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Task offloading is a promising technology to exploit the available resources in edge cloud efficiently. Many incentive mechanisms for offloading systems have been proposed. However, most of existing works study the centralized incentive mechanisms under the assumption that all mobile edge infrastructures are operated by a central cloud. In this paper, we aim to design the auction-based truthful incentive mechanisms for heavily loaded task offloading system in heterogeneous MECs. We first study the homogeneous MEC situation and present a global auction executed in the central cloud as a benchmark. For the heterogeneous MEC situation, we model the system as a dual auction framework, which enables the heterogeneous MECs to perform cross-edge task offloading without the participation of central servers. Specifically, we design two dual auction models: secondary auction-based model, which enables the system to offload tasks from a large-scale region in a single auction, and double auction-based model, which is suitable for the time sensitive tasks. Then the auctions for these two dual auction models are proposed. Through rigorous theoretical analysis, we demonstrate that the proposed auctions achieve desirable properties of computational efficiency, individual rationality, budget balance, truthfulness, and guaranteed approximation. The simulation results show that the secondary auction and double auction can obtain 14.5% and 4.2% more social welfare than comparison algorithm on average, respectively. In addition, the double auction has great advantage in terms of computation efficiency.

Double auction mechanisms have been designed to trade a variety of divisible resources (e.g., electricity, mobile data, and cloud resources) among distributed agents. In such divisible double auction, all the agents (both buyers and sellers) are expected to submit their bid profiles, and dynamically achieve the best responses. In practice, these agents may not trust each other without a market mediator. Fortunately, smart contract is extensively used to ensure digital agreement among mutually distrustful agents. The consensus protocol helps the smart contract execution on the blockchain to ensure strong integrity and availability. However, severe privacy risks would emerge in the divisible double auction since all the agents should disclose their sensitive data such as the bid profiles (i.e., bid amount and prices in different iterations) to other agents for resource allocation and such data are replicated on all the nodes in the network. Furthermore, the consensus requirements will bring a huge burden for the blockchain, which impacts the overall performance. To address these concerns, we propose a hybridized TEE-Blockchain system (system and auction mechanism co-design) to privately execute the divisible double auction. The designed hybridized system ensures privacy, honesty and high efficiency among distributed agents. The bid profiles are sealed for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments and empirical studies to validate the system and auction performance using two real-world applications.

With the rapid popularity of IoT devices and smart city technologies, mobile edge computing considered as a promising technology appears, which can support real-time applications at the edge of the network. In this paper, we focus on the auction mechanism in mobile edge computing to allocate resources between IoT devices regarded as buyers and edge servers regarded as sellers and establish a two-sided relationship based on network economics to depict the process of resource allocation. Next, two double auction mechanisms named TMF and EMF are proposed, which aim at optimizing the rationality of resource allocation by considering successful trades and social welfare. Both TMF and EMF are applied to the scenario that each seller services to multi-buyers and each buyer also requests for multi-sellers with different sizes of resources requirements. The TMF mechanism we proposed is a truthful mechanism for both buyers and sellers and EMF is an efficient mechanism with high successful trades and social welfare. Then, a theoretical analysis is given and our proposed mechanisms are proved to be computational efficiency, individual rationality and incentive compatibility. Finally, the simulation results indicate that our mechanisms perform better than traditional mechanisms and improve successful trades and social welfare in mobile edge computing.

Published online: 28 September 2022 We study minimal conditions for competitive behavior with few agents. We adapt a price-quantity strategic market game to the indivisible commodity environment commonly used in double auction experiments, and show that all Nash equilibrium outcomes with active trading are competitive if and only if there are at least two buyers and two sellers willing to trade at every competitive price. Unlike previous formulations, this condition can be verified directly by checking the set of competitive equilibria. In laboratory experiments, the condition we provide turns out to be enough to induce competitive results, and the Nash equilibrium appears to be a good approximation for market outcomes. Subjects, although possessing limited information, are able to act as if complete information were available in the market. This article was published Open Access with the support from the EUI Library through the CRUI - Springer Transformative Agreement (2020-2024)

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.

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.

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.

Fees are omnipresent in markets but, with few exceptions, are omitted in economic models-such as Double Auctions-of these markets. Allowing for general fee structures, we show that their impact on incentives and efficiency in large Double Auctions hinges on whether the fees are homogeneous (as, e.g., fixed fees and price fees) or heterogeneous (as, e.g., bid-ask spread fees). Double Auctions with homogeneous fees share the key advantages of Double Auctions without fees: markets with homogeneous fees are asymptotically strategyproof and efficient. We further show that these advantages are preserved even if traders have misspecified beliefs. In contrast, heterogeneous fees lead to complex strategic behavior (price guessing) and may result in severe market failures. Allowing for aggregate uncertainty, we extend these insights to market organizations other than the Double Auction.

The article explores the essence of online auction as an environment for the promotion of goods through e-commerce technology, which is rapidly developing and changing. The aim of the article is to study theoretical issues related to the identification of the peculiarities of online auctions functioning to stimulate sales of used cars. The processes occurring in the e-commerce environment are the object of research. The basic principles of the formation of a business model of an online auction are determined, the main types of auctions are considered, namely: standard, Dutch, simultaneous bid auction, auction of closed bid and double auction. The advantages and disadvantages of online auctions during the bidding process are disclosed. The groups of auctions in Ukraine are determined. The process of selling used cars at the auction is investigated and its main stages are described. Based on the experience of the specialized international company "ALD Automotive Ukraine", the activities of an online auction for the sale of used cars are analyzed in the article. The strengths and the weaknesses of using online auctions to drive sales of used cars have been identified. The strengths are the following: the access to complete information, the availability of the most powerful tools for selecting a vehicle according to technical specifications, cost reduction related to the advertising campaign, full automation of the process of servicing potential customers, the support of customer feedback, quick informing of the partners and customers about new services, the lack of corruption schemes, after-sales support and resolving contentious issues, etc. The weaknesses are the following: frequent lack of opportunity for the buyer to inspect the car personally, possible long-term delivery of goods, etc.

The bubble-and-burst pattern in asset markets is among the most replicable results in experimental economics. Using controlled laboratory experiments, we compare mispricing in markets organized by standard double auction rules with mispricing in markets organized by two alternative sets of clock auctions. The double Dutch auction, shown to be more efficient than the double auction in past commodity market experiments, does not eliminate bubbles. However, the English Dutch auction yields prices reflective of underlying fundamentals and succeeds in taming bubbles even with inexperienced traders in a declining fundamental value environment with an increasing cash-to-asset ratio. (JEL C91, D44, G12)

Periodic Double Auctions (PDAs) are commonly used in the real world for trading, e.g. in stock markets to determine stock opening prices, and energy markets to trade energy in order to balance net demand in smart grids, involving trillions of dollars in the process. A bidder, participating in such PDAs, has to plan for bids in the current auction as well as for the future auctions, which highlights the necessity of good bidding strategies. In this paper, we perform an equilibrium analysis of single unit single-shot double auctions with a certain clearing price and payment rule, which we refer to as ACPR, and find it intractable to analyze as number of participating agents increase. We further derive the best response for a bidder with complete information in a single-shot double auction with ACPR. Leveraging the theory developed for single-shot double auction and taking the PowerTAC wholesale market PDA as our testbed, we proceed by modeling the PDA of PowerTAC as an MDP. We propose a novel bidding strategy, namely MDPLCPBS. We empirically show that MDPLCPBS follows the equilibrium strategy for double auctions that we previously analyze. In addition, we benchmark our strategy against the baseline and the state-of-the-art bidding strategies for the PowerTAC wholesale market PDAs, and show that MDPLCPBS outperforms most of them consistently.

Cognitive radio can improve spectrum utilization through spectrum sharing between primary and secondary users, thereby alleviating spectrum scarcity. Spectrum auction is one of the effective methods that can be utilized to achieve spectrum sharing. However, traditional spectrum auction is unable to guarantee the truth of the price given by bidders comprehensively, while neglecting the online characteristics of the buyers request arrival. Existing truthful online auction mechanisms do not consider the privacy leakage problem and fail to provide adequate privacy protection for bidders. In this paper, we propose a privacy-preserving spectrum allocation strategy based on a double online truthful auction mechanism. Specifically, we first propose a critical bid determination algorithm to guarantee the truth of the auction. Then, the channel allocation is modeled as a social welfare maximization optimization problem. Since the problem is NP-hard and it is difficult to solve in polynomial time. We propose a greedy algorithm that can effectively obtain a feasible solution to the problem. An online dynamic accessing mechanism is further designed by considering the arrival of requests of secondary users. Finally, differential privacy is introduced to encrypt sensitive information such as buyers location privacy and time dynamic. Experimental results validate our auction mechanism achieving higher social welfare and spectrum utilization.

In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows electric vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designing an effective auction market in the smart grid. First, the auction market scheme shall be online, allowing buyers and sellers to enter the market at any time, and satisfy several critical economic properties (individual rationality, incentive compatibility, and so on.). Second, the sensitive information of participants shall be protected in the auction process. To address these challenges, we present a novel privacy-preserving online double auction scheme based on differential privacy. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, aiming at solving the social welfare maximization problem to match buyers and sellers. The principle of differential privacy is leveraged to protect the privacy of EVs’ sensitive bidding information. Via theoretical analysis, we demonstrate that our designed auction scheme satisfies both economic and privacy-preserving properties, including individual rationality, incentive compatibility, weak budget balance, and $\varepsilon $ -differential privacy. We conduct an extensive performance evaluation to measure the effectiveness of our proposed scheme. Our experimental results show that the proposed auction scheme can not only ensure the privacy of participants but also effectively facilitates demand response in the smart grid, with respect to social welfare, satisfaction ratio, social efficiency, and computational overhead.

Cloud manufacturing is a new kind of networked manufacturing model. In this model, manufacturing resources are organized and used on demand as market-oriented services. These services are highly uncertain and focus on users. The information between service demanders and service providers is usually incomplete. These challenges make the resource scheduling more difficult. In this study, an iterative double auction mechanism is proposed based on game theory to balance the individual benefits. Resource demanders and providers act as buyers and sellers in the auction. Resource demanders offer a price according to the budget, the delivery time, preference, and the process of auction. Meanwhile, resource providers ask for a price according to the cost, maximum expected profit, optimal reservation price, and the process of auction. A honest quotation strategy is dominant for a participant in the auction. The mechanism is capable of guaranteeing the economic benefits among different participants in the market with incomplete information. Furthermore, the mechanism is helpful for preventing harmful market behaviors such as speculation, cheating, etc. Based on the iterative double auction mechanism, manufacturing resources are optimally allocated to users with consideration of multiple objectives. The auction mechanism is also incentive compatibility.

In this paper, a general-purpose double auction mechanism that is specifically relevant for energy transactions among competing buyers and sellers, i.e., agents, in a microgrid has been investigated. This auction algorithm is implemented by means of a microgrid controller (MC), and is individually rational, weakly budget balanced, and efficient (social welfare maximizing). Furthermore, the mechanism does not violate any physical grid constraints and does not require agents’ private information. These characteristics of the auction are accomplished by a suitable surrogate objective function which implicitly maximizes the social welfare while the agents place bids in a selfish manner and can sell or procure energy. The sellers of energy are modeled as prosumers that are equipped with photovoltaic panels as well as advanced smart meters and are willing to participate in selling their excess energy due to saturation in their energy consumption utility curves. Buyers of energy are assumed to be conventional consumers with smart meter capabilities to communicate with the MC and place bids to get optimal allocations. Buyers aim to maximize their energy usage utility plus their payment amount whereas sellers maximize their own energy consumption utility plus the reimbursement that they receive for selling their excess generation. Furthermore, the mechanism allows the possibility of sellers’ coalition where a fair redistribution of the energy supply is possible in a competitive market with less demand and more supply.

A combinatorial double auction is a type of double-side auction which makes buyers and sellers trade goods more conveniently than multiple combinatorial auctions. In this paper, we consider the combinatorial double auction problem in which there are transaction costs, supply constraints and surplus constraints. We formulate the WDP of combinatorial double auction problem as an integer programming problem formulation. The winner determination problem (WDP) in combinatorial double auctions poses a challenge due to computational complexity. Particle swarm optimization (PSO) is a well-known approach to deal with complex optimization problems. In the existing literature, different variants of PSO algorithms have been proposed. However, there still lacks a comparative study on effectiveness of applying different variants of PSO algorithms in combinatorial double auctions. As standard discrete PSO (DPSO) algorithm suffers from premature convergence problem, we adopt a coevolution approach to develop a discrete cooperatively coevolving particle swarm optimization (DCCPSO) algorithm that can scale with the problem. The effectiveness of the proposed algorithm is verified by comparing the results with several variants of PSO algorithms and Differential Evolution algorithms through simulation. Simulation results indicate that the proposed DCCPSO algorithm significantly outperforms these variants of PSO algorithms and Differential Evolution algorithms in most test cases of combinatorial double auctions.

Spectrum auction is deemed as an efficient approach for redistributing spectrum and realizing dynamic spectrum access. Though there have been extensive research endeavors in designing spectrum auctions that are truthful with desirable social welfare, security and privacy concerns give rise to new challenges and calls for the design of privacy- preserving spectrum auction mechanisms. In this article, we begin with an overview of conventional spectrum auction mechanisms, then discuss the potential security threats in them. In particular, bidders' private information, for example, bid values and geo-locations, are submitted to the non-trustworthy auctioneer and can be observed by rival bidders who may leverage this information to rig the auction. To address this problem, we outline the framework for privacy-preserving auction mechanism design, which applies cryptographic tools to the original auction and ensures that the private information of bidders is protected, while the basic functionalities of the auction are maintained. Based on the proposed framework, we further construct a privacy-preserving heterogeneous spectrum double auction, which is shown to uphold the closely similar allocation efficiency with the original auction, and realizes privacy preservation with moderate communication and computation overheads.

With the technologies of distributed energy resources and digital communication systems, energy consumers can behave like proactive entities, called prosumers, and participate in peer-to-peer energy trading market. This paper proposes a new market model for peer-to-peer energy trading based on double auction approach. By doing this, the proposed method provides the market participants more flexibility to decide the agreed price between any two partners of contract when compared to the other types of double auctions, i.e., uniform double auction, discriminatory double auction; pay-as-bid, pay buyer’s price, and pay seller’s price, and k-double auction. In the simulation, the generation and consumption data of all the market participants are randomized by using the real data from photovoltaic owner company and utility load research. Moreover, the trading period is set to be 1 hour which is separated into 2 intervals: bidding and power exchange. This paper focuses on the first 30 minutes for bidding period. The simulation results show that the proposed method can create more flexibility of setting exchange price.

In this paper, we propose a secure and efficient blockchain-based data trading approach for the Internet of Vehicles (IoV). First, we apply consortium blockchain technologies to ensure secure and truthful data trading, and propose a general blockchain-based data trading framework for IoV. Second, to improve the efficiency of data trading and encourage more participants to trade data, we propose an iterative double auction mechanism with the purpose of achieving social welfare maximization, in which pricing rules of buyers and sellers are designed to induce participants to submit bids and to decide the amount of traded data and its price among buyers and sellers. In particular, in our algorithm, the hidden information of individuals can be extracted gradually so that the privacy of participants in data trading can be protected well. Finally, the experimental results show the efficiency of our proposed algorithm. Moreover, the correctness of social welfare maximization, incentive compatibility, individually rationality, and weakly budget balance of our auction mechanism are verified in the experiments.

Purpose In this paper, the main focus is on supply and demand auction systems with resource pooling in modern supply chain from a theoretical modeling perspective. The supply and demand auction systems in modern supply chains among manufacturers and suppliers serve as information sharing mechanisms. The purpose of this paper is to match the supply and demand such that a modern supply chain can achieve incentive compatibility and economic efficiency. The authors design such a supply and demand auction system that can integrate resources to efficiently match the supply and demand. Design/methodology/approach The authors propose three theoretic models of modern supply chain auctions with resource pooling according to the Vickrey auction principle. They are supply auction model with demand resource pooling, demand auction model with supply resource pooling, and double auction model with demand and supply resource pooling. For the proposed auction models, the authors present three corresponding algorithms to allocate resources in the auction process by linear programming, and study the incentive compatibility and define the Walrasian equilibriums for the proposed auction models. The authors show that the solutions of the proposed algorithms are Walrasian equilibriums. Findings By introducing the auction mechanism, the authors aim to realize the following three functions. First is price mining: auction is an open mechanism with multiple participants. Everyone has his own utility and purchasing ability. So, the final price reflects the market value of the auction. Second is dynamic modern supply chain construction: through auction, firm can find appropriate partner efficiently. Third is resources integration: in business practices, especially in modern supply chain auctions, auctioneers can integrate resources and ally buyers or sellers to gain more efficiency in auctions. Originality/value In the paper, the authors propose three theoretic models and corresponding algorithms of modern supply chain auctions with resource pooling according using the Vickrey auction principle, which achieves three functions: price mining, dynamic modern supply chain construction and resources integrating. Besides, these proposed models are much closer to practical settings and may have potential applications in modern supply chain management.

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.

Policy-mapping mechanisms can efficiently help to realize the exchange and the sharing of cross-domain information at low cost. However, due to concerns over policy conflicts, if not sufficient incentives, most selfish domains are often disinterested in helping others to implement policy mapping cooperatively. Thus an appropriate incentive mechanism is required. In this paper, we propose an incentive mechanism to encourage selfish domains to take part in policy mapping and resolve policy conflicts. Formulating conflict resolution as a double auction and solving Bayesian Nash equilibrium, we design the optimal asking/bidding price scheme to maximize the benefits of the domains involved. Simulations demonstrate that our approach can efficiently incentivize selfish domains to take part in cooperation.

Auction mechanisms for exchanging divisible resources (e.g., electricity, cloud resources, and network bandwidth) among distributed agents have been extensively studied. In particular, divisible double auction allows both buyers and sellers to dynamically submit their prices until convergence. However, severe privacy concerns may arise in the double auctions since all the agents may have to disclose their sensitive data such as the bid profiles (i.e., bid amounts and prices in different iterations) to other agents for resource allocation. To address such concerns, we propose an efficient and private auction system ETA by co-designing the divisible double auction mechanism with the Intel SGX, which executes the computation for auction while ensuring confidentiality and integrity for the buyers/sellers’ sensitive data. Furthermore, ETA seals the bid profiles to achieve a Progressive Second Price (PSP) auction for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments to validate the performance of private auction system ETA.

We model and analyze a peer-to-peer (p2p) energy trading market under uncertainty in the traded energy, in a setting with multiple sellers and buyers. A set of prosumers sell in the market their energy surplus units, which are subject to uncertainty for being actually available, while other prosumers buy energy to cover their deficit units, which are subject to uncertainty for being actually needed. Given the different levels of uncertainty of different prosumers and different energy units, the p2p trading problem is to match energy demand and supply and to specify the payments of buyers and compensations to sellers. We propose an innovative variant of the Vickrey-Clarke-Groves (VCG) auction customized to our setting, motivated by the properties of the standard form of the VCG auction, namely maximizing social welfare while ensuring participants' truthfulness. We determine the bidding profiles of players by considering the uncertainty in the declared amounts of energy surplus or deficit. Moreover, we develop a low-complexity allocation rule, that provably leads to maximization of the expected social welfare, where the expectation is with respect to uncertainties of energy units. We also derive closed-form expressions for winners' payments. We compare our mechanism to a double auction, which is currently used as a p2p trading mechanism. The results reveal that our mechanism outperforms the double auction one, and lead to interesting intuitions and guidelines that shed light into p2p energy market design.

The electricity market, due to its complex under-lying physical constraints, suffers from market manipulation. Most existing markets employ the double auction to organize the markets: the independent system operator collects the bidding information from both the supply sides and solves the Walrasian equilibrium to conduct dispatch. Market manipulation arises when market players strategically bid their information. Hence to contain the manipulation, one promising solution is to design the double auction mechanism to induce truthful bidding. In this work, we customize four double auction mechanisms (Walrasian equilibrium Mechanism, VCG mechanism, MUDA (Lottery) mechanism, and MUDA (VCG) mechanism) for the electricity market. After proposing key metrics to evaluate the performance of various mechanisms, we conduct extensive numerical studies based on the real market data for a thorough comparison between the four mechanisms and identify the unique features for each mechanism. This could serve as the theoretical guidance for the double auction mechanism design for the electricity market.

The advent of 5G networks around the world has resulted in greater bandwidth being available to more users, but has also led to an unprecedented increase in network traffic. Device-to-Device (D2D) communication has emerged as one of the effective strategies for reduction of network load and sharing of bandwidth among users. The present work examines a double auction game based strategy for efficient sharing of bandwidth among users who may act as buyers or sellers of the bandwidth. Standard strategies such as natural ordering are contrasted with a Bayesian approach. Finally, the Bayesian Nash Equilibrium is achieved through simulation of the network and contrasted with results obtained through natural ordering based auction.

With the development of large-scale data centers, how to reduce the energy consumption of data centers has becoming one of the most important issues in the cloud computing. To provide the better solution to resource allocation across data centers, one resource allocation scheme based on the Priority Combinatorial Double Auction (PCDA) is proposed. In the PCDA, the auction process of cloud resources includes three phases: cloud user brokers quote, cloud resource providers bidding, and the resource administrate negotiation the bidding. Through the priority of users and the urgency of tasks, PCDA would estimate the SLA (Service Level Agreement) violations for each task during the auction process, and compute the profit of cloud providers. At the same time, multiple transactions are allowed per round. Cloud resources would be allocated reasonably based on the user levels. Simulation results demonstrate that the proposed PCDA can guarantee the bidding success rate. Compared with the traditional combinational double auction (CDA) - one auction, one transaction, the proposed PCDA can reduce the energy consumption by 35.00% during the auction period, and increase the profit of cloud providers by about 38.84%.

The present study contributes to the ongoing debate on possible costs and benefits of insider trading. We present a novel call auction model with insider information. Our model predicts that more insider information improves informational efficiency of prices, but this comes at the expense of reduced gains from trade. The model further implies that in the presence of insider information the call auction performs worse than continuous double auction. Testing these hypotheses in the lab we find that insider information increases informational efficiency of call auction prices but does not decrease the realized gains from trade. Contrary to the theoretical prediction, the call auction does not perform worse than the continuous double auction. In fact, when the probability of insider information is high, the call auction has the most informative prices and highest realized gains from trade. Our experiment provides new evidence, from markets with very asymmetrically dispersed information, that lends support to the decision by many stock exchanges to use call auctions when information asymmetries are severe and the need for accurate prices is large, e.g., at the open or close of the trading day.

We study uniform-price double auctions augmented with a class of subsidy schemes. Using these subsidies, any profile of linear demand schedules can be implemented as an equilibrium outcome. By revenue equivalence, all other mechanisms which implement linear auction equilibria are essentially equivalent to some subsidy scheme in our class. We show that, under a linear dependency condition on primitives, fully efficient, budget balanced, and individually rational trade is possible. Thus, the welfare loss from equilibria in uniform-price auctions without subsidies is a non-fundamental distortion, which can be fixed with better mechanism design. However, we show that monopolist trading platforms have incentives to reduce allocative efficiency to increase revenue, even as the number of traders becomes large.

The double auction marketplaces usually charge fees to traders to make profits, but little work has been done on analyzing how marketplaces charge appropriate fees to make profits in multiple marketplaces competing environment. In this paper, we investigate this problem by using game theory. Specifically, we consider four typical types of fees, and use Fictitious Play algorithm to analyze the Nash equilibrium market selection and bidding strategy of traders in competing environment when different types of fees are charged. Building on this, we draw insights about how the marketplaces charge strategies in equilibrium when sellers and buyers have made the choice of the marketplace and bid in Nash equilibrium. Furthermore, we investigate which type of fees is more competitive in terms of maximizing profits while keeping traders staying in the marketplaces. Our experimental results provide useful insights on setting charging strategies for competing double auction marketplaces.

The auction mechanism is deemed to be an effective method to address the problem of spectrum scarcity. Numerous spectrum auction mechanisms can alleviate spectrum shortage under the consideration of truthfulness, social welfare maximization and spectrum reusability, while the privacy preservation and preferences of primary/secondary users have not been fully discussed. In this paper, we propose a matching based double auction mechanism for spectrum trading with differential privacy (MastDP) to protect the privacy of buyers/sellers from the untrustworthy auctioneer, other buyers/sellers and other potential parties. Each participant adds distributed differential private noise following Geom(α) distribution to his bid value and encrypts the noisy bid value. The auctioneer can decrypt only the sum of all uploaded noisy bid values and determines the clearing price by using its private key. Based on the clearing price, the matching theory is adopted to maximize the winning participants' revenue while fully considering their preferences and spectrum reuse. Simulation results show that MastDP achieves satisfactory performance in terms of economic properties' privacy preservation and spectrum trading efficiency.

This paper studies a double auction market where a set of sellers sell homogeneous items to a set of buyers and each buyer (seller) buys (sells) one item. Moreover, each buyer is linked to a set of other potential buyers via a network. The goal is to design a double auction such that the buyers are incentivized to invite their neighbours to join the auction to improve the allocation efficiency and the sellers' revenue, which is not achievable under the existing double auction mechanisms. Moreover, it is well-known that budget balance is not compatible with other desirable properties at the same time. Hence, we design double auction mechanisms that both incentivize buyers to invite their neighbours and guarantee budget balance. We first show that an extension of McAfee's trade reduction [8] cannot achieve this. Therefore, we propose a new mechanism called Double Network Auction (DNA) to guarantee the properties.

Double auction-based pricing model is an efficient pricing model to balance users’ and providers’ benefits. Existing double auction mechanisms usually require both users and providers to bid with the unit price and the number of VMs. However, in practice users seldom know the exact number of VMs that meets their task requirements, which leads to users’ task requirements inconsistent with providers’ resource. In this paper, we propose a truthful double auction mechanism, including a matching process as well as a pricing and VM allocation scheme, to bridge users’ task requirements and providers’ resources in two-sided cloud markets. In the matching process, we design a cost-aware resource algorithm based on Lyapunov optimization techniques to precisely obtain the number of VMs that meets users’ task requirements. In the pricing and VM allocation scheme, we apply the idea of second-price auction to determine the final price and the number of provisioned VMs in the double auction. We theoretically prove our proposed mechanism is individual-rational, truthful and budget-balanced, and analyze the optimality of proposed algorithm. Through simulation experiments, the results show that the individual profits achieved by our algorithm are 12.35 and 11.02 percent larger than that of scale-out and greedy scale-up algorithms respectively for 90 percent of users, and the social welfare of our mechanism is only 7.01 percent smaller than that of the optimum mechanism in the worst case.

Considering a parking platform with multiple slot demanders and multiple slot suppliers, this paper addresses two truthful double auction mechanisms for shared parking problem, where the demanders targeted by the proposed mechanisms are with flexible schedules and for non-work activities, and the suppliers are with the typical daily ‘driving go out early and come back at dusk’ pattern. To provide flexible match schemes and increase the trading scale, we allow “all-or-nothing” principle for demanders and “one-to-many” principle for suppliers. Based on the parking slot allocation rule and the transaction payment rule, we first relax the single output restriction (suppliers can only submit bids on a single unit of one commodity) and propose a “demander competition padding method (DC-PM)” auction mechanism, which is used to solve potential budget deficit. To avoid the likely distorted social welfare resulted by the DC-PM auction mechanism and to add budget surplus, we further modify the parking slot allocation rule and the transaction payment rule in the DC-PM auction mechanism, and propose a “modified demander competition padding method (MDC-PM)” auction mechanism. Three economical properties of both auction mechanisms, i.e. incentive compatibility, individual rationality and budget balance, are proved. Numerical experiments show that the proposed two auction mechanisms can realize asymptotic efficiency as both demanders and suppliers approach infinity. Moreover, the DC-PM auction mechanism is superior to the MDC-PM auction mechanism with respect to participants’ utilities and can maintain the strict lexicographic allocation order, and the MDC-PM auction mechanism outperforms the DC-PM auction mechanism in terms of welfare efficiency and platform's payoff. Issues of managerial implications for shared parking problem are further discussed in this paper.

We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers' preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers' "fairness" expectations. The auction's termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system's profit, indicating its effectiveness for a seller who needs to operate under the "fairness" constraint., NA

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 ( $\mathsf {SINR}$ ) constrained interference model; this model is more realistic as users can simultaneously communicate as long as their requirements $\mathsf {SINR}\text{s}$ 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.

A classical trading experiment consists of a set of unit demand buyers and unit supply sellers with identical items. Each agent’s value or opportunity cost for the item is his private information, and preferences are quasilinear. Trade between agents employs a double oral auction (DOA) in which both buyers and sellers call out bids or offers that an auctioneer recognizes. Transactions resulting from accepted bids and offers are recorded. This continues until there are no more acceptable bids or offers. Remarkably, the experiment consistently terminates in a Walrasian price. The main result of this article is a mechanism in the spirit of the DOA that converges to a Walrasian equilibrium in a polynomial number of steps, thus providing a theoretical basis for the empirical phenomenon described previously. It is well known that computation of a Walrasian equilibrium for this market corresponds to solving a maximum weight bipartite matching problem. The uncoordinated but mildly rational responses of agents thus solve in a distributed fashion a maximum weight bipartite matching problem that is encoded by their private valuations. We show, furthermore, that every Walrasian equilibrium is reachable by some sequence of responses. This is in contrast to the well-known auction algorithms for this problem that only allow one side to make offers and thus essentially choose an equilibrium that maximizes the surplus for the side making offers. Our results extend to the setting where not every agent pair is allowed to trade with each other.

With the unprecedented proliferation of mobile devices, Mobile Crowd Sensing (MCS) emerges as a promising computing paradigm which utilizes sensor-embedded smart devices to collect sensory data. Recently, a number of privacy-preserving auction-based incentive mechanisms have been proposed. However, none of them guarantees the quality of sensing data in double-side auction scenarios. In this paper, we propose a Differentially Private Double Auction With Reliability-Aware in Mobile Crowd Sensing (DPDR). Specifically, we design the incentive mechanism by employing the exponential mechanism in double-side auction to select the clearing price tuple. Moreover, to collect precise sensory data, we heuristically choose more reliable workers as candidates for each clearing price tuple. We further improve the social welfare of the mechanism by designing the utility function with less sensitivity, or adopting a more practical pricing strategy. Through theoretical analysis, we demonstrate that our mechanisms can guarantee both differential privacy and economic properties, including individual rationality, budget balance, approximate truthfulness and approximate maximal social welfare. Extensive experimental results show that the improved mechanisms can achieve better performance than DPDR in term of social welfare, and all proposed mechanisms can produce high-quality data.

We study a double auction environment where buyers and sellers have interdependent valuations and multi-unit demand and supply. We propose a new mechanism which satisfies ex post incentive compatibility, individual rationality, feasibility, non-wastefulness, and no budget deficit. Moreover, this mechanism is asymptotically efficient in that the trade outcome in the mechanism converges to the efficient level as in a competitive equilibrium as the numbers of the buyers and sellers become large. Our mechanism is the first double auction mechanism with these properties in the interdependent values setting.

Auction is a highly effective trading form for distributing resource among buyers in a market at competitive prices, and has been applied to many domains, e.g. spectrum allocation in wireless networks and the virtual machine allocation in cloud computing. Most of existing auction mechanisms based on McAfee double auction calculate the uniform clearing prices for winning buyers no matter what channel they acquired, which does not reflect the differences of buyers' personalised preferences for heterogeneous spectrums. Hence, in this paper, we propose a truthful double auction scheme, named TRUDA, which incorporates the marginal effect of buyer–seller pair into auction mechanism design and considers the case in which buyers are mutually exclusive. We show analytically that this auction mechanism guarantees the economic-robustness of the auction and has polynomial time complexity.

For procuring from sellers with decreasing returns, there are known efficient dynamic auction formats. In this paper, we design an efficient dynamic procurement auction for the case where goods are homogeneous and bidders have increasing returns. Our motivating example is the procurement of vaccines, which often exhibit large fixed costs and small constant marginal costs. The auctioneer names a price and bidders report the interval of quantities that they are willing to sell at that price. The process repeats with successively lower prices, until the efficient outcome is discovered. We demonstrate an equilibrium that is efficient and generates VCG prices. (JEL D24, D44, F53, H57, I11, L14, L65)

We study the design of CDS auctions, which determine the payments by CDS sellers to CDS buyers following the defaults of bonds. Through a simple model, we nd that the current two-stage design of CDS auctions leads to biased prices. First, dealers may manipulate the rst-stage quotes to prot from their CDS positions. Second, various restrictions imposed on both stages of CDS auctions prevent certain investors from participating in the price-discovery process. The resulting allocations of bonds are also inecient. As a remedy, we propose a double auction design that delivers more ecient price discovery and allocations.

This paper presents an auction mechanism for energy trading between multiple microgrids. We consider a region consisting of multiple interconnected microgrids wherein given time t, some microgrids have excessive energy that they wish to sell, whereas other microgrids desire to buy additional energy to meet their local demands. In this paper, we introduce a combinatorial double auction mechanism for such trades to happen. The participants can bid as a combination of bids along with a single bid format. We also present a novel winner determination solution for combinatorial double auctions using evolutionary algorithms. Two algorithms combining genetic algorithm and particle swarm optimization are presented in this paper. Price determination for each trade is also explained. Using MATLAB, performance evaluation and stability tests of the proposed auction technique are performed and presented.

Cloud computing is able to allocate different resources as virtual machines (VMs) to users, who need only pay for the amount of resources used. Two of the challenges in clouds are resource allocation and pricing in such a way to satisfy both cloud providers and users. Existing allocation and pricing mechanisms cannot guarantee increased profits due to various reasons. A better solution to increase the satisfaction of both parties, which is supported by economic theory, is the employment of auction-based allocation and pricing mechanisms. In these mechanisms, cloud resources and services are awarded based on the highest bids, while winners receive the quality of services expected. However, most existing auction-based mechanisms are inefficient and cannot be used in real clouds due to high computational or communication overhead, the bid function’s time complexity, and/or its inaccurate estimates. In the present paper, a lightweight mechanism is introduced which can be utilized in the real-world application of clouds. The currently proposed mechanism is a winner-bid auction game that seals users’ bids by a multi-criteria valuation-based bid function and sends them to the auctioneer. During scheduling, the auctioneer awards VMs exclusively to users with the highest bids. The presented approach is an online auction whose main aim is to increase the profits of the provider and user from different criteria. While determining the Nash equilibrium, the current study specifies the prices to be paid by users in various cases and proves the truthfulness of the proposed method. Finally, the effectiveness of the presented mechanism is examined through extensive experiments on different simulation scenarios and actual workload data.

In a sealed-bid auction, bidders simultaneously submit their sealed bids to the auctioneer without knowledge of the others’ bids. The auctioneer will then declare the bidder with the highest price (or second-highest price) as the winner during the opening stage. Although existing bidding solutions focus on ensuring certain characteristics of the auction, including correctness, fairness, privacy protection, and confidentiality, it is difficult for losing bidders to verify whether the winner is a genuine bidder or just a fake bidder that is manipulated by a malicious party (e.g., a malicious auctioneer). In this paper, we introduce a fair indictment mechanism for an online sealed-bid auction that includes self-enforcing privacy. Our solution allows for an honest bidder to detect malicious activity and provides the bidder with verifiable evidence to indict a dishonest party. A successful indictment will give an incentive to the honest bidder and will result in a withdrawal of the result of the auction, whereas a failed indictment will require the involved complainer to pay a penalty. We achieve this goal using a scheme designed with an oblivious polynomial evaluation and homomorphic cryptosystem. We also involve a semi-honest verification agent in the indictment process to help the honest party verify the winning bid. This prevents the auctioneer from controlling the entire auction process. We also provide an analysis of the indictment, requirements, security and efficiency of the proposed mechanism and demonstrate the use of our solution in a multiple-item sealed-bid auction (i.e., combinatorial auction).

A detailed study of the double auction mechanisms in cloud is provided.A framework for double auction in cloud is proposed for a future cloud market.A model TMDA is proposed to infer how a truthful double auction can be designed.TMDA is asymptotically efficient, individual rational, truthful and budget-balanced.Various challenges and future scope in double auction in cloud are also presented. The cloud system is designed, implemented and conceptualized as a marketplace where resources are traded. This demands efficient allocation of resources to benefit both the cloud users and the cloud service providers. Accordingly, market based resource allocation models for cloud computing have been proposed. These models apply economy based approaches e.g. auction, negotiation etc. This work makes a detailed study of the double auction mechanisms and their applicability for the cloud markets. A framework for a future cloud market using double auction is also proposed. As most of the existing works in double auction confines only resource allocation, therefore, a Truthful Multi-Unit Double Auction mechanism (TMDA) is proposed that would help researchers to understand how a truthful double auction mechanism can be designed. TMDA is proven to be asymptotically efficient, individual rational, truthful and budget-balanced. TMDA would also encourage researchers to contribute in this emerging area. The performance of TMDA, which addresses the interests of both the cloud user and the provider, has been validated through simulation study. Various challenges in the realization of double auction mechanisms in cloud computing along-with the future possibilities are also presented.

Mobile cloud computing offers an appealing paradigm to relieve the pressure of soaring data demands and augment energy efficiency for future green networks. Cloudlets can provide available resources to nearby mobile devices with lower access overhead and energy consumption. To stimulate service provisioning by cloudlets and improve resource utilization, a feasible and efficient incentive mechanism is required to charge mobile users and reward cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. Truthfulness and system efficiency are two crucial properties in addition to computational efficiency, individual rationality and budget balance. In this paper, we first propose a feasible and truthful incentive mechanism (TIM), to coordinate the resource auction between mobile devices as service users (buyers) and cloudlets as service providers (sellers). Further, TIM is extended to a more efficient design of auction (EDA). TIM guarantees strong truthfulness for both buyers and sellers, while EDA achieves a fairly high system efficiency but only satisfies strong truthfulness for sellers. We also show the difficulties for the buyers to manipulate the resource auction in EDA and the high expected utility with truthful bidding.

In opportunistic networks, selfish nodes will refuse to forward messages for others to save their precious resources such as bandwidth, buffer and energy, and the system performance will degrade significantly. Therefore, it is necessary to motivate the selfish nodes to cooperatively forward messages. However, current cooperation mechanisms for opportunistic networks mainly focus on encouraging nodes to participate in message forwarding, but fail to consider the node congestion problem. When many messages are forwarded to the nodes with high connection degree, these nodes will become congested and discard most messages, which will seriously degrade the routing performance. To stimulate the selfish nodes with limited buffer to cooperatively forward messages, this paper proposes a congestion-aware node cooperation mechanism based on double auction, called CANCMDA. In CANCMDA, nodes first determine the self congestion degree according to current free buffer and message receiving speed. Then, combining message delivery probability and congestion degree, nodes trade messages based on double auction model. The double auction trade process is a bayesian game, and nodes get the optimal bid by solving the bayesian equilibrium to trade messages. The experimental results show that CANCMDA can effectively stimulate selfish nodes to cooperatively forward messages when congested, and achieves higher message delivery ratio with lower overhead ratio, compared with other mechanisms.

Spectrum auction is one of the most effective solutions to allocate the spectrum resource following the market rules and has attracted much attention from both academia and industry. However, most of the existing studies assume that the spectrum buyers’ demands are homogeneous and the interference relationship is fixed without any change with the variation of spectrum. Furthermore, the economical efficiency of auction outcome has not drawn enough attention. That motivates us to design an auction scheme to jointly consider the multi-demand of buyers, heterogeneous spectrum, and economical efficiency. In this paper, we propose a novel overlapping coalition formation-based double auction, called VERACITY, to address this problem. The auctioneer groups the conflict free buyers into the same coalition and allows a buyer to join multiple coalitions based on the heterogeneous demand. Dynamic overlapping coalition formation implemented by the auctioneer is to find the approximately optimal coalition structure corresponding to the economical efficiency outcome, i.e., maximizing the social welfare. Furthermore, we prove that VERACITY is individually rational, budget balanced, truthful, and economically efficient. Simulation results are presented to show the convergence and effectiveness of the proposed VERACITY.