In this paper, we propose a robust design for overt-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure sensing and aggregation quality. We then develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while maintaining desired sensing and communication quality. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm that uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality. Numerical results show that accurate sensing effectively suppresses target echoes on the uplink, preserving model aggregation quality despite interference.

This study tackles the problem of edge caching within dynamic settings, where increasing traffic demands put pressure on backhaul links and core network infrastructures. We introduce a caching method based on Proximal Policy Optimization (PPO) that integrates essential file characteristics, including size, lifetime, importance, and popularity, while also accommodating random file request patterns to better mirror real-world edge caching situations. Dynamic environments often experience fluctuations in content popularity and request rates, rendering previously established policies less effective since they were tailored to earlier conditions. Although training a new policy from scratch in a changed environment is feasible, it is often inefficient and resource-intensive. To solve this issue, we present a PPO algorithm enhanced with transfer learning, which improves convergence in new environments by utilizing previously acquired knowledge. Our simulation results highlight the substantial advantages of our approach, outperforming recent transfer learning-based methods in terms of convergence rate, demonstrating its potential to enhance edge caching in dynamic real-world scenarios.

Efficient resource allocation is essential for optimizing various tasks in wireless networks, which are usually formulated as generalized assignment problems (GAP). GAP, as a generalized version of the linear sum assignment problem, involves both equality and inequality constraints that add computational challenges. In this work, we present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address GAP in vehicular networks (VNets). Our approach leverages a hybrid quantum-classical structure, integrating a tailored cost function that balances both objective and constraint-specific penalties to improve solution quality and stability. Using the CVaR-VQE model, we handle the GAP efficiently by focusing optimization on the lower tail of the solution space, enhancing both convergence and resilience on noisy intermediate-scale quantum (NISQ) devices. We apply this framework to a user-association problem in VNets, where our method achieves 23.5% improvement compared to the deep neural network (DNN) approach.

Federated learning (FL) enables decentralized model training while preserving data privacy, but remains vulnerable to model poisoning attacks, where malicious clients inject harmful updates to degrade global model performance. In this work, we propose Dual-Variational Autoencoder with truncated Gaussian (DVTG), an unsupervised defense framework for anomaly detection. The first VAE filters out low-reconstruction-error updates from potentially poisoned data. These are used to train a second VAE with a truncated Gaussian prior. This prior constrains latent representations to high-density regions of normal behavior, improving robustness against noise and adversarial manipulation. Experiments on MNIST with sign-flipping and additive Gaussian noise attacks show that our method outperforms both single-VAE and robust aggregation baselines in anomaly detection and global model performance.

The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security breaches, or fraud. As datasets become more complex and high-dimensional, traditional detection methods struggle to effectively capture intricate patterns. Advances in deep learning have made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data. This survey provides a comprehensive review of over 190 recent studies, focusing on deep learning-based AD techniques. We categorize and analyze these methods into reconstruction-based and prediction-based approaches, highlighting their effectiveness in modeling complex data distributions. Additionally, we explore the integration of traditional and deep learning methods, highlighting how hybrid approaches combine the interpretability of traditional techniques with the flexibility of deep learning to enhance detection accuracy and model transparency. Finally, we identify open issues and propose future research directions to advance the field of AD. This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning.

This paper addresses the challenge of edge caching in dynamic environments, where rising traffic loads strain backhaul links and core networks. We propose a Proximal Policy Optimization (PPO)-based caching strategy that fully incorporates key file attributes such as size, lifetime, importance, and popularity, while also considering random file request arrivals, reflecting more realistic edge caching scenarios. In dynamic environments, changes such as shifts in content popularity and variations in request rates frequently occur, making previously learned policies less effective as they were optimized for earlier conditions. Without adaptation, caching efficiency and response times can degrade. While learning a new policy from scratch in a new environment is an option, it is highly inefficient and computationally expensive. Thus, adapting an existing policy to these changes is critical. To address this, we develop a mechanism that detects changes in content popularity and request rates, ensuring timely adjustments to the caching strategy. We also propose a transfer learning-based PPO algorithm that accelerates convergence in new environments by leveraging prior knowledge. Simulation results demonstrate the significant effectiveness of our approach, outperforming a recent Deep Reinforcement Learning (DRL)-based method.

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem.

In the 6G era, user demand for low-latency, cost-effective extreme services such as extended reality (XR) and holographic communications has significantly increased. Multi-domain networks, known for their vast capacity and coverage, are essential in fulfilling the growing demand for high-performance services. Despite their potential, these networks face challenges with domain isolation, requiring a software defined network (SDN) controller for inter-domain communication. Network function virtualization (NFV) enhances flexibility of service delivery with customizable service function chain (SFC), yet prior research falls short in delivering low-latency, cost-efficient services in multi-domain NFV networks alongside an unreasonable assumption that software on physical nodes can support the execution of all virtualization network functions (VNFs). In this paper, we study the problem of SFC deployment with VNF-dependent software migration (SD-VDSM) in multi-domain networks. Particularly, we first formulate the problem by setting an objective to minimize the end-to-end communication delay and the associated costs of service provisioning, while simultaneously ensuring load balancing across multi-domain networks. However, complexity of the issue escalates to an intractable level due to the intertwined nature of SFC deployment strategies and VNF-dependent software migration tactics, which mutually influence each other intricately. To tackle this issue, we propose an innovative heuristic algorithm, designated as the Joint SFC Deployment with VNF-Dependent Software Migration Algorithm (JSD-VDSMA). Comprising three fundamental steps, this algorithm is crafted to adeptly resolve the complexities of service provisioning across multi-domain networks. A suite of rigorous experimental assessments is detailed, demonstrating the capability of our proposed JSD-VDSMA. Through these comparative analyses, we demonstrate its effectiveness not only to increase the service acceptance rate but also to diminish both the end-to-end communication delay and resource utilization costs in comparison to its counterparts.

Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramér-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.

Deep learning (DL)-based Semantic Communications (SemCom) is becoming critical to maximize overall efficiency of communication networks. Nevertheless, SemCom is sensitive to wireless channel uncertainties, source outliers, and suffer from poor generalization bottlenecks. To address the mentioned challenges, this paper develops a latent diffusion model-enabled SemCom system with three key contributions, i.e., 1) to handle potential outliers in the source data, semantic errors obtained by projected gradient descent based on the vulnerabilities of DL models, are utilized to update the parameters and obtain an outlier-robust encoder, 2) a lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter and is placed before the decoder at the receiver, enabling adaptation for out-of-distribution data and enhancing human-perceptual quality, and 3) an end-to-end consistency distillation (EECD) strategy is used to distill the diffusion models trained in latent space, enabling deterministic single or few-step low-latency denoising in various noisy channels while maintaining high semantic quality. Extensive numerical experiments across different datasets demonstrate the superiority of the proposed SemCom system, consistently proving its robustness to outliers, the capability to transmit data with unknown distributions, and the ability to perform real-time channel denoising tasks while preserving high human perceptual quality, outperforming the existing denoising approaches in semantic metrics such as multi-scale structural similarity index measure (MS-SSIM) and learned perceptual image path similarity (LPIPS).

In the coming sixth-generation era, space-air–ground integrated network (SAGIN) is a technology with the potential for seamless coverage and high-data rate transmission. However, the inherent broadcast nature of wireless communication forces us to consider physical-layer security. This article explores secure communications with the aid of hybrid free space optical/radio frequency (FSO/RF) links in a two-phase uplink transmission. Specifically, in the first-phase transmission, a ground device transmits secrecy data to an unmanned aerial vehicle (UAV) via an radio frequency (RF) link, while the UAV emits artificial noise to confuse an eavesdropper. In the second-phase transmission, the UAV sends the secrecy data to a satellite via an FSO link to defend against RF eavesdropping. More specifically, we design two transmission schemes, i.e., slot-based scheme and period-based scheme, which are suitable for transmitting delay-sensitive data and delay-insensitive data, respectively. In order to maximize the average secrecy rate of the system, the trajectory and power allocation of the UAV are jointly optimized. The objective functions of these two schemes are both nonconvex, which are mathematically intractable to tackle by the interior-point method. Therefore, we use block coordinate descent and successive convex approximation techniques to obtain approximate solutions. Numerical results reveal the impact of the UAV trajectory and power allocation optimization on the average secrecy rate during different flight periods in different schemes. In addition, other benchmark schemes are considered for comparison, and the results indicate that our proposed schemes can achieve higher average secrecy rates.

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) have been deployed to complement fixed charging stations. Typically, MCSs are assigned to charge the electric vehicles with insufficient electricity which have made charging requests (termed IEVs). Moreover, there are some electric vehicles with insufficient electricity which have not made charging requests (termed quasi-IEVs). If idle MCSs are allowed to actively track quasi-IEVs according to their potential charging demand, then more IEVs could be promptly charged, and thus the charging profits of MCSs could be increased. However, due to the private ownership of electric vehicles, some private information cannot be provided in the potential charging demand of quasi-IEVs (e.g., the destinations and residual electricity), making the potential charging profits of idle MCSs hard to be evaluated, and thereby the proper assignments of idle MCSs are difficult to decide. To this end, we introduce the profit-maximizing heat maps to depict the potential charging demand of quasi-IEVs and evaluate the potential charging profits of idle MCSs. A profit-maximizing heat map remarks the positions around quasi-IEVs and displays them as continuous areas. Specifically, the different shades of colours are used to distinguish the quantities of potential charging profits of idle MCSs, and the sizes of coloured areas are used to indicate the possibility of quasi-IEVs passing through these positions. In this paper, we propose a Profit-Maximizing Assignment Strategy of Idle MCSs (PMASIM) to properly assign the idle MCSs to charge IEVs at selected charging positions, or track some quasi-IEVs according to the profit-maximizing heat maps. Extensive simulations and comparisons demonstrate the superior performance of PMASIM, i.e., with the profit-maximizing heat maps, the charging profits of MCSs are increased, and the proportion of charged IEVs is enhanced as well.

The introduction of network function virtualization (NFV) leads to service function chain (SFC) deployment problems, promoting the idea of composing network services as virtualized network functions (VNFs). Meanwhile, the rapid development of edge computing, artificial intelligence and big data has led to a surge in data volume and explosive growth in computing and forwarding demands. As such, a traditional central processing unit (CPU)-based data forwarding mode in the NFV network appears to be a bottleneck, and a CPU-only computing framework can no longer meet the forwarding needs of diverse business scenarios and services. The data processing unit (DPU)-based architecture allows better forwarding performance to be achieved more cost-effectively, largely alleviating the computing pressure of the CPU and reducing the node forwarding delay. Therefore, in this paper, a heterogeneous CPU+DPU architecture is investigated to solve the SFC deployment problem. To handle diverse service needs, we establish a multi-objective SFC deployment scheme to optimize the service latency, deployment cost and service acceptance rate. Because extreme services require better real-time performance, DPUs are adopted for fast processing according to the requirement of service requests. To address the unacceptable delay in sequential mode, a parallel strategy is proposed to process SFCs. To solve the multi-objective SFC deployment problem, a deep reinforcement learning (DRL)-based heterogeneous algorithm that includes multiple subalgorithms is designed, named parallelizable, shared and horizontally scaled service function chain deployment (PSHD), which uses diverse processing algorithms to deploy SFCs and break the delay bottleneck in NFV-based networks.The performance of PSHD is evaluated through extensive experiments. PSHD is found to be time-efficient, and it achieves a higher request acceptance rate and 37.73% and 34.26% lower latencies than state-of-the-art methods.

Due to the low cost and agile service provision, mobile charging stations (MCSs) have been deployed to complement fixed charging stations (FCSs). In the Internet of Electric Vehicles (IoEV) with MCSs, a major concern is to enhance the charging efficiency of MCSs. The charging efficiency of MCSs can be improved by prolonging the charging durations of MCSs, i.e., MCSs should undertake the charging tasks as more as possible, which can increase the charging profits of MCSs and reduce the charging expenses of IEVs (EVs with insufficient electricity). Besides, EVs and MCSs are selfish in terms of charging expenses and charging profits, respectively. In this article, we propose a Bayesian game based Bidding Scheme for Mobile Charging enabled Electric Vehicles (BBS-MCEV). In BBS-MCEV, each IEV first calculates the maximum charging price (MCP) according to the potential expense if charged by nearby FCSs, and then the optimal charging price (OCP) is determined by the Bayesian game model. Each MCS accepts the charging request with the largest charging profit. Extensive simulations and comparisons demonstrate the superior performance of our proposed BBS-MCEV, i.e., with the Bayesian game model, IEVs can rationally bid for the mobile charging services from MCSs, and thus BBS-MCEV can increase the charging profits of MCSs and reduce the charging expenses of IEVs effectively. Besides, a proper tradeoff between the charging profits of MCSs and the charging expenses of IEVs can be achieved.

Over-the-air federated learning (OTA-FL) is an emerging technique to reduce the computation and communication overload caused by the orthogonal transmissions of the model updates in conventional federated learning (FL). This reduction is achieved at the expense of introducing aggregation error that can be efficiently suppressed by means of receive beamforming via large array-antennas. This paper studies OTA-FL in massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF)-chains. For this setting, the beamforming for over-the-air model aggregation needs to be addressed jointly with antenna selection. This leads to an NP-hard problem due to its combinatorial nature. We develop three different algorithms to solve the problem. First, we use the penalty dual decomposition (PDD) technique and propose a two-tier algorithm for joint antenna selection and beamforming. The second algorithm interprets the antenna selection task as a sparse recovery problem and invokes the least absolute shrinkage and selection operator (Lasso) algorithm to approximate the sparse solution. The third algorithm invokes the same sparse recovery based interpretation, but employs the low-complexity method of fast iterative soft-thresholding to find a sparse solution. Convergence and complexity analysis is presented for all the algorithms. The numerical investigations depict that the two algorithms based on the sparse recovery interpretation outperform the PDD-based algorithm, when the number of RF-chains at the edge server is much smaller than its array size. However, as the number of RF-chains increases, the PDD-based algorithm outperforms. Our simulations further depict that learning performance with all the antennas being active at the parameter server (PS) can be closely tracked by selecting less than 20% of the antennas at the PS.

To meet the surging wireless traffic demand, underlaying cellular networks with device-to-device (D2D) communication to reuse the cellular spectrum has been envisioned as a promising solution. In this paper, we aim to secure the D2D communication of the D2D-underlaid cellular network by leveraging covert communication to hide its presence from the vigilant adversary. In particular, there are adversaries aiming to detect D2D communications according to their received signal powers. To avoid being detected, the legitimate entity, i.e., D2D-underlaid cellular network, performs power control aiming to hide the D2D communication. We model the conflict between the adversaries and the legitimate entity as a two-stage Stackelberg game. Therein, the adversaries are the followers intending to detect D2D communication at the lower stage while the legitimate entity is the leader and aims to maximize its utility constrained by the D2D communication covertness and the cellular quality of service (QoS) at the upper stage. Different from the conventional works, the study of the combat is conducted from the system-level perspective, where the scenario that a large-scale D2D-underlaid cellular network threatened by massive spatially distributed adversaries is considered and modeled by stochastic geometry. We obtain the adversary’s optimal strategy as the best response from the lower stage and also both analytically and numerically verify its optimality. Taking into consideration the best response from the lower stage and based on the successive convex approximation (SCA) method, we devise a bi-level algorithm to find the optimal strategy of the legitimate entity, which together with the best response from the lower stage constitute the Stackelberg equilibrium. Numerical results are presented to evaluate the network performance and reveal practical insights that instead of improving the legitimate utility by strengthening the D2D link reliability, increasing D2D transmission power will degrade it due to the security concern.

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractable approach to accurately evaluate the network performance. In this paper, we propose a theoretical framework for an LEO satellite-aided shore-to-ship communication network (LEO-SSCN), where LEO satellites are distributed as a binomial point process (BPP) on a specific spherical surface. The framework aims to obtain the end-to-end transmission performance by considering signal transmissions through either a marine link or a space link subject to Rician or Shadowed Rician fading, respectively. Due to the indeterminate position of the serving satellite, accurately modeling the distance from the serving satellite to the destination ship becomes intractable. To address this issue, we propose a distance approximation approach. Then, by approximation and incorporating a threshold-based communication scheme, we leverage stochastic geometry to derive analytical expressions of end-to-end transmission success probability and average transmission rate capacity. Extensive numerical results verify the accuracy of the analysis and demonstrate the effect of key parameters on the performance of LEO-SSCN. Notably, with common parameter settings, after incorporating the space link, the transmission success probability increases by 886% with a 13 dB predefined signal-to-noise ratio (or signal-to-interference-plus-noise-ratio) threshold. This superior performance is attributed to the fact that the space link uses a wider bandwidth and greater power for signal transmission compared to the maritime link. It’s undeniable that the integration of the space link inevitably incurs additional expenses.

Unmanned aerial vehicles (UAVs) are an emerging technology that can be effectively utilized to perform data collection tasks in the Internet of Things (IoT) networks. However, both the UAV and the sensors in these networks are energy-limited devices, necessitating an energy-efficient data collection procedure to ensure network lifetime. In this paper, we consider a UAV-assisted network, where a UAV flies to the ground sensors according to a predetermined schedule and controls the sensor’s transmit power when hovering above the sensor. Our goal is to minimize the total energy consumption of the UAV and the sensors, which is needed to accomplish the data collection mission. We formulate this problem into two sub-problems of UAV navigation and sensor power control and model each part as a finite-horizon Markov Decision Process (MDP). We deploy the deep deterministic policy gradient (DDPG) method to generate the best trajectory for the UAV in an obstacle-constrained environment and to control the sensor’s transmit power during data collection. Our simulations show that the UAV can find a safe and energy-efficient path for each trip. In addition, continuous sensor power control achieves better performance against the fixed-power and fixed-rate approaches in terms of the total energy consumption during data collection.

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between global and local objectives, making the FL model slow to converge. In this paper, we proposed the Optimal Aggregation algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a Probabilistic Node Selection framework (FedPNS) to dynamically change the probability for each node to be selected based on the output of Optimal Aggregation. FedPNS can preferentially select nodes that propel faster model convergence. Experimental results demonstrate the effectiveness of FedPNS in accelerating the FL convergence rate, as compared to FedAvg with random node selection.

Harvesting wireless power to energize miniature devices has been envisioned as a promising solution to sustain future-generation energy-sensitive networks, e.g., Internet-of-Things systems. However, due to the limited computing and communication capabilities, wirelessly powered networks (WPNs) may be incapable of employing complex security practices, e.g., encryption, which may incur considerable computation and communication overheads. This challenge makes securing energy harvesting communications an arduous task and, thus, limits the use of WPNs in many high-security applications. In this context, security at the physical layer (PHY) that exploits the intrinsic properties of the wireless medium to achieve secure communication has emerged as an alternative paradigm. This article first introduces the fundamental principles of primary PHY attacks, covering jamming, eavesdropping, and detection of covert, and then presents an overview of the prevalent countermeasures to secure both active and passive communications in WPNs. Furthermore, a number of open research issues are identified to inspire possible future research.

Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.

Non-orthogonal multiple access (NOMA)-based cognitive heterogeneous multi-homing networks is a very important scenario in the future wireless networks. In this work, we formulate a joint video packet assignment, power control and user scheduling problem as a mixed integer non-linear programming (MINLP) to maximize the total video transmission quality for cognitive multi-homing heterogeneous NOMA networks, which is subject to the maximum accessed number of secondary users at each subchannel, video encoding characteristics, maximum interference power constraint and total available power constraint. For the joint video packet assignment, power control and user scheduling problem, we divide it into a video packet assignment subproblem, a power control subproblem and a secondary user scheduling subproblem for cognitive multi-homing heterogeneous NOMA networks. Firstly, the secondary user scheduling algorithm is proposed using the greedy method. Then, we utilize successive convex approximation (SCA) method to transform the power control subproblem into a convex programming problem, and an approximated optimal power control algorithm is proposed with the dual decomposition method. Finally, a heuristic video packet assignment algorithm is designed, which utilizes the auction theory. Numerical simulation results demonstrate that the proposed algorithms not only improve the video transmission quality, but also enhance the total throughput of cognitive multi-homing heterogeneous NOMA networks.

After several decades of development on cyber security techniques, one clear conclusion can be drawn: no cyber security solution can completely remove the risks faced by the users. In this regard, cyber-insurance has been introduced as a means to enable the users to alleviate the damage from the cyber threats by transferring the cyber risks to an insurer. In this article, we study a cloud security service market, which is composed of cloud users and cloud security service vendors (CSSVs). The CSSVs work as the insurers for selling the cloud security plan, which is consisted of cloud security service and cloud-insurance. The users in the cloud platform can purchase the cloud security plan from the CSSVs to secure their cloud service. If the cloud service is attacked and loss happens, the users will receive the claim from the CSSVs. To lower the successful attack probability, the CSSV has an incentive to invest in improving its cloud security service. Specifically, we model and study the cloud security service market in the framework of a two-stage Stackelberg game. On the upper stage, the CSSVs lead to decide on their own strategies, i.e., the price of the cloud security plan and the security investment to improve their offered cloud security service. On the lower stage, the users follow to decide on the purchase of the cloud security plan according to the price of the cloud security plan and the perceived cyber breach probability of the cloud security service. We analytically verify that the Stackelberg equilibrium exists and is unique. Extensive simulations have been conducted to evaluate the performance of the Stackelberg game. The performance evaluation shows some insightful results. For example, when the users have strong interdependency, the profits of the CSSVs become lower.

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge. In this paper, we propose Fed erated Adaptive Weighting (FedAdp) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset. Through mathematical and empirical analysis, we observe the implicit connection between the gradient of local training and data distribution on local node. We then propose to assign different weight for updating global model based on node contribution adaptively through each training round, which is measured by the angle between local gradient vector and global gradient vector, and is quantified by a designed non-linear mapping function. The simple yet effective strategy can reinforce positive (suppress negative) node contribution dynamically, that results in communication round reduction drastically. With extensive experiments performed in Pytorch and PySyft, we show that FL training with FedAdp can reduce the number of communication rounds by up to 54.1% on MNIST dataset and up to 45.4% on FashionMNIST dataset, as compared to the commonly adopted Federated Averaging (FedAvg) algorithm.

As the paradigm of crowdsensing involves the data collection from users, the issue of designing reward to incentivize the users is fundamentally important to be addressed, thereby effectively enhancing the participation. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in crowdsensing-based healthcare services, the accuracy of diet recommendation for a certain user can be promoted by exploiting the nutritional information contributed and shared by the socially-connected friends of him/her taking similar types of food. To be more general and practical, we study the incentive schemes in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive schemes. Considering this, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically validate the existence and uniqueness of the Stackelberg equilibrium. Simulations are conducted to evaluate game equilibrium properties, and the results are presented to assess and demonstrate the performance effectiveness of the proposed game model.

A sharded blockchain with the Proof-of-Stake (PoS) consensus protocol has advantages in increasing throughput and reducing energy consumption, enabling the resource-limited participants to manage transactions and in a decentralized way and obtain rewards at a lower cost, e.g., Internet-of-Things (IoT) users. However, the latest PoS (e.g., Casper) requires a steep security deposit, which is the key to provide more robust security guarantees than Proof of Work, but not practical for the owners of heterogeneous IoT devices. This article considers any individual and institute who owns the IoT devices as the potential participant and focuses on designing the proper security deposits in a practical scenario with hidden information and hidden action. To bridge blockchain and the IoT users, we study the problem of balancing the security incentive and the economic incentive under two cases: 1) stake oriented and 2) effort oriented. We propose two joint models under the contract theory framework to efficiently address the problems: 1) joint adverse selection and moral hazard and 2) joint adverse selection and tournament. Both optimal contracts can provide a maximized profit for blockchain. The optimal rewards and security deposits for different types of participants can be determined accordingly. Simulations indicate that the proposed models can overcome asymmetric information and offer feasible contracts. Moreover, it demonstrates that both joint models can provide an economic incentive for the participants without reducing security incentives for the sharded blockchain.

Fog computing has gained tremendous popularity due to its capability of addressing the surging demand on high-quality ubiquitous mobile services. Nevertheless, the highly virtualized environment in fog computing leads to vulnerability to cyber attacks such as advanced persistent threats. In this paper, we propose a novel game approach of cyber risk management for the fog computing platform. We adopt the cyber-insurance concept to transfer cyber risks from fog computing platform to a third party. The system model under consideration consists of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform which is composed of multiple fog nodes. Meanwhile, the attacker dynamically adjusts the allocation of its attack computing resources to increase the probability of successful attack. Additionally, to prevent from the potential loss due to the attacks, the provider also makes a dynamic decision on the subscription of cyber-insurance for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. To model this dynamic interactive decision making problem, we formulate a dynamic Stackelberg game. In the lower-level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper-level, the cyber-insurer optimizes its premium strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable, and we investigate the Stackelberg equilibrium by capitalizing on tools from the optimal control theory. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.

Recently, due to the staggering growth of wireless data traffic, heterogeneous networks have drawn tremendous attention due to the capabilities of enhancing the capacity/coverage and reducing energy consumption for the next generation wireless networks. In this paper, we study a long-run user-centric network selection problem in the 5G heterogeneous network, where the network selection strategies of the users can be investigated dynamically. Unlike the conventional studies on the long-run model, we incorporate the memory effect and consider the fact that the decision-making of the users is affected by their memory, i.e., their past service experience. Namely, the users select the network based on not only their instantaneous achievable service experience but also their past service experience within their memory. Specifically, we model and study the interaction among the users in the framework of fractional evolutionary game based on the classical evolutionary game theory and the concept of the power-law memory. We analytically prove that the equilibrium of the fractional evolutionary game exists, is unique and uniformly stable. We also numerically demonstrate the stability of the fractional evolutionary equilibrium. Extensive simulations have been conducted to evaluate the performance of the fractional evolutionary game. The numerical results have revealed some insightful findings. For example, the user in the fractional evolutionary game with positive memory effect can achieve a higher cumulative utility compared with the user in the fractional evolutionary game with negative memory effect. Moreover, the fractional evolutionary game with positive memory effect can reduce the loss in the user's cumulative utility caused by the small-scale fading.

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

Open-access blockchains based on proof-of-work protocols have gained tremendous popularity for their capabilities of providing decentralized tamper-proof ledgers and platforms for data-driven autonomous organization. Nevertheless, the proof-of-work based consensus protocols are vulnerable to cyber-attacks such as double-spending. In this paper, we propose a novel approach of cyber risk management for blockchain-based service. In particular, we adopt the cyber-insurance as an economic tool for neutralizing cyber risks due to attacks in blockchain networks. We consider a blockchain service market, which is composed of the infrastructure provider, the blockchain provider, the cyber-insurer, and the users. The blockchain provider purchases from the infrastructure provider, e.g., a cloud, the computing resources to maintain the blockchain consensus, and then offers blockchain services to the users. The blockchain provider strategizes its investment in the infrastructure and the service price charged to the users, in order to improve the security of the blockchain and thus optimize its profit. Meanwhile, the blockchain provider also purchases a cyber-insurance from the cyber-insurer to protect itself from the potential damage due to the attacks. In return, the cyber-insurer adjusts the insurance premium according to the perceived risk level of the blockchain service. Based on the assumption of rationality for the market entities, we model the interaction among the blockchain provider, the users, and the cyber-insurer as a two-level Stackelberg game. Namely, the blockchain provider and the cyber-insurer lead to set their pricing/investment strategies, and then the users follow to determine their demand of the blockchain service. Specifically, we consider the scenario of double-spending attacks and provide a series of analytical results about the Stackelberg equilibrium in the market game.

The mobile crowdsensing paradigm facilitates a broad range of emerging sensing applications by leveraging ubiquitous mobile users to cooperatively perform certain sensing tasks with their smart devices. As this paradigm involves data collection from users, the issue of designing rewards to incentivize users is fundamentally important to ensure participation in crowdsensing. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in healthcare-based crowdsensing services, the fun of tracking daily nutrition information for a certain user can be promoted by comparing her nutritional information with that contributed and shared by her socially-connected friends. To be more general and practical, we study the incentive mechanisms in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive mechanisms. With this focus, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically prove the existence and uniqueness of the Stackelberg equilibrium. We conduct extensive simulations to investigate game equilibrium properties, and the real-world dataset is applied to evaluate and demonstrate the performance effectiveness of the proposed game model.

Mobile data rewards is now leading a new economic trend in wireless networks, where the operators stimulate mobile users to view ads with data rewards and ask for corresponding payments from advertisers. Yet, due to the uncertain nature of users' preferences, it is always challenging for the advertiser to find the best choice of data rewards to attain an optimum balance between ad revenue and rewards spent. In this paper, we develop a general contract-theoretic framework to address the problem of data rewards design in a realistic asymmetric information scenario, where each user is associated with multidimensional private information. Specifically, we model the interplay between the advertiser and users by using a multidimensional contract design approach, and theoretically analyze optimal data rewarding schemes. To ensure global incentive compatibility, we convert the multi-dimensional contract problem into an equivalent one-dimensional contract problem. Necessary and sufficient conditions for an optimal and feasible contract are then derived to provide incentives for engagement of users in data rewarding scheme. We leverage numerical results to evaluate the performance of the designed multi-dimensional contract for data rewarding scheme.

A sponsored content policy enables a content provider (CP) to pay a network service provider (SP), and thereby mobile users (MUs) can access contents from the CP through network services from the SP with a lower charge. Thus, more users want to access the contents which potentially generates more profit gain to the CP. In this article, we study the interactions among three entities under the sponsored content policy, namely, the network SP, which is referred to as SP for brevity, the CP and MUs. We model the interactions as a hierarchical Stackelberg game, where the SP and the CP act as the leaders determining the pricing and sponsoring strategies, respectively, and the MUs act as the followers deciding on their content demand. The model incorporates the network effects in a social domain and congestion in a network domain which enables us to obtain insights from the sponsored content policy. In the model, we investigate the mutual interplay between the SP and the CP in three scenarios: 1) sequential competition, where the SP first optimizes its pricing strategy for maximizing its revenue, and then the CP optimizes its sponsoring strategy for maximizing its profit sequentially; 2) simultaneous competition, where the CP and the SP optimize their individual strategies separately and simultaneously; and 3) cooperation, where both providers jointly optimize their strategies with the purpose of maximizing their aggregate payoff. Through backward induction, we derive the unique Nash equilibrium among the MUs. Furthermore, the existence and uniqueness of the Stackelberg equilibrium under three proposed scenarios are validated analytically. Via extensive simulations, it is shown that the network effects significantly improve the utilities of MUs, the profit of the CP, and the revenue of the SP.

In the era of the Internet of Things (IoT), an immense amount of data is generated from numerous sensors and devices. Data as a service (DaaS) represents a new market whose time has come, and DaaS-based businesses are emerging quickly. Businesses across sectors begin seeing their data not only as fundamentally valuable but economically viable to distribute. Due to the exponential growth of the DaaS market, the current pricing models gradually become less suitable for the selling of data sets. A more sophisticated pricing strategy is needed to unlock the value of that data for the data vendor's (DV's) revenue growth and their customers' benefits such as online service providers (SPs). In this article, we aim to maximize the DV's profits by designing a mixed sales mechanism, which allows the DV to sell data sets separately or bundled. Particularly, we apply a multidimensional adverse selection model from contract theory to model the data set trading between DVs and SPs. The DV's surplus maximization problem is solved in the single-product case first, then extended to the multiproduct case. Furthermore, the analysis of the solution of the pricing strategy in single-product and multiproduct cases is provided. Finally, the simulation results show that the proposed pricing model can improve the DV's profits efficiently.

Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.

By enabling revenue sharing between the network operators and the sponsors, the sponsored data has been proven to be a promising solution and is becoming a ubiquitous trend in the fifth generation (5G) networks for improving data connectivity for the users, increasing mobile engagement for the sponsors, and ensuring revenue for the network operators. In this paper, we investigate the data sponsored 5G system on a long-run basis. Compared with the conventional dynamic, i.e., long-run, model, the users in the system are memory-affecting, i.e., the users' decision-making is affected by their past service experience. In the system under our consideration, the users decide on the communication service access by jointly taking into account their instantaneous achievable utility and the history of their service experience, e.g., the past improved utility corresponding to the data sponsorship. The 5G system works as the utility provider for managing the communication service. Specifically, by using the concept of the power-law fading memory and the classical evolutionary game theory, we formulate a population game to model and study the dynamic behaviors of the players in the data sponsored 5G system. In the game, the interaction among the memory-affecting rational users is formulated as a fractional evolutionary game, and the communication service management of the 5G system is formulated as a classical evolutionary game. We analytically prove the existence and uniqueness of the solution to the population game. We both analytically and numerically verify the stability of the solution. The performance evaluation shows some insightful results. For example, the data sponsorship can significantly increase the data consumption for the users when they are heavily memory-affecting. Following this, we study a data sponsorship pricing problem with the objective to maximize the data consumption at the expense of the minimal data sponsorship.

The Internet of Things (IoT) connects mobile and wireless devices, and enables the IoT service providers to deliver IoT services to the mobile users in various applications, e.g., transportation and communications. In this paper, the problem of IoT service delivery management is studied with the consideration of substitutability, complementarity, and externalities of delivering IoT services due to the diversity of different IoT components in mobile systems. The substitutable IoT services have similar functionalities to serve IoT users, and the IoT users can switch to buy service from any IoT service provider. The complementary IoT services have different functionalities to serve IoT users, and the IoT users may request a bundle of IoT services from multiple IoT service providers as their IoT services can be integrated. Externalities represent the situation in which IoT users in the same system can affect the utilities of each other due to the connections and interference among the IoT users, which leads to the presence of network effect and congestion effect. To analyze the impact of these factors on the performance of IoT systems, a multi-leader multi-follower Stackelberg game model is introduced. Therein, the IoT service providers and IoT users make their strategic decisions in terms of pricing and service requests, respectively, toward their individual objectives in a distributed manner. A closed-form equilibrium solution is derived analytically through backward induction.

The mining process in public blockchains with the Nakamoto consensus protocol requires solving a computational puzzle, i.e., proof-of-work, which is resource expensive to implement in lightweight devices with limited computing resources and energy. Thus, renting mining service from cloud providers becomes a reasonable solution, which is called cloud mining. This enables users who want to mine, i.e., miners, to purchase and lease an amount of hashing power from the cloud/edge providers without any hassle of managing the infrastructure. In this paper, we study the interactions among the cloud/edge providers and miners in blockchain using a multi-leader multi-follower game-theoretic approach, in order to support proof-of-work based blockchains application. Due to the inherent complexity of the formulated game, we employ the Alternating Direction Method of Multipliers (ADMM) algorithm to investigate the optimum solution. Utilizing the decomposition characteristics and fast convergence of ADMM, we obtain the optimum results in a distributed manner. Simulation results demonstrate that with the proposed solutions, the optimization of the utilities of miners and the profits of providers can be jointly achieved.

Data rewarding is a novel business model leading a new economic trend in mobile networks, in which the operators stimulate mobile users to watch ads with data rewards and ask for corresponding payments from advertisers. Yet, due to the uncertain nature of users' preferences, it is always challenging for the advertiser to find the best choice of data rewards to attain an optimum balance between ad revenue and rewards spent. In this paper, we build a general contract-theoretic framework to address the problem of data rewards design in a realistic asymmetric information scenario, where each user is associated with multi-dimensional private information, i.e., data valuation, ad valuation, and ad sensitivity. In particular, we model the interplay between the advertiser and users by using a multi-dimensional contract approach, and theoretically analyze optimal data rewarding schemes. To ensure global incentive compatibility, we utilize the structural properties of our contract problem and convert the multi-dimensional contract into an equivalent one-dimensional contract. Necessary and sufficient conditions for an optimal and feasible contract are then derived to provide incentives for engagement of users in data rewarding scheme. Extensive numerical evaluations validate the efficiency of the designed multi-dimensional contract for data rewarding compared to other benchmark schemes.

Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods sequentially and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator offers the data price simultaneously. We analytically demonstrate that the proposed dynamic pricing scheme can help the operator gain greater revenue and users achieve higher total utilities than those of the baseline static pricing scheme. We construct the social graph using Erdös-Rényi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator.

Per sponsored content policy, a content provider can pay the network operator on behalf of mobile users to lower the data usage fees so as to generate more advertising revenue. Under such a scheme, how to offer proper sponsorship to the users in response to varying data prices becomes an important issue. Furthermore, the information asymmetry between the content provider and users makes the problem more challenging. In this paper, we propose a Stackelberg game based framework to tackle this challenge. In the framework, the network operator determines the data price first as the leader of the game, and the content providers as well as users make the decisions based on the data price as the followers. Specifically, the decision making process of the followers with the presence of asymmetric information is formulated as a contract game. In the contract game, the content provider designs a contract that contains sponsoring strategies toward all types of the users. After obtaining the optimal contract that maximizes the profit of the content provider, we also derive the optimal pricing of the network operator through backward induction. The Stackelberg equilibrium is proved to be unique, and numerical results are presented for performance evaluation.

With the development of decentralized consensus protocols, permissionless blockchains have been envisioned as a promising enabler for the general-purpose transaction-driven, autonomous systems. However, most of the prevalent blockchain networks are built upon the consensus protocols under the crypto-puzzle framework known as proof-of-work. Such protocols face the inherent problem of transaction-processing bottleneck, as the networks achieve the decentralized consensus for transaction confirmation at the cost of very high latency. In this paper, we study the problem of consensus formation in a system of multiple throughput-scalable blockchains with sharded consensus. Specifically, the protocol design of sharded consensus not only enables parallelizing the process of transaction validation with sub-groups of processors, but also introduces the Byzantine consensus protocols for accelerating the consensus processes. By allowing different blockchains to impose different levels of processing fees and to have different transaction-generating rate, we aim to simulate the multi-service provision eco-systems based on blockchains in real world. We focus on the dynamics of blockchain-selection in the condition of a large population of consensus processors. Hence, we model the evolution of blockchain selection by the individual processors as an evolutionary game. Both the theoretical and the numerical analysis are provided regarding the evolutionary equilibria and the stability of the processors' strategies in a general case.

Recently, an unmanned aerial vehicle (UAV), as known as drone, has become an alternative means of package delivery. Although the drone delivery scheduling has been studied in recent years, most existing models are formulated as a single objective optimization problem. However, in practice, the drone delivery scheduling has multiple objectives that the shipper has to achieve. Moreover, drone delivery typically faces with unexpected events, e.g., breakdown or unable to takeoff, that can significantly affect the scheduling problem. Therefore, in this paper, we propose a multi-objective and three-stage stochastic optimization model for the drone delivery scheduling, called multi-objective optimization for drone delivery (MODD) system. To handle the the multi-objective optimization in the MODD system, we apply $\varepsilon$-constraint method. The performance evaluation is performed by using a real dataset from Singapore delivery services.

Embedding sensors on wireless devices for collaborative environment sensing has been envisioned as a cost-effective solution for IoT applications. However, existing IoT platforms faces challenges, e.g., unsustainablility due to the limited on-device battery and tremendous cost of deploying middlewares for centralized task dispatching. In this paper, we employ wireless energy transfer and permissionless blockchains to construct a sustainable and decentralized IoT crowdsensing platform. Therein, IoT sensing cloud composed of multiple co-located sensors is wirelessly powered by RF-energy beacons for data sensing and transmission. The data is then forwarded to the blockchain for distributed data/transaction verification and trading. The data users access the crowdsensing service by renting sensors from the sensing clouds. Both the sensing clouds and data users are self-interested and aim to maximize their individual profits. The sensing clouds handle the interference of concurrent wireless transmissions and the on-chain transaction cost. Meanwhile, each user distributes its limited budget over the sensing clouds to optimize the service quality. We formulate a Stackelberg differential game to analyze the interaction among the sensing clouds and data users. Then, we investigate the Stackelberg equilibrium by capitalizing on Pontryagin's maximum principle. Furthermore, we provide a series of insightful numerical results about the Stackelberg equilibrium.

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

Public blockchain networks using proof of work (PoW)-based consensus protocols are considered as a promising platform for decentralized resource management with financial incentive mechanisms. In order to maintain a secured, universal state of the blockchain, PoW-based consensus protocols financially incentivize the nodes in the network to compete for the privilege of block generation through cryptographic puzzle solving. For rational consensus nodes, i.e., miners with limited local computational resources, offloading the computation load for PoW to the cloud/fog providers (CFPs) becomes a viable option. In this paper, we study the interaction between the CFPs and the miners in a PoW-based blockchain network using a game theoretic approach. In particular, we propose a lightweight infrastructure of the PoW-based blockchains, where the computation-intensive part of the consensus process is offloaded to the cloud/fog. We formulate the computation resource management in the blockchain consensus process as a two-stage Stackelberg game, where the profit of the CFP and the utilities of the individual miners are jointly optimized. In the first stage of the game, the CFP sets the price of offered computing resource. In the second stage, the miners decide on the amount of service to purchase accordingly. We apply backward induction to analyze the subgame perfect equilibria in each stage for both uniform and discriminatory pricing schemes. For uniform pricing where the same price applies to all miners, the uniqueness of the Stackelberg equilibrium is validated by identifying the best response strategies of the miners. For discriminatory pricing where the different prices are applied, the uniqueness of the Stackelberg equilibrium is proved by capitalizing on the variational inequality theory. Further, the real experimental results are employed to justify our proposed model.

Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this paper, we propose an optimal and low-complexity dynamic spectrum access framework for the RF-powered ambient backscatter system. In this system, the secondary transmitter not only harvests energy from ambient signals but also reflects these signals to transmit its modulated data to the receiver. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to “learn” from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods.

In sponsored content and service markets, the content and service providers are able to subsidize their target mobile users through directly paying the mobile network operator to lower the price of the data/service access charged by the network operator to the mobile users. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the content/service providers. In this paper, we study the interactions among the three parties in the market, namely, the mobile users, the content/service providers, and the network operator, as a two-level game with multiple Stackelberg (i.e., leader) players. Our study is featured by the consideration of global network effects owning to consumers' grouping. Since the mobile users may have bounded rationality, we model the service-selection process among them as an evolutionary-population follower sub-game. Meanwhile, we model the pricing-then-sponsoring process between the content/service providers and the network operator as a non-cooperative equilibrium searching problem. By investigating the structure of the proposed game, we reveal a few important properties regarding the equilibrium existence and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence of the proposed algorithm and demonstrate how sponsoring helps improve both the providers' profits and the users' experience.

As computing services are increasingly cloud-based, corporations are investing in cloud-based security measures. The Security-as-a-Service (SECaaS) paradigm allows customers to outsource security to the cloud, through the payment of a subscription fee. However, no security system is bulletproof, and even one successful attack can result in the loss of data and revenue worth millions of dollars. To guard against this eventuality, customers may also purchase cyber insurance to receive recompense in the case of loss. To achieve cost effectiveness, it is necessary to balance provisioning of security and insurance, even when future costs and risks are uncertain. To this end, we introduce a stochastic optimization model to optimally provision security and insurance services in the cloud. Since the model we design is a mixed integer problem, we also introduce a partial Lagrange multiplier algorithm that takes advantage of the total unimodularity property to find the solution in polynomial time. We also apply sensitivity analysis to find the exact tolerance of decision variables to parameter changes. We show the effectiveness of these techniques using numerical results based on real attack data to demonstrate a realistic testing environment, and find that security and insurance are interdependent.

Unmanned aerial vehicles, also known as drones, have emerged as a promising mode of fast, energy-efficient, and cost-effective package delivery. A considerable number of works have studied different aspects of drone package delivery service by a supplier, one of which is delivery planning. However, existing works addressing the planning issues consider a simple case of perfect delivery without service interruption, e.g., due to accident, which is common and realistic. Therefore, this paper introduces the joint ground and aerial delivery service optimization and planning (GADOP) framework. The framework explicitly incorporates uncertainty of drone package delivery, i.e., takeoff and breakdown conditions. The GADOP framework aims to minimize the total delivery cost, given practical constraints, e.g., travelling distance limit. Specifically, we formulate the GADOP framework as a three-stage stochastic integer programming model. To deal with the high complexity issue of the problem, a decomposition method is adopted. Then, the performance of the GADOP framework is evaluated by using two data sets including the Solomon benchmark suite and the real data from one of the Singapore logistics companies. The performance evaluation clearly shows that the GADOP framework can achieve significantly lower total payment than that of the baseline methods, which do not take uncertainty into account.

The energy efficiency of future networks is becoming a significant and urgent issue, calling for greener network designs. However, the increasing complexity in network structure and resource space lead to growing problem scales and coupled resource dimensions, which bring great challenges in obtaining a joint solution in optimizing the energy efficiency. In this paper, we develop a multi-dimensional network model on the basis of tuple-links associated with transmission patterns (TPs) and formulate the optimization problem as a TP based scheduling problem which jointly solves transmission scheduling, routing, power control, radio, and channel assignment. In order to tackle the complexity issues, we propose a novel algorithm by exploiting the delay column generation technique to decompose the coupled problem into recursively solving a master problem for scheduling and a sub-problem for power allocation. Further, we theoretically prove that the performance gap between the proposed algorithm and the optimum is upper bounded by that for the sub-problem solution, where the latter is derived by solving a relaxed version of the sub-problem. Numerical results demonstrate the effectiveness of the multi-dimensional framework and the benefit of the proposed joint optimization in improving network energy efficiency.

As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of “block mining” by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network, which is constrained by the computing resource and deployed as an infrastructure for decentralized data management applications. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.

This paper investigates the time scheduling for a backscatter-aided radio-frequency-powered cognitive radio network, where multiple secondary transmitters transmit data to the same secondary gateway in the backscatter mode and the harvest-then-transmit mode. With many secondary transmitters connected to the network, the total transmission demand of the secondary transmitters may frequently exceed the transmission capacity of the secondary network. As such, the secondary gateway is more likely to assign the time resource, i.e., the backscattering time in the backscatter mode and the transmission time in the harvest-then-transmit mode, to the secondary transmitters with higher transmission valuations. Therefore, according to a variety of demand requirements from secondary transmitters, we design two auction-based time scheduling mechanisms for the time resource assignment. In the auctions, the secondary gateway acts as the seller as well as the auctioneer, and the secondary transmitters act as the buyers to bid for the time resource. We design the winner determination, the time scheduling, and the pricing schemes for both the proposed auction-based mechanisms. Furthermore, the economic properties, such as individual rationality and truthfulness, and the computational efficiency of our proposed mechanisms are analytically evaluated. The simulation results demonstrate the effectiveness of our proposed mechanisms.

Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.

In a sponsored content scheme, a wireless network operator negotiates with a sponsored content service provider where the latter can pay the former to lower the cost of the mobile subscribers/users to access certain content. As such, the scheme motivates the entities in the sponsored content ecosystem to be more actively involved. Meanwhile, with the forthcoming 5G cellular networks, edge caching becomes a promising technology for traffic offloading to reduce cost and improve service quality of the content service. The key idea is that an edge caching content service provider caches content on edge networks. The cached content is then delivered to mobile users locally, reducing latency substantially. In this paper, we propose the joint sponsored and edge caching content service market model. We investigate an interplay between the sponsored content service provider and the edge caching content service provider under the non-cooperative game framework. Furthermore, the interactions among the wireless network operator, content service providers, and mobile users are modeled as a hierarchical three-stage Stackelberg game. In the game model, we analyze the sub-game perfect equilibrium in each stage through backward induction analytically. Additionally, the existence of the proposed Stackelberg equilibrium is validated by capitalizing on the bilevel optimization programming. Based on the analysis of the game properties, we propose a sub-gradient-based iterative algorithm, which guarantees to converge to the Stackelberg equilibrium.

Benefit from the capabilities of providing decentralized tamper-proof ledgers and platforms for data-driven autonomous organization, open-access blockchains based on proof-of-work protocols have gained tremendous popularity. Yet, the proof-of-work based consensus protocols under threats, e.g., double-spending. In this paper, by adopting the cyber-insurance as an economic tool to neutralize cyber risks, we propose a novel approach of cyber risk management for blockchain-based service. The blockchain service market under our consideration is composed of four entities, i.e., the infrastructure provider, blockchain provider, cyber-insurer, and users. The blockchain provider purchases the computing resources, e.g., a cloud, from the infrastructure provider to maintain the blockchain consensus and then offers blockchain services to the users. The blockchain provider optimize its profit by strategizing its investment in the infrastructure in order to improve the security of the blockchain and the service price charged to the users. In the meantime, to prevent the potential damage incurred by the attacks and then fully secure the cyber-space, the blockchain provider purchases a cyber-insurance from the cyber-insurer. In return, the cyber- insurer adjusts the insurance premium according to the perceived risk level of the blockchain service and will pay the claim to the blockchain provider once attacks happen. Based on the rationality of the market entities, we model the interaction among the blockchain provider, users, and cyber-insurer as a two- stage Stackelberg game. Specifically, the blockchain provider and cyber-insurer lead to set their pricing/investment strategies in the upper level subgame, and then the users follow to determine their demand of the blockchain service in the lower level subgame. Specifically, we consider the scenario of double-spending attacks and provide a series of analytical results about the Stackelberg equilibrium in the market game.

For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior-and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods.

Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In this paper, in order to effectively and efficiently recruit sufficient MUs, i.e., participants, we investigate an optimal reward mechanism of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and participating as a two-stage game, and analyze the MUs' participation level and the CSP's optimal reward mechanism using backward induction. At the same time, the reward is designed taking the underlying social network effects amid the mobile social network into account, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbours in social networks. We derive the analytical expressions for the discriminatory reward as well as uniform reward with complete information, and approximations of reward incentive with incomplete information. Performance evaluation reveals that the network effects tremendously stimulate higher mobile participation level and greater revenue of the CSP. In addition, the discriminatory reward enables the CSP to extract greater surplus from this Crowdsensing service market.

With a sponsored content scheme in a wireless network, a sponsored content service provider can pay to a network operator on behalf of the mobile users/subscribers to lower down the network subscription fees at the reasonable cost in terms of receiving some amount of advertisements. As such, content providers, network operators and mobile users are all actively motivated to participate in the sponsored content ecosystem. Meanwhile, in 5G cellular networks, caching technique is employed to improve content service quality, which stores potentially popular contents on edge networks nodes to serve mobile users. In this work, we propose the joint sponsored and edge caching content service market model. We investigate an interplay between the sponsored content service provider and the edge caching content service provider under the non-cooperative game framework. Furthermore, a three-stage Stackelberg game is formulated to model the interactions among the network operator, content service provider, and mobile users. Sub-game perfect equilibrium in each stage is analyzed by backward induction. The existence of Stackelberg equilibrium is validated by employing the bilevel optimization programming. Based on the game properties, we propose a sub-gradient based iterative algorithm, which ensures to converge to the Stackelberg equilibrium.

Blockchains enable tamper-proof, ordered logging for transactional data in a decentralized manner over open-access, overlay peer-to-peer networks. In this paper, we propose a decentralized framework of proactive caching in a hierarchical wireless network based on blockchains. We employ the blockchain-based smart contracts to construct an autonomous content caching market. In the market, the cache helpers are able to autonomously adapt their caching strategies according to the market statistics obtained from the blockchain, and the truthfulness of trustless nodes are financially enforced by smart contract terms. Further, we propose an incentive-compatible consensus mechanism based on proof-of-stake to financially encourage the cache helpers to stay active in service. We model the interaction between the cache helpers and the content providers as a Chinese restaurant game. Based on the theoretical analysis regarding the Nash equilibrium of the game, we propose a decentralized strategy-searching algorithm using sequential best response. The simulation results demonstrate both the efficiency and reliability of the proposed equilibrium searching algorithm.

Blockchain, an emerging decentralized security system, has been applied in many applications, such as bitcoin, smart grid, and Internet-of-Things. However, running the mining process may cost too much energy consumption and computing resource usage on handheld devices, which restricts the use of blockchain in mobile environments. In this paper, we consider deploying edge computing service to support the mobile blockchain. We propose an auction-based edge computing resource allocation mechanism for the edge computing service provider. Since there is competition among miners, the allocative externalities are taken into account in the model. In our auction mechanism, we maximize the social welfare while guaranteeing the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we evaluate the performance of our auction mechanism which shows that the proposed mechanism can efficiently solve the social welfare maximization problem for the edge computing service provider.

In this paper, we consider wireless caching helper networks (WCHNs) consisting of cache-enabled device-to-device (D2D) transmitters and caching helpers (CHs), which deliver data by exploiting cached contents. We consider two types of modes at a typical user, namely D2D and CH modes. In the D2D and CH modes, after requesting a content, the user receives the content from a D2D transmitter and a CH caching the content, respectively. In practical scenarios, to mitigate interference, the CHs may not be placed close to each other, and thus there exists a form of repulsion among the CHs' locations. In this context, we model the spatial distribution of the CHs as a β-Ginibre point processe, which reflects the repulsive behavior and contains the Poisson point process as a special case. Then, we provide analytical expressions for the coverage probabilities in the WCHNs.

With the increasing use of smart objects, such as wearable health gadgets, household automation devices, and personal electronics, there is a growing demand for a globally interconnected information network, known as the Internet of Things (IoT). IoT is featured with low-power communications among a massive number of ubiquitously-deployed and energy-constrained electronics, like sensors and actuators. In this context, wireless-powered cooperative relaying emerges as a promising solution to extend coverage and solve energy scarcity problems for IoT devices. In this paper, we propose a novel hybrid relay by combining wireless-powered communications and ambient backscattering functions for improved applicability and performance. To well adapt the hybrid relay to the network environments, we design a mode selection protocol to coordinate between the two functions. Moreover, we analyze the successful transmission probability of a dual-hop relaying system with the hybrid relay. Through numerical results, we demonstrate the performance gain of the hybrid relay and the impact of the system parameters.

Blockchain has recently been applied in many applications such as bitcoin, smart grid, and Internet of Things (IoT) as a public ledger of transactions. However, the use of blockchain in mobile environments is still limited because the mining process consumes too much computing and energy resources on mobile devices. Edge computing offered by the Edge Computing Service Provider (ECSP) can be adopted as a viable solution for offloading the mining tasks from the mobile devices, i.e., miners, in the mobile blockchain environment. However, a mechanism for edge resource allocation to maximize the revenue for the ECSP and to ensure incentive compatibility and individual rationality is still open. In this paper, we develop an optimal auction based on deep learning for the edge resource allocation. Specifically, we construct a multi-layer neural network architecture based on an analytical solution of the optimal auction. The neural networks first perform monotone transformations of the miners' bids. Then, they calculate allocation and conditional payment rules for the miners. We use valuations of the miners as the training data to adjust parameters of the neural networks so as to optimize the loss function which is the expected, negated revenue of the ECSP.We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue.

In this paper, we investigate the profit maximization problem for the mobile virtual network operator in cognitive radio networks considering the uncertain property of users' spectrum demand. In order to achieve more revenues while simultaneously satisfying the needs of users, the cognitive mobile virtual network operator chooses to dynamically sense the idle spectrum in the licensed band which is more economic, and at the same time leases the spectrum from the spectrum owner which guarantees more stable spectrum resources. However, the fluctuant spectrum demand of users imposes unprecedented challenges on the decision making process. To deal with the uncertain features of the users' demand, a flexible distribution uncertainty model is developed. Particularly, a reference distribution is introduced based on historical data and then a uncertainty set is defined to confine the spectrum demand. The uncertainty model developed allows the actual users' spectrum requirement to fluctuate around the reference distribution. Chance constraint approximations and robust optimization approaches are developed to transform and then solve the optimization problem. Simulation results based on the real-world traces evaluate the performance of the proposed scheme and investigate the parameter impacts on the system utilities. Our research may also help shed some insights on the investment policy making for the mobile virtual network operator.

Date offloading is an efficient way to address the mobile traffic congestion by redirecting some traffic of a cellular network to complimentary networks. Meanwhile, multicast applications (e.g., video conference, IPTV, and the bulk software upgrade) are very popular. However, few of existing work investigates incentive mechanism design for multicast applications in mobile data offloading. In this paper, we focus on the multicast incentive mechanism design and propose a social-aware multicast incentive mechanism (SAMIM) for data offloading, where the cellular operator will offload the data of a multicast group to some WiFi access points (APs), then these APs serve mobile users (MUs) within their coverage area through multicast transmission. Numerical simulations demonstrate the good performance of SAMIM by comparing with IM-only. In specific, SAMIM improves the system social welfare by 20% compared with IMonly with the number of selected APs |W| = 15.

As the core issue of blockchain, the mining requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power needed. Thus, the development of blockchain in mobile applications is restricted. In this paper, we, for the first time, consider the edge computing as the network enabler for mobile blockchain. In particular, we study optimal pricing-based edge computing resource management to support mobile blockchain applications where the mining process can be offloaded to an Edge computing Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly maximize the profit of the ESP and the individual utilities of different miners. In Stage~I, the ESP sets the price of edge computing services. In Stage~II, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for uniform and discriminatory pricing schemes. Further, the existence and uniqueness of Stackelberg game are validated for both pricing schemes. At last, the performance evaluation shows that the ESP intends to set the maximum possible value as optimal price for profit maximization under uniform pricing. In addition, the discriminatory pricing helps the ESP to encourage higher total service demand from miners and achieve greater profit correspondingly.

Cyber-insurance has been employed as the mean to transfer cyber risks to an insurance company, i.e., insurer. Thereby the users are covered by the insurance to alleviate the loss from cyber threats. In this work, we consider the security vendors (e.g., Symantec) as cyber-insurers selling cyber-insurance in the market. Security service will be attached to the cyber-insurance by the cyber-insurers for the purpose to reduce the probability of paying claims, where the security level of the security service is measured as the security quality. Our proposed model consists of two stages, i.e., the Stackelberg game. In the first stage, cyber-insurers set the price of cyber-insurance charging to the users while security service will be attached to these cyber-insurance. In the second stage, the users decide on the amount of these cyber-insurances to purchase based on the observed prices and the qualities of the security service. The existence and uniqueness for the equilibrium of the Stackelberg game are validated analytically. The performance evaluation presents some interesting results. For example, the cyber-insurer, who provides the security service with higher quality than other cyber-insurers, earns more profit in the market with strong interdependency than that in the market with weak interdependency while other cyber-insurers earn less profit simultaneously. This is due to the fact that the users can be influenced more easily by their peers, when one cyber-insurer provides the security service with higher quality, it can attract more users easily and be more competitive.

5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed solution is efficient not only in providing slice-as-a-service based on service requirements, but also in maximizing the provider's revenue.

Recently, unmanned aerial vehicles (UAVs), also known as drones, has emerged as an efficient and cost-effective solution for package delivery. Especially, drones are expected to incur lower cost, and achieve fast and environment friendly delivery. While most of existing drone research concentrates on surveillance applications, few works studied the drone package delivery planning problem. Even so, the previous works only focus on the drone delivery planning of a single supplier. In this paper, thus we propose the supplier cooperation in drone delivery (CoDD) framework. The framework considers jointly package assignment, supplier cooperation, and cost management. The objective of the framework is to help suppliers minimize and achieve fair share of the cost as well as reach a stable cooperation. The trade-off between using drones and outsourcing package delivery to a carrier is also investigated. The performance evaluation of the CoDD framework is conducted by using the Solomon benchmark suite and a real Singapore dataset which evidently confirms the practical findings.

Cyber insurance has been introduced as the mean to transfer cyber risks to an insurance company, namely, insurer. The users are thus covered by the insurance to alleviate the damage from cyber threats. In this paper, we investigate the joint pricing and security investment in a cloud-insurance market. The market is composed of users, cloud providers, and cloud-insurers. The users subscribes to use the cloud service (platform) from the cloud providers. To protect from the damage, the users can buy a cloud-insurance product from the cloud-insurers which will pay a claim to the users if an attack happens to the cloud service. The users are interdependent in which they can take advantage of the positive security effects generated by other users' investments in security. We assume that the cloud provider and cloud-insurer are the business partners. Therefore, the cloud-insurers can invest in the cloud platform to improve the security level, i.e., quality, of the cloud service and hence reduce the probability of paying claim. Our proposed model consists of two stages, i.e., the Stackelberg game. In the first stage, cloud-insurers set the price charging to the users and decide on the investment for improving the cloud security quality. In the second stage, the users decide on the amount of these cloud-insurances to purchase based on the observed prices and qualities. The existence and uniqueness for the equilibrium of the Stackelberg game are proved analytically. The performance evaluation shows some interesting results. For example, when the users have strong interdependency, the price of the cloud-insurance becomes lower. This is from the fact that the users can be influenced more easily by their peers, when one cloud-insurer decreases the price, it can attract more users easily.

The development of mobile cloud computing has brought many benefits to mobile users as well as cloud service providers. However, mobile cloud computing is facing some challenges, especially security-related problems due to the growing number of cyberattacks which can cause serious losses. In this paper, we propose a dynamic framework together with advanced risk management strategies to minimize losses caused by cyberattacks to a cloud service provider. In particular, this framework allows the cloud service provider to select appropriate security solutions, e.g., security software/hardware implementation and insurance policies, to deal with different types of attacks. Furthermore, the stochastic programming approach is adopted to minimize the expected total loss for the cloud service provider under its financial capability and uncertainty of attacks and their potential losses. Through numerical evaluation, we show that our approach is an effective tool in not only dealing with cyberattacks under uncertainty, but also minimizing the total loss for the cloud service provider given its available budget.

With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.

The data sponsored scheme allows the content provider to cover parts of the cellular data costs for mobile users. Thus the content service becomes appealing to more users and potentially generates more profit gain to the content provider. In this paper, we consider a sponsored data market with a monopoly network service provider, a single content provider, and multiple users. In particular, we model the interactions of three entities as a two-stage Stackelberg game, where the service provider and content provider act as the leaders determining the pricing and sponsoring strategies, respectively, in the first stage, and the users act as the followers deciding on their data demand in the second stage. We investigate the mutual interaction of the service provider and content provider in two cases: (i) competitive case, where the content provider and service provider optimize their strategies separately and competitively, each aiming at maximizing the profit and revenue, respectively; and (ii) cooperative case, where the two providers jointly optimize their strategies, with the purpose of maximizing their aggregate profits. We analyze the sub-game perfect equilibrium in both cases. Via extensive simulations, we demonstrate that the network effects significantly improve the payoff of three entities in this market, i.e., utilities of users, the profit of content provider and the revenue of service provider. In addition, it is revealed that the cooperation between the two providers is the best choice for all three entities.

Internet of Things (IoT) technology enables various physical devices to collect, process and exchange information. Market oriented models become important for IoT systems to efficiently utilize information, as IoT network nodes operate in a highly distributed and autonomous manner. In this work, we propose a three-player game theoretic market model for IoT information trading, considering direct and indirect externalities among market participants. In the model, an IoT service provider collects and processes IoT information, and then delivers the processed information as IoT services to IoT users. Then, an IoT content vendor senses and generates raw information for the IoT service provider to collect, and receives rewards from the provider. Finally, an IoT user pays a fixed service fee to the IoT service provider to access the IoT services. To jointly derive the optimal market decisions of the three participants in the model, we employ a Stackelberg game approach. The equilibria are obtained as the closed form solutions of the game, with which the existence and uniqueness properties are proved. The analytical results show that the IoT service provider operates as an intermediary agent between the IoT content vendor and users, reducing the information trading complexity of both user and vendor sides.

With the advancement and emergence of new network services, such as social network, Internet of Things, and crowd-sensing, large volume of diverse data is collected, shared, and leveraged to develop analytics services. The data analytics service has become a key commodity that can be traded among various economic entities. In this paper, we address the optimal pricing mechanisms and data management for data analytics services and further discuss the perishable services in the time varying environment. We first propose a data market model and define the data utility based on the impact of data size on the performance of data analytics, e.g., prediction and verification accuracy. For perishable services, we study the perishability of data that affects the service quality and provide a quality decay function. The data analytics services are considered as digital goods and uniquely characterized by “unlimited supply” compared to conventional goods. Therefore, we apply the Bayesian profit maximization mechanism in selling data analytics services, which is truthful, rational, and computationally efficient. The optimal service price, data amount, and service update interval are obtained to maximize the profit under different customer's valuation distributions. Finally, experimental results on realworld datasets show that our data market model and pricing mechanism effectively solve the profit maximization problem and provide useful strategies for the data analytics service provider.

With the advancement of wireless energy harvesting and transfer techniques, an Internet of Things (IoT) node equipped with a wireless charging facility can request and receive energy from wireless chargers deployed at different locations. This provides more opportunity for the mobile IoT node to replenish its battery and be able to operate without interruption due to shortage of energy supply. In this paper, we develop an optimal energy charging scheme for the mobile IoT node, considering the states of location, traffic generation, and energy storage. We formulate the problem of energy charging as a Markov decision process (MDP) to obtain the mobile IoT node’s optimal policy. The objective is to maximize the expected utility. Furthermore, we prove that the optimal policy of the proposed MDP has a threshold structure. The numerical results show the performances of the mobile IoT node under various scenarios and parameter setting. Furthermore, the proposed MDP-based wireless energy charging scheme outperforms conventional baseline schemes.

The fifth-generation (5G) wireless networks are expected to provision value-added services with ubiquitous coverage, which makes data security unprecedentedly critical. In this context, physical layer security has emerged as a promising solution to safeguard data transmission by exploiting characteristics of the wireless medium. Despite the recent technological advance in physical layer security and wireless transmission, secrecy outages (i.e., data breaches) and service outages (i.e., connection failures) will inevitably happen and incur financial losses. This economical consequence is a fact that is mostly overlooked by the existing literature. To provide financial protection against secrecy outage and service outage, we introduce a cyber-insurance framework for wireless users in cellular networks, where each user pays a premium to an insurer for a future financial compensation if an outage occurs to him/her. In particular, we derive the network risks of the cellular users in terms of secrecy outage probability and service outage probability as well as the financial risk of the cyber insurer in terms of the ruin probability that indicates the chance that the insurer experiences a deficit in affording the losses of outage users. Through numerical evaluation, we demonstrate the impact of network performance on the financial risk of the insurer. The numerical results also show that the ruin probability of the insurer can be effectively reduced by equipping a larger number of antennas at the base stations or increasing network frequency reuse.

The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS' power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance.

Non-orthogonal multiple access (NOMA)-based cognitive wireless networks can improve the spectral efficiency to utilize the vacant spectrum resource and exploit the power domain diversity. In this paper, we formulate a max-min resource allocation problem for video traffic in NOMA-based cognitive wireless networks as a mixed integer non-linear programming (MINLP) problem. The max-min video transmission problem is subject to the constraints of maximum accessed user number at each subchannel, total available energy of each secondary user, video encoding characteristics, and interference power threshold. To solve the formulated MINLP problem, we divide it into two subproblems, i.e., a power allocation and secondary user scheduling subproblem, and a video packet scheduling subproblem. First, we apply a successive convex approximation to transform the joint power allocation and secondary user scheduling subproblem into a bi-convex programming problem. Second, the binary search and dual decomposition methods are combined to obtain the approximated optimal power allocation and secondary user scheduling solutions. Finally, we propose a heuristic packet scheduling algorithm. Simulation numerical results show that the proposed algorithm improves the video quality and guarantees the fairness among different secondary users.

Microgrid (MG) represents one of the major drives of adopting Internet of Things for smart cities, as it effectively integrates various distributed energy resources. Indeed, MGs can be connected with each other and presented as a system of multimicrogrid (MMG). This paper proposes the optimal operation of MMGs by a cooperative energy and reserve scheduling model, in which energy and reserve can be cooperatively utilized among MMGs. In addition, values of Shapely are introduced to allocate economic benefits of the cooperative operation. Finally, a case study based on a system of MMGs is conducted, and simulation results verify the effectiveness of the proposed cooperative scheduling model.

Mobile cloud computing has been introduced to improve the performance of mobile application clients by offloading data processing and storage to cloud. By deploying the service on several cloud-enabled data centers, the service provider can optimally locate service instances on the cloud to provide qualified services at a reasonable cost. However, a centralized approach for both request allocation and response routing does not scale efficiently due to a large number of mobile clients involved in the mobile service traffic management. Moreover, the random and unpredictable wireless network performance (e.g., delay) complicates the problem further. In this paper, we present a stochastic distributed optimization framework for mobile cloud traffic management in 5G networks. The framework takes the impact of random wireless network characteristics into account. Utilizing the alternating direction method of multipliers, the optimization problem is decomposed into independent subproblems, which are solved in a parallel fashion on distributed agents and coordinated through dual variables. The convergence issue under the stochastic setting is addressed, and the numerical tests validate the effectiveness of the proposed algorithm.

In this paper, we consider fog radio access networks (F-RANs) consisting of cache-enabled device-to-device (D2D) transmitters and fog access points (F-APs), which deliver data by exploiting cached contents or leveraging cloud processing. We consider three types of modes at a typical user, namely, D2D, F-AP, and cooperative modes. In the D2D and the F-AP modes, when the user requests content, the user receives the content from a D2D transmitter and an F-AP caching the content, respectively. In the cooperative mode, F-APs located near the user send data aided by a centralized cloud processing unit. We also examine a mode selection algorithm in which the user adaptively selects one of the three modes. In practical scenarios, to mitigate interference, the transmitters may not be placed close to each other, and thus, there may exist a form of repulsion among the transmitters' locations. In this context, we model the spatial distributions of the D2D transmitters and the F-APs as $\beta $ -Ginibre point processes, which reflect the repulsive behavior and contain the Poisson point process as a special case. Then, we provide analytical expressions for the coverage probabilities in the F-RANs. Our results are corroborated by Monte Carlo simulations.

In this paper, we investigate wireless networks consisting of a legitimate transmitter (Alice), a legitimate receiver (Bob), eavesdroppers (Eves), and friendly jammers. Two network scenarios are considered depending on whether Alice and the jammers have the ability to detect the existence of Eves in their vicinity. If they do not have the ability, as a means to enhance the secrecy, Alice transmits artificial noise and each jammer selectively radiates a jamming signal based on the channel gain between the jammer and Bob. On the other hand, when they have the ability, Alice sends a confidential message to Bob if no Eve is detected within its guard zone, and the jammers transmit jamming signals when there exists at least one Eve in their vicinity. We model the spatial distributions of Eves and jammers as $\beta $ -Ginibre point processes, which can characterize repulsion among the nodes and include the Poisson point process (PPP) as a special case. Then, we analyze both the probability that Bob successfully decodes the confidential message and the probability that the message is secure against eavesdropping. Also, we show that our analysis is a generalization of previous works on the networks with PPPs by recovering them from our analytical results.

In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes.

The sponsored content/service market is an emerging platform, where the Content/Service Providers (CSPs) pay the Mobile Network Operator (MNO) and subsidize the Mobile Users (MUs) to access their services at a lower price. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the CSPs. In this paper, we study the interactions among the three entities in the market, namely, the MUs, the CSPs and the MNO, as a two-level hierarchical game. Our study is featured by the consideration of global network effects owning to consumers' grouping. We model the service- selection process among the MUs as an evolutionary population sub-game, and the sponsoring-pricing process between the CSPs and the MNO as a non- cooperative sub-game. By investigating the structure of the proposed game, we discover a few important properties regarding the existence of the hierarchical equilibrium, and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence property of the proposed algorithm, and demonstrate how sponsoring helps to improve both the CSPs' profits and the MUs' experience.

Backscatter communications allows the wireless radio to work in passive mode that transmits information by reflecting incident radio frequency signals. It consumes significantly less power compared to the conventional active radio that modulates information on self-generated carrier signals. However, the active radio is deemed more reliable as it can adapt to the varying channel conditions via transmit power control. In this paper, we aim to maximize the throughput of a multi-user network wirelessly powered by a power beacon station (PBS), assuming that each transceiver can switch between the passive and active radio modes. The joint optimization of the radios' mode selection, the PBS' energy beamforming and time allocation is formulated into a mixed integer nonlinear program (MINLP). Relying on an approximate upper bound of the MINLP, we employ a heuristic mode selection algorithm to determine each user's radio mode under uncertain channel state information. Simulation reveals that passive mode is preferred by the radios with better channel conditions and the active mode will be preferred if we ensure higher system reliability when the channels are subject to uncertainties.

In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and energy storages. In such systems, the uncertain renewable energy will impose unprecedented scheduling challenges. To cope with the fluctuate nature of the renewable energy, an uncertainty model based on renewable energies' moment statistics is developed. Specifically, we obtain the mean vector and second-order moment matrix according to predictions and field measurements and then define uncertainty set to confine the renewable energy generation. The uncertainty model allows the renewable energy generation distributions to fluctuate within the uncertainty set. We develop chance constraint approximations and robust optimization approaches based on a Chebyshev inequality framework to firstly transform and then solve the scheduling problem. Numerical results based on real-world data traces evaluate the performance bounds of the proposed scheduling scheme. It is shown that the temporal-correlation information of the renewable energy within a proper time span can effectively reduce the conservativeness of the solution. Moreover, detailed studies on the impacts of different factors on the proposed scheme provide some interesting insights which shall be useful for the policy making for the future microgrids.

Mobile data demand is increasing tremendously in wireless social networks, and thus efficient pricing for socialenabled services is urgently needed. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile service provider in a social data market, where the provider, i.e., the seller, individually offers each user, i.e., the buyer, a certain price in multiple time periods dynamically and repeatedly. The proposed scheme exploits the network effects in the behavior model of mobile users that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Through both the mathematical analysis and simulation, we demonstrate that our proposed sequential dynamic pricing can help the service provider to achieve greater revenue and mobile users achieve higher total utilities than those of existing optimal static pricing scheme.

Sponsored content policy enables a content provider to pay a network operator, and thereby their users access contents from the content provider through network services from the network operator with lower charge. In this paper, we study the interaction among three entities under the sponsored content policy, namely, the network operator or service provider, the content provider and the end-users. We consider a hierarchical three-stage setting to formulate the game theoretic model to analyze the interaction. Using the game model, we derive the user content demand, optimal sponsoring of content provider, and pricing of service provider based on backward induction. The model incorporates the network effects in social domain and congestion in network domain which enables us to obtain insights from the sponsored content policy. We derive the closed-form solution, i.e., equilibrium, and prove its existence and uniqueness in each stage of the game. Additionally, we develop an iterative algorithm to obtain the Stackelberg equilibrium of the entire three-stage game. The simulation results indicate that the revenue, profit, and utility of the service provider, content provider, and end-users have been improved to a large extent under the sponsored content policy because of the network effects.

In cloud computing, cloud services can be allocated to users upon requests in an on-demand basis. Heterogeneous cloud service providers may join the cloud systems to serve various types of users. Cloud services can be complementary or substitutable. For the complementary services, users may request for a bundle of the services, e.g., CPU and storage, to gain higher benefit from requesting them alone. The substitutable services have similar functionalities to serve users, e.g., different cloud database services, obtaining one of them can replace another one. Furthermore, the users of the cloud systems also influence each other because of externalities, particularly, network effect and congestion effect. From the perspective of each user, the existence of other users may introduce positive or negative impacts on the user utility, in the case of network and congestion effects, respectively. In this work, the participants in the cloud systems are treated as social enabled rational individuals. We model the complementarity, substitutability and externalities in cloud services by employing a multiple-leader multiple- follower Stackelberg game approach, including a two-stage service transaction process where service providers and users make their transaction decisions in a distributed manner. The analytical expressions of equilibria, service pricing strategies, and service allocations are derived with numerical results. We also find in the numerical results that both collusive and competitive service pricing schemes may lead to the optimized provider and user performances simultaneously.

In this paper, we propose a novel network model for RF-powered cognitive radio networks and ambient backscatter communications. In the network under consideration, each secondary transmitter is able to backscatter primary signals to the gateway for data transfer or to harvest energy from the primary signals and then use that energy to transmit data to the gateway. To maximize overall network throughput of the network, we formulate an optimization problem with the aim of finding not only an optimal tradeoff between data backscattering time and energy harvesting time, but also time sharing among multiple secondary transmitters. Through the numerical results, we demonstrate that the solution of the optimization problem always achieves the best performance compared with two other baseline schemes.

In this paper, we study an overlay RF-powered cognitive radio network with ambient backscatter communications. In the network, when the channel is occupied, the secondary transmitter (ST) can perform either energy harvesting or data transmission using ambient backscattering technique to a gateway. We consider the case that the gateway charges the ST a certain price if the ST transmits information. This leads to questions of how to determine the best price for the gateway and how to find the optimal backscatter time. To address this problem, we propose a Stackelberg game in which the gateway is the leader adapting the price to maximize its profit in the first stage. Meanwhile, the ST chooses its backscatter time to maximize its utility in the second stage. To analyze the game, we apply the backward induction technique. We show that the game always has a unique subgame perfect Nash equilibrium. Additionally, our results provide insights on the impact of the competition on the players' profit and utility.

Due to its mathematical tractability, the homogeneous Poisson point process (PPP) has been employed to model wireless networks and analyze their performance. The PPP has the fundamental property that in a network with n nodes, the n nodes are distributed independently from each other. As such the PPP is not a suitable model for many networks where there exists a repulsion among the nodes. In order to address this limitation, in this paper we model the spatial distribution of transmitters in wireless networks as a Poisson hard-core process (PHCP) in which no two nodes can be closer to each other than a given repulsion radius from one another. We first provide an exact expression of the coverage probability of the networks and then introduce the method to efficiently evaluate the derived expression. Additionally, we derive approximations of the coverage probability which have low computational complexities. The accuracy and efficiency of our analytical results are validated by our simulations.

In this paper, we analyze the performance of wireless sensor networks using stochastic geometry. In practical networks, since sensor nodes in the networks are not independently placed, there exists a correlation among the locations of the nodes. In order to capture the effect of the correlation, we model the spatial distribution of nodes as α-Ginibre point processes (GPPs) which reflect the repulsion. It is assumed that each sensor node is associated with the closest gateway and employs a channel inversion power control which adjusts transmit power based on the contact distance. We first identify the characteristics of the contact distance and transmit power, and then investigate the outage performance of the networks using the derived characteristics. Since the α-GPP contains the Poisson point process (PPP) as a particular case, our analysis can be interpreted as a generalization of previous works on the networks modeled by PPPs. The accuracy of our analysis is validated through simulation results.

This paper introduces a cyber insurance policy management for the mobile networks in which if a mobile user agrees to purchase an insurance policy from an insurer, the loss of the mobile user, i.e., the insured, will be covered by the insurance policy when the risks happen. To protect mobile users from cyber attacks, the insurer can deploy security protection solutions, e.g., anti-virus software or personal firewall, to the insureds, thereby reducing the risks for mobile users. However, when the solutions are deployed, they will incur a certain cost to the insurer. Therefore, we propose a stochastic optimization based on the reserve state of the insurer and the number of active mobile users to determine whether the protection solutions should be deployed or not to maximize the revenue for the insurer. The performance evaluation reveals that the optimal policy can achieve significantly higher revenue than those of baseline schemes for the insurer. Alternatively, the coalitional game is studied to share the reward among the insurers, and we show that the insurers can gain higher individual rewards through the cooperation.

Self-sustainable communications based on advanced energy harvesting technologies have been under rapid development, which facilitate autonomous operation and energy-efficient transmission. Recently, ambient backscattering that leverages existing RF signal resources in the air has been invented to empower data communication among low-power devices. In this paper, we introduce hybrid device-to-device (D2D) communications by integrating ambient backscattering and wireless-powered communications. The hybrid D2D communications are self-sustainable, as no dedicated external power supply is required. However, since the radio signals for energy harvesting and backscattering come from external RF sources, the performance of the hybrid D2D communications needs to be optimized efficiently. As such, we design two mode selection protocols for the hybrid D2D transmitter, allowing a more flexible adaptation to the environment. We then introduce analytical models to characterize the impacts of the considered environment factors, e.g., distribution, spatial density, and transmission load of the ambient transmitters, on the hybrid D2D communications performance. Extensive simulations show that the repulsion factor among the ambient transmitters has a non-trivial impact on the communication performance. Additionally, we reveal how different mode selection protocols affect the performance metrics.

Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers. Most of the existing VRPPC works consider deter- ministic parameters which may not be practical and uncertainty has to be taken into account. In this paper, we propose the Optimal Stochastic Delivery Planning with Deadline (ODPD) to help a supplier plan and optimize the package delivery. The aim of ODPD is to service all customers within a given deadline while considering the randomness in customer demands and traveling time. We formulate the ODPD as a stochastic integer programming, and use the cardinality minimization approach for calculating the deadline violation probability. To accelerate computation, the L-shaped decomposition method is adopted. We conduct extensive performance evaluation based on real customer locations and traveling time from Google Map.

With an increasing demand from emerging logistics businesses, Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been introduced to manage package delivery services from a supplier to customers. However, almost all of existing studies focus on the deterministic problem that assumes all parameters are known perfectly at the time when the planning and routing decisions are made. In reality, some parameters are random and unknown. Therefore, in this paper, we consider VRPPC with hard time windows and random demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to minimize the total package delivery cost while meeting the customer time window constraints. We use stochastic integer programming to formulate the optimization problem incorporating the customer demand uncertainty. Moreover, we evaluate the performance of the ODP using test data from benchmark dataset and from actual Singapore road map.

A big data service is any data-originated resource that is offered over the Internet. The performance of a big data service depends on the data bought from the data collectors. However, the problem of optimal pricing and data allocation in big data services is not well-studied. In this paper, we propose an auction-based big data market model. We first define the data cost and utility based on the impact of data size on the performance of big data analytics, e.g., machine learning algorithms. The big data services are considered as digital goods and uniquely characterized with ''unlimited supply'' compared to conventional goods which are limited. We therefore propose a Bayesian profit maximization auction which is truthful, rational, and computationally efficient. The optimal service price and data size are obtained by solving the profit maximization auction. Finally, experimental results on a real-world taxi trip dataset show that our big data market model and auction mechanism effectively solve the profit maximization problem of the service provider.

With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared with the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

This paper introduces a new solution to improve the performance for secondary systems in radio frequency (RF) powered cognitive radio networks (CRNs). In a conventional RF-powered CRN, the secondary system works based on the harvest-then-transmit protocol. That is, the secondary transmitter (ST) harvests energy from primary signals and then uses the harvested energy to transmit data to its secondary receiver (SR). However, with this protocol, the performance of the secondary system is much dependent on the amount of harvested energy as well as the primary channel activity, e.g., idle and busy periods. Recently, ambient backscatter communication has been introduced, which enables the ST to transmit data to the SR by backscattering ambient signals. Therefore, it is potential to be adopted in the RF-powered CRN. We investigate the performance of RF-powered CRNs with ambient backscatter communication over two scenarios, i.e., overlay and underlay CRNs. For each scenario, we formulate and solve the optimization problem to maximize the overall transmission rate of the secondary system. Numerical results show that by incorporating such two techniques, the performance of the secondary system can be improved significantly compared with the case when the ST performs either harvest-then-transmit or ambient backscatter technique.

This paper studies the data scheduling and admission control problem for a backscatter sensor network (BSN). In the network, instead of initiating their own transmissions, the sensors can send their data to the gateway just by switching their antenna impedance and reflecting the received RF signals. As such, we can reduce remarkably the complexity, the power consumption, and the implementation cost of sensor nodes. Different sensors may have different functions, and data collected from each sensor may also have a different status, e.g., urgent or normal, and thus we need to take these factors into account. Therefore, in this paper, we first introduce a system model together with a mechanism in order to address the data collection and scheduling problem in the BSN. We then propose an optimization solution using the Markov decision process framework and a reinforcement learning algorithm based on the linear function approximation method, with the aim of finding the optimal data collection policy for the gateway. Through simulation results, we not only show the efficiency of the proposed solution compared with other baseline policies, but also present the analysis for data admission control policy under different classes of sensors as well as different types of data.

Wireless radio frequency energy harvesting has been adopted in wireless networks as a method to supply energy to wireless nodes. In this paper, we analyze a wireless energy harvesting network based on a Boolean-Poisson model. This model assumes that energy sources are distributed according to a Poisson point process and have disc-shaped coverage regions with random radii. We introduce a distribution for the coverage radii, which takes aggregated harvested power into account. The union of the coverage regions of the energy sources forms the energy harvesting zone. We derive the transmission success probability of single-hop networks characterized by the probability that two sensor nodes are located in the energy harvesting zone. Then, we analyze the performance of multi-hop networks in the cases, where the locations of the sensor nodes are either fixed or randomly distributed. Moreover, we consider a star-shaped topology, which reflects the scenario wherein some sensor nodes simultaneously transmit data to a data collector. In this setting, we derive an approximation of the average throughput at the data collector. Numerical results validate the accuracy of our analysis in the single-hop and multi-hop networks and confirm the tightness of our approximation in the case of the star-shaped topology.

The Poisson point process (PPP) has been widely employed to model wireless networks and analyze their performance. The PPP has the property that nodes are conditionally independent from each other. As such, it may not be a suitable model for many networks, where there exists repulsion among the nodes. In order to address this limitation, we adopt a Poisson hardcore process (PHCP), in which no two nodes can be closer than a repulsion radius from one another. We consider two-tier heterogeneous networks, where the spatial distributions of transmitters in the first-tier and the second-tier networks follow a PHCP and a PPP, respectively. To alleviate inter-tier interference, we consider a guard zone for the first-tier network and presume that the second-tier transmitters located in the zone are deactivated. Under this setup, the activated second-tier transmitters form a Poisson hard-core hole process. We first derive exact computable expressions of the coverage probability and introduce a method to efficiently evaluate the expressions. Then, we provide approximations of the coverage probability, which have lower computational complexities. In addition, as a special case, we investigate the coverage probability of single-tier networks by modeling the locations of transmitters as a PHCP.

In this paper, we analyze the performance of wireless sensor networks using stochastic geometry. In practical networks, since nodes in the networks are not independently placed, there exists a correlation among the locations of the nodes. In order to capture the effect of the correlation, we model the spatial distribution of the nodes as α-Ginibre point processes (GPPs), which reflect the repulsion. It is assumed that each sensor node is associated with the closest gateway and employs a fractional channel inversion power control, which adjusts transmit power based on the contact distance. We first identify the characteristics of the contact distance and transmit power, and then investigate the outage performance of the networks using the derived characteristics. We also examine energy harvesting networks where each sensor harvests energy from radio frequency signals radiated by energy sources and transmits data to its serving gateway when the harvested energy is enough to conduct the fractional channel inversion power control. Since the α-GPP contains the Poisson point process (PPP) as a particular case, our analysis can be interpreted as a generalization of previous works on the networks modeled by PPPs. The accuracy of our analysis is validated through simulation results.

In this paper, a call admission control algorithm based on inter-network cooperation is proposed via a Stackelberg game framework for cognitive heterogeneous networks. The call admission control problem is subject to the variable bandwidth rate traffic, network service selection, feasible subchannel allocation, and call blocking probability. The call admission control algorithm is based on spectrum price at primary heterogeneous networks and subchannel allocation price and network selection at cognitive heterogeneous networks. In order to determine the call blocking probability, a probability upper bound of exceeding the maximum admission number for secondary mobile terminals (MTs) is analyzed based on M/M/∞ model. Then, the subchannel allocation price and network selection are designed via the dual decomposition method, and the vacant spectrum price is determined with Bertrand game theory. Finally, a call admission control algorithm is proposed. Simulation results demonstrate that the proposed algorithm not only improves quality of service at each secondary MT, but also reduces the call blocking probability for cognitive heterogeneous networks.

In this paper, we introduce a new model for RF-powered cognitive radio networks with the aim to improve the performance for secondary systems. In our proposed model, when the primary channel is busy, the secondary transmitter is able either to backscatter the primary signals to transmit data to the secondary receiver or to harvest RF energy from the channel. The harvested energy then will be used to transmit data to the receiver when the channel becomes idle. We first analyze the tradeoff between backscatter communication and harvest-then-transmit protocol in the network. To maximize the overall transmission rate of the secondary network, we formulate an optimization problem to find time ratio between taking backscatter and harvest-thentransmit modes. Through numerical results, we show that under the proposed model can achieve the overall transmission rate higher than using either the backscatter communication or the harvest-then-transmit protocol.

Big data has been emerging as a new approach in utilizing large datasets to optimize complex system operations. Big data is fueled with Internet-of-Things (IoT) services that generate immense sensory data from numerous sensors and devices. While most current research focus of big data is on machine learning and resource management design, the economic modeling and analysis have been largely overlooked. This paper thus investigates the big data market model and optimal pricing scheme. We first study the utility of data from the data science perspective, i.e., using the machine learning methods. We then introduce the market model and develop an optimal pricing scheme afterward. The case study shows clearly the suitability of the proposed data utility functions. The numerical examples demonstrate that big data and IoT service provider can achieve the maximum profit through the proposed market model.

Wireless caching has been used to improve network performance and reduce bandwidth and energy consumption. In this paper, we study the issue of joint admission control and content caching for wireless access points with energy harvesting capability. Given limited energy supply, the access points, in a competitive environment, aim to maximize their payoff defined in terms of revenue by optimizing their admission control and content caching policy. Moreover, the throughput of the content transmission by the access point has to be maintained above a certain threshold. Thus, we propose a constrained stochastic game to model this competitive caching scenario. The equilibrium policy, which is a mapping from the energy, cache, and demand states to the action, is obtained from the model. From the performance evaluation, the joint admission control and content caching policy can achieve significantly better performance than that of the baseline schemes, especially when the energy harvesting rate becomes constricted.

Caching has shown the success in performance improvement for many wireless communications and networking systems. In this paper, we introduce a caching mechanism for the energy harvesting based Internet of Things (IoT) sensing service. In the service, a sensor harvests energy from an environment. The energy is stored in the battery, and the sensor uses it for sensing and transmitting the reading to the user. A sensing cache can be implemented at a wireless gateway of the sensor to avoid activating the sensor too frequently, hence reducing its energy consumption. We develop an analytical model to investigate the benefit of the proposed caching mechanism. We also introduce the threshold adaptation algorithm that allows the sensing cache dynamically to adjust the parameter of caching to maximize the combined hit rate of the sensing service from multiple sensors. The performance evaluation clearly shows the tradeoff between energy consumption and caching.

A wireless powered communication network is a potential application of wireless energy harvesting to improve convenience and flexibility. However, wireless energy transfer from a wireless energy source has to be scheduled to minimize energy usage while meeting quality of service (QoS) requirements of sensor nodes in the network. In this paper, we consider wireless powered sensor network whose sensor nodes have auxiliary energy sources in addition to dedicated wireless energy transfer. We propose a distributed wireless energy transfer scheduling to achieve the aforementioned objective and meet the requirements. We formulate a constrained stochastic game model to obtain a multi-policy constrained Nash equilibrium of wireless energy transfer request. This equilibrium instructs the sensor node to request for wireless energy transfer based on its local state. The performance evaluation shows that the analytical model is well verified by numerical simulations.

Wireless powered sensor networks are composed of multiple sensor nodes with limited energy supply and storage. In this paper, we consider the wireless powered sensor networks, where an energy gateway can supply energy wirelessly to the sensor nodes. The sensor nodes use the energy to transmit their data. We then propose an opportunistic energy scheduling scheme. In this scheme, the energy gateway with limited energy supply transfers energy to sensor nodes based on channel conditions. We formulate an optimization problem based on a Markov decision process to obtain the optimal energy transfer policy. The objective is to maximize the weighted energy received by the sensor nodes. We prove that the optimal policy is a structure policy, in which the numerical studies confirm the result.

Ambient radio frequency (RF) energy harvesting methods have drawn significant interests due to their ability to provide energy to wireless devices from ambient RF sources. This paper considers ambient RF energy harvesting wireless sensor networks where a sensor node transmits data to a data sink using the energy harvested from the signals transmitted by the ambient RF sources. We analyze the performance of the network, i.e., the mean of the harvested energy, the power outage probability, and the transmission outage probability. In many practical networks, the locations of the ambient RF sources are spatially correlated and the ambient sources exhibit repulsive behaviors. Therefore, we model the spatial distribution of the ambient sources as an α-Ginibre point process (α-GPP), which reflects the repulsion among the RF sources and includes the Poisson point process as a special case. We also assume that the fading channel is Nakagami-m distributed, which also includes Rayleigh fading as a particular case. In this paper, by exploiting the Laplace transform of the α-GPP, we introduce semi-closed-form expressions for the considered performance metrics and provide an upper bound of the power outage probability. The derived expressions are expressed in terms of the Fredholm determinant, which can be computed numerically. In order to reduce the complexity in computing the Fredholm determinant, we provide a simple closed-form expression for the Fredholm determinant, which allows us to evaluate the Fredholm determinant much more efficiently. The accuracy of our analytical results is validated through simulation results.

RF-enabled wireless power transfer and energy harvesting has recently emerged as a promising technique to provision perpetual energy replenishment for low-power wireless networks. The network devices are replenished by the RF energy harvested from the transmission of ambient RF transmitters, which offers a practical and promising solution to enable self-sustainable communications. This paper adopts a stochastic geometry framework based on the Ginibre model to analyze the performance of self-sustainable communications over cellular networks with general fading channels. Specifically, we consider the point-to-point downlink transmission between an access point and a battery-free device in the cellular networks, where the ambient RF transmitters are randomly distributed following a repulsive point process, called Ginibre α-determinantal point process (DPP). Two practical RF energy harvesting receiver architectures, namely time-switching and power-splitting, are investigated. We perform an analytical study on the RF-powered device and derive the expectation of the RF energy harvesting rate, the energy outage probability and the transmission outage probability over Nakagami-m fading channels. These are expressed in terms of so-called Fredholm determinants, which we compute efficiently with modern techniques from numerical analysis. Our analytical results are corroborated by the numerical simulations, and the efficiency of our approximations is demonstrated. In practice, the accurate simulation of any of the Fredholm determinant appearing in the manuscript is a matter of seconds. An interesting finding is that a smaller value of α (corresponding to larger repulsion) yields a better transmission outage performance when the density of the ambient RF transmitters is small. However, it yields a lower transmission outage probability when the density of the ambient RF transmitters is large. We also show analytically that the power-splitting architecture outperforms the time-switching architecture in terms of transmission outage performances. Lastly, our analysis provides guidelines for setting the time-switching and power-splitting coefficients at their optimal values.

Mobile cloud computing offers an appealing paradigm to relieve the pressure of soaring data demands and augment energy efficiency for future green networks. Cloudlets can provide available resources to nearby mobile devices with lower access overhead and energy consumption. To stimulate service provisioning by cloudlets and improve resource utilization, a feasible and efficient incentive mechanism is required to charge mobile users and reward cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. Truthfulness and system efficiency are two crucial properties in addition to computational efficiency, individual rationality and budget balance. In this paper, we first propose a feasible and truthful incentive mechanism (TIM), to coordinate the resource auction between mobile devices as service users (buyers) and cloudlets as service providers (sellers). Further, TIM is extended to a more efficient design of auction (EDA). TIM guarantees strong truthfulness for both buyers and sellers, while EDA achieves a fairly high system efficiency but only satisfies strong truthfulness for sellers. We also show the difficulties for the buyers to manipulate the resource auction in EDA and the high expected utility with truthful bidding.

Internet of Things (IoT) objects and devices, e.g., sensors and actuators, can be accessed as a service to meet the users' demand from various applications. In this paper, we propose an optimal service auction to determine which user to access an IoT device. The auction decision to accept the highest bid is obtained as a policy of a Markov decision process (MDP) with an objective to maximize the reward of the IoT device defined as a function of the revenue from the bid minus the costs from energy replenishment and channel access for data transfer. We consider system dynamics in terms of wireless energy transfer and wireless transmission which can incur different costs. The optimal policy obtained from the MDP shows the adaptability of the IoT device owner to accept the highest bid and to request for wireless energy transfer. The performance evaluation shows clearly that the proposed optimal service auction achieves significantly higher reward than a static scheme.

Massive MIMO will be one of the technologies adopted in 5G cellular networks due to its ability to enhance transmission performance. However, resource management issues remain unsolved, especially with quality of service (QoS) requirements from users. This paper focuses on cell association and antenna allocation problems in such networks. We analyze the competitive situations where users in different classes with different QoS (i.e., data rate) requirement can choose to associate with any cell rationally and independently. Likewise, access points can allocate their antennas to different users. The users and access points are self-interested to maximize their own benefits in terms of data rate and total revenue, respectively. We formulate a hierarchical evolutionary game framework which is composed of the games for cell association and antenna allocation. We apply both deterministic and stochastic approaches to obtain the equilibrium solutions of the game.

In mobile social networks, mobile users can help each other to disseminate and deliver contents utilizing social relationship (e.g., physical contact). In this paper, we consider content delivery in mobile social networks, where a mobile user (i.e., a content source) transfers not only content, but also energy to an intermediate user (i.e., a mobile content messenger). The messenger uses this energy to store, carry, and forward the content to the destination (i.e., a sink). We particularly address the content messenger selection and wireless energy transfer problem of the content source to determine which messenger to deliver the content and the amount of energy to be transferred to the messenger. We formulate a Markov decision process (MDP) to obtain the optimal policy. The numerical results show clearly the improved performance in terms of higher throughput as compared with a baseline static policy.

In wireless energy harvesting communication networks, a user harvests energy from an environment and uses the energy for data transmission. However, the user's data transmission is susceptible to a jamming attack by jammers, which also harvest energy from the environment. To address this problem, therefore we introduce a user's deception mechanism in which the user can transmit fake signals (i.e., blank transmission) to trigger the jammers to perform the attack, wasting their energy. We propose an analysis of the network with the deception mechanism based on a Markov chain. The performance evaluation reveals some interesting results. For example, the user can adjust the number of blank transmissions to achieve the highest throughput. We provide a benchmarking scheme based on an optimization. The benchmarking is useful for developing an effective deception mechanism with minimum complexity and knowledge about the network and jammers.

In wireless powered networks, a user can make a request and use the wireless energy transferred from an energy source for its data transmission. However, due to broadcast nature of wireless energy transfer (e.g., RF energy), a malicious node (i.e., an attacker) can also intercept the energy and use it to perform an attack by jamming the data transmission of the user. We consider such a jamming attack where the user and attacker are aware of each other. We formulate a game theoretic model to analyze the energy request and data transmission policy of the user and the attack policy of the attacker when the user and the attacker both want to maximize their own rewards. We use an iterative algorithm designed based on the best response dynamics to obtain the solution defined in terms of the constrained Nash equilibrium. The numerical results show not only the convergence of the proposed algorithm, but also the optimal reward of the user under different energy cost constraints.

Delivering high-speed mobile social networks requires smart mechanisms that can explore the social relations between users to improve data delivery and content dissemination performance. In this paper, a novel approach for energy sharing in mobile social energy networks is proposed. In this proposed system, pairs of users that have a friendship relationship can share their energy, e.g., from batteries or power banks, to improve the data transmission performance. An analytical model is introduced to derive some important performance measures (e.g., energy outage probability and average transferred energy) of the friend users. Based on this proposed analytical model, it is observed that being friends may not always be beneficial for some of the user. To this end, a friend matching algorithm is proposed to determine the best friendship pairings between users that allow to minimize the energy outage probability. Using the proposed approach, it is shown that that there exist certain regions of system parameters, such as the transmission probability and the capacity of an energy storage, within which the stability of the friend relationship between users can be maintained. Simulation results were used to evaluate the performance of the proposed approach and to gain more insights on the potential of mobile social energy networks.

We consider a cognitive radio network, where primary users (PUs) share their spectrum with energy harvesting (EH) enabled secondary users (SUs), conditioned on a limited SUs' interference at PU receivers. Due to the lack of information exchange between SUs and PUs, the SU-PU interference channels are subject to uncertainty in channel estimation. Besides channel uncertainty, SUs' EH profile is also subject to spatial and temporal variations, which enforce an energy causality constraint on SUs' transmit power control and affect SUs' interference at PU receivers. Considering both the channel and EH uncertainties, we propose a robust design for SUs' power control to maximize SUs' throughput performance. Our robust design targets at the worst-case interference constraint to provide a robust protection for PUs, while guarantees a transmission probability to reflect SUs' minimum QoS requirements. To make the non-convex throughput maximization problem tractable, we develop a convex approximation for each robust constraint and successfully design a successive approximation approach that converges to the global optimum of the throughput objective. Simulations show that SUs will change transmission strategies according to PUs' sensitivity to interference, and we also exploit the impact of SUs' EH profile (e.g., mean, variance, and correlation) on SUs' power control.

The advance in RF energy transfer and harvesting technique over the past decade has enabled wireless energy replenishment for electronic devices, which is deemed as a promising alternative to address the energy bottleneck of conventional battery-powered devices. In this paper, by using a stochastic geometry approach, we aim to analyze the performance of an RF-powered wireless sensor in a downlink simultaneous wireless information and power transfer (SWIPT) system with ambient RF transmitters. Specifically, we consider the point-to-point downlink SWIPT transmission from an access point to a wireless sensor in a network, where ambient RF transmitters are distributed as a Ginibre α-determinantal point process (DPP), which becomes the Poisson point process when a approaches zero. In the considered network, we focus on analyzing the performance of a sensor equipped with the power-splitting architecture. Under this architecture, we characterize the expected RF energy harvesting rate of the sensor. Moreover, we derive the upper bound of both power and transmission outage probabilities. Numerical results show that our upper bounds are accurate for different value of a.

Device-to-Device (D2D) communications, which allow direct communication among mobile devices, have been proposed as an enabler of local services in 3GPP LTE-Advanced (LTE-A) cellular networks. This work investigates a hierarchical LTE-A network framework consisting of multiple D2D operators at the upper layer and a group of devices at the lower layer. We propose a cooperative model that allows the operators to improve their utility in terms of revenue by sharing their devices, and the devices to improve their payoff in terms of end-to-end throughput by collaboratively performing multi-path routing. To help understanding the interaction among operators and devices, we present a game-theoretic framework to model the cooperation behavior, and further, we propose a layered coalitional game (LCG) to address the decision making problems among them. Specifically, the cooperation of operators is modeled as an overlapping coalition formation game (CFG) in a partition form, in which operators should form a stable coalitional structure. Moreover, the cooperation of devices is modeled as a coalitional graphical game (CGG), in which devices establish links among each other to form a stable network structure for multi-path routing. We adopt the extended recursive core, and Nash network, as the stability concept for the proposed CFG and CGG, respectively. Numerical results demonstrate that the proposed LCG yields notable gains compared to both the non-cooperative case and a LCG variant and achieves good convergence speed.

In wireless energy harvesting communication networks, a user receives wireless energy released by an ambient or dedicated energy source, and uses that energy for delay constrained data transmission. However, such data transmission can be susceptible to a jamming attack from a nearby attacker also harvesting from the wireless energy source. In this paper, we consider such a scenario and present performance analysis. In particular, we develop an analytical model for the network based on a Markov chain to obtain various performance measures for the user including throughput and delay distribution. The performance evaluation shows some interesting results. For example, under the jamming attack, there is a maximum achievable throughput of the user. We also validate the analytical model using simulation.

Publish-subscribe mobile social networks enable content providers to disseminate up-to-date contents to end users with the help of mobile content relays by opportunistic wireless contacts. Since content providers, relays and end users are independent and self-interest entities in the mobile social networks, the content relay has to take a content requesting/transferring action to achieve the lowest cost. In this paper, we propose and solve a Markov decision process (MDP) based scheme for the content relay to optimally take the actions to receive contents from content providers, and to transfer/forward contents to end users. The relay takes an action based on the observed content price, the number of end users of contents, as well as the queue length. The proposed MDP scheme aims to minimize an expected cost of the content relay. The numerical results show that the proposed MDP scheme significantly outperforms baseline schemes.

The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users' local load and availability of cloudlets. We examine users' mobility patterns and cloudlets' admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

The increasing number of cloud-based Internet applications has led to the demand for efficient resource and cost management. This paper proposes a real-time group auction system for the cloud instance market. The system is designed based on a combinatorial double auction, and its applicability and effectiveness are evaluated in terms of resource efficiency and monetary benefits to auction participants (e.g., cloud users and providers). The proposed auction system helps them decide when and how providers will allocate their resources and to which users. Furthermore, we propose a distributed algorithm using a group formation game that determines which users and providers will trade resources by their cooperative decisions. To find how to allocate the resources, the utility optimization problem is formulated as a binary integer programming problem and the nearly optimal solution is obtained by a heuristic algorithm with quadratic time complexity. In comparison studies, the proposed real-time group auction system with cooperation outperforms an individual auction in terms of the resource efficiency (e.g., the request acceptance rate for users and resource utilization for providers) and monetary benefits (e.g., average payments for users and total profits for providers).

Ambient radio frequency (RF) energy harvesting technique has recently been proposed as a potential solution for providing proactive energy replenishment for wireless devices. This paper aims to analyze the performance of a battery-free wireless sensor powered by ambient RF energy harvesting using a stochastic geometry approach. Specifically, we consider the point-to-point uplink transmission of a wireless sensor in a stochastic geometry network, where ambient RF sources, such as mobile transmit devices, access points and base stations, are distributed as a Ginibre α-determinantal point process (DPP). The DPP is able to capture repulsion among points, and hence, it is more general than the Poisson point process (PPP). We analyze two common receiver architectures: separated receiver and time-switching architectures. For each architecture, we consider the scenarios with and without co-channel interference for information transmission. We derive the expectation of the RF energy harvesting rate in closed form and also compute its variance. Moreover, we perform a worst-case study which derives the upper bound of both power and transmission outage probabilities. Additionally, we provide guidelines on the setting of optimal time-switching coefficient in the case of the time-switching architecture. Numerical results verify the correctness of the analysis and show various tradeoffs between parameter setting. Lastly, we prove that the RF-powered sensor performs better when the distribution of the ambient sources exhibits stronger repulsion.

In cognitive radio networks, it is challenging for secondary users (SUs) to estimate and control their interference at the receivers of primary users (PUs), due to incomplete or erroneous channel information between SUs and PUs. Thus, SUs need to estimate the worst-case aggregate interference at PU receivers to ensure guaranteed protection for PUs from excessive interference. As it is rare that all SU-PU channels experience the worst-case conditions simultaneously, we propose a practical model (namely, the worst-case selective robust model) for SUs to estimate their aggregate interference power. This model employs an adjustable parameter to control the number of SU-PU channels that are in the worst-case conditions. For an individual SU-PU channel, the estimation of worst-case channel gain is subject to a distribution uncertainty. Given this robust model, we study SUs' power control problem in a non-cooperative game where each SU selfishly maximizes its own throughput performance subject to coupled interference constraints at PU receivers. We study the existence and uniqueness of Nash equilibrium and propose an iterative algorithm for SUs to achieve the equilibrium in a distributed manner. Numerical results show that our algorithm provides guaranteed protection for PUs and fair throughput performance for SUs, provided with uncertain SU-PU channel information.

We study the performance optimization problem for a cognitive radio network with radio frequency (RF) energy harvesting capability for secondary users. In such networks, the secondary users are able to not only transmit packets on a channel licensed to a primary user when the channel is idle, but also harvest RF energy from the primary users' transmissions when the channel is busy. Specifically, we propose a system model where the secondary users are able to cooperate to maximize the overall network throughput through sensing a set of common channels. We first consider the case where the secondary users cooperate in a TDMA fashion and propose a novel solution based on a learning algorithm to find optimal channel access policies for the secondary users. Then, we examine the case where the secondary users cooperate in a decentralized manner and we formulate the cooperative decentralized optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve the cooperative decentralized stochastic optimization problem, we apply a decentralized learning algorithm based on the policy gradient and the Lagrange multiplier method to obtain optimal channel access policies. Extensive performance evaluation is conducted and it shows the efficiency and the convergence of the learning algorithms.

Abstract In smart grid, the real‐time pricing is implemented to motivate power consumers to change their consumption profile dynamically. With the real‐time pricing, a deferrable load can be scheduled by its scheduler optimally so that the power consumption cost will be minimized. However, when the data communication in smart grid suffers from interference, congestion, malfunction in devices, or even cyber attack, it is possible that the power price information cannot be transmitted successfully to the scheduler. As a result, the scheduling performance will be negatively affected by the suboptimal decision‐making because of incomplete power price information. To overcome this problem, a partially observable Markov decision process based deferrable load scheduling algorithm is proposed. Besides, the implementation of a standby alternative channel with the purpose to improve the reliability of the data communication in smart grid is also discussed in this paper. The numerical results show that the proposed partially observable Markov decision process based algorithm and the implementation of standby channel can effectively improve the scheduling performance when the scheduler lacks actual price information. Copyright © 2014 John Wiley & Sons, Ltd.

Ambient RF (Radio Frequency) energy harvesting techniques have recently been proposed as a potential solution to provide proactive energy replenishment for wireless devices. This paper aims to analyze the performance of a battery-free wireless sensor powered by ambient RF energy harvesting using a stochastic-geometry approach. Specifically, we consider a random network model in which ambient RF sources are distributed as a Ginibre α-determinantal point process which recovers the Poisson point process when α approaches zero. We characterize the expected RF energy harvesting rate. We also perform a worst-case study which derives the upper bounds of both power outage and transmission outage probabilities. Numerical results show that our upper bounds are accurate and that better performance is achieved when the distribution of ambient sources exhibits stronger repulsion.

In cognitive radio networks, it is challenging for secondary users (SUs) to keep track of their interference at the receivers of primary users (PUs), due to the error in channel estimation and irregular information exchange between SUs and PUs. In this paper, we practically consider that SUs have only partial knowledge about the channel gains from SUs to PUs, based on which SUs estimate the worst-case channel gains and decide transmit power to robustly protect PUs. As it is rare that all SU-PU channels experience the worst-case conditions simultaneously, we proposed the worst-case selective robust model for SUs to estimate the aggregate interference power at PU receivers by predicting that only a part of SU-PU channels are in the worst-case conditions. We study SUs' robust power control problem in a non-cooperative game, where each SU maximizes its own throughput subject to interference constraints at PU receivers. We propose an iterative algorithm for SUs to achieve unique Nash equilibrium in a distributed manner. Extensive numerical results show that our algorithm provides guaranteed protection for PUs provided with uncertain SU-PU channel information.

Decentralized security software deployment is important to achieve reliable and secure network operations. In this paper, we consider the joint traffic routing and security software deployment problem. In this problem, the network can make decisions about routing, security protection and recovery software to minimize total energy consumption of all nodes in the network. Specifically, the network has to trade off between energy consumption and preventing security damages by running protection software based on uncertainty about attacks. The protection software has to be deployed along the route of traffic flow, and hence the network has to optimize traffic routing. We first formulate this problem as a stochastic programming model. To obtain the solution, we transform the stochastic programming model into the deterministic problem, which can be solved using a standard solver. The performance evaluation reveals that the optimal solution depends largely on energy budget and energy consumption of the nodes for transferring traffic and running security software.

Radio frequency (RF) energy harvesting capability allows wireless nodes to harvest and convert ambient RF signal into energy supply for their operations and data transmission. Cognitive radio networks can employ such capability that enables secondary users to opportunistically not only transmit data on an idle channel, but also harvest RF energy from primary users' transmission on a busy channel. In this paper, we propose a queueing model to analyze performance of the cognitive radio networks with opportunistic RF energy harvesting. The queueing model captures the channel state and considers multiple secondary users whose transmissions are scheduled using the round-robin policy. Then, we introduce a simple network selection strategy for the secondary users. We also develop an analytical model to analyze the network selection decision at the steady state.

In this paper, we consider a decentralized wireless communication system with wireless energy transfer capability. We aim to minimize the total number of packets waiting at wireless nodes for the whole system. We first formulated the optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve an optimization problem with constraints, we applied the Lagrangian multiplier and the policy gradient method. In addition, to reduce the complexity of DEC-POMDP, we proposed a decentralized online learning algorithm with minimum communication among the wireless nodes. Under appropriate conditions, we showed that the proposed algorithm converges to a local optimal solution. The simulation results clearly showed the convergence as well as the efficiency of the proposed algorithm.

Mobile social network (MSN) has been widely studied as a novel and effective way for communication in the current era of fast developing mobile devices, which enables the contents and services to be delivered opportunistically when mobile users contact. In this paper, we aim to optimize the content delivery from the content provider (CP) to the subscribers via relays in between. Tandem queueing model is applied to model the content delivery process, based on which absorbing Markov chain is used to derive the quality of the content delivery service received by the subscribers. The validity of the proposed queuing model has been verified by our simulation results. Observing that the content provider always has dominant control on the content delivery process and is pursuing the maximum profit by strategically designing the “rights” and “obligation” items for the subscribers, contract theory is adopted to reach an economically optimal solution. The numerical results verify the effectiveness of the contract-theoretic approach in maximizing the content provider's profit, and the capability to ensure the satisfaction of the heterogeneous subscribers with different quality of service (QoS) requirements.

Wireless or RF energy transfer will be capable of supplying energy to mobile nodes for packet transmission without physical battery changing or replacement. In this paper, we consider the network in which the access point can transfer wireless energy to the nodes and the nodes use that energy to transmit packets. The nodes can send requests (i.e., bids) to the access point for transferring wireless energy. As the access point adopts auction mechanism for wireless energy transfer, we formulate a noncooperative game to analyze the competitive wireless energy transfer bidding of the nodes. The Nash equilibrium is considered to be a solution of the game. To reach the Nash equilibrium, the nodes as players can adapt their strategies based on stochastic response dynamic. The Markov chain is used to analyze the properties of the game (e.g., convergence). The numerical results clearly show the convergence of the bidding strategy adaptation of the nodes toward the Nash equilibrium.

With RF (radio frequency) energy transfer capability, an access point not only communicates with wireless nodes, but also supplies them with energy for data transmission. In this paper, we consider the wireless network with RF energy transfer. To support quality of service (QoS) in the network, we introduce an admission control policy, which decides whether incoming nodes can be admitted into the network or not. Also, the policy determines the RF energy transfer strategy to maximize the reward of the network, while the performance requirement in terms of node throughput is maintained at the target level. We present optimization and queueing models to obtain an optimal admission control policy and performance measures of the node in the network, respectively. The performance evaluation shows that the admission control policy can successfully achieve the network objective and meet the node constraint on QoS requirement.

Radio frequency (RF) energy harvesting is a promising technique to sustain an operation of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. We consider such a network where the secondary user can select one of the channels to transmit data when it is not occupied by a primary user, and to harvest RF energy when the primary user transmits data. Specifically, we formulate an optimization problem to determine an optimal channel selection policy for the secondary user. The secondary user selects a channel based on the energy level in its battery (i.e., energy queue) and the number of packets in its data queue. The optimization considers complete information and incomplete information cases, where the secondary user has and does not have the knowledge about channel states, respectively. The performance obtained in the complete information case can serve as an upper bound for the secondary user.

The emergence of mobile cloud computing enables mobile users to dynamically offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve execution efficiency. However, due to mobility, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by a mobile user may fail (e.g., the user moves out of transmission range of cloudlets). In this paper, we model and develop an optimal offloading algorithm for the mobile user, considering the users' local load and availability of cloudlets. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with an objective to minimize the computation and offloading cost. The numerical results show that the proposed dynamic offloading algorithm outperforms conventional baseline schemes.

With wireless energy transfer, a mobile node can operate perpetually without having a wired connection to charge its battery. In this paper, we present a quality of service (QoS) aware data transmission and wireless energy transfer for the mobile node. The node can request for wireless energy transfer or transmit a packet when the node is in a coverage area of an access point. The node supports service differentiation for different type of traffic (i.e., low and high priority data). To meet the QoS requirement of each traffic type, we present the performance modeling and optimization framework. The objective is to maximize the throughput, while the packet loss probabilities are maintained below the target levels. The optimal policy for the node is obtained from solving the constrained Markov decision process (CMDP). In addition, we present an application of the framework to a data mule for collecting, carrying, and transmitting data from sensors to the access point.

Radio frequency (RF) energy harvesting is a promising technique to sustain operations of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. In this paper, we consider such a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively. We present an optimization formulation to obtain the channel access policy for the secondary user to maximize its throughput. Both the case that the secondary user knows the current state of the channels and the case that the secondary knows the idle channel probabilities of channels in advance are considered. However, the optimization requires model parameters (e.g., the probability of successful packet transmission, the probability of successful RF energy harvesting, and the probability of channel to be idle) to obtain the policy. To obviate such a requirement, we apply an online learning algorithm that can observe the environment and adapt the channel access action accordingly without any a prior knowledge about the model parameters. We evaluate both the efficiency and convergence of the learning algorithm. The numerical results show that the policy obtained from the learning algorithm can achieve the performance in terms of throughput close to that obtained from the optimization.

We focus on the problems of cooperative virtual machine management of cloud users in a smart grid environment. In such an environment, the cloud users can cooperate to share the available computing resources in private cloud and public cloud to reduce the total cost. To achieve an optimal and fair solution, we develop the framework composed of the virtual machine allocation, cost management, and cooperation formation models. The problem is challenging due to the uncertainties (e.g., uncertain power price and unpredictable users' demand). Therefore, for the virtual machine allocation, we develop the stochastic programming model to obtain the optimal solutions of virtual machines to be hosted in the local data center, to be hosted on the public cloud servers, or to be migrated to the data centers of other cooperative cloud users. Then, among cooperative cloud users, the cost management is formulated as the coalitional game whose fair share of the total cost is obtained as the Shapley value. Next, given that the cloud users are rational, we formulate the cooperation formation as the network formation game to analyze the stability of the cooperation. In the experiment, we evaluate our proposed framework with real trace data. The results clearly show that the cooperative virtual machine management can achieve the minimum total cost of cloud users compared with expected value and worst case formulations.

With the pervasiveness of wireless devices, machine-to-machine (M2M) communications or machine-type-communications (MTC) is emerging to support data transfer among devices without human interaction. In this paper, we introduce a tractable queueing model for performance modeling and analysis for heterogeneous MTC. We then demonstrate versatile applications of the proposed queueing model. Firstly, we use the queueing model to study the coexistence between M2M communications of MTC devices and human-to-human (H2H) communications in the same networks. We also consider more sophisticated settings, where the MTC user equipments (UEs) are able to perform the transmission to the macro Evolved Node B (eNodeB) or small-cell eNodeB, or perform the relay transmission. In addition, we extend our study to analyze the eNodeB selection and coalition formation for relay transmission when MTC UEs coexist with H2H UEs. In this case, we formulate the non-transferable utility (NTU) coalitional game to model the eNodeB selection and coalition formation for relay transmission. The performance evaluation reveals some interesting results. For example, the throughput of MTC UEs can be improved when the MTC UEs spend more time inactive due to lower contention in the network, compared with the case when the MTC UEs are mostly active.

With wireless energy transfer capability, network nodes can rely on the energy supplied wirelessly from a network hub or access point. As a result, there is no need for the nodes to replace or recharge their battery using any wire. This paper considers such a scenario and proposes the performance analysis and optimization framework for the network operating on a TDMA protocol with wireless energy transfer. We first present the analysis and optimization of an individual node in the network. The objective is to maximize the network utility defined in terms of throughput and the number of packets in the queue such that the packet loss probability is maintained below the threshold. We solve the optimization problem to obtain an optimal policy to operate the node (i.e, to be active or inactive). We next formulate the network optimization problem for the network hub. The problem can be solved to determine the amount of wireless energy transfer to meet the quality of service (QoS) requirements of all the nodes in the network. We reveal the special structure of the problem, that we can decompose the network optimization into small subproblems. These subproblems can be solved efficiently using the standard algorithm.

ABSTRACT Medium Access Control is an important component in cognitive radio that allow secondary users to identify and access spectrum opportunity without interfering with primary users. In this paper, a queueing model to analyze the performances of the secondary users in a cognitive radio network is presented. The queueing model considers the transmissions of a secondary system where a Medium Access Control algorithm is used to enable the secondary users to sense and access the channels. Also, a simple scheduler is employed to assign transmission time slots to the secondary users. Because the value of the system parameters can be perturbed and cannot be determined precisely, the analysis is extended to take uncertainty into account. In this case, a robust optimization method to study the Markov chain with uncertainty is applied to obtain the stationary probabilities of the queueing model under uncertainty. The lower and upper bounds of performance measures are obtained and compared with the nominal value. Copyright © 2012 John Wiley & Sons, Ltd.

Although extensive research has been carried out on the issue of how to optimally select relays in wireless relay networks, relay selection for mobile users is still a challenging problem because of the requirement that the dynamic selection should adapt to user mobility. Moreover, because the selected relays consume their energy on relaying data for the users, it is required that the users have to pay for this relay service. The price of selecting relays will affect the users' decisions. Assuming that different relays can belong to different service providers, we consider the situation that the service providers can strategically set the prices of their relays to maximize their profits. In this paper, we jointly study the dynamic relay selection for mobile users and profit maximization for service providers. Also, we design a Stackelberg-game hierarchical framework to obtain the solution. At the lower level, we investigate the relay selection problem for the mobile users under given prices of selecting the relays. It is formulated as a Markov decision process problem with the objective to minimize the mobile user's long-term average cost (which consists of the payment to the relay service and the cost due to packet loss), and solved by applying the linear programming technique. At the upper level, we study the game of setting relay prices for the service providers, with the knowledge that the mobile users will make relay selections based on their given prices. Nash equilibrium is obtained as the solution. Our results can help to provide a guidance for service providers to compete for providing relay services.

The harmonic coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to identify the idle spectrum bands. One common approach to achieve spectrum awareness is through spectrum sensing, which usually assumes known distributions of the received signals. However, due to the nature of wireless channels, such an assumption is often too strong to be realistic, and leads to unreliable detection performance in practical networks. In this paper, we study the sensing performance under distribution uncertainty, i.e., the actual distribution functions of the received signals are subject to ambiguity and not fully known. Firstly, we define a series of uncertainty models based on signals' moment statistics in different spectrum conditions. Then we present mathematical formulations to study the detection performance corresponding to these uncertainty models. Moreover, in order to make use of the distribution information embedded in historical data, we extract a reference distribution from past channel observations, and define a new uncertainty model in terms of it. With this uncertainty model, we propose two iterative procedures to study the false alarm probability and detection probability, respectively. Numerical results show that the detection performance with a reference distribution is less conservative compared with that of the uncertainty models merely based on signal statistics.

The public utility can implement real-time pricing (RTP) as a demand response (DR) program in smart grid to shed the power consumption (e.g., by reducing consumption during peak period). In this paper, we consider the optimization of deferrable load scheduling to minimize the power consumption cost. The constrained Markov decision process (CMDP) model is formulated and solved to obtain the optimal scheduling policy. In addition, we consider the case that the data communications system to support RTP (i.e., to broadcast power price information to the deferrable load) can be under attack, which makes the power price information unavailable or falsified. The loss due to such attack can be analyzed using the proposed CMDP model. The results show that the power consumption cost in a scenario with attack is higher than that in a scenario without attack. The analysis will be useful for improving the intrusion detection system (IDS) to defend the attack.

In cognitive radio networks, channel information is desired by unlicensed secondary users (SUs) to perform effective power control so as to avoid undue interference to licensed primary users (PUs). However, in general, there is no regular information exchange between PUs and SUs, which implies that SUs are unable to obtain up-to-date channel information at the PU side. Besides, the small-scale fading, in addition to shadowing, brings great uncertainty in SUs' channel estimation. In this paper, we consider limited information exchange between SUs and PUs, and study the impact of channel uncertainty on SUs' throughput performance with power control. We model the uncertain channel gain to be a random variable following a state-dependent probability distribution function, and design a power control method that is robust against the channel uncertainty. We formulate the robust power control problem as a chance constrained robust optimization and solve it by an iterative algorithm. Numerical results show that the proposed power control can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement.

In cognitive radio networks, it is often impossible to have regular information exchange between PUs and SUs. This implies that SUs are unable to obtain up-to-date channel information at the PU side, and will face technical challenges in accurately controlling their interference to PUs through power control. In this paper, we assume that SUs can estimate the channel information in the reciprocal channel, and study the channel uncertainty due to estimation errors and its impact on SUs' performance and PUs' protection. Specifically, we model the uncertain channel gain to be a random variable following a state-dependent distribution function, and propose a power control mechanism that is robust against the channel uncertainty. We study the robust power control in two cases. In the first case, all SU transmitters (e.g., secondary base stations) transmit with the same power, while in the second case each SU transmitter may choose distinct transmit power based on its own preference. In either case, we formulate the power control problem as a chance constrained robust optimization problem and design an iterative algorithm, respectively. Numerical results show that our robust power control mechanism can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement, and the second-case power control generally provides better Quality of Service (QoS) for SUs than that in the first case.

Mobile cloud computing is an emerging technology to improve the quality of mobile services. In this paper, we consider the resource (i.e., radio and computing resources) sharing problem to support mobile applications in a mobile cloud computing environment. In such an environment, mobile cloud service providers can cooperate (i.e., form a coalition) to create a resource pool to share their own resources with each other. As a result, the resources can be better utilized and the revenue of the mobile cloud service providers can be increased. To maximize the benefit of the mobile cloud service providers, we propose a framework for resource allocation to the mobile applications, and revenue management and cooperation formation among service providers. For resource allocation to the mobile applications, we formulate and solve optimization models to obtain the optimal number of application instances that can be supported to maximize the revenue of the service providers while meeting the resource requirements of the mobile applications. For sharing the revenue generated from the resource pool (i.e., revenue management) among the cooperative mobile cloud service providers in a coalition, we apply the concepts of core and Shapley value from cooperative game theory as a solution. Based on the revenue shares, the mobile cloud service providers can decide whether to cooperate and share the resources in the resource pool or not. Also, the provider can optimize the decision on the amount of resources to contribute to the resource pool.

Channel allocation is a crucial concern in variable-width wireless local area networks. This work aims to obtain the stable and fair nonoverlapped variable-width channel allocation for selfish access points (APs). In the scenario of single collision domain, the channel allocation problem reduces to a channel-width allocation problem, which can be formulated as a noncooperative game. The Nash equilibrium (NE) of the game corresponds to a desired channel-width allocation. A distributed algorithm is developed to achieve the NE channel-width allocation that globally maximizes the network utility. A punishment-based cooperation self-enforcement mechanism is further proposed to ensure that the APs obey the proposed scheme. In the scenario of multiple collision domains, the channel allocation problem is formulated as a constrained game. Penalty functions are introduced to relax the constraints and the game is converted into a generalized ordinal potential game. Based on the best response and randomized escape, a distributed iterative algorithm is designed to achieve a desired NE channel allocation. Finally, computer simulations are conducted to validate the effectiveness and practicality of the proposed schemes.

The successful coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to be spectrum aware and know which spectrum bands are occupied by PUs. Such awareness can be achieved in several ways, one of which is spectrum sensing. While existing spectrum sensing methods usually assume known distributions of the received primary signals, such an assumption is often too strong and unrealistic, and leads to unreliable detection performance in practical networks. In this paper, we design robust spectrum sensing algorithms under the distribution uncertainty of primary signals. After formulating the optimal sensing design as a robust optimization problem, we decompose it into a series of analytically tractable semi-definite programs, and propose an iterative algorithm to search the optimal decision threshold while maintaining the desirable false alarm probability during the iterations. Numerical results verify that our robust sensing algorithm improves the worst-case detection probability and reduces the system sensitivity on decision variables.

ABSTRACT Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we present a novel framework for spectrum sharing in cognitive radio networks. The secondary users (SUs) can share the spectrum resource with primary users (PUs) in a cooperative manner, where PUs trade their information and surplus resource, and SUs access the primary spectrum intelligently based on SUs' heterogeneous demands and PUs' resource prices. After paying PUs a subscription fee for the spectrum information, SUs become spectrum-aware and avoid the overhead on spectrum sensing. During SUs' channel access, PUs further charge SUs based on the amount of resource taken by SUs. We model this sharing problem in a flexible time-slotted structure, where SUs' decisions include the selection of proper transmission channel and slot length to meet their demands. This joint decision problem is studied as a spectral temporal allocation game. We prove the existence of a Nash equilibrium and design a strategy update process which can converge to an equilibrium.

The successful coexistence of cognitive radio systems with licensed system requires the secondary users the capability of interference-awareness, i.e., knowing which spectrum bands are occupied by primary users, i.e., the legacy users. Spectrum sensing thus is a key enabling module, which usually models the sensing process as a binary hypothesis testing assuming known signal distribution. However, an unrealistic assumption regarding the signal distribution easily leads to unreliable detection probability. In this paper, we study the sensing performance considering the distribution uncertainty in hypothesis testing, i.e., the actual distribution function of the received signal strength is not known. According to different signal characteristics, we define appropriate uncertainty sets respectively for different hypotheses. Then we present an approximate approach to determine the robust decision threshold, and investigate the performance bounds for the detection probability under distribution uncertainty. Moreover, we provide an analytical expression for the lower bound of detection probability. Numerical results are given to validate our conclusions.

The successful coexistence of cognitive radio systems and licensed systems requires the secondary users to have the capability of sensing and keeping track of primary transmissions. While existing spectrum sensing methods usually assume known distributions of the primary signals, such an assumption is often not true in practice. As a result, applying existing sensing methods directly will often lead to unreliable detection performance in practical networks. In this paper, we try to improve the sensing performance under the distribution uncertainty of primary signals. We formulate the optimal sensing design as a robust optimization problem, and propose an iterative algorithm to determine the optimal decision threshold for each user. Extensive simulations demonstrate the effectiveness of our proposed algorithm.

We propose an adaptive power management (APM) algorithm for a data center with an objective to minimize the total cost of power bought from an electrical grid. This APM algorithm is developed for a smart grid environment which is envisioned to be a cooperative, responsive, and economical power system. In particular, APM algorithm takes the spot power price from an electrical grid, the power supply from a renewable power source, and users' demand in terms of application workload processing into account when managing the power consumption. Therefore, an APM algorithm is considered to be the demand side management in a smart grid. To obtain an optimal decision of the APM algorithm, an optimization model based on stochastic programming with multi-stage recourse is developed. This optimization model considers various uncertainties and is able to determine the optimal solution for the APM algorithm. The APM algorithm is evaluated by numerical studies. The numerical results clearly show that the APM algorithm can minimize the power cost of a data center.

The smart grid uses data communications techniques to gather and transfer information for scheduling and decision making so that the electric power can be used more efficiently and economically. To reduce the cost of data communications in smart grid, in this paper, we assume that cognitive radio technique is used to transmit the meter data from smart meters to the data aggregator unit (DAU). Smart meters are deployed in the houses representing consumption nodes to measure and report power demand. Also, smart meters are used by generators of the community renewable power generation facility (CRPGF) which is one of the distributed energy resources (DERs), to collect and estimate renewable power production capacity. However, the transmission of meter data must be performed within a limited period of time (i.e., deadline). By using absorbing Markov chain, we analyze the average packet loss probability of meter data, and study the impact of packet loss on the power supply cost optimization made by the meter data management system (MDMS).

We consider an admission control problem and adaptive resource allocation for running mobile applications on a cloudlet. We formulate an optimization problem for dynamic resource sharing of mobile users in mobile cloud computing (MCC) hotspot with a cloudlet as a semi-Markov decision process (SMDP). SMDP is transformed into a linear programming (LP) model and it is solved to obtain an optimal solution. In the optimization model, the quality of service (QoS) for different classes of mobile user is taken into account under resource constraints (i.e., bandwidth and server). The numerical results are presented to illustrate that the proposed admission control scheme can achieve a desirable performance and improve throughput of an MCC hotspot significantly.

Mobile cloud computing aims at improving the performance of mobile applications and to enhance the resource utilization of service providers. In this paper, we consider a mobile cloud computing environment in which the service providers can form a coalition to create a resource pool to support the mobile applications. First, an admission control mechanism is used to provide services of mobile applications to the users given the available long-term reserved resources in a pool. An optimization formulation is introduced to obtain the optimal decision of admission control. Then, for a given coalition of service providers, the revenue obtained from utilizing the resource pool has to be shared among the service providers. A coalitional game model is developed for sharing the revenue. In addition, since the service providers can decide on short-term capacity expansion of the resource pool, a game model is introduced to obtain the optimal strategies of service providers on capacity expansion such that their profits are maximized.

Cooperative relay network will be supported in IEEE 802.16m to improve the coverage and performance of mobile broadband wireless access service. In this paper, we jointly consider the problem of dynamic service selection and bandwidth allocation in IEEE 802.16m mobile relay networks. Specifically, the advanced mobile stations (AMSs) perform the selection of advanced base station (ABS) and transmission mode (i.e., direct transmission or relay-cooperation transmission) for a better service quality. The ABSs allocate the bandwidth for different transmission modes to maintain the desired queue level at base stations and user distribution for satisfying performance requirements. This problem is challenging when the strategies of both ABSs and AMSs influence each other and the decisions are made dynamically. To address this problem, a two-level dynamic game framework based on an evolutionary game and a differential game is developed. Since the mobile stations can adapt their strategies according to the received service quality, the dynamic service selection is modeled as an evolutionary game at the lower level. At the upper level, a differential game is formulated for a dynamic bandwidth allocation of base stations and a closed-loop Nash equilibrium is obtained as the solution. Viewing the fluctuation of traffic flow rate as disturbance, the robust bandwidth allocation strategy design is performed. Both stochastic optimal control and H_∞ optimal control approaches are adopted for average performance and worst-case performance design, respectively.

Offloading is an effective method for extending the lifetime of handheld mobile devices by executing some components of applications remotely (e.g., on the server in a data center or in a cloud). In this article, to achieve energy saving while satisfying given application execution time requirement, we present a dynamic offloading algorithm, which is based on Lyapunov optimization. The algorithm has low complexity to solve the offloading problem (i.e., to determine which software components to execute remotely given available wireless network connectivity). Performance evaluation shows that the proposed algorithm saves more energy than the existing algorithm while meeting the requirement of application execution time.

We consider a problem of dynamic spectrum leasing in a spectrum secondary market of cognitive radio networks where secondary service providers lease spectrum from spectrum brokers to provide service to secondary users. The problem is challenging when the optimal decisions of both secondary providers and secondary users are made dynamically under competition. To address this problem, a two-level dynamic game framework is developed in this paper. Since the secondary users can adapt the service selection strategies according to the received service quality and price, the dynamic service selection is modeled as an evolutionary game at the lower level. The replicator dynamics is applied to model the service selection adaptation and the evolutionary equilibrium is considered to be the solution. With dynamic service selection, competitive secondary providers can dynamically lease spectrum to provide service to secondary users. A spectrum leasing differential game is formulated to model this competition at the upper level. Both simultaneous play model and asynchronous play model are considered. The service selection distribution of the underlying evolutionary game describes the state of the upper differential game. Both open-loop and closed-loop Nash equilibria are obtained as the solution of dynamic control of the differential game. Numerical comparison shows the advantages over static control in terms of profit and convergence speed.

Abstract The cognitive radio (CR) technology appears as an attractive solution to effectively allocate the radio spectrum among the licensed and unlicensed users. With the CR technology the unlicensed users take the responsibility of dynamically sensing and accessing any unused channels (frequency bands) in the spectrum allocated to the licensed users. As spectrum sensing consumes considerable energy, predictive methods for inferring the availability of spectrum holes can reduce energy consumption of the unlicensed users to only sense those channels which are predicted to be idle. Prediction‐based channel sensing also helps to improve the spectrum utilization (SU) for the unlicensed users. In this paper, we demonstrate the advantages of channel status prediction to the spectrum sensing operation in terms of improving the SU and saving the sensing energy. We design the channel status predictor using two different adaptive schemes, i.e., a neural network based on multilayer perceptron (MLP) and the hidden Markov model (HMM). The advantage of the proposed channel status prediction schemes is that these schemes do not requirea prioriknowledge of the statistics of channel usage. Performance analysis of the two channel status prediction schemes is performed and the accuracy of the two prediction schemes is investigated. Copyright © 2010 John Wiley & Sons, Ltd.

Mobile infostation networks which achieve content distribution by exploiting the opportunistic contact among mobile users (MUs) and access points (APs) have attracted a lot of attention in the last few years. It is found that cooperation either between APs or MUs has a great impact on the network performance. However, in most of the existing works wherever cooperation is involved, cooperation among the APs or MUs is discussed independently, while few of them has jointly taken both levels of cooperation into account. In this paper, we propose a more general framework which allows two levels of cooperation. The benefit as well as the cost of such hierarchical cooperation are taken into consideration. With properly defined payoffs of both APs and MUs, each AP may gain more benefit by strategically forming coalition with other APs, while the MUs can also choose to cooperate with one another to further maximize their payoffs. To obtain the optimal structure of two-level cooperation, an implementable distributed algorithm is proposed. Through extensive numerical experiments, our scheme shows the high effectiveness in achieving the stable cooperation structure, and the impact of different system parameters is also extensively investigated.

In wireless mesh network (WMN), multiple service providers (SPs) can cooperate to share resources (e.g., relay nodes and spectrum), to serve their collective subscribed customers for better service. As a reward, SPs are able to achieve more individual benefits, i.e., increased revenue or decreased cost, through efficient utilization of shared network resources. However, this cooperation can be realized only if fair allocation of aggregated payoff, which is the sum of the payoff of all the cooperative SPs, can be achieved. We first formulate such cooperation as a coalitional game with transferable utility, specifically, a linear programming game, in which, each SP should obtain the fair share of the aggregated payoff. Then we study the problem of allocating aggregated payoff which leads to stable service coalition of SPs in WMN based on the concepts of dual payoff and Shapley value.

We consider the problem of dynamic bandwidth allocation among different service classes under uncertainty in cognitive radio networks. In such networks, the secondary users compete for bandwidth resources and the service providers compete for users access (e.g., subscription). To address this problem, a two-level dynamic game framework is developed. The underlying dynamic service selection of secondary users is modeled as an evolutionary game based on replicator dynamics. The randomly irrational churning behavior of secondary users is modeled as a stochastic disturbance to the service selection distribution evolution. At the upper level, a bandwidth allocation stochastic differential game is formulated to model the competition among different service providers. The service selection distribution of the underlying evolutionary game describes the state of the upper stochastic differential game and a Markov perfect Nash equilibrium is considered to be the solution. The decentralized nature of the framework makes the system flexible and simple for implementation.

Dynamic spectrum access (DSA) is an important design aspect for the cognitive radio networks. Most of the existing DSA schemes are to govern the unlicensed user (i.e., secondary user) traffic in a licensed spectrum without compromising the transmissions of the licensed users, in which all the unlicensed users are typically treated equally. In this paper, prioritized unlicensed user traffic is considered. Specifically, we prioritize the unlicensed user traffic into two priority classes (i.e., high and low priority). Two different DSA policies are proposed to manage the prioritized unlicensed user traffic. These two policies are different in which one does not allow the high priority secondary user to be dropped, while the other allows if the system is full and has some low priority secondary users. We also study the impact of sub-channel reservation for the high priority secondary users in both DSA policies. Both DSA policies are analyzed using Markov chain. For performance measures, we derive the blocking probability, the probability of forced termination and the throughput for both high, and low priority unlicensed users. The numerical results are verified using simulations.

Cooperative communication has attracted dramatic attention in the last few years due to its advantage in mitigating channel fading. Despite much effort that has been made in theoretical analysis of the performance gain, cooperative relay selection, which is one of the fundamental issues in cooperative communications, is still left as an open problem. In this paper, the tradeoff between improvement and corresponding cost of cooperative communication, focusing on relay selection is addressed. We consider a challenging scenario which takes user mobility into consideration. Based on user mobility pattern, a dynamic relay selection scheme aiming at minimizing the long-term average cost while satisfying the QoS requirement is proposed. For relay selection to achieve maximal performance, an optimization model based on the constrained Markov decision process (CMDP) is formulated and solved by applying the linear programming (LP) technique. Comprehensive analysis and comparison with several other relay selection schemes are presented. Through extensive simulations, our scheme shows its high effectiveness and flexibility in balancing the cost and QoS performance.

We consider a cognitive relay network (CRN) where the secondary users (SUs) are involved as cooperative relays in a primary user's (PU) communication. To avoid generating interference to PU transmissions, it is assumed that SUs can transmit only when the PU's channel is idle. On one hand, SUs use some relay powers to speed up the PU's transmissions. Consequently, PU's buffer will be depleted faster, resulting in more channel idle times (i.e., more transmission opportunities for SUs). On the other hand, due to the energy limit, less power can be used for SU's own transmissions if it uses too much power on relaying. Thus, an optimal power allocation strategy is necessary to address this tradeoff. In this paper, the power allocation problem for both single-SU case and multiple-SU case is investigated. The former is formulated as a utility maximization problem, while the latter is modeled as a non-cooperative game. The existence and uniqueness of the Nash equilibrium (NE) are proved, and the impact of different system parameters on NE is comprehensively analyzed through numerical results.

The evolving smart grid will use advanced data communications and networking techniques to improve efficiency and reliability of electric power generation, transmission, distribution, and consumption. Packet loss performance of the data communications networks used in smart grid will have impact on the cost of power supply. In this paper, we model and analyze the impact of packet loss performance on the optimization of cost for power supply in smart grid. To optimize (i.e., minimize) the cost of power supply, the power demand from consumers has to be estimated based on the power usage data, which are transferred from smart meters through data aggregator unit (DAU) to the meter data management system (MDMS). First, we present a model to optimize the cost of power supply given demand uncertainty. Then the probability distribution of power demand is estimated with and without packet loss. Subsequently, we analyze and show how the packet loss increases the cost of power supply. Next, a queueing model is used to quantify the packet loss due to congestion at DAU, and then the transmission rate from the DAU is optimized to minimize the impact of packet loss. The modeling and analysis of packet loss performance presented in this paper is a step toward optimal network design for future smart grid.

Relay transmission or cooperative communication is an advanced technique that can improve the performance of data transmission among wireless nodes. However, while the performance (e.g., throughput) of a source node can be improved through cooperation with a number of relays, this improvement comes at the expense of a degraded performance for the relay nodes due to the resources that they dedicate for helping the source node in its transmission. In this paper, we formulate a coalitional game among the wireless nodes that seek to improve their performance by relaying each other's data. The game is classified as a coalition formation game in which the nodes can take individual and distributed decisions to join or split from a given coalition while ensuring that their individual throughput is maximized. A Markov chain model is proposed to investigate the stability of the resulting coalitional structures. Further, we consider the practical case in which the wireless nodes do not have an exact and perfect knowledge of the parameters (e.g., channel quality) in coalition formation. For this scenario, we analyze the stability of the partitions resulting from the proposed coalition formation game under uncertainty. We also define the conditions needed for obtaining the stable and unstable coalitional structures among the nodes that are performing cooperative transmission.

The mobile social network (MSN) combines techniques in social science and wireless communications for mobile networking. The MSN can be considered as a system which provides a variety of data delivery services involving the social relationship among mobile users. This paper presents a comprehensive survey on the MSN specifically from the perspectives of applications, network architectures, and protocol design issues. First, major applications of the MSN are reviewed. Next, different architectures of the MSN are presented. Each of these different architectures supports different data delivery scenarios. The unique characteristics of social relationship in MSN give rise to different protocol design issues. These research issues (e.g., community detection, mobility, content distribution, content sharing protocols, and privacy) and the related approaches to address data delivery in the MSN are described. At the end, several important research directions are outlined.

We consider the problem of optimal channel access to provide quality of service (QoS) for data transmission in cognitive vehicular networks. In such a network, the vehicular nodes can opportunistically access the radio channels (referred to as shared-use channels) which are allocated to licensed users. Also, they are able to reserve a channel for dedicated access (referred to as exclusive-use channel) for data transmission. A channel access management framework is developed for cluster-based communication among vehicular nodes. This framework has three components: opportunistic access to shared-use channels, reservation of exclusive-use channel, and cluster size control. A hierarchical optimization model is then developed for this framework to obtain the optimal policy. The objective of the optimization model is to maximize the utility of the vehicular nodes in a cluster and to minimize the cost of reserving exclusive-use channel while the QoS requirements of data transmission (for vehicle-to-vehicle and vehicle-to-roadside communications) are met, and also the constraint on probability of collision with licensed users is satisfied. This hierarchical optimization model comprises of two constrained Markov decision process (CMDP) formulations - one for opportunistic channel access, and the other for joint exclusive-use channel reservation and cluster size control. An algorithm is presented to solve this hierarchical optimization model. Performance evaluation results show the effectiveness of the optimal channel access management policy. The proposed optimal channel access management framework will be useful to support mobile computing and intelligent transportation system (ITS) applications in vehicular networks.

Abstract With recent advances in the development of wireless communication networks, wireless mesh networks (WMNs) have been receiving considerable research interests in recent years. The need to support integrated services and ensure quality of service (QoS) satisfaction for various applications is one of the fundamental challenges for successful WMN deployment. In order to provide differentiated services, medium access control (MAC) should have priority management at the link layer. In code division multiple access (CDMA)‐based WMNs, the interference phenomenon and simultaneous transmissions must be considered. We propose two priority schemes for MAC in a distributed CDMA‐based WMN, taking into account interference, multimedia services, QoS requirements, and simultaneous transmissions. The first priority scheme is within a node. Each node has an independent queue for each traffic class. According to QoS requirements, the queue that should be served first is determined. The second priority scheme is among neighbor nodes. It is proposed for multiple simultaneous transmissions in the CDMA network. This scheme gives a larger chance of correct transmission to high priority traffic than low priority traffic. In addition, we propose to use adaptive spreading gain and a frame structure to achieve high resource utilization. Simulation results demonstrate that the proposed schemes can achieve effective QoS guarantee. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Mobility management is one of the most challenging research issues for vehicular networks to support a variety of intelligent transportation system (ITS) applications. The traditional mobility management schemes for Internet and mobile ad hoc network (MANET) cannot meet the requirements of vehicular networks, and the performance degrades severely due to the unique characteristics of vehicular networks (e.g., high mobility). Therefore, mobility management solutions developed specifically for vehicular networks would be required. This paper presents a comprehensive survey on mobility management for vehicular networks. First, the requirements of mobility management for vehicular networks are identified. Then, classified based on two communication scenarios in vehicular networks, namely, vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communications, the existing mobility management schemes are reviewed. The differences between host‐based and network‐based mobility management are discussed. To this end, several open research issues in mobility management for vehicular networks are outlined. Copyright © 2009 John Wiley & Sons, Ltd.

In a cognitive radio network, the full-spectrum is usually divided into multiple channels. However, due to the hardware and energy constraints, a cognitive user (also called secondary user) may not be able to sense two or more channels simultaneously. As different channels may have different primary user activities and time-varying channel qualities, an important task is to select which channels to sense and access for a given time period so that the available spectrum left by the primary users can be fully utilized by the secondary user. In this paper, we propose an optimal sensing channel selection policy based on partially observable Markov decision process (POMDP). The proposed policy takes the time-varying channel state into consideration and intends to optimally exploit spectrum resources for the secondary user. In addition to selecting optimal channel to sense, we also derive the optimal sensing time which leads to maximized throughput of the secondary user.

We consider the problem of dynamic content distribution in publish-subscribe mobile social networks. Mobile users subscribe to the content provider. Then, when new content is created, the provider transmits it to subscribers (i.e., mobile users) through the base station. The mobile users who have the social relationship (i.e., mobile users in the same community) can transfer the fresh content when they move and meet each other. From the content provider's perspective, the content transmission policy by the base station can be optimized so that the number of mobile users having the fresh data is maximized subject to the constraint on the maximum waiting time of the new content. An optimization formulation is presented for the content provider to obtain this optimal content transmission policy. Performance evaluation results show that with the optimal content transmission policy, the average number of mobile users having fresh content is larger than that if the new content is transmitted immediately.

Delay tolerant networks (DTNs) can be composed of multiple heterogeneous groups (i.e., communities) of nodes. The nodes from these communities can cooperate with each other in order to carry and forward data packets so that the performance (e.g., delay) can be improved. However, this cooperation will incur additional cost on the nodes. In this paper, we first develop an analytical model to investigate the performance gain from cooperation of multiple communities in a DTN. Then, we propose a coalitional game model for analyzing the cooperation decisions of multiple rational communities based on the tradeoff between performance gains and associated costs. As a solution to the proposed game, we determine the stable coalitional structure, i.e., the structure where no community can improve its payoff by changing its cooperation decisions. The proposed analytical and game models will be useful for the performance and cost optimization of multi-community DTNs.

Bandwidth allocation for different service classes in heterogeneous wireless networks is an important issue for service provider in terms of balancing service quality and profit. It is especially challenging when considering the dynamic competition both among service providers and among users. To address this problem, a two-level game framework is developed in this paper. The underlying dynamic service selection is modeled as an evolutionary game based on replicator dynamics. An upper bandwidth allocation differential game is formulated to model the competition among different service providers. The service selection distribution of the underlying evolutionary game describes the state of the upper differential game. An open-loop Nash equilibrium is considered to be the solution of this linear state differential game. The proposed framework can be implemented with minimum communication cost since no information broadcasting is required. Also, we observe that the selfish behavior of service providers can also maximize the social welfare.

Positioning of real world objects (e.g., people) in indoor environment will facilitate location dependent or context-aware applications. Due to severe multi-path fading effect in indoor wireless environment, received signal strength indicator (RSSI) based indoor positioning systems usually require a great amount of human intervention for data measurement during the system initiation. This paper proposes a novel two-phase positioning technique that has been implemented and tested in real environment. Experiment results show that our method can significantly cut down the requirements on data acquisition and achieve satisfactory performance in terms of error distance.

The Cognitive Radio (CR) technology enables the unlicensed users to share the spectrum with the licensed users on a non-interfering basis. Spectrum sensing is an important function for the unlicensed users to determine availability of a channel in the licensed user's spectrum. However, spectrum sensing consumes considerable energy which can be reduced by employing predictive methods for discovering spectrum holes. Using a reliable prediction scheme, the unlicensed users will sense only those channels which are predicted to be idle. By achieving a low probability of error in predicting the idle channels, the spectrum utilization can also be improved. Since the traffic characteristics of most licensed user systems encountered in real life are not known a priori, we design the spectrum predictor using the neural network model, multilayer perceptron (MLP), which does not require a prior knowledge of the traffic characteristics of the licensed user systems. The performance of the spectrum predictor is analyzed through extensive simulations.

In this paper, we develop an opportunistic spectrum scheduling scheme for cognitive radio networks. In the proposed scheme, the channel status (i.e., whether being occupied by primary users) and the instantaneous channel quality (SNR at the secondary receiver) are assumed to vary fast within a frame which consists of a fixed number of slots. At each scheduling epoch, each secondary user will observe the channel conditions, namely the channel status and the channel quality. Based on the queue size and the observed channel conditions, each secondary user will estimate the throughput for each channel over the frame to be scheduled. A scheduling algorithm is performed to maximize the expected aggregate throughput of all the secondary users. The performances of the proposed scheduling scheme in terms of throughput and percentage of packet drop are evaluated.

In cognitive mobile multihop relay (CMMR) network, the mobile user as the primary user is allocated with the channel for transmitting data. Relay station as the secondary user can help primary user to relay transmitted data while in return primary user grants channel access to secondary user. In this paper, credit-based spectrum sharing for primary and secondary user in CMMR network is proposed. With this sharing scheme, the credit is given to the secondary user when primary user requests relay transmission. Primary user can then later allow secondary user to access the allocated channel in return given available credit. An optimization model based on Markov decision process is formulated and solved to obtain optimal policy for primary user in this credit-based spectrum sharing. Knowing that the primary user will adopt optimal policy to grant channel access, the secondary user can optimize the relay transmit power such that the cost is minimized. The performance evaluation shows that the credit-based spectrum sharing can benefit both primary user to gain better QoS performance and secondary user to minimize the cost due to QoS degradation and relay transmit power.

In vehicular-to-roadside (V2R) communications the bandwidth from roadside units (RSUs) can be shared among the vehicular users in order to improve the resource utilization and reduce the costs of bandwidth reservation. We formulate a coalitional game model to analyze the situation in which multiple vehicular users can cooperate for sharing the bandwidth from serving RSUs. First, we consider a \emph{rational coalition formation} approach in which each vehicular user is self-interested, and, hence, decides to join the coalition which maximizes its individual utility. For this approach, we propose a dynamic model based on Markov chain which allows to obtain a stable coalitional structure. Further, for implementation of rational coalition formation, we propose a distributed algorithm based on well-defined merge and split mechanisms. Then, we consider the optimal coalition formation process in which the coalitions are formed so that the social welfare of all vehicular users is maximized. The performance evaluation shows that optimal coalition formation yields a higher utility than rational coalition formation due to the group-interest of all vehicular users. Also, both optimal and rational coalition formation achieve a significantly higher utility than the case without bandwidth sharing (non-cooperative case).

Enabling users to connect to the best available network, dynamic network selection scheme is important for satisfying various quality of service (QoS) requirements, achieving seamless mobility and load balancing in heterogeneous wireless networks. In this paper, we formulate the network selection problem in heterogeneous wireless networks with incomplete information as a Bayesian game. In general, the preference (i.e., utility) of a mobile user is private information. Therefore, each user has to make the decision of network selection optimally given only the partial information of the preferences of other users. To study the dynamics of such network selection, the Bayesian best response dynamics and aggregate best response dynamics are applied. Bayesian Nash equilibrium is considered to be the solution of this game, and there is a one-to-one mapping between the Bayesian Nash equilibrium and the equilibrium distribution of the aggregate dynamics. The numerical results show the convergence of the aggregate best response dynamics for this Bayesian network selection game. This result ensures that even with incomplete information, the equilibrium of network selection decisions of mobile users can be reached.

We propose a wireless access scheme which is based on a channel reservation sharing method for a group of mobile users. This proposed scheme exploits the mobility diversity of the mobile users in order to reduce the cost of wireless access. Another aspect of the proposed scheme is contention resolution among mobile users belonging to the same group in order to access the reserved channel while they are at the same location. A game theoretic model is developed for this wireless access scheme through which the rational mobile users can minimize the cost of wireless access while satisfying their quality-of-service (QoS) requirements (e.g., packet loss rate and average packet waiting time). The proposed game model consists of two interrelated formulations: a coalitional game for channel reservation and a stochastic game for channel access. The stable coalitional structure and equilibrium channel access policy are obtained from this game model.

Abstract Future mobilead hocnetworks are expected to support voice traffic. The requirement for small delay and jitter of voice traffic poses a significant challenge for medium access control (MAC) in such networks. User mobility presents unique difficulties in this context due to the associated dynamic path attenuation. In this paper, a MAC scheme for mobilead hocnetworks supporting voice traffic is proposed. With the aid of a low‐power probe prior to DATA transmissions, resource reservation is achieved in a distributed manner, thus leading to small packet transmission delay and jitter. The proposed scheme can automatically adapt to dynamic path attenuation in a mobile environment. Statistical multiplexing of on/off voice traffic can also be achieved by partial resource reservation for off voice flows. Simulation results demonstrate the effectiveness of the proposed scheme. Copyright © 2009 John Wiley & Sons, Ltd.

This paper considers the problem of optimal and competitive wireless access for data streaming over vehicle-to-roadside (V2R) communication. In a service area, the onboard units (OBUs) in vehicles use wireless access to download streaming data from the roadside units (RSUs). The downloaded streaming data can be stored in proxy buffer for the application to playout. The wireless access can be in reservation or on-demand mode. While the price of wireless access in reservation mode is fixed, that of on-demand mode is determined from the total demand from all OBUs. The OBUs compete with each other for wireless access to a particular RSU. The objective of an OBU is to minimize the cost for wireless access while the quality-of-service (QoS) requirement (e.g., buffer underrun probability) of the streaming application is met. A stochastic game is formulated to model this competitive situation in which OBUs are the players of this game. The strategy of an OBU is the wireless access policy (i.e., the amount of bandwidth to be used for downloading streaming data). The constrained Nash equilibrium is considered to be the solution of this stochastic game. This solution ensures that the cost of each OBU is minimized given the wireless access policies of other OBUs and thus none of the OBUs would unilaterally change its policy for wireless access. In addition, the solution guarantees that the QoS requirement of streaming application is met.

In this paper, quality of service (QoS) provisioning for voice service over cognitive radio networks is considered. As voice traffic is sensitive to delay, the presence of primary users and the requirement that secondary users should not interfere with them pose many challenges for QoS support for secondary voice users. Two cognitive medium access schemes are proposed in this paper for the secondary voice users to access the available channel. An analytical model is developed to obtain the voice service capacity (i.e., the maximum number of voice users that can be supported with QoS guarantee) for the secondary users, taking the impact of primary users' activities into consideration. The analytical model is validated by the simulation. The analytical results will be useful to support voice service in cognitive radio networks.

Delay tolerant network (DTN) based on vehicular communications (i.e., vehicular delay tolerant network or VDTN) is considered in this paper. In VDTN, there is no direct end- to-end connection between the source and destination (i.e., sink). The traffic source transmits data to a mobile router. This mobile router in a vehicle receives and stores data in a buffer. The vehicle can move and once it is in vicinity of the sink, the data in the buffer is forwarded to the sink. The buffer management and its queueing model are proposed to analyze the performance of a mobile router in this VDTN. With this analytical model, various performance measures (e.g., throughput and delay) can be obtained. This queueing model is then used as a tool to study the behavior of the traffic source in a competitive environment which is due to the fact that the transmission resources of a mobile router are shared among multiple traffic sources. Therefore, the traffic sources have to noncooperatively optimize their transmission strategies to achieve the highest utility. The proposed analytical models will be useful to investigate the performance and behavior of the vehicular delay tolerant network.

Wireless mesh networking is a promising wireless technology for future broadband Internet access. In this paper, a novel collision-free medium access control (MAC) scheme supporting multimedia applications is proposed for wireless mesh backbone. The proposed scheme is distributed, simple, and scalable. Benefiting from the fixed locations of wireless routers, the proposed MAC scheme reduces the control overhead greatly as compared with conventional contention-based MAC schemes (e.g., IEEE 802.11). In addition, the proposed scheme can provide guaranteed priority access to real-time traffic and, at the same time, ensure fair channel access to the routers with data traffic. Unlike most of the existing MAC schemes which focus on single-hop transmissions, the proposed MAC scheme takes the intra-flow correlations between up-stream and downstream hops of a multi-hop flow into consideration. To avoid buffer overflow at bottleneck routers, a simple but effective congestion control mechanism is proposed. Simulation results demonstrate that the proposed scheme significantly improves the delay performance of real-time traffic and the end-to-end data throughput, as compared with IEEE 802.11 and distributed packet reservation multiple access (DPRMA). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results is verified by computer simulations.

In this paper, with the cross-layer design principle, a novel cooperative triple busy tone multiple access (CTBTMA) scheme is proposed for wireless networks to achieve cooperative diversity gain. A utility-based algorithm is presented to determine the capability of a node in helping other nodes' transmissions. With the use of three busy-tone channels, not only collisions can be avoided, but also an optimal helper can be determined without disturbing existing transmissions. Simulation results demonstrate that the proposed scheme can effectively increase the throughput in a low SNR environment, as compared with IEEE 802.11a single-hop transmissions. On the other hand, transmit power can be greatly reduced in the proposed scheme in order to achieve the same throughput as in the single-hop transmissions.

The need to support integrated services and provide quality of service (QoS) for various applications is one of the fundamental challenges for successful wireless mesh network (WMN) deployment. In order to provide differentiated services, medium access control (MAC) should have priority management at the link layer. In code division multiple access (CDMA) based WMNs, the interference phenomenon and simultaneous transmissions must be considered. We propose two priority schemes for MAC in a distributed CDMA-based WMN, taking into account interference, multimedia services, QoS requirements, and simultaneous transmissions. In addition, we propose to use an adaptive spreading gain and a frame structure to achieve high resource utilization. Simulation results demonstrate that the proposed schemes can achieve effective QoS guarantee.

In this paper, a novel collision-free MAC scheme supporting multimedia applications is proposed for wireless mesh backbone. The proposed scheme is distributed, simple, and scalable. Benefiting from the fixed locations of wireless routers, the proposed MAC scheme reduces the control overhead greatly as compared with the conventional contention-based MAC protocols (e.g., IEEE 802.11). In addition, the proposed scheme can provide guaranteed priority access to real-time traffic and, at the same time, ensure fair channel access from the routers with data traffic. Unlike most of the existing works which focus on single-hop transmissions, the proposed MAC scheme takes the intra-flow correlations between up-stream and down-stream hops of a multi-hop flow into consideration. To avoid buffer overflow at bottleneck routers, a simple but effective congestion control mechanism is proposed. Simulation results demonstrate that the proposed scheme significantly improves the delay performance of real-time traffic, the fairness of data traffic, and the end-to-end data throughput, as compared with IEEE 802.11.

In wireless ad hoc networks, in addition to the well-known hidden terminal and exposed terminal problems, the location-dependent contention may cause serious unfairness and priority reversal problems. These problems can severely degrade network performance. To the best of our knowledge, so far there is no comprehensive study to fully address all these problems. In this paper, a new busy-tone based medium access control (MAC) scheme supporting voice/data traffic is proposed to address these problems. Via two separated narrow-band busy-tone channels with different carrier sense ranges, the proposed scheme completely resolves the hidden terminal and exposed terminal problems. Furthermore, with the use of transmitter busy-tones in the node backoff procedure, the proposed scheme ensures guaranteed priority access for delay-sensitive voice traffic over data traffic. The priority is also independent of the user locations, thus solving the priority reversal problem. The fairness performance for data traffic in a non-fully-connected environment is also greatly improved (as compared with the popular IEEE 802.11e MAC scheme) without the need for extra information exchanges among the nodes.

Distributed medium access control (MAC) is essential for a wireless network without a central controller. In previous work of the authors, a distributed MAC scheme has been proposed to achieve guaranteed priority and enhanced fairness performance. For a wireless network, the service time distribution at the MAC sub-layer is important for performance analysis (e.g., in terms of packet delay, packet dropping rate, and admission region) at the network layer, because the network layer performance is largely dependent on the high-order time- domain statistics of the service provided by the MAC sub-layer. This paper presents a service time distribution analysis of the previously proposed distributed MAC scheme. Specifically, the respective distributions of the node service time and the system service time are derived. Simulation results verify the accuracy of this analysis.

In this paper, it is shown that it is challenging to evaluate service fairness in multi-hop wireless networks due to intra-flow contention and unequal channel capacity. The conventional fairness criterion in wireline networks in terms of flow rate is not appropriate in the wireless environment. Thus, the channel time in the maximal clique is proposed here as an alternative criterion. Based on this criterion, a new definition of max-min fairness for wireless networks is given. This definition is shown to be general for both wireless and wireline networks. Under certain conditions, it is seen to be equivalent to the proportional fairness definition.

Most of the existing medium access control (MAC) protocols for wireless local area networks (WLANs) provide prioritized access by adjusting the contention window sizes or inter-frame spaces for different traffic classes. Those MAC protocols can only provide statistical priority access and limited service differentiation. In this paper, a novel token-based scheduling scheme is proposed for a fully-connected WLAN that supports both voice and data traffic. The proposed scheme can provide guaranteed priority access to voice traffic and, at the same time, provide more precise and quantitative service differentiation for data traffic, which provides great flexibility and facility to the network service provider for service class management. Simulation results demonstrate that the proposed scheme can guarantee a small delay for voice traffic. For data traffic, it can effectively achieve proportional differentiation among different classes, while achieving fair resource sharing within the same class. In addition, compared with a contention based scheme and a centralized polling scheme, the proposed scheme significantly improves the channel utilization by avoiding collisions (in the contention based scheme) and the polling overhead (in the polling scheme). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results is verified by computer simulations.

In this paper, based on a cross-layer design prin- ciple, we propose an interference aware distributed medium access control (MAC) scheme for a code-division multiple access (CDMA)-based wireless mesh backbone. Specifically, benefiting from the fixed location of wireless routers, the power allocation is based on the length of the transmission path, so as to ensure some level of fairness in resource allocation among the routers. For call admission and slot/rate allocation, based on the maximum sustainable interference concept, we propose to estimate the interference from the viewpoint of the receiver (rather than the transmitter). Each receiver estimates its experienced interference level for the hypothesis that one or more new calls are admitted. If the interference is not tolerable, the receiver rejects the new call(s). The main advantages of our proposed scheme are the low control message overhead for easy implementation, and the accurate interference estimation. Simulation results are presented to evaluate the performance of our scheme. router. In such a backbone, fine-granularity QoS provisioning is desired or required. Carrier sense multiple access (CSMA)- based random access schemes, the major stream for traditional ad hoc networks, may not be a choice, due to their limited QoS provisioning capability. Thus reservation-based MAC schemes should be more suitable for the wireless mesh backbone. When resources are reserved for each active flow, fine-granularity QoS can be achieved. This paper presents an effective distributed MAC scheme for the wireless mesh backbone, taking into account the unique networking characteristics. Specifically, we consider a wireless mesh backbone based on code-division multiple access (CDMA) technology, and propose a MAC scheme based on the cross-layer design principle. The merits of our proposed scheme are four-fold: 1) it is fully distributed; 2) each link does not need to have the dynamic information of other links in terms of transmission power, tolerable interference, etc., thus requiring a low information exchange overhead and increasing the robustness and scalability of the MAC scheme; 3) accurate interference estimation can be achieved for each receiver; and 4) fine-granularity QoS can be achieved by burst- based resource reservation. If a traffic burst is admitted into the network, it can use the reserved resources until the completion of the burst.

Mobile ad hoc networks are expected to support voice traffic. The requirement for small delay and jitter of voice traffic poses a significant challenge for medium access control (MAC) in such networks. User mobility makes it more complex due to the associated dynamic path attenuation. In this paper, a MAC scheme for mobile ad hoc networks supporting voice traffic is proposed. With the aid of a low-power probe prior to DATA transmissions, resource reservation is achieved in a distributed manner, thus leading to small delay and jitter. The proposed scheme can automatically adapt to dynamic path attenuation in a mobile environment. Simulation results demonstrate the effectiveness of the proposed scheme.

Distributed channel access is essential for a wireless network without a central controller. In our previous research, we have proposed a distributed channel access scheme to achieve guaranteed priority and enhanced fairness performance. To better understand the properties of the scheme, and also for the sake of the network design, it is important to investigate the time domain statistics of the scheme. In this paper, we derive the distributions of node service time and system service time, respectively, for the distributed channel access scheme in the saturated case. Simulation results verify the accuracy of our analysis.

Most of the existing WLAN MAC protocols can only provide limited service differentiation. In this paper, we propose a novel token-based scheduling scheme for precise and quantitative service differentiation, which can provide great flexibility and facility to the network service provider for service class management. Simulation results demonstrate that the proposed scheme can effectively achieve proportional differentiation among different classes, while achieving fair resource sharing within the same class. In addition, compared with the contention based scheme and the centralized polling scheme, the proposed scheme significantly improves the channel utilization by avoiding collisions (with the contention based scheme) and the polling overhead (with the polling scheme). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results are verified by computer simulations.

Although the IEEE 802.11e enhanced distributed channel access (EDCA) can differentiate high priority traffic such as real-time voice from low priority traffic such as delay- tolerant data, it can only provide statistical priority, and is characterized by inherent short-term unfairness. In this paper, we propose a new distributed channel access scheme through minor modifications to EDCA. Guaranteed priority is provided to real time voice traffic over data traffic, while a certain service time and short-term fairness enhancement are provided to data traffic. We also present analytical models to calculate the percentage of time to serve voice traffic and the achieved data throughput. Both analysis and simulation demonstrate the effectiveness of our proposed scheme.

In this paper, with a cross-layer design principle, we propose an interference aware distributed resource management scheme for a code-division multiple access (CDMA)-based wireless mesh backbone (consisting of a number of wireless routers at fixed sites). Specifically, benefiting from the fixed location of wireless routers, the power allocation is based on the length of the transmission path, so as to ensure a certain level of fairness among the routers. For a new call arrival, based on the maximum sustainable interference concept, each existing receiver (rather than the potential sender) estimates its experienced interference level under the hypothesis that the new call is admitted. If the interference is not tolerable, the existing receiver rejects the new call by sending a blocking-signal. The main advantages of our proposed scheme are the low control message overhead for easy implementation, and the accurate interference estimation. Simulation results are presented to evaluate the performance of our scheme.

Voice over wireless local area network (VoWLAN) is an emerging application taking advantage of the promising voice over Internet Protocol (VoIP) technology and the wide deployment of WLANs all over the world. The real-time nature of voice traffic determines that controlled access rather than random access should be adopted. Further, to fully exploit the capacity of the WLAN supporting voice traffic, it is essential to explore statistical multiplexing and to suppress the large overhead. In this paper, we propose mechanisms to enhance the WLAN with voice quality of service (QoS) provisioning capability when supporting hybrid voice/data traffic. Voice multiplexing is achieved by a polling mechanism in the contention-free period and a deterministic priority access for voice traffic in the contention period. Header overhead for voice traffic is also reduced significantly. Delay-tolerant data traffic is guaranteed an average portion of service time in the long run. A session admission control algorithm is presented to admit voice traffic into the system with QoS guarantee. Analytical and simulation results demonstrate the effectiveness and efficiency of our proposed solutions.

In wireless ad hoc networks, in addition to the well-known hidden terminal and exposed terminal problems, the location-dependent contention may cause serious unfairness and priority reversal problems. These problems can severely degrade network performance. In this paper, a new busy- tone based medium access control (MAC) scheme supporting voice/data traffic is proposed to address these problems. Via two separated narrow band busy-tone channels with different carrier sense ranges, the proposed scheme completely resolves the hidden terminal and exposed terminal problems. Furthermore, by extending busy-tones in the transmitter busy-tone channel, the proposed scheme ensures guaranteed priority access for delay-sensitive voice traffic independent of the user locations. The long- term and short-term fairness performance for data traffic in a multi-hop environment is also greatly improved as compared with the popular IEEE 802.11e MAC scheme.

Although the IEEE 802.11e enhanced distributed channel access (EDCA) can differentiate high priority traffic such as real-time voice from low priority traffic such as delay-tolerant data, it can only provide statistical priority, and is characterized by inherent short-term unfairness. In this paper, we propose a new distributed channel access scheme through minor modifications to the EDCA. Guaranteed priority is provided to real-time voice traffic over data traffic, while a certain service time and short-term fairness enhancement are provided to data traffic. We also present analytical models to calculate the percentage of time to serve voice traffic and the achieved data throughput. Both analysis and simulation demonstrate the effectiveness of our proposed scheme.

Voice over wireless local area network (VoWLAN) is an emerging application taking advantage of the promising voice over Internet Protocol (VoIP) technology and the wide deployment of WLANs all over the world. To fully exploit the capacity of WLAN supporting voice traffic, it is essential to explore statistical multiplexing and to suppress the large overhead. In this paper, we propose mechanisms to enhance the WLAN with voice quality of service (QoS) provisioning capability in supporting hybrid voice/data traffic. Voice multiplexing is achieved by a polling mechanism in the contention-free period and a deterministic priority access for voice traffic in the contention period. Header overhead for voice traffic is also reduced significantly. Delaytolerant data traffic is guaranteed an average portion of service time in the long run. A session admission control algorithm is presented to admit voice traffic into the system with QoS guarantee. Analytical and simulation results demonstrate the effectiveness and efficiency of our proposed solutions.

In a single-channel wireless ad hoc network, the collisions caused by hidden terminals can severely reduce the network capacity. In this paper, a new busy-tone based scheme is proposed, which can completely avoid collisions (including DATA packet and RTS packet collisions) caused by hidden terminals. This is achieved by adding dual busy-tone channels, and setting a larger carrier sense range of the transmitter busy-tone channel than those of the information and receiver busy-tone channels. The proposed scheme also resolves the exposed terminal problem incurred by the increased carrier sense range of the transmitter busy-tone channel. The simulation results demonstrate that the proposed scheme has an improved performance in terms of throughput in the hidden terminal scenario as compared with the traditional busy-tone solution, and also achieves a high channel utilization in the exposed terminal scenario.