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

The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the DCOs and the SSs are regarded as the leaders and followers, respectively. In the proposed game, each SS selects its serving DCO with preferred price and purchases the optimal amount of resources for the SS's computing requirements. Based on the responses of the SSs' and the other DCOs', the DCOs decide their resource prices so as to receive the highest profit. When the coordination among DCOs is weak, we consider all DCOs are noncooperative with each other, and propose a sub-gradient algorithm for the DCOs to approach a sub-optimal solution of the game. When all DCOs are sufficiently coordinated, we formulate a coalition game among all DCOs and apply Kalai-Smorodinsky bargaining as a resource division approach to achieve high utilities. Both solutions constitute the Stackelberg Equilibrium. The simulation results verify the performance improvement provided by our proposed approaches.

Utilizing the illuminating light-emitting diode (LED) for signal transmission, the visible light communication (VLC) offloads the congested traffic in licensed spectrum while providing users with high quality of service. However, in shaded areas, the performance of VLC is affected. In this paper, we apply Device-to-Device (D2D) technology on the VLC network, where some mobile users are able to relay the data transmission to nearby end mobile users. Considering the autonomous behaviors of mobile users as relays (RUs), cellular service provider (CSP) and VLC service provider (VLCSP), we propose a hierarchical game framework and analyze the distributive strategies for each individual. In the game, the data packet size, the price of licensed spectrum and data rates in possible data transmission routes are sequentially determined with equilibrium solutions, and each VLC transmitter (VLCT) determines the optimal data transmission route with stable matching. Simulations depict the high performance when combining VLC and D2D with the proposed strategies.

The radio frequency spectrum crunch has triggered the harnessing of other sources of bandwidth, for which visible light is a promising candidate. Even though visible light communication (VLC) ensures high capacity, coverage is limited. This necessitates the integration of VLC and device-to-device (D2D) technologies into heterogeneous networks. In particular, mobile users which are accessible by the VLC transmitters can relay data to mobile users which are not, by means of D2D communication. However, due to the distributed behaviors of mobile users, determining optimal data transmission routes from VLC transmitters to end mobile devices is a major challenge. In this paper, we propose a reinforcement learning (RL)-based approach to determine multi-hop data transmission routes in an indoor VLC-D2D heterogeneous network. We obtain the rewards for the RL-based method dynamically, by formulating the interactions between the mobile users relaying the data as an equilibrium problem with equilibrium constraints and using alternating direction method of multipliers to solve it. The proposed technique can achieve optimal data transmission routes in a distributed manner. The simulation results demonstrate the effectiveness of the proposed approach, showing that transmission routes with low delays and high capacities can be achieved through the learning algorithm.

Fog Computing potentially plays a pivotal role in delivering real-time data services to users, where large number of Fog Nodes (FNs) are deployed by various Data Service Operators (DSOs) to provide efficient services to the Authorized Data Service Subscribers (ADSSs). As a result, there exist tradings between DSOs and ADSSs in providing and purchasing these services, respectively. There also exists competition among all DSOs for providing these services at the prices that can maximize their profits. Moreover, competition exists among all ADSSs for purchasing the required amount of resources at the lowest available prices, and thus minimizing their costs. In this paper, we model the aforementioned competitions in fog computing as an Equilibrium Problem with Equilibrium Constraints (EPEC). In the EPEC, the DSOs provide incentives to the ADSSs and balance the utilities between the DSOs and the ADSSs. At the same time, the ADSSs leverage the incentives provided by the DSOs to their advantage. As the size of a typical fog computing network is large, the Alternating Direction Method of Multipliers (ADMM) algorithm, that has been recognized as a key method in large scale optimization can be employed. Utilizing the fast convergence and decomposition properties of ADMM, we achieve optimum results. Simulation results show that with the proposed framework, optimization of the utility functions of DSOs and ADSSs can be achieved in real-time. It is also shown that compared to the profit in traditional cloud computing and data center services, the total maximum profit of the ADSSs is improved to a great extent in fog computing.

With large coverage area, high data rate, low latency and high spectral efficiency, LTE-V has been considered as a promising communication technology in the vehicular networks. However, as all vehicles share the same wireless resource, in LTE-V, how to design power allocation strategy for each vehicle while motivating other vehicles to forward the data remains challenging. In this paper, we model the data transmission in the uplink scenario of the vehicular network as an equilibrium program with equilibrium constraints (EPEC), where the upper-layer vehicles as receivers (VaRs) provide priced relaying service to vehicle as a transmitter (VaT) in the bottom layer. Observing the prices set by serving VaRs in allocated channels, we adopt multi-level water- filling algorithm at the VaT for power allocation. With joint consideration on the optimal reaction of the VaT and the pricing strategy of other VaRs, each VaR optimizes its setting price by adopting the sub-gradient algorithm such that the equilibrium of the formulated EPEC is finally achieved. Simulation results corroborate our theoretical analysis and demonstrate the performance superiority of our proposal as compared with classic pricing strategies of VaRs.

Network virtualization facilitates radio access network (RAN) sharing by decoupling the physical network infrastructure from the wireless services. This paper considers a scenario in which a virtual network operator (VNO) leases wireless resources from a software-defined networking based virtualized RAN set up by a third-party infrastructure provider (InP). In order to optimize the revenue, the VNO explores jointly the delay tolerance in mobile traffic and the weak load coupling across the base stations (BSs) when making the resource scheduling decisions to serve its mobile users (MUs). The problem faced by the VNO can be straightforwardly transformed to the problem of minimizing the payments to the InP, which is formulated as a finite time horizon constrained Markov decision process (MDP). However, for a large-scale network with a huge number of MUs, the problem solving becomes extremely challenging. Through the dual decomposition approach, we decompose the problem into a series of per-MU MDPs, which can be solved distributedly. Moreover, the independence of channel conditions between a MU and the BSs is expected to further simplify solving each per-MU MDP. The simulations carried out in this paper show that our proposed scheme achieves minimal average payments compared with other existing approaches in literature.

Vehicle-to-Vehicle (V2V) communication becomes an emerging topic because of its capability to provide efficient solution which guarantees more pleasant driving environment and eliminates the possibility of traffic accidents. However, the limitation of resource in V2V communication determines that a dynamic resource allocation strategy must be implemented to provide a balanced communication resource usage. Instead of focusing on the small topology of vehicular wireless communication, we look at a bigger picture of the scenery to deal with the challenge of limitation of resource in V2V communication. In this paper, we propose a long short-term memory (LSTM) based regression model to predict 24-hour traffic counts data. The main steps of our work are as follow: First, we collect 24-hour traffic counts data online and label those data. Second, we construct a stacked LSTM model to implement regression. Third, compared with the performance of logistic regression, the efficiency of our regression model is found out. Finally, we analyze the potential resource allocation patterns according to the regression results.

Fog 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.

It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs' interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

With increasing mobile data services in the wireless communication networks, the LTE unlicensed is put forward as an effective way to offload the crowded traffic in the licensed spectrum. However, when there are multiple wireless cellular operators (WCOs) sharing the common unlicensed spectrum together, how to manage the competitions among all WCOs while guaranteeing the performance of the Wi-Fi networks still remains to be a technical challenge. In this paper, we model a layered power control game among all the WCOs and the Wi-Fi access point (WAP). In the game, we first fix the behaviors of all the other WCOs and propose the zero- determinant strategy for the power control of one considered WCO. Based on the predicted strategies of every WCO in all situations, all WCOs then play a non- cooperative game and determine their optimal strategies to achieve the Nash equilibrium results. Simulation results show the correctness of the analysis and the high performance gain achieved with the proposed zero- determinant strategies.

The visible light communication (VLC) technology is able to provide indoor users with high quality of service and low cost. However, the performance of the VLC is limited in shaded or strong sun- light areas. In this paper, we combine the VLC network with Device-to-Device (D2D) technology. We consider that some mobile users accessible to the VLC network is able to relay the transmitted data to the nearby mobile users. We suppose all the mobile users as relays (MUaRs), cellular service provider (CSP) and VLC service provider (VLCSP) are autonomous individuals, and propose a hierarchical game to analyze the optimal strategies for each of them. In the game, the VLCSP determines the data transmission route and data packet size first. Based on the behaviors of the VLCSP, the CSP determines the prices of wireless licensed spectrum. According to the behaviors of the VLCSP, the CSP and the MUaRs in the neighborhood, all MUaRs determines their transmission data rates for optimal utility. Therefore, there is a graphical game among all MUaRs. When all MUaRs consider the corresponding reactions of other MUaRs in the neighborhood and achieve Nash Equilibrium, the Stackelberg Equilibriums can be achieved in the Stackelberg games between the VLCSP and the CSP, between the CSP and all MUaRs and between the VLCSP and all MUaRs. Simulation results show the correctness of the analysis and the VLC network with multi-hop D2D relay is able to bring high profits for the CSP.

With the increasing popularity of data services and applications, data center networks have been introduced to serve users in a centralized fashion. Furthermore, the flexibility of the data service subscribers' (DSSs') requirements motivates data center virtualization so as to optimize the resource allocation among all DSSs. However, as the massive data centers are usually far away from the DSSs, the quality of services are severely affected for delay-sensitive DSSs. Accordingly, fog computing is considered to solve the problem, where some virtualized edge data centers acting as fog nodes (FNs) are added in the network and help massive data center operators (MDCOs) serve DSSs. In this paper, we analyze the resource management problem in the multi-FN multi-MDCO, and multi-DSS networks. We model the network architecture with 3-layer model, where the FNs are in the upper layer, MDCOs in the middle layer, the DSSs in the bottom layer. The FNs first share computing resource with the MDCOs, and thus the MDCOs are able to serve their DSSs with low delay. Based on the model, we propose a hierarchical game, where the interaction between FNSs and MDCOs is regarded as a multi-leader multi-follower Stackelberg game, and the interactions between MDCOs and DSSs are regarded as the single-leader single-follower Stackelberg games. By making decisions distributively, all FNs, MDCOs, and DSSs receive high utilities. Simulation results show the correctness of the analysis and the performance improvement of the proposed strategies in the fog computing networks.

Cooperation in resource sharing among wireless users and network operators has been widely studied in wireless communication. However, because of the limited coordination capability or cheating strategies, each participant of the cooperation may cease its cooperative behavior or duties unilaterally during the resource sharing, resulting in unsatisfying quality of services (QoSs) for all other participants. In this paper, we model the resource sharing among participants as an iterated game. Specifically, we first define the participant who is responsible for maintaining the social welfare as an administrator of cooperation (AoC), and other selfish participants as the regular participants of cooperation (PoCs). Then we consider three scenarios, i.e., with two-player applying discrete strategy, two-player applying continuous strategy, and multi-player applying continuous strategy, Finally, we investigate the power control problem in each of scenarios, and apply the zero-determinant strategies for the AoC to find the maximum social welfare that the AoC can achieve with existence of PoCs. Simulation results show that the high and stable social welfare can be maintained by the the AoC with the proposed zero-determinant algorithm.

Allowing cellular operators to offload data traffic to unlicensed spectrum has the potential to significantly increase the capacity of the cellular network systems. This paper considers the spectrum sharing among multiple cellular operators in the unlicensed spectrum. One of the main challenges for this system is how to control the interference between the cellular users and the unlicensed users in other networks, e.g., Wi-Fi, and the interference among cellular users of different operators. As such, we develop a hierarchical game where there is a Kalai-Smorodinsky bargaining game among leaders and a Stackelberg game between operators and mobile users (MU). Accordingly, multiple operators can negotiate with each other for the revenue obtained from the unlicensed spectrum and use a pricing mechanism to control the interference caused by each MU to other operators and users in other unlicensed networks. Simulation results show that our proposed strategy significantly increases the revenue and utility for both operators and MUs.

Game theory is a powerful tool to deal with the interaction of decision makers with conflicting interests. However, for certain game models such as Chicken-Dare games, traditional strategies in game theory cannot achieve stable and high social welfare because of the competition between players. In this paper, we suppose one player in the game as an administrator, who concerns about the performance of the whole network, and the other player aims to improve its own utility based on the behavior of its opponent. Then we propose a zero-determinant strategy for the administrator so as to reach an equilibrium where the social welfare is satisfying. Such equilibrium can be widely applied in resource management of wireless network, and simulation results show the correctness and superiority of the proposed strategy, compared with other equilibrium concepts such as the correlated equilibrium.

Wi-Fi, small cells and macrocell networks serve users with different advantages and drawbacks. In this paper, we propose a multi-leader multi-follower Stackelberg game between these three types of networks and mobile users. In the multi-leader game, each network first sets the optimal price according to the behaviors of the other networks and the prediction of each mobile user's optimal strategies. Subsequently, mobile users choose their optimal network, accordingly. In the proposed game, there exists a Stackelberg equilibrium between the leader level and the follower level, and two Nash Equilibria within leaders and within followers, respectively. This fact differentiates the proposed work from most existing literature. Simulation results show that the proposed approach yields high social welfare at the equilibrium.

Cooperation of resource sharing among wireless users and network operators has been widely studied in wireless communication. However, during the resource sharing, because of the weak communication signals or cheating strategies, each participant of the cooperation may sometimes stop its cooperative behavior unilaterally. Such behavior causes non-cooperation, resulting in unsatisfying quality of services for all participants. In this paper, we model the resource sharing between two participants as an iterated prisoner's dilemma game. Based on the applications of wireless cooperations, we define the participant who is responsible to maintain the high social welfare as the administrator of cooperation (AoC), and the other rational selfish participant as the regular participant of cooperation (PoC). Then, we propose a zero-determinant strategy for the AoC, and find the maximum social welfare that the AoC can maintain regardless of the strategy of PoC. Simulation results show that when the AoC applies the proposed zero-determinant strategy, the high social welfare can be maintained, and both AoC and PoC receive better performances than those of noncooperation.