Browsing by Author "Niyato, Dusit"
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Item A Hierarchical Game Framework for Distributive Resource Allocation in Future Heterogeneous Network(2017-12) Zhang, Huaqing; Han, Zhu; Pan, Miao; Nguyen, Hien Van; Cai, Lin X.; Niyato, DusitThe explosive development of mobile data service makes our lives convenient and efficient. However, with the increasing demands of wireless data transmission, it is difficult to fulfill real-time requirements of mobile users in the traditional cellular architecture. In future wireless communication, the network is expected to be heterogeneous. On one hand, the large amount and different sizes of small cells are expected to be overlaid within the wireless network. On the other hand, wireless networks will be coordinated with other networks for expansion of available resources. Nevertheless, due to the distributive behaviors of multiple individuals in the heterogeneous network (HetNet), it is challenging to adopt resource allocations to achieve stable and high quality of service (QoS) for all mobile users. In this dissertation, we overview the development of wireless networks and summarize the wireless service into a 4-layer service architecture, consisting of the service layer, resource layer, infrastructure layer and user layer. Considering the heterogeneous architecture of future wireless network, a hierarchical game framework is proposed to determine distributive strategies for high performance and equilibrium solutions. We first analyze the distributive behaviors during the cooperation of multiple infrastructure providers, and propose a zero-determinant strategy for the administrator of the cooperation to maintain a high social welfare. Then, we analyze the distributive behaviors of multiple resource providers as well as infrastructure providers, with the applications of LTE unlicensed (LTE-U) and visible light communication (VLC). In LTE-U, multi-leader multi-follower Stackelberg game is employed among operators and users for resource management of licensed spectrum and unlicensed spectrum. In VLC, we combine VLC with Device-to-Device (D2D) communication and employ the Stackelberg game with a graphical game to analyze the equilibrium behaviors of all individuals. Finally, we consider the general heterogeneous network with the application of fog computing. With network virtualization, a hierarchical game framework combining the Stackelberg game and matching game are applied, where each mobile user is allocated with the optimal amount of computing resources from the selected fog node or cloud server.Item A Hierarchical Game With Strategy Evolution for Mobile Sponsored Content and Service Markets(IEEE Transactions on Communications, 9/24/2018) Wang, Wenbo; Xiong, Zehui; Niyato, Dusit; Wang, Ping; Han, ZhuIn 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.Item A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks(IEEE Communications Surveys & Tutorials, 3/9/2016) Wang, Wenbo; Kwasinski, Andres; Niyato, Dusit; Han, ZhuThe framework of cognitive wireless networks is expected to endow the wireless devices with the cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In many practical scenarios, the complexity of network dynamics makes it difficult to determine the network evolution model in advance. Thus, the wireless decision-making entities may face a black-box network control problem and the model-based network management mechanisms will be no longer applicable. In contrast, model-free learning enables the decision-making entities to adapt their behaviors based on the reinforcement from their interaction with the environment and (implicitly) build their understanding of the system from scratch through trial-and-error. Such characteristics are highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Therefore, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the state-of-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models on which those applications are based, a systematic overview of the learning algorithms in the domains of single-agent system, multiagent systems, and multiplayer games is provided. The applications of model-free learning to various problems in cognitive wireless networks are discussed with the focus on how the learning mechanisms help to provide the solutions to these problems and improve the network performance over the model-based, non-adaptive methods. Finally, a broad spectrum of challenges and open issues is discussed to offer a guideline for the future research directions.Item Applications of Economic and Pricing Models for Wireless Network Security: A Survey(IEEE Communications Surveys & Tutorials, 7/27/2017) Luong, Nguyen Cong; Hoang, Dinh Thai; Wang, Ping; Niyato, Dusit; Han, ZhuThis paper provides a comprehensive literature review on applications of economic and pricing theory to security issues in wireless networks. Unlike wireline networks, the broadcast nature and the highly dynamic change of network environments pose a number of nontrivial challenges to security design in wireless networks. While the security issues have not been completely solved by traditional or system-based solutions, economic and pricing models recently were employed as one efficient solution to discourage attackers and prevent attacks to be performed. In this paper, we review economic and pricing approaches proposed to address major security issues in wireless networks including eavesdropping attack, denial-of-service (DoS) attack such as jamming and distributed DoS, and illegitimate behaviors of malicious users. Additionally, we discuss integrating economic and pricing models with cryptography methods to reduce information privacy leakage as well as to guarantee the confidentiality and integrity of information in wireless networks. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to wireless security issues.Item Applications of Repeated Games in Wireless Networks: A Survey(IEEE Communications Surveys & Tutorials, 6/16/2015) Hoang, Dinh Thai; Lu, Xiao; Niyato, Dusit; Wang, Ping; Kim, Dong In; Han, ZhuA repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model interactions among players in only one period, in repeated games, interactions of players repeat for multiple periods. Thus, the players become aware of other players' past behaviors and their future benefits, so as to adapt their strategies accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games in encouraging wireless nodes into cooperations, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Item Cloud/Fog Computing Resource Management and Pricing for Blockchain Networks(IEEE Internet of Things Journal, 9/24/2018) Xiong, Zehui; Feng, Shaohan; Wang, Wenbo; Niyato, Dusit; Wang, Ping; Han, ZhuPublic blockchain networks using proof of work-based consensus protocols are considered a promising platform for decentralized resource management with financial incentive mechanisms. In order to maintain a secured, universal state of the blockchain, proof of work-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 proof of work to the cloud/fog providers becomes a viable option. In this paper, we study the interaction between the cloud/fog providers and the miners in a proof of work-based blockchain network using a game theoretic approach. In particular, we propose a lightweight infrastructure of the proof of work-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 cloud/fog provider and the utilities of the individual miners are jointly optimized. In the first stage of the game, the cloud/fog provider 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 sub-game 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.Item Coalition Formation Games for Distributed Cooperation Among Roadside Units in Vehicular Networks(IEEE Journal on Selected Areas in Communications, 12/30/2010) Saad, Walid; Han, Zhu; Hjorungnes, Are; Niyato, Dusit; Hossain, EkramVehicle-to-roadside (V2R) communications enable vehicular networks to support a wide range of applications for enhancing the efficiency of road transportation. While existing work focused on non-cooperative techniques for V2R communications between vehicles and roadside units (RSUs), this paper investigates novel cooperative strategies among the RSUs in a vehicular network. We propose a scheme whereby, through cooperation, the RSUs in a vehicular network can coordinate the classes of data being transmitted through V2R communication links to the vehicles. This scheme improves the diversity of the information circulating in the network while exploiting the underlying content-sharing vehicle-to-vehicle communication network. We model the problem as a coalition formation game with transferable utility and we propose an algorithm for forming coalitions among the RSUs. For coalition formation, each RSU can take an individual decision to join or leave a coalition, depending on its utility which accounts for the generated revenues and the costs for coalition coordination. We show that the RSUs can self-organize into a Nash-stable partition and adapt this partition to environmental changes. Simulation results show that, depending on different scenarios, coalition formation presents a performance improvement, in terms of the average payoff per RSU, ranging between 20.5% and 33.2%, relative to the non-cooperative case.Item Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching(IEEE Internet of Things Journal, 3/29/2017) Zhang, Huaqing; Xiao, Yong; Bu, Shengrong; Niyato, Dusit; Yu, F. Richard; Han, ZhuFog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.Item Contract Theory Framework for Cryptoeconomics(2022-05-12) Li, Jing; Han, Zhu; Pan, Miao; Shi, Weidong; Nguyen, Hien Van; Niyato, Dusit; Zhang, Xiao-PingCryptoeconomics is the research on how incentives should construct a decentralized and distributed cryptographic system. Economic incentives are used to motivate the efforts and govern the allocation of resources in the cryptoeconomic ecosystem, ensuring specific types of information security qualities. Compared to the costly and time-consuming cryptography, incentives obtained through game theory are much more cost-efficient and easier to implement. However, there lacks sufficient research on the incentive issue of cryptoeconomics. We investigate the various incentives of blockchain networks to fill in the gaps in cryptoeconomics research. The first research focuses on the blockchain network with shards and adopts the security-deposit-based consensus protocol, studying the problem of how to balance the security incentive and the economic incentive. The contract theory is utilized to formulate the problem between temporary blockchain leaders and validators. Compared with fixed deposits, flexible deposits can provide sufficient financial incentives for the participants without losing the security incentives. In the second work, we adopt the cyber insurance idea and propose the insurance contract to help determine the withdrawal delay and the insurance claim to relieve the loss of victims. Specifically, instead of requiring the insurance premium from the validators, the cyber insurer first signs the contract with the blockchain representative (e.g., beacon chain). Then the blockchain representative would sign a series of contracts with the validators. Through the simulations, we demonstrate that the proposed model can provide adaptive insurance contracts for the different validators and keep the profits of the blockchain network and the cyber insurer. In the last work, we propose a random-contract-based scheme to maximize the service provider's revenue and assign the service buyers the feasible service price under the framework of a sidechain linked to the public blockchain. We systematically demonstrate random contracts' superiority under the increasing absolute risk-aversion assumption. The simulation results show that random contracts can provide more significant revenue for sidechains by an average of 24.70% compared to deterministic contracts. Efficient service payments can be reduced by an average of 44.65% compared to the main chain's cost.Item Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey(IEEE Communications Surveys & Tutorials, 6/21/2016) Luong, Nguyen Cong; Hoang, Ding Thai; Wang, Ping; Niyato, Dusit; Kim, Dong In; Han, ZhuThis paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks (WSNs) are the main components of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in machine-to-machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.Item Deferrable load scheduling under imperfect data communication channel(Wireless Communications and Mobile Computing, 3/10/2014) Dong, Qiumin; Niyato, Dusit; Wang, Ping; Han, ZhuIn 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.Item Game Theoretic and Machine Learning Techniques for Efficient Resource Allocation in Next Generation Wireless Networks(2019-12) Raveendran, Neetu; Han, Zhu; Pan, Miao; Nguyen, Hien Van; Shakkottai, Srinivas G.; Niyato, DusitThe rationale behind the next generation wireless networks is the handling of the recent massive surge in wireless traffic, especially due to the advent of the Internet of Things (IoT) ecosystem. Tremendously high data rates, extremely low latency, and significantly high Quality of Service (QoS) are among the key objectives of the forthcoming fifth generation (5G) standard. Some of the concepts which act as the driving forces behind realizing these goals are network virtualization, fog computing, heterogeneous networks, and spectrum sharing. Taking these into account, a few efficient resource allocation frameworks for these techniques are proposed in this dissertation. Considering the distributed behaviors of the different sets of entities involved and their interrelationships, we incorporate the potentials of game theory and Machine Learning (ML) as powerful mathematical tools for strategic decision making. Firstly, two resource allocation frameworks for network virtualization based on matching theory are proposed: a three-sided matching based model involving radio resources, physical infrastructure, and mobile users for wireless network virtualization, and a similar model involving Tracking Areas (TAs), Virtual Network Function (VNF) instances, and Cloud Networks (CNs) for Network Function Virtualization (NFV). Secondly, an Equilibrium Problem with Equilibrium Constraints (EPEC) and a many-to-many matching based framework is proposed for NFV integrated IoT fog computing: a large-scale model for the optimization of resource pricing for the Data Service Operators (DSOs), as well as for the optimization of resource allocation from the Fog Nodes (FNs) as per the requirements of the Authorized Data Service Subscribers (ADSSs). Thirdly, a resource allocation framework for heterogeneous networks based on Reinforcement Learning (RL) and EPEC is proposed: a multi-hop data transmission route determination model for an indoor Visible Light Communication (VLC) and Device-to-Device (D2D) heterogeneous network. Finally, a framework to enhance the spectrum utilization of a Cognitive Radio Network (CRN) is proposed: a classification approach to detect Primary User Emulation (PUE) attacks using Generative Adversarial Networks (GANs), which are effective ML models to train classifiers in a semi-supervised manner. In this dissertation, a comprehensive discussion of these frameworks is performed, followed by the validation of their effectiveness through extensive simulations.Item Joint Communication, Computation, Caching, and Control in Big Data Multi-access Edge Computing(IEEE Transactions on Mobile Computing, 3/29/2019) Ndikumana, Anselme; Tran, Nguyen H.; Ho, Tai Manh; Han, Zhu; Saad, Walid; Niyato, Dusit; Hong, Choong SeonThe concept of Multi-access Edge Computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to the resourceful cloud, MEC server has limited resources. When each MEC server operates independently, it cannot handle all computational and big data demands stemming from users' devices. Consequently, the MEC server cannot provide significant gains in overhead reduction of data exchange between users' devices and remote cloud. Therefore, joint Computing, Caching, Communication, and Control (4C) at the edge with MEC server collaboration is needed. To address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency. However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed. To solve the proximal upper bound problem, the block successive upper bound minimization method is applied. Simulation results show that the proposed approach satisfies computation deadlines and minimizes bandwidth consumption and network latency.Item Joint Incentive Mechanism for Paid Content Caching and Price Based Cache Replacement Policy in Named Data Networking(IEEE Access, 6/18/2018) Ndikumana, Anselme; Tran, Nguyen H.; Ho, Tai Manh; Niyato, Dusit; Han, Zhu; Hong, Choong SeonInternet traffic volume is continuing to increase rapidly. Named data networking (NDN) has been introduced to support this Internet traffic growth through caching contents close to consumers. While caching in NDN is beneficial to both Internet service providers (ISPs) and content providers (CPs), ISPs serve cached contents independently without any coordination with CPs. By authorizing the ISPs to cache and distribute the contents accessible on payments, it becomes impractical for CPs to control content access and payments. In this paper, we address these challenges by proposing a joint incentive mechanism and a price-based cache replacement (PBCR) policy for paid content in NDN that improves the ISP's and CPs' profits. We use an auction theory, where the ISP earns profits from caching by alleviating traffic load on transit links and participating in contents selling. Therefore, before the ISP starts selling cached contents, it needs to cache them first. Furthermore, the ISP cache capacity is limited; therefore, we propose PBCR, where the PBCR triggers the content that needs to be replaced when the cache storage is full based on both content price and link cost. The simulation results show that our proposal increases the profits of all the network players involved in paid content caching and improves cache hit ratio.Item Learning for Robust Routing Based on Stochastic Game in Cognitive Radio Networks(IEEE Transactions on Communications, 1/30/2018) Wang, Wenbo; Kwasinski, Andres; Niyato, Dusit; Han, ZhuThis paper studies the problem of spectrum-aware routing in a multi-hop, multi-channel cognitive radio network when malicious nodes in the secondary network attempt to block the path with mixed attacks. Based on the location and time-variant path delay information, we model the path discovery process as a non-cooperative stochastic game. By exploiting the structure of the underlying Markov Decision Process, we decompose the stochastic routing game into a series of stage games. For each stage game, we propose a distributed strategy learning mechanism based on stochastic fictitious play to learn the equilibrium strategies of joint relay-channel selection in the condition of both limited information exchange and potential routing-toward-primary attacks. We also introduce a trustworthiness evaluation mechanism based on a multi-arm bandit process for normal users to avoid relaying to the sink-hole attackers. Simulation results show that without the need of information flooding, the proposed algorithm is efficient in bypassing the malicious nodes with mixed attacks.Item Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach(Journal of Signal Processing Systems, 9/30/2015) Zhang, Yanru; Liu, Lanchao; Gu, Yunan; Niyato, Dusit; Pan, Miao; Han, ZhuThe proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs’ idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs’ participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.Item Privacy Management and Optimal Pricing in People-Centric Sensing(IEEE Journal on Selected Areas in Communications, 3/15/2017) Alsheikh, Mohammad Abu; Niyato, Dusit; Leong, Derek; Wang, Ping; Han, ZhuWith 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.Item Resource Allocation in Wireless Powered Relay Networks: A Bargaining Game Approach(IEEE Transactions on Vehicular Technology, 12/20/2016) Zheng, Zijie; Song, Lingyang; Niyato, Dusit; Han, ZhuInformation and power transfer in mobile relay networks have recently emerged simultaneously, where the relay can harvest the radio frequency (RF) energy and then use this energy for data forwarding and system operation. Most of the previous works do not consider that the relay may have its own objectives, such as using the harvested energy for its own transmission instead of maximizing transmission of the network. Therefore, in this paper, we propose a Nash bargaining approach to balance the information transmission efficiency of source-destination pairs and the harvested energy of the relay in a wireless powered relay network with multiple source-destination pairs and one relay. We analyze and prove that the Nash bargaining problem has several desirable properties such as the discreteness and quasi-concavity, when it is decomposed into three subproblems: the energy transmission power optimization, the power control for data transmission, and the time division between energy transmission and data transmission. Based on the theoretical analysis, we propose an alternating power control and time-division algorithm to find a suboptimal solution. Simulation results clearly show and demonstrate the properties of the problem and the convergence of our algorithm.Item Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey(IEEE Communications Surveys & Tutorials, 1/5/2017) Luong, Nguyen Cong; Wang, Ping; Niyato, Dusit; Wen, Yonggang; Han, ZhuThis paper presents a comprehensive literature review on applications of economic and pricing models for resource management in cloud networking. To achieve sustainable profit advantage, cost reduction, and flexibility in provisioning of cloud resources, resource management in cloud networking requires adaptive and robust designs to address many issues, e.g., resource allocation, bandwidth reservation, request allocation, and workload allocation. Economic and pricing models have received a lot of attention as they can lead to desirable performance in terms of social welfare, fairness, truthfulness, profit, user satisfaction, and resource utilization. This paper reviews applications of the economic and pricing models to develop adaptive algorithms and protocols for resource management in cloud networking. Besides, we survey a variety of incentive mechanisms using the pricing strategies in sharing resources in edge computing. In addition, we consider using pricing models in cloud-based software defined wireless networking. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to cloud networking.Item Smart data pricing models for the internet of things: a bundling strategy approach(IEEE Network, 3/21/2016) Niyato, Dusit; Hoang, Dinh Thai; Luong, Nguyen Cong; Wang, Ping; Kim, Dong In; Han, ZhuThe Internet of Things (IoT) has emerged as a new paradigm for the future Internet. In IoT, devices are connected to the Internet and thus are a huge data source for numerous applications. In this article, we focus on addressing data management in IoT through using a smart data pricing (SDP) approach. With SDP, data can be managed flexibly and efficiently through intelligent and adaptive incentive mechanisms. Moreover, data is a major source of revenue for providers and partners. We propose a new pricing scheme for IoT service providers to determine the sensing data buying price and IoT service subscription fee offered to sensor owners and service users, respectively. Additionally, we adopt the bundling strategy that allows multiple providers to form a coalition and offer their services as a bundle, attracting more users and achieving higher revenue. Finally, we outline some important open research issues for SDP and IoT.