Proposed Game Model Research Articles

Aspiration-based co-evolution of cooperation with resource allocation on interdependent networks

Abstract In order to reveal the underlying mechanism of aspiration on cooperation with resource allocation, in this article, an aspiration-based game model on interdependent networks is proposed, where the risk-averse voluntary players are introduced into the game model. Each player has a self-adaptive aspiration tuned by the obtained payoff, which will be evolving with cooperation. In reality, limited resources are always related to games. The allocation of resources is generally decided by the results of games and in turn affects the choice of game strategies. Here, limited resources as a co-evolutionary factor is also involved in the proposed model, which exerts influence on the aspiration of players. Based on the prisoner's dilemma, the evolutionary dynamics of the proposed model is discussed in detail. As the results, the self-adaptive aspiration mechanism can significantly improve the level of cooperation. Meanwhile, both the strength of interdependence and the quantity of resources owned by players have great influence on the choice of game strategies. Interestingly, players with higher resources and aspirations are mostly located in the centers of cooperator (loner) strategy clusters. Moreover, based on the proposed game model, public good game and snow game are also implemented to verify the robustness of the results.

Evolutionary Game Model of Stock Price Synchronicity from Investor Behavior

Institutional and individual investors are the two important players in the stock market. Together, they determine the price of the stock market. In this paper, an evolutionary game model that contains the two groups of players is proposed to analyze the stock price synchronicity considering the impacts of investors’ decisions on stock investment. Factors affecting investors’ decisions include the potential revenue or loss, the probability of gain or loss, and the cost of corresponding behavior. The proposed game model is analyzed by replicator dynamics equations and simulation of the evolutionary equilibrium strategy under different circumstances. The analysis shows that the operating cost of institutional investors, the cost of information collection before trading, and the expected loss that may be punished by regulators are the key factors that affect the evolutionary game system between institutional investors and individual investors. In addition, reducing the speculation in the market and increasing the information access of investors through the serious operation mode of institutional investors and the strengthening of the market information disclosure mechanism are beneficial to alleviate price synchronicity in stock market.

An efficient strategy for warning dissemination in crowds during emergencies

In emergencies, warnings can flow amongst crowds via person-to-person communications. Interpersonal warning dissemination is important for warning people when the government does not have sufficient time or personnel to notify all affected people. Moreover, if interpersonal warning dissemination can be used to warn people in emergencies, the warning efficiency of the crowd can be improved so that the emergency response efficiency of people can be improved. Similarly, anti-warnings that can cause a negative influence on emergency responses can also flow amongst crowds. In this scenario, how can interpersonal dissemination be properly utilized to improve warning efficiency? In this paper, we build a game model that includes warning and anti-warning dissemination based on multi-agent modelling. Moreover, we propose an efficient strategy based on dynamic dissemination characteristics for improving warning efficiency in a crowd. To ensure the reliability of the game model, the Monte Carlo method is applied to conduct our experiments. To verify the practicability of the proposed strategy, 40 Monte Carlo experiments are conducted in synthetic and real crowds. The experimental results demonstrate the effectiveness of our proposed strategy. The proposed game model can be applied to study the dissemination of information in a crowd during emergencies. The proposed strategy can be used to train people in emergency drills to improve their emergency response capabilities.

Pricing for private charging pile sharing considering EV consumers based on non-cooperative game model

The supply of public charging infrastructure is insufficient to meet the charging demand of a large number of electric vehicles (EVs). Private charging pile sharing is an emerging solution to alleviate this imbalance. However, a reasonable price for charging pile sharing has not yet been determined. This study employs a non-cooperative game model to determine a charging pile sharing price considering EV consumers’ charging behaviors. First, a multi-logit model is constructed to measure the probability of EV consumers’ charging behavior choices. Then, a two-matrix game model is established between the private charging pile sharing and public charging mode. Using Beijing as a sample, the sharing rate and price strategies of private and public charging piles are calculated based on the proposed game model. The results show that the optimal sharing rate is 20.01% private charging pile sharing with 1.14 yuan/kWh and 79.99% public charging with 1.7946 yuan/kWh. Sensitivity analysis shows that economics is the most sensitive factor affecting the charging price of private charging pile sharing. Finally, policy recommendations are outlined to improve the private charging pile sharing rate and service efficiency, to broaden the charging options for EV consumers, reduce the construction of public charging piles, and save the government subsidy.

A network-constrained energy consumption game in dynamic pricing markets

The electricity distribution network is experiencing a profound transformation with the concept of the smart grid, providing possibilities for selfish consumers to interact with the distribution system operator (DSO) and to maximize their individual energy consumption utilities. However, this profit-seeking behavior among consumers may violate the network constraints, such as line flows, transformer capacity and bus voltage magnitude limits. Therefore, a network-constrained energy consumption (NCEC) game among active load aggregators (ALAs) is proposed to guarantee the safety of the distribution network. The temporal and spatial constraints of an ALA are both considered, which leads the formulated model to a generalized Nash equilibrium problem (GNEP). By resorting to a well-developed variational inequality (VI) theory, we study the existence of solutions to the NCEC game problem. Subsequently, a two-level distributed algorithm is proposed to find the variational equilibrium (VE), one fair and stable solution to the formulated game model. Finally, the effectiveness of the proposed game model and the efficiency of the distributed algorithm are tested on an IEEE-33 bus system.

A QoS-aware virtual resource pricing service based on game theory in federated clouds

Recently, federated cloud platform has become a promising paradigm to provide cloud services for various kinds of users in a distributed manner. To compete for cloud users, it is critically important for each cloud provider to select an optimal price that best corresponds to their service qualities as well as remains attractive to cloud users. In this paper, we first formulate the pricing strategy of individual cloud provider as a constrained optimisation programming problem to analyse the behaviours of both cloud users and cloud providers. Then, we present game-based model which introduces a set of virtual resource agents to help providers adjusting their prices with aiming at achieving a global optimal solution. Theoretical analysis is present to prove the validity and effectiveness of the proposed game model, and extensive experiments are conducted in a real-world cloud platform to evaluate its performance. The experimental results show that the proposed pricing model can significantly improve the resource revenue for cloud providers and provide desirable quality-of-service (QoS) for user tasks in terms of various performance metrics.

ISAR Imaging Task Allocation for Multi-Target in Radar Network Based on Potential Game

In this paper, a task allocation strategy for multi-target inverse synthetic aperture radar (ISAR) imaging in radar network is proposed by using the potential game theory. The multi-target ISAR imaging task allocation problem in this paper refers to that the targets are in different radar beams and the purpose is to accomplish all the imaging tasks with high resolution within the limited time. First of all, the multi-target imaging task allocation problem is formulated. According to the imaging task allocation problem, a resolution optimization game is proposed to search for a satisfied strategy profile for all the targets, and the proposed game is proven to be an exact potential game which has at least one Nash equilibrium (NE). The optimal and suboptimal solution of the imaging task allocation problem can converge to a pure-strategy NE. Then, a best response-based imaging task allocation algorithm is proposed to solve the proposed game model. The simulation experiments demonstrate that the proposed game approach which can achieve a satisfied strategy profile for the targets with a lower computation complexity is an effective method for the multi-target imaging task allocation problem.

An Interactive Strategy of Grid and Electric Vehicles Based on Master-Slaves Game Model

In order to avoid the adverse effects of the grid caused by disorder large-scale electric vehicles and improve supporting function of energy storage battery on the power grid, interaction model of electric vehicles (EVs) and the power grid is constructed according to a master-slave game method. Then, the utility functions of each participant are established, and the dynamic constraints of the batteries for EVs are established considering the travel demand of users. Then, based on the YALMIP/CPLEX solver, the optimal response strategy of the EVs can be obtained, and the genetic algorithm is used to solve the Nash equilibrium solution of the master-slaves game model. Moreover, the Monte Carlo method is employed to simulate the user travel data and the basic parameters of EVs in a certain area. Based on these data, the optimal time-of-use price in this area is offered, and the optimal response strategy of EVs under this price is analyzed. Finally, the study of the case show that the proposed game model can make the strategies of both the EVs and the grid, which can restrict the load fluctuation of the grid and improve the benefit of EVs without affecting their travel.

Task-Driven Relay Assignment in Distributed UAV Communication Networks

In this paper, we study the distributed relay assignment problem in multi-channel multi-radio unmanned aerial vehicle (UAV) communication networks. Multi-UAVs are driven by the overall task and fly in certain formation, where UAVs with different tasks have various transmission requirements. Source UAVs equipped with multi-radio can select more than one relay radios to achieve high data rate, and each relay radio can be shared by multiple source UAVs. We construct distributed game models to promote the global transmission performance by self-organizing coordination among UAVs. Specifically, the channel competition relationship between relay UAVs is modeled as a congestion game model, while the task-driven relay selection among UAVs is modeled as a many-to-many matching market without substitutability. With the proposed game models, the optimizing of local optimized process will lead to the improvement of global transmission results. After that, we design algorithms for the stable and changeable topology structures, respectively. Based on the given formation shape of UAVs, a learning matching algorithm is proposed to reach the optimum result with a large probability. A fast potential matching algorithm is propose to deal with the topological change of UAV networks. We prove that two proposed algorithms can both achieve the stable matching results. Simulation results show that the proposed relay assignment approaches yield good performances in terms of the global transmission satisfaction and fairness. Particularly, the result of the learning algorithm is close to the global optimum and the fast potential matching approach is robust to the perturbation of UAV networks.

Enhancement of Distribution System Reliability: A Framework Based on Cournot Game Model

The enhancement of load-point reliability level in power distribution systems is usually based on traditional methods including value-based planning (VBP), performance- based rates (PBR) and reliability insurance scheme (RIS). The implementation of these methods has shown that due to weaknesses in their structures, customer reliability requirements were fulfilled rather infrequently. However, satisfying a desired level of reliability for distribution level customers remains to be a major challenge for distribution utilities. In this paper, a new competition model that is based on game theory is introduced which can conclude to more satisfactory results for customers and utilities. The problem formulation shows that the proposed model is similar to the Cornout model in game theory. The presented model has been applied to a sample radial feeder and the results show how the proposed game model can be a more appropriate structure for providing customer reliability in power distribution systems.

Pricing Game With Complete or Incomplete Information About Spectrum Inventories for Mobile Virtual Network Operators

In network virtualization, mobile virtual network operators (MVNOs) lease spectrum resources from mobile network operators (MNOs) and offer certain wireless services to end users. Each MVNO competes with others using optimal price and spectrum inventory so as to profit. However, the inventory of one MVNO is private information and maybe unknown to others, which makes pricing decisions difficult for MVNOs. In this work, we first study the pricing strategy given others’ inventory information. We model the pricing decision problem using the non-cooperative game theory, and develop an optimal price setting algorithm based on an ordinal potential game. Then we put forward three cooperation strategies for MVNOs and analyze the impacts of the coalitions structure on pricing decision. For the situation where the inventory of one MVNO is unknown to others, we use the Bayesian coalition formation game to formulate the pricing decision problem and propose an optimal price setting algorithm based on the Minimum Mean-Square Error to resolve the conflicts resulting from the uncertainty. Next we define a Belief Pareto Order to characterize the preferences of MVNOs regarding the coalition structures. Then we devise a distributed coalition formation algorithm with the proposed belief Pareto order to achieve a Bayesian-Nash stable coalition structure that enables each MVNO to maximize its own revenue. Finally, simulation results demonstrate comprehensive performance evaluation of the proposed game model and provide a guidance on pricing strategies for MVNOs.

Demand aware transmission power cost optimization based on game theory and distributed learning algorithm for wireless body area network

This paper studied the issue of communication transmission power cost optimization for the personal body base station in wireless body area network (WBAN). With the limited energy capacity, the energy cost for the data exchange between the personal body base station and the medical surveillance network should be controlled. At the same time, the users’ demand should also be guaranteed. The transmission AI choosing distributed optimization model is established, by taking the transmission power consumption as the optimization goal. In this model, the user’s data transmission requirements, wireless environment, location of the AI, and other users ‘choices are comprehensively analyzed. To achieve the optimal users’ AI choosing result for the transmission power cost optimization, the AI choosing game model for transmission power cost of WBAN is constructed, and the game is proved to be an accurate potential game. A transmission power cost optimization AI choosing distributed decision-making algorithm is designed, and the convergence of the algorithm is proved. Experiment analysis verifies the theoretical analysis of the proposed game model and learning algorithm, and show that the proposed algorithm can effectively optimize the AI choosing results of the WBAN to reduce the energy cost.

Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming

A group of collaborative nodes can efficiently complete spatial long-distance transmission tasks using beamforming technology. However, a high sidelobe level interferes with communication quality, and uneven energy consumption of nodes affects network lifetime. This paper proposes an energy-efficient collaborative transmission algorithm based on potential game theory for beamforming. First, the minimum number of cooperative nodes is determined in accordance with the energy consumption and spacing limitation condition. A group of nodes satisfying the node spacing condition is selected as cooperative nodes based on the ring array to minimize communication interference among nodes. Second, a potential game model is proposed as a joint method for optimizing the collaborative parameters of the cooperative nodes and their energy consumption balancing features. Finally, the game process is continuously executed until the Nash equilibrium is reached. According to simulation results, the sidelobe level caused by the cooperative nodes is reduced and the transmission conflicts are lessened. Thus, the quality of communication links in between nodes in the network is improved. Energy efficiency is also promoted because a balancing of energy consumption is involved in the proposed potential game model, and network lifetime is effectively prolonged accordingly.

Defense against unknown broadband jammer for time‐critical operation in smart grid

In this work, the authors consider the communication network of a power substation, where multiple intelligent electronic devices (IEDs) transmit their delay sensitive control and monitoring messages to a common receiver over a wireless network; in the presence of a broadband jammer, which is capable of jamming multiple channels simultaneously. The objective of the IEDs is to successfully transmit their messages within a specified time, whereas the jammer wants to obstruct IED's transmission. A novel utility function is designed for the players, that addresses the time-critical nature of communication. Due to the conflicting interest of the IEDs and the jammer, they model the interaction between them as a repeated Bayesian zero-sum game, which also addresses the repeated interaction among the IEDs and the jammer, and the unavailability of exact information about the jammer. The equilibrium strategies for both the scenarios of perfect and imperfect monitoring are derived and verified through simulation results. Further, the performance of the proposed game model in various scenarios is thoroughly compared in the result section. Finally, the efficacy of the proposed defence strategies is tested in a practical communication network of a power substation under jamming, which is simulated in Optimised Network Engineering Tool (OPNET).

A Game Theoretic Model for the Multiperiodic Smart Grid Demand Response Problem

Today's smart grid faces many challenges due to the rapid evolution of generation, distribution, and storage means which enable users to produce, store, and sell energy back to the providers. Demand response management plays a key role in achieving the objective of balancing electricity supply and demand efficiently. It also helps leveling consumption during peak hours. To do so, this paper proposes a game theoretic model for the multiperiodic smart grid demand side management problem with shifted demand. The proposed model has two major sets of players: Homeowners and electricity providers. We develop a 0–1 mixed linear programming approach to compute the Nash equilibria of the proposed game. We analyze the structure of the Nash equilibria to maintain the viability of the smart grid infrastructure. We also discuss the order relations between the users and the providers utility coefficients. Finally, we conduct extensive experiments on smart grid demand response synthetic data with different size. The obtained results demonstrate the scalability of the proposed game model.

A Continuous Time Bertrand Duopoly Game With Fractional Delay and Conformable Derivative: Modeling, Discretization Process, Hopf Bifurcation, and Chaos

The purpose of this paper is threefold. First, we present a discretization process to obtain numerical solutions of a conformable fractional-order system with delays. Second, we extend the classical Bertrand duopoly game with integer delays to that with fractional delays. Third, we extend the game based on ordinary differential derivative to that based on conformable fractional-order derivative. Finally, we analyze the local stability, Hopf bifurcation, and chaos of the proposed game model.

A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid

With the rapid growth of distributed energy sources, power grid has become a flexible and complex networked control system. However, it increases the chances of being a denial-of-service attack, which degrades the performance of the power grid, even causing cascading failures. To mitigate negative effects from denial-of-service attack and enhance the reliability of the power grid, we propose a networked control system structure based optimization scheme that is derived from a Stackelberg game model for the frequency regulation of a power grid with distributed energy sources. In the proposed game model, both denial-of-service attacker and control system designer as a defender are considered without using any analytical model. For defenders, we propose a sparse neural network based DES control and system structure design scheme. The neural network is used to approximate the desired control output and reinforce signals for the improvements of short- and long-term performance. It also introduces the sparse regulation of column grouping in the neural network learning process to explore the structure of control system that involves the placement of sensor, distributed energy sources actuator, and communication topology. For denial-of-service attackers, the related attack constraints and attack rewards are established. The solution of game equilibrium is considered as an optimal solution for both denial-of-service attack strategy and control structure. An offline optimization algorithm is proposed to solve the game equilibrium. The effectiveness of proposed scheme is verified by two cases, which illustrate the optimal solutions of both control structure and denial-of-service attack strategy.

Multi‐energy Demand Response Management in Energy Internet: A Stackelberg Game Approach

A multi-energy Demand response management (DRM) approach in Energy Internet is proposed by employing Stackelberg game theory. The multi-energy trading problem in DRM is formulated as a Stackelberg game, where an energy provider, as a leader, adjusts energy prices, and residential smart energy hubs, as followers, dynamically schedule multiple energy flows according to the prices. The existence and uniqueness of the equilibrium of the proposed game model are analyzed. A multi-energy DRM algorithm for reaching the game equilibrium is developed. Simulation results show that the proposed game-based approach can effectively reduce residential energy costs and improve revenues of the energy provider.

An evolutionary game model for analysis of rumor propagation and control in social networks

Nowadays, social networks are widely used as fast and ubiquitous media for sharing information. Rumor as unverified information also considerably spreads in social networks. The study of how rumor spreads and how it can be controlled, plays an important role in reducing social and psychological damages of rumor in social networks. Although recent researches have mainly focused on epidemic models and structure of social networks, they ignore the impact of people’s decision on rumor process. In this paper, an evolutionary game model is proposed to analyze the rumor process in social network considering the impacts of people’s decisions on rumor propagation and control. The model considers a rumor control mechanism via sending anti-rumor messages through rumor control centers. Factors affecting the people’s decisions including social anxiety, people’s attitude toward rumor/anti-rumor, strength of rumor/anti-rumor, influence of rumor control centers, and participation of people in discussions are studied in the model. The proposed game model is analyzed by replicator dynamics equations and simulation of the imitation update rule on a synthetic (Barabasi–Albert) and two real-world graphs of Twitter and Facebook. We further analyze the model in various environments considering people characteristics and society situation. Also we use a real rumor dataset of Twitter (Pheme dataset) to first compare the trends of people strategies (rumor/anti-rumor spreader and ignorant) derived by the model with the real trends of the traits of people in the rumor spreading on Twitter. Then we conduct a number of sensitivity analysis experiments to show the impact of different factors on rumor process. In fact, we analyze the trends of people strategies in Pheme dataset assuming various possible conditions. The analysis show that propagation of convincing anti-rumor messages and locating rumor control centers impact debunking the rumor. Moreover, it is shown that people attitude toward rumor/anti-rumor has significant impact on rumor spreading. Besides, factors such as social anxiety and strength of rumor accelerates rumor propagation.

Resource Allocation in Wireless-Powered Mobile Edge Computing Systems for Internet of Things Applications

Wireless devices in Internet of Things (IoT) applications, such as wireless sensors and Radio Frequency Identifications (RFIDs), are faced with challenges of heavy computation tasks and limited energy, which can be solved by the importation of mobile edge computing (MEC) and wireless power transfer (WPT) techniques. As MEC can effectively enhance computation capability, and the wireless power transfer can ensure a sustainable supply of energy, it has drawn significant research interest in IoT applications. In this paper, we will study the resource allocation problem in the wireless-powered MEC system for IoT applications with one access point (AP) and many other wireless devices, and propose a Stackelberg dynamic game model to obtain the optimal allocated resource for the nodes in the IoT environment. The AP is a wireless power source that can charge wireless devices based on wireless power transfer techniques. The AP is also integrated with a MEC server that can carry out computation tasks that offload from wireless devices. The wireless devices can use the harvested energy to execute and offload computation tasks to the AP. Based on the proposed game model, the AP and wireless devices can control their optimal transmit power for energy transfer, and computation tasks offloading to the AP, respectively. The numerical simulation results show the correctness and effectiveness of the proposed model.