Showing papers on "Stackelberg competition published in 2023"

A Coordinated Multi-Energy Trading Framework for Strategic Hydrogen Provider in Electricity and Hydrogen Markets

Abstract: This paper proposes a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs). In this framework, the HP can harvest hybrid RESs for green H2 production based on electrochemical effects of biomass electrolysis, and procure stacked profits from both the electricity and H2 markets by the flexibility of electricity-H2 conversion. A Vickrey auction-based pricing mechanism is developed to determine the trading price and quantity of H2 while eliciting truthful offers and bids in a competitive H2 market. Then, a single-leader-multiple-follower Stackelberg game with an iterative solution algorithm is formulated to capture the interactions between the H2 auctioneer and hydrogen fueling stations (HFSs) for achieving the win-win goal. Furthermore, a hybrid-renewable-to-H2 production and control method is proposed for the HP to raise the production efficiency of green H2 and suppress large fluctuations in electrolysis current caused by RES uncertainties. Comparative studies have validated the superiority of the proposed methodology on economic performance and RES accommodation.

AoI Optimization in the UAV-Aided Traffic Monitoring Network Under Attack: A Stackelberg Game Viewpoint

Abstract: Intelligent Vehicle Systems (IVSs) devote to integrating the data sensing, processing, and transmission in the Vehicle to Everything (V2X) scenarios, where the Unnamed Aircraft Vehicle (UAV)-aided traffic monitoring network is one of the most significant applications. Moreover, since the central premise to support the IVS is timely and effectively sensing data processing, Age of Information (AoI) can precisely reflect the timeliness and effectiveness of the communication process in the UAV-aided traffic monitoring network. However, recent researches pay little attention to AoI minimization issue, especially when the malicious attacker attempts to deteriorate the network performance. The accurately modelling of the adversarial relationship between legitimate UAVs and attacker is not fully investigated. To make up this research gap, we start from the Stackelberg game viewpoint to investigate the AoI optimization problem in the UAV-aided traffic monitoring network under attack. Firstly, the system model and three-layer Stackelberg game-based optimization goal are established. Secondly, based on the Backward Induction (BI) analysis, the follower’s data sensing rate, transmission power, and the leader’s attacking power are determined by the Lagrange duality optimization technology successively. Moreover, the sub-gradient update-based optimization technology is used to achieve the Stackelberg Equilibrium (SE). Finally, simulations are performed under various parameters. The evaluation results present better performance of our proposed approach when compared with the typical baselines.

A Dynamic Hierarchical Framework for IoT-Assisted Digital Twin Synchronization in the Metaverse

Abstract: Metaverse, also known as the Internet of 3-D worlds, has recently attracted much attention from both academia and industry. Each virtual subworld, operated by a virtual service provider (VSP), provides a type of virtual service. Digital twins (DTs), namely, digital replicas of physical objects, are key enablers. Generally, a DT belongs to the party that develops it and establishes the communication link between the two worlds. However, in an interoperable metaverse, data-like DTs can be “shared” within the platform. Therefore, one set of DTs can be leveraged by multiple VSPs. As the quality of the shared DTs may not always be satisfying, in this article, we propose an agile solution, i.e., a dynamic hierarchical framework, in which a group of Internet of Things devices in the lower level are incentivized to collectively sense physical objects’ status information and VSPs in the upper level determine synchronization intensities to maximize their payoffs. We adopt an evolutionary game approach to model the devices VSP selections and a simultaneous differential game to model the optimal synchronization intensity control problem. We further extend it as a Stackelberg differential game by considering some VSPs to be first movers. We provide open-loop solutions based on the control theory for both formulations. We theoretically and experimentally show the existence, uniqueness, and stability of the equilibrium to the lower level game and further provide a sensitivity analysis for various system parameters. Experiments show that the proposed dynamic hierarchical game outperforms the baseline.

Distributed Incentives for Intelligent Offloading and Resource Allocation in Digital Twin Driven Smart Industry

Abstract: Mobile edge computing is one of the key enabling technologies of smart industry solutions, providing agile and ubiquitous services for mobile devices (MDs) through offloading latency-critical tasks to edge service providers. However, it is challenging to make optimal decisions of computation offloading and resource allocation while ensuring the privacy and information security of MDs. Consequently, we consider a new vision of digital twin (DT) empowered edge networks, where the optimization problem is formulated as a two-stage incentive mechanism. First, the resource allocation strategy is determined by the interaction among DTs according to the credit-based incentives. Afterward, a distributed incentive mechanism based on the Stackelberg-based alternating direction method of multipliers is opted to obtain the optimal offloading and privacy investment strategies in parallel. Numerical results show that the proposed two-stage incentive mechanism achieves effective resource allocation and computation offloading while simultaneously improving the privacy and information security of MDs.

UAV-Aided Computation Offloading in Mobile-Edge Computing Networks: A Stackelberg Game Approach

Abstract: Unmanned aerial vehicles (UAVs) are considered as a promising method to provide additional computation capability and wide coverage for mobile users (MUs), especially when MUs are not within the communication range of the infrastructure. In this article, a UAV-aided mobile-edge computing (MEC) network, including one UAV-MEC server, one BS-MEC server, and several MUs, is investigated for computation offloading, in which the edge service provider (ESP) manages two kinds of servers. It is considered that MUs have a large number of computation tasks to conduct, while the ESP has idle computational resources. MUs can choose to offload their tasks to the ESP to reduce their pressure and cost, and the ESP can make a profit by selling computational resources. The interaction among the ESP and MUs is modeled as a Stackelberg game, and both the ESP and MUs want to maximize their utility. The proposed game is analyzed by using the backward induction method, and it is proved that a unique Nash equilibrium can be achieved in the game. Then, a gradient-based dynamic iterative search algorithm (GDISA) is proposed to get the approximate optimal solution. Finally, the effectiveness of GDISA is verified by extensive simulations, and the results show that GDISA performs better than other benchmark methods under different scenarios.

Stackelberg evolutionary game theory: how to manage evolving systems

Abstract: Stackelberg evolutionary game (SEG) theory combines classical and evolutionary game theory to frame interactions between a rational leader and evolving followers. In some of these interactions, the leader wants to preserve the evolving system (e.g. fisheries management), while in others, they try to drive the system to extinction (e.g. pest control). Often the worst strategy for the leader is to adopt a constant aggressive strategy (e.g. overfishing in fisheries management or maximum tolerable dose in cancer treatment). Taking into account the ecological dynamics typically leads to better outcomes for the leader and corresponds to the Nash equilibria in game-theoretic terms. However, the leader’s most profitable strategy is to anticipate and steer the eco-evolutionary dynamics, leading to the Stackelberg equilibrium of the game. We show how our results have the potential to help in fields where humans try to bring an evolutionary system into the desired outcome, such as, among others, fisheries management, pest management and cancer treatment. Finally, we discuss limitations and opportunities for applying SEGs to improve the management of evolving biological systems. This article is part of the theme issue ‘Half a century of evolutionary games: a synthesis of theory, application and future directions’.

Optimal Pricing Strategy for Data Center Considering Demand Response and Renewable Energy Source Accommodation

Abstract: With the continuous development of information technology, data centers (DCs) consume significant and ever-growing amounts of electrical energy. Renewable energy sources (RESs) can act as clean solutions to meet this requirement without polluting the environment. Each DC serves numerous users for their data service demands, which are regarded as flexible loads. In this paper, the willingness to pay and time sensitivities of DC users are firstly explored, and the user-side demand response is then devised to improve the overall benefits of DC operation. Then, a Stackelberg game between a DC and its users is proposed. The upper-level model aims to maximize the profit of the DC, in which the time-varying pricing of data services is optimized, and the lower-level model addresses user's optimal decisions for using data services while balancing their time and cost requirements. The original bi-level optimization problem is then transformed into a single-level problem using the Karush-Kuhn-Tucker optimality conditions and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are conducted to demonstrate the feasibility and effectiveness of the proposed method, as well as the effects of the time-varying data service price mechanism on the RES accommodation.

A Stackelberg Game based Optimization Method for Heterogeneous Building Aggregations in Local Energy Markets

Abstract: This paper proposes an optimal operational method and a customized pricing strategy for a Microgrid (MG) incorporating heterogeneous buildings from different communities in a local energy market (LEM). A customized pricing strategy in the LEM for the heterogeneous building aggregations (BAs) is proposed considering their heterogeneous insulation performances. In this regard, a hierarchical framework with two levels for optimal decision-making of the MG as the price maker and BAs as the price takers in the LEM are presented. At the upper level, the MG aims to maximize its profit by optimizing the customized prices and its energy schedules. At the lower level, a detailed physical model of the building with adjustable heating, ventilation, and air conditioning (HVAC) systems is developed while considering the building's thermal dynamics. The objective of each BA in each community is to minimize its operating cost while ensuring the users' thermal comfort according to MG's prices. The Stackelberg game is used to model the interactions between the MG (leader) as the price maker and multiple heterogeneous BAs (followers) as the price takers in the LEM. Then, the proposed optimization model is converted into a mixed integer linear programming using the Karush-Kuhn-Tucker conditions and strong duality theorem. Numerical results show that the hierarchical method benefits both the MG and heterogeneous BAs with customized pricing schemes. The HVAC of the building with good thermal insulation performance can make more adjustments to indoor temperature to reduce its operating costs. Moreover, it has a more significant impact on MG's electricity prices.

Distributed Task Offloading and Resource Purchasing in NOMA-enabled Mobile Edge Computing: Hierarchical Game Theoretical Approaches

Abstract: As the computing resources and the battery capacity of mobile devices are usually limited, it is a feasible solution to offload the computation-intensive tasks generated by mobile devices to edge servers in mobile edge computing (MEC). In this paper, we study the multi-user multi-server task offloading problem in mobile edge computing systems, where all the users compete for the limited communication resources and computing resources. We formulate the offloading problem with the goal of minimizing the cost of the users and maximizing the profits of the edge servers. We propose a hierarchical Economic and Efficient Task Offloading and Resource Purchasing (EETORP) framework that includes a two-stage joint optimization process. Then, we prove that the problem is NP-complete. For the first stage, we formulate the offloading problem as a multi-channel access game (MCA-Game) and prove theoretically the existence of at least one Nash equilibrium strategy in the MCA-Game. Next, we propose a game-based multi-channel access (GMCA) algorithm to obtain the Nash equilibrium strategy and analyze the performance guarantee of the obtained offloading strategy in the worst case. For the second stage, we model the computing resource allocation between the users and edge servers by Stackelberg game theory, and reformulate the problem as a resource pricing and purchasing game (PAP-Game). We prove theoretically the property of incentive compatibility and the existence of Stackelberg equilibrium. A game-based pricing and purchasing (GPAP) algorithm is proposed. Finally, a series of both parameter experiments and comparison experiments are carried out, which validate the convergence and effectiveness of the GMCA and GPAP algorithms.

Toward Secure Data Sharing for the IoT devices with limited resources: A Smart Contract–Based Quality-Driven Incentive Mechanism

Abstract: With the rapid deployment of Internet of Things (IoT) devices in various industries and fields, the massive amount of data produced by these devices can yield greater value through sharing. A critical challenge in the data sharing process is ensuring that the data is high quality. However, the quality of data provided by a large number of IoT devices is impacted by the variability of factors contributing to the data quality (DQ). Effective and safe sharing of perception data by the limited resources of IoT devices is a problem worth investigating. In this article, we propose a smart contract–based and DQ-driven incentive mechanism. First, a smart contract is proposed to realize security in the data-sharing process, while the proposed DQ evaluation mechanism ensures the quality of the shared data. Second, a two-layer Stackelberg game of Nested Coalitional(TLSNC) scheme is designed to obtain the maximum overall social welfare according to the trust score obtained during DQ evaluation while satisfying the limitation of loose and insufficient computing resources. Moreover, we designed a smart contract for automatic execution of the data-sharing transaction and used a trusted execution environment (TEE) to complete the security calculation of shared data. Finally, the numerical results reveal the effectiveness of the DQ evaluation mechanism and the security of our TEE-based model. Based on the proposed scheme, sustainable incentives for user participation and high-quality data sharing can be achieved. In addition, our system can significantly improve the overall social welfare compared to traditional solutions.

The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things

Abstract: The aim is to improve small and medium-sized enterprises (SMEs)' core competitiveness and financing attainability using deep learning (DL) under economic globalization. Accordingly, this work constructs a supply chain symbiosis system based on DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs. Afterward, a structural framework of supply chain financing (SCF) is designed. Further, it verifies the effectiveness of the proposed back propagation neural network (BPNN) credit evaluation model through specific enterprise data. The results show that the proposed internet of things (IoT)-based SCF SMEs-oriented BPNN credit evaluation model reaches a prediction accuracy of 91.4%. It effectively eliminates information asymmetry between banks and various capitals. As a result, banks can guarantee operation funds for the supply chain SMEs and help them minimize project risks by lowering financing leverage and through information transparency.

Low-Carbon Supply Chain Coordination Based on Carbon Tax and Government Subsidy Policy

Abstract: To meet the demands of society’s transition to a low-carbon economy, this study analyzes and designs a coordination contract that is suitable for a low-carbon supply chain, under the circumstances of a carbon tax policy and government subsidies; this is to achieve a reduction in emissions and a growth in the total profits of the supply chain, while simultaneously improving the sustainable competitiveness and coordination of the supply chain. Manufacturers and retailers make up the two levels of the supply chain that are the focus of this study. Both centralized and decentralized decision-making models are created using the Stackelberg game method. By analyzing the supply chain decision-making and emission-reduction strategies in both cases, the revenue-sharing contract is designed to achieve the sustainable coordination of the y chain. The results of the numerical analysis show the following: first, that more orders are placed and emissions are reduced under centralized decision-making than under decentralized decision-making; second, that the total supply chain’s profits are higher when all parties comply with the revenue-sharing contract than when there are no contracts; third, that the revenue-sharing contract allows for the free allocation of supply chain gross margins in the enterprise for supply chain coordination.

An Efficient Load-Balancing Scheme for UAVs in 5G Infrastructure

Abstract: Deploying caches at the macro base station (MBS), unmanned aerial vehicle (UAV), and mobile user caches can effectively reduce the retransmission of duplicate content in the 5G cellular wireless hotspot network. As the storage capacity of MBS is much higher than UAVs and other hotspot cache nodes, the MBS advertises its vacant storage space so that the participating nodes can rent it. In this article, we proposed an efficient load-balancing scheme by using the Stackelberg equilibrium game model. The proposed scheme sets a unit price ( $\xi$ ) based on constraints to avoid data traffic uncertainty caused by participation nodes and rent vacant space of MBS. Furthermore, we proposed an efficient scheme for the placement and delivery of hotspot content by using Knapsack and Zipf. Moreover, ensuring the device-to-device link support also minimizes transportation costs. The results validate that considering the above-mentioned techniques significantly improves the overall hotspot network performance.

Operator-as-a-Consumer: A Novel Energy Storage Sharing Approach Under Demand Charge

Abstract: Energy storage systems (ESSs)-based demand response (DR) is an appealing way to save electricity bills for consumers under demand charge and time-of-use (TOU) price. In order to counteract the high investment cost of ESS, a novel operator-enabled ESS sharing scheme, namely, the "operator-as-a-consumer (OaaC)," is proposed and investigated in this article. In this scheme, the users and the operator form a Stackelberg game. The users send ESS orders to the operator and apply their own ESS dispatching strategies for their own purposes. Meanwhile, the operator maximizes its profit through optimal ESS sizing and scheduling, as well as pricing for the users' ESS orders. The feasibility and economic performance of OaaC are further analyzed by solving a bilevel joint optimization problem of ESS pricing, sizing, and scheduling. To make the analysis tractable, the bilevel model is first transformed into its single-level mathematical program with equilibrium constraints (MPEC) formulation and is then linearized into a mixed-integer linear programming (MILP) problem using multiple linearization methods. Case studies with actual data are utilized to demonstrate the profitability for the operator and simultaneously the ability of bill saving for the users under the proposed OaaC scheme.

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

Abstract: The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in rendering tasks. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the baseline schemes, the proposed scheme can improve the utility of metaverse service provider and average profit of CDC workers.

The Impact of Social Preferences on Supply Chain Performance: An Application of the Game Theory Model

Abstract: Traditional supply chain literature on contracting only considers agents’ economic motivation. Nowadays, with the development of behavioral economics, social preference theory has been widely used in supply chain research. These social preferences are distinct from economic motivation and will influence agents’ behaviors in the supply chain. Agents will make decisions based on not only self-interests but also the interests of others, reciprocity, and fairness. This paper introduces the relationship and status preferences in the utility function. We aim to analyze the impact of social preference on individual competition intensity in the supply chain. A Stackelberg game model (tacit collusion) is used as the theoretical framework of the choice behavior between competition and cooperation. The theoretical results and numerical simulation analysis show that under some conditions, suppliers and retailers who take the social preference factors into account can realize multiple-stage channel coordination through revenue sharing. Moreover, social preference factors will influence the choice behavior of agents in competition and cooperation. Specifically, the relationship preference promotes close cooperation among enterprises and significantly improves the supply chain and individual performance. Status preference causes fierce competition among enterprises and adversely affects supply chain performance and individual performance, making it more unstable. These findings can provide useful insights for supply chain coordination.

Investment of Green Technology in a Tourism Supply Chain Under Competition

Abstract: This article considers a green tourism supply chain (GTSC) composed of one scenic spot (SS) and two travel agencies (TAs) and derives the optimal green technology investments of the SS and TAs in the noncooperation, unilateral cooperation, and bilateral cooperation scenarios by establishing Stackelberg differential games. In addition, we investigate how the green tourism preference of tourists and the competition intensities of the TAs affect the green technology investments of the GTSC members and the greenness levels of tourism products. Our study indicates that the green technology investments of the GTSC members and the greenness levels of tourism products increase with the increasing of tourists’ green tourism preference. We also find that the green technology investments of the TAs and the greenness levels of tourism products change along with the competition intensity of the TAs, but the green technology investment of the SS remains unchanged. Meanwhile, we propose a bilateral cooperation policy that can achieve Pareto improvement of the GTSC performance.

Efficient Task Allocation for Computation Offloading in Vehicular Edge Computing

Abstract: In vehicular edge computing, the edge servers may be overloaded once too many vehicles request the task offloading service, which will cause task offloading failure or the high service delay. To provide high-quality VEC service, the processing capabilities of the vehicles, the edge servers, and the cloud should be utilized simultaneously in task offloading. In this article, we focus on task offloading allocation for the requesting vehicle and the pricing schemes for the edge server and the cloud. From a market perspective, we model the competition and cooperation among the requesting vehicle, the edge server, and the cloud as a Stackelberg game. Then, based on the backward induction method, we transform the game problem into a convex optimization problem and theoretically prove that the game has a unique Nash equilibrium, thereby the optimal task allocation for the requesting vehicle can be obtained. Meanwhile, a genetic algorithm-based searching algorithm is proposed to find the optimal pricing schemes for the edge server and the cloud, and the proposed algorithm has a rapid convergence due to the convex feature of the objective problem. Simulation results demonstrate that the proposed task allocation strategy has better performance than other solutions in terms of task offloading delay and cost, thus, can make the existing resources fully used to undertake more offloading tasks.

Pricing and Budget Allocation for IoT Blockchain with Edge Computing

Abstract: Attracted by the inherent security and privacy protection of the blockchain, incorporating blockchain into Internet of Things (IoT) has been widely studied in these years. However, the mining process requires high computational power, which prevents IoT devices from directly participating in blockchain construction. For this reason, edge computing service is introduced to help build the IoT blockchain, where IoT devices could purchase computational resources from the edge servers. In this paper, we consider the case that IoT devices also have other tasks that need the help of edge servers, such as data analysis and data storage. In this scenario, IoT devices will allocate their limited budgets to purchase different resources from different edge servers, such that their profits can be maximized. Moreover, edge servers will set "best" prices such that they can get the biggest benefits. We model the interaction between edge servers and IoT devices as a multi-leader multi-follower Stackelberg game, whose objective is to reach the Stackelberg Equilibrium (SE). We prove the existence and uniqueness of the SE point, and design efficient algorithms to reach the SE point. In the end, we verify our model and algorithms by performing extensive simulations.

Logistics Service Supply Chain Vertical Integration Decisions under Service Efficiency Competition

Abstract: In the logistics sector, price competition is no longer the only form of horizontal competition between logistics service integrators; instead, it frequently takes the form of service efficiency competition among chains. Facing fierce market competition, vertical resource integration gradually becomes the trend in logistics industry integration. Using the inverse derivation method and comparative analysis, this study examines the relationship between the overall profit of its chain and that of the rival chain under service efficiency competition with or without the integration strategy. Furthermore, it builds two parallel competition logistics service supply chain models based on the inter-chain Nash competition and Stackelberg game of the chain members. The study results demonstrate that when the cost per unit of service efficiency is fixed, the greater the intensity of competition between chains, the more managers should tend to choose an integration strategy to maximize their profits. More interestingly, we find that the optimal integration decision of the supply chain is independent of the competitive intensity when the cost required to improve the unit service efficiency is extremely high.