Hierarchical Game Model Research Articles

Coordinated Multi-Beam Transmissions for Reliable Millimeter-Wave Communications With Independent and Correlated Blockages

Millimeter-Wave (mmWave) communication has been considered as one of enabling technologies for the sixth generation (6G) network and beyond to boost the system throughput, which however is hard to provide robust and reliable transmissions since it is easy to be blocked. Coordinated multi-beam transmission has emerged as an effective way to overcome this challenge. In this paper, we first define an incomplete blockage probability and a correlated blockage probability for each user equipment (UE) to measure the robustness and reliability in coordinated multi-beam transmissions, and then formulate the coordinated multi-beam selection and transmission power allocation problem to maximize the sum rate of all UEs in consideration of the independent blockage probability and dependent blockage probability constraints of each UE in mmWave networks. To solve the considered problem efficiently, we reformulate it as a hierarchical game model and design a decentralized algorithm to search the Nash Equalibriums (NEs) of the games. Finally, we present extensive simulation results to demonstrate the effectiveness of the proposed scheme.

Blockchain-assisted computation offloading collaboration: A hierarchical game to fortify IoT security and resilience

With the rapid development of the Internet of Things (IoT), computation offloading collaboration has emerged as an important solution to the data processing challenges faced by resource-constrained devices. Nonetheless, the collaboration process introduces intricate, multi-layered security and privacy issues. This paper presents a blockchain-assisted computation offloading collaboration (BACOC) scheme that leverages blockchain technology to reinforce security and resilience during computation offloading. Concurrently, we adopt a hierarchical game model grounded in Stackelberg game and contract theory, which helps ascertain the optimal incentive strategies for the purpose of incentive compatibility. Experimental and simulation results demonstrate the effectiveness and feasibility of the BACOC scheme.

Joint Channel Access and Power Control Optimization in Large-Scale UAV Networks: A Hierarchical Mean Field Game Approach

With the development of the new generation communications, unmanned aerial vehicle base stations (UAV-BSs) have been widely used to enhance the terrestrial communications. Because of the increasing demand for communication, the scale of the networks is constantly expanding. This paper investigates a joint channel access and power control optimization in large-scale UAV coverage networks, and proposes a hierarchical mean field game approach. Firstly, considering the quality of service (QoS) requirement and energy consumption, the cost function of UAV-BSs is designed. A joint channel access and power control optimization framework is built to improve the efficiency of resource utilization by minimizing the cost function. Then, the joint optimization problem is formulated as a hierarchical game model whose exterior is a channel access game and interior is a multiple mean fields game (MMFG). The channel access game is proved to be a potential game and have at least one Nash Equilibrium (NE). As feedback for channel access optimization, power control policy is obtained by MMFG. A near-optimal algorithm for joint optimizations is designed to achieve the NE. Besides, aiming to the networks with limited computing capability, we also propose a fast optimization approach. The original optimization problem is divided into two independent sub-problems. The channel optimization sub-problem is formulated as a local altruistic game model, then the power policy sub-problem is solved by MMFG. A distributed algorithm based on spatial adaptive play and better response is proposed. Finally, the simulation results verify the validity of the proposed algorithms. The near-optimal optimization method has a better performance while the fast optimization method has a higher speed of the convergence. Compared with the traditional approaches, both of them improve resources utilization efficiency greatly.

Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game

With the rise of integrated energy systems, the load aggregator's role in forming a connection between the demand and supply sides has been gaining increasing importance. Traditional demand response has gradually developed into integrated demand response. Considering consumer psychology, a bilateral integrated demand response model between the integrated energy production base, load aggregator, and users is established; the model is a hierarchical Stackelberg game. Simultaneously, a mixed integer quadratic programming-multi-verse optimizer distributed algorithm is proposed to solve the game model. During the continuous interactive gaming process, the utilities of all parties can be maximized while achieving a balance of benefits. Considering whether price incentives should be implemented and including consumers' psychological characteristics, three scenarios were established for case analysis. The results show that the proposed trading strategy can increase the benefits of the integrated energy production base, load aggregator, and users by 5%, 4%, and 23%, respectively. In addition, the penalty cost of abandoning wind and solar power and of power reserve capacity are reduced by $34.4 and $9, respectively. The introduction of the hierarchical game model ensures the sustainability of the mechanism and provides innovative ideas for the development of integrated energy systems.

The Heterogeneous Demands Satisfaction in IoT Network: Air-Ground Collaborative Deployment

This paper investigates the joint optimization of the three-dimensional (3D) position deployment of multiple unmanned aerial vehicles (UAVs) and the access UAVs selection of ground machine type devices (MTDs). Cache-enabled UAVs are deployed over the Internet of Things (IoT) networks to provide flexible and reliable delivery services for ground devices. Since each MTD has different data demands and the ultimate goal of communication is to provide personalized services for MTDs, we propose an air-ground collaborative deployment strategy focusing on optimizing users’ satisfaction. In the proposed strategy, the hierarchical game model is proposed. Specifically, the 3D position deployment of multiple UAVs is at the upper layer and the access UAV selection of ground MTDs is at the lower. Since there is no central controller and the users are autonomous, we structure a local interaction game to achieve global optimization for the network satisfaction problem. We design a hierarchical deployment access based log-linear learning (HDALL) algorithm to achieve the desirable solution. Finally, the simulation results demonstrate the validity and effectiveness of air-ground collaborative deployment strategy as well as HDALL algorithm.

A Hierarchical Game Model for OFDM Integrated Radar and Communication Systems

This paper studies the spectrum allocation problem between spectrum service providers (SSPs) and terminals equipped with orthogonal frequency division multiplexing (OFDM) integrated radar and communication (IRC) systems. In particular, IRC-equipped terminals such as autonomous vehicles need to buy spectrum for their radar functions, e.g., sensing and detecting distant vehicles, and communication functions, e.g., transmitting sensing data to road-side units. The terminals determine their spectrum demands from the SSPs subject to their IRC performance requirements, while the SSPs compete with each other on the service prices to attract terminals. Taking into account the complicated interactions, a hierarchical Stackelberg game is proposed to reconcile the spectrum demand and service price, where the SSPs are the leaders and the terminals are the followers. Due to the spectrum constraints of the SSPs, we model the lower-layer subgame among the terminals as a generalized Nash equilibrium problem. An iterative searching algorithm is then developed that guarantees the convergence to the Stackelberg equilibrium. Numerical results demonstrate the effectiveness of our proposed scheme in terms of social welfare compared to baseline schemes.

An Intelligent Game-Based Offloading Scheme for Maximizing Benefits of IoT-Edge-Cloud Ecosystems

Nowadays, with the explosive growth of sensor-based devices connected to Internet of Things (IoT), massive amount of data are generated every day with potential tremendous value. We argue that the value of those data can be extracted through monetize data platform in IoT-Edge-Cloud ecosystems for many parts of the business. In such monetize data platform, the data can be computed and transformed into services in IoT-Edge-Cloud ecosystems and provide data-as-a-service (DAAS) for applications. The key to implement such a monetize data platform is to evenly distribute DAAS computing tasks to network devices to maximize the benefits of the system. So, in this article, we study the task type-based computation offloading algorithm (TTCO) to implement such platform. We use the “IoT-Edge-Cloud” three-layer multihop model, which is closer to the complex scene in monetize data platform. We divide tasks into data-intensive tasks and CPU-intensive tasks, and then combine the cost model of computation offloading with task type to make data-intensive tasks prefer local computing and CPU-intensive tasks prefer offload computing, thereby reducing the monetize data platform transmission volume and improving the overall quality of computation offloading. We then use a hierarchical game model combined with fictitious play to solve the Nash equilibrium (NE) of the system and obtain the mixed strategies of the devices. Finally, we propose a TTL-constrained flood strategy transmission mechanism to make the algorithm apply to practice. The experimental results prove that our algorithm has a large performance gain in various scenarios, which can be severed as a monetize data platform for IoT-Edge-Cloud ecosystems.

Air–ground integrated deployment for UAV‐enabled mobile edge computing: A hierarchical game approach

In this study, the air–ground integrated deployment method is studied for the unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system. The UAV can help to reduce the delay and energy consumption of MEC. However, the limited coverage range of UAV limits the quality of data offloading. To improve the efficiency of data transmission, a hierarchical game model is designed. Ground nodes form multiple coalitions actively according to the position of UAV and the UAV adjusts the position based on the data distribution of ground networks. The relationship between the UAV and ground nodes is modelled as a Stackelberg game. A coalition formation game (CFG) is constructed for the data gathering among ground nodes. It is proved that the proposed CFG is an exact potential game with at least one Nash equilibrium. Moreover, the property of Stackelberg equilibrium is proven for the air–ground cooperative relationship. Based on the hierarchical model, a distributed air–ground integrated deployment algorithm is proposed to jointly optimise the position of the UAV and the coalition formation of ground nodes. The simulation results show that the proposed method promotes the efficiency of data transmission greatly and can converge to a stable state with reasonable iteration times.

Opportunistic Utilization of Dynamic Multi-UAV in Device-to-Device Communication Networks

In this paper, we investigate the problem of opportunistic UAV transmission in D2D communication networks. UAVs are supposed to help transmissions of D2D users when they are employed to perform flying missions with given trajectories. On one hand, users can select appropriate UAVs as real-time relays according to the topology in the sky at different moments. On the other hand, due to flight characteristics, UAVs can receive the uploading data when they are approaching transmitters, and then offload the data to corresponding receivers in the appropriate later time. Users need to select and adjust transmission modes dynamically, including multi-UAV selection, time allocation of data loading and offloading, as well as the competition of channel access. We design a hierarchical game model to analyze the complicated relationship among devices. Specifically, a predictable dynamic matching market is constructed to address the issue of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. After that, distributed algorithms are proposed and the properties of convergence are discussed. Simulation results confirm that the effective opportunistic UAV transmission approach can improve the global network significantly, while unreasonable optimization approaches may lead to the decline of the transmission performance.

Security Enhanced Content Sharing in Social IoT: A Directed Hypergraph-Based Learning Scheme

Security is a critical element to the existing Internet of Things (IoT) deployment, where any user may actively or passively attack the content sharing of others reusing the same channel. As most smart devices are carried by human, we may leverage their owners’ social trust to avoid being intercepted by untrusted users, which conforms to the Social Internet of Things (SIoT) paradigm. In this paper, we propose a secure content sharing (SCS) scheme to strike the trade-off between security and quality of experience (QoE) by exploring the social trust. Firstly, to dynamically extract the social trust, the random walk strategy is employed for prediction based on the proposed “User-Content-Social Group” graph which models users’ preference over time. Given the social trust value, we propose a hierarchical game model to decouple the optimization problem into two sub-problems: user pairing and channel selection. More specifically, the user pairing sub-problem is formulated as a matching sub-game with peer effect, and the embedded rotation-swap matching algorithm can accommodate the dynamics caused by mutual interference. The second sub-problem can be formulated as a secure channel selection sub-game with the directed hypergraph being game space, which is proved to be an exact potential game. Then, we design an uncoupled-user concurrent learning algorithm (UUCL) to search for the optimal pure Nash equilibrium, and thereby the global optimum of this sub-game is achieved. Finally, simulation results generated on realistic social dataset verify that our proposed scheme can notably enhance the security without sacrificing users’ QoE.

Robust interference waveform design in fuzzy colored noise based on mutual information

Traditional waveform design methods mostly assume that the noise in the environment is Gaussian white noise. However, as the electronic warfare environment for the jammer becomes increasingly complicated, there is more likely to be a noise source that can emit fuzzy colored noise and a situation where the radar and jammer game, and the existing waveform design methods cannot meet jammer’s performance demand. Therefore, to reduce the radar’s estimation performance in complex environments, a fuzzy colored noise environment and a robust interference waveform design method under the hierarchical game model between the radar and jammer are proposed successively, which can minimize the mutual information of the radar echo signal and the target impulse response. Extensive simulation experiments show that the designed robust interference waveform can finally optimize the strategy of the jammer and provide a meaningful reference for the interference waveform energy allocation strategy in the future.

A hierarchical game model for computation sharing in smart buildings

Recently, initiatives to integrate Internet of Things (IoT) technologies into smart buildings have attracted extensive attention for improving the performance of buildings and the comfort of occupants. However, the amount of data generated by IoT devices remains a challenge to the building management systems (BMSs) in terms of intensity and complexity. Different from cloud computing and edge computing, we propose a computation sharing architecture in smart buildings to incentivize idle computing devices (ICDs, sellers) to offload computational tasks for the BMS (buyer). In this paper, we design a hierarchical game model, consisting of a Stackelberg game and a Cournot game, to achieve a dynamic increase in computational capacity for the BMS. To guarantee the utility of BMS and ICDs, the Stackelberg game model is built to analyze the interactions between BMS and ICDs. Then, the Cournot game model is presented to formulate the internal competition among multiple ICDs. Under the premise of the subgame perfect Nash equilibrium, the BMS can quote the optimal pricing strategy, and the ICDs can share the corresponding optimal amount of computing resources. Finally, the simulation results show that the BMS's computational capacity is enhanced on-demand, and each participant in the game obtains maximal utility.

A Game Theory Approach for Joint Access Selection and Resource Allocation in UAV Assisted IoT Communication Networks

The growing popularity of Internet of Things (IoT) with the requirements of highly reliable and low latency has imposed huge challenges to current cellular networks. Using small aerial platforms like unmanned aerial vehicles (UAVs) to assist terrestrial base stations (BSs) is attractive, but it often challenged by the lack of UAV access selection and resource allocation algorithm to balance the network performance and service cost. In this paper, we study the UAV access selection and BS bandwidth allocation problems in a UAV assisted IoT communication network, where a hierarchical game framework is presented. The complicated interactions among UAVs and BSs as well as the cyclic dependency is studied by applying the Stackelberg game theory. Wherein, the access competition among groups of UAVs is formulated as a dynamic evolutionary game and solved by an evolutionary equilibrium. On the other hand, the problem of how much bandwidth should BSs allocate to the UAVs is modeled as a noncooperative game, where the existence and the uniqueness of Nash equilibrium is analyzed. Stochastic geometry tool is used to model the position distribution of network nodes and drive the payoff expressions by taking into account different network parameters. The analytical results for the proposed hierarchical game model and the corresponding solutions are evaluated via simulations, which verify both the validity of our analysis and the effectiveness of the proposed algorithms.

MI‐Based Robust Waveform Design in Radar and Jammer Games

Due to the uncertainties of the radar target prior information in the actual scene, the waveform designed based on the radar target prior information cannot meet the needs of parameter estimation. To improve the performance of parameter estimation, a novel transmitted waveform design method under the hierarchical game model of radar and jammer, which maximizes the mutual information (MI) between the radar target echo and the random target spectrum response, is proposed. In the hierarchical game model of radar and jammer, the radar is in a leading position while the jammer is in a following position. The strategy of the jammer is optimized based on the radar transmitted waveform of previous moment, then the radar selects its own strategy based on the strategy of the jammer. It is generally assumed that the radar and the jammer have intercepted the real target spectrum and then the optimal jamming and the optimal transmitted waveform spectrum are obtained. However, the exact characteristic of the real target spectrum is hard to capture accurately in actual scenes. To simulate this, the real target spectrum is considered to be within an uncertainty range which is confined by known upper and lower bounds. Then, the minimax robust jamming and the maximin robust transmitted waveform are designed successively based on the MI criteria, which optimizes the performance under the most unfavorable condition of the radar and the jammer, respectively. Simulation results demonstrate that the robust transmitted waveform design method guarantees the parameter estimation performance effectively and provides useful guidance for waveform energy allocation.

A Stackelberg game approach for multiple energies trading in integrated energy systems

In this paper, we propose a novel game model based on the hierarchical Stackelberg game for analyzing the multiple energies trading (MET) problem in integrated energy systems (IESs). In the proposed game model, a number of distributed energy stations (DESs) lead the game deciding the unit prices of electricity and cooling energies they generated, while multiple energy users (EUs) perform as followers determining the amounts of energies to consume. In addition to maximizing the profit of each DES, the proposed hierarchical game model also considers the benefit of each EU, who actively participates in the MET. We prove that, for the first time, there exists a unique Stackelberg Equilibrium (SE) in the MET, so that the existence of an equilibrium strategy optimizing the objectives of all participants can be guaranteed. Moreover, we found that the price setting game played by DESs is a submodular game when energy dispatching is performed during their generation process, which indicates that the behaviors of DESs are strategic substitutes. Furthermore, the SE is obtained in a closed-form, by which the effects of coupling generation of multiple energies are analyzed. Finally, a best response algorithm is provided to obtain the SE in an iterative way. Numerical studies demonstrate the convergency of the proposed best response algorithm, corroborate the jointly effects of market scale and exogenous parameter on the SE and verify the practicability of the proposed game-theoretic approach.

On the Two-User Multi-Carrier Joint Channel Selection and Power Control Game

In this paper, we propose a hierarchical game approach to model the energy efficiency maximization problem where transmitters individually choose their channel assignment and power control. We conduct a thorough analysis of the existence, uniqueness and characterization of the Stackelberg equilibrium. Interestingly, we formally show that a spectrum orthogonalization naturally occurs when users decide sequentially about their transmitting carriers and powers, delivering a binary channel assignment. Both analytical and simulation results are provided for assessing and improving the performances in terms of energy efficiency and spectrum utilization between the simultaneous-move game (with synchronous decision makers), the social welfare (in a centralized manner) and the proposed Stackelberg (hierarchical) game. For the first time, we provide tight closed-form bounds on the spectral efficiency of such a model, including correlation across carriers and users. We show that the spectrum orthogonalization capability induced by the proposed hierarchical game model enables the wireless network to achieve the spectral efficiency improvement while still enjoying a high energy efficiency.

Coercion builds cooperation in dynamic and heterogeneous P2P live streaming networks

Sustaining reasonable performance in a peer-to-peer (P2P) network is contingent upon cooperation among peers. As autonomous agents, peers cooperate only when they are incentivized to do so. Typically, incentives are provided through bilateral exchange of useful data, in a tit-for-tat manner. Unfortunately, good for file sharing and video-on-demand systems as they are, such incentive schemes are ineffective in live streaming systems due mainly to the lack of sufficient opportunity to allow such reciprocity to happen, as recently demonstrated in a pioneering quantitative study. The key insight is that with stringent time constraint, good system performance can only be sustained by judicious peer selection.Despite that some pioneering efforts are done in peer selection, it is as yet a difficult challenge to tackle the inherent non-cooperation of peers in a dynamic and bandwidth-diverse network. In this paper, we meet this challenge by first presenting a novel hierarchical game model, covering strategic interactions among peers, trackers, and the content provider. Based on the analytical insights derived from the repeated game model, we propose a Striker strategy to coerce peers to cooperate, leading to significantly enhanced system performance, as demonstrated by our analytical and simulation results. Most importantly, our proposed incentive schemes are highly practicable in a real-life P2P network.

Novel Pre-pushing Scheme for Peer-Assisted Streaming Network based on Multi-leader Multi-follower Stackelberg Model

Peer-assisted streaming network plays an important role in people's daily lives, for users can watch multimedia resources anywhere, anytime. In this kind of network, servers need pre-pushing certain resources to users for caching which can be further retrieved by other users, and then the network performance can be improved. A key design algorithm in pre-pushing scheme is that which user should be selected to be pushed and how to price to keep the effectiveness and fairness of this action. To address this issue, we turn to multi-leader multi-follower Stackelberg model which is a hierarchical game model dealing with the resource competing among leaders and followers. In our model, we set that users are leaders who own bandwidth and define the price of a unit amount of bandwidth. Servers are followers and each of them selects a best user for pushing according to leaders' equilibrium results. We build utility functions for each server and user, respectively, and maximize them according to the built Stackelberg model. The numerical simulation demonstrates the effectiveness and fairness of our proposed pushing scheme. Further, the comparison between traditional pre-pushing scheme and our proposed one is simulated which also shows that our proposed one can make more payoffs.

Hierarchical Game Model Incorporating Cooperation for Electric Generating System Development—Resource Supply and Public Participation

Incorporating the social siting concern and efficient utilization of limited resource, the present paper represents hierarchical game models for the electric generating system development. The interperiod balances in electric generating capacity and resource supply are firstly investigated. Several evaluation factors such as cost and siting concern are secondly introduced. The integrated problem of electric generating system development and resource supply is then formulated as multi-level multifollower Stackelberg games, where the noncooperative and cooperative approaches to the public participation are investigated from the point of view of social siting concern. Characteristics of the models are investigated by simulation analyses. The model provides various interaction analyses between energy, environment and resource problems in the electric generating system development.