Bayesian Game Research Articles

Shipper Cooperation in Stochastic Drone Delivery: A Dynamic Bayesian Game Approach

With the recent technological innovation, unmanned aerial vehicles, known as drones, have found numerous applications including package and parcel delivery for shippers. Drone delivery offers benefits over conventional ground-based vehicle delivery in terms of faster speed, lower cost, more environment-friendly, and less manpower needed. However, most of existing studies on drone delivery planning and scheduling focus on a single shipper and ignore uncertainty factors. As such, in this paper, we consider a scenario that multiple shippers can cooperate to minimize their drone delivery cost. We propose the Bayesian Shipper Cooperation in Stochastic Drone Delivery (BCoSDD) framework. The framework is composed of three functions, i.e., package assignment, shipper cooperation formation and cost management. The uncertainties of drone breakdown and misbehavior of cooperative shippers are taken into account by using multistage stochastic programming optimization and dynamic Bayesian coalition formation game. We conduct extensive performance evaluation of the BCoSDD framework by using customer locations from Solomon benchmark suite and a real Singapore logistics industry. As a result, the framework can help the shippers plan and schedule their drone delivery effectively.

SINR-based power schedule of sensors and DoS attackers in wireless network

In this paper, the game theory approaches are used to solve the power schedule problem in the wireless communication network. All sensors can be affected by DoS attackers when transmitting data. The game process between sensors and attackers is established as Bayesian game process. First, a Nash Equilibrium (NE) framework is proposed based on the object function consist of signal-to-interference-and-noise-ratio (SINR). Second, the total power of sensors is considered to have two types, and each type has some power levels. Unlike NE, both sensors and attackers no longer learn the specific total power of each other. However, sensors and attackers can get the type distribution of each other. In this situation, the strategies of sensors and attackers are formulated by introducing the Harsanyi transformation, and the Bayesian Nash Equilibrium (BNE) is solved. Finally, the Bayesian equilibrium strategies used by both offensive and defensive players are compared in the numerical example, which can illustrate the advantage of making full use of incomplete information.

SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience

With the development of edge computing, edge storage solutions are attracting widespread attention. When facing the requirements of lower latency and faster access speed from end devices, edge storage solutions are considered to be an alternative to the cloud. However, edges are usually owned by small organizations which have limited operations and maintenance capabilities. This makes these edge devices can be easily disabled by external attacks or internal hardware failures. Besides, the heterogeneity of the edge devices will also make it difficult to price the edge resources uniformly. To tackle these problems, we propose SmartStore: an auction mechanism based on blockchain to allocate edge resources. Considering centralized solutions have access bottlenecks and trust issues, we built SmartStore on the smart contract. With Bayesian game theory, SmartStore can analyze how data owners (DO) and edges price the resources can maximize their benefits. From an economic perspective, both DO and edges can make full use of edge heterogeneous resources with SmartStore. Besides, a two-stage submission strategy is proposed to complete the sealed auction. Furthermore, considering the reliability of edge storage, we propose a cluster-based block distribution algorithm for SmartStore's intelligent edge recommendation process. SmartStore ensures the reliability of edge storage while maximizing the benefits and resource utilization of both parties. Finally, we conduct specific experiments on the proposed auction smart contract through “Ethereum” and the experimental results of implementation show the effectiveness and efficiency of our SmartStore.

Attack path prediction based on Bayesian game model

The current network risk assessment model often ignores the impact of attack cost and intrusion intention on network security. In order to better solve the problem of information security defense strategy selection and accurately assess the target network risk, this paper proposes an attack path prediction method based on game model.The atomic attack probability is calculated by vulnerability value, attack cost and attack benefit. The static risk assessment model is established combined with Bayesian belief network quantitative attack graph. And the dynamic update model of intrusion intention is used to realize the effective prediction of attack action under rational assumption, which provides the basis for dynamic defense measures of attack surface. The experimental results verify the feasibility and effectiveness of the model and method.

Trading mechanism and pricing strategy of integrated energy systems based on credit rating and Bayesian game

In the integrated energy market, monitoring and managing the irregularities of market members is the key to ensuring efficient and stable of the market. This paper focuses on a multi-energy market in which the energy hub (EH) as a retailer purchases electricity from the main network and sells electricity and heat to energy users. We use a credit rating model based on Fisher discriminant analysis to evaluate the credit of the EH and set up three levels of punishment to punish the irregularities in trading. Furthermore, we establish a Bayesian game to model and analyze price strategy for EH. The cross-price elasticity of demand is a private information and serve as the type of EH. Each EH estimates the others’ prices and chooses the optimal price strategy that maximizes the expected benefits. Finally, real data are adopted to evaluate the proposed model.

Clustering in network games

In many circumstances, behavior and well-being of people depend on the actions taken by their social contacts. Previous research has mainly studied how individuals’ choices are shaped by their number of connections, while the incidence that other features of their social networks have on their behavior has been understudied. This paper analyzes the role of network clustering in Bayesian games of strategic substitutes and strategic complements played on networks, which reflect for instance public good provision and technology adoption, respectively. In our framework, players have incomplete information about the interaction network that includes the number of triads in the network — information labeled as perceived clustering throughout the paper. We show that equilibrium actions are non-decreasing (non-increasing) in perceived clustering under strategic substitutes (complements). Greater perceived clustering thus increases public good provision and reduces the adoption of complementary technologies in the Bayesian equilibria.

Blockchain-Enabled Two-Way Auction Mechanism for Electricity Trading in Internet of Electric Vehicles

As people pay more attention to environment protection, the number of electric vehicles (EVs) is gradually increasing. Energy trading management for EVs is becoming a challenge. However, existing research has not considered the problem of information sharing between energy traders and issues surrounding the protection of user privacy. Therefore, in this article, we propose a vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) electricity trading architecture based on blockchain. All energy transactions of EVs can be recorded on the blockchain ledger to ensure privacy and smart contracts work as agents for pricing and optimal energy allocation. Furthermore, we introduce a two-way auction mechanism based on the Bayesian game and design a new price adjustment strategy. Finally, we propose a bidirectional auction mechanism based on the Bayesian game approach. We use extensive simulations to evaluate the performance of our proposed algorithm. Simulation results show that the social welfare and cost performance of our algorithm can be improved by up to 102.8% and 319%, respectively.

Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry

Network congestion games are a well-understood model of multi-agent strategic interactions. Despite their ubiquitous applications, it is not clear whether it is possible to design information structures to ameliorate the overall experience of the network users. We focus on Bayesian games with atomic players, where network vagaries are modeled via a (random) state of nature which determines the costs incurred by the players. A third-party entity—the sender—can observe the realized state of the network and exploit this additional information to send a signal to each player. A natural question is the following: is it possible for an informed sender to reduce the overall social cost via the strategic provision of information to players who update their beliefs rationally? The paper focuses on the problem of computing optimal ex ante persuasive signaling schemes, showing that symmetry is a crucial property for its solution. Indeed, we show that an optimal ex ante persuasive signaling scheme can be computed in polynomial time when players are symmetric and have affine cost functions. Moreover, the problem becomes NP-hard when players are asymmetric, even in non-Bayesian settings.

Evolution Strategies for Approximate Solution of Bayesian Games

We address the problem of solving complex Bayesian games, characterized by high-dimensional type and action spaces, many (> 2) players, and general-sum payoffs. Our approach applies to symmetric one-shot Bayesian games, with no given analytic structure. We represent agent strategies in parametric form as neural networks, and apply natural evolution strategies (NES) [wierstra2014natural] for deep model optimization. For pure equilibrium computation, we formulate the problem as bi-level optimization, and employ NES in an iterative algorithm to implement both inner-loop best response optimization and outer-loop regret minimization. In simple games including first- and second-price auctions, it is capable of recovering known analytic solutions. For mixed equilibrium computation, we adopt an incremental strategy generation framework, with NES as strategy generator producing a finite sequence of approximate best-response strategies. We then calculate equilibria over this finite strategy set via a model-based optimization process. Both our pure and mixed equilibrium computation methods employ NES to efficiently search for strategies over the functional space, given only black-box simulation access to noisy payoff samples. We experimentally demonstrate the efficacy of all methods on two simultaneous sealed-bid auction games with distinct type distributions, and observe that the solutions exhibit qualitatively different behavior in these two environments.

Bayesian Stackelberg games for cyber-security decision support

A decision support system for cyber-security is here presented. The system aims to select an optimal portfolio of security controls to counteract multi-stage attacks. The system has several components: a preventive optimisation to select controls for an initial defensive portfolio, a learning mechanism to estimate possible ongoing attacks, and an online optimisation selecting an optimal portfolio to counteract ongoing attacks. The system relies on efficient solutions of bi-level optimisations, in particular, the online optimisation is shown to be a Bayesian Stackelberg game solution. The proposed solution is shown to be more efficient than both classical solutions like Harsanyi transformation and more recent efficient solvers. Moreover, the proposed solution provides significant security improvements on mitigating ongoing attacks compared to previous approaches. The novel techniques here introduced rely on recent advances in Mixed-Integer Conic Programming (MICP), strong duality and totally unimodular matrices.

A Fog-based Cyber Security Risk Management System using Bayesian Games

A Fog-based Cyber Security Risk Management System using Bayesian Games

The Family of Alpha,[a,b] Stochastic Orders: Risk vs. Expected Value

In this paper we provide a novel family of stochastic orders that generalizes second order stochastic dominance, which we call the α,[a,b]-concave stochastic orders. These stochastic orders are generated by a novel set of “very” concave functions where α parameterizes the degree of concavity. The α,[a,b]-concave stochastic orders allow us to derive novel comparative statics results for important applications in economics that cannot be derived using previous stochastic orders. In particular, our comparative statics results are useful when an increase in a lottery’s riskiness changes the agent’s optimal action in the opposite direction to an increase in the lottery’s expected value. For this kind of situation, we provide a tool to determine which of these two forces dominates — riskiness or expected value. We apply our results in consumption–savings problems, self-protection problems, and in a Bayesian game.

A class of [formula omitted]-player Colonel Blotto games with multidimensional private information

In this paper, we study N-player Colonel Blotto games with incomplete information about battlefield valuations. Such games arise in job markets, research and development, electoral competition, security analysis, and conflict resolution. For M≥N+1 battlefields, we identify a Bayes–Nash equilibrium in which the resource allocation to a given battlefield is strictly monotone in the valuation of that battlefield. We also explore extensions such as heterogeneous budgets, the case M≤N, full-support type distributions, and network games.

Tripartite Coordinated Regulation of New Energy Vehicles Based on Dynamic Bayesian Game

Manufacturers are disseminating false or ambiguous information regarding new energy vehicles (NEVs), which has led to skepticism from consumers about the quality of NEVs. In this research, we simultaneously considered the relationship among manufacturers, consumers, and governments from the perspective of stakeholders, and then we analyzed the tripartite coordinated regulation. In view of the serious information asymmetry of NEVs, we innovatively developed the Bayesian dynamic game model. By solving refined Bayesian equilibrium strategies, this study explores the effects of key influencing factors on strategic choices. On the basis of the conclusion, relevant countermeasures and suggestions are put forward to engender effective regulation by governments.

Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing

This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as “masters” which offload their computations to edge devices acting as “workers”. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master; ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.

Analysis of offering behavior of generation-side integrated energy aggregator in electricity market:A Bayesian evolutionary approach

Integrated energy systems (IESs) as the widely concerned multi-energy systems have significant contributions to enhancing energy utilization efficiency and renewable energy (RE) consumption. With the increasing proportion of RE, integrated energy aggregators (IEAs), who coordinate IESs with multiple generators, will have more important roles in the future smart grid. This paper presents a Bayesian evolutionary game (BEG) method to study the optimal supply strategy for generating units of different energy types to maximize their own profits in an unregulated power market. The competition among IEAs lowers their willingness to share their information and restricts the profit themselves. Given this information asymmetry, the interaction of three types of generators in IESs is captured by the Bayesian game to transform the incomplete game into a complete game with imperfect information. Given the dynamic of the spot market, this paper combines the Evolutionary game theory with Bayesian theory to study the symbiotic evolution among them. Simulations are introduced to examine the asymptotic stability of various evolutionary stabilization strategies. The results verify the effectiveness of the proposed model. Finally, the implications of different renewable energy penetration, market-clearing rules, market share, and the market supply-demand ratio on IEAs’ offering behavior are explored by applying the experimental economics principle.

Distributed Cooperation Under Uncertainty in Drone-Based Wireless Networks: A Bayesian Coalitional Game

We study the resource sharing problem in a drone-based wireless network by considering a distributed control setting under uncertainty (e.g., due to lack of full information). The drones cooperate in serving the users while pooling their spectrum and energy resources in the absence of prior knowledge about different system characteristics such as the amount of available power at the other drones. Compared to the state-of-the-art research in drone-based wireless networks, which is mainly based on the assumption of accurate global information availability at every drone, our setting is realistic and practical. We cast the efficient resource pooling problem as a Bayesian cooperative game in which the agents (drones) engage in a coalition formation process, where the goal is to maximize the overall transmission rate of the network. The drones update their beliefs by using a novel technique that combines the maximum likelihood estimation with Kullback-Leibler divergence. We propose a decision-making strategy for repeated coalition formation that converges to a stable coalition structure. We analyze the performance of the proposed approach by both theoretical analysis and simulations. We provide the comparison of our scheme with the baseline and the socially optimal solution obtained from the exhaustive search. Simulation results demonstrate the superior performance of the proposed method in terms of the sum-rate of the network, the individual rate of the drones, and convergence properties.

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

The Cognitive radio network (CR) is a widespread technology in which the Secondary users are assumed to be of the winning users to acquire the spectrum by reducing the false alarm possibilities and the false detection of the user assumed to be original user in nature is restricted with the usage of Spectrum monitoring agents. The collaborative spectrum sensing (CSS) is an approach that will identify the false intruder in the CR networks, here it is proposed with the Enhanced Q-Learning model with Coalition Game approach (EQLCG) to outline the energy enhancement. Besides an approach on Greedy Bidding is used to allocate the spectrum to the winning secondary user (SU) based on the idle primary user to strengthen the spectrum sensing. The winning secondary user forms a communication establishment with the neighbouring SU to eradicate the miss detection probability based on group level cooperation. The simulation experiment analyses the cluster level security with energy monitoring that has been performed using the analysis of interference by applying the coalition game theory modelling and the information obscured by the attacker is reduced with the usage of enhanced Q-learning, and the results prove that overhead is substantially monitored. The proposed paper enhances the security in physical layer with energy conservation and maintains the spectrum usage for application purpose. The proposed simulation approach reduces the miss detection and false alarm probabilistic approach while compared with Stackelberg and Bayesian game models.

Decentralized Consensus Decision-Making for Cybersecurity Protection in Multimicrogrid Systems

Multimicrogrid (MMG) systems play an increasingly important role in the smart grid. They come with various potential cyberattacks, which may cause power supply interruption or even human casualties. Therefore, decision-making for timely mitigation of cyberattack risks is highly desirable in the security protection of power systems. However, there is a lack of effective decentralized decision-making strategies that are able to deal with MMG scenarios through distributed consensus. To address this issue, a decentralized consensus decision-making (DCDM) approach is proposed in this article for the security of MMG systems. It achieves decentralized consensus without the need of a trusted authority or central server, making it distinct from existing consensus methods. Meanwhile, it guarantees the consistency and nonrepudiability of consensus results, which are stored on the blockchain in sequence. In each of the distributed agents, the approach consists of a fuzzy static Bayesian game model (FSB-GM) to determine the optimal security strategy and a hybrid consensus algorithm to achieve consensus. The FSB-GM considers the fuzzy preferences of different types of attackers and defenders. The hybrid consensus algorithm is implemented by the fusion improvement of two consensus mechanisms in the blockchain. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.

Bayesian Game Approach to Mitigate DoS Attack in Vehicular Ad-Hoc Networks

Background: Trustful message transmission within the Vehicular ad-hoc Networks is needed as traffic related safety applications needs successful and reliable delivery of messages. The biggest issue is how only the trustworthy parties can be retained and the misbehaved are revoked. From the background analysis, it gives a detailed view of various vulnerabilities in these networks and the techniques used by the researchers to identify and mitigate the attack. Based on the drawbacks observed in the literature the proposed Bayesian Game Mechanism has been designed. Objective: The major objective of this manuscript is to identify the Denial of Service Attack in Vehicular Ad-hoc Networks as it depletes availability of the resources. This attack is identified using Bayesian Game approach. Methods: The Bayesian game approach is used to identify the attack. It analyzes the behavior of vehicles and classifies them into trustworthy or malicious. Results: The simulation is conducted using Network simulator 2.34 and the traffic model is designed using by considering 100 nodes. From the results it is inferred that Packet drop ratio has been improved by 9.82 % and the delay and throughput has been minimized, when the proposed mechanism is used in the presence of attackers. Conclusion: This paper identifies Denial of Service attack as the most vulnerable attack in this network and has designed game theoretic approach namely Bayesian approach for preventing this attack. The proposed method has minimized the delay and packet drop and improved the throughput when compared against the bench mark.