Search

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.

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 and share the resources. 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. In this paper, we propose a multi-unit double auction mechanism called TARA 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. TARA achieves some important properties like truthfulness, individual rationality and budget balance for both buyers and sellers. TARA is also computationally efficient and possess high system efficiency. The simulation results also show that total utility of buyer is more than some existing double auction mechanisms.

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.

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory . Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks.

Blockchain has been widely applied in various fields, such as finance, Internet of Things, law, etc. However, it is a challenge to apply blockchain to mobile applications, as some mobile devices cannot afford computing resources required by the mining processes. Therefore, edge computing is introduced to provide computing resources for mobile devices. We propose an allocation mechanism based on combinatorial double auction to offload the mining tasks to the edge servers. The corresponding allocation and payment schemes are proposed to generate allocation results and calculate clearing prices, respectively. Moreover, we prove that the proposed mechanism is computation efficient, and it satisfies three auction properties: budget balance, individual rationality and truthfulness. Experimental results show that the proposed mechanism is able to yield higher total utility, together with good scalability. For the case of 500 miners in the auction, the total utility increases by 6 times, and utilization ratio of edge server increases by 1.5 times, in comparison to the existing benchmark approaches.

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.

Cloud Computing is a growing technology in which resources are provided as a service. The efficiency in providing the resources as a service depends on various factors. One of the major concerns is the suitable allocation of resources to the job. Hence, this paper introduces an Auction based model (CMCDA) for selecting the best customer-providers pairs based on credibility for resource provisioning in cloud computing environment. The Credibility based Multi-attribute Combinative Double Auction (CMCDA) model reduces the complexity in providing the resources for the execution of jobs and fulfill the expectations of both the customers and providers in cloud computing environment. The model also finds the best customer-providers pairs in an efficient way by calculating the credibility values before the resource provision. The highest credibility values pairs are selected as the best customer-providers pairs in the list. Here, the credibility value represents the level of customers and providers satisfaction. The time complexity of the proposed CMCDA algorithm is O(nlog(n)), Since the algorithm only goes through the sorted bid and tries to match them, being executed at most l(n + m) times. The performance of the proposed CMCDA is compared with the Combinatorial Double Auction Resource Allocation (CDARA) model which is the existing cloud double auction model, using CloudAuction simulator. The experimental results demonstrate that the proposed CMCDA performs efficiently than the existing CDARA model for resource provisioning in cloud environment.

Resource provisioning by cloud computing is one of the emerging fields for providing computing services anywhere at any time without physical barriers. It not only reduces the cost of set-up and maintenance of infrastructure at the on-site level but also helps in optimal resource utilization and reduction of e-waste generation to save the environment. It provides versatile resources to run different types of applications efficiently without worrying about delay or failure. Auction emerges as one of the most promising pricing schemes for the provisioning of resources in cloud computing. Providers and customers have different mind-set. The providers desire to earn more profit and lead the market, while customers prefer economical rates with high quality of service for the resources. In the virtual world, the providers’ genuineness is crucial, and the feedback from the customers plays a vital role that quantifies this genuineness. This paper proposes a Feedback-based Combinatorial Fair Economical Double Auction Resource Allocation Model (FCFEDARA) that investigates the genuineness of providers based on the offered prices for the required resources along with the feedback given by the customers. The proposed framework provides a common platform for the customers to access resources from different providers at optimal prices, as well as prioritizes genuine providers with good feedback over in-genuine providers with bad feedback. In the combinatorial double auction model, customers and providers submit their bundle bid along with a list of resources in the auction. The proposed model takes care of the truthfulness of the providers, penalizes market spoilers, and gives priority to those providers who have good customer feedback. Three different cases with their implications have analyzed, and evaluated based on the bid, configuration of resources, and customer feedback. Case 1 focuses on feedback given by the customers. In Case 2 and Case 3, providers’ genuineness, as well as their feedback from the customers, are key factors to determine winner providers. It is in contrast with earlier models where genuineness is based only on the bid and configuration of resources offered by providers. The proposed model was simulated on the CloudSim simulator, a Java-based simulator that provides a cloud computing environment, and results are evaluated and compared with two popular existing models: CDARA, its variant model, and the author’s previous approach (CFEDARA). The experimental results and the graphs have clearly shown the promotion of genuine providers with good feedback, fair allocation with economical pricing while maintaining the overall profit of the system. The participation ratio of customers and providers is maintained to sustain demand and supply in the virtual market.

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.

Residential energy trading systems (RETS) enable homeowners with distributed energy resources (DERs) to participate in virtualized energy markets that have the potential to reduce the peak demand of residential communities. Blockchains are key enablers of RETS, by virtue of providing a decentralized, self-governed network that mitigates concerns regarding privacy and transparency. However, more real-world case studies are needed to evaluate the techno-economic viability of blockchain-based RETS to improve their positive uptake. Thus, this article develops a permissioned blockchain-based RETS, which enables homeowners to select bidding strategies that consider the individual preferences of their DERs, and further evaluates the impact of the bidding strategies on reducing the peak demand of the community. The proposed system is implemented on the permissioned Hyperledger Fabric platform, where a decentralized ledger is used to store all energy bids, and a smart contract is used to execute a double auction mechanism and dispatch the homeowner DERs. The proposed system is validated by conducting simulations on a 8-home community using real-world data, and also by deploying the system to a Canadian microgrid, where the smart contract execution time is benchmarked. Simulation results demonstrate the efficacy of the proposed system by achieving a peak demand reduction of up to 48 kW (62%), which leads to an average savings of $1.02 M for the distribution system operator by avoiding transformer upgrades. Also, the simulation results show that the execution time of the proposed smart contract is 17.12 seconds across 12 nodes, which is sufficient for RETS.

In a smart grid, each residential unit with renewable energy sources can trade energy with others for profit. Buyers with insufficient energy meet their demand by buying the required energy from other houses with surplus energy. However, they will not be willing to engage in the trade if it is not beneficial. With the aim of improving participants’ profits and reducing the impacts on the grid, we study a peer-to-peer (P2P) energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, the auctioneer controls the game, and the seller does not participate in the game but finally achieves the maximum social welfare. The proposed method not only benefits the participants but also hides their information, such as their bids and asks, for privacy. We further study individual rationality and incentive compatibility properties in the proposed method’s auction process at the game’s unique Stackelberg equilibrium. For practical applicability, we implement our proposed energy trading system using blockchain technology to show the feasibility of real-time P2P trading. Finally, simulation results under different scenarios demonstrate the effectiveness of the proposed method.

This paper introduces a transactive market design for a combined heat and power (CHP) based energy hub (hub). The proposed model allows a hub operator to supply the hub’s demands by participating in the day-ahead market and a transactive market with CHPs and also in the real-time market by using a recursive moving window algorithm. The proposed local energy market for a hub operator and CHPs is based on the double auction P2P trading mechanism. The model develops an optimal bidding and offering strategies for CHPs and hub operators, respectively, to achieve optimal transactions. The CHPs may be equipped with boiler unit and heat buffer tank (HBT) beside CHP units. The uncertain nature of the hub’s electrical load, real-time and day-ahead markets prices and wind speed is addressed by using robust optimization. The procedure aimed at minimizing the worst-case CHP-based hub’s demand procurement cost even though flexibly regulating the solution robustness. Further, case studies investigate the economic impact of robustness on the hub’s cost.

The explosive growth of the Internet of Things (IoT) is posing a high pressure on the spectrum resources. Dynamic spectrum allocation is an effective solution for supporting massive IoT devices with limited spectrum resources, often implemented in the form of spectrum auction. Among diverse auction formats, double auctions feature multiple buyers and multiple sellers, which is applicable to a wide range of IoT scenarios. Nevertheless, the existing double auction mechanisms mostly focus on the design of truthfulness and ignore the importance of privacy preservation. Hence, in this article, we propose PP-SPEC, a truthful and privacy-preserving double auction mechanism that takes spatial reuse, spectrum heterogeneity, and multiminded bidders into account, and provides full privacy in terms of asking prices, bidding values, and locations. To tackle the challenge of privacy-preserving comparison over ciphertext field, we carefully designed a set of generic privacy-preserving ciphertext comparison protocols (PIC), including basic PIC (B-PIC) and extended PIC (E-PIC), by integrating additive homomorphic encryption and garbled circuits. Furthermore, we have theoretically proved the truthfulness and security of PP-SPEC. We have implemented and evaluated PP-SPEC, whose experimental results show high performance and practicability of PP-SPEC for real-life applications.

Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.

The Cloud is a new computing paradigm that has experienced exponential growth in recent years. Therefore, success in the cloud services industry can be a challenge, as satisfying both cloud service users and providers is challenging. Cloud users expect to receive a higher service quality with the lowest possible prices. On the other hand, cloud service providers need to maximize their revenues by utilizing energy-efficient resources while adopting prices that attract the most customers. As such, the success of a business in cloud computing depends on the equal consideration of cloud service providers’ and users’ needs. This paper proposes a long-term organic solution based on a double auction model that encompasses the varying natures of both strategic players and cloud resources. We develop the dynamic online double auction mechanism (DODAM) and coin the concept of sustainable pricing for cloud computing services. A dynamic online platform that is capable of realistically modelling cloud ecosystems distinguishes the DODAM model from other studies using a double auction mechanism. Based on experimental results, we prove that the proposed model outweighs other double auction models in terms of the social welfare and truthfulness pillars.

In recent years, we have witnessed the compelling application of the Internet of Things (IoT) in our daily life, ranging from daily living to industrial production. On account of the computation and power constraints, the IoT devices have to offload their tasks to the remote cloud services. However, the long-distance transmission poses significant challenges for latency-sensitive businesses, such as autonomous driving and industrial control. As a remedy, mobile edge computing (MEC) is deployed at the edge of the network to reduce the transmission delay. With the MEC joining in, how to allocate the limited computing resource of MEC is a critical problem to guarantee efficient working of the whole IoT system. In this article, we formulate the resource management among MEC and IoT devices as a double auction game. Also, for searching the Nash equilibrium, we introduce the experience-weighted attraction (EWA) algorithm performing behind each participant. With this AI method, auction participants acquire and accumulate experience by observing others’ behavior and doing introspection, which accelerates the trading policy’s learning process of each agent in such an opaque environment. Some simulation results are presented to evaluate the convergence and correctness of our architecture and algorithm.

Blockchain technology is developing rapidly and has been applied in various aspects, among which there are broad prospects in Internet of Things (IoT). However, IoT mobile devices are restricted in communication and computation due to mobility and portability, so that they can’t afford the high computing cost for blockchain mining process. To solve it, the free resources displayed on non-mining-devices and edge cloud are selected to construct collaborative mining network(CMN) to execute mining tasks for mobile blockchain. Miners can offload their mining tasks to non-mining-devices within a CMN or the edge cloud when there are insufficient resources. Considering competition for resource of non-mining-devices, resource allocation problem in a CMN is formulated as a double auction game, among which Bayes-Nash Equilibrium (BNE) is analyzed to figure out the optimal auction price. When offloading to edge cloud, Stackelberg game is adopted to model interactions between edge cloud operator and different CMNs to obtain the optimal resource price and devices’ resource demands. The mechanism realizes improving the mining utility in mining networks while ensuring the maximum profit of edge cloud operator. Finally, profits of mining networks are compared with an existing mode which only considers offloading to edge cloud. Under the proposed mechanism, mining networks obtain 6.86% more profits on average.

Double auction is an auction in which multiple buyers and sellers looking for a price where supply and demand balance. Since the electronic double auction based on secure multiparty computation (DABSMPC) cannot guarantee its fairness, we propose a blockchain-based fair and secure electronic double auction protocol (BFSDA). BFSDA modifies the data input and distribution mechanism of participants in DABSMPC, which improves the security of the protocol. Then, the BFSDA introduces and improves the blockchain-based fair and secure multiparty computation protocol (BFSMPC) to ensure fairness while increasing the success rate of secret recovery. In addition, BFSDA uses a fairer and more efficient protocol for secure two-party comparing to obtain the final marketing clearing price. The schema analysis result of BFSDA shows that: first, the private input data will not be revealed as long as data owner is not compromised, second, honest participants can get the result or economic compensation, and third, participants only need to pay the deposit once and a large amount of complicated verification operations are carried out off the chain, which ensures the efficiency of the protocol.

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 exter...

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 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.

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.

In a mobile blockchain network, many mobile devices have insufficient computational capacity to execute computation-intensive tasks locally. To tackle this problem, blockchain tasks can be offloaded to edge servers with the aid of auction. However, most auction mechanisms on mobile blockchain ignore the automatic parallel execution and long-term performance. This paper aims to solve the problem of computation offloading in a mobile blockchain network. We transform this problem into a multi-choice multi-dimensional knapsack problem which is NP-hard. To improve the total utility of auction participants, this paper proposes a smart-contract-based double auction mechanism, named long-term auction for mobile blockchain (LAMB). The subtasks can be offloaded from one mobile device to heterogeneous edge servers. Also, LAMB satisfies the economic properties of an auction mechanism. Experimental results demonstrate that, the utility and utilization ratio can be achieved by 130.55% higher and 138.64% higher, respectively, in comparison to the existing auction algorithm WBD. Furthermore, the proposed LAMB can guarantee long-term performance for task offloading, and it can achieve automatic execution in an autonomous and secure environment.

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.

This study aims to plan a cost-minimizing charging schedule for electric buses with fast charging stations. The paper conceptualizes the problem as a three-stage procedure, which is oriented around the participation of an electric bus aggregator in a day-ahead energy auction. First, the aggregation stage determines the bid parameters of buses. With bid parameters, aggregated cost-minimizing charging plans are obtained in the second stage conceived as the hourly day-ahead auction. The disaggregation of hourly plans into feasible minutely charging schedules is the third stage. The main contribution is the formulation of mixed-integer linear programming aggregation models to determine charging availability expressed as minimum and maximum hourly energy requirements taking into account detailed, minutely characteristics and constraints of the charging equipment and the buses. No price forecasts are required, and the plans adjust to the wholesale prices of energy. Defining only a few aggregated bids parameters used in linear programming constraints and incorporating them into the auction model is another contributing factor of this paper, allowing the scheduling of storage-based participants economically. The proposed methodology has been verified on a recently published case study of a real-world bus service operated on the Ohio State University campus. We show that the auction-based charging of all 22 buses outperforms as-soon-as-possible schedules by 7% to even 28% of daily cost savings. Thanks to the aggregated bids, buses can flexibly shift charges between high- and low-price periods while preserving constraints of the charging equipment and timetables.

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.

Trading of used goods in secondhand markets is considered to have both environmental and economic benefits. For the trading of used goods as well as new ones, this study proposes a periodic double auction model for Internet-based electronic markets whereby each bidder can place both asks and bids for selling and purchasing goods respectively during a trading period. The proposed model allows each bidder to place a spending limit order such that the difference between the cost of the purchased goods and the income obtained from the sold goods does not exceed this limit. Furthermore, since a bidder may be indifferent to multiple goods, e.g. copies of a same title, the model also provides a mechanism so that bidders may combine a number of bids inside a set and put a limit on the number of goods to be purchased in this set. The model is mathematically defined, and the corresponding winner determination problem is formulated using linear integer programming. Since the problem is shown to be NP-hard, a number of heuristic methods are also proposed. Performances of these methods are evaluated on a comprehensive test suite and statistical analyses of the results are presented. Furthermore, possible economical contribution of the model is also evaluated. The results indicate that the proposed model can be used efficiently in large-scale markets with tens of thousands of bidders.

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.

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

In this paper, we address the issue of demand response (DR) in microgrids via vehicle-to-vehicle technology in the smart grid with consideration for location privacy protection supported by Internet of Vehicles. To enable effective DR, the online double auction is a viable approach to support energy trading between electric vehicles (EVs) that have surplus or insufficient energy, while the utility of each participant can be considered. Nonetheless, there are three primary challenges in designing such an online double auction approach. First, as EVs are allowed to enter the market at any time, the auctioneer should make the best decision without further information about bids and asks. Second, as EVs are allowed to enter the market in different places, the auctioneer should perform routing optimization for EV charging after determining the winner. Third, there is a risk of leakage in the location of EVs that needs to be protected. To tackle these issues, we present a new truthful online double auction scheme, which features multiunit energy trading among EVs, routing optimization for EV charging, and location privacy protection. We conduct a theoretical analysis and demonstrate that our online double auction scheme is capable of achieving several important economic properties as well as the privacy guarantee (i.e., k -anonymity). Our experimental results show that the proposed scheme can achieve good performance with respect to social welfare, satisfaction ratio, total profit of EV owners, peak load shifting, state of charge, driving distance satisfaction, and computing time, and can further ensure location privacy protection.

This study aims to devise efficient double auction mechanisms for the intermodal transportation service procurement problem (ITSPP) in the context of the “Belt and Road Initiative”. In ITSPP, the transportation demand of each shipper may contain a bundle of loads in different lanes, while the transportation service provided by each carrier may cover either one or multiple lanes. Three efficient double auction mechanisms that consider transaction costs are proposed. Moreover, a series of computational experiments were conducted to evaluate the performances of the proposed mechanisms. According to the experimental results, several managerial implications are also provided.

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

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.

Over the past decades, there have been extensive research endeavors in spectrum auction design. However, most solutions only focus on the allocation efficiency while ignoring the privacy leakage inherent in the process of spectrum auction. So far, the very few existing works on secure spectrum auctions either provide inadequate privacy protection or incur performance loss in terms of spectrum reusability. In this paper, for the first time, we propose PS-TAHES, a privacy-preserving and truthful double auction mechanism for heterogeneous spectrum. PS-TAHES is constructed based on our carefully designed security primitives, which can support various arithmetics over encrypted data, including multiplication, bid comparison, and sorting matrix, and they are well applicable in other contexts. We theoretically analyze the security and efficiency of PS-TAHES, which is proved to ensure a full and strong privacy protection for bidders while preserving the allocation efficiency of the original auction mechanism. Experimental results, consistent with the theoretical analysis, further validate the practical use of PS-TAHES in real-world applications.

PurposeDouble auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized fashion and compare macro market outputs of different micro bidding strategies.Design/methodology/approachIn this study, two novel bidding strategies based on fuzzy logic are presented. Also, four new bidding strategies based on price targeting are introduced for the aim of comparison. The proposed bidding strategies are based on agent-based computational economics approach. The authors performed multi-agent simulations of double auction market for each suggested bidding strategy. For the aim of comparison, the zero intelligence strategy is also used in the simulation study. Various market outputs are obtained from these simulations. These outputs are market efficiencies, price means, price standard deviations, profits of sellers and buyers, transaction quantities, profit dispersions and Smith’s alpha statistics. All outputs are also compared to each other usingt-tests and kernel density plots.FindingsThe results show that fuzzy logic-based bidding strategies are superior to price targeting strategies and the zero intelligence strategy. The authors also find that only small number of inputs such as the best bid, the best ask, reference price and trader valuations are sufficient to take right action and to attain higher efficiency in a fuzzy logic-based bidding strategy.Originality/valueThis paper presents novel bidding strategies for dynamic double auction markets. New bidding strategies based on fuzzy logic inference systems are developed, and their superior performances are shown. These strategies can be easily used in market-based control and automated bidding systems.

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs’ storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs’ caching utilities and MNO’s caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results.

Spectrum auction is an effective way to redistribute scarce spectrum resources. However, most spectrum auction designs only target at economic robustness, while neglecting the inherent privacy leakage problem. Existing secure spectrum auction mechanisms fail to provide adequate security, and they all neglect the online fashion of spectrum request arrival. In this paper, for the first time, we propose a Privacy-pReserving and truthful Online double auction mechanism for Spectrum allocaTion in wireless networks, PROST . Compared with the state-of-the-art solutions, PROST provides a comprehensive and strong protection for users’ sensitive information, especially for location privacy and time dynamics. PROST is constructed based on our carefully designed security building blocks, which support various arithmetics over encrypted real numbers, and they are also well applicable in other spectrum auctions. Besides, we improve on the existing online spectrum auction mechanisms by designing a novel privacy-preserving buyer grouping protocol for spectrum reuse. We not only theoretically prove that PROST can realize an all-round security against semi-honest adversaries but also extensively evaluate its performance. Experimental results validate that PROST achieves nice spectrum allocation efficiency with light computation and communication costs.

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.

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.

In this paper, we propose PreDA– a p reference-based truthful d ouble a uction for dynamic spectrum access (DSA) networks where multiple heterogeneous spectrum bands are sold by the primary users and bought by the secondary users. Unlike existing double auctions, we not only consider channels’ heterogeneity and multi-bids from buyers, but also consider buyers’ preferences for the channels. We use the signal to interference and noise ratio (SINR) as a metric for the preference; channels with higher SINR are preferred and hence bids are more compared to the bids for channels that offer lower SINRs. In order to maximize the spatial and temporal reuse of channels, we use the concept of virtual grouping of conflict-free buyers. Virtual groups allow us to transform multi-unit bids to single-unit bids. We propose a novel winner determination and pricing mechanism to allocate the unused spectrum bands to the most appropriate buyers. We prove that the proposed double auction achieves the three economic properties of truthfulness, individual rationality, and ex-post budget balance. We validate PreDA through simulation experiments and show its performance in terms of the number of allocated bands, utilization, revenue, and fairness.

In this paper, we address the sharing incentive issue in multi-tenant passive optical networks (PONs). We propose an economic-robust and efficient sharing platform for new emerging multi-tenant PON networks. This platform is capable of accommodating a diverse range of service providers and enhancing the network utilization. We propose a sharing platform that provides sharing incentives for the incumbent network operators through monetization of inter-operator network sharing. Meanwhile, the platform allows the incumbent operators to operate a virtual instance of the bandwidth scheduling algorithm that enables them to meet their quality of service and latency requirements. Therefore, the proposed sharing platform grants a high degree of control to the operators co-operating the same network while, thanks to the higher resource efficiency, reduces the initial investment. We first model the multi-tenant PON as a market and define the roles of the virtual network operators and the infrastructure provider along with their utility functions. We propose a double auction mechanism to facilitate the trading of excess resources. The proposed double auction satisfies the crucial economic properties of a market while it achieves more efficient resource allocation among the market players. We have theoretically proven the economic robustness of the mechanism including incentive compatibility, individual rationality, and weak budget balance. Through extensive market simulations, we confirmed that the proposed mechanism achieves superior allocative efficiency compared to a reference baseline mechanism.

© 2021 by the authors. This work presents a novel blockchain-based energy trading mechanism for electric vehicles consisting of day-ahead and real-time markets. In the day-ahead market, electric vehicle users submit their bidding price to participate in the double auction mechanism. Subsequently, the smart match mechanism will be conducted by the charging system operator, to meet both personal interests and social benefits. After clearing the trading result, the charging system operator uploads the trading contract made in the day-ahead market to the blockchain. In the real-time market, the charging system operator checks the trading status and submits the updated trading results to the blockchain. This mechanism encourages participants in the double auction to pursue higher interests, in addition to rationally utilize the energy unmatched in the auction and to achieve the improvement of social welfare. Case studies are used to demonstrate the effectiveness of the proposed model. For buyers and sellers who successfully participate in the day-ahead market, the total profit increase for buyer and seller are 22.79% and 53.54%, respectively, as compared to without energy trading. With consideration of social welfare in the smart match mechanism, the peak load reduces from 182 to 146.5 kW, which is a 19.5% improvement. Department of Finance and Education of Guangdong Prov- ince 2016 [202]: Key Discipline Construction Program, China; the Education Department of Guang- dong Province: New and Integrated Energy System Theory and Technology Research Group [Pro- ject Number 2016KCXTD022]; Brunel University London BRIEF Funding; National Natural Science Foundation of China (51907031).

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.

International audience; Local energy markets allow neighbours to exchange energy among them. Their traditional implementation using sequential auctions has proven to be inefficient and even counterproductive in some cases. In this paper we propose a combinatorial double auction for the exchange of energy for several time-slots simultaneously. We suppose that participants have a flexible demand; flexibility being obtained, for example, by the usage of a battery. We show the benefits of the approach and we provide an example of how it can improve the utility of all the participants in the market.

It is increasingly popular that platforms integrate various services into mobile applications due to the high usage and convenience of mobile devices, many of which demand high computational capacities and energy, such as cryptocurrency services based on blockchain. However, it is hard for mobile devices to run these services due to the limited storage and computational capacity. In this paper, the problem of computation offloading that requires sufficient computing resources with high utilization in large-scale users and multiprovider MEC system was investigated. A mechanism based on the combinatorial double auction, G-TRAP, is proposed in this paper to solve the above problem. In the mechanism, resources are provided both in the cloud and at the edge of the network. Mobile users compete for these resources to offload computing tasks by the rule that the edge-level resources will be allocated at first while cloud-level resources could be the supplement for the edge level. Given that the proof-of-work (PoW), the core issue of blockchain application, is resource-expensive to implement in mobile devices, we provide resource allocation service to users of blockchain application as experimental subjects. Simulation results show that the proposed mechanism for serving large-scale users in a short execution time outperforms two existing algorithms in terms of social utility and resource utilization. Consequently, our proposed system can effectively solve the intensive computation offloading problem of mobile blockchain applications.

Double auctions are considered to be effective price-scheduling mechanisms to resolve cloud resource allocation and service pricing problems. Most of the classical double auction models use price-based mechanisms in which determination of the winner is based on the prices offered by the agents in the market. In cloud ecosystems, the services offered by cloud service providers are inherently time-constrained and if they are not sold, the allocated resources for the unsold services are wasted. Furthermore, cloud service users have time constraints to complete their tasks, otherwise, they would not need to request these services. These features, perishability and time-criticality, have not received much attention in most classical double auction models. In this paper, we propose a cloud priority-based dynamic online double auction mechanism (PB-DODAM), which is aligned with the dynamic nature of cloud supply and demand and the agents’ time constraints. In PB-DODAM, a heuristic algorithm which prioritizes the agents’ asks and bids based on their overall condition and time constraints for resource allocation and price-scheduling mechanisms is proposed. The proposed mechanism drastically increases resource allocation and traders’ profits in both low-risk and high-risk market conditions by raising the matching rate. Moreover, the proposed mechanism calculates the precise defer time to wait for any urgent or high-priority request without sacrificing the achieved performance in resource allocation and traders’ profits. Based on experimental results in different scenarios, the proposed mechanism outperforms the classical price-based online double auctions in terms of resource allocation efficiency and traders’ profits while fulfilling the double auction’s truthfulness pillar.