Research on pricing game strategy for load-balancing in VANET

In vehicle Ad-hoc netwok （VANET）, traffic load is often unevenly distributed among access points （APs）. Such load imbalance hampers the network from fully utilizing the network capacity. To alleviate such imbalance, the paper introduces a novel pricing game model. The research scene is at the intersection when the traffic light is green. As vehicles are highly mobile and the network typology changes dynamically, the paper divides the green light time into equal slots and calculates APs＇ prices with the presented pricing game in each time slot. The whole process is a repeated game model. The final equilibrium solution set is APs＇ pricing strategy, and the paper claim that this equilibrium solution set can affect vehicles＇ selection and ensure APs＇ load-balancing. Simulation results based on a realistic vehicular traffic model demonstrate the effectiveness of the game method.

A Fair and Trusted Trading Scheme for Medical Data Based on Smart Contracts

Data is regarded as a valuable asset,and sharing data is a prerequisite for fully exploiting the value of data.However,the current medical data sharing scheme lacks a fair incentive mechanism,and the authenticity of data cannot be guaranteed,resulting in low enthusiasm of participants.A fair and trusted medical data trading scheme based on smart contracts is proposed,which aims to encourage participants to be honest and improve their enthusiasm for participation.The scheme uses zero-knowledge range proof for trusted verification,verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction,and realizes privacy protection.At the same time,the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price.The smart contract is used to complete the verification and game bargaining process,and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial.Finally,by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations,it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment.The experimental results show that the model improves the credibility and fairness of medical data transactions,maximizes social benefits,encourages more patients and medical institutions to participate in the circulation of medical data,and more fully taps the potential value of medical data.

Game-theoretical Models of a Two-echelon Supply Chain Involving Two Substitutable Products

This paper considers tripartite pricing issues in a two-echelon supply chain involving duopolistic manufacturers and a single retailer.Firstly,a tripartite competitive model is conducted,in which both a Stackelberg game and a Bertrand game occur simultaneously.It is shown that the manufacturer who possesses a higher sales quantity gains more profits than the other one.Secondly,a definition of optimal vertical pricing alliance is proposed when cooperation exists between the retailer and some manufacturer.We conduct two-player games when partial cooperation exists among the three participants.It is demonstrated that the total profit of the alliance is higher than the sum profit of the corresponding two participants in the tripartite competition model,and meanwhile the profit of the manufacturer who is not in the alliance suffers a loss.Further,a criterion is given to judge which manufacturer the retailer will choose to cooperate in order to maximize the total increased profits.From the perspective of game theory,we examine the stability of the vertical alliance with considering the dominance of the retailer.Finally,a numerical illustration is designed to examine the judging criteria of which manufacturer is the member of the optimal alliance under different potential market demands.

Price Competition in the Random Coefficient Attraction Choice Models with Linear Cost

We study the pricing game between competing retailers under various random coefficient attraction choice models.We characterize existence conditions and structure properties of the equilibrium.Moreover,we explore how the randomness and cost parameters affect the equilibrium prices and profits under multinomial logit(MNL),multiplicative competitive interaction(MCI)and linear attraction choice models.Specifically,with bounded randomness,for the MCI and linear attraction models,the randomness always reduces the retailer’s profit.However,for the MNL model,the effect of randomness depends on the product’s value gap.For high-end products(i.e.,whose value gap is higher than a threshold),the randomness reduces the equilibrium profit,and vice versa.The results suggest high-end retailers in MNL markets exert more effort in disclosing their exact product performance to consumers.We also reveal the effects of randomness on retailers’pricing decisions.These results help retailers in making product performance disclosure and pricing decisions.