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Building upon the concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources. Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services. First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare. Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and show that the proposed mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms. Moreover, we put forth a trustworthiness-driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair reward allocation. We formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values. The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities.

Mobile users provision their multiple resources, such as cores, memory, and cameras which are then allocated to the requesters to execute tasks in crowd sensing systems. Existing studies focus on the divisible task or one-to-one mapping for single resource allocation. However, this assumption does not hold for crowd sensing systems. Owing to the task attribute, some tasks cannot be divided into multiple parts to run on different devices. In addition, a high performance mobile device can execute multiple tasks simultaneously. We address the problem of multi-resource allocation in crowd sensing systems for the auction-based model considering many-to-one mapping for indivisible tasks. In this paper, we study, for the first time to the best of our knowledge, a truthful mechanism that stimulates mobile users and requesters to declare their true values. We design a truthful double auction mechanism together with a payment scheme tailored to fit it that would help researchers understand how a truthful double auction mechanism can be designed. In addition, we prove that our proposed mechanism maintains budget-balance, individual rationality, and computational tractability. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. Experimental results demonstrate that our proposed mechanism has high computation efficiency and good performance.

With the rapid development of Internet of Things (IoT), digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of digital twin in IoT. In this paper, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through access point (AP) to help resource limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging Directed Acyclic Graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.

Owing to energy liberalization and increasing penetration of renewables, renewable energy trading among suppliers and users has gained much attention and created a new market. This article investigates a double-auction scheme operated by an aggregator with limited supervision for energy trading. To ensure beneficial bidding for renewable generators and end users (EUs) considered as agents, a multiagent Q-learning (MAQL) based bidding strategy is developed to maximize their cumulative reward. Each agent first provides their information about renewable supply or demand to an aggregator who will then return the information about aggregate supply and demand. Without knowing the business model of the aggregator, the agents use Q-tables to estimate the expected cumulative reward and determine their bidding prices accordingly. Finally, the aggregator coordinates energy trading between agents who will then update their Q-tables on the basis of the amount of power bought or sold at the prices they bid. The proposed approach can avoid some unnecessary or unrealistic assumptions generally made by model-based approaches, such as the assumption on the knowledge of others’ bidding profiles or the assumption of an oligopoly; it can consider the influence of bidding strategies on the market, which cannot be properly addressed by a conventional proportional allocation mechanism. A numerical analysis using real-world data and considering a profit maximization model for the aggregator shows that the proposed approach outperformed comparable methods in terms of profits of renewable generators and energy satisfaction level of EUs: iterative double auction by approximately 29%, heuristic bidding by 39.9%, random bidding by 38.1%, NSGA-II-based multiobjective approach by 62%, and MOEA/D-based multiobjective approach by 83.1% on average.

Due to the proliferation of WiFi devices and the high verification precision, researchers have shown interests in WiFi-based multilateration location verification (WMLV), where multiple WiFi APs (a.k.a. verifiers) verify the location information claimed by a prover. However, it is a high expenditure for any single location-based service provider to deploy densely covered WiFi facilities. Incentivizing independent WiFi owners to corporately verify location information is thus a feasible solution to this plight, yet none of the previous research has taken this into consideration. To this point, we design a double auction-based incentive mechanism for WMLV which motivates the participation of both provers and verifiers. More importantly, we consider practical situations where the provers has various verification precision requirements, and different number of verifiers are required by different provers. The proposed double auction mechanism achieves desirable economical properties including truthfulness, individual rationality, computational efficiency, budget balance, and non-negative social welfare. The desired properties are validated through both theoretical analysis and extensive simulations.

The development of smart vehicles and rich cloud services have led to the emergence of vehicular edge computing. To perform the distributed computation tasks efficiently, Coded Distributed Computing (CDC) was proposed to reduce communication costs and mitigate the straggler effects through the use of coding techniques. In this paper, we propose a double auction mechanism to allocate the resources of the edge servers to the vehicles in order to complete the CDC tasks. Specifically, the vehicles use the PolyDot codes to manage the tradeoff between communication costs and recovery threshold. Given the requirements of various vehicles, the double auction mechanism matches the edge servers with the required resources to the vehicles. Besides, the double auction mechanism also determines the prices that the vehicles need to pay for the resources of the edge servers. The analyses show that the double auction mechanism satisfies the properties of individual rationality, incentive compatibility and budget-balance. From the simulation, the utility of auctioneer increases when the number of vehicles and edge servers increases. Ministry of Education (MOE) National Research Foundation (NRF) This work was supported in part by the Programme DesCartes, in part by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise Programme, in part by Alibaba Group through Alibaba Innovative Research Program and Alibaba-NTU Singapore Joint Research Institute, in part by the National Research Foundation, Singapore, under the AI Singapore Programme under Grant AISG2-RP-2020-019, in part by WASP/NTU under Grant M4082187 (4080), in part by the Singapore Ministry of Education under Grant Tier 1 (RG16/20), in part by the SUTD SRG-ISTD-2021-165, and in part by the SUTD-ZJU IDEA under Grant (SUTD-ZJU (VP) 202102).

The fast-growing cloud computing market puts tremendous provisioning pressure on cloud service providers. Instead of keeping investing in costly cloud infrastructures, trading resources among different cloud service providers is another cost-effective way to expand the capacity. The key is to design an effective market-based mechanism to facilitate resource trading and motivate high-quality service provisioning. This paper proposes a distributed Feedback-based Combinatorial Multi-unit Double Auction mechanism backed by blockchain to establish a cloud resource market that not only produces high social welfare but also motivates participants to provide high-quality service. We first design an improved greedy allocation mechanism with a novel sorting method to achieve high social welfare. Then we propose a feedback-based second-price pricing mechanism to limit strategic manipulation. Finally, we design a feedback system combined with the auction mechanism to motivate high-quality service provisioning. All these mechanisms are implemented in the form of smart contracts on a consortium blockchain which guarantees the effectiveness of the feedback system and eliminates the dependence on a central auctioneer. Theoretical analysis demonstrates that the proposed auction mechanism maintains budget balance, individual rationality, truthfulness on the buyer side and computational efficiency. In comparison with other popular mechanisms, extensive experiments are conducted to demonstrate higher efficiency of the allocation mechanism, good limitation on strategic manipulation of the pricing mechanism and effectiveness of the feedback system.

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.

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

The rapid and wide adoption of microgrids (MGs) and the increasing popularity of electric vehicles (EVs) have created a unique opportunity for the integration of these technologies. In this paper, we address the issue of demand response of EVs during MG outages by leveraging Vehicle-to-Grid (V2G) technology. We investigate an auction trading market, allowing EVs with surplus energy to act as sellers, and EVs that want to be charged to act as buyers. A novel distributed double auction scheme is proposed to allow each buyer EV to submit multiple bids to seller EVs in different parking lots. Nonetheless, the locations of buyer EVs could be inferred by adversaries through analyzing the valuations, posing serious risks. A valuation-based attack scheme is investigated to validate the potential privacy risk. To defend against this attack, we present a location privacy-preserving double auction scheme, protecting the privacy of participants via homomorphic encryption. Theoretical analysis is conducted to validate our scheme in satisfying designed economic and privacy properties. The experimental results show that our auction scheme can not only mitigate the demand response problem in MGs, but also provides good performance with respect to social welfare, satisfaction ratio, computational and communication overhead, and privacy leakage.

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.

With the increasingly high penetration of distributed energy resources (DERs), more consumers with the capability of producing energy (hence called prosumers) appear in the electricity distribution network. Different from conventional consumers, prosumers are allowed to trade their energy with the power grid to generate income. To facilitate the active participation of prosumers in the distribution-level market, we develop a continuous group-wise double auction (CGDA) mechanism to coordinate the energy transactions among prosumers. In particular, the prosumers submit continuous double-sided bids to describe their cost-minimizing buying and selling strategies at different market prices. A distribution system operator (DSO), acting as the auctioneer, decides the clearing prices, the clearing power quantities, and the rewards for all prosumers. We prove that the proposed CGDA mechanism achieves several desirable economic properties, including truthfulness, individual rationality, weakly balanced budget, and asymptotic efficiency. We further demonstrate the superior performance of the proposed mechanism compared with other benchmarks through extensive simulations.

In this chapter, we address the issues associated with the centralized marketplaces in 5G networks. We study how a distributed alternative based on blockchain and smart contract technology could replace the costly and inefficient third‐party‐based trust intermediaries. We propose a smart contract based on a sealed‐bid double auction to allow resource providers and enterprise users to trade resources on a distributed marketplace. We have explained the implementation of this marketplace application on Hyperledger Fabric permissioned blockchain while deploying the network using a pragmatic scenario over a public, commercial cloud. We evaluated the distributed marketplace's performance under different transaction loads, and the results showed that the marketplace could process up to 400 auction transactions per second while maintaining the latency under 1 s.

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.

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.

An E-auction mechanism has been considered as the most popular activity in E-commerce model. However, it relies on the assumption that a fully trusted third party is required to guarantee the system, which is hard to be implemented in the real-world. In this article, we aim to design an ad-hoc combinatorial double auction framework based on the blockchain technique. The properties of decentralization and nontempering can be used to realize a fair transaction and enhance the flexibility without the third party. A participant can follow his/her will to launch an auction play with the adoption of smart contract, and an improved genetic algorithm is designed to figure out an optimal pairing solution within a reasonable period. Experiments have demonstrated that the new framework can fulfill most transactions to benefit both seller and buyer no matter how many players and commodities have involved into the auction.

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

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

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.

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

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.

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.

Double auction is becoming the preferred negotiation protocol for cloud service negotiation due to its economic efficiency, capability in facilitating dynamic pricing, and suitability for handling a large number of customers and service providers. However, as far as this research work is concerned, there is no framework using double auction that simultaneously and comprehensively addresses the heterogeneity of cloud services in multi-attributes negotiation. Before such a framework can be designed, suitable multi-attributes negotiation techniques and its attributes should be identified. Therefore, this paper’s objective is to distinguish and provide an overview of multi-attributes techniques used in double auction negotiations. The sources are from the Scopus Database. It is found that the current multi-attributes techniques lacked in addressing the preferential dependency, selective attributes and utility optimization simultaneously in double auction frameworks. These concerns need to be addressed in order to devise a practical framework. The future direction for double auction framework in multi-attributes negotiation is suggested.

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.

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

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.

Most assets clear independently rather than jointly. This paper presents a model based on the uniform‐price double auction which accommodates arbitrary restrictions on market clearing, including independent clearing across assets (allowed when demand for each asset is contingent only on the price of that asset) and joint market clearing for all assets (required when demand for each asset is contingent on the prices of all assets). Additional trading protocols for traded assets—neutral when the market clears jointly—are generally not redundant innovations, even if all traders participate in all protocols. Multiple trading protocols that clear independently can be designed to be at least as efficient as joint market clearing for all assets. The change in price impact brought by independence in market clearing can overcome the loss of information, and enhance diversification and risk sharing. Except when the market is competitive, market characteristics should guide innovation in trading technology.

Double auction is one of the most promising solutions to allocate virtual machine (VM) resources in two-sided cloud markets, which can increase the utilization rate of VM resources. However, most cloud auction mechanisms simply assume that the auctioneer is fully trusted while ignoring bid-privacy preservation and trade fairness in the process of auction. Previous studies have indicated that some cryptographic tools can be used to resolve the above issues, but the poor performance makes those techniques difficult to practice. In this paper, we propose a Secure and Fair Double AuCtion framework (named SF-DAC) for cloud virtual machines, which performs cloud auction efficiently while guaranteeing both bid privacy and trade fairness. We design secure 3-party computation protocols that support secure comparison and secure sorting, which enable us to construct a secure double auction scheme that outperforms all prior comparable solutions. Furthermore, we propose a fair trading mechanism based on smart contracts to prevent the bidders from halting the auction without financial penalties. The extensive experiments demonstrate that SF-DAC achieves an order of magnitude reduction in computation and communication costs than prior arts.

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.

A recent study predicts that by 2025, up to 75 billion internet of things (IoT) devices will be connected to the internet, in which data sharing is increasingly needed by massive IoT applications as a major driver of the IoT market. However, how to meet the interests of all participants in complex multi-party interactive data sharing while providing secure data control and management is the main challenge in building an IoT data sharing ecosystem. In this article, the authors propose a blockchain-empowered data sharing architecture that supports secure data monitoring and manageability in complex multi-party interactions of IoT systems. First, to build trust among different data sharing parties, the authors apply blockchain technologies to IoT data sharing. In particular, on-chain/off-chain collaboration and sharding consensus process are used to improve the efficiency and scalability of the large-scale blockchain-empowered data sharing systems. In order to encourage IoT parties to actively participate in the construction of shared ecology, the authors use an iterative double auction mechanism in the proposed architecture to maximize the social welfare of all parties as a case-study. Finally, simulation results show that the proposed incentive algorithm can optimize data allocations for each party and maximize the social welfare while protecting the privacy of all parties.

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

We propose a new estimation method and use experimental data from multiple double auction experiments in the literature to directly estimate how much information is incorporated in financial market prices. We find that public information is almost completely reflected in prices, but that surprisingly little private information—less than 50%—is incorporated in prices. Our estimates therefore suggest that while semi-strong informational efficiency is consistent with the data, financial market prices may be very far from strong-form efficiency. We compare our estimates with beliefs of economists surveyed at the Econometric Society Meetings, and find that economists and finance researchers alike expect market prices to reflect considerably more private information than what we estimated.

Background: Medical image analysis application has complex resource requirement. Scheduling Medical image analysis application is the complex task to the grid resources. It is necessary to develop a new model to improve the breast cancer screening process. Proposed novel Meta scheduler algorithm allocate the image analyse applications to the local schedulers and local scheduler submit the job to the grid node which analyses the medical image and generates the result sent back to Meta scheduler. Meta schedulers are distinct from the local scheduler. Meta scheduler and local scheduler have the aim at resource allocation and management. Objective: The main objective of the CDAM meta-scheduler is to maximize the number of jobs accepted. Methods: In the beginning, the user sends jobs with the deadline to the global grid resource broker. Resource providers sent information about the available resources connected in the network at a fixed interval of time to the global grid resource broker, the information such as valuation of the resource and number of an available free resource. CDAM requests the global grid resource broker for available resources details and user jobs. After receiving the information from the global grid resource broker, it matches the job with the resources. CDAM sends jobs to the local scheduler and local scheduler schedule the job to the local grid site. Local grid site executes the jobs and sends the result back to the CDAM. Success full completion of the job status and resource status are updated into the auction history database. CDAM collect the result from all local grid site and return to the grid users. Results: The CDAM was simulated using grid simulator. Number of jobs increases then the percentage of the jobs accepted also decrease due to the scarcity of resources. CDAM is providing 2% to 5% better result than Fair share Meta scheduling algorithm. CDAM algorithm bid density value is generated based on the user requirement and user history and ask value is generated from the resource details. Users who, having the most significant deadline are generated the highest bid value, grid resource which is having the fastest processor are generated lowest ask value. The highest bid is assigned to the lowest Ask it means that the user who is having the most significant deadline is assigned to the grid resource which is having the fastest processor. The deadline represents a time by which the user requires the result. The user can define the deadline by which the results are needed, and the CDAM will try to find the fastest resource available in order to meet the user-defined deadline. If the scheduler detects that the tasks cannot be completed before the deadline, then the scheduler abandons the current resource, tries to select the next fastest resource and tries until the completion of application meets the deadline. CDAM is providing 25% better result than grid way Meta scheduler this is because grid way Meta scheduler allocate jobs to the resource based on the first come first served policy. Conclusion: The proposed CDAM model was validated through simulation and was evaluated based on jobs accepted. The experimental results clearly show that the CDAM model maximizes the number of jobs accepted than conventional Meta scheduler. We conclude that a CDAM is highly effective meta-scheduler systems and can be used for an extraordinary situation where jobs have a combinatorial requirement.

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.

Artificial multi-agent systems can be applied to replicate complex processes, including the stock market. In this manuscript, an artificial stock market was simulated. Heterogeneous agents are set to trade by using different investiment strategies through a double auction market. The main innovative aspect of this work is to evaluate the performance and effectiveness of agents with different investment strategies based on the combination of Technical/Fundamental Analysis. According to the simulations performed, the results showed that the time series resulting from the interactions among these agents resemble to the real financial series, reproducing some of the stylized facts found in the Literature. Moreover, the statistical analysis reported no significant differences in the distribution mean performance between these diverse agents types.

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

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

We address some open issues regarding the characterization of double auctions. Our model is a two-sided commodity market with either finitely or infinitely many traders. We first unify existing formulations for both finite and infinite markets and generalize the characterization of market clearing in the presence of ties. Second, we define a mechanism that achieves market clearing in any, finite or infinite, market instance and show that it coincides with the k-double auction by Rustichini et al. (1994) in the former case. In particular, it clarifies the consequences of ties in submissions and makes common regularity assumptions obsolete. Finally, we show that the resulting generalized mechanism implements Walrasian competitive equilibrium.

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.

In this article, we provide mechanisms for exchange economies with private information and interdependent values, which are ex post individually rational, incentive compatible, generate budget surplus, and are ex post nearly efficient, with many agents. Our framework is entirely prior-free, and we make no symmetry restrictions. The mechanisms can be implemented using a novel discriminatory conditional double auction, without knowledge of information structure or utility functions. We also show that no other mechanism satisfying the constraints can generate inefficiency of smaller order.

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.