Showing papers on "Stackelberg competition published in 2021"

Distribution Network-Constrained Optimization of Peer-to-Peer Transactive Energy Trading Among Multi-Microgrids

Abstract: This article proposes a two-level network-constrained peer-to-peer (P2P) transactive energy for multi-microgrids (MGs), which guarantees the distribution power network security and allows MGs to trade energy with each other flexibly. At the lower level, a P2P transactive energy is employed for multi-MGs to trade energy with each other. A multi-leader multi-follower (MLMF) Stackelberg game approach is utilized to model the energy trading process among MGs. We prove the existence and the uniqueness of the Stackelberg equilibrium (SE) and provide the closed-form expression for SE. For privacy concerns, we provide several distributed algorithms to obtain SE. At the upper level, the distribution system operator (DSO) reconfigures the distribution network based on the P2P transactive energy trading results by applying the AC optimal power flow considering the distribution network reconfiguration. If there are any network violations, DSO requests trading adjustments at the lower level for network security. We reformulate the DSO operation problem in a mixed-integer second-order cone programming (MISOCP) model and ensure its solution accuracy. Numerical results for a 4-Microgrid system, a modified IEEE 33-bus and 123-bus distribution power system show the effectiveness of the proposed transactive model and its solution technique.

Competitive pricing of substitute products under supply disruption.

Abstract: There has been an increased interest in optimizing pricing and sourcing decisions under supplier competition with supply disruptions. In this paper, we conduct an analytical game-theoretical study to examine the effects of supply capacity disruption timing on pricing decisions for substitute products in a two-supplier one-retailer supply chain setting. We investigate whether the timing of a disruption may significantly impact the optimal pricing strategy of the retailer. We derive the optimal pricing strategy and ordering levels with both disruption timing and product substitution. By exploring both the Nash and Stackelberg games, we find that the order quantity with the disrupted supplier depends on price leadership and it tends to increase when the non-disrupted supplier is the leader. Moreover, the equilibrium market retail prices are higher under higher levels of disruption for the Nash game, compared to the Stackelberg game. We also uncover that the non-disrupted supplier can always charge the highest wholesale price if a disruption occurs before orders are received. This highlights the critical role of order timing. The insights can help operations managers to proper design risk mitigation ordering strategies and re-design the supply contracts in the presence of product substitution under supply disruptions.

Volunteer Assisted Collaborative Offloading and Resource Allocation in Vehicular Edge Computing

Abstract: As a promising new paradigm, Vehicular Edge Computing (VEC) can improve the QoS of vehicular applications by computation offloading. However, with more and more computation-intensive vehicular applications, VEC servers face the challenges of limited resources. In this paper, we study how to effectively and economically utilize the idle resources in volunteer vehicles to handle the overloaded tasks in VEC servers. First, we present a model of volunteer assisted vehicular edge computing, in which the cost and utility functions are defined for requesting vehicles and VEC servers, and volunteer vehicles are encouraged to assist the overloaded VEC servers via obtaining rewards from VEC servers. Then, based on Stackelberg game, we analyze the interactions between requesting vehicles and VEC servers, and find the optimal strategies for them. Furthermore, we prove theoretically that the Stackelberg game between requesting vehicles and VEC servers has a unique Stackelberg equilibrium, and propose a fast searching algorithm based on genetic algorithm to find the best pricing strategy for the VEC server. In addition, to maximize the reward of volunteer vehicles, we propose the volunteer task assignment algorithm for optimal mapping between the tasks and volunteer alliances. Finally, the effectiveness of the proposed scheme is demonstrated through a large number of simulations. Compared with other schemes, the proposed scheme can reduce the offloading cost of vehicles and improve the utility of VEC servers.

Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

Abstract: Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers’ traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human-like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for AVs regarding driving safety, ride comfort and travel efficiency, which are considered in the modeling process of decision making. Then, Nash equilibrium and Stackelberg game theory are applied to the noncooperative decision making. In addition, potential field method and model predictive control (MPC) are combined to deal with the motion prediction and planning for AVs, which provides predicted motion information for the decision-making module. Finally, two typical testing scenarios of lane change, i.e., merging and overtaking, are carried out to evaluate the feasibility and effectiveness of the proposed decision-making framework considering different human-like behaviors. Testing results indicate that both the two game theoretic approaches can provide reasonable human-like decision making for AVs. Compared with the Nash equilibrium approach, under the normal driving style, the cost value of decision making using the Stackelberg game theoretic approach is reduced by over 20%.

A New Service Mechanism for Profit Optimizations of a Cloud Provider and Its Users

Abstract: In this paper, we try to design a service mechanism for profit optimizations of both a cloud provider and its multiple users. We consider the problem from a game theoretic perspective and characterize the relationship between the cloud provider and its multiple users as a Stackelberg game, in which the strategies of all users are subject to that of the cloud provider. The cloud provider tries to select and provision appropriate servers and configure a proper request allocation strategy to reduce energy cost while satisfying its cloud users at the same time. We approximate its servers selection space by adding a controlling parameter and configure an optimal request allocation strategy. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value under the strategy of the cloud provider. We formulate the competitions among all users as a generalized Nash equilibrium problem (GNEP). We solve the problem by employing variational inequality (VI) theory and prove that there exists a generalized Nash equilibrium solution set for the formulated GNEP. Finally, we propose an iterative algorithm (IA), which characterizes the whole process of our proposed service mechanism. We conduct some numerical calculations to verify our theoretical analyses. The experimental results show that our IA algorithm can benefit both of a cloud provider and its multiple users by configuring proper strategies.

FedParking: A Federated Learning Based Parking Space Estimation With Parked Vehicle Assisted Edge Computing

Abstract: As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parking space estimation without exchanging the raw data. Furthermore, we investigate the management of Parked Vehicle assisted Edge Computing (PVEC) by FedParking. In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking. We formulate the interactions among the PLOs and vehicles as a multi-lead multi-follower Stackelberg game. Considering the dynamic arrivals of the vehicles and time-varying parking capacity constraints, we present a multi-agent deep reinforcement learning approach to gradually reach the Stackelberg equilibrium in a distributed yet privacy-preserving manner. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.

Multi-Operator Spectrum Sharing for Massive IoT Coexisting in 5G/B5G Wireless Networks

Abstract: With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.

A Blockchain-Enabled Ecosystem for Distributed Electricity Trading in Smart City

Abstract: Along with the development in the Internet of Things technology and smart city, a distributed network has been formed among cities. This makes it easy to integrate distributed electric energy into the power grid, thus become an efficient way to use energy. However, how to guarantee the security and privacy protection of distributed electricity trading has not been solved effectively. In this article, we propose a blockchain-based electricity trading (B-ET) ecosystem and design a smart contract to ensure transactions are conducted in a safe and reliable manner. To overcome the shortcomings of high latency in traditional Proof-of-Work (PoW) consensus, we proposed a credit-based PoW consensus mechanism by integrating the concept of “stake” to improve the consortium blockchain under the B-ET ecosystem. Then, we take combined cooling, heating, and power (CCHP) system as an example that supplies distributed energy, and model its interactions with the agent of power grid by a novel Stackelberg game. We show that the optimal utilities of entities in a city can be obtained at the Stackelberg equilibrium by a distributed algorithm, which is guaranteed to exist and be unique. In the end, we conduct a number of numerical simulations to evaluate our proposed model and verify our algorithms, which demonstrate their correctness and efficiency completely.

Peer-to-Peer Energy Trading in Smart Grid Through Blockchain: A Double Auction-Based Game Theoretic Approach

Abstract: In a smart grid, each residential unit with renewable energy sources can trade energy with others for profit. Buyers with insufficient energy meet their demand by buying the required energy from other houses with surplus energy. However, they will not be willing to engage in the trade if it is not beneficial. With the aim of improving participants’ profits and reducing the impacts on the grid, we study a peer-to-peer (P2P) energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, the auctioneer controls the game, and the seller does not participate in the game but finally achieves the maximum social welfare. The proposed method not only benefits the participants but also hides their information, such as their bids and asks, for privacy. We further study individual rationality and incentive compatibility properties in the proposed method’s auction process at the game’s unique Stackelberg equilibrium. For practical applicability, we implement our proposed energy trading system using blockchain technology to show the feasibility of real-time P2P trading. Finally, simulation results under different scenarios demonstrate the effectiveness of the proposed method.

An Incentive Mechanism for Privacy-Preserving Crowdsensing via Deep Reinforcement Learning

Abstract: With the rise of the Internet of Things (IoT), the number of mobile devices with sensing and computing capabilities increases dramatically, paving the way toward an emerging paradigm, i.e., crowdsensing that facilitates the interactions between humans and the surrounding physical world. Despite its superiority, particular attention is paid to be able to submit sensing data to the platform wherever possible to avoid leaking the sensitive information of participants and to incentivize them to improve sensing quality. In this article, we propose an incentive mechanism for participants, aiming to protect them from privacy leakage, ensure the availability of sensing data, and maximize the utilities of both platforms and participants by means of distributing different sensing tasks to different participants. More specifically, we formulate the interactions between platforms and participants as a multileader–multifollower Stackelberg game and derive the Stackelberg equilibrium (SE) of the game. Due to the difficulty to obtain the optimal strategy, a reinforcement learning algorithm, i.e., $Q$ -learning is adopted to obtain the optimal sensing contributions of participants. In order to accelerate learning speed and reduce overestimation, a deep learning algorithm combined with $Q$ -learning in a dueling network architecture, i.e., double deep $Q$ network with dueling architecture (DDDQN) is proposed to obtain the optimal payment strategies of platforms. To evaluate the performance of our proposed mechanism, extensive simulations are conducted to show the superiority of our proposed mechanism compared with state-of-the-art approaches.

Resource-Aware Detection and Defense System against Multi-Type Attacks in the Cloud: Repeated Bayesian Stackelberg Game

Abstract: Cloud-based systems are subject to various attack types launched by Virtual Machines (VMs) manipulated by attackers having different goals and skills. The existing detection and defense mechanisms might be suitable for simple attack environments but become ineffective when the system faces advanced attack scenarios wherein simultaneous attacks of different types are involved. This is because these mechanisms overlook the attackers’ strategies in the detection system’s design, ignore the system’s resource constraints, and lack sufficient knowledge about the attackers’ types and abilities. To address these shortcomings, we propose a repeated Bayesian Stackelberg game consisting of the following phases: risk assessment framework that identifies the VMs’ risk levels, live-migration-based defense mechanism that protects services from being successful targets for attackers, machine-learning-based technique that collects malicious data from VMs using honeypots and employs one-class Support Vector Machine to learn the attackers’ types distributions, and resource-aware Bayesian Stackelberg game that provides the hypervisor with the detection load’s optimal distribution over VMs that maximizes the detection of multi-type attacks. Experiments conducted using Amazon’s datacenter and Amazon Web Services honeypot data reveal that our solution maximizes the detection, minimizes the number of attacked services, and runs efficiently compared to the state-of-the-art detection and defense strategies, namely Collabra , probabilistic migration, Stackelberg, maxmin, and fair allocation.

Peer-to-Peer Energy Sharing With Social Attributes: A Stochastic Leader–Follower Game Approach

Abstract: Distributed energy resources bring about challenges related to the participation of an increasing number of prosumers with strong social attributes in peer-to-peer (P2P) energy sharing markets, resulting in the increased complexity of socio-technical systems. Previous research has focused on energy sharing analysis based on rational games without considering the social attributes of prosumers, which are not typically used in real scenarios. In this article, an interdisciplinary P2P energy sharing framework that considers both technical and sociological aspects is proposed. It is based on prospect theory (PT) and stochastic game theory, in which the prosumers work as followers with subjective load strategies, while an energy sharing provider (ESP) serves as the leader with a dynamic pricing scheme. A subjective utility model with risk utility (RU) determined by PT is designed for prosumers, and a profit model for dynamic prices is suggested for ESP. Moreover, a solution algorithm that consists of interpolation and curve fitting to obtain the RU function, the aggregation of prosumers to a Markov decision process, and a differential evolution algorithm to solve the game are proposed to solve the problems of the “curse of dimensionality” and discreteness arising from the social attributes of prosumers. Numerical analysis reveals the results of the Stackelberg equilibrium and demonstrates the effectiveness of this method in terms of the social behavior of prosumers, i.e., radicalness when losing and conservatism when gaining.

Multiagent Actor-Critic Network-Based Incentive Mechanism for Mobile Crowdsensing in Industrial Systems

Abstract: Mobile crowdsensing (MCS) is an appealing sensing paradigm that leverages the sensing capabilities of smart devices and the inherent mobility of device owners to accomplish sensing tasks with the aim of constructing powerful industrial systems. Incentivizing mobile users (MUs) to participate in sensing activities and contribute high-quality data is of paramount importance to the success of MCS services. In this article, we formulate the competitive interactions between a sensing platform (SP) and MUs as a multistage Stackelberg game with the SP as the leader player and the MUs as the followers. Given the unit prices announced by MUs, the SP calculates the quantity of sensing time to purchase from each MU by solving a convex optimization problem. Then, each follower observes the trading records and iteratively adjusts their pricing strategy in a trial-and-error manner based on a multiagent deep reinforcement learning algorithm. Simulation results demonstrate the efficiency of the proposed method.

Operational decisions and financing strategies in a capital-constrained closed-loop supply chain

Abstract: This paper aims to find operational decisions and financing strategies in a closed-loop supply chain (CLSC) consisting of a financially constrained manufacturer and a retailer. Based on a benchmark...

Beam-Domain Anti-Jamming Transmission for Downlink Massive MIMO Systems: A Stackelberg Game Perspective

Abstract: In this paper, beam-domain (BD) anti-jamming transmission in a downlink massive multiple-input multiple-output (MIMO) system is investigated. A smart jammer with multiple antennas attempts to interfere with the signal reception of users with the desired energy efficiency (EE), whereas a base station (BS) tries to minimize the transmission cost while ensuring uninterrupted communication. A Bayesian Stackelberg game between the BS and jammer, where the jammer is the follower and the BS acts as the leader, is modeled. In the follower subgame, the optimal jamming precoding with a closed-form power solution is introduced. The optimal jamming power is proportional to the transmission power in the downlink, and thus, for the BS, the strategy of suppressing malicious attacks by increasing the transmission power fails. In the leader subgame, generalized zero-forcing (ZF), whose closed-form power solution constitutes the unique Stackelberg equilibrium (SE) with that of the jammer, is found to be the optimal anti-jamming precoding for robust transmission. The results show that there always exists a precoding solution for the BS that ensures reliable transmission when the SE is obtained. A proper increase in the minimum signal-to-interference-and-noise ratio (SINR) threshold or the BD channel approximation error helps the BS save power during the resistance against the jammer. Then, a simplified power solution without the instantaneous channel state information (CSI) of jamming channels is further introduced for practical implementation. Numerical results are provided to verify the proposed solutions.

Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks

Abstract: Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association and resource allocation in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose a hierarchical game framework to study the dynamics of edge association and resource allocation in self-organizing HFL networks. In the lower-level game, the edge association strategies of the workers are modelled using an evolutionary game. In the upper-level game, a Stackelberg differential game is adopted in which the model owner decides an optimal reward scheme given the expected bandwidth allocation control strategy of the edge server. Finally, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

Abstract: This article proposes a novel Stackelberg game approach for activating demand response (DR) program in a residential area aiming at addressing the mismatch between the demand and renewable energy generation. In this study, two major players, namely the aggregator as a leader and the consumers as followers, are considered. The aggregator, which owns a wind farm and also receives power from the independent system operator (ISO), strives to obtain the maximum matching between the consumers’ demand and forecasted wind power by incentivizing consumers to adjust their load through offering a bonus to them. On the other hand, consumers change their load profiles for obtaining the highest amount of bonuses. Each consumer has two kinds of loads including critical loads, which must be maintained under any circumstances, and the flexible loads, e.g., heating, ventilation, and air conditioning (HVAC) system, which can be regulated for DR purposes. In order to consider the uncertainty associated with the wind generation and the demands, a scenario-based stochastic programming model has been adopted in this work. Results show the effectiveness of the Stackelberg game model used for interaction between the aggregator and consumers, and the best response that can be served to both of them.

Stochastic Optimal Energy Management and Pricing for Load Serving Entity With Aggregated TCLs of Smart Buildings: A Stackelberg Game Approach

Abstract: With the development of demand-side management in the smart grid, load-serving entity (LSE) plays a more important role for consumers, which purchases energy from the electricity market and sells it to consumers. Moreover, aggregated thermostatically controlled loads (TCLs) in smart buildings can provide additional demand response capacities and require efficient energy management methods. This article proposes a stochastic optimal energy management and pricing model for LSE with aggregated TCLs and energy storage based on the Stackelberg game and stochastic programming. The energy management and pricing problem are formulated as a bilevel optimization model. The upper level model aims to maximize LSE's expected profit under market price uncertainties and determines the offering prices to consumers. According to the offering price from upper level model, the lower level model optimizes the power purchasing pattern for consumers of two types of buildings with TCLs: factory and office buildings. The nonlinear bilevel model is reformulated and converted into mixed-integer linear programming using a strong duality theory. The proposed model is validated by numerical studies based on real market prices from the PJM electricity market. In addition, the impacts of energy storage, number of buildings, comfortable indoor temperature limits, and offering price limits on LSE's profit are analyzed.

Low-carbon supply chain optimization considering warranty period and carbon emission reduction level under cap-and-trade regulation

Abstract: Greenhouse gas emissions have gradually became an important problem hindering the sustainable development of human beings. This paper considers the carbon emissions generated by the manufacturer during the product warranty process. The impacts of cap-and-trade regulation on product warranty policy and carbon emission reduction strategy are studied. According to Stackelberg theory, two supply chain game models are established. In Model I, market demand only depends on the selling price and warranty period of the product. In Model II, the carbon emission reduction level of the product is also considered as a new important factor affecting market demand. The optimal decisions of Model I and Model II in the centralized and decentralized game structure are solved and a revenue-sharing contract is proposed to coordinate the low-carbon supply chain. According to theory analysis, the following conclusions are obtained: (1) Compared with Model I, the key decisions in Model II have better response. (2) Under all game structures, the products in the centralized scheme have higher carbon emission reduction levels and warranty periods. (3) The unit carbon trading price has a negative correlation with the carbon emission reduction level and warranty period of the product. Eventually, through a set of numerical experiments, the validity of the proposed contract is verified and sensitivity analysis on key parameters of the optimal strategies of the two models under different game policies is performed.