Water Resource Pricing Study Based on Water Quality Fuzzy Evaluation: A Case Study of Hefei City

Price plays an important role in water resources management. The price of water resources can also be considered as a “water resource tax” which reflects the value and opportunity cost of water, and people will pay for the right to use water. Currently, the water resource fees’ effect of regulating resource differential revenues is not manifest and it’s not enough to reflect the principle of paid use of resources as well as regulating resources differential revenues. Due to the ambiguity and complexity of water resources price, this paper uses methods relating to fuzzy mathematics for modeling and processing. The study had a comprehensive consideration of five factors including water quality, water resources per capita, household consumption level, per capita GNP, population or population density to evaluate the water resource price.

Dynamic Resource Pricing and Allocation in Multilayer Satellite Network

The goal of delivering high-quality service has spurred research of 6G satellite communication networks.The limited resource-allocation problem has been addressed by next-generation satellite communication networks,especially multilayer networks with multiple low-Earth-orbit(LEO)and nonlow-Earth-orbit(NLEO)satellites.In this study,the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved.The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users.The resource allocation and dynamic pricing problems are combined,and a dynamic gamebased resource pricing and allocation model is proposed to maximize the market advantage of LEO satellites and reduce interference between LEO and NLEO satellites.In the proposed model,the resource price is formulated as the dynamic state of the LEO satellites,using the resource allocation strategy as the control variable.Based on the proposed dynamic game model,an openloop Nash equilibrium is analyzed,and an algorithm is proposed for the resource pricing and allocation problem.Numerical simulations validate the model and algorithm.

A Reverse Auction Mechanism for Time-Varying Multidimensional Resource Allocation in Vehicular Fog Computing with Cloud and Edge Collaboration

It is a hot issue to allocate resources using auction mechanisms in vehicular fog computing(VFC)with cloud and edge collaboration.However,most current research faces the limitation of only considering single type resource allocation,which cannot satisfy the resource requirements of users.In addition,the resource requirements of users are satisfied with a fixed amount of resources during the usage time,which may result in high cost of users and even cause a waste of resources.In fact,the actual resource requirements of users may change with time.Besides,existing allocation algorithms in the VFC of cloud and edge collaboration cannot be directly applied to time-varying multidimensional resource allocation.Therefore,in order to minimize the cost of users,we propose a reverse auction mechanism for the time-varying multidimensional resource allocation problem(TMRAP)in VFC with cloud and edge collaboration based on VFC parking assistance and transform the resource allocation problem into an integer programming(IP)model.And we also design a heuristic resource allocation algorithm to approximate the solution of the model.We apply a dominant-resource-based strategy for resource allocation to improve resource utilization and obtain the lowest cost of users for resource pricing.Furthermore,we prove that the algorithm satisfies individual rationality and truthfulness,and can minimize the cost of users and improve resource utilization through comparison with other similar methods.Above all,we combine VFC smart parking assistance with reverse auction mechanisms to encourage resource providers to offer resources,so that more vehicle users can obtain services at lower prices and relieve traffic pressure.