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In peer-to-peer (P2P) energy trading, members locally trade energy. Blockchain-based systems are employed for the above trading. These systems are limited in speed because of the time required in the consensus process to audit and verify transactions. Further energy is traded using auction, which also consumes time. In this paper, we propose a blockchain and machine learning-based system to speed up trading. This system uses Byzantine Fault Tolerance method to reduce consensus time and machine learning to reduce auction time. In addition, the proposed system provides loans to cash-deficient buyers thereby enabling them to purchase energy. Machine learning is used to arrive at optimal loan rates for buyers. All these claims have been verified by the simulation results. Simulation results show that the new mechanism finds the optimal value of the trader's quotes prices quickly by altering the value of the profit margin, and the new loan system increases the number of transactions in this blockchain-based system. Hence, the proposed system will promote P2P trading by making it faster and enabling greater participation of cash-deficient buyers in the trading. [ABSTRACT FROM AUTHOR]

Blockchain, a promising technology, has been extensively applied in numerous fields, such as network security, finance, and medical care. However, due to the low power consumption and weak computing power of the mobile environment, the application of blockchain in this environment still faces many challenges. Therefore, edge computing has been introduced to improve the computing power of mobile devices and encourage more mobile edge devices to join the blockchain network. In this paper, we propose a double auction model to address the issue of edge computing resource allocation in blockchain networks. Based on this auction model, we first propose a truthful double auction mechanism based on breakeven (TDAMB) to determine matched pairs of edge computing service providers (ECSPs) and miners. Furthermore, to improve the system efficiency, we propose a double auction mechanism based on a critical value (DAMCV). We also theoretically analyze the individual rationality, budget balance and truthfulness of the proposed mechanisms. Extensive experiments show that TDAMB and DAMCV have good effects on edge computing resource allocation in blockchain networks. [ABSTRACT FROM AUTHOR]

We investigate effects of tax reporting institutions on evasion and incidence using an experimental double auction market. We find that 28% of the sellers are truthful when only sellers report, but that 88% and 64% of them are truthful under costless and costly third-party reporting by buyers, respectively. Reporting behavior therefore responds to the intensity of deterrence. However, we find that prices do not fully reflect the lower taxes of the evaders. Thus, when sellers can unilaterally evade taxes, tax incidence deviates from the prediction of the standard model, and there is deadweight loss even if tax revenue is low. Pricing, incidence, and reporting patterns in all treatments are consistent with a model with heterogeneous lying costs, or a combination of lying costs and image concerns that give rise to a motivation to appear honest. [ABSTRACT FROM AUTHOR]

External effects can be triggered through trade and depend on the locations of buyers and sellers who are matched. Inspired by electricity markets, we experimentally investigate markets in which net trades between two locations induce social costs. Based on a modified double auction setting, we compare the performance of market platforms that are location-blind with those where information on the location of (potential) trading partners or the level of the externality is given. We demonstrate that locational information is sufficient to reduce the externality. Imposing the full external costs on individual trades leads to maximal price differentiation between locations and further reduces net trades, while welfare improvements are limited. Reasons for not achieving the typically high efficiency of double auctions are discussed. [ABSTRACT FROM AUTHOR]

This study aimed to improve the truthfulness and efficiency of double auction mechanism in the cloud computing environment. In an auction, bidders have a high incentive to announce an unrealistic price, that is, each cloud user tends to announce a lower price and each cloud service provider tends to bid higher price than the actual one. This untruthful behavior will have catastrophic consequences to the market, leading to participants' fear of being misused. Therefore, it seems vital to provide a mechanism for the realization of truthfulness involving no unrealistic offer by the users. On the other hand, it has been proven that it is not possible to achieve the characteristics of truthfulness, budget balance and economic efficiency at the same time. There is a weak budget balance in a truthful mechanism with a price difference greater than zero between the total price paid by the cloud users and that received by the cloud service providers. In this paper, a mechanism is proposed to encourage participants to offer truthful prices by rewarding honest participants. The proposed called the reward mechanism uses a repository to store bidders' information, including the number of bids and the number of honest bids per auction as well as a ranked list of bidders based on their bidding history in offering honest prices. The results show that the proposed mechanism not only motivates the bidders to announce honest bids, but also solve the problem of weak budget balance by dividing the bid amount among the winning bidders. [ABSTRACT FROM AUTHOR]

The paper investigates two-player double-auction bargaining with private values in a setting with discrete two-point overlapping distributions of traders' valuations. We characterize parameter settings in which there exists a fully efficient equilibrium, and show that if there are traders that behave naively, i.e., set bid or ask equal to their valuation, then there is no equilibrium achieving full efficiency. We conduct an experiment to test the theoretical possibility that the presence of naive traders can reduce efficiency. We find, however, that efficiency is not lower in the presence of naive traders. Subjects mostly set bid/ask prices strategically but they do not coordinate on a fully efficient equilibrium and the extent of strategic behavior is not different in the presence of naive players. We show that a learning model of noisy strategy adjustment explains the observed behavior better than other (equilibrium or non-equilibrium) models. [ABSTRACT FROM AUTHOR]

In order to avoid the occurrence of transaction failure of allocated parking spaces due to users􀆳 breach of trust or default in the process of shared parking, and aim to improve the utilization rate of parking spaces, a berth allocation model considering the user􀆳s credit was proposed. Firstly, combined with the existing credit evaluation model, an allocation model considering the credit worthiness of users was built. Then, based on price and time constraints, a bidding mechanism was established for shared berth participants (owner, user, platform) based on the double auction model to determine the final transaction price. In addition, the total user utility value was calculated, the optimal allocation sequence based on the utility value was obtained, and the berth requests submitted by the user are allocated orderly according to the sequence. Finally, the proposed model was compared with the traditional auction model and first-come-first-stop through MATLAB software simulation. The results show that the proposed model is on the first-come, first-stop scheme and the traditional auction model, with revenue growth rates increasing by 2. 15% and 32. 07%, respectively, compared to the traditional auction model. The average utilization rate of berths increases by 0. 93% and 29. 99%, respectively. The research results can effectively circumvent the user default phenomenon and provide an effective way to optimize the existing berth allocation model. [ABSTRACT FROM AUTHOR]

The development of local electricity markets (LEMs) and energy communities is accelerating the shift from consumerism to prosumerism. However, there is no concrete understanding of how electricity sharing in LEMs should be organized, a local wholesale market within or centralized sharing? This paper explores trading algorithms that can represent a competitive market and bidding conditions within a LEM. That is, how well trading algorithms can represent the wholesale market of an energy community?; What is a fair LEM reference price to create bidding simulations? How do the system characteristics affect the outcome of the trading algorithms? We address these questions by analyzing a community (residential buildings) in Steinkjer (Norway) and London (UK), including PV systems and wind turbines. We first determine bids and offers based on different bidding simulations and develop a market reference price. Afterward, we applied the trading algorithms Peer-to-Peer (P2P) and Multi-unit-Double-Auction (MUDA) for local electricity trading. We compared the results in selected KPIs such as self-sufficiency, traded energy, and curtailment. We find that P2P provides a more economically efficient trading algorithm than MUDA as it generally enables more trading and thus lowers grid imports. However, there are concerns that P2P brings disadvantages such as unfair trading. • Collaborative (energy community) and competitive (Peer-to-peer and Vickrey-MUDA) local electricity markets. • Understanding how a market algorithm will determine local prices in the UK and Norway. • Local market results based on different bidding simulations that reflect participants' behavior. • Developed a reference price index tailored for Local Electricity Markets. • P2P gives better results in terms of trading efficiency compared to MUDA. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading, whereby customers can trade energy with one another rather than the energy supplier only, has the potential to save money for consumers whilst also incentivising more efficient and environmentally beneficial behaviour. Many existing models for P2P only consider a real-time or hour-ahead market, which does not allow proper scope for the planning of flexible demand or for energy storage. Accordingly, in this model we employ a day-ahead continuous double auction (CDA), in which all the upcoming timeslots are simultaneously open for trading. This allows schedules for device dispatch to be developed properly. We consider the flexibility and interdependence of bidding across different timeslots and develop strategies to address this. Furthermore, we consider the trade of heat as well as power, via a low temperature heat network. Heat and power trading interact due to the use of air source heat pumps (ASHPs) as well as reversible solid oxide cells (rSOCs), which can provide combined heat and power, or alternatively produce hydrogen via water electrolysis. In our case study, the P2P market is simulated with 25 houses participating, for two week-long periods in different climate conditions. P2P electricity trading is found to bring a marked reduction in reliance on grid electricity, and a reduction in peak grid load. This is brought about mainly by the incentive for rSOCs to generate at a higher average load factor, and the average house makes savings of ca. £10 / week in winter weather. Heat trading brings a further decrease in reliance on grid electricity, and largely eliminates the use of inefficient resistive heat. However, the heat trading may not be financially worthwhile in all conditions. • Simulation of peer-to-peer (P2P) energy trading via a continuous double auction • Heat and power both considered, with fuel cells and heat pumps coupling these • Auction format allows flexible devices to plan schedules in advance • With electricity trading, higher load factor for fuel cells, cuts reliance on grid • Heat trading brings extra technical benefits though cost savings are not certain [ABSTRACT FROM AUTHOR]

The decentralization of power generation driven by the rise in the adoption of distributed energy resources paves the way for a new paradigm in grid operations. P2P energy trading is beneficial to the grid as well as the connected peers. A blockchain-based smart contract is well suited to transparently facilitate trades between energy consumers and producers without the services of intermediaries. In this paper, Ethereum-based smart contracts that facilitate double energy auction and spinning reserve trading are developed with Solidity, compiled, and deployed within the Remix IDE. Willing energy sellers/buyers submit offers/bids to a contract that implements the double auction procedure. In order to fulfil energy supply obligations, sellers are also able to purchase spinning reserves via another smart contract. The smart contracts' effectiveness in performing the auction procedure and making payments is confirmed using an energy/reserve market scenario. The proposed scheme encourages further adoption of distributed energy resources and participation in local P2P energy trading. [ABSTRACT FROM AUTHOR]

• We design double auction mechanisms for online freight platforms. • We propose two mechanisms with transaction costs and asymmetric demand information. • We show that the mechanisms have good properties. • Numerical studies show advantages of mechanisms over two commonly used mechanisms. In recent years, many online freight platforms have appeared to improve transport market efficiency. An important problem faced by such platforms is how to elicit the agents' information and to match their supplies and demands efficiently. To address the problem, we propose the modified Multi-unit Trading Reduction and Vickery‒Clarke‒Groves (MMTR-MVCG) mechanism for considering heterogeneous transaction costs. Under this mechanism, the shippers and carriers first decide their bids, and then the platform determines the trading agents, sets the transaction prices, and matches the shippers' demands and the carriers' supplies. We show that this mechanism is incentive-compatible, individually rational, budget-balanced, and asymptotically efficient. For the special case with homogeneous transaction costs, we first show that the MMTR-MVCG mechanism can be simplified. In addition, we design a new double auction mechanism for the case with asymmetric demand information and homogeneous transaction costs and show that this mechanism also is incentive- compatible, individually rational, budget-balanced, and asymptotically efficient. Moreover, we conduct numerical studies to assess the impacts of transaction costs and information asymmetry on the mechanisms and compare the performance of our mechanisms with those of two commonly used mechanisms. Our mechanisms can yield greater social welfare, more trading agents, and larger trading volumes than the other two mechanisms. We also discuss the impact of the subsidy strategy. [ABSTRACT FROM AUTHOR]

With the rapid advancement of distributed energy resources (DERs), artificial intelligence, and smart meter technologies, traditional consumers are undergoing a paradigm shift towards 'prosumers'. In this context, peer-to-peer (P2P) energy trading emerges as an effective approach to enhance local energy utilization. Nevertheless, the inherent intermittency and forecasting challenges associated with renewable energy resources may magnify uncertainties in the markets, and pose a potential threat to destabilize the markets. To address this challenge, this paper presents a method to assess the economic impacts of forecasting errors and introduces a metric, the bill deviation index. Additionally, the consequences of forecasting errors on market outcomes are examined based on the mathematical model of three different pricing mechanisms. Our findings indicate that forecasting errors can lead to significant financial discrepancies, the magnitude of which is closely related to the pricing mechanisms and their dependency on energy quantity. The paper further underscores the role of variability in clearing price, balancing cost, and the supply–demand relationship in determining the economic fallout of forecasting errors. It concludes by providing insights for managing energy trading in markets marked by high forecasting errors and suggests strategies to mitigate the associated economic risks. • An economic impact assessment method considering pricing calculation principles is proposed. • The influence of forecasting errors on market outcomes is analyzed through three pricing mechanisms. • A P2P energy trading model is developed to evaluate crucial variables under various market conditions. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) renewable energy trading, facilitated by designing P2P market smart contracts on blockchain servers, is a promising approach to increase investments in cleaner energy generation. To enhance trading efficiency and pricing fairness, as the major challenges of P2P market designs, this study introduces two new market mechanisms, Hybrid Auction Coalition (HAC), and innovative coalition business model (ICBM), respectively. The performance of these market mechanisms is contrasted with existing mechanisms from the literature with respect to electricity bills, market efficiency, fairness, and blockchain feasibility using a comprehensive list of indicators including costs and profits, fairness, market efficiency, and technical viability. Compared to traditional billing, ICBM and HAC increase sellers' profit by 88% and 66% respectively, while both impose 13% more costs on buyers. ICBM and HAC also set the fairest prices compared to the existing markets. ICBM is shown to improve blockchain feasibility and market efficiency due to lighter on-chain calculations, and absolute clearing mechanisms. The results also demonstrate that HAC outperforms standalone auctions in every aspect which endorses the hybridization benefits. • Novel and efficient P2P Models, and blockchain-based mechanisms for P2P renewable energy trading. • Enhanced fairness concerns in P2P trading through a novel pricing mechanism. • Improved P2P trading efficiency & potentially increased local grid self-sufficiency. • Technical comparison of state-of-the-art blockchain-based P2P trading markets. [ABSTRACT FROM AUTHOR]

This paper presents a new prosumer-centric peer-to-peer (P2P) energy trading framework incorporating the network-secure export–import limits, known as dynamic operating envelope (DOE), for prosumers considering their preferences to trade in the local market. In the proposed framework, a DOE-integrated energy management algorithm is developed for prosumers considering electric vehicles (EVs) charge–discharge and photovoltaic (PV) systems. The framework allows prosumers to operate within defined DOE, aligning with the preferences of EV-owning prosumers by managing the desired EV battery charge levels at their departure. An auction-based P2P energy trading mechanism for prosumers is proposed incorporating the allocated DOE to ensure secure network operation and valuing prosumers' preferences to benefit them. The proposed framework is implemented on a 78-node low-voltage (LV) residential distribution network to evaluate its performance. The results and comparative analyses verify the effectiveness of the proposed framework in addressing network issues such as voltage and line congestion problems, meeting prosumers' preferences in asset management, and providing benefits to all participants in the energy trading process. • A prosumer-centric P2P energy trading using network-secure export–import limits. • Hybrid DER marketplace model-based framework consisting of the DSO and the DMO. • Energy management within the limits considering EVs charge–discharge and PV systems. • DOE-integrated Double auction-based market mechanism for P2P energy. • Fairness in allocating DOEs with high RPI, PIEI, QoS, QoE and MiM values. [ABSTRACT FROM AUTHOR]

The development of smart vehicles and rich cloud services have led to the emergence of vehicular edge computing. To perform the distributed computation tasks efficiently, Coded Distributed Computing (CDC) was proposed to reduce communication costs and mitigate the straggler effects through the use of coding techniques. In this paper, we propose a double auction mechanism to allocate the resources of the edge servers to the vehicles in order to complete the CDC tasks. Specifically, the vehicles use the PolyDot codes to manage the tradeoff between communication costs and recovery threshold. Given the requirements of various vehicles, the double auction mechanism matches the edge servers with the required resources to the vehicles. Besides, the double auction mechanism also determines the prices that the vehicles need to pay for the resources of the edge servers. The analyses show that the double auction mechanism satisfies the properties of individual rationality, incentive compatibility and budget-balance. From the simulation, the utility of auctioneer increases when the number of vehicles and edge servers increases. [ABSTRACT FROM AUTHOR]

Cloud computing is a business model where having users and providers are competing to sell and buy services. Due to diverse software applications and dynamic cloud market, customers' requirements are ever changing. Dynamic auction-based resource allocation models, namely: combinatorial double auction resource allocation (CDARA) model attracted the researchers' attention. In existing work services are allocated to users based on less price, which may lead to service-level agreement (SLA) violation and increase user's dissatisfaction in the cloud marketplace. Decision on single attribute price may create genuine and ungenuine providers issue, as the providers will try to provide low-quality services. To solve these issues, we proposed adaptive market-oriented combinatorial double auction resource allocation (AMO-CDARA) model that allocates services to users based on multiple parameters such as less price, QoS, and providers ranking. In the proposed model, bids are received by auctioneer from multiple providers and users. Auctioneer calculates users and providers bid densities and allocates services to the most efficient users from most efficient providers according to their required request. After successful running of tasks, the auctioneer asks for feedback of the used services from users and calculates the final ranking of providers for future auctions. The simulation results show that the proposed model guaranteed SLA violation up to 99% due to twice punishment penalty and ranking of providers. The guaranteed QoS users/brokers will provide extra payment for that, and the extra payment will be from 1 to 10% of the total price, according to the final price. Furthermore, we solved the bidder drop problem up to 10% by increasing service price by adding QoS price. [ABSTRACT FROM AUTHOR]

Industrial parks are regarded as a possible fashion to reduce the manufacturing costs and increase the efficiency by sharing cost-effective infrastructure and communal services. However, the waste of manufacturing resources could impede the development of manufacturing enterprises that locate in an industrial park when market demands fluctuate dynamically. This article aims to use the cloud manufacturing (CMfg) to relieve the temporary shortage of manufacturing resources and capabilities by sharing them among these enterprises in an industrial park. This article focuses on manufacturing cloud service allocation (MCSA), which is one of the critical processes to implement CMfg. Two multiunit double auction mechanisms are proposed to allocate the manufacturing cloud services (MCSs) with reasonable trade price in bilateral markets, and their properties and bidding strategies are proved. Numerical studies are conducted, and the results show the effectiveness and efficiency of two mechanisms for dealing with MCSA and verify the theoretical proofs. Note to Practitioners—MCSA in CMfg is a complicated and challenging task. The current industrial applications rely on optimization methods, such as all-in-one methods (e.g., genetic algorithm) and multidiscipline design optimization methods (e.g., analytical target cascading), to allocate MCSs. While these methods can help cloud operators obtain feasible allocation solutions rapidly, there exist several disadvantages: 1) they can only solve problems in one-sided settings (one customer with many providers); 2) there is an assumption that the customer’s demand can be provided by any providers, which means providers’ capacities are infinite; and 3) the problem is optimized based on the fixed prices provided by providers, without considering supply and demand. This article presents two multiunit double auction mechanisms that allow multiple customers to trade with multiple providers. Allocation rules can break the assumption and providers’ capacities are limited. MCSs are also priced dynamically based on supply and demand. We show how cloud operators apply two mechanisms first and demonstrate their properties and bidding strategies of customers and providers when the mechanism is not incentive compatible. Finally, our experiments and analysis fully validate the applicability and efficiency of two mechanisms and illustrate how cloud operators can maximize their utilities. [ABSTRACT FROM AUTHOR]

We investigate equilibrium in the buyer's bid double auction (BBDA) in a model with correlated private values/costs. Using a combination of theorems and computed examples, we demonstrate that simple equilibria exist even in small markets. Moreover, we bound traders' strategic behavior as a function of market size and derive rates of convergence to zero of (i) inefficiency in the allocation caused by strategic behavior and (ii) the error in the market price as an estimate of the rational expectations price. These rates together with computed examples suggest that strategic behavior can be inconsequential even in small markets in its effect on allocational efficiency and information aggregation. The BBDA thus simultaneously accomplishes both the informational and allocational goals that markets ideally fulfill; it does this perfectly in large markets and approximately in small markets, with the error due mainly to the smallness itself and not the strategic behavior of traders. [ABSTRACT FROM AUTHOR]

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user's private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey–Clarke–Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Peer-to-Peer (P2P) energy trading scheme between EVs and the SPs to manage the demand response in V2G environment. Unlike the traditional schemes, which consists of complex energy-transportation meshes, the proposed scheme achieves a balance between demand and response by providing incentives to EVs out of their self-interests. However, the online transactions security and privacy protection of EVs poses challenges with respect to confidentiality and integrity preservation. To cope up with the issues, we design a consortium blockchain-based scheme to ensure secure energy transactions between EVs and the SPs without trusted third-party intervention. Moreover, the energy pricing and the amount of traded energy problems for demand response are solved by a double auction mechanism to maximize the social welfare. Numerical results based on a real-time implementation on a private Ethereum network having smart contract demonstrated that the auction-based mechanism achieves social welfare maximization with privacy protection of online transactions between EVs and the SPs. The results obtained show that the rate of convergence, scalability metric average latency, and standard deviation of the energy transactions have been improved by approximately 18.18%, 15%, and 28.1%, respectively in comparison to the existing schemes. Moreover, security and privacy analysis of the proposed scheme shows that it improves the transaction security substantially in comparison to the existing state-of-the-art proposals. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) resource sharing promotes local resource-hungry task offloading to other mobile devices and balances the resource consumption between mobile devices. Most of existing P2P task offloading systems aims to solve the resource sharing between one pair exclusively without considering the cost of resource supply and the strategic behaviors of mobile users. In this paper, we propose two user models for the P2P task offloading system: honest user model and strategy user model. For the honest user model, we formulate the resource allocation maximization problem with latency and energy consumption constraints as an Integer Linear Programming. We show that the solution for honest user model can output 189% resource transactions of that for the strategic users. For the strategy user model, we propose a double auction-based P2P task offloading system, and design a truthful multi-resource transaction mechanism to maximize the number of resource transactions. We first group the mobile users based on the connected components to improve the efficiency of double auction. Then we utilize the McAfee Double Auction to price the resource transactions. Finally, we split each winning mobile user of double auction into multiple virtual mobile users, and use the matching approach to calculate the resource allocation. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed multi-resource transaction mechanism satisfies the desirable properties of computational efficiency, individual rationality, budget balance, truthfulness for resource request/supply, and general truthfulness for bid/ask price. [ABSTRACT FROM AUTHOR]

The intermittent nature of renewable energy sources challenges the ability of grid operators to maintain balance between supply and demand of electricity. Demand response, or flexibility, is the ability of a load to modify its consumption in response to a control signal. The necessary two-way communications between end-users and grid operators introduce new risks on consumer privacy and data security. Consumer engagement in such programs tends to be low not only for privacy issues, but also because of the inappropriate incentive schemes. In this work, secure Multiparty Computation is used in a flexibility market where end-consumers provide demand response to grid operators in exchange for a monetary compensation. Multiparty computation is a cryptographic method that performs operations over encrypted data. Users submit their offers in encrypted form, and a novel way to characterize flexibility as a commodity and to determine device constraints with very little information from the consumers is developed. The market is cleared via a double auction-like process. The method is validated using flexibility offers from 200 devices of different types. We demonstrate the possibility to clear the market and allocate resources with encrypted orders for sole information. • Private-by-design flexibility market for trading demand flexibility as a commodity. • Market orders for balancing services are submitted in encrypted form. • The market is cleared using secure Multiparty Computation. • Novel flexibility characterization method based on minimal information. • Priority-based, private dispatch algorithm to safely allocate demand-response. [ABSTRACT FROM AUTHOR]

This paper employs and extends the auction method for the re-allocation of emission permits (RAEP) at the China Beijing Environment Exchange (CBEE) to meet pollution reduction targets. An optimization method is first proposed to calculate the optimal production quantity and emission permit demand/supply volume for firms with high/low pollution abatement cost. Then, the double auction method is adopted and extended to construct the RAEP double auction mechanism based on the principle of maximizing the total social welfare utility. To further explain this auction method, three matching mechanisms are proposed. Each mechanism achieves a balance between supply and demand of emission permits. Finally, a computational analysis of the real CBEE case is used to verify both the validity and practicability of the mechanism. The results show that the extended auction method presented in this paper could effectively increase the number of traded participants, improve the auction transaction efficiency, and increase the utilities of trading participants, compared to the auction method currently used in the CBEE; the extended method is always applicable regardless of the size of the permit market; the method could effectively realize the incentive compatibility, thus encouraging each firm to provide a real bid price. [ABSTRACT FROM AUTHOR]

The advent of big data era has given rise to the big data trading market because of the potentially enormous economic value. However, designing an effective trading mechanism for the data trading market is still in its infancy. Existing several incentive mechanisms have neglected the important fact that data consumers have both preferences and complex conflicts of interest (CoI) relations among them. In response to the limitations of existing trading mechanisms, we propose DTPCI , a truthful double auction mechanism for a Data Trading market with two unique characteristics of consumers' Preferences and complex CoI relations among them. However, three major challenges have to be addressed, i.e. diverse market preferences, the complex CoI relations of data consumers and the strategic behaviors of both two sides. To jointly address the three challenges, we propose DTPCI to achieve nonnegative social welfare, which features a group rule and a data trading rule. The group rule generates all conflict-free virtual groups based on the CoI graph. The data trading rule adopts the group buying to share data and expense. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that DTPCI achieves all the desired economic properties. [ABSTRACT FROM AUTHOR]

Virtualization technology plays an important role in cloud computing. Virtual machine (VM) migration not only enables load balancing of hosts in data center to avoid overload anomalies, but also reduces the cost of cloud computing data centers. Our work mainly focused on the communication costs of VMs migration in data center. In this paper, a double auction-based VM migration algorithm is proposed, which takes the cost of communication between VMs into account under normal operation situation. The algorithm of VM migration is divided into two parts: (i) selecting the VMs to be migrated according to the communication and occupied resources factors of VMs and (ii) determining the destination host for VMs which to be migrated. In the first process of VM migration, we proposed VMs greedy selection algorithm (VMs-GSA) to select VMs. A VM Migration Double Auction Mechanism was applied to the second process of VM migration to obtain the mappings between VMs and underutilized hosts. The simulation result shows that the proposed VM migration algorithm-based heuristic is efficient. The traffic generated by VMs-GSA is 35% less than the random algorithm, and the success rate of VM migration is very high. [ABSTRACT FROM AUTHOR]

Transactive coordination and control has recently emerged as a new methodology to facilitate large-scale integration of various distributed energy resources into power distribution systems. Various transactive energy systems (TESs) are being developed for power system problems involving self-interested asset owners with privacy concerns. However, there has been very limited work on evaluating and comparing their performance. In this paper, a systematic performance evaluation procedure is presented for the comparison among different TESs in terms of their performance under practical conditions such as information uncertainties, network effects, individual rationality, etc. Both quantitative and qualitative performance metrics are proposed. This procedure is then applied to evaluate and compare two existing TESs using the double-auction market. The detailed case studies also illustrate the performance limitations of double-auction-based TESs and provide useful insights on the necessary design improvements for practical applications. [ABSTRACT FROM AUTHOR]

Autonomous electric vehicles (AEVs) are gaining ground around the world. They are equipped with intelligent decision-making capabilities that allow them to optimize on their battery usage and engage in energy trading for profit maximization. This paper envisions a market model, where energy aggregators considers buying and selling electricity from AEVs. In this system, the aggregators reside on cloudlets at the edge of a cloud computing system for prompt communication and negotiation with AEVs. Whether they are parked or en-route, AEVs can be crowdsourced to provide energy to consumers at needed times. This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing. The proposed system implements dynamic pricing structures, which are applicable to various scenarios of energy trading. To evaluate the proposed system, this paper provides extensive theoretical analysis and simulation experiments to demonstrate that the proposed auctioning models are computationally efficient, truthful, individually rational, and budget balanced. [ABSTRACT FROM AUTHOR]

With a view to the expected increased data traffic volume and energy consumption of the fifth generation networks, the use of renewable energy (RE) sources and infrastructure sharing have been embraced as energy and cost-saving technologies. Aiming at reducing cost and grid energy consumption, in the present paper, we study RE exchange (REE) possibilities in late-trend network deployments of energy harvesting (EH) macrocell and small cell base stations (EH-MBSs, EH-SBSs) that use an EH system, an energy storage system, and the smart grid as energy procurement sources. On this basis, we study a two-tier network composed of EH-MBSs that are passively shared among a set of mobile network operators (MNOs), and EH-SBSs that are provided to MNOs by an infrastructure provider (InP). Taking into consideration the infrastructure location and the variety of stakeholders involved in the network deployment, we propose as REE approaches 1) a cooperative RE sharing, based on bankruptcy theory, for the shared EH-MBSs and 2) a non-cooperative, aggregator-assisted RE trading, which uses double auctions to describe the REE acts among the InP provided EH-SBSs managed by different MNOs, after an initial internal REE among the ones managed by a single MNO. Our results display that our proposals outperform baseline approaches, providing a considerable reduction in SG energy utilization and costs, with satisfaction of the participant parties. [ABSTRACT FROM AUTHOR]

Abstract Motivated by the taxi refusal problem in Bangkok, this paper presents a novel approach of demand-supply matching for taxi services through double auction, where passengers and drivers can offer and ask for a surcharge in addition to a regular meter fare. This system is equivalent to a variable surcharge policy as opposed to the existing fixed surcharge policy. The objective of this paper is twofold. First, we examine the design of information content for taxi service auction under two conditions: with and without time-cost. Laboratory experiments with student subjects were conducted to investigate the effect of different levels of information content on bidding strategies and market efficiency. The results show that time-cost has a significant effect on individuals' bidding strategy and market efficiency. In addition, providing bidders with greater information does not necessarily lead to better efficiency. Second, we compare the performance of the proposed method with that of the fixed surcharge policy. It is found that the variable surcharge policy outperforms the fixed surcharge policy in several aspects. In particular, the variable surcharge policy provides a better opportunity for passengers to get the taxi service and obtains a higher market efficiency at least as good as the fixed surcharge policy. Highlights • Double auction is proposed for demand-supply matching of taxi services. • Various designs of information content exposed to the bidders were investigated. • We conduct laboratory experiments to evaluate bidding strategy and market efficiency. • Providing bidders with greater information does not lead to better market efficiency. • The proposed variable surcharge policy outperforms the existing policy. [ABSTRACT FROM AUTHOR]

Double auctions are commonly used market mechanisms on the world. Double auctions constitute one of the most complex market mechanisms by allowing both buyers and sellers to make price offers. Complexity theory is an emerging scientific paradigm which deals with complex systems and sometimes called a scientific revolution. One of the main analysis tools in complexity paradigm is agent based simulations. With agent based simulations agent behaviors can be modeled and collective outputs of agent interactions can be obtained from the simulations. Double auctions are complex systems in which buyers and sellers interact with each other. Although double auctions are widely used on the world theoretical works on double auctions are few. Main reason of this is the complexity of double auctions. Complexity theory and agent based simulations promises new opportunities for modeling and understanding double auctions. In this work, we investigated a kind of double auction which is called dynamic double auction by using agent based simulations. In our simulations we modeled trader (agent) behaviors with bidding strategies. We utilized seven bidding strategies. Two of them are based on fuzzy logic (FFL and SFL) and four of them are based on price targeting. We also used zero intelligence strategy for comparison purposes. In our simulations we compared second fuzzy logic (SFL) strategy with other strategies and demonstrated that SFL strategy has always higher profit than other strategies. [ABSTRACT FROM AUTHOR]

We report boundary experiments testing the robustness of price convergence in double auction markets for non-durable goods in which there is extreme earnings inequality at the competitive equilibrium (CE). Following up on a conjecture by Smith (1976a), we test whether the well-known equilibrating power of the double auction institution is robust to the presence of complete information about traders’ values and costs and the presence of symmetric market power. We find that complete information is insufficient to impede convergence to CE prices; however, introducing market power consistently causes prices to deviate from the CE, whether or not subjects possess complete information. Our design highlights the value of boundary experiments in understanding how market institutions shape behavior, and our findings help delineate the limits of the double auction institution to generate competitive outcomes. [ABSTRACT FROM AUTHOR]

We consider a simplified model of the continuous double auction where prices are integers varying from 1 to N with limit orders and market orders, but quantity per order limited to a single share. For this model, the order process is equivalent to two M ∕ M ∕ 1 queues. We study the behavior of the auction in the low-traffic limit where limit orders are immediately matched by market orders. In this limit, the distribution of prices can be computed exactly and gives a reasonable approximation of the price distribution when the ratio between the rate of order arrivals and the rate of order executions is below 1 ∕ 2 . This is further confirmed by the analysis of the first-passage time in 1 or N . [ABSTRACT FROM AUTHOR]

Deploying small cells in cellular networks, as a technique for capacity and coverage enhancement, is an indispensable characteristic of future cellular networks. In this paper, two novel online approaches for enabling energy trading in multitier cellular networks with noncooperative energy-harvesting base stations (BSs) are proposed. The goal is to minimize the nonrenewable energy consumption in a multitier cellular network with an arbitrary number of tiers. In the first approach, a decentralized energy trading framework is established in which BSs are stimulated to compensate their energy shortage with the extra harvested energy from other BSs rather than using the nonrenewable energy. Matching theory is used to assign BSs with energy deficit to the BSs with extra harvested energy. In the second approach, which is centralized, BSs with extra harvested energy and BSs with energy deficit enter a double auction for energy trading. The centralized approach also motivates the BSs with deficient energy to use other BSs extra harvested energy and satisfies a number of properties including truthfulness, individual rationalities, and budget balance. Both approaches achieve Nash equilibrium and motivate noncooperative BSs to share their extra harvested energy. The extra harvested energy is exchanged by the smart grid. We show that the amount of information exchanged in the network to enable BSs to trade energy is reduced in the centralized algorithm compared to the decentralized algorithm at the expense of using a control center. Simulation results verify that the proposed approaches reduce the nonrenewable energy consumption conspicuously. Furthermore, by applying the proposed approaches, BSs gain more profit, and consequently, their utility functions enhance. [ABSTRACT FROM AUTHOR]

Auction is often applied in cognitive radio networks due to its efficiency and fairness properties. An important issue in designing an auction mechanism is how to utilize the limited spectrum resource in an efficient manner. In order to achieve this goal, we propose a predictive double spectrum auction model in this paper. Our auction model first obtains the bidding range from statistical analysis, and then separates the interval into independent states and employees a Markovian prediction based algorithm to generate guidelines for the bidding range of primary and secondary users, respectively. Comparing with existing approaches, our proposed auction model is more efficient in spectrum utilization and satisfies the economic properties. Extensive simulation results show that our work achieves an utilization ratio up to 91 %. [ABSTRACT FROM AUTHOR]

In order to promote the value circulation of energy resources and improve energy efficiency, credible energy sharing between Internet of Things Devices (IoTDs) came into being. However, sometimes IoTDs do not obtain the required energy in the required time period, resulting in less active participation in energy sharing. To address these challenges, this paper first proposes a credible energy transaction model based on the distributed ledger blockchain at the Edge of the Internet of Things, where the Edge Cloud Server (ECS) can collect a large number of surplus energy resources of IoTDs in a secure and credible energy sharing environment and share them with other IoTDs in urgent need of charging. Meanwhile, in order to attract IoTDs to participate in energy sharing for a long time and meet the energy demand of ECS to the maximum extent, a smart contract-based Expected Social Welfare Maximized double auction incentive mechanism of Single ECS to Multi-IoTDs (ESWM-StM) is proposed to enable dynamic and adaptive energy sharing from multiple IoTDs to a single ECS. In addition, this paper compares the proposed algorithm with the benchmark method in terms of energy-sharing cost and long-term utility. The simulation results show that the proposed incentive mechanism can enable IoTDs to provide more surplus energy per unit cost to meet the energy demand of ECSs, and can sustainably attract more energy trading participants to enhance the expected social welfare in the long term. [ABSTRACT FROM AUTHOR]

• A comprehensive review of existing research and pilot projects on P2P energy trading. • Detailed analysis of the implementation methodologies with mathematical formulations. • Discussion on the developed P2P testbed and live demonstration projects. • Directions for future research work in developing P2P energy trading frameworks. Energy sector is undergoing a massive transformation that includes key aspects such as integrating renewables, improving operational efficiency, leveraging smart grid infrastructure, and handling the dynamics of transactive energy; all of which necessitates ecosystem players to refine their role, devise efficient regulatory/policy frameworks, and experiment with new business models. Given these circumstances, Peer-to-Peer (P2P) energy trading appears to be one of the viable solutions in which an end-user can sell/buy power to/from other users instead of fully relying on the utility for the same. However, the implementation of P2P energy trading in the distribution networks introduce new challenges, such as network constraint violations, increased communication requirements, compromised end-user privacy, the threat to the financial viability of the utility companies, etc. Various pilot projects and research are being carried out at different levels worldwide in this regard. This paper provides a detailed analysis of the existing research and implementation activities on P2P energy trading, and suggests potential future research avenues. Readers gain an understanding of P2P energy trading frameworks, implementation methodologies, and demonstration projects through this article. [ABSTRACT FROM AUTHOR]

With the rapid development of Distributed Energy Sources (DERs), Peer-to-Peer (P2P) energy trading is regarded as an effective scheme to improve local energy utilization. Nevertheless, unlike wholesale electricity markets of the current grid size, small-scale prosumers and highly unpredictable intermittent DERs account for a significant proportion of P2P markets, leading to an escalation of market uncertainties. To facilitate effective market functionality, penalty mechanisms for unqualified participants are essential, as is typically the case in the wholesale electricity market. However, there has been little discussion of the use of penalty mechanisms in P2P markets. In this context, we propose a novel adaptive penalty mechanism (APM) to drive the defaulting prosumers to fulfill orders. Unlike the traditional two-dimensional (price, quantity) penalty price, APM uses a three-dimensional penalty and introduces deviation percentage factors to reduce the risk of excessive penalty rates. Penalty prices are determined by utilizing the distributed default clearing algorithm to adapt to market conditions, thereby preventing deviations in clearing prices. Case studies are conducted to demonstrate the feasibility and efficiency of the proposed APM in the P2P market. The results indicate that the APM strike an appropriate between cost-effectiveness and regulation, requiring about 20% less reserve capacity than the severe penalty. • A novel adaptive penalty mechanism is designed for the Peer-to-Peer energy trading. • Participants are encouraged to compensate for energy deviations to prevent clearing price deviations. • A three-dimensional penalty is developed to reduce excessive penalty risks for market participants. • A distributed default clearing algorithm is devised to implement the proposed penalty mechanism. [ABSTRACT FROM AUTHOR]

Video traffic offloading through the helper peers can significantly alleviate the server workload and enhance users' quality of experience. In this paper, the complex interactions of a number of helpers seeking to trade their bandwidth surplus in a peer-to-peer market are studied using non-cooperative game theory and double auction. In particular, a non-cooperative game is formulated among the helpers in which each helper can make a decision on the maximum amount of upload bandwidth surplus that it is willing to sell so as to maximize its utility function that reflects the tradeoff between the benefits from bandwidth sharing and its associated costs. Then, the price governing the bandwidth trading market between the helpers and peers is determined via a truthful double auction mechanism. To solve the game, an algorithm based on the best response dynamics is proposed using which helpers can reach a Nash equilibrium point. Simulation results exhibit that the proposed algorithm achieves significant performance improvements in terms of utility per selling helpers. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Sometimes experimentalists draw far-reaching policy conclusions from their findings. However, this is dangerous if there is ambiguity with respect to the right interpretation of the results. A good example for this danger is the well-known study by Falk and Szech (Science, 2013), who conclude that “markets erode moral values”. If this were true, economists, who have emphasized the efficiency enhancing effects of markets for centuries, would have to reconsider their judgments fundamentally. In this note we demonstrate that the claim made by Falk and Szech is unfounded for at least four reasons and that their experimental results can be (and should be) interpreted, if anything, in the opposite way. [ABSTRACT FROM AUTHOR]

Auction mechanism has been widely applied to cognitive radio networks to motivate spectrum redistribution among unlicensed users and spectrum holders, in which unlicensed users with cognitive radio capability can access the licensed spectrum by compensating spectrum holders with monetary payment. However, most of prior studies on auction are mainly restricted to channel allocation with the assumption of fixed transmitting power and/or neglecting spectrum holders’ interference restrictions. Few of them jointly considers the channel and power allocation. Additionally, power allocation may cause non-identical interference relationships among unlicensed users due to the variable interference ranges. In this paper, we propose two truthful double auction schemes under single-channel demand and multi-channel demand, respectively.We theoretically prove that the two auction schemes both achieve the desired economic properties. Results from numerical evaluation demonstrate our analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel automated double auction mechanism called convergent linear function submission-based double-auction (CLFS-DA) for a prosumers' decentralized smart grid. The target decentralized smart grid is a regional electricity network that consists of many prosumers that have a battery and a renewable energy-based generator, such as photovoltaic cells. In the proposed double-auction mechanism, each intelligent software agent representing each prosumer submits linear demand and supply functions to an automated regional electricity market where they are registered. It is proven that the CLFS-DA mechanism is guaranteed to obtain one of the global optimal price profiles in addition to it achieving an exact balance between demand and supply, even through the learning period. The proof of convergence is provided on the basis of the theory of LFS-DA, which gives a clear bridge between a function submission-based double auction and a dual decomposition (DD)-based real-time pricing procedure. The performance of the proposed mechanism is demonstrated numerically through a simulation experiment. [ABSTRACT FROM AUTHOR]

A linear function submission-based double auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer, and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market. The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework, except for a constant factor. [ABSTRACT FROM AUTHOR]

Recently, as it can greatly increase the capacity of cellular networks through spatial diversity, cooperative communications has been obtaining more and more attention. As a widely used schema, user (user equipment, UE) cooperation presents its advantage. However, due to the users' selfish nature, UE may be unwilling to serve as a relay node if they cannot obtain a corresponding reward. On the contrary, to save energy for longer standby time, particularly when there is energy shortage, many source users would like to purchase the relay service from other idle UE. Therefore, reconciling the demand of both sides is a challenging problem to be resolved. In this paper, a realistic double-auction scenario is constructed between multiple source UE and multiple idle UE in a cellular network. We take the avaricious psychology of network entities into consideration by determining the ask and bid prices through a markup, which is the function of their residual energy. To solve this double-auction-based optimal relay assignment problem, we transform it into maximum matching (MM) and maximum weighted matching (MWM) problems, respectively, and we solve them by corresponding algorithms. Experimental results show that the proposed algorithms dramatically maximize the system performance involving successful source–relay pairs, system throughput, and social welfare (SW), compared with other idealized truthful double-auction scheme that only consider the system throughput. [ABSTRACT FROM PUBLISHER]

Abstract: We consider a market for indivisible items with m buyers and m sellers. Traders privately know their values/costs, which are statistically dependent. Two mechanisms are considered. The buyer's bid double auction collects bids and asks from traders and determines the allocation by selecting a market-clearing price. It fails to achieve all possible gains from trade because of strategic bidding. The designed mechanism is a revelation mechanism in which honest reporting of values/costs is incentive compatible and all gains from trade are achieved. This optimality, however, comes at the expense of plausibility: (i) the monetary transfers among the traders are defined in terms of the traders' beliefs about each other's value/cost; (ii) a trader may suffer a loss ex post; (iii) the mechanism may run a surplus/deficit ex post. We compare the virtues of the simple yet mildly inefficient buyer's bid double auction to the flawed yet perfectly efficient designed mechanism. [Copyright &y& Elsevier]

Abstract: We study double auction market design where the market maker wants to maximize its total revenue by buying low from the sellers and selling high to the buyers. We consider a Bayesian setting where buyers and sellers have independent probability distributions on the values of products on the market. For the simplest setting, each seller has one kind of indivisible good with a bounded (integer) amount that can be sold to a buyer, who may demand a bounded number of copies. We develop a maximum mechanism for the market maker to maximize its own revenue. For the more general case where each seller's product may be different, we consider a number of variants in terms of constraints on supplies and demands. For each of them, we develop a polynomial time computable truthful mechanism for the market maker to achieve a revenue at least a constant α times the revenue of any other truthful mechanism. [Copyright &y& Elsevier]

Abstract: We study a sealed-bid auction between two bidders with asymmetric independent private values. The two bidders own asymmetric shares in a partnership. The higher bidder buys the lower bidderʼs shares at a per-unit price that is a convex combination of the two bids. The weight of the lower bid is denoted by . We partially characterize equilibrium strategies and show that they are closely related to equilibrium strategies of two well-studied mechanisms: the double auction between a buyer and a seller and the first-price auction between two buyers (or two sellers). Combining results from those two branches of the literature enables us to prove equilibrium existence. Moreover, we find that there is a continuum of equilibria if whereas the equilibrium is unique if . Our approach also suggests a procedure for numerical simulations. [Copyright &y& Elsevier]

We experimentally compare the performance of two multi-type environmental credit trading markets. The first trading market is called the multiple market (MM) institution, which allows the providers to sell jointly produced credits of all types. The second trading market is called the single market (SM) institution, where the providers of jointly produced credits can only choose one type of credit to sell. We investigate several key indicators, including the trading price, quantity, and net social benefit, to compare the performance of the SM and MM institutions. We find that the trading prices are significantly lower in MM compared to SM, indicating that MM potentially benefits the credit buyers. We also find that SM leads to more credit productions. Since not all credits can be traded in SM, the increase in the production cost outweighs the increase in the total social benefit. As a result, the net social benefit is lower in SM compared to MM. We expand the literature by firstly designing a market platform for multi-type credit trading and then assessing their performance with lab experiment evidence. [ABSTRACT FROM AUTHOR]

Abstract: The pricing mechanism in a deregulated electricity market is consistently a critical issue to improve market operations. Along with the development of electricity market, large consumers may purchase electric power from generators directly. In this paper, with an effort to introduce McAfee’s second-price sealed auction mechanism into multi-unit electric power transaction between competitive generators and large consumers, an auction model is developed to decide electric power price between competitive generators and large consumers based on the inclusion of the transaction cost and power transmission cost in a wholesale electricity market. After the description of pricing rules in the deregulated power market, auction rules and transaction rules are discussed. In particular, auction rules can increase the probability that generators providing a large number of supply and large consumers with high demand win the auction. On the other hand, transaction rules can optimize the whole transmission cost and transaction cost under market equilibrium, and the whole system is dispatched to maximize social welfare. A numerical example with eight suppliers and eight large consumers is provided to illustrate the essential features of this method. [Copyright &y& Elsevier]

Cognitive networks are designed based on the concept of dynamic and intelligent network management, characterizing the feature of self-sensing, self-configuration, self-learning, self-consciousness etc. In this paper, focusing on the spectrum sharing and competition, we propose a novel OODA (Orient-Observe-Decide-Act) based behavior modeling methodology to illustrate spectrum access problem in the heterogenous cognitive network which consists of multiple primary networks (PN, i.e. licensed networks) and multiple secondary networks (SN, i.e. unlicensed networks). Two different utility functions are designed for primary users and secondary users respectively based on marketing mechanism to formulate the decide module mathematically. Also, we adopt expectation and learning process in the utility design which considers the variance of channels, transmission forecasting, afore trading histories and etc. A double auction based spectrum trading scheme is established and implemented in two scenarios assorted from the supply-and-demand relationship i.e. LPMS (Less PNs and More SNs) and MPLS (More PNs and Less SNs). After the discussion of the Bayesian Nash Equilibrium, numerical results with four bidding strategies of SNs are presented to reinforce the effectiveness of the proposed utility evaluation based decision modules under two scenarios. Besides, we prove that the proposed behavior model based spectrum access method maintains frequency efficiency comparable with traditional centralized cognitive access approaches and reduces the network deployment cost. [ABSTRACT FROM AUTHOR]