Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain.

Demand response (DR) has been studied widely in the smart grid literature, however, there is still a significant gap in approaches that address security, privacy, and robustness of settlement processes simultaneously. The need for security and robustness emerges as a vital property, as Internet of Things (IoT) devices become part of the smart grid; in the form of smart meters, home energy management systems (HEMSs), intelligent transformers, and so on. In this paper, we use energy blockchain to secure energy transactions among customers and the utility. In addition, we formulate a mixed-strategy stochastic game model to address uncertainties in DR contributions of agents and achieve optimal demand response decisions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. We use a real dataset of residential demand profiles and photovoltaic (PV) generation to validate the performance of the proposed scheme. The results show the impact of electric vehicle (EV) discharging and customer demand reduction on increasing the probability of successful block mining and improving customer profits. Moreover, the results demonstrate the security and robustness of our consensus algorithm for detecting malicious activities.

Performance evaluation of regulatory schemes for retired electric vehicle battery recycling within dual-recycle channels.

As electric vehicles (EVs) are developing at a rapid pace, the foreseeable "scrap tide" of EV batteries poses a severe challenge to ecological protection. This article investigates a dual-recycle channel closed-loop supply chain and provides regulatory solutions to retired EV batteries' recycling. Specifically, we construct four supervision scenarios: S1 no policy intervention, S2 reward-penalty scheme, S3 deposit-refund scheme, and S4 dual scheme combining S2 and S3. Based on the Stackelberg game and empirical data, all scenarios' recycling performance is evaluated and compared with a view to "society, economy, and environment". The results revealed: (1) Compared with S1, the recycling rate and carbon reduction rate in S2∼S4 increase by 2.6049%/0.0092%, 4.0379%/0.0285%, and 6.6660%/0.0379%, respectively; (2) The difference between S2 and S3 in recycling performance depends on regulatory intensities, yet the latter places greater burdens on consumers and firms. The S4 presents optimal environmental performance but at the expense of socioeconomic development; (3) As regulatory intensity increases, social welfare rises driven by environmental benefits, then falls due to overburdened supply chain profits, consumer surplus, and policy expenditures; (4) Carbon trading prices and EVs' potential market sizes affect regulatory schemes' operations. Our results contribute to policy-making and managerial practices for EV battery recycling.

A bi-level model for state and county aquatic invasive species prevention decisions.

Recreational boats are important vectors of spread of aquatic invasive species (AIS) among waterbodies of the United States. To limit AIS spread, state and county agencies fund watercraft inspection and decontamination stations at lake access points. We present a bi-level model for determining how a state planner can efficiently allocate inspection resources to county managers, who independently decide where to locate inspection stations. In our formulation, each county manager determines a set of optimal plans for the locations of inspection stations under various resource constraints. Each plan maximizes inspections of risky boats that may carry AIS from infested to uninfested lakes within the county. Then, the state planner selects the set of county plans (i.e., one plan for each county) that maximizes the number of risky boats inspected throughout the state subject to a statewide resource constraint. We apply the model using information from Minnesota, USA, including the infestation status of 9182 lakes and estimates of annual numbers of boat movements from infested to uninfested lakes. Comparison of solutions of the bi-level model with solutions of a state-level model where a state planner selects lakes for inspection stations statewide shows that when state and county objectives are not aligned, the loss in efficiency at the state-level can be substantial.

Incentive strategies for retired power battery closed-loop supply chain considering corporate social responsibility.

Global environmental concerns and resource scarcity are driving the growth in sales of electric vehicles (EVs). Reusing and recycling retired batteries from EVs has significant economic value and reduces the environmental burden. Rising raw material prices have intensified competition among recyclers; in particular, recyclers without corporate social responsibility (CSR) have been added. These observations lead to a game-theoretical model consisting of three players: a battery manufacturer, a recycler with CSR and a recycler without CSR (non-CSR). The non-CSR recycler enjoys a cost advantage over the CSR recycler, but may not be considered by the consumers with high environmental awareness (CEA). We explore the incentive strategies for CSR recyclers outperform, and how the equilibrium is affected by the recyclers' Stackelberg game. Results show that (1) the deposit- refund is the most profitable strategy for all members and the whole supply chain if raw material price rises high enough; otherwise, a contract strategy should be adopted. (2) Improving CEA and echelon utilization ratio is more conducive to the implementation of revenue-sharing contract. In addition, increasing CEA contributed to CSR recycler collects more retired batteries instead of non-CSR recycler. (3) Stackelberg game between recyclers may hurt supply chain. However, CSR recycler may benefit from the non-CSR recycler-led Stackelberg game. Our work provides the basis of incentive strategies for different participants in the closed-loop supply chain of retired batteries, in particular, to encourage retired batteries flow to CSR recyclers.

Risk mitigation services in cyber insurance: optimal contract design and price structure.

As the cyber insurance market is expanding and cyber insurance policies continue to mature, the potential of including pre-incident and post-incident services into cyber policies is being recognised by insurers and insurance buyers. This work addresses the question of how such services should be priced from the insurer's viewpoint, i.e. under which conditions it is rational for a profit-maximising, risk-neutral or risk-averse insurer to share the costs of providing risk mitigation services. The interaction between insurance buyer and seller is modelled as a Stackelberg game, where both parties use distortion risk measures to model their individual risk aversion. After linking the notions of pre-incident and post-incident services to the concepts of self-protection and self-insurance, we show that when pricing a single contract, the insurer would always shift the full cost of self-protection services to the insured; however, this does not generally hold for the pricing of self-insurance services or when taking a portfolio viewpoint. We illustrate the latter statement using toy examples of risks with dependence mechanisms representative in the cyber context. Supplementary Information: The online version contains supplementary material available at 10.1057/s41288-023-00289-7.

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach.

This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm's feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

Comparative evaluation for recycling waste power batteries with different collection modes based on Stackelberg game.

The new energy vehicle industry is booming, but the subsequent problem of vehicle power batteries' "scrap tide" is still severe. How to establish and improve the end-of-life power battery recycling system to avoid the "catastrophic" environmental consequences has become an urgent global problem needing a solution. This article constructs three recycling models for manufacturer recycling, retailer recycling, and mixed recycling. By using Stackelberg game and market real data, the influence of carbon trading policy outside the supply chain, power battery endurance capacity and advertising effects within the supply chain on the selection of recycling channels was studied. The results showed: (1) Different recycling channels did not affect the wholesale price, retail price, and market demand for raw material power batteries in the positive supply chain; (2) The total profit function of manufacturers and retailers had a "U-shaped" non-linear relationship with power battery endurance capacity and has a positive linear relationship with the advertising effect. Taking the R&D endurance capacity of 0.4 and the total endurance capacity of 62 kWh as the lowest dividing point, it will decrease first and then increase; (3) The increase in the recycling competition coefficient had a greater impact on the consumption of carbon emission rights in the mixed recycling model than on savings in carbon emission rights, and retailers were the indirect "victims" of rising carbon trading prices; (4) Endurance capacity, advertising effects, and carbon trading prices determined the economics of the recycling model and the carbon emission reduction potential. Manufacturers, retailers, and governments can refer to the value range of each variable to select the most appropriate recycling mode.

Investment strategy for blockchain technology in a shipping supply chain.

In the post-COVID-19 pandemic era, how to promote blockchain technology to improve the efficiency of port customs clearance and logistics transparency has become a hot research question in the shipping industry. In this paper, we investigate the value of blockchain-based vertical cooperation led by a port or a shipping company in a one-to-two shipping service competition model. A status quo scenario and two different investment scenarios led by different stakeholders are constructed, and equilibrium solutions of the Stackelberg game in three scenarios are proposed. Meanwhile, consumer surplus and social welfare under different cooperation frameworks are discussed. We find that i) investment in blockchain technology can significantly increase the profits of shipping supply chain participants. ii) From the point of view of profit, when the investment efficiency of the port and the shipping company satisfies a certain relationship, there is a balanced strategy for both parties to invest in blockchain technology. iii) The more intense the competition for the services of shipping companies, the lower the level of blockchain technology to improve the logistics capabilities of the shipping supply chain participants. iv) The port's investment in blockchain technology brings more consumer surplus and social welfare. The abovementioned findings can provide managerial insights for ports and shipping companies and present decision support for the government to formulate blockchain technology promotion policies.

Task Offloading Strategy Based on Mobile Edge Computing in UAV Network.

When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy edge servers near the UAV. The UAV can offload some of the computationally intensive and real-time tasks to edge servers. In this paper, a mobile edge computing offloading strategy based on reinforcement learning is proposed. Firstly, the Stackelberg game model is introduced to model the UAV and edge nodes in the network, and the utility function is used to calculate the maximization of offloading revenue. Secondly, as the problem is a mixed-integer non-linear programming (MINLP) problem, we introduce the multi-agent deep deterministic policy gradient (MADDPG) to solve it. Finally, the effects of the number of UAVs and the summation of computing resources on the total revenue of the UAVs were simulated through simulation experiments. The experimental results show that compared with other algorithms, the algorithm proposed in this paper can more effectively improve the total benefit of UAVs.

Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning.

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

Prevention schemes for future fresh agricultural products (FAPs) supply chain: mathematical model and experience of guaranteeing the supply of FAPs during the COVID-19 pandemic.

BACKGROUND: The COVID-19 outbreak caused short-term disruptions in the supply chain of fresh agricultural products (FAPs), which exposed the vulnerability of the existing FAP supply chain. With pandemic control being widely coordinated, the supply chain of FAPs was gradually optimized and improved. However, after the outbreak of COVID-19, achieving an effective supply of FAPs in future pandemics has become a key issue. The present work therefore aimed to construct a three-level supply chain based on the Stackelberg game model, consisting of suppliers, third-party logistics (TPL), and retailers, to guarantee the supply of FAPs. COVID-19 pandemic factors such as virus infection coefficients and pandemic prevention efforts were fully integrated into the model. RESULTS: Compared with the wholesale prices of FAPs, preservation efforts and pandemic prevention efforts have huge impacts on the retail prices of FAPs. When suppliers are in the leading position, the quality assurance effort level is positively correlated with the optimal profit. Compared with this situation, when FAP retailers are in the leading position, TPL providers show higher levels of pandemic prevention effort and FAP preservation effort. With an increase in consumer preference for pandemic prevention, the profits of supply-chain members when FAP retailers are in the leading position will gradually increase. CONCLUSION: This study reveals an effective supply mechanism for FAPs in metropolitan areas during the COVID-19 pandemic and describes the authors' experience of guaranteeing the quality and safety of FAPs for future pandemic cases. © 2021 Society of Chemical Industry.

Competitive pricing of substitute products under supply disruption.

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.

A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs.

As a key technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been promising to provide safety and infotainment for drivers and passengers. To support different applications about traffic safety, traffic efficiency, autonomous driving and entertainment, it is important to investigate how to effectively deliver content in VANETs. Since it takes resources such as bandwidth and power for base stations (BSs) or roadside units (RSUs) to deliver content, the optimal pricing strategy for BSs and the optimal caching incentive scheme for RSUs need to be studied. In this paper, a framework of content delivery is proposed first, where each moving vehicle can obtain small-volume content files from either the nearest BS or the nearest RSU according to the competition among them. Then, the profit models for both BSs and RSUs are established based on stochastic geometry and point processes theory. Next, a caching incentive scheme for RSUs based on Stackelberg game is proposed, where both competition sides (i.e., BSs and RSUs) can maximize their own profits. Besides, a backward introduction method is introduced to solve the Stackelberg equilibrium. Finally, the simulation results demonstrate that BSs can obtain their own optimal pricing strategy for maximizing the profit as well as RSUs can obtain the optimal caching scheme with the maximum profit during the content delivery.

A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing.

Selection of the optimal users to maximize the quality of the collected sensing data within a certain budget range is a crucial issue that affects the effectiveness of mobile crowdsensing (MCS). The coverage of mobile users (MUs) in a target area is relevant to the accuracy of sensing data. Furthermore, the historical reputation of MUs can reflect their previous behavior. Therefore, this study proposes a coverage and reputation joint constraint incentive mechanism algorithm (CRJC-IMA) based on Stackelberg game theory for MCS. First, the location information and the historical reputation of mobile users are used to select the optimal users, and the information quality requirement will be satisfied consequently. Second, a two-stage Stackelberg game is applied to analyze the sensing level of the mobile users and obtain the optimal incentive mechanism of the server center (SC). The existence of the Nash equilibrium is analyzed and verified on the basis of the optimal response strategy of mobile users. In addition, mobile users will adjust the priority of the tasks in time series to enable the total utility of all their tasks to reach a maximum. Finally, the EM algorithm is used to evaluate the data quality of the task, and the historical reputation of each user will be updated accordingly. Simulation experiments show that the coverage of the CRJC-IMA is higher than that of the CTSIA. The utility of mobile users and SC is higher than that in STD algorithms. Furthermore, the utility of mobile users with the adjusted task priority is greater than that without a priority order.

A Stackelberg Security Game for Adversarial Outbreak Detection in the Internet of Things.

With limited computing resources and a lack of physical lines of defense, the Internet of Things (IoT) has become a focus of cyberattacks. In recent years, outbreak propagation attacks against the IoT have occurred frequently, and these attacks are often strategical. In order to detect the outbreak propagation as soon as possible, t embedded Intrusion Detection Systems (IDSs) are widely deployed in the IoT. This paper tackles the problem of outbreak detection in adversarial environment in the IoT. A dynamic scheduling strategy based on specific IDSs monitoring of IoT devices is proposed to avoid strategic attacks. Firstly, we formulate the interaction between the defender and attacker as a Stackelberg game in which the defender first chooses a set of device nodes to activate, and then the attacker selects one seed (one device node) to spread the worms. This yields an extremely complex bilevel optimization problem. Our approach is to build a modified Column Generation framework for computing the optimal strategy effectively. The optimal response of the defender's problem is expressed as mixed-integer linear programming (MILPs). It is proved that the solution of the defender's optimal response is a NP-hard problem. Moreover, the optimal response of defenders is improved by an approximate algorithm--a greedy algorithm. Finally, the proposed scheme is tested on some randomly generated instances. The experimental results show that the scheme is effective for monitoring optimal scheduling.

Equilibrium strategy based waste load allocation using simulated annealing optimization algorithm.

This study focuses on development of equilibrium strategy based on simulated annealing (SA) algorithm for balancing economic and environmental concerns in waste load allocation (WLA) problem. To resolve conflicts among various stakeholders, including Iran Department of Environment (DoE) as governmental authority and industrial and municipal dischargers, Stackelberg and Nash bargaining games have been applied in this WLA problem and the results have been compared. SA algorithm has been coupled to QUAL2Kw model to derive optimal WLA program and the environmental penalty tariff (EPT) in Nash bargaining and Stackelberg games. The proposed tools and methodologies were illustrated in a case study of multi-stakeholders WLA problem in Gheshlagh River, Sanandaj, Kordestan, Iran. The results indicate that lower BOD removal rates are allocated to the pollutant dischargers in the Stackelberg game compared to the Nash bargaining game. Furthermore, the EPT assigned by Iran DoE in Stackelberg and Nash bargaining games are 11.25 and 3.6 Rials/(gr/month), respectively. The estimated EPT in the Stackelberg game is close to the current tariff (10 Rials/(gr/month)) specified by Iran DoE on impermissible BOD discharges.

Dynamic Defense against Stealth Malware Propagation in Cyber-Physical Systems: A Game-Theoretical Framework.

Stealth malware is a representative tool of advanced persistent threat (APT) attacks, which poses an increased threat to cyber-physical systems (CPS) today. Due to the use of stealthy and evasive techniques, stealth malwares usually render conventional heavy-weight countermeasures inapplicable. Light-weight countermeasures, on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures, which normally is a challenging task. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game with both static version (SSPTI) and multi-stage dynamic version (DSPTI), and safety requirements of CPS are introduced as constraints in the defender's decision model. The attacker aims to stealthily penetrate the CPS at the lowest cost (e.g., time, effort) by selecting optimal network links to spread, while the defender aims to retard the malware epidemic as much as possible. Both games are modeled as bi-level integer programs and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg equilibrium of SSPTI, and design a Model Predictive Control strategy to solve DSPTI approximately by sequentially solving an 1+δ approximation of SSPTI. Extensive experiments have been conducted by comparing proposed algorithms and strategies with existing ones on both static and dynamic performance metrics. The evaluation results demonstrate the efficiency of proposed algorithms and strategies on both simulated and real-case-based CPS networks. Furthermore, the proposed dynamic defense framework shows its advantage of achieving a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.

Sustainable municipal solid waste disposal supply chain analysis under price-sensitive demand: A game theory approach.

The impacts of price and sustainability on municipal waste disposal demand have largely been ignored by waste management researchers. This paper considers a waste disposal supply chain that includes a disposal facility and a contractor. Both parties try to improve source sorting for waste collection to reduce the recycling cost at the disposal facility. Improving source sorting requires investment that would increase the price of the waste disposal service, thereby affecting the price-sensitive demand. The relationship between the service price and investments in waste sorting motives is analyzed in this paper via studying the trade-off between the optimal source sorting and the waste disposal service prices. Different scenarios based on the various players' power structures are developed. Nash and Stackelberg games have been applied in order to find the optimal decision values in each scenario. The impact of cost sharing on optimal supply chain decisions is also studied. The numerical results show that the waste supply chain is more profitable when it is working under an integrated management structure. Moreover, reducing the required investment motivates supply chain players to select higher levels of waste sorting at the source. A numerical example is provided, followed by some managerial insights.

Price-Based Resource Allocation in Wireless Power Transfer-Enabled Massive MIMO Networks.

This paper considers the price-based resource allocation problem for wireless power transfer (WPT)-enabled massive multiple-input multiple-output (MIMO) networks. The power beacon (PB) can transmit energy to the sensor nodes (SNs) by pricing their harvested energy. Then, the SNs transmit their data to the base station (BS) with large scale antennas by the harvesting energy. The interaction between PB and SNs is modeled as a Stackelberg game. The revenue maximization problem of the PB is transformed into the non-convex optimization problem of the transmit power and the harvesting time of the PB by backward induction. Based on the equivalent convex optimization problem, an optimal resource allocation algorithm is proposed to find the optimal price, energy harvesting time, and power allocation for the PB to maximize its revenue. Finally, simulation results show the effectiveness of the proposed algorithm.

Priority-Aware Price-Based Power Control for Co-Located WBANs Using Stackelberg and Bayesian Games.

According to the IEEE 802.15.6 standard, interference within each wireless body area network (WBAN) can be well addressed by the time division multiple access (TDMA)-based media access control (MAC) protocol. However, the inter-WBAN interference will be caused after multiple WBANs are gathered together. This paper proposes a priority-aware price-based power control (PPPC) scheme for mitigating the inter-WBAN interference. Specifically, to maximize the transmission data rate of sensors and control the aggregate interference suffered by coordinators, a Stackelberg game is established, in which the coordinators issue interference prices and the active sensors adjust their transmission power accordingly. On the other hand, since the information about the identities of the active sensors in a specific time slot is kept private, a Bayesian game is designed to model the interaction among sensors. Moreover, the timeliness and reliability of data transmission are guaranteed by designing the sensors' priority factors and setting a priority-related active probability for each sensor. At last, a power control algorithm is designed to obtain optimal strategies of game players. Simulation results show that compared with other existing schemes, the proposed scheme achieves better fairness with a comparable network sum data rate and is more energy efficient.