network management. Recently, deep learning and machine learning-based TC have become popular than the traditional port-based and protocol-based TC due to practices such as port disguise and payload encryption. The flow-based TC is reliable as it relies on time-related statistical features. Federated learning is a distributed machine learning technique to train improvised deep/machine learning models with less privacy distress. The organizations or enterprises having similar business models may take participation in building a federated model for their network traffic characterization. In this study, we build a cross-silo horizontal federated model for TC using flow-based time-related features. The federated model shows comparable performance to the centralized model.

A new communications infrastructure is needed for users to experience the contents of 5G-based VR/AR in High-Speed Train (HST). Therefore, it is proposed that the Unmanned Aerial Vehicle (UAV) can be used as a communication equipment on behalf of the general Rail-side Units (RSUs) supporting the communication of the HST. To maintain reliable communications, initial deployment and trajectory considered altitude and direction of UAV are determined. Also, limited energy in UAV is an important constraint on trajectory optimization. Thus, this paper proposes initial deployment and trajectory optimization techniques for stable communication between HST and Multi-UAV with the energy constraints of UAV. This paper uses Soft Actor-Critic (SAC), one of the methods of reinforcement learning, as a way to optimize the UAV trajectory. It also uses the Support Vector Machine to carry out optimal initial deployment based on data on the maximum UAV communication distance according to the speed of HST and the energy of UAV, which is the result of trajectory optimization. As a result, this study quickly and accurately derives the optimal trajectory of Multi-Uav according to the speed of HST and the energy of UAV and also maintain stable communication by optimal initial deployment.

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

As the basis of the most existing blockchain networks, Proof of Work (PoW) consensus protocol highly relies on the computational resources, and thus causing a huge waste of energy. Proof of Stake (PoS) is the alternative to relieve the PoW dilemma. However, it is also under threat, i.e., discouragement attack, which is a way to bring down the blockchain networks without any effective defense against it. To prevent the discouragement attack, the founders of Ethereum argue that the system should set a withdraw delay instead of allowing the validators entry/exit quickly. But how to determine the delay is still an open question. In this paper, we adopt the cyber insurance idea and propose the insurance contract to help determine the withdraw delay, as well as the insurance claim to relieve the loss of victims. Specifically, instead of requiring the insurance premium from the validators, the cyber insurer first signs the contract with the blockchain representative (e.g., beacon chain). Then the blockchain representative would sign a series of contracts with the validators. By such design, the validators can obtain the insurance claim without paying the premium, while the blockchain networks can keep the validators staying online to resist the discouragement attack. Finally, through the simulations, we demonstrate that the proposed model is capable of providing adaptive insurance contracts for the different validators and keeping the profits of the blockchain network and the cyber insurer.

The tower sharing approach has been widely used by Mobile Network Operators (MNOs) to save their Capital Expenditure (CAPEX) by sharing the physical infrastructure hosted by a third party tower provider. In addition, multiple Computing Resource Providers (CRP) are deploying their servers at towers by cooperating with tower providers to grant low latency, real time services to users. Thus, the resource allocation has become a challenging issue where users of different MNOs need to share the computing resources provided by CRPs. In this paper, the joint allocation of uplink, downlink and computing resources is considered to minimize the end to end latency of users where the offloading process is modeled as a network of queues. Since the resource allocation of MNOs and the CRP are coupled with each other, we formulate it as a Generalized Nash Equilibrium Problem (GNEP). We propose a penalty based algorithm to solve the formulated GNEP with an effective initialization approach to improve the performance of the algorithm. Then, we perform the simulation to analyze the performance of the algorithm.

Vehicular Mobile Edge Computing is a promising technology to leverage the bottleneck at a base station (BS) at peak hours. However, to deploy Vehicular Mobile Edge Computing requires to deal with the challenges in how to incentive vehicles to resource sharing and how to assign tasks and computation resource to minimize the total network delay. In this paper, we develop a two-stage incentive mechanism and task assignment and resource allocation scheme by combining auction game, matching theory, and convex optimization method. In the first stage, we present the incentive problem between the BS and nearby vehicles, which leverages a reserve auction. Then we study the network delay minimization problem. The problem is decoupled into two subproblems for determining task assignment and computing resource allocation, respectively. Finally, numerical results show the effectiveness and efficiency of our scheme.

Federated learning is an prominent machine learning technique that model is trained distributively by using local data of mobile users, which can preserve the privacy of users and still guarantee high learning performance. In this paper, we deal with the problem of incentive mechanism design for motivating users to participate in training. In this paper, we employ the randomized auction framework for incentive mechanism design in which the base station is a seller and mobile users are buyers. Concerning the energy cost incurred due to join the training, the users need to decide how many uplink subchannels, transmission power and CPU cycle frequency and then claim them in submitted bids to the base station. After receiving the submitted bids, the base station needs algorithms to select winners and determine the corresponding rewards so that the social cost is minimized. The proposed mechanism can guarantee three economic properties, i.e., truthfulness, individual rationality and efficiency. Finally, numerical results are provided to demonstrate the effectiveness, and efficiency of our scheme.

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Recently, a rapid growth in the number of vehicles on the road has led to an unexpected surge of parking demand. Consequently, finding a parking space has become increasingly difficult and expensive. One of the viable approaches is to utilize both public and private parking lots (PLs) to effectively share the parking spaces. However, when the parking demands are not balanced among PLs, a local congestion problem occurs where some PLs are overloaded, and others are underutilized. Therefore, in this article, we formulate the parking assignment problem with two objectives: 1) minimizing parking expenses and 2) balancing parking demand among multiple PLs. First, we derive a matching solution for minimizing parking expenses. Then, we extend our study by considering both parking expenses and balancing parking demand, formulating this as a mixed-integer linear programming problem. We solve that problem by using an alternating direction method of multipliers (ADMM)-based algorithm that can enable a distributed implementation. Finally, the simulation results show that the matching game approach outperforms the greedy approach by 8.5% in terms of parking utilization, whereas the ADMM-based algorithm produces performance gains up to 27.5% compared with the centralized matching game approach. Furthermore, the ADMM-based proposed algorithm can obtain a near-optimal solution with a fast convergence that does not exceed eight iterations for the network size with 1000 vehicles. Note to Practitioners-The efficiency of the parking assignment is critical to the parking management systems in order to provide the best parking guides. This article investigates the cost minimization problem for parking assignment while balancing parking demand among multiple parking lots (PLs). Previous parking assignment approaches do not jointly investigate the cost of parking and the cost of PL utilization. Therefore, they can fail to the local congestion problem caused by a large number of vehicles driving toward the same PL. In this article, a new method that considers both of minimizing parking expenses and balancing parking demand is proposed. It is obtained by using the alternating direction method of multipliers (ADMM)-based proposal that distributively solves a constrained optimization problem. Based on the experimental results, the ADMM-based algorithm outperforms the matching-based algorithm and the greedy algorithm in terms of the balancing parking demand and reducing parking expenses. The proposed method can be readily implemented in real-world industrial PLs. In the future work where parking assignments for electric vehicles are needed, our proposed mechanism can then be extended to solve the balanced electricity overload multiple charging stations.

The concept of Multi-access Edge Computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to the resourceful cloud, MEC server has limited resources. When each MEC server operates independently, it cannot handle all computational and big data demands stemming from users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction of data exchange between users devices and remote cloud. Therefore, joint Computing, Caching, Communication, and Control (4C) at the edge with MEC server collaboration is needed. To address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency. However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed. To solve the proximal upper bound problem, the block successive upper bound minimization method is applied. Simulation results show that the proposed approach satisfies computation deadlines and minimizes bandwidth consumption and network latency.

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges in terms of adaptation to diverse network appliances and reduction of the capital and operational expenses of the service providers. In particular, to deploy service chains, providers must consider different objectives, such as minimizing the network latency or the operational cost, which are coupled objectives that have traditionally been addressed separately. In this paper, the problem of virtual network function (vNF) placement for service chains is studied for the purpose of energy and traffic-aware cost minimization. This problem is formulated as an optimization problem named the joint operational and network traffic cost (OPNET) problem. First, a sampling-based Markov approximation (MA) approach is proposed to solve the combinatorial NP-hard problem, OPNET. Even though the MA approach can yield a near-optimal solution, it requires a long convergence time that can hinder its practical deployment. To overcome this issue, a novel approach that combines the MA with matching theory, named as SAMA, is proposed to find an efficient solution for the original problem OPNET. Simulation results show that the proposed framework can reduce the total incurred cost by up to 19 percent compared to the existing non-coordinated approach.

Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22% gain in the offered reward.

Long-Term Evolution in the unlicensed spectrum (LTE-U) is considered as an indispensable technique to mitigate the spectrum scarcity in wireless networks. Typical LTE transmissions are contention-free and centrally controlled by the base station (BS); however, the wireless networks that work in unlicensed bands use contention-based protocols for channel access, which raises the need to derive an efficient and fair coexistence mechanism among different radio access networks. In this work, we propose a novel neural networks (NNs) based mechanism for the coexistence of an LTE-U base station (BS) in the unlicensed spectrum alongside with a WiFi access point (WAP). Specifically, we model the coexistence problem as a Hopfield Neural Network (HNN) based optimization problem that aims a fair coexistence considering both the LTE-U data rate and the QoS requirements of the WiFi network. Using the energy function of HNN, precise investigation of its minimization property can directly provide the solution of the optimization problem. Numerical results show that the proposed mechanism allows the LTE-U BS to work efficiently in the unlicensed spectrum while protecting the WiFi network.

In this paper, we study the minimum input energy for an unmanned aerial vehicle (UAV) while maneuvering in a smart factory. The deployment of UAV in a factory environment for wireless communication between sensors to the central controller promises to provide these services of efficient data collection. A UAV has to traverse the path for data collection from sensors and monitors the production line in a factory. The trajectory optimization of UAV is an important parameter for energy efficiency. We consider the UAV flight on the horizontal plane with constant altitude. In our objective, we aim to minimize the UAV traversal path for predefined coordinates with time-bound. To this end, the theoretical model is derived in the form of a discrete-time linear dynamical system (DTLS). To obtain the energy-efficient path of UAV flight, we optimized the input control vector of the derived system. Simulation results showed that the proposed technique of UAV deployment has significant performance.

With recent developments in the blockchain technology, the energy industry is devoting an overwhelming interest towards it. Moreover, the future society will move towards a zero-energy internet to share energy and minimize the energy usage that contributes in producing greenhouse gas consumption. As a result, energy sharing system is undergoing a paradigm shift from centralized energy supply systems to ICT-based distributed energy supply systems. In this regards, a major challenge for the energy market case study is the presence of the energy double spending problem in Micro-Grid (MG), which threatens the security of the trading infrastructure. In this paper, we propose a mechanism that the problem of double energy spending is solved by applying off-chain Hybrid Blockchain (HB) technology to provide a secure environment where energy can be safely shared between prosumer and consumer at a closer distance, without intermediary intervention.

Unmanned Aerial Vehicle (UAV) is mobile and has the advantage of being equipped with cameras, sensors, computing resources and devices for communication. By combining the advantage of communication technology and UAV, rapid response can be made at disaster area and used as a mobile base station in place where traffic demand is high, such as concert venue and sports stadium. In addition, by using data that have been collected by mounted sensors and cameras, the UAV can provide data-based application. However, these services usually use big data processing and machine learning techniques which require high computing power and the UAV is not sufficiently capable of process those applications due to lack of computing resources and battery limitation. To overcome this problem, the UAV can offload task to near Mobile Edge Server that can provide computing resources. Mobile Edge Server can be cellular base station, Wi-Fi access point and so on. When tasks occur in a specific area, one or more UAV need to move the location to acquire data and process. If data processing is too heavy to process at local, the UAV can cooperate mobile edge server. In this situation, we can find out two problems. (i)When the tasks occur, which UAV can process occurred task (ii) When UAV is assigned a task, then a mobile edge server can cooperate with UAV. In this paper, based on Hungarian algorithm which is one of matching algorithm, we propose optimal task-UAV-edge server matching algorithm which minimizes energy consumption and processing time.

Federated learning (FL) is a decentralized learning method that deviated from the conventional centralized learning. The FL progresses learning locally on each device and gradually improves the learning model through interaction with the central server. However, it can cause network overload because of limited communication bandwidth and the participation of a huge number of users. One of the ways to minimize the network load is for the model to converge rapidly and stably with target learning accuracy. In this paper, we propose blockchain based federated learning scenario. Blockchain can efficiently induce users to participate in learning and can separate each participating user as a `node'. In addition, it can be pursued the integrity, stability, and so on. We consider two types of weights to choose the subset of clients for updating the global model. First, we consider the weight based on local learning accuracy of each client. Second, we consider the weight based on participation frequency of each client. We choose two key performance indicators, learning speed and standard deviation, to compare the performance of our proposed scheme with existing schemes. The simulation results show that our proposed scheme achieves higher stability along with fast convergence time for targeted accuracy compared to others.

In this paper, we propose blockchain network based architecture called “FLchain” for enhancing security of Federated Learning (FL). We leverage the concept of channels for learning multiple global models on FLchain. Local model parameters for each global iteration are stored as a block on the channel-specific ledger. We introduce the notion of “the global model state trie” which is stored and updated on the blockchain network based on the aggregation of local model updates collected from mobile devices. Qualitative evaluation shows that FLchain is more robust than traditional FL schemes as it ensures provenance and maintains auditable aspects of FL model in an immutable manner.

Recently, the deployment of unmanned aerial vehicles (UAVs) in the wireless networks has gained attention to achieve a better quality of service (QoS) for the mobile nodes. This paper studies the use of UAV network to assist the terrestrial network to meet the QoS requirements of the mobile nodes. However, such cellular traffic offloading to the energy constrained UAVs is not trivial. Therefore, we propose a weighted power allocation scheme in the UAV assisted heterogeneous networks (UAV-HetNets) where both UAV and terrestrial networks are efficiently utilized to meet the QoS requirements of the mobile nodes. For that purpose, the weighted energy minimization problem is formulated under the QoS constraints of the mobile node. Simulation results are drawn to show the significance of the proposed energy efficient resource allocation scheme.

The ongoing development of edge computing in fifth-generation (5G) networks promises to provide an artificial intelligence-as-a-service (AIaaS) for meeting the stringent requirements of everything as a service (XaaS) in the edge of the networks. Therefore, the concept of edge-artificial intelligence (edge-AI) is not only evolving but also emergent enabler toward AI service fulfillment. In this paper, we investigate an AI-based service aggregation problem for a mobile agent in AIaaS-enabled edge computing. First, we propose an optimization problem for the mobile agent and the objective is to maximize the AI service fulfillment achieved rate while satisfying the computational, memory, and delay requirements. Thus, we show that this optimization problem is NP-hard. Second, we compel the formulated problem in a community discovery problem and derive a solution by executing a data-driven approach. To do this, we incorporate density-based spatial clustering of applications with noise (DBSCAN) and flow control algorithm, and propose a low computational complexity algorithm for AI service aggregation of the mobile agent. Finally, numerical analysis shows the proposed model can perform better over other baseline methods in terms of deprived AI services, server utilization, and complexity analysis.

Multi-access Edge Computing (MEC) is recently acknowledged as one of the key pillars for the next revolution of mobile communications area to provide lower latency and more computation capability for cellular base stations (BSs). The trade-off of delay performance and energy cost which is fully controlled by the association decisions among systems of edge sites of mobile users and the service rate scaling of MEC servers to serve the user tasks is analyzed. In this paper, we formulate a joint user association and MEC server scaling optimization problem, namely MEC - MP. Accordingly, we first propose a centralized algorithm by iteratively solving the user association and MEC server scaling subproblems to obtain a suboptimal solution approach. We then propose a distributed algorithm based on a greedy user association strategy and decentralized solutions of the MEC server scaling problem.

Due to commercialization of the 5G network, many base stations need to enhance a reliable communication quality. Thus, many studies have still worked to provide mobility and economic benefits to the VAVs-Base Station (VAVs-BS) on behalf of ground base stations. In this paper, we propose a system to find a location where multiple users can have an optimal service throughput by considering users' requirements in Multi-VAVs communication. Based on the Air-To-Ground Path Loss Model, the virtual communication environment is established and Airtime Fairness is applied for equitable channel usage time distribution according to user requirements. Thus, we apply a collaborative algorithm with modified K-means that can distribute users to each VAV and solve communication overload problems. In addition, the Proximal Policy Optimization (PPO) algorithm is applied to set an optimal location with the maximum throughput. As a result, the proposed systems allow the Multi-VAVs to be in the locations with high service throughput for users with different demands.

The aim of Influence maximization (IM) techniques is to mine social networks to find a small set of influential seed users that maximize the viral marketing profit. On the other hand, the Reverse Influence Maximization (RIM) maximizes the profit by minimizing the viral marketing cost. Here, the cost is estimated by the lowest number of nodes which are needed to activate seed nodes. On the other hand, the profit is computed by the highest number of nodes that can be influenced by seed users. However, most of the existing works assume that the seed nodes are either initially activated or offered free products for motivation. Thus, most of the studies do not address the seed activation cost. Therefore, in this research, we propose a Susceptible-Infection-based Greedy Reverse Influence Maximization (SIG-RIM) model to maximize the profit by minimizing the seeding cost. The proposed SIG-RIM model employs the Susceptible-Infected (SI) mechanism in reverse order to compute the seeding cost and a greedy technique to optimize the cost. Moreover, the SIG-RIM model tackles RIM challenges more efficiently. Finally, we conduct the performance evaluation of our model with real datasets of two popular social networks, and the result shows that the proposed model outperforms state-of-the-art models.

With the help of multi-access edge computing (MEC) system, traditional mobile network operators (MNOs) can provide various services to their mobile users with the minimum delay by installing micro-datacenters at the base stations (BSs)(i.e., at the edge of the radio access network). However, the capital and operational expenditures become significant challenge for the MNOs. Fortunately, multiple MNOs can coexist on the same infrastructure and share the network resources with the help of upcoming technologies such as network virtualization (i.e., network slicing) and software defined networking (SDN). In this work, we introduce a virtualized MEC system in which an infrastructure provider (InP) deploys a BS integrated with a micro-datacenter and owns the wireless network resource i.e., bandwidth. Then, InP creates the virtual network by slicing its network resources including bandwidth and the computation resource of the MEC server, and shares these resource slices to multiple virtual network operators (MVNOs) where MVNOs provide specific services to their mobile users. To solve our proposed problem, we first decompose the original problem into two subproblems. Then, we apply the Karush-Kuhn-Tucker (KKT) conditions to solve each subproblem. Moreover, simulation results prove that our proposed algorithm for joint communication and computation resources sharing outperforms the existing schemes.

Recently, with big data and high computing power, deep learning models have achieved high accuracy in prediction problems. However, the challenging issues of utilizing deep learning into the content's popularity prediction remains open. The first issue is how to pick the best-suited neural network architecture among the numerous types of deep learning architectures (e.g., Feed-forward Neural Networks, Recurrent Neural Networks, etc.). The second issue is how to optimize the hyperparameters (e.g., number of hidden layers, neurons, etc.) of the chosen neural network. Therefore, we propose the reinforcement (Q-Learning) meta-learning based deep learning model deployment scheme to construct the best-suited model for predicting content's popularity autonomously. Also, we added the feedback mechanism to update the Q-Table whenever the base station calibrates the model to find out more appropriate prediction model. The experiment results show that the proposed scheme outperforms existing algorithms in many key performance indicators, especially in content hit probability and access delay.

The nature of multi-access edge computing (MEC) is to deal with heterogeneous computational tasks near to the end users, which induces the volatile energy consumption for the MEC network. As an energy supplier, a microgrid is able to enable seamless energy flow from renewable and non- renewable sources. In particular, the risk of energy demand and supply is increased due to nondeterministic nature of both energy consumption and generation. In this paper, we impose a risk- sensitive energy profiling problem for a microgrid-enabled MEC network, where we first formulate an optimization problem by considering Conditional Value-at-Risk (CVaR). Hence, the formulated problem can determine the risk of expected energy shortfall by coordinating with the uncertainties of both demand and supply, and we show this problem is NP-hard. Second, we design a multi-agent system that can determine a risk- sensitive energy profiling by coping with an optimal scheduling policy among the agents. Third, we devise the solution by applying a multi-agent deep reinforcement learning (MADRL) based on asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This approach mitigates the curse of dimensionality for state space and also, can admit the best energy profile policy among the agents. Finally, the experimental results establish the significant performance gain of the proposed model than that a single agent solution and achieves a high accuracy energy profiling with respect to risk constraint.

Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to the central aggregator for improving the global model. However, a key challenge is to maintain communication efficiency (i.e., the number of communications per iteration) when participating clients implement uncoordinated computation strategy during aggregation of model parameters. We formulate a utility maximization problem to tackle this difficulty, and propose a novel crowdsourcing framework, involving a number of participating clients with local training data to leverage FL. We show the incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Further, we illustrate the efficacy of our proposed framework with simulation results. Results show that the proposed mechanism outperforms the heuristic approach with up to 22% gain in the offered reward to attain a level of target accuracy.

There is an increasing interest in a new machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), and each UE contributes to the learning model by independently computing the gradient based on its local training data. Federated Learning has several benefits of data privacy and potentially a large amount of UE participants with modern powerful processors and low-delay mobile-edge networks. While most of the existing work focused on designing learning algorithms with provable convergence time, other issues such as uncertainty of wireless channels and UEs with heterogeneous power constraints and local data size, are under-explored. These issues especially affect to various trade-offs: (i) between computation and communication latencies determined by learning accuracy level, and thus (ii) between the Federated Learning time and UE energy consumption. We fill this gap by formulating a Federated Learning over wireless network as an optimization problem FEDL that captures both trade-offs. Even though FEDL is non-convex, we exploit the problem structure to decompose and transform it to three convex sub-problems. We also obtain the globally optimal solution by charactering the closed-form solutions to all sub-problems, which give qualitative insights to problem design via the obtained optimal FEDL learning time, accuracy level, and UE energy cost. Our theoretical analysis is also illustrated by extensive numerical results.

The computational tasks at multiaccess edge computing (MEC) are unpredictable in nature, which raises uneven energy demand for MEC networks. Thus, to handle this problem, microgrid has the potentiality to provides seamless energy supply from its energy sources (i.e., renewable, nonrenewable, and storage). However, supplying energy from the microgrid faces challenges due to the high uncertainty and irregularity of the renewable energy generation over the time horizon. Therefore, in this paper, we study about the microgrid-enabled MEC networks' energy supply plan, where we first formulate an optimization problem and the objective is to minimize the energy consumption of microgrid-enabled MEC networks. The problem is a mixed integer nonlinear optimization with computational and latency constraints for tasks fulfillment, and also coupled with the dependencies of uncertainty for both energy consumption and generation. Therefore, we show that the problem is an NP-hard problem. As a result, second, we decompose our formulated problem into two subproblems: 1) energy-efficient tasks assignment problem for MEC into community discovery problem and 2) energy supply plan problem into Markov decision process. Third, we apply a low complexity density-based spatial clustering of applications with noise to solve the first subproblem for each base station distributedly. Sequentially, we use the output of the first subproblem as a input for solving the second subproblem, where we apply a model-based deep reinforcement learning. Finally, the simulation results demonstrate the significant performance gain of the proposed model with a high accuracy energy supply plan.

Edge-of-Things (EoT)-based healthcare services are forthcoming patient-care amenities related to autonomic and persuasive healthcare, where an EoT broker usually works as a middleman between the Healthcare Service Consumers (HSC) and Computing Service Providers (CSP). The computing service providers are the edge computing service providers (ECSP) and cloud computing service provider (CCSP). Sensor observations from a patient’s body area networks (BAN) and patients’ medical and genetic historical data are very sensitive and have a high degree of interdependency. It follows that EoT based patient monitoring systems or applications are tightly coupled and require obstinate synchronization. Therefore, this paper proposes a portfolio optimization solution for the selection of virtual machines (VMs) of edge and/or cloud computing service providers. The dynamic pricing for an EoT computation service is considered by the EoT broker for optimal VM provisioning in an EoT environment. The proposed portfolio optimization solution is compared with the traditional certainty equivalent approach. As the portfolio optimization is a centralized solution approach, this paper also proposes an alternating direction method of multipliers (ADMM) based distributed provisioning method for the healthcare data in the EoT computing environment. A comparative study shows the cost-effective provisioning for the healthcare data through portfolio optimization and ADMM methods over the traditional certainty equivalent and greedy approach, respectively.

Wireless network slicing (i.e., network virtualization) is one of the potential technologies for addressing the issue of rapidly growing demand in mobile data services related to 5G cellular networks. It logically decouples the current cellular networks into two entities: infrastructure providers (InPs) and mobile virtual network operators (MVNOs). The resources of base stations (e.g., resource blocks, transmission power, and antennas), which are owned by the InP, are shared with multiple MVNOs who need resources for their mobile users. Specifically, the physical resources of an InP are abstracted into multiple isolated network slices, which are then allocated to MVNO's mobile users. In this paper, two-level allocation problem in network slicing is examined while enabling efficient resource utilization, inter-slice isolation (i.e., no interference among slices), and intra-slice isolation (i.e., no interference between users in the same slice). A generalized Kelly mechanism (GKM) is also designed, based on which the upper level of the resource allocation issue (i.e., between the InP and MVNOs) is addressed. The benefit of using such a resource bidding and allocation framework is that the seller (InP) does not need to know the true valuation of the bidders (MVNOs). For solving the lower level of resource allocation issue (i.e., between MVNOs and their mobile users), the optimal resource allocation is derived from each MVNO to its mobile users by using Karush-Kuhn-Tucker (KKT) conditions. Then, bandwidth resources are allocated to the users of MVNOs. Finally, the results of the simulation are presented to verify the theoretical analysis of our proposed two-level resource allocation problem in wireless network slicing.

In recent years, in order to provide a better quality of service (QoS) to Internet of Things (IoT) devices, the cloud computing paradigm has shifted toward the edge. However, the resource capacity (e.g., bandwidth) in fog network technology is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the fog network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the fog network infrastructure (i.e., fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game that occurs due to job delay and solve the network resource allocation problem by applying the “best-fit” resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.

In wireless network virtualization, guaranteeing service contracts with different mobile virtual network operators (MVNOs) and optimizing energy efficiency are crucial for the success of the virtualization scheme deployed by an infrastructure provider (InP). In this paper, a novel design framework is proposed for resource allocation in an OFDMAvirtualized wireless network (VWN). Treating the virtual resources for a VWN as commodities, the InP wants to maximize its revenue by leasing the infrastructure and resources to the MVNOs while meeting certain contract agreements. Moreover, MVNOs want to serve their users at the best performance and pay the minimum cost to the InP. A Lyapunov based online algorithm is proposed to solve the InP's long-term optimization problem. The shortterm optimization problem of the InP is considered as a combinatorial nonconvex problem. A multiple time-scale framework is proposed to solve the optimization problem of the InP, which decomposes the pricing decision, base station assignment, and resource allocation into different time-scale algorithms to achieve the design objectives. First, a distributed matching based algorithm is proposed to solve the base station assignment problem. Second, we propose a successive convex approximation approach to solve the joint subchannel assignment and energy efficiency problem. Finally, we propose a branch and bound based algorithm to optimally solve the price decision problem. Simulation results show the trade-off between energy efficiency, InP's revenue, and the isolation provisioning.

Long term evolution (LTE)-unlicensed, an extension of LTE Advanced to unlicensed spectrum, can provide high performance and seamless user experience. To reap the full benefits of the LTE-unlicensed deployment, efficient resource allocation and interference management are critical to ensuring a harmonious coexistence between LTE-unlicensed and WiFi systems. In this paper, we study a resource orchestration scheme for an LTE-unlicensed network where small cells share the same unlicensed spectrum with a WiFi system. An optimization problem for channel and power allocations is formulated to maximize the overall network utility, which is an NP-hard problem. The problem is constrained on meeting the desired data rate demands of the served small-cell users, the capacity-limited backhaul links, and the maximum tolerable interference at the WiFi access point. To solve this challenging problem, a distributed solution based on Lagrangian relaxation is proposed to assist the LTE-unlicensed network in making decisions on channel allocation and transmit power. Furthermore, low-complexity solutions are devised upon applying the one-to-one matching game theory. The simulation results with practical parameter settings show that the proposed algorithms converge to the suboptimal solution after a small number of iterations in the considered examples.

LTE over unlicensed band (LTE-U) has emerged as an effective technique to overcome the challenge of spectrum scarcity. Using LTE-U along with advanced techniques such as carrier aggregation (CA), one can boost the performance of existing cellular networks. However, if not properly managed, the use of LTE-U can potentially degrade the performance of co-existing Wi-Fi access points which operate over the unlicensed frequency bands. Moreover, most of the existing works consider a macro base station (MBS) or a small cell base station (SBS) for their proposals. In this paper, an effective coexistence mechanism between LTE-U and Wi-Fi systems is studied. The goal is to enable the cellular network to use LTE-U with CA to meet the quality-of-service (QoS) of the users while protecting Wi-Fi access points (WAPs), considering multiple SBSs from different operators in a dense deployment scenario. Specifically, an LTE-U sum-rate maximization problem is formulated under a user QoS and WAP-LTE-U co-existence constraints. To solve this problem, a cooperative Nash bargaining game is proposed. This game allows LTE-U and WAPs to share time resource while protecting Wi-Fi system. For allocating unlicensed resource among LTE-U users, a heuristic algorithm is proposed. Simulation results show that the proposed method is better than the comparing methods regarding per user achieved rate, percentage of unsatisfied users and fairness. The result also shows that the proposed method protects Wi-Fi user far better way than basic listen-before-talk (LBT) does.

We study the problem of joint user clustering and resource allocation for wireless network virtualization (WNV) using non-orthogonal multiple access (NOMA). We aim to maximize the weighted total sum-rate while taking into account the isolation constraint of the mobile virtual network operators (MVNOs). To solve the non-convex formulated problem, we decouple it into three subproblems, i.e., user clustering, resource block (RB) allocation and power assignment. We apply the framework of matching game with externalities to solve the user clustering problem while the solutions for RB and power allocation are derived by using the Lagrange dual approach and complementary Geometric programming, respectively. An alternative maximization algorithm is provided to achieve a suboptimal solution for the original problem. We propose to classify user equipments (UEs) into three classes, i.e., strong, normal and weak UEs and compare our proposed scheme with general NOMA scheme with two UEs per cluster. Simulation results revel a performance gain of 2.5% in terms of throughput. Moreover, the proposed scheme outperforms the traditional OFDMA scheme in terms of throughput and energy efficiency by up to 40% and 58%, respectively.

Installation of backup power supply plays a vital role in maintaining communication services which can save billions of dollars as well as human lives during natural disasters. Due to the higher capital and operational expense compared to public power, pooling and sharing the backup power supplies can be an economical solution since the backup power capacity can be sized based on the aggregate demand of co-located operators. However, how to pool and share the backup power at multi-operator cellular sites in a fair manner should be considered due to the limited capacity and high user demands. In this paper, we adopt the Nash Bargaining Solution (NBS) of a bargaining problem which can guarantee the fairness of backup power sharing and design a decentralized algorithm approach with limited information exchange among the operators. Our simulation demonstrates that the sharing the backup power reduces the average delay and requires less BS power consumption than the non-sharing approach, especially for high traffic load scenarios. In addition, we also extend the formulation with respect to admission control for very high traffic demand cases.

In recent years, Influence Maximization (IM) has gained great research interest in the field of social network research. The IM is a viral marketing based approach to find the influential users on the social networks. It determines a small seed set that can activate a maximum number of nodes in the network under some diffusion models such as Linear Threshold model or Independent Cascade model. However, previous works have not focused on the opportunity cost defined by the minimum number of nodes that must be motivated in order to activate the initial seed nodes. In this work, we have introduced a Reverse Influence Maximization (RIM) problem to estimate the opportunity cost. The RIM, working in opposite manner to IM, calculates the opportunity cost for viral marketing in the social networks. We have proposed the Extended Randomized Linear Threshold RIM (ERLT-RIM) model to solve the RIM problem. The ERLT-RIM is a Linear Threshold (LT)-based model which is an extension to the existing RLT-RIM model. We also have evaluated the performance of the algorithm using three real-world datasets. The result shows that the proposed model determines the optimal opportunity cost with time efficiency as compared to existing models.

Network virtualization (NV) allows multiple heterogeneous virtual networks (VNs) to coexist and operate over the same physical network (PN) infrastructures. Some of the benefits of this advancement include flexibility in VN topologies, heterogeneity in VN technologies, and modularity of network operations. However, there are a few areas, such as resource allocation and economics, which challenge the implementation of NV. In this paper, we first introduce some NV parameters that influence the resource allocation and economics of an NV system. Next, we formulate an economic model for NV using the prey-predator food chain model. This model takes into account the dynamics in an NV system, such as the service, payoff, failure, and competition rates within each VN and PN. The solution point to this model represents the resource strategy of the service provider (SP) given the number of users trying to use its VN, as well as the resource strategy of the infrastructure provider (InP) given the strategy of the VN leasing its PN. In addition, we establish economic models that relate the capacities of the end users, the SP, and the InP. Finally, we provided simulations that show how the prey-predator food chain model fits well on an NV system.

Phishing detection is recognized as a criminal issue of Internet security. By deploying a gateway anti-phishing in the networks, these current hardware-based approaches provide an additional layer of defense against phishing attacks. However, such hardware devices are expensive and inefficient in operation due to the diversity of phishing attacks. With promising technologies of virtualization in fog networks, an anti-phishing gateway can be implemented as software at the edge of the network and embedded robust machine learning techniques for phishing detection. In this paper, we use uniform resource locator features and Web traffic features to detect phishing websites based on a designed neuro-fuzzy framework (dubbed Fi-NFN). Based on the new approach, fog computing as encouraged by Cisco, we design an anti-phishing model to transparently monitor and protect fog users from phishing attacks. The experiment results of our proposed approach, based on a large-scale dataset collected from real phishing cases, have shown that our system can effectively prevent phishing attacks and improve the security of the network.

Keeping wireless base stations operating continually and providing uninterrupted communications services can save billions of dollars as well as human lives during natural disasters and/or electricity outages. Toward this end, wireless operators need to install backup power supplies whose capacity is sufficient to support their peak power demand, thus incurring a significant capital expense. Hence, pooling together backup power supplies and sharing it among co-located wireless operators can effectively reduce the capital expense, as the backup power capacity can be sized based on the aggregate demand of co-located operators instead of individual demand. Turning this vision into reality, however, faces a new challenge: how to fairly share the backup power supply? In this paper, we propose fair sharing of backup power supply by multiple wireless operators based on the Nash bargaining solution (NBS). In addition, we integrate our analysis with multiple time slots for emergency cases in which the study the backup energy sharing based on model predictive control and NBS subject to an energy capacity constraint regarding future service availability. Our simulations demonstrate that sharing backup power/energy improves the communications service quality with lower cost and consumes less base station power than the non-sharing approach.

Daily-life oriented applications and multimedia entertainments through smart devices are creating immense stress to the current cellular foundation. To overwhelm from this loopholes, both academia and industry are trying their best by incorporating different technologies with existing ones using licensed spectrum. On the other side, Device-to-Device (D2D) communications are also being used to improve the spectrum efficiency and user experience by reutilizing licensed cellular spectrum. But with insufficient licensed spectrum, it is impossible to meet the demand of users in the current scenario. So, peoples are thinking to utilize unlicensed spectrum with licensed one to alleviate this scarcity issue and provide guaranteed Quality of Service (QoS) to the users. In this paper, we want to extend D2D communication and LTE-A network into the unlicensed spectrum. But this initiative will harm the performance of other technologies which are already working in the same unlicensed band. Moreover, if multiple mobile network operators (MNOs) use the same unlicensed band then they will diminish the benefits of each other. So this paper wants to maximize the sum-rate of LTE users and D2D pairs by allocating licensed and unlicensed subchannels considering their QoS requirements while protecting minimum requirements of WiFi Access Points (WAPs). Then we solve this problem with the help of Nash bargaining game (NBG) between Small Cell Base Stations (SBSs) and WAPs by the cooperative approach. Simulation results show the effectiveness and efficiency of the proposed approach.

As technologies and services that leverage cloud computing evolve, more and more businesses or individuals are using them. However, as users increasingly use and store personal information in cloud storage, research on privacy protection models in the cloud environment is becoming more important. A self-destructing scheme has been proposed to prevent the decryption of encrypted user data after a certain period of time using a DHT network. However, the existing privacy protection model does not mention the method of setting the threshold value considering the availability and security of the data. Therefore, in this paper, we propose an optimal threshold finding method considering both data availability and security of privacy protection model by applying regression analysis.

Green transportation such as electric vehicles are emerging as an alternative to the traditional vehicles primarily due to the increasing cost and need of petroleum energy worldwide. These electric vehicle operate by using electric charging and the way to charge an electric car is to use a mobile charger or use a charging infrastructure. Therefore, when a mobile charger is used, a billing system is required through which a user is billed who has charged the electric vehicle. In this paper, we propose a mobile charger billing system that utilizes Blockchain technology. This technology has been applied to achieve more secure online transactions in a peer-to-peer manner. Moreover, we analyze the requirements of mobile charger for billing and propose a lightweight scheme that can overcome the challenge of data size in existing Blockchain.

Content-Centric Networking(CCN) is emerging technology for the future network infrastructure. In CCN, CCN routers support cache space called Content Store(CS), which can store passing contents. In this paper, we define two representative problems that can occur in CS of CCN routers, namely content search problem and cache replacement problem, and propose FERA(File Extension and Regression Analysis based Caching Scheme) which can solve these problems. In FERA, two approaches are proposed. Specifically, the first proposal is to divide CS into four types of CSs based on file extensions. Thereafter, when the user sends a request including the content name, the CCN router extracts and identifies the file extension from the content name, and transmits the user's request to the corresponding CS. After being transmitted, the user request is processed in the corresponding CS. The second proposal is to predict the content that is continuously decreasing in popularity based on regression analysis which is one of the machine learning algorithms and evicts that content. Finally, we verify the proposed scheme using CCN simulator. The simulation results show that the proposed scheme improves the cache hit ratio and the cache hit distance over the LRU.

Mobile operators are providing mobile data services through numerous Wi-Fi access points (APs). In such a wireless network, when mobile devices select a APs, recent works only consider to provide guarantee QoS (Quality of Service), however, they ignore the backhaul link limitations which refers to the link between AP and backbone routers. Therefore, even if there exist many APs satisfying the QoS, the mobile devices selects a specific AP, thus, creating congestion on the AP's backhaul link. Therefore, we propose a novel AP selection scheme that classifies mobile traffic by applying a machine learning based scheme and also considers the backhaul link status of APs along with the QoS requirements. Moreover, prior to AP selection, we prioritized the requests and implement an efficient approach that can balance the load between APs. Simulation results state that our proposal is suitable to provide an efficient network environment by distributing the backhaul link load of APs with guaranteed QoS.

Long Term Evolution-UnUcensed (LTE-U) is a 5G enabling technology in which both LTE and Wi-Fi systems operate together using the same frequency spectrum. LTE can co-exist with Wi-Fi in both 2.5GHz and 5GHz bands for fully utilizing the spectrum. Aggregating these two technologies while implementing a fair allocation of resources among them is a challenge. Moreover, the LTE technology provides better spectral efficiency and is more bandwidth hungry, thus, it can consume more spectrum which is unfair for Wi-Fi systems. Therefore, appropriate spectrum management scheme is needed to be developed that can maintain the fairness among the two coexisting technologies. This paper proposes a ruin theory based redundant spectrum allocation to LTE-U users while providing sufficient fairness to Wi-Fi systems.

Mobile data traffic is increasing astronomically. This enormous extent was not only caused by the increasing in the number of mobile devices, but also the growing number of applications running on clouds. Most of these mobile devices have limited resources for computing, they are relying on cloud computing, which is inadequate to realize millisecond-scale latency in the 5G network. To deal with this issue, Mobile Edge Computing (MEC) has been introduced to supplement cloud computing by pushing Computing, Caching, Communication, and Control (4C) to the edges. However, the proposed MEC server at each base station is not enough to deal with 4C, when it operates independently, and without any collaboration with other MEC servers. This results in increasing the delay and making backhaul to continue suffering from huge data exchange between end-users and remote clouds. To address this challenge, we propose collaborative cache allocation and computation offloading, where the MEC servers collaborate for executing computation tasks and data caching. We formulate an optimization problem that aims at maximizing the resource utilization. The simulation results show that our proposal is easy to be implemented in production network.

There have been many recent studies on the Demand Response (DR) of Datacenters (DCs). Nonetheless, (i) DR of Colocation Datacenters (CDCs), and (ii) the role of Demand Response Provider (CSP) have been largely overlooked. CDCs differ from big owner-operated DCs in that the operator has no control over their tenants, and thus, requiring a mechanism for the operator to give tenants incentives to reduce their electricity usage. CSP uses compensation price as a guidance for customers' response. To fill the gap, we propose an incentive mechanism for CDC DR that studies the interaction between the CSP, CDCs and tenants. Firstly, the strategic behaviors of these interactions are formulated as a three-stage Stackelberg game which contains a separate problem at each stage. In Stage I, the CSP solves an optimal compensation pricing problem. In Stage II, each CDC operator finds its own optimal procurement and reward strategy. In Stage III, the optimal tenants' energy reduction is calculated. Secondly, we examine both exact and approximate solution at Stage II, and propose an efficient algorithm to obtain the optimal CSP price in Stage I. Finally, the extensive numerical analysis (a) shows that the CLT-based approximation achieves similar solutions compared to the exact analysis, and (b) illustrates the comparisons between the optimal CSP individual cost and the social cost.

Social networks have gained huge research interest, especially in viral marketing due to their rapid boom in the past years. It is very crucial to identify the influential users in the social networks for viral and target marketing. Influence maximization (IM) problem estimates such influential users in the social networks. With an initial seed set, the IM finds a maximum number of nodes that can be activated in the network under some diffusion models e.g. Linear Threshold model or Independent Cascade model. But previous works in this field have not studied about the minimum cost, termed as opportunity cost (OC), to motivate those seed nodes. In this work, we define a novel Reverse Influence Maximization (RIM) problem to determine the opportunity cost of influence maximization. Employing the influence propagation in opposite order, the RIM determines the minimum number of nodes that must be activated in order to motivate a set of target nodes. We propose Random RIM (R-RIM) and Randomized Linear Threshold RIM (RLT-RIM) models to tackle the RIM problem. We also perform a simulation to evaluate the performance of the algorithms using two real world datasets. The result shows that the proposed models determine the optimized opportunity cost with faster running time margin.

Virtualized wireless networks is one of the techniques to satisfy the increasing demand of mobile data services and for future wireless network. Nework virtualization enables efficient resource utilization and it also reduces capital expenditures and operational expenditures of mobile network operators (MNOs). In this paper, we present virtualized wireless network where users of each service provider (SP) are served by an infrastructure provider (InP) who owns a set eNBs. We propose the problem of power allocation of each user that belongs to service providers. We aim to maximize the total rate of mobile users with limited power. We introduce Dual decomposition technique to address the optimization problem.

Modern day's User Equipments (UEs) are equipped with rich resources which encourage them to be used for more sophisticated applications. On the other hand, with these equipments in hand, users demand for high-quality video on the move is increasing day-by-day. Moreover, Information/Content Centric Networking (ICN/CCN) has changed the network dynamics by getting the desired contents regardless of the location. Unused memory in UEs can be used to cache the contents and provide it to the other nearby users on demand. In this paper, we propose to provide the requested video to users from other users cache, using D2D link, if it is present there. Our objective is to reduce the download delay for the users' requested video. We formulate the problem as a matching game in which the resources are assigned to the users in the uplink period. The UEs select the content node for D2D communication and the suitable channel. We have evaluated the proposed mechanism by implementing it in Matlab and have compared it with greedy approach and no D2D communication scheme. The experimental results show the effectiveness of our proposed mechanism.

For 5G cellular networks, non-orthogonal multiple access (NOMA) has been recognized as a promising technique. NOMA allows multiple users to access the same channel by adapting successive interference cancellation (SIC) and multiplexing in power domain. This causes the clustering problem to become critical as higher channel gain users need to cancel out the signals of lower channel gain users while the latter has to receive less interference. In this paper, we formulate the user clustering as a correlation clustering problem. We transform the problem by relaxing the clustering variables, and solve using semidefinite programming (SDP). Moreover, a simple iterative power allocation algorithm is nominated. In the simulation results, it can be seen that the correlation clustering using semidefinite programming outperforms the random clustering of users.

In the commercial caching system, both Infrastructure Provider (InP), who owns the infrastructure and wireless network resource and Service Providers (SPs), who provide service to its users based on the virtual resource provided by the InP, are beneficial in leasing and renting the cache space. By partitioning the cache space at the BS into slices and leasing each partition to the SPs, the InP can receive a payment. Meanwhile, the SPs can serve their users with faster download service with local caching. However, both SPs and InP are selfish and want to maximize their own benefits. In addition, in practice, there is asymmetric information between SPs and InP. Thus, some SPs may declare inaccurate private information to get more cache space or less payment. To deal with these problems, in this paper, we propose an incentive mechanism based on contract theory, in which the InP, the employer, who designs and offers contracts to SPs, the employees. In particular, SPs are specified into types based on their valuation parameters and request rates. Different from the traditional contract model with two feasible contract conditions, we impose cache capacity constraints, which induces the interaction among SPs and makes the contract design more complicated. We propose an algorithm that achieves the optimal contract so that the InP can motivate SPs to participate into renting caching space while maximize its utility. Simulation results show that the proposed approach not only ensures no SP has incentive to select another contract but also outperforms the baseline allocation algorithm.

Caching is the key feature of Content Centric Networking (CCN) that allows the Internet Service Provider (ISP) to reduce network traffic crossing its network, and save bandwidth usage cost. On the other hand, it is also on benefit of the Content Providers (CPs) to cache the contents within the ISP network near the consumers. However, caching paid contents (the contents that only paying consumers can access), which are the main source of income for CP, in the ISP network complicates the CP's task of controlling content access and payment. Thus, ISP manages content placement inside its cache-enabled routers and serves content based on user demands, without any coordination with CP. There is no profit sharing mechanism between both ISP and CPs. Therefore, a payment mechanism between ISP and CPs that considers paid content caching and distribution inside the ISP network is needed. To address this challenge, we propose a new incentive mechanism for paid content caching that satisfies both ISP and CPs through the use of reverse auction. The ISP monetizes its cache storage through caching contents from multiple CPs and selling them to its customers. The reverse auction helps the ISP to get prices from multiple CPs, and to select the price that minimize its total payment. The simulation results show that our proposal satisfies all network players involved in in- network caching through increasing their utilities.

In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.

Ability of caching the contents is one of the most important feature of an Information Centric Networking (ICN) node. By managing the cache space intelligently we can improve network’s performance and increase users’ Quality of Experience (QoE). Moreover, scalable video streaming in ICN is envisioned to be very beneficial as well as a challenging issue. In this paper, we are proposing a mechanism for cache management and request forwarding policies for scalable video streaming in ICN. Our proposed cache decision policy ensures to cache the base layer of a scalable video near to the users, which is mandatory layer for decoding any SVC encoded video and is needed by all the users with any data-rate budget, and consequently cache the higher layers in the upper nodes in the CCN/ICN within a specific RTT range. Furthermore, our intelligent cache decision cover fairness by considering router’s cache capacity (inside the RTT range) and at the same time giving more priority to the nodes that are nearer to the users. A limited cooperative request forwarding mechanism, which is the part of our proposal, plays a role to improve users’ QoE by providing the popular requested contents quickly. We have intensively simulated our proposed cache management and request forwarding scheme. The simulation results show that our proposed solution outperforms the current cache management schemes and improve the cache utilization. Also our proposed scheme decrease the traffic flowing inside the network by eliminating the request flooding and providing the requested contents from the nearby location to the users. The proposed scheme provides video faster to the users, specially the mandatory base layer is provided very quickly to the users.

Device to device (D2D) communication is considered as an effective technology for enhancing the spectral efficiency and network throughput of existing cellular networks. However, enabling it in an underlay fashion poses a significant challenge pertaining to interference management. In this paper, mode selection and resource allocation for an underlay D2D network is studied while simultaneously providing interference management. The problem is formulated as a combinatorial optimization problem whose objective is to maximize the utility of all D2D pairs. To solve this problem, a learning framework is proposed based on a problem-specific Markov chain. From the local balance equation of the designed Markov chain, the transition probabilities are derived for distributed implementation. Then, a novel two phase algorithm is developed to perform mode selection and resource allocation in the respective phases. This algorithm is then shown to converge to a near optimal solution. Moreover, to reduce the computation in the learning framework, two resource allocation algorithms based on matching theory are proposed to output a specific and deterministic solution. The first algorithm employs the one-to-one matching game approach whereas in the second algorithm, the one-to many matching game with externalities and dynamic quota is employed. Simulation results show that the proposed framework converges to a near optimal solution under all scenarios with probability one. Moreover, our results show that the proposed matching game with externalities achieves a performance gain of up to 35 percent in terms of the average utility compared to a classical matching scheme with no externalities.

The use of heterogeneous small cell-based networks to offload the traffic of existing cellular systems has recently attracted significant attention. One main challenge is solving the joint problems of interference mitigation, user association, and resource allocation. These problems are formulated as an optimization which is then analyzed using two different approaches: Markov approximation and log-linear learning. However, finding the optimal solutions of both approaches requires complete information of the whole network which is not scalable with the network size. Thus, an approach based on a Markov approximation with a novel Markov chain design and transition probabilities is proposed. This approach enables the Markov chain to converge to the bounded near optimal distribution without complete information. In the game-theoretic approach, the payoff-based log-linear learning is used, and it converges in probability to a mixed-strategy ε-Nash equilibrium. Based on the principles of these two approaches, a highly randomized self-organizing algorithm is proposed to reduce the gap between optimal and converged distributions. Simulation results show that all of the proposed algorithms effectively offload more than 90 percent of the traffic from the macrocell base station to small cell base stations. Moreover, the results also show that the algorithms converge quickly irrespective of the number of possible configurations.

In this paper, the problem of distributed resource allocation is studied for an Internet of Things (IoT) system, composed of a heterogeneous group of nodes compromising both machine-type devices (MTDs) and human-type devices (HTDs). The problem is formulated as a noncooperative game between the heterogeneous IoT devices which seek to find the optimal time allocation so as to meet their quality-of-service (QoS) requirements in terms of energy, rate, and latency. Since the strategy space of each device is dependent on the actions of the other devices, the generalized Nash equilibrium (GNE) solution is first characterized, and the conditions for uniqueness of the GNE are derived. Then, to explicitly capture the heterogeneity of the devices, in terms of resource constraints and QoS needs, a novel and more realistic game-theoretic approach, based on the behavioral framework of cognitive hierarchy (CH) theory, is proposed. This approach is then shown to enable the IoT devices to reach a CH equilibrium (CHE), a concept that takes into account the various levels of rationality corresponding to the heterogeneous computational capabilities and the information accessible for each one of the MTDs and HTDs. Simulation results show that the CHE solution maintains a stable performance. In particular, the proposed CHE solution keeps the percentage of devices with satisfied QoS constraints above 96% for IoT networks containing up to 10000 devices without considerably degrading the overall system performance in terms of the total utility. Simulation results also show that the proposed CHE solution brings a two-fold increase in the total rate of HTDs and deceases the total energy consumed by MTDs by 78% compared with the equal time policy.

To reap the benefits of cache-enabled small cell networks, new backaul management mechanisms are needed to prevent the predicted files that are downloaded at the small base stations (SBSs) for caching purposes from jeopardizing the urgent requests that need to be served via the backhaul. Such mechanisms must account for the heterogeneity of the backhaul that will encompass both wireless backhaul links (at various frequency bands) and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. A self-organizing reinforcement learning algorithm is then proposed and shown to converge to a unique Boltzmann-Gibbs equilibrium, which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.

The demand for data rate is increasing exponentially in the cellular networks due to the emergence of more and more data-driven applications and smart-devices. Currently, these demands cannot be met by cellular networks with any single technology. As a consequence, Quality-of-service (QoS) needs to be sacrificed by some users. To provide guaranteed QoS to the users, researchers are considering massive MIMO, LTE-A, cooperative communications etc as the suitable candidate for the next generation cellular network. In this paper, we propose a collaborative communication mechanism with incentive for downlink in the cellular network for providing guaranteed QoS by utilizing multiple connectivity of the user's smart equipments. We formulate the problem as an optimization problem first and afterwards, we solve this problem with the help of matching theory. Simulation results are shown to represent performance of the technique.

Wireless Power Transmission (WPT) is a technique to charge electrical devices (EDs) remotely. In WPT, power source (Smart Wireless Charger) transmits power wirelessly, using air as the medium, to EDs. In this paper, we present an Auction mechanism to obtain the energy trading between Smart Wireless Chargers (SWCs) and EDs. In our proposed Double-Auction based charging trade mechanism, our priorities are to increase utility of the SWCs as well as increase EDs utilities. In our proposal, first we analyze the system architecture of the WPT environment and then form an optimization problem for the auction system. We then, introduce two algorithms to solve the combinatorial optimization problem such that the wireless charging system is stable and have high efficiency. We have numerically analyzed our system using python, the results show that the proposed mechanism achieve higher total utility for the whole system with satisfying budget balancing, individual rationality and truthfulness.

Cognitive femotcell networks can opportunistically access the licensed spectrum to enhance spectrum utilization. However, interference management plays a crucial role to effectively utilize the spectrum. In this paper, we consider the joint resource allocation and power control problem for an uplink transmission for a network consisting of a licensed macrocell and multiple cognitive femtocells. Furthermore, our problem imposes crucial constraints of both cross-tier interference for macrocell base station and quality of service for femtocell user. The joint problem is shown to be mix-integer nonlinear nonconvex optimization problem, which is NP-hard. To solve this problem efficiently, we employ a scheme consisting of two distributed algorithms. Numerical results show that the proposed scheme converges to the optimal power and resource allocation with a fast convergence speed. Additionally, our scheme guarantees the interference threshold at MBS and outage QoS for all cognitive femtocell users.

The proliferation of novel network access devices and demand for high quality of service by the end users are proving to be insufficient and are straining the existing wireless cellular network capacity. An economic and promising alternate to enhance the spectral efficiency and network throughput is device to device (D2D) communication. However, enabling D2D communication poses significant challenges pertaining to the interference management. In this paper, we address the resource allocation problem for underlay D2D pairs. First, we formulate the resource allocation optimization problem with an objective to maximizes the throughput of all D2D pairs by imposing interference constraints for protecting the cellular users. Second, to solve the underlying mixed-integer non linear resource allocation problem, we propose a stable, self-organizing and distributed solution using matching theory. Finally, we simulate our proposition to validate the convergence, cellular user protection, and network throughput gains achieved by the proposal. Simulation results reveal that D2D pairs can achieve significant throughput gains (i.e., up to 45 - 91%) while protecting the cellular users compared to the scenario in which no D2D pairs exist.

Most existing work on path computation has been focused on the shortest-path problem, which is to find the optimal path between an origin and destination pair. To get an optimal route, they usually consider travel time moving forward on the road and distance from a source to a target as crucial factors to value and select a path. However, it is not sufficient for real life traffic. Firstly, when we drive on the road, the road driving time includes the duration of moving forward on the road section, going through an intersection and performing lane changes. Secondly, it is not possible, in practice, to go on any road section. Some road sections have restricted rules. Turn restrictions are commonly restricted in a real network to reduce disruption to traffic. Therefore, in our work, we direct to find not only the shortest path but also realistic or feasible path. Both lane changes and turn restrictions are considered in our work. Simulation results show that two these additional constraints are necessarily considered to achieve a realistic shortest path. In addition, our proposal is not only giving a practical path but also a satisfied path for each individual car.

In this paper, we propose a framework to jointly optimize user association and power control for the uplink cognitive small cell network (CSN). In the considered CSN, small cell base stations (SBSs) are deployed to serve a set of small cell user equipments (SUEs) by sharing the same licensed spectrum with a macrocell base station. The problem of joint user association and power allocation is formulated as an optimization problem, in which the goal is to maximize the overall uplink throughput while guaranteeing the SINR requirements of the served SUEs and MBS protection. To solve this problem, a distributed framework based on dual decomposition is proposed to find sub-optimal solution of the user association and transmit power in distributed manner. Moreover, distributed frameworks to achieve suboptimal solutions with low-computation complexity are also proposed. Simulation results show that the proposed approaches obtain suboptimal solutions with a small number of iterations.

Recently, wireless virtualization has attracted more and more attentions from research communities. With virtualization, resource utilization is higher, system performance is improved and the investment capital is lower. However, there are remaining challenges to be addressed before wireless virtualization is widespread deployed. One challenge is user association which has great influence on the performance of the wireless virtualization network. Traditionally, user associates to the base station (BS) who provides the highest signal to interference plus noise ratio (max-SINR). However, this scheme may not guarantee the quality of service (QoS) of users and backhaul constraint of infrastructure providers (InPs). This motivates us to investigate the user association problem in virtualized cellular networks in this paper. We formulate the user-BS association as a matching game. We show that the preferences used to rank one another are independent and affected by the existing matching. Therefore, this game can be classified as the one-to-many matching game with externalities. In addition, we present a distributed algorithm to find the stable outcome of this game. Simulation results show that the presented association scheme is better than traditional scheme.

Recently, the mashup services linking multiple separated platforms have appeared on the Internet. However, to communicate between different platforms, those services need the Smart Mediator as a common relaying platform. The Smart Mediator receives data from various platforms and provides a uniform OpenAPI to the mashup service developer to ease the development procedures. One of the most important task of a Smart Mediator is to safeguard the user's privacy. Thus, it needs to provide anonymized personal data to the mashup services. In this paper, we propose an efficient anonymization scheme using entropy in the Smart Mediator. And we evaluate this scheme in the Smart Mediator. As a result, this scheme is more efficient other k when we are comparing attack risk rate and data loss rate. Through this scheme, we expect to shelter user's privacy and provide enough personal information to the mashup services.

Content Centric Networking (CCN) is one of the Future Internet architectures that aims at improving content distribution and retrieval, where content is requested by name rather than IP address. Each content is partitioned into small units, namely chunks. In CCN, the consumer sends Interest packet in order to get a chunk of Data. Upon successful reception of the requested chunk, the consumer sends the next Interest packet. This policy of send Interest, wait for the Data to reach and generate next Interest makes the situation worst in case of large sized content. The result is uplink underutilization. To overcome the above highlighted issue, we propose Interest forwarding in CCN, which is based on packet aggregation through combining multiple chunk requests in one Interest packet, and content name aggregation. The Interest packet aggregation will reduce the number of Interest packets need to be sent in the network and downsize Pending Interest Table (PIT), while organizing content based on aggregated name reduces Content Store (CS) entries. The simulation results show that our proposal achieves high performance with throughput improvement, reduced number of Interest packets in network, PIT and CS entries over other existing proposal in the literature.

An autonomous car is a self-driving car that is to keep the human being out of the car and to relieve them from the task of driving. An autonomous car can make more convenient, safer, and less energy intensive. In addition, Green Light Optimal Speed Advisory (GLOSA) systems reduce the travel time and CO2emission. In this paper, we propose a novel GLOSA, called R-GLOSA system to support to autonomous cars. We assume that an autonomous car can access to all traffic light schedules that it will encounter on its route. The route is divided into each segment according to traffic lights. In each segment, an autonomous car can communicate to Road Side Units (RSUs) distributed among the road. An autonomous car collects the road information transmitted by an RSU and then, it optimizes speed in order to arrive at the intersection when the light is green. The R-GLOSA system provides an autonomous car with speed advisory for each RSU's coverage. The simulation results show that an autonomous car using R-GLOSA system outperforms in terms of travel time and waiting time compared to using single-segment and multi-segment GLOSA system.

Recommender agents are widely used in online markets, social networks and search engines. The recent online news recommendation systems such as Google News and Yahoo! News produce real-time decisions for ranking and displaying highlighted stories from massive news and users access per day. The more relevant highlighted items are suggested to users, the more interesting and better feedback from users achieve. Therefore, the distributed online learning can be a promising approach that provides learning ability for recommender agents based on side information under dynamic environment in large scale scenarios. In this work, we propose a distributed algorithm that is integrated online K-Means user contexts clustering with online learning mechanisms for selecting a highlighted news. Our proposed algorithm for online clustering with lower bound confident clustering approximates closer to offline K-Means clusters than greedy clustering and gives better performance in learning process. The algorithm provides a scalability, cheap storage and computation cost approach for large scale news recommendation systems.

Information-Centric Networking (ICN) and Wireless Network Virtualization (WNV) are two emerging technologies for the next-generation network infrastructure. ICN provides the key technology to reduce the network traffic by caching the contents temporarily and aggregating the same content requests. WNV enables the resources sharing among Infrastructure Providers (InPs) and Mobile Virtual Network Operators (MVNOs), to reduce capital expenditures and operating expenses. Also, the network resource management becomes easier because of WNV. In this paper, we combine these two technologies to improve the performance of the network and the profit of the MVNOs. We formulate the optimization problem to solve the cache allocation problem and maximize the profit of MVNOs by controlling the usage of cache space, backhaul link, and radio resources. Finally, we validate our proposed scheme using a chunk-level simulator. The simulation results show that the proposed mechanism can improve the profit of MVNOs and user's QoS.

Information Centric Networking (ICN) is envisioned to be the future Internet architecture and mobile access network e.g., Long Term Evolution (LTE), and 5G will be the major access networks. In this paper, we present a cache management and cooperative request forwarding schemes for Scalable Video Streaming (SVS) in Information Centric Networking (ICN) enabled mobile access networks. H.264/SVC encoded video is consisted a mandatory baselayer and multiple optional enhancement layers. Baselayer, which is enough to decode the video, though with the lowest quality, is needed by every user who want to watch the video while enhancement layers are used to improve the video quality. Only a subset of users download enhancement layers of the video. Therefore, caching the baselayer nearer to the users will increase their Quality of Experience. Furthermore, we introduce cooperative request forwarding for the baselayer of video to take more benefits from cache of neighboring base stations. We have intensively simulated our proposed schemes by extending chunk level simulator ccnsim which is developed over Omnet++. Our experimental results show that, cache hit rate can be improved significantly by adopting our proposed caching and Interest forwarding schemes.

Even though demand response of datacenters recently has received increasing attention due to huge demands and flexible power control knobs, most of current studies focus on the owner-operated datacenters, leaving behind another critical segment of datacenter business: multi-tenant colocation. In colocation datacenters, while there exist multiple tenants who manage their own servers, the colocation operator only provides other facilities such as cooling, reliable power, and network connectivity. Therefore, colocation has its unique feature that challenges any attempts to design its demand response program: uncoordinated power management among tenants. To tackle this challenge, we consider incentive mechanisms that can coordinate tenants' power consumption for emergency demand response, where a fixed energy reduction target must be fulfilled. For two types of price-taking and price-anticipating tenants, we propose two incentive schemes with distributed algorithms that can achieve the same optimal social cost. Finally, trace-based simulations are also provided to illustrate the efficacy of our proposed incentive schemes.

In this paper, a novel framework is proposed for joint subchannel assignment and power allocation in the uplink of cognitive femtocell network (CFN). In the studied model, femtocell base stations (FBSs) are deployed to serve a set of femtocell user equipments (FUEs) by reusing subchannels in a macrocell network. The problem of optimal allocation of subchannels and transmit power is formulated as an optimization problem in which the goal is to maximize the overall uplink throughput while guaranteeing minimum rate requirement of the served FUEs and macrocell base station (MBS) protection. To solve this problem, a novel framework based on matching theory is proposed to model and analyze the competitive behaviors among the FUEs and FBSs. Using this framework, distributed algorithms are implemented to enable the CFN to make decisions on subchannel allocation and power control. The developed algorithms are then shown to converge to stable matchings. Simulation results show that the proposed approach yields a notable performance improvement, in terms of the overall network throughput and outage probability while requiring only a small number of iterations for convergence.

The use of heterogeneous small cell-based networks to offload the traffic of existing cellular systems has recently attracted significant attention. One main challenge is solving the joint problems of user association, resource allocation, and interference mitigation. The goal of this paper is to design a self-organizing algorithm that can solve these problems, simultaneously. To this end, this joint resource allocation problem is formulated as an optimization problem which is then solved using log-sum-exp approximation. This solution is then shown to require complete information of the whole network which is not scalable with the network size. To address this scalability issue, a novel Markov chain approach is proposed and its transition probabilities are shown to eventually converge to the near optimal solution without complete information. Furthermore, the gap between the optimal and converged solutions is shown to be bounded. Simulation results show that our proposed algorithm effectively offloads the traffic from macro-cell base station to small-cell base stations. Moreover, the results also show that this algorithm converges very quickly independent of the number of possible configurations.

In this paper, the problem of resource allocation in cloud datacenters, that own highly complex and heterogeneous tasks and servers, is considered. To address this problem, a novel framework, dubbed joint operation cost and network traffic cost (JOT) framework, is proposed. This framework combines notions from Gibbs sampling and matching theory to find an efficient solution addressing the NP-hard problem JOT. The proposed model is shown to be capable of controlling the active server set, in a coordinated manner while allocating VMs in order to reduce both operation cost and network traffic cost of the cloud datacenter. We also conduct a case-study to validate our proposed algorithm and the results show that JOT can reduce the total incurred cost by up to 19% compared to the existing non-coordinated approach.

In this paper, a renewable energy powered millimeter wave (mmW) backhaul network is studied. In the considered model, the wireless operator must request renewable energy from multiple renewable power suppliers (RPSs) to serve the end mobile users using the mmW backhaul. The unit price of renewable energy depends on the RPS's production capacity/lead time for the corresponding backhaul node. A lead time-dependent pricing scheme is proposed thus enabling the operator to manage the traffic latency over the backhaul and co-ordinate independent RPSs' decisions on the renewable energy storage levels with uncertain wireless traffic demand. Toward this end, the problem is formulated as a Stackelberg game between the operator and multiple RPSs. In this game, the operator first specifies a pricing scheme for RPSs and each RPS should then make its own decision in stocking the renewable energy. Then, efficient distributed algorithms are proposed to find the operator's optimal pricing scheme, and the RPSs' Pareto equilibrium storage strategies, respectively. Our results provide useful insights for understanding the tradeoff between the benefit of energy savings and the cost of quality-of-service (QoS) reducing for the operator. Also, simulation results show how renewable energy production capacities affect the revenue of individual RPSs in decentralized renewable-powered backhaul systems. The results also show that the proposed scheme can enable the operator to achieve more profit compared to a centralized solution.

Even though demand response of data centers has attracted many studies, there are very limited attempts on an important segment: colocation datacenters. Unlike large-scale (Google-type) datacenters, the colocation operator lacks control over its tenant servers, which entails a special interest in a design of incentive mechanisms, such that the operator can coordinate tenants to reduce the power usage for demand response. However, most previous studies ignore the role of the demand response provider (DRP), who uses pricing signals as a guide for customer response and as a compensation for their cutting electricity usage. To address this oversight, we propose an incentive mechanism Reward-to-Reduce for colocation's economic demand response, which shows an interaction between the DRP compensation to the colocation operator, and the colocation operator reward to tenants. Observing that this interaction contains strategic behaviors, we first formulate a two-stage Stackelberg game, where we show a unique competitive equilibrium of the operator strategy in the second stage, and a nonconvex problem of finding the optimal DRP compensation price in the first stage. We next analyze the second-stage equilibrium using an exact analysis and design an algorithm that can efficiently search the first-stage optimal DRP price with a reduced search space. Since the exact analysis can be impractical due to required tenants' private information, we also propose an approximate approach with limited tenant information. Extensive case studies show that the approximate approach can have the same performance as the exact analysis in a wide array of case studies and the optimal DRP price can be determined effectively, with which the corresponding DRP individual cost is compared with the social cost.

The proliferation of the market in patient care services is attracting attention in the healthcare industry; however, a remote mental healthcare system is still unattainable. In this paper, an ambient intelligent system of in-home psychiatric care service for emergency psychiatry (EM-psychiatry) is proposed for the remote monitoring of psychiatric emergency patients. The emergency psychiatric states of patients are modeled as the states of the maximum-entropy Markov model (MEMM), in which sensor observations, psychiatric screening scores, and patients’ histories are considered as the observations of MEMM. A modified Viterbi, a machine-learning algorithm, is used to generate the most probable psychiatric state sequence based on such observations; then, from the most likely psychiatric state sequence, the emergency psychiatric state is predicted through the proposed algorithm. The ambient EM-psychiatry model is implemented and the performance of the proposed prediction model is analyzed using the receiver operator characteristics curves, which demonstrates that the use of the EM-psychiatric screening questionnaire with biosensor observations enhances the prediction accuracy.

Music mood classification is a crucial component in the field of multimedia database retrieval and computational musicology. There is a constantly growing interest in developing and evaluating music information retrieval (MIR) systems that can provide automated access to the music mood. The proposed method considers the different types of audio features. From each feature's frame, a bin histogram has been calculated to preserve all important information associated with it. The histogram bins of each feature are used to calculate the similarity matrix, and the number of similarity matrices depends on the number of audio features. Therefore, there are 59 similarity matrixes from the corresponding same amount of audio features. The intra and inter similarity matrix are used to calculate the intra-inter similarity ratio. These similarity ratios are sorted in descending order in each feature. Among them, some of the selected similarity ratios are ultimately used as prototypes from each feature and are used for classification by designing the nearest multi-prototype classifier. The Coimbra mood dataset is used to measure the overall performance of the proposed method. We achieved competitive classification accuracies as compared with other existing state-of-the-art music mood classification techniques.

The revolutionized vision of IoT has united heterogeneous devices to foster the systems of cohesive intelligent things. In addition, Fog computing has also envisioned a new form of cloud computing paradigm. Therefore, Fog provides edge computing to such IoT devices with varied capabilities and resources. However, a balanced and efficient pairing or matching strategy for edge IoT nodes is crucial to achieve the user requisite. Hence, this paper addresses the utility based matching or pairing problem within the same domain of IoT nodes by using Irving's matching algorithm under the node specified preferences to endure a stable IoT node pairing. We studied the performance of the proposed matching algorithm through simulation. The simulation results show the higher utility gain of the node pairs through refined matching algorithm over greedy approach.

In heterogeneous networks (HetNets), low power smallcells, i.e., Wifi, can be offered an economic incentive in order to offload traffic from high-power macrocell, which is usually overloaded. This becomes important in order to maintain efficient operation of the network and generate benefit of tradeoff between macrocell and smallcells. The benefit to smallcells comes from the economic incentive offered by macrocell and the benefit to macrocell is achieved by reducing the load and saving spectrum. However, two important challenges are faced in this cooperation: 1) How much economic incentive can be offered by macrocell, and 2) How much offloading traffic volumes can be admitted by the smallcells. In this paper, we propose a novel game based approach for data offloading scheme to determine the amount of economic incentive a macrocell should offer to smallcells and to determine how much traffic each smallcell should admit from the macrocell. In our proposal, a two-stage non-cooperative Stackelberg game theory is applied to optimize the strategies of both macrocell and smallcells in order to maximize their utilities.

Recently cloud service has been introduced in order for many enterprises to achieve purposes such as improvement in efficiency, cost reduction and revolution in business process. However spoofing or poison attacks on VM inside the cloud cause the deterioration of cloud system and those attacks can make the huddle for spreading the cloud services. Many researches are now under way to solve such problems but most of these seem to be passive and limited in terms of detecting attacks and applying to large scale of networks. In this paper, we propose a defense technique for loss of VM resources against the network attacks called spoofing of poison on OpenStack environment. In our proposal, we use reliable ARP table which makes our proposal more reliable in cloud computing environment. Finally we conclude this paper showing that the proposed mechanism is an effective way to defend the ARP spoofing attack.

The installation of low power small cells under macro cells using the same spectrum is a promising approach to enhance the spectral efficiency and data-rate for the end users. These installations are becoming very dense in order to support the users' requirements (especially 5G networks) which make resource allocation using the same spectrum a very challenging problem. In this study, we address the downlink resource allocation problem for underlay small cell tier. We formulate the optimization problem for resource (channel) allocation in small cells while keeping the total interference to macro tier under an acceptable level. The objective of resource allocation is to maximize the throughput of small cells under the cross tier interference constraint. We employ matching theory to find a stable match for the resource allocation problem. We simulate our proposition to validate the stability of the network and the convergence of the resource allocation algorithm in terms of rate in a dense heterogeneous network. The matching results in an optimal solution which outperforms the existing sub-optimal resource allocation solutions.

At the present, the traffic is really in a mess when the number of vehicles is increasing rapidly. As a consequence, finding a parking space is remarkably difficult and expensive. Therefore, solving this problem has attracted the attention of both scientists and companies. Our study also focuses on solving parking problem to relieve the traffic congestion, reduce air pollution and enhance driving effectively. However, unlike other studies, we consider parking problem in the view of IoT. From this perspective, Fog Computing and Roadside Cloud are utilized to find a vacant spot. By utilizing this infrastructures, any parking space at many places can be shared. Then, we analyze and apply the matching theory to solve the parking problem. Accordingly, our proposal not only helps drivers finding an ideal available space but also brings the owners of these places profit. Simulation results demonstrate that the proposed approach is a reliable solution for the finding parking slot.

In this paper, we study the relay-aided cooperative modeling that supports the load-sharing in uplink two-tier cellular networks. In our model, users in heavily loaded macrocell are shifted to lightly loaded smallcells with the assistance of relay users to mitigate Signal to Interference plus Noise Ratio (SINR) degradation problem in conventional direct handover. In order to promote relaying data of users which are selfish and rational, a trading exchange model based on Stackelberg game is proposed to optimize strategies of users. Relay users have pricing-based strategies on theirs power unit while shifted heavily loaded macrocell users have strategies to buy power levels of relay users. Optimal strategies are investigated using the backward induction analysis. Specifically, problems of NP-hard combinatorial optimization in relay user selections in the game are solved with a distributed algorithm based on matching theory. We intensively evaluate our proposed model by simulating it in Matlab which shows the efficiency of our proposal.

The increase in the number of connected smart mobile devices has fueled the exponential growth in mobile data. The next-generation networks must meet the demand for higher capacity. Heterogeneous cognitive networks with multiple base station tiers are a promising approach to achieving the higher data rate target. The user association problem is a major issue in the heterogeneous cognitive networks because of the disparity in transmission powers of the base stations involved. Our objective is to achieve the optimal user association under interference constraints. We formulate the problem into an optimization problem and employ matching theory to propose an algorithm to obtain the optimal user association. The proposed matching algorithm for the optimal user association plays the role of SDN application. We then perform simulations and compare our proposed algorithm with existing ones. The simulations results depict that our proposed algorithm outperforms others.

Internet has grown very rapidly in the last couple of decades and still growing because of the expansion and utilization of various services and applications. Consequently, demand of delay and throughput sensitive services, like audio/video is also increasing. Information Centric Networking (ICN) is proposed as an architecture for the future Internet to meet the modern users and application requirements. In ICN users send requests (Interest Packets) for the Data they need. Interest packet is assigned a lifetime, which greatly affects the Quality of Experience (QoE) because user needs to resend the Interest, when the lifetime expires. Interest lifetime may be expired because of congestion, or Interest lifetime is shorter than the network delay, etc. Waiting for the expiration of an Interest lifetime to resend it is merely appropriate for best effort traffic, rather than services which require high throughput and are delay sensitive. In this paper, we propose Network-Assisted Congestion Control mechanism in ICN, which detects the congestion before it happens, and provides notification to downstream node. On reception of the notification, downstream node continuously reduces the traffic rate. However, when the downstream node fails to adjust the sending rate, the same procedure continues, until the sending node reduces the traffic rate through adjusting its congestion window. We have intensively evaluated our proposal by comparing it with similar proposal using ndnSIM. The experimental results show that our proposal achieves up to 59 percent performance improvement over other proposal in the literature.

Heterogeneous cellular networks offload the mobile data traffic to small cell base stations to reduce the workload on the macro base stations. Our objective is to maximize the sum rate of the down-links for the whole network under outage QoS constraint. To achieve the objective, we have to jointly solve the user association problem and resource allocation problem. We formulate the two problems into a joint optimization problem and convert it into an equivalent game theoretic formulation. We employ payoff based log linear learning and propose an algorithm that converges to one of the existing Nash equilibrium. We then provide extensive simulation results to verify the performance of our proposed algorithm.

Content Centric Networking (CCN) is one of the most promising network architectures of future Internet. In CCN, Content Router (CR) floods request in the network to find the content. This flooding may degrade the network performance by generating too much traffic. Also, in legacy CCN architecture, each CR caches all contents that pass through it. Thus, same contents are replicated in all CRs along the request path, which incurs faster cache replacement and degrades cache utilization consequently. Furthermore, caching and forwarding decisions are made by the CR only on the basis of its local knowledge, which may not be optimum decisions. In this paper we propose a hybrid caching, cache replacement and requests forwarding approaches to overcome the above drawbacks. In our proposal, there is a virtual controller inside the data center with high performance computational capacity. The controller makes all the forwarding and caching decisions and passes it to the physical CRs via virtualized core router. Physical CRs are grouped inside regions on the basis of their geographical location, in order to enhance users' QoE. We have intensively simulated the proposed mechanism in a chunk level simulator and the performance is compared with existing schemes. The simulation results show that the proposed mechanism outperforms the existing state-of-the-art schemes.

In the area of cloud infrastructure environment, the management tool to monitor and control the cloud resources is the important factor that can drive the cost benefit of the cloud vendors. But most these tools are bundled within the high cost commercial platforms and are optimized to run on desktop computers. With the vision that Mobile Cloud Computing will be the future technology paradigm that dominates the IT industry, we want to create a cloud management tool that is open source, fast, lightweight and mobile friendly. We take the initial steps by implementing our framework using several popular technologies such as RESTful, Java Message Service, JSON, and we call it “High performance and Lightweight Mobile Cloud Infrastructure Monitor and Benchmark Service” or HiLiCloud. The initial testings show competitive evaluation results.

We take the first step to study the price competition in a heterogeneous market cloud computing formed by public provider and cloud broker, all of which are also known as cloud service providers. We formulate a price competition between cloud broker and public provider as a two-stage non-cooperative game. In stage one, where cloud service providers set their service prices to maximize their revenue, we use the Nash equilibrium concept to study the equilibria for the price setting game. Cloud users can select the services (from the cloud broker or public provider) that provide them the best payoff in terms of performance (i.e., delay) and price. To that end, cloud users can adapt their service selection behavior by observing the variations in price and quality of service offered by the different cloud service providers. For the service selection game of cloud users in stage two, we use the evolutionary game model to study the evolution and the dynamic behavior of cloud users. Furthermore, the Wardrop equilibrium and replicator dynamics is applied to determine the equilibrium and its convergence properties of the service selection game. Numerical results illustrate that our game model captures the main factors behind the heterogeneous market cloud pricing and service selection, thus represents a promising framework for the design and understanding of the heterogeneous market cloud computing.

In two-tier LTE heterogeneous networks (HetNets), picocells can be offered radio resource in order to mitigate interference to picocell users in downlink transmission from high-power macrocell base station (MBS). This becomes important in order to maintain efficient operation of the network and generate benefit tradeoff between macrocell and picocells. In this paper, we propose a game based approach for joint resource partitioning and data offloading scheme to determine the amount of radio resource a MBS should offer to picocells and to determine how much traffic each picocell access point (AP) should admit from MBS. In our proposal, a two-stage Stackelberg game theory is applied to optimize the strategies of both MBS and APs in order to maximize both of their utilities and this scheme is implemented using the notion of Almost Blank Subframes (ABS) proposed in the LTE standard.

Demand response programs have been considered critical for power grid reliability and efficiency. Especially, the demand response of datacenters has recently received encouraging efforts due to huge demands and flexible power control knobs of datacenters. However, most current efforts focus on owner-operated datacenters, omitting another critical segment of datacenter business: multitenant colocation. In colocation datacenters, while there exist multiple tenants who manage their own servers, the colocation operator only provides facilities such as cooling, reliable power, and network connectivity. Therefore, colocation has a unique feature that challenges any attempts to design a demand response program: uncoordinated power management among tenants. To tackle this challenge, two incentive mechanisms are proposed to coordinate tenant power consumption for demand response under two different scenarios. First, in the case of economic demand response where the operator can adjust an elastic energy reduction target, we show that there is an interaction between the operator and tenant strategies, where each side maximizes its own benefit. Hence, we apply a two-stage Stackelberg game to analyze this scenario and derive this game's equilibria. However, computing these equilibria can be intractable with exhaustive search; therefore, we propose an algorithm to find the Stackelberg equilibria with linear complexity. Second, in the case of emergency demand response where a fixed energy reduction target must be fulfilled, we devise two incentive schemes with the distributed algorithms that can achieve the same optimal social cost. While the first algorithm is based on the dual-decomposition method that is suitable for nonstrategic tenants, the second one is designed for strategic tenants to achieve a unique Nash equilibrium of a bidding game. Finally, trace-based simulations are also provided to illustrate the efficacy of our proposed incentive schemes.

In the dynamic spectrum access (DSA), pricing is an efficient approach providing economic incentives for operators, whereas load balancing yields congestion-avoidance incentives for secondary users (SUs). Despite complexities of 1) the couplings among pricing, load balancing, and SUs' spectrum access decision, and 2) the heterogeneity of primary users' traffic and SUs classes/types, we tackle the joint load balancing and pricing problem to maximize operators' revenue in two cognitive radio markets: monopoly and duopoly. For the monopoly market, we first show there exists a unique SUs' equilibrium arrival rate to the monopolist's channels. We then show that the joint problem can be solved efficiently by exploiting its convex structure. For the duopoly market, we first characterize a unique SUs' equilibrium arrival rate to two operators employing different DSA approaches. When two operators are noncooperative, we show that there exists a unique Nash equilibrium for each operator's revenue. When they are cooperative, we show that the social revenue optimization can achieve a unique optimal solution. Using the Nash bargaining framework, we also present a sharing contract that determines the optimal fraction of the social revenue for each operator. In both markets, we propose two algorithms that can find the largest SU class supportable by the operators.

Vehicular Ad hoc Networks (VANETs) should provide the vehicles with reliable safety message broadcasts and efficient non-safety message transmissions. The IEEE 1609.4 MAC is designed for VANETs to support multi-channel operations, but the safety message broadcast is not much reliable and the Service Channel (SCH) resources are not fully utilized. In this paper, we propose a new multi-channel MAC for VANETs, named HER-MAC, which supports both TDMA and CSMA multiple access schemes. The HER-MAC allows vehicle nodes to send safety messages without collision on the Control Channel (CCH) within their reserved time slots and to utilize the SCH resources during the control channel interval (CCHI) for the non-safety message transmissions. Compared to the current IEEE 1609.4, the proposed HER-MAC protocol is more reliable in the safety message broadcast, efficient in the service channel utilization.

Vehicular Ad hoc NETworks (VANETs) should provide the reliable safety message broadcasts and the efficient non-safety message transmissions to vehicles. The IEEE 1609.4 MAC, which supports multi-channel operations in VANETs, is not reliable enough for the safety message broadcast and not efficient in the Service CHannel (SCH) resources utilization. In this paper, we propose a MAC protocol which supports a Reliable Multi-hop Safety message Broadcast (RMSB-MAC) in VANETs. Each Multi-hop Forwarder (MF) collects the safety messages from the neighbor vehicle nodes, and then the MF uses its reserved time slot to broadcast them to all vehicle nodes in its transmission range as well as to forward them to the next MF. Moreover, by allowing vehicle nodes to exchange non-safety messages during the Control CHannel Interval (CCHI), the RMSB-MAC utilizes the SCH resources more efficiently.

In this paper, we optimize resource allocation for multimedia cloud based on queuing model. Specifically, we optimize the resource allocation in both single multimedia service provider (MSP) scenario and multiple MSPs scenario. In each scenario, we formulate and solve the MSPs' revenue maximization problem under eviction probability constraint of users. Numerical results demonstrate that the proposed optimal allocation scheme can optimally utilize the cloud resources to achieve a maximum revenue.

According to increasing performance of mobile devices, like smart phone, tablet PC and etc, and diffusing network infrastructures, like LTE, WiFi and etc, various types of content delivery services based on PC services can serve into mobile devices using cloud. In this paper we proposed content delivery framework with SDN (Software Defined Networking) and CCN (Content Centric Networking) to improve content delivery QoS in mobile cloud environment. Additionally to serve autonomic optimal services, we proposed reinforcement learning based context-aware content delivery scheme. Using our framework, we can guarantee QoS to provide context-aware content delivery scheme.

In this paper, we develop an opportunistic scheduling policy for allocating spectrum in cognitive radio networks. We maximize the throughput utility of secondary users subject to maximum collision constraints with the primary users. Particularly, we consider a cognitive radio network with a subset of the secondary users desire to use the licensed channels of primary system in a stochastic environment. Based on Lyapunov technique, we formulate the above problem as a Lyapunov optimization problem on stability region of virtual and actual queues. Then, we propose an online flow control, scheduling and spectrum allocation algorithm that meets the desired objectives and provides explicit performance guarantees.

The VeMAC, a TDMA-based MAC protocol for Vehicular Ad-hoc NETworks (VANET), assigns disjoint sets of the time slots to vehicles moving in opposite directions and to road side units. So the VeMAC protocol reduces the access and merging collisions. Moreover, the VeMAC protocol employs slot release prevention condition which avoids unnecessarily releasing time slots when a node just enters the communication range of each other. Although VeMAC supports reliable and efficient transmission, it is still not full applicable for VANET in parallel transmission. In this paper, we propose an e-VeMAC protocol: an enhanced vehicular MAC protocol to mitigate the exposed terminal problem. The simulation results show that the e-VeMAC protocol supports more parallel transmissions than the VeMAC protocol.

The original Content Centric Network (CCN) employs a simple caching scheme, Leave Copy Everywhere (LCE). However, this scheme is not efficient because cache redundancy reduces the storage capacity of the CCN network. In this paper, to resolve the issue of cache redundancy, we propose a cooperative caching decision and forwarding mechanism which is based on consistent hashing and virtual routers. We divide the Autonomous System (AS) into several groups of routers. The routers in the group cooperatively store the contents (Data) and also forward the requests (Interest) cooperatively in order to increase the caching performance of the CCN network. Finally, we evaluate our proposal by using a chunk-level simulator. The results show that the cache hit ratio of our proposed scheme is better than other proposed schemes.

In-memory Computing (IMC) is the new trend for enabling high-performance computation and fast data processing. It is currently being used for large enterprises, e-commerce shops who need real-time interactions, low latency responses and instant results. Given the enhancement of the IMC, we apply this new paradigm to the Virtual Desktop Environment (VDE), and look into the performance differences in comparison with traditional VDE. The end results shows positive feedback, but there are trade-offs we need to concern for the In-memory Virtual Desktop Environment.

In dynamic spectrum access (DSA) control, the prevalent approach to provide economics incentives for operators is pricing, whereas load balancing gives congestion-avoidance incentives to secondary users (SUs). Despite complexities of i) the couplings between pricing, load balancing and SUs' spectrum access decision, and ii) the heterogeneity of primary users' traffic and SUs types, we propose to solve the joint load balancing and pricing problem to maximize operator' revenue in a monopoly market. In this market, we first show there exists a unique SUs' equilibrium arrival rate to the monopolist's channels, and then we show that the joint problem can be solved efficiently by exploiting its convex structure. We next propose a low-complexity algorithm that enable the operator to maximize its revenue.

In this paper, we focus on a scheme that supports mobility for groups of sensor networks. Mobility in wireless sensor networks (WSNs) is one of the most important WSN technologies for applications such as healthcare, vehicular communication systems, intelligent transport systems, and logistics applications. This paper proposes a new mobility management architecture for WSNs that is based on the ID/LOC separation concept for ID-based communications with location-based routing. The proposed architecture supports energy-efficient lightweight mobility control for a large number of WSNs by distributed management manner. Furthermore, the discover-before-forward concept is proposed for route optimization. Performance results show that the proposed ID/LOC separation-based mobility architecture for supporting 6LoWPAN is more efficient and lighter than the existing HIP scheme in terms of energy consumption, total signaling cost and packet delivery cost ratio.

Pricing is an effective approach for spectrum access control in cognitive radio (CR) networks. In this paper, we study the pricing effect on the equilibrium behaviors of selfish secondary users' (SUs') data packets which are served by a CR base station (BS). From the SUs' point of view, a spectrum access decision on whether to join the queue of the BS or not is characterized through an individual optimal strategy that is joining the queue with a joining probability. This strategy also requires each SU to know the average queueing delay, which is a non-trivial problem. Toward this end, we provide queueing delay analysis by using the M/G/1 queue with breakdown. From the BS's point of view, we consider a duopoly market based on the two paradigms: the opportunistic dynamic spectrum access (O-DSA) and the mixed O-DSA & dedicated dynamic spectrum access (D-DSA). In the first paradigm, two co-located opportunistic-spectrum BSs utilize freely spectrum-holes to serve SUs. Then, we show the advantages of the cooperative scenario due to the unique solution that can be obtained in a distributed manner by using the dual decomposition algorithms. For the second paradigm, there are one opportunistic-spectrum BS and one dedicated-spectrum BS. We study a price competition between two BSs as a Stackelberg game. The cooperative behavior between two BSs is modeled as a bargaining game. In both paradigms, bargain revenues of the cooperation are always higher than those due to competition in both cases. Extensive numerical analysis is used to validate our derivation.

We study the pricing mechanisms and their effects on equilibrium behaviors of self-optimizing secondary users (SUs) sharing a single channel of primary users (PUs) operated by a service provider (SP) in cognitive radio networks. From SUs' point of view, a spectrum access decision on whether to join a queue or not is characterized through an individual optimal strategy. With this strategy, we show that there exists a unique equilibrium in terms of SUs' joining probability. This strategy also requires each SU to know its average queueing delay, which is a non-trivial problem because of multiple SUs service's interruptions from the returns of PUs; we, however, can analyze this queueing delay based on the general distribution of SUs's service time and PUs' traffic model by using renewal theory. We also provide a sufficient condition and iterative algorithms for the convergence of equilibrium points. From the SP's point of view, two pricing mechanisms are proposed with different goals: revenue maximization and social welfare maximization. And the optimal price can be solved efficiently using numerical methods.

In addition to spectrum scarcity, unfair and insufficient channel contention resolution has become one of the major bottlenecks for a good throughput in multi-hop wireless ad-hoc networks (MHAHNs). In this paper, we propose an optimal cognitive MAC protocol for MHAHNs under opportunistic spectrum access (OSA) approach in which channel contention resolution is done on the basis of interference-dependent random access addressing both social welfare maximization and energy efficiency. We show that, in OSA-based MHAHNs, contention control at MAC layer has an interaction relationship with power control at physical layer and congestion control at transport layer. Studying their mutual effect on such an OSA-based MHAHNs' performance motivates an optimal cognitive MAC framework which is shown as an NP-hard problem. To solve the problem, we introduce some auxiliary variables which are interpreted as interference weights and develop a distributed solution, which has been proved to converge to the global optimum.

The IEEE 1609.4 [1] is a MAC extension of IEEE 802.11p [2] to support multi-channel operations. However, the IEEE 1609.4 does not allow nodes to exchange non-safety messages during the CCH interval. This paper proposes a Vehicular Enhanced Multi-channel MAC protocol (VEMMAC) for Vehicular Ad hoc Networks (VANETs). The VEMMAC adopts the IEEE 1609.4 with alternating sequences of the Control Channel (CCH) interval and the Service Channel (SCH) interval. Different from the IEEE 1609.4, the VEMMAC allows nodes to transmit non-safety messages during CCH interval and broadcast safety messages twice with each in the CCH and SCH interval. Our proposal can utilize the channel resources more efficiently than the IEEE 1609.4. The simulation results show that the proposed VEMMAC protocol achieves higher throughput for service data and is more reliable for safety messages broadcast than other protocols.

Cognitive radio network technology is regarded as a new way to improve the spectral efficiency of wireless networks. It has been well studied for more than a decade and numerous precious works have been proposed. However, very few existing works consider how to broadcast messages in cognitive radio networks that operate in multichannel environments and none of these provides a full broadcast mechanism. Therefore, in this paper, we propose a broadcasting mechanism for multichannel cognitive radio ad hoc networks. Then, we analyze the mechanism regarding the speed of message dissemination, number of transmissions, portion of the users that receive the broadcast message and so forth.

In this paper, we consider the probabilistic channel contention resolution problem for net revenue maximization in multi-hop wireless ad-hoc networks (MHAHNs) under collision-rate-constrained opportunistic spectrum access (OSA) approach. Specifically, we focus on the interference-dependent contention model, in which secondary users (SUs) must coordinate to each other to simultaneously balance between interference and collision, leading a more efficient MAC protocol than the location-dependent one proposed in the literature. By introducing some auxiliary variables and noisy channel estimations, we can then develop a novel heuristic cross-layer cognitive MAC protocol (HCC-MAC) in OSA-based MHAHNs to solve the formulated MAC optimization problem which is shown non-convex and inseparable. More importantly, our proposed protocol can achieve near-optimal throughput in a distributed manner without control overhead. Finally, the numerical results show that HCC-MAC can outperform the existing MAC protocols under OSA paradigm.

This paper studies price-based spectrum access control in cognitive radio networks, which characterizes network operators' service provisions to delay-sensitive secondary users (SUs) via pricing strategies. Based on the two paradigms of shared-use and exclusive-use dynamic spectrum access (DSA), we examine three network scenarios corresponding to three types of secondary markets. In the first monopoly market with one operator using opportunistic shared-use DSA, we study the operator's pricing effect on the equilibrium behaviors of self-optimizing SUs in a queueing system. We provide a queueing delay analysis with the general distributions of the SU service time and PU traffic using the renewal theory. In terms of SUs, we show that there exists a unique Nash equilibrium in a non-cooperative game where SUs are players employing individual optimal strategies. We also provide a sufficient condition and iteraIntive algorithms for equilibrium convergence. In terms of operators, two pricing mechanisms are proposed with different goals: revenue maximization and social welfare maximization. In the second monopoly market, an operator exploiting exclusive-use DSA has many channels that will be allocated separately to each entering SU. We also analyze the pricing effect on the equilibrium behaviors of the SUs and the revenue-optimal and socially-optimal pricing strategies of the operator in this market. In the third duopoly market, we study a price competition between two operators employing shared-use and exclusive-use DSA, respectively, as a two-stage Stackelberg game. Using a backward induction method, we show that there exists a unique equilibrium for this game and investigate the equilibrium convergence.

We study the cross-layer design of congestion control and power allocation with outage constraint in an interference-limited multihop wireless networks. Using a complete-convexification method, we first propose a message-passing distributed algorithm that can attain the global optimal source rate and link power allocation. Despite the attractiveness of its optimality, this algorithm requires larger message size than that of the conventional scheme, which increases network overheads. Using the bounds on outage probability, we map the outage constraint to an SIR constraint and continue developing a practical near-optimal distributed algorithm requiring only local SIR measurement at link receivers to limit the size of the message. Due to the complicated complete-convexification method, however the congestion control of both algorithms no longer preserves the existing TCP stack. To take into account the TCP stack preserving property, we propose the third algorithm using a successive convex approximation method to iteratively transform the original nonconvex problem into approximated convex problems, then the global optimal solution can converge distributively with message-passing. Thanks to the tightness of the bounds and successive approximations, numerical results show that the gap between three algorithms is almost indistinguishable. Despite the same type of the complete-convexification method, the numerical comparison shows that the second near-optimal scheme has a faster convergence rate than that of the first optimal one, which make the near-optimal scheme more favorable and applicable in practice. Meanwhile, the third optimal scheme also has a faster convergence rate than that of a previous work using logarithm successive approximation method.

In this paper, we investigate the cross-layer design for congestion, contention, and power control in multi-hop cognitive radio ad-hoc networks (CRAHNs). In particular, we develop a unified optimization framework achieving flexible tradeoff between energy efficiency and network utility maximization where we design two novel cross-layer cognitive algorithms comprising efficient powered-controlled MAC protocols for CRAHNs based on the concepts of social welfare and net revenue in economics. The proposed framework can balance interference, collision, and congestion among cognitive users (CUs) including cognitive sources and cognitive links while utilizing stochastic spectrum holes vacated by licensed users (LUs). The former allows both cognitive sources and cognitive links to simultaneously adjust their transmission parameters (i.e., transmit power, persistence probability, and rate) following the law of diminishing returns whereas the latter forces cognitive links to control the persistence probability and transmit power in order to asymptotically balance the offered load regulated by cognitive sources. Our proposed protocols are then validated and their performance is compared with the existing MAC schemes in the literature via numerical studies.

We exploit helper-to-helper (h-2-h) cooperation using the broadcast nature of wireless sensor networks. The potential helpers overhear the packet from the transmission of previous hop and the helper transmits the packet in the current hop on behalf of the assigned node (sender). We call it friendship relaying cooperative medium access control (FRC-MAC) protocol. This mechanism eliminates the sender transmission phase, improving the energy efficiency and end-2-end delay. Our innovative proposed MAC protocol provides alternate path when a link is poor temporarily, hence no need to find a new path frequently. In this protocol, each node manages a helper queue in additional to its data queue to store cooperative data so that the helper can transmit cooperative data with higher priority than its own data. We performed a simulation study to investigate network performance and compared with existing protocols. We found that FRC-MAC outperforms existing protocols in terms of end-to-end delay and energy efficiency.

Coexistence of different wireless networks is a common phenomenon in today's smart communication infrastructure. Now, the big issue is to explore benefits from the heterogeneous nature of communication technology. Load balancing among the heterogeneous wireless networks is the primary goal of this paper. Load balancing without considering Quality of Service (QoS) merely inadequate in convergence of resource utilization and grade of service. So, this paper proposed a load balancing algorithm with QoS provisioning. This paper is based on a semi-distributed load balancing architecture. Firstly, IP-flow dividing ratio based soft load balancing approach is discussed for high speed features of next generation wireless networks. Secondly, an admission control function of QoS requirements is developed. Thirdly, a joint optimization function is derived and a load balancing algorithm is proposed by using the cost function. Finally, simulation results are presented for performance appraisal.

In the wireless ad hoc network, utilizing the multiple channels at Medium Access Control (MAC) layer is on of the key techniques to improve the network performance. It can be done by multi-channel MAC to utilize the channels resource as much as possible. Wireless nodes are usually powered by battery and thus are limited in power capacity. The IEEE 802.11 Power Saving Mechanism (PSM) is used to conserve energy for the ad hoc networks by allowing nodes to enter doze mode when there is no need for data exchange. In this paper, we propose a hybrid and adaptive protocol, named H-MMAC, by adopting IEEE 802.11 PSM. In H-MMAC, nodes exchange control messages on default channel to negotiate the data channel during the ATIM (Ad hoc traffic Indication Message) window. The difference of H-MMAC compared to other multi-channel MAC protocols is that the other nodes can transmit data packets on data channels based on the network traffic load. This means H-MMAC can utilize the channel resources more efficiently. The simulation results show that our proposed H-MMAC improves the network performance significantly in terms of aggregate throughput, average delay and energy efficiency.

Recently overlay/underlay framework in Cognitive Radio (CR) have been studied and demonstrated the benefits such as spectrum efficiency and channel capacity maximization. We assume Secondary Users (SUs) can choose to either acquire a dedicated spectrum with constant payment or to use a Primary User (PU) band for free. However, using PU band yields delayed transmission cost. In this paper, we apply M/M/1 queueing model with heterogeneous service rate to derive the explicit expressions for the expected delays of an arbitrary SU data packets. Based on this, the SUs need to decide whether to use the Primary User (PU) band or to acquire the dedicated band. The interaction between selfish SUs is modeled as a noncooperative game. We prove the existence and uniqueness of a symmetric Nash equilibrium, and characterize the equilibrium behavior explicitly. Then an appropriate price of the dedicated band can be defined. Numerical analysis are used to prove a high degree of accuracy for the derived expressions.

Recently smart devices include various interfaces to communicate using 3G, LTE, WiFi and Bluetooth. Additionally a research, named flow mobility, which maintains seamless communication session has progressed using different interfaces in the smart device. Content Centric Network which is one of the Future Internet research issues is studied to solve a problem which is occurred by node mobility. In this paper we propose an efficient and seamless content delivery scheme using flow mobility in CCN. To solve mobility problem in CCN, we introduce a mobility scenario with proposed scheme.

Machine to Machine (M2M), communication between the machines without or with the least-human involvement, is going to be an important part of life. Healthcare is one of the areas on which M2M is going to play major roles. At present time, healthcare sensors monitor patient information and notify remote doctors. However, if we can integrate more intelligence in healthcare sensors, then these can sense patient's emergency condition by themselves and can notify doctor before severe condition. In this context, we have developed intelligent mobile sensor agents in a healthcare scenario in a M2M context. This sensor agent can sense blood pressure of a patient and can notify remote doctor, with the help of an intelligent adapter and a manager in a M2M healthcare scenario.

The IEEE 802.11 [1] provides multiple channels for wireless communications at the Physical Layer, but the Medium Access Control (MAC) protocol is only designed for a single channel. If the multiple channels can be exploited by multi-channel MAC protocol, there can be multiple transmissions on different channels. Besides that, the power control algorithm can improve the spatial reuse of wireless channels. In this paper, we combine the multi-channel MAC protocol and the power control algorithm together to exploit multiple channels and improve frequency reuse. The main idea of our proposal is to use IEEE 802.11 Power Saving Scheme (PSM) with different transmission power levels in the ATIM window and the data window. All nodes transmit ATIM/ATIM-ACK/ATIM-RES messages with the maximum power while contending the data channel during the ATIM window, and use the minimum required transmission power in the data window on their negotiated channels. The simulation results show that the proposed E-MMAC improve the performance of the network: aggregate throughput, average delay and energy efficiency.

In regular wireless ad hoc network, the Medium Access Control (MAC) coordinates channel access among nodes and the throughput of the network is limited by the bandwidth of a single channel. The multi-channel MAC protocols can exploit the multiple channels to achieve a high throughput by enabling more concurrent transmissions in the network. Dynamic Channel Assignment (DCA [5]), Multi-channel MAC (MMAC [2]) and Pipelining Multi-channel MAC (π-Mc [7]) are three multichannel MAC protocol representatives. In the DCA protocol, the dedicated control channel cannot be fully utilized or causes the bottleneck depending on the number of channels. In the MMAC protocol, the data channel resources are wasted during the ATIM window. The data packet size impacts on the performance of π-Mc protocol. In this paper, we propose a hybrid protocol that utilizes the multi-channel resources more efficiently than MMAC and other protocols. The H-MMAC protocol allows nodes to transmit data packets while other nodes try to negotiate the data channel during the ATIM window. The simulation results show that the proposed H-MMAC can improve the performance of the network: aggregate throughput, average delay and energy efficiency.

Body sensor nodes those are implanted inside human body for comparatively long term monitoring, are termed as implanted or implantable or in vivo sensor nodes. In recent years, in vivo sensor nodes are getting increasing interest from clinicians or researchers around the world. These nodes are noninvasive and can provide more accurate information in terms of medical science being implanted on/near the targeted body-organ. Moreover, movement of human or body organ does not affect the functionality of in vivo nodes. However, in vivo sensor nodes exhibit temperature at processing or communication time, which might be dangerous for human tissue in long term monitoring. Thermal aware routing algorithms have been proposed to deal with the problem. But, these algorithms suffer from limitations like hotspot creation, computational complexity, delay etc. A virtual backbone is a small subset of connected sensor nodes and it is connected to all other sensor nodes of the set. All virtual backbone nodes involve less interference, congestion and lightweight communication those are significant for an in vivo sensor network. In this context, we have proposed fault-tolerant virtual backbone construction algorithm to schedule temperature(routing cost) in an in vivo environment. Fault-tolerance virtual backbone is significant in the sense that when any node or nodes of a in vivo virtual backbone has/have higher temperature, the path is disconnected and communication is continued with an alternative path. Therefore, temperature is scheduled in an in vivo sensor network.

Interference due to the sharing of common spectrum band among links and congestion due to the contention among flows sharing the same link have become obstacles to good performance in wireless networks, especially in multi-hop cognitive radio networks (MHCRNs). Consequently, the high end-to-end throughput for MHCRNs calls for a framework of cross-layer optimization design. In this paper, by taking into account the problem of joint optimal rate, power, and spectrum allocation (JORPS), we propose a new cross-layer optimization framework for MHCRNs under spectrum underlay manner using orthogonal frequency division multiple access (OFDMA). The formulation is shown to be a mix-integer non-linear non-convex optimization problem, which is μμ-hard in general. To tackle this cumbersome, we firstly applied a partially distributed solution. Then, we showed that this approach fast converges to the global optimum at a cost of computational complexity.

In this paper, we solve the network utility maximization (NUM) problem for networks with both multi-path and single-path users. To deal with the non-strictly convexity and non-separability of the problem, we approximate it to a new strictly convex and separable problem which is efficiently solved by the standard dual-based decomposition approach. After a sequence of approximations, the solution to the approximation problem converges to a globally optimal solution of the original NUM. From the theoretical analysis, we also introduce a design of multi-path Reno (mReno) based on the reverse engineering framework of TCP Reno. The fairness among multi-path users and single-path users is guaranteed.

In this paper, we have devised a reinforcement learning algorithm, which helps in enabling autonomic control loops in Policy based Autonomic Network Management (PBANM). We have proposed two autonomic control loops for optimal configuration and policy optimization in PBANM system. Simulations are performed to validate our proposal.

Recently overlay/underlay framework in Cognitive Radio have been studied and it demonstrated the benefits such as spectrum efficiency and channel capacity maximization. Arising from these work, we suppose the secondary users can operate under overlay mode when the primary user is absent and operate under underlay mode when the primary user is present. In this paper, a hybrid overlay/underlay cognitive radio system is modeled as a M/M/1 queue where the rate of arrival and the service capacity are subject to Poisson alternations. Each packet (as a customer) arriving at the queue makes decision to join or balk the queue. Upon arrival, the individual decision of each packet is optimized based on his observation about the queue length and the state of system. We will present the individual strategy of each customer in detail in this paper.

We consider the problem of joint congestion control and power control with outage constraint in an interference limited multihop wireless network. We transform the original nonconvex problem into a convex programming problem and develop a message passing distributed algorithm that can attain the global optimal source rate and link transmit power. This algorithm however requires larger control message size than that of the conventional scheme, which increases network overheads. We continue developing a practical near-optimal distributed algorithm which only requires local SIR measurement to limit the size of the message. Numerical results show that both schemes have nearly identical performance and outperform the conventional scheme.

Efficient and fair power allocation associated with congestion control in orthogonal frequency division multiplexing (OFDM)-based multi-hop cognitive radio networks (CRNs) is a challenging and complicated problem. In this paper, we consider their mutual relationship through a cross-layer optimization design that addresses both aggregate utility maximization and energy consumption minimization. By introducing the unique outage constraint of primary user (PU) protection, the joint congestion control and power control (JCPC) formulation is shown to be a nonlinear non-convex optimization problem. Using dual decomposition approach, we first propose a distributed algorithm that can attain the optimal solution via message passing while maintaining the architectural modularity between the layers. Next, we develop a suboptimal algorithm using a new heuristic method to alleviate the overhead burden of the first solution. Finally, the numerical results confirm that the OFDM-based multi-hop CRNs can optimally exploit the spectrum opportunity if the PU outage probability is kept below the target.

In this paper, we focus on a scheme that supports mobility for IPv6 over Low power Wireless Personal Area Network (6LoWPAN) sensor nodes. We define a protocol for 6LoWPAN mobile sensor node, named 6LoMSN, based on Proxy Mobile IPv6 (PMIPv6). The conventional PMIPv6 standard supports only single-hop networks and cannot be applied to multihop-based 6LoWPAN. It does not support the mobility of 6LoMSNs and 6LoWPAN gateways, named 6LoGW, cannot detect the PAN attachment of the 6LoMSN. Therefore, we define the movement notification of a 6LoMSN in order to support its mobility in multihop-based 6LoWPAN environments. The attachment of 6LoMSNs reduces signaling costs over the wireless link by using router solicitation (RS) and router advertisement (RA) messages. Performance results show that our proposed scheme can minimize the total signaling costs and handoff latency. Additionally, we present the design and implementation of the 6LoMSN mobility based on PMIPv6 for a healthcare system. According to the experimental results, the 6LoMSN of the proposed PMIPv6-based 6LoWPAN can be expected to use more of the battery lifetime. We also verify that the 6LoMSN can maintain connectivity, even though it has the freedom of being able to move between PANs without a mobility protocol stack.

Intrusion detection system (IDS) is one of the basic components of network security. IDS can use signatures or abnormal behaviors to detect attacks. When IDS uses signatures, in order to detect more attacks, we have to implement more attack signatures on IDS. However, in resource-constrained wireless sensor networks, it is not efficient to implement all of attack signatures. And it is hard to maintain attack signature database because it should be updated frequently when new kind of attacks appear. In this paper, we propose a distributed IDS mechanism which reduces the usage of memory and overhead. Analysis of our proposal shows that our proposal requires less memory.

Content-Centric Network (CCN) sends data to avoid duplicated transmission with content name in the network. However, CCN may cause increase in overhead while maintaining contents management table and create storage problem while storing contents information in the node. In this paper, we proposed a new searching scheme using interesting keyword and manage contents table via overlay network. The content providers with high frequency are chosen in the network. Then an overlay network is constructed using content providers. Via overlay network, it is possible to reduce the number of message transmissions and overhead to manage content management table in the network.

With the penetration of smart-devices and intelligent home-appliances, M2M (machine to machine) communication has opened the door of new prospects for us. The success of such M2M networks depends on its human involvements at the least possible level. In this context, policy based management has huge potentials to be used for M2M communication. This paper presents such a policy based management framework for M2M networks and services. We have considered two M2M communication platforms namely smart-devices and sensor network in our system.

In social networks, user profile can be used as an important factor to define the character of a user and divide users in many social groups and provide suitable services to each social group. Recently, launched mobile devices can perform many functions, such as taking pictures, listening to music, web browsing etc. So, a mobile device can collect a user's data in his/her daily life and create his/her profile. In this paper, we propose a novel system, which can create a user profile automatically based on collected data from a mobile device and build an Ad-hoc social network.

The forthcoming Intelligent Transportation System aims to achieve safety and productivity in transportation using vehicular ad hoc networks (VANETs) to support the communications system required. Currently, some clustering approaches have been proposed to improve the performance of VANETs due to their dynamic nature, high scalability and load balancing results. However, the host mobility and the constantly topology change continue to be main problems of this technique due to the lack of models which represent the vehicular behavior and the group mobility patterns. Therefore, we propose an Adaptable Mobility-Aware Clustering Algorithm based on Destination positions (AMACAD) to accurately follow the mobility pattern of the network prolonging the cluster lifetime and reducing the global overhead. In an effort to show the efficiency of AMACAD, a set of simulation was executed. The obtained results reveal an outstanding performance in terms of the lifetime of the cluster heads, lifetime of the members and the re-affiliation rate under varying speeds and transmission ranges.

In this paper, we propose protocols and mechanism for creation and working of Virtual Sensor Networks (VSN). Through simulations we support the concept and show that the proposal can achieve better results than ordinary WSN. We provide an application scenario for configuration management of WSN using the WSN concept.

The current optimal joint rate and power control algorithms for wireless networks are mainly for elastic traffic which has strictly concave utility functions. In the multiclass service networks such that elastic and inelastic, the inelastic traffic is usually associated with the sigmoidal utilities which are nonconcave functions. Therefore, the corresponding Network Utility Maximization (NUM) problem is nonconvex in both objective and constraints. The current approaches cannot be applied and the problem is difficult to solve for the global optimal solution even with the centralized method. This paper proposes the joint rate and power control algorithm which can be distributively implemented. We approximate the nonconvex NUM to the convex problem which is easily solved by dual decomposition method. After a series of approximations, the algorithm converges the local optimal solution.

We consider a network with two kinds of traffic: inelastic and elastic. The inelastic traffic requires fixed throughput and high priority, while the elastic traffic has the rate that can be controlled and low priority. Given the fixed rate of inelastic traffic, the problem of how to inject the elastic traffic into the network to achieve the maximum utility of elastic traffic is solved in this paper. The node-centric formulation in cross-layer design framework is applied. By using Lagrange duality method, the congestion control, back-pressure routing and Max-weight scheduling are integrated naturally when decomposing the Larangian. The Greedy Distributed scheduling is introduced to not only decentralize scheduling, but also decrease the complexity of the Max-weight scheduling. The Back-pressure routing has presented the poor performance at the light load, the packets take an unnecessary long route from the source node to the destination node. As the result, the delay is excessively large at light load. The enhancement algorithm is implemented to maximize the utility while minimizing the resource usage. An admission control scheme is also introduced to check if the new inelastic flow is admissible.

Design of a Set-top Box (STB) is presented to provide virtual platforms such that it can support multiple service providers. Each service provider is able to manage its own multimedia streams and ensures the desired network performance for each flow and enables QoS in IP Multimedia Subsystem (IMS)-based networks. Simulations are done to assert the correctness of the algorithm.

We study joint rate control and resource allocation with QoS provisioning that maximizes the total utility of secondary users in cognitive radio networks. We formulate and decouple the original utility optimization problem into separable subproblems and then develop an algorithm that converges to optimal rate control and resource allocation. The proposed algorithm can operate on different time-scale to reduce the amortized time complexity.

In this paper, we propose a new routing protocol to support the selection of stable and high speed route in Multi-Rate Mobile Ad-hoc Networks (MANET). We introduce another approach for modeling the mobility aspect under the relation with the variation of communication rate. Maximize the proposed “Route Selection Indicator” (RSI) ensures that the the selected route is the most stable, has highest speed among route candidates. The correctness of proposal is proved and the simulation results show that our proposed approach provides an accurate and efficient method for evaluating and selecting the best route in mobile environments.

This paper presents an enhanced channel access mechanism for the newly introduced Mesh Deterministic Access (MDA) mechanism for IEEE 802.11s based Wireless Mesh Networks (WMNs). The MDA is an optional scheduled access mechanism which enables mesh nodes to negotiate periodic transmission opportunity (referred to as Mesh Deterministic Access Opportunity (MDAOP)) for a collision free transmission of QoS frames. However, the performance of MDA is affected by the presence of contention from non-MDA nodes in the neighborhood. In this work, we first identify how a non-MDA node affects the operation of MDA and then, propose an enhanced channel access mechanism referred to as Enhanced Mesh Deterministic Access (EMDA) that ensures guaranteed access to the medium during an MDAOP by an MDA-owner by means of reduced Interframe Space (IFS) and the preemption capability. Finally, we study the performance of EMDA through simulation, and results show that EMDA outperforms others in terms of throughput, end-to-end delay, packet loss rate and MDA utilization.

Considering energy as a crucial resource, several duty cycle based MAC have already been proposed for Wireless Sensor Networks (WSNs) to gain higher energy efficiency during long idle period of the sensors, and are optimized for light traffic loads. Contrastively, considering the bandwidth constraint of WSNs, and to optimize the heavy traffic loads, another research trend is continuing in devising rate and congestion control at the MAC layer. To provide an integrated solution of both these distinct research trends at the MAC layer, in this paper, we present a Load-independent Energy-efficient and Rate-control Integrated Asynchronous Duty Cycle MAC (LER-MAC) for WSNs. Performance of LER-MAC has been evaluated using ns-2 which demonstrates that, LER-MAC conserves energy considerably during light traffic loads along with procures higher throughput and lower latency avoiding packet drops through maximum utilization of the channel during heavy traffic loads.

This paper is a contribution to the inception of multiple tokens in solving distributed mutual exclusion problem. The proposed algorithm is based on the token ring approach and allows simultaneous existence of multiple tokens in the logical ring of the network. Each competing process generates a unique token and sends it as request to enter the critical section that travels along the ring. The process can only enter the critical section if it gets back its own token. The algorithm also handles the coincident existence of multiple critical sections (if any) in the system. The algorithm eliminates the idle time message passing, increases overall throughput and provides fault-tolerance. We discuss the impact of process failures and loss of tokens and propose corresponding recovery methods. The results of simulation show that the proposed algorithm overcomes the key limitations of the major token ring algorithms.

Sensor nodes report the sensed data packets to the sink and depending on the application these packets may have diverse attributes: time-critical (TC) and non time-critical (NTC). In such a heterogeneous traffic environment, designing a data dissemination framework that can achieve both the reliability and delay guarantee while preserving the energy efficiency, namely multi-constraint QoS (MCQoS), is a challenging problem. This paper proposes a new aggregate routing model and a localized algorithm (DARA) that implements the model. DARA is designed for multi-sink multipath location aware network architecture. Delay-differentiated multi-speed packet forwarding and in-node packet scheduling mechanisms are also incorporated with DARA. The simulation results demonstrate that DARA effectively improves the reliability, delay guarantee and energy efficiency.

In wireless mesh network, efficient channel allocation and link scheduling is essential for throughput improvement. We investigate the problem of how to schedule a maximal set of feasible transmission under physical interference model by using the Spatial TDMA access scheme and channel allocation which relieves the interference effect between nearby transmissions. We also consider the fairness enhancement to prevent some border nodes of the network from starvation. By using Minimum Spanning Tree as network subgraph constructed from original network graph, we propose centralized algorithms for scheduling and channel allocation to maximize the aggregate throughput and to provide the fairness of the network. We also evaluate our algorithms through extensive simulations and the results show that our algorithms can achieve good performance.

In wireless sensor networks, majority of routing protocols considered energy efficiency as the main objective and assumed data traffic with unconstrained delivery requirements. However, the introduction of image and video sensors demands certain quality of service (QoS) from the routing protocols and underlying networks. Managing real-time data requires both energy efficiency and QoS assurance in order to ensure efficient usage of sensor resources and correctness of the collected information. In this paper, we present a novel QoS-aware routing protocol to support high data rate for wireless multimedia sensor networks. Being multi-channel multi-path the foundation, the routing decision is made according to the dynamic adjustment of the required bandwidth and path-length-based proportional delay differentiation for real-time data. The proposed protocol works in a distributed manner to ensure bandwidth and end- to-end delay requirements of real-time data. At the same time, the throughput of non-real-time data is maximized by adjusting the service rate of real-time and non-real-time data. Results evaluated in simulation demonstrate a significant performance improvement in terms of average delay, average lifetime and network throughput.

Heterogeneous applications could be assimilated within the same wireless sensor network with the aid of modern motes that have multiple sensor boards on a single radio board. Different types of data generated from such types of motes might have different transmission characteristics in terms of prio

Our study on the deployment topology and communication characteristics of wireless mesh enterprise networks (WMENs) leads to three critical security challenges: (a) deployment of network devices are not planar, rather devices are deployed over three-dimensional space, (b) message generated/received by a mesh client traverses through mesh routers in a multi-hop fashion, and (c) mesh clients being mostly mobile in nature may result in misbehaving or spurious during communications. We address these challenges for WMENs that may be a small network within an office or a medium-size network for all offices in an entire building, or a large scale network among offices in multiple buildings. We develop a matrix key distribution technique that perfectly suits the network topology. A session key establishment protocol is presented to achieve the client-router and router-router communication security. Finally, a misbehaving client detection algorithm is developed based on the communication history. We analyze and evaluate the performance to show the suitability of our proposed security solutions.

This paper presents a group-based security scheme for distributed wireless sensor networks. The scheme is proposed with 3-types of entities: one or more base stations, Y number of group dominator nodes, and N number of ordinary sensor nodes. We model the group-based deployment using Gaussian (normal) distribution and show that more than 85% network connectivity can be achieved with the proposed model. The small groups with pre-shared secrets form the secure groups where group dominators form the backbone of the network. The scheme is devised for dealing with sensory data aggregated by groups of collocated sensors; i.e., local sensed data are collected by the dominating nodes and sent an aggregated packet to the base station via other group dominators. The scheme is shown to be light-weight, and it offers a stronger defense against node capture attacks. Analysis and simulation results are presented to defend our proposal. Analysis shows that robustness can significantly be improved by increasing the deployment density using both the dominating and/or ordinary sensor nodes.

Energy efficiency is one of the most challenging issues in wireless sensor network as the sensors have to serve unattended. Cluster based communication can reduce the traffic on the network and gives the opportunity to other sensors for periodic sleep and awake and thus saves energy. Passive clustering is less computational and light weight. In cluster based approach, cluster heads and gateways have to have maximum energy to be awaken for all the times. Existing passive clustering algorithm uses first declaration method without any priority generates severe collisions in the network and form the clusters very dense with large amount of overlapping regions. This results increased number of gateways. We have proposed several modifications for the existing passive clustering algorithm to prolong the life time of the network with better cluster formation. More -over, our proposed solution finds the optimum path between sources and sinks using the tiny cache memory of the intermediate nodes. Simulation result shows that EAREC saves significant amount of energy and at the same time keeps the delay and success rate satisfactory.

This paper proposes a new approach, segment-based path protection, which provides much more enhanced network resource utilization than the local protection scheme and achieves much more fast restoration than the global protection scheme. This segment-based protection scheme consists of two subsequent steps: determination of the optimal restoration scope taking the working path state, bandwidth, and delay constraints into account and calculation of optimal segmented backup path that is link and node disjoint path with the found working path. In addition, we evaluate the performance of the proposed protection scheme under the randomly generated Waxman's network topology

In this paper, we propose a novel hierarchical architecture for a large wireless sensor network (WSN) wherein sensors are arranged into a multi-layer architecture with the nodes at each layer interconnected as a de Bruijn graph and provide a hierarchical routing algorithm in the network. Using our approach, every sensor obtains a unique node identifier addressed by binary addressing fashion. We show that our algorithm has reasonable fault-tolerance, admits simple and decentralized routing, and offers easy extensibility. We also present simulation results showing the average delay, success data delivery radio in our approach. And we received acceptable results for some potential applications.

The paper proposes a hierarchical restoration scheme that can be applied to the restoration of working label switched paths (LSPs) and pre-provisioned backup LSPs. Our hierarchical restoration scheme is composed of two subsequent algorithms, one to determine the reasonable restoration scope (RS) and the other to compute an optimal alternative path that avoids the fault location. The first algorithm for dynamic RS determination increases the restoration speed by minimizing the complexity of the network topology for restoration as soon as possible. By increasing the reusability of the existing path, we can minimize the length of the new alternative path. In addition, the newly proposed concept of the hierarchical RS extension minimizes the probability of restoration failure by dynamically widening the restoration scope until the RS is equal to the whole network topology. Through simulation, we evaluate the performance of our restoration scheme and existing protection schemes (Huang, C. et al., 2000; Haskin, D. and Krishnan, R., 2000) in terms of the restoration speed, packet loss, network resource utilization, and resource reusability of the existing working LSP.

The number of ADSL subscribers in Korea Telecom is remarkably increasing by as many as eight hundred new subscribers every day, with a total number now already at 3 million. Because of such a large number of customers, we need a systematic networking system that can easily accommodate the explosive growth in the number of ADSL subscribers, and efficiently and uniformly manage the various heterogeneous network equipments that are increasing in proportion to the growth of ADSL subscribers. This paper, thus, proposes the distributed networking system architecture for Internet-access service provision using ATM over ADSL. In this paper, we describe the hierarchical network model in deploying ADSL services across the ATM access networks, which can easily accommodate the explosive growth ADSL subscribers in the future. In addition, this paper describes the distributed networking system and its capability to provide a systemic network management using the principal networking concepts of service ordering, addressing, routing, adaptation and switching. All of the networking system components with CORBA objects in favor of the distribution and location transparency are defined and described using the CORBA interface description language (IDL) for commonality. Lastly, we present its implementation and operation in Korea Telecom.

This paper describes an integrated service and network management system for provisioning the point-to-multipoint reservation service (PMRS) in an ATM network. There are two major issues confronting the network service provider in relation to this service: one is to rapidly confirm the acceptability of the subscriber's reservation at subscription time, and the other is to punctually activate the reserved point-to-multipoint service. To meet these requirements, this paper proposes a service provision model (SPM) and a network resource model of bandwidth allocation timetable (BAT). This paper likewise proposes a point-to-multipoint routing algorithm composed of ordering and backtracking procedures, which can find an optimal branch point under the complex network topology and can add more destinations to the existing point-to-multipoint route. Lastly, this paper shows the feasibilities of the SPM, the BAT and the point-to-multipoint routing algorithm by means of implementation and performance analysis under the real ATM network of Korea Telecom.

In order to support a service level assurance (SLA) mechanism and make it visible, this paper proposes an efficient and simple SLA mechanism by extending the legacy CNM (customer network management) concept suitable for multilayer networks.

This paper proposes management architecture of a multi-layer virtual private network over the broadband network that will play an important role at the initial stages of the broadband era. To provide flexible management capabilities with the administrator of the VPN, we adopt a layering concept and abstraction mechanism to give a simple view of the real connection at service management level. The multilayer VPN management architecture we propose includes the concept of customer network management for a subscriber's control of their own VPN, and the information/computational model of VPN with emphasis on the layering concept based on ITU-T G.803 and G.805. In addition, some advanced multi-layer VPN features and a generic bearer connectivity model are presented. We also outline our early experiences with the implementations of the proposed VPN management architecture based on the common object request broker architecture and Web technologies.

The service system which is built on a distributed processing environment (DPE) is becoming one of the most promising techniques for the flexible and rapid introduction of services. We propose a service networking architecture suitable for VoD services based on the TINA compliant model. The applications in this architecture are deployed as units of software managers. Each manager provides a layered view for the effective management and control of the multimedia network resources and services according to the concept of TMN and TINA. For the purpose of flexible service provision to users and effective service introduction by service providers, this architecture proposes the adoption of ad hoc service managers such as a video on demand (VoD) manager and a protocol for transactions between those. This paper also proposes a reference scenario for interworking of the TINA-like model with the WWW environment. The proposed architecture is implemented on a DPE platform that provides various transparencies.

This paper addresses a novel networking architecture model on DPE for various multimedia services suitable to high speed networks and for the flexible and rapid introduction of services. In this model, applications are assembled from software "building blocks" which cope with information service and network providers. Each building block provides a layered view, enabling the effective management of multimedia network resources and services according to the concept of TMN and TINA. In this paper, we also propose the use of a directory system and its naming structure for the management of user profiles and session profiles, and a control model of effective multimedia logical device objects that uses a stream process approach. Our model is implemented on a DPE platform that provides various transparencies. For the purpose of flexible service provision to users, this architecture presents ad hoc service building blocks such as a video on demand building block and a CSCW building block.

The paper presents a networking architecture model for multimedia applications and their flexible management suitable for B-ISDN. In this architecture, the applications are separated by building blocks according to their functionality in a distributed processing environment. This architecture provides a layered view of an effective management of the multimedia network resources and services. Also, we propose a model for managing and controlling multimedia terminal resources. Finally, we discuss interoperability in order to facilitate connections between multimedia network elements.>

This paper describes and discusses the modeling of multimedia communication networking architecture suitable to high speed networks such as LAN, B-ISDN, etc. for the flexible management and rapid introduction of services. In this model, the applications are deployed in units of software building blocks called group call server, service control server, resource management server and client agent. Each building block provides a layered view for the effective management of the multimedia network resources and services. In this paper, we propose a service scenario scheme and multimedia logical devices model for the flexible generation and effective management of services, and abstracting the network resources, respectively. Finally, this paper presents an architecture evolution model toward interoperability in a distributed processing environment (DPE). This can be applied for interoperability between multimedia network resources provided by different vendors.>