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.

In personalized Federated Learning (pFL), high data heterogeneity can cause significant gradient divergence across devices, adversely affecting the learning process. This divergence, especially when gradients from different users form an obtuse angle during aggregation, can negate progress, leading to severe weight and gradient update degradation. To address this issue, we introduce a new approach to pFL design, namely Federated Learning with Layer-wise Aggregation via Gradient Analysis (FedLAG), utilizing the concept of gradient conflict at the layer level. Specifically, when layer-wise gradients of different clients form acute angles, those gradients align in the same direction, enabling updates across different clients toward identifying client-invariant features. Conversely, when layer-wise gradient pairs make create obtuse angles, the layers tend to focus on client-specific tasks. In hindsights, FedLAG assigns layers for personalization based on the extent of layer-wise gradient conflicts. Specifically, layers with gradient conflicts are excluded from the global aggregation process. The theoretical evaluation demonstrates that when integrated into other pFL baselines, FedLAG enhances pFL performance by a certain margin. Therefore, our proposed method achieves superior convergence behavior compared with other baselines. Extensive experiments show that our FedLAG outperforms several state-of-the-art methods and can be easily incorporated with many existing methods to further enhance performance.

The ability to interpret and intervene model decisions is important for the adoption of computer-aided diagnosis methods in clinical workflows. Recent concept-based methods link the model predictions with interpretable concepts and modify their activation scores to interact with the model. However, these concepts are at the image level, which hinders the model from pinpointing the exact patches the concepts are activated. Alternatively, prototype-based methods learn representations from training image patches and compare these with test image patches, using the similarity scores for final class prediction. However, interpreting the underlying concepts of these patches can be challenging and often necessitates post-hoc guesswork. To address this issue, this paper introduces the novel Concept-based Similarity Reasoning network (CSR), which offers (i) patch-level prototype with intrinsic concept interpretation, and (ii) spatial interactivity. First, the proposed CSR provides localized explanation by grounding prototypes of each concept on image regions. Second, our model introduces novel spatial-level interaction, allowing doctors to engage directly with specific image areas, making it an intuitive and transparent tool for medical imaging. CSR improves upon prior state-of-the-art interpretable methods by up to 4.5% across three biomedical datasets. Our code is released at https://github.com/tadeephuy/InteractCSR.

Industrial control systems (ICSs) are becoming increasingly interconnected, rendering them susceptible to cyber attacks. Timely detection of anomalies in encrypted data flows is crucial for ensuring the reliability and security of ICS. Although deep learning-based anomaly detection methods have made significant strides, their implementation in point-by-point mapping paradigms often necessitates a tradeoff between feature representation and computational efficiency, especially in resource-constrained environments. In addition, these methods face challenges with class imbalance and limited labeled anomaly data. To address these challenges, we propose a frequency-domain anomaly detection (FreAD) framework specifically tailored for encrypted data flows in ICS. FreAD utilizes a frequency-domain feature fusion encoding module to capture global temporal dependencies. An anomaly scoring network integrates a small amount of labeled data along with pseudolabeled data generated from the modified Z-score algorithm, effectively addressing class imbalance. Furthermore, the frequency-domain deviation prior module can alleviate the contraction of interclass distances during unsupervised training. Extensive experiments demonstrate that FreAD significantly surpasses other state-of-the-art anomaly detection algorithms.

Blockchain technology has emerged as a transformative force in various sectors, including finance, healthcare, supply chains, and intellectual property management. Beyond Bitcoin’s role as a decentralized payment system, Ethereum represents a notable application of blockchain, featuring Smart Contract functionality that enables the development and execution of decentralized applications (DApps). A key feature of Ethereum , and public blockchains in general, is pseudonymity, typically achieved by using public keys as pseudonyms for users. Despite implementing several privacy-preserving techniques, the public recording of user activities on the blockchain allows various deanonymization methods that can profile users, reveal sensitive information , and potentially re-identify them. Most blockchains, such as Bitcoin , Litecoin , and Cardano, employ the Unspent Transaction Output (UTXO) model for accounting, which focuses on individual transactions and is susceptible to various deanonymization techniques. In contrast, Ethereum uses an account-based transaction model, integrating the concepts of accounts and wallets at the protocol level. This makes most UTXO-based deanonymization techniques ineffective for Ethereum. However, alternative methods with the potential to deanonymize Ethereum users have been proposed and developed. Privacy preservation techniques have been used to counteract deanonymization attempts; however, the challenges related to these techniques, their effectiveness and efficiency, and the trade-off between usability and protection levels remain areas for further exploration. This survey presents a comprehensive analysis of state-of-the-art privacy preservation along with deanonymization techniques in the blockchain and Ethereum ecosystems. This survey examines the intrinsic mechanisms supporting pseudonymity in Ethereum, providing a detailed assessment of the advantages and disadvantages of privacy preservation techniques, and suggests potential countermeasures against those deanonymization methods. It also discusses the implications arising from the intersection of DApps and data protection legislation , which is vital for ensuring the coexistence and advancement of groundbreaking blockchain capabilities and protecting user data.

Non-independent and identically distributed (non-IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL). Several approaches to handle non-IID data, such as personalized FL and federated multitask learning (FMTL), are of great interest to research communities. In this work, first, we formulate the FMTL problem using Laplacian regularization to explicitly leverage the relationships among the models of clients for multitask learning. Then, we introduce a new view of the FMTL problem, which, for the first time, shows that the formulated FMTL problem can be used for conventional FL and personalized FL. We also propose two algorithms FedU and decentrali- zed FedU ( dFedU ) to solve the formulated FMTL problem in communication-centralized and decentralized schemes, respectively. Theoretically, we prove that the convergence rates of both algorithms achieve linear speedup for strongly convex and sublinear speedup of order 1/2 for nonconvex objectives. Experimentally, we show that our algorithms outperform the conventional algorithm FedAvg, FedProx, SCAFFOLD, and AFL in FL settings, MOCHA in FMTL settings, as well as pFedMe and Per-FedAvg in personalized FL settings.

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.

Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off. Memory based approaches with dominant performances like PatchCore or Coupled-hypersphere-based Feature Adaptation (CFA) require an external memory bank, which significantly lengthens the execution time. Another approach that employs Reversed Distillation (RD) can perform well while maintaining low latency. In this paper, we revisit this idea to improve its performance, establishing a new state-of-the-art benchmark on the challenging MVTec dataset for both anomaly detection and localization. The proposed method, called RD++, runs six times faster than PatchCore, and two times faster than CFA but introduces a negligible latency compared to RD. We also experiment on the BTAD and Retinal OCT datasets to demonstrate our method's generalizability and conduct important ablation experiments to provide insights into its configurations. Source code will be available at https://github.com/tientrandinh/Revisiting-Reverse-Distillation.

Object re-identification (ReID) is prone to errors under variations in scale, illumination, complex background, and object occlusion scenarios. To overcome these challenges, attention mechanisms are employed to focus on the object's characteristics, thereby extracting better discriminative features. This paper introduces a local-global vision transformer (LoGoViT) for object re-identification by learning a hierarchical-level representation from fine-grained (local) to general (global) context features. It comprises two components: (i) shift and shuffle operations to generate robust local features and (ii) local-global module to aggregate the multi-level hierarchy features of an object. Extensive experiments show that our method achieves state-of-the-art on the ReID benchmarks. We further investigate effective augmentation operations and discuss how the patch modifications improve the proposed model's generalization under occlusion scenarios. The source code is available at https://github.com/nguyenphan99/LoGoViT.

In modern cyber-physical systems, the vast interconnected processes generated from sensor networks necessitate advanced modeling techniques to exploit decentralized data considering edge computation and data access issues. As sensors emit correlated real-life time series, successful forecasting hinges on revealing the spatio-temporal structures and qualities of data. Matrix Estimation-based (ME) methods, as state-of-the-art techniques, excel at denoising and forecasting high-dimensional correlated time series by representing spatio-temporal data as a temporal matrix. However, ME methods face challenges in handling the decentralized data and access restrictions, due to existing licensing agreements and the inherent burden of centralized modeling. To address this limitation, we propose the Federated Multivariate Singular Spectrum Analysis (Fed-mSSA), a federated matrix estimation-based framework, to denoise and predict correlated time series in the presence of noisy and decentralized data. Specifically, we introduce a novel consensus optimization problem to jointly learn the low-rank matrix representation, capturing spatio-temporal patterns to recover latent states and missing data. Furthermore, we present a federated prediction method that privately and efficiently extracts non-linear temporal dynamics using the denoised temporal matrix. Our results show that our proposed framework achieves state-of-the-art prediction performance in a distributed setting, particularly in the presence of missing data

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices’ computing resources compromise the practical effectiveness of PCA. We propose a federated PCA learning using Grassmann manifold optimization, which coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices’ traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and low detecting latency with limited computational resources. Finally, we show that the computational complexity of the Grassmann manifold-based algorithm is satisfactory for hardware-constrained IoT devices. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches.

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.

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.

Abstract In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user’s historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user’s browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.

We study the multi-machine task scheduling problem in an integrated serverless edge and cloud computing system, where tasks can be scheduled locally on edge processors or offloaded to cloud servers, with the objective of minimizing the makespan, i.e., the total time to finish all tasks. The system is semi-online, where the edge processing delays of the tasks are known as priori, but the cloud processing delays remain unknown due to the uncertainty introduced by uploading and loading delay (loading the software environment). The problem is NP-hard in nature, and therefore we resort to approximation schemes and propose a novel algorithm named multi-machine with restart scheduling (MRS). MRS utilizes task restart, where a task that is cancelled will be restarted later when its processing time exceeds the threshold, and the threshold can be adaptively adjusted. We derive an competitive ratio for MRS so that its worst-case gap from the optimal solution is bounded. We also implement the MRS scheduler in a real-world system, which schedules a diverse set of Deep Neural Network (DNN) inference tasks. It shows that MRS achieves significant reduction in makespan compared to existing benchmark schemes.

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, the exponential growth of the Internet-of-Things (IoT) services with heterogeneous requirements becomes a burden to the traditional cloud/data center platform. Edge computing is an emerging solution to gain business value of IoT services where real-time demands become satisfied by moving computing resources close to data sources. Nevertheless, edge resources are still limited to be able to fulfill all demands at the same time. Among new approaches, resource sharing between edge/cloud service providers has been considered as a promising mechanism to address resource scarcity and pursue cost reduction. In this article, we propose an allocation and sharing model in the edge cloud network where providers team up to efficiently utilize resources, named the share-to-run IoT services (SRIS). In particular, we formulate a resource allocation and sharing optimization model to implement IoT services of multiple edge/cloud providers that can maximize the providers’ utility while satisfying service constraints. We relax SRIS into a tractable form that can be solved efficiently using well-known distributed convex frameworks, such as the dual decomposition and alternating direction method of multipliers. Finally, we evaluate our methods by providing several simulation cases, in which our proposed mechanisms show outstanding outcomes by obtaining a faster convergence, increasing by 6.9% of utilization, and 16% of acceptance rate compared to the nonoptimal approach.

Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks. Of great interest is the design and optimization of new wireless network structures that support the stable and fast operation of FL. Cell-free massive multiple-input–multiple-output (CFmMIMO) turns out to be a suitable candidate, which allows each communication round in the iterative FL process to be stably executed within a large-scale coherence time. Aiming to reduce the total execution time of the FL process in CFmMIMO, this article proposes choosing only a subset of available users to participate in FL. An optimal selection of users with favorable link conditions would minimize the execution time of each communication round while limiting the total number of communication rounds required. Toward this end, we formulate a joint optimization problem of user selection, transmit power, and processing frequency, subject to a predefined minimum number of participating users to guarantee the quality of learning. We then develop a new algorithm that is proven to converge to the neighborhood of the stationary points of the formulated problem. Numerical results confirm that our proposed approach significantly reduces the FL total execution time over baseline schemes. The time reduction is more pronounced when the density of access point deployments is moderately low.

Automotive infotainment systems are expected to be first deployed on highways to service drivers travelling long distances, who are more likely to utilize the infotainment applications. In order to meet the stringent requirements of the infotainment systems, road side units (RSUs) are installed along the highway to facilitate a continuous vehicle-to-infrastructure (V2I) connectivity. Due to the long travelling distance and small coverage of the individual RSU, a more cost-effective solution would be to combine V2I with the vehicle-to-vehicle (V2V) communications to maintain the continuous connectivity. In this paper, we propose a new dynamic cooperation scheme that employs a dynamic forwarder selection strategy to generate an adaptive multi-hop V2V path for connectivity maintenance and throughput enhancement at a vehicle located outside of the RSU’s coverage range. For the commonly assumed scenario that all vehicles travel in the same direction and at the same speed, we develop an analytical model and derive closed-formed expressions for the average out-of-range connection time, number of service resumptions and achieved throughput. The developed analytical model provides insights into the impacts of inter-RSU distance, vehicles’ assistance willingness and the target vehicle’s buffer size to the network performance. Simulation results with practical parameter settings show that our proposed scheme is effective in improving connectivity while offering a high throughput for the target vehicle. In particular, a high vehicle density, more assistance willingness by the forwarders and a large buffer size at the target vehicle are shown to be helpful in sparse RSU deployments.

There is growing interest in applying distributed machine learning to edge computing, formingfederated edge learning. Federated edge learning faces non-i.i.d. and heterogeneous data, and the communication between edge workers, possibly through distant locations and with unstable wireless networks, is more costly than their local computational overhead. In this work, we propose${{\sf DONE}}$, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning. First, with strongly convex and smooth loss functions,${{\sf DONE}}$approximates the Newton direction in a distributed manner using the classical Richardson iteration on each edge worker. Second, we prove that${{\sf DONE}}$has linear-quadratic convergence and analyze its communication complexities. Finally, the experimental results with non-i.i.d. and heterogeneous data show that${{\sf DONE}}$attains a comparable performance to Newton's method. Notably,${{\sf DONE}}$requires fewer communication iterations compared to distributed gradient descent and outperforms DANE, FEDL, and GIANT, state-of-the-art approaches, in the case of non-quadratic loss functions.

With the capability of decoupling the control plane and the data plane of networks, Software-Defined Network (SDN) enables flexible and efficient implementations in networks. In addition, Network Function Virtualization (NFV) with Virtual Network Function (VNF) service chain capabilities provides high-performance networks with greater scalability, elasticity, and adaptability. Such an elastic deployment of service chains results in different Service Level Agreements (SLA) and resource requirements on the control plane. In this work, we illustrate the impact of service chains on the control plane and formulate the dynamic controller/switch mapping (DCSM) problem in NFV networks in order to reduce the operational cost. We address the combinatorial optimization problem, DCSM, by designing a novel mechanism to relax DCSM into a tractable problem based on the Penalty Successive Upper Bound Minimization (PSUM) method. In doing so, we conduct several simulation scenarios to evaluate the performance. The experimental results show that our proposed algorithms can achieve a near-optimal result and reduce the operational cost up to 31.7% and 28.3% compared to K-Mean and the matching game-based approaches, respectively.

Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. Conventional FL (FedAvg) employs gradient descent algorithm, which may not be efficient enough. Momentum is able to improve the situation by adding an additional momentum step to accelerate the convergence and has demonstrated its benefits in both centralized and FL environments. It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far. This motives us to propose FedNAG, which employs NAG in each worker as well as NAG momentum and model aggregation in the aggregator. We provide a detailed convergence analysis of FedNAG and compare it with FedAvg. Extensive experiments based on real-world datasets and trace-driven simulation are conducted, demonstrating that FedNAG increases the learning accuracy by 3–24% and decreases the total training time by 11–70% compared with the benchmarks under a wide range of settings.

As an improvement over the standard simple online real-time tracking (SORT) method, DeepSORT introduces a cascade matching mechanism to track objects during a certain period of occlusion, effectively reducing the number of identity (ID) switches. However, DeepSORT lacks the capability of lost-identities recovery, which enables robustness and performance in face recognition systems. To address the issue, we propose a novel multi-face tracking method, named ReSORT, that can recover lost identities. Our method removes the cascade matching block in DeepSORT and extends a similarity matching (SM) block after the Kalman filter to assign uncertain tracks to their probable tracking IDs. Such arrangement significantly reduces the processing time while maintaining the longevity of tracking IDs. The SM block functions by storing existing facial features and comparing the similarity between the new and the existing facial features, enabling ReSORT to recover the lost IDs or IDs from other cameras. To benchmark the ID-recovery ability, we introduce three new metrics, calling IDnew, TIDRate, and TReRate. We also produce face tracking annotations for three public surveillance camera datasets, i.e., LAB, MSU-AVIS, and ChokePoint. Extensive experiments conducted on the three datasets with various resolutions and frame-rates settings demonstrate the superiority of ReSORT over DeepSORT, i.e. reducing the identity switches by average 36.38%, and the processing time by 5.19 times. Source code and annotations of all three datasets are available at https://github.com/tantm97/ReSORT.

Straggler effect is the main bottleneck in realizing federated learning (FL) in wireless networks. This work proposes a novel user (UE) selection approach to mitigate this effect with UE sampling in cell-free massive multiple-input multiple-output networks. Our proposed approach selects only a small subset of UEs for participating in one FL process. Importantly, since the UEs are selected before any FL process is executed, the performance of FL during the executing time is not affected by our method. Here, we select UEs by solving an FL transmission time minimization problem that jointly optimizes UE selection, power control, and data rate. The problem is formulated to capture the complex interactions among the FL training time, UE selection, and straggler effect. This mixed-integer mixed-timescale stochastic nonconvex problem is constrained by the minimum number of UEs to guarantee the quality of learning. By employing online successive convex approximation, we propose a novel algorithm to solve the formulated problem with guaranteed convergence to the neighbourhood of their stationary points. Our approach can significantly reduce the FL transmission time over baseline approaches, especially in the networks that experience serious straggler effect due to the moderately low density of access points.

Various recurrent neural network (RNN) architectures have been implemented successfully for time series prediction in recent years.However, real-world time series data usually contain noise, which decreases the performance of the neural networks.Despite the substantial efforts to understand the pattern of time series, there is a lack of research on detecting and filtering out the inherent noise when predicting time series based on training RNN models.We propose a dual RNN strategy, namely Robust Dual Recurrent Neural Networks (RDRNN), for noisy time series prediction.We designed and trained two RNNs simultaneously and used the loss value to classify different samples into noise-free samples and noisy samples.We exchanged the small-loss samples (which were likely to be noise-free data) to fit the main pattern of time series data, and re-weighted the large-loss samples (which were likely to be noisy data) to alleviate the impact of noise.Empirical results on three popular Chinese stock market indexes demonstrate that the new learning paradigm significantly outperforms baseline approaches.Our code is available at https://jiayuheusyd.github.io/

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, 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.

Wireless network virtualization is basically the abstraction, isolation, and sharing of wireless resources among different entities. Consequently, virtualization provides great flexibility and higher network efficiency, and enables easier migration to new technologies in wireless networks. Traditionally, a wireless network virtualization controller manages the virtual resources (including radio resources and infrastructure resources) known as slices which are available to the Service Providers (SPs). The SPs then allocate their purchased resources to serve their subscribed mobile users. Such a centralized allocation decouples the Quality-of-Service (QoS) management by the SPs from the virtual resource management by the controller. In this paper, we propose a matching based wireless network virtualization resource allocation mechanism: a distributed three-sided (3D) matching between radio resources, physical infrastructure and mobile users. The Restricted Three-sided Matching with Size and Cyclic preference model (R-TMSC) is implemented to obtain a stable solution. Simulation results show that our proposed spectrum-oriented and user-oriented algorithms outperform the traditional resource allocation schemes. The spectrum-oriented algorithm enhances the user throughput and the system performance, within a lesser run time. Furthermore, for an increasing number of users, the proposed algorithms serve more users than traditional methods.

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.

Internet of Things (IoT) services have been implemented for several network applications from smart cities to rural areas. However, there are many barriers to provide an efficient solution for the IoT service deployment underlying innovation SDN/NFV-based technologies. First, though an IoT service can flexibly deploy via virtual network functions (VNFs), a deployment scheme needs to solve the joint routing and resource allocation problem, which becomes more difficult than the traditional centralized cloud/datacenter solution due to distributed resources in the edge-cloud network. In addition, due to uncertain workloads in IoT services, static optimization solutions may not deal with uncompleted knowledge of the entire input, which is often given by assumptions, but unrealistic in current provisioning approaches. Aiming to address these issues, we model an online mechanism for the dynamic IoT service chain deployment to optimize the operational cost in a finite horizon. We propose a JOint Routing and Placement problem for IoT service chain (JORP) that can dynamically scale in/out the number of VNF instances. We then propose a learning method to efficiently solve JORP based on branch-and-bound (BnB). Our proposed learning mechanism can intelligently imitate the branching/pruning actions of BnB, and remove unlikely solutions in the search space based on the deep neural network model to improve the performance. In that respect, we take an intensive simulation that illustrates the promising result of our proposed deep learning method compared to BnB and the greedy baseline in terms of the performance of the algorithm and the operational cost reduction.

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%.

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.

As an emerging solution to latency requirements of Internet of Things (IoT) services, edge computing can bring powerful processing capacity closer to data sources. However, with the limited resources at edge nodes, a major challenge is finding optimal resources in distributed edges to reduce the operational costs of service deployment. Prior works focus mainly on static optimization which may not work efficiently with the time-varying workloads and resource constraints. In this paper, we, therefore, consider a dynamic allocation framework in the edge-cloud network over the long run with uncertainty workloads. In such a system, we introduce a JOint Routing and Placement problem for IoT services, called JORP, that dynamically assigns resources according to workload demand in order to reduce the operational costs in long term. Inspired from the well-known algorithm, branch-and-bound (BnB), for solving the mixed-integer non linear problems (MINLPs) like JORP, we bring the learning concept to address the high complexity of BnB when the search space is huge. Particularly, we design a deep neural network (DNN) and train it under the imitation learning to mimic branching behaviors in BnB for searching the optimal solution. Finally, simulations show our solution outperforms baselines in terms of convergence and operational cost.

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.

Federated Learning (FL) is a fast-developing distributed machine learning technique involving the participation of a massive number of user devices. While FL has benefits of data privacy and the abundance of user-generated data, its challenges of heterogeneity across users’ data and devices complicate algorithm design and convergence analysis. To tackle these challenges, we propose an algorithm that exploits proximal stochastic variance reduced gradient methods for non-convex FL. The proposed algorithm consists of two nested loops, which allow user devices to update their local models approximately up to an accuracy threshold (inner loop) before sending these local models to the server for global model update (outer loop). We characterize the convergence conditions for both local and global model updates and extract various insights from these conditions via the algorithm’s parameter control. We also propose how to optimize these parameters such that the training time of FL is minimized. Experimental results not only validate the theoretical convergence but also show that the proposed algorithm outperforms existing Stochastic Gradient Descent-based methods in terms of convergence speed in FL setting.

With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed Graph Convolutional Networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high quality graph's node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. For evaluation, we design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show our HeteGraph's encouraging performance on the first task and state-of-the-art performance on the second task.

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data. One challenge associated with FL is statistical diversity among clients, which restricts the global model from delivering good performance on each client's task. To address this, we propose an algorithm for personalized FL (pFedMe) using Moreau envelopes as clients' regularized loss functions, which help decouple personalized model optimization from the global model learning in a bi-level problem stylized for personalized FL. Theoretically, we show that pFedMe convergence rate is state-of-the-art: achieving quadratic speedup for strongly convex and sublinear speedup of order 2/3 for smooth nonconvex objectives. Experimentally, we verify that pFedMe excels at empirical performance compared with the vanilla FedAvg and Per-FedAvg, a meta-learning based personalized FL 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.

This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency. This mixed-timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to 55% over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO.

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.

In recent years, the rapid development of edge computing enables us to process a wide variety of intelligent applications at the edge, such as real-time video analytics. However, edge computing could suffer from service outage caused by the fluctuated wireless connection or congested computing resource. During the service outage, the only choice is to process the deep neural network (DNN) inference at the local mobile devices. The obstacle is that due to the limited resource, it may not be possible to complete inference tasks on time. Inspired by the recently developedearly exit of DNNs, where we can exit DNN at earlier layers to shorten the inference delay by sacrificing an acceptable level of accuracy, we propose to adopt such mechanism to process inference tasks during the service outage. The challenge is how to obtain the optimal schedule with diverse early exit choices. To this end, we formulate an optimal scheduling problem with the objective to maximize a general overall utility. However, the problem is in the form of integer programming, which cannot be solved by a standard approach. We therefore prove the Ordered Scheduling structure, indicating that a frame arrived earlier must be scheduled earlier. Such structure greatly decreases the searching space for an optimal solution. Then, we propose the Scheduling Early Exit (SEE) algorithm based on dynamic programming, to solve the problem optimally with polynomial computational complexity. Finally, we conduct trace-driven simulations and compare SEE with two benchmarks. The result shows that SEE can outperform the benchmarks by 50.9%.

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.

In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs' licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.

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.

Accelerated Software Defined Networking (SDN) adoption makes SDN paradigm emerging in the state of the art. Especially, the combination of SDN and network functions virtualization (NFV) becomes a promising trend in deploying virtual network services for network operators. Although SDN can decouple networks into the control plane and the data plane to obtain flexible operation and programmability, there are many open issues that need to be addressed for SDN deployments, such as i) where to place SDN controllers in a given network, ii) how to assign connections from controllers to switches in terms of satisfying multiple objectives (e.g., resource utilization, failure, quality of services, etc.). In this work, we focus on the efficient assignment between SDN controllers and switches to guarantee a low operational cost, quality of services, and fault tolerance, in which the complexity of virtual links in network services, an omitted factor in current works, is considered and addressed. We formulate an optimization problem for dynamic controller/switch mapping (DCSM) in the network virtualization. We then proposed approximation algorithms in terms of relaxing the binary variables to solve the NP-hard problem, DCSM, in both centralized and distributed mechanisms. We also create various simulation schemes to evaluate our methods where they outperform state-of-the-art methods.

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.

As the critical infrastructure for supporting Internet and cloud computing services, massive geo-distributed data centers are notorious for their huge electricity appetites and carbon footprints. Nonetheless, a lesser-known fact is that data centers are also “thirsty”: to operate data centers, millions of gallons of water are required for cooling and electricity production. The existing water-saving techniques primarily focus on improved “engineering” (e.g., upgrading to air economizer cooling, diverting recycled/sea water instead of potable water) and do not apply to all data centers due to high upfront capital costs and/or location restrictions. In this paper, we propose a software-based approach towards water conservation by exploiting the inherent spatio-temporal diversity of water efficiency across geo-distributed data centers. Specifically, we propose a batch job scheduling algorithm, called WACE (minimization of WAter, Carbon and Electricity cost), which dynamically adjusts geographic load balancing and resource provisioning to minimize the water consumption along with carbon emission and electricity cost while satisfying average delay performance requirement. WACE can be implemented online without foreseeing the far future information and yields a total cost (incorporating electricity cost, water consumption and carbon emission) that is provably close to the optimal algorithm with lookahead information. Finally, we validate WACE through a trace-based simulation study and show that WACE outperforms state-of-the-art benchmarks: 25 percent water saving while incurring an acceptable delay increase. We also extend WACE to joint scheduling of batch workloads and delay-sensitive interactive workloads for further water footprint reduction in geo-distributed data centers.

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.

In current wireless communication systems, there is a rapid development of location based services, which will play an essential role in the future 5G networks. One key feature in providing the service is the mobile crowdsourcing in which a central cloud node denoted as the principal collects location based data from a large group of users. In this paper, we investigate the problem of how to provide continuous incentives based on user's performances to encourage users' participation in the crowdsourcing, which can be referred to the moral hazard problem in the contract theory. We not only propose the one-dimensional performance-reward related contract, but also extend this basic model into the multi-dimensional contract. First, an incentive contract which rewards users by evaluating their performances from multiple dimensions is proposed. Then, the utility maximization problem of the principal in both one-dimension and multi-dimension are formulated. Furthermore, we detailed the analysis of the multi-dimensional contract to allocate incentives. Finally, we use the numerical results to analyze the optimal reward package, and compare the principal's utility under the different incentive mechanisms. Results demonstrate that by using the proposed incentive mechanism, the principal successfully maximizes the utilities, and the users obtain continuous incentives to participate in the crowdsourcing activity.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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 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.