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This paper proposes a simple descriptive model of discrete-time double auction markets for divisible assets. As in the classical models of exchange economies, we consider a finite set of agents described by their initial endowments and preferences. Instead of the classical Walrasian-type market models, however, we assume that all trades take place in a centralized double auction where the agents communicate through sealed limit orders for buying and selling. We find that, under nonstrategic bidding, double auction clears with zero trades precisely when the agents' current holdings are on the Pareto frontier. More interestingly, the double auctions implement Adam Smith's "invisible hand" in the sense that, when starting from disequilibrium, repeated double auctions lead to a sequence of allocations that converges to individually rational Pareto allocations. [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]

This paper investigates the existence of a nondegenerate pure-strategy Bayesian-Nash equilibrium in a double auction between one seller and several heterogeneous buyers in the independent private value framework. It begins with three examples describing some of the model's particular features. After studying a number of continuity-related properties of the interim payoff functions, we construct a sequence of strategy profiles that converges to a nondegenerate monotone Bayesian-Nash equilibrium of the game under some strict monotonicity conditions. The equilibrium existence result is applied to a double auction with risk-averse bidders. [ABSTRACT FROM AUTHOR]

Cloud computing provides a feasible solution to data outsourcing, and hence forming a cloud-based data market, where data users buy data from owners through querying cloud servers. However, it also incurs new privacy and security problems, as data is under a centralized third-party instead of the data owner’s direct control. Existing data markets are also questioned on their inflexible and opaque pricing, where the value of data ownership and the cost of query searches are mixed. In this paper, we consider blockchain-based storage as a better choice to ensure safe data outsourcing since data is spread out across many data points. We propose an Ethereum-based data market that provides distributed storage and correct remote data search. We design a new pricing model, where each query will be charged by two parties: owner (paid for providing his data) and miner (rewarded by performing query searches). We study a new cooperative search scheme through a proxy to reduce cost on the user side. Given that each user query is charged based on its number of keywords, then a cooperative search can reduce user-side cost by combining multiple queries into a group so that overlapped keywords will only be charged for one time. To ensure user QoE, a combined query should not be significantly larger than any of its original queries in terms of the number of keywords. The total price is based on the total number of keywords in all groups. Since it is a cooperative model with shared resources, we also study various incentive properties on the user side, yielding a cost sharing mechanism to split joint cost in a truth-revealing and fair manner. We further extend our market with a set of substitute data owners and propose a double auction mechanism to match users and owners based on their requirements. Experiments have been conducted on real query trace to demonstrate the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

Opportunistic availability of licensed frequency bands enables the secondary users (SUs) to avail the radio spectrum dynamically. Cognitive radio (CR) paradigm extends the dynamic spectrum access techniques to sense for free channels (called spectrum holes) which can be efficiently redistributed amongst SUs. Motivated by the adaptive technology in CR, this paper introduces a sealed-bid double auction mechanism which aims to obtain an effective allocation of the unused radio spectrum. The proposed auction model adopts multi-channel allocation where one SU can access more than one available channel, while imposing the constraints for dynamics in spectrum opportunities and varying channel availability time amongst SUs. Previously designed double auctions miss out the CR constraints which can further degrade the network performance. Also, multi-winner allocation is induced in the model which encourages spectrum reuse by allowing a common channel to be assigned to multiple non-interfering SUs. A preference list of channels is maintained at each SU using which SUs offer their bid values for the heterogeneous channels which the primary owners are competing to lease. To organize channel specific groups of non-interfering SUs, a bidder group formation algorithm is developed such that members of a winner group get access to a common channel. The auctioneer formulates a winner determination strategy and a pricing strategy which achieves truthfulness while assigning the idle spectrum. Effectiveness of the proposed model is studied by comparing it with an existing work which shows that channel allocation gets significantly improved on deploying the proposed model.

With an aim of maximizing the efficiency of edge offloading and the resource utilization of edge computing server simultaneously, a new flow-of-traffic prediction based edge computing offloading solution was proposed for Internet of vehicles (IoV).Firstly, both the efficiency utility function of vehicle and the resource utilization of mobile edge computing (MEC) server were established by considering task priority.Next, the formulated dual-objective optimization problem was transformed into a double auction problem between vehicles and MEC servers.Finally, based on the designed flow-of-traffic based pricing function of vehicle and MEC server, a McAfee auction algorithm was adopted to complete the edge computing process.Simulation results show that benefiting from the flow-of-traffic prediction information, the proposed solution can significantly improve both the efficiency of computation offloading and the utilization of computation resource.

Cloud computing is a growing technology where lot of heterogeneous resources are available and large amount of requests are submitted by the customers simultaneously. So it is difficult to match the requests and resources based on the expectations of customers and providers. This paper proposes the resource allocation using auction based technique to reduce the complexity of providing the resources for customers job execution and fulfill the expectations of both customers and providers in cloud environment. In the proposed work the Enhanced Multi-attribute Combinative Double Auction (EMCDA) resource allocation algorithm is used to conduct the auction to the customers bids with the providers bids by the cloud auctioneer for finding the best customer-providers pairs and achieves the customers and providers satisfaction using the normalization factors during price calculation in the cloud computing environment. The experimental result demonstrates that the proposed Enhanced Multi-attribute Combinative Double Auction (EMCDA) resource allocation algorithm performs efficiently than the existing Combinatorial Double Auction Resource Allocation (CDARA) model. The proposed EMCDA model is incentive-compatible, which encourage the participants of an auction to reveal their true valuation during bidding. [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]

The growing market of cloud computing resulted in increased demand for cloud resources and it will become difficult for individual service providers (SPs) to fulfill all resource requests. That leads to a situation where two or more SPs may form a group (federation) and share the resources in order to fulfill the cloud users’ demand and gain economic advantage. Now, due to the formation of more than one federations by different cloud providers, it may be difficult for users to select a suitable federation who can deliver cloud services at a fair price. In this context, it is necessary to have a framework that will efficiently allocate resources of cloud federations to the users at a fair price and stop market manipulation. In this article, we propose a multi-unit double auction mechanism called TARA (Truthful Double Auction for Resource Allocation) that can be used to efficiently choose cloud federations for users from which they can get resources. Here, we consider a multi-seller and multi-buyer double auction mechanism for heterogeneous resources, where every buyer submits their bids and every seller places their ask (the price of a resource that is offered by a federation). TARA achieves some important properties like truthfulness (also known as incentive compatibility), individual rationality and budget balance for both buyers and sellers. TARA is also computationally efficient and posses high system efficiency. The simulation results also show that total utility of buyer is more than some existing double auction mechanisms. [ABSTRACT FROM AUTHOR]

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In the traditional double auction market, the number of achievable tradable pairs are usually limited by the market size. To solve this problem, we propose new double auction mechanisms based on social networks to realize market expansion. In this paper, we examine the double auction market, which consists of a group of sellers and a group of buyers, where the sellers provide the same type of items, and the buyers respectively purchase one from them. In addition, every buyer can invite other potential buyers to enter the market through social networks. The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism. We found that the extended McAfee auction mechanism cannot motivate existing buyers to invite other potential ones, while the traditional Vickrey-Clark-Groves (VCG) mechanism cannot guarantee to break even. To solve these problems, we propose two mechanisms, which are called information network auction (INA) mechanism and double network auction (DNA) mechanism. Both of these mechanisms can encourage old buyers in the market to invite new potential customers to participate. Moreover, INA focuses on achieving more effective allocation and DNA focuses on ensuring a balance between income and expenditure.

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.

Smith's (1962) demonstration that prices and allocations quickly converge to the competitive equilibrium in the continuous double auction (CDA) remains one of the most important results in experimental economics. Market experiments and exchange models have added considerably to our knowledge of how markets reach equilibrium, and how they respond to disruptions. Perhaps the best-known model of exchange in CDA market experiments is the random behavior "zero-intelligence" (ZI) model by Gode and Sunder (1993). They argue that the CDA generates efficient allocations and "convergence of transaction prices to the proximity of the theoretical equilibrium price," provided only that agents meet their budget constraints. We demonstrate that prices do not converge in their simulations. Their budget constraint requires that a buyer's currency never exceeds her commodity value, which is an unnatural restriction. Their conclusion that market efficiency results from the structure of the CDA independent of traders' profit seeking behavior rests on their claim that the constraints that they impose are a part of the market institution. We show that actually they impose individual rationality. Misinterpretation of this behavioral constraint has led to unproductive debate on market adjustment processes. [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]

Distributed energy resources, together with information and communication technologies, have transformed the traditional electricity consumers into proactive consumers, namely prosumers. Prosumers can exchange their surplus energy with consumers through peer-to-peer (P2P) energy sharing. In this paper, the framework of P2P energy exchange in active distribution networks is developed using a multi-round double auction (MRDA) with average pricing mechanism (APM) integrated with distributional locational marginal price. The advantages of the proposed P2P framework include, 1) modeling and integration of the costs of voltage regulation, congestion, and power loss into the payments of agents for each transaction; 2) the entire distribution network clustered into multiple zones with transactions cleared at different levels, which decreases the additional costs for successful transactions, reduces the computational time, and increases the number of successful transactions; and 3) the matching algorithm encourages more prosumers and consumers to participate in P2P energy sharing and increases the efficiency and benefit from P2P market. The proposed MRDA-APM framework is validated by testing on the 33-node and 141-node distribution test systems. Simulation results demonstrate the effectiveness of the proposed mechanism for P2P energy exchange from both technical and computational viewpoints. [ABSTRACT FROM AUTHOR]

This paper presents an auction mechanism for the coordination of dynamic, uncapacitated lot-sizing problems in two-party supply chains where parties' local data are private information and no external mediator is involved. In the first stage of the mechanism, the buyer generates supply proposals which potentially lead to system-wide improvements, and communicates them to the supplier. Then both parties evaluate the cost impact of the different proposals for their local planning domains. In the second stage, buyer and supplier submit sealed bids on each proposal. Then the bids are opened and the proposal with the greatest surplus is implemented. The procedure for proposal generation is efficient and scalable. It identifies the system-wide optimum in special cases, finds close-to-optimal solutions for general settings in computational tests, while the number of needed proposals increases linearly with the number of time periods and items supplied. Further analytical and numerical analyses indicate that the auction in the second stage is highly efficient. [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]

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 selfishness and uncertainty of user behaviors in the mobile crowd sensing network make them unwilling to participate in sensing activities,which may result to a lower sensing task completion rate.To deal with these problems,an incentive mechanism based on auction model was proposed.In order to maximize the utility of each user,the proposed incentive method based on reverse auction (IMRA) leveraged a task-centric method to choose winners,and payed them according to a critical-price strategy.Furthermore,the proposed user-bidirectional interaction incentive mechanism (UBIM) helped drop-out users (buyers) to transfer their unfinished tasks to new users.Simulation results show that,compared with TRAC and IMC-SS,IMRA can achieve a better performance in terms of average user utility and tasks coverage ratio,and the task completion ratio can also be improved by UBIM.

The selfishness and randomness of users in the mobile crowd sensing network could cause them unwilling to participate in sensing activities and lead to lower completion rates of sensing tasks. In order to deal with these problems, this paper proposes a novel incentive mechanism based on a new auction model for mobile crowd sensing, which consists of two consecutive stages. In the first stage, a novel Incentive Method based on Reverse Auction for Location-aware sensing (IMRAL) is proposed to maximize user utility. By introducing a task-centric method to determine the winning bids, it can provide higher user utility and higher task coverage ratio. To ensure the truthfulness of IMRAL, we design a unique payment determination algorithm based on critical payment for the incentive platform. In the second stage, we propose a user-interaction incentive model (UIBIM) to cover the situation that a user may drop out of the sensing activity. This new incentive model includes a dynamic double auction framework prompting users' interaction and a user matching algorithm based on a bipartite graph. The proposed new mechanism achieves the goal of improving task completion rates without increasing the cost of the incentive platform. The simulation results show that comparing with other solutions, such as a truthful auction for location-aware collaborative sensing in mobile crowdsourcing and incentive mechanism for crowdsourcing in the single-requester single-bid-model, IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio, and the UIBIM can significantly improve task completion rates.

The double auction is a widely applicable trading mechanism used to converge to competitive equilibrium in different markets from which multiple equilibriums and incomplete information may arise. Therefore, different learning models have been applied to facilitate bidding strategies for buyers and sellers in the market. However, due to the existence of problems in double auction markets such as individual bounded rationality and information incompleteness, it is still necessary to explore a more general learning model to depict the learning mechanism in double auction markets and predict the evolution processes of bidding strategies for both sides. Therefore, this paper aims at introducing the use of the experience-weighted attraction (EWA) model for double auction because it combines reinforcement learning with belief learning that then converts EWA in a suitable and interesting learning model for describing and improving individuals' learning behavior. It can become an effective learning model for bidding strategies in the double auction. In addition to the use of the EWA for strategy evolution in the double auction, the impact of its different bidding strategy performance parameters will be also analyzed and compared with other learning models.

With the explosive growth of mobile data traffic in cellular networks, indoor users are always suffering poor data services. Femtocells are widely accepted as an effective way to solve this problem by providing better coverage. In this way, the user quality of service (QoS) can be significantly enhanced. However, a major obstacle to implement the fashion is lacking market-driven mechanisms to incentivize femtocell owners to trade their access permissions (ACPs). Therefore, designing a robust auction mechanism for ACP trading has attracted lots of attention. A critical challenge of designing such an auction mechanism is to ensure the economic property of truthfulness. Most of the prior works on this issue focus on single-sided scenario, where there is only one seller or one buyer. However, multiple femtocells and multiple macrocell users equipments (MUEs) are always involved in practical systems. In this paper, we study a general market model where multiple femtocells can trade with multiple MUEs and show that designing such a truthful auction mechanism for this scenario is challenging. Therefore, we propose a truthful double auction for access permission (TDAP) allocation. We show analytically that our auction mechanism is economic robust (i.e., satisfying three economic properties including truthfulness, individually rationality, and budget balance) and computationally efficient. Moreover, through extensive simulation experiments, we show TDAP can highly improve auction efficiency outperforming prior auction design.

With the increasing demands for mobile users to obtain content, the heterogeneous networks (HetNets), which consolidate the backbone networks with mobile edge networks, have been regarded as a promising paradigm to provide mobile users with high quality of experience (QoE). However, the limited network resources and caching capacity become a new challenge to deliver content in HetNets. Therefore, in this paper, a cooperative scheme between edge server and content provider in HetNets is proposed to improve the performance of content delivery. Firstly, a novel framework of content delivery with backbone networks and mobile edge networks is introduced. The edge servers are deployed at the edge of networks and close to users. Secondly, a Q-learning based scheme for content caching is developed to securely cache contents with the cooperation between edge server and content provider. Thirdly, the cooperative interaction between edge server and content provider is modeled as the double auction game. Each player can obtain the maximum utility from the equilibrium strategy. Finally, simulation results show that the proposed scheme can improve the efficiency of content delivery and bring more utilities to edge server and content provider than the conventional schemes.

Physical layer security (PLS) is considered as a promising technique to prevent information eavesdropping in wireless systems. In this context, cooperative relaying has emerged as a robust solution for achieving PLS due to multipath diversity and relatively lower transmission power. However, relays or the relay operators in the practical environment are unwilling for service provisioning unless they are incentivized for their cost of services. Thus, it is required to jointly consider network economics and relay cooperation to improve system efficiency. In this paper, we consider the problem of joint network economics and PLS using cooperative relaying and jamming. Based on the double auction theory, we model the interaction between transmitters seeking for a particular level of secure transmission of information and relay operators for suitable relay and jammer assignment, in a multiple source-destination networks. In addition, theoretical analyses are presented to justify that the proposed auction mechanism satisfies the desirable economic properties of individual rationality, budget balance, and truthfulness. As the participants in the traditional centralized auction framework may take selfish actions or collude with each other, we propose a decentralized and trustless auction framework based on blockchain technology. In particular, we exploit the smart contract feature of blockchain to construct a completely autonomous framework, where all the participants are financially enforced by smart contract terms. The security properties of the proposed framework are also discussed.

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.

This article proposes a double auction-based mechanism that captures the interaction within a community energy sharing market consisting of distributed solar power prosumers and consumers. All agents are assumed to have battery energy storage systems, and can use battery for demand response. Agents can optimize the charging/discharging schedules of their battery systems for community sharing to reduce electricity costs. To determine the double-side auction market spot price, a non-cooperative game is formulated among all participants involved in the community sharing. An iterative algorithm is first designed to clear the market and mitigate the uncertainty in supply and demand. Then, an adaptive pricing strategy is designed to assist agents better estimate the market and predict the future price. A case study with 10 agents is provided to evaluate the effectiveness of the proposed community sharing market. [ABSTRACT FROM AUTHOR]

Typically, an industry contracts a given electrical supply capacity to the energy distribution company for a relatively long time period. Such kind of contract is often expensive for the industry, because large supply capacities must be considered in order to avoid penalty fees due to an eventually high peak power demand that could occur even along a short time period. This paper proposes a dynamic strategy for simultaneously assigning the price and the fraction of power supply capacity to every industry located in an industrial park. The assignment strategy aims at reaching a joint benefit for all the involved enterprises (i.e., the industries and the energy distribution company). The proposed procedure involves 3 sequential algorithms. The strategy for supply capacity re-assignment is based on a double-auction mechanism, which can be applied at time periods of arbitrary duration. The proposal is evaluated on the basis of two synthetic examples that involve different number of industries and electric power consumptions. [ABSTRACT FROM AUTHOR]

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With the development of cognitive radio networks in recent years, spectrum utilization has been enhanced, as secondary users can lease under-utilized spectrum from the spectrum owners. Spectrum is allocated through auctions in wireless communication networks. The auction can provide benefits for both primary users and secondary users. Existing auction mechanisms for spectrum are mainly based on interference graphs and consider the heterogeneity of spectrums only to a minimal degree. The economic efficiency of the auction is usually neglected due to the focus on improving spectrum utilization. In this paper, we consider a signal-to-interference-plus-noise ratio (SINR) constrained interference model; this model is more realistic as users can simultaneously communicate as long as their requirements SINRs are satisfied. We propose a truthful profit maximization double auction mechanism to improve the benefit of networks with low energy. At the same time, security concerns are guaranteed because buyers and sellers make their true critical decision, i.e., they cannot improve their utility by misreporting their asks and bids. Moreover, our proposed novel auction mechanism is individually rational and budget-balanced. The experiments demonstrate that our auction mechanism efficiently increases the number of winners and improves the auctioneer's profit.

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]

Nowadays, data-based analysis plays a crucial role in both academia and industry, and thus data has been recognized a valuable resource to be traded. Because data generated from various end devices is dispersive, heterogeneous and stored in a distributed way, an impartial and trustworthy platform is indeed essential to make a market for trading entities (data buyers and sellers). In a practical market, all the entities tend to maximize their profits rather than the overall social welfare, while the platform, instead, should focus on social welfare maximization by applying a sophisticated mechanism that is imperative to guide the entities to distribute data efficiently. In this paper, we propose the Many-to-Many Data Trading Algorithm (MMDTA), and then formulate a data trading model with multiple entities based on MMDTA. Finally, the simulations validate convergence behavior and economic properties of MMDTA which is effective in different market scales and with different market power. [ABSTRACT FROM AUTHOR]