Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and industry.Trading can achieve maximal data value and prepare data for smart city services.Due to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading entities.Meanwhile,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social welfare.To solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

Negotiation Based Combinatorial Double Auction Mechanism in Cloud Computing

Cloud computing is a demanding business platform for services related to the field of IT.The goal of cloud customers is to access resources at a sustainable price,while the goal of cloud suppliers is to maximize their services utilization.Previously,the customers would bid for every single resource type,which was a limitation of cloud resources allocation.To solve these issues,researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle.Still,in this allocation mechanism,some drawbacks need to be tackled,such as due to the lower average bid price the users are dropped from the auction process.To solve this problem,we proposed a“Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation(N-CDARA)in cloud computing”.The proposed method negotiates with dropped users.Lower average bid price users are asked by our proposed mechanism to increase their bids,as by the quoted bids they will be dropped by the auctioneer.Most of the users that are close to winning accept the proposal and increase their bid prices.The proposed mechanism is implemented in a CloudSim simulation toolkit.Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4%than the existing schemes.

A Consortium Blockchain-enabled Double Auction Mechanism for Peer-to-peer Energy Trading among Prosumers

This paper investigates a double auction-based peer-to-peer(P2P)energy trading market for a community of renewable prosumers with private information on reservation price and quantity of energy to be traded.A novel competition padding auction(CPA)mechanism for P2P energy trading is proposed to address the budget deficit problem while holding the advantages of the widely-used Vickrey-Clarke-Groves mechanism.To illustrate the theoretical properties of the CPA mechanism,the sufficient conditions are identified for a truth-telling equilibrium with a budget surplus to exist,while further proving its asymptotical economic efficiency.In addition,the CPA mechanism is implemented through consortium blockchain smart contracts to create safer,faster,and larger P2P energy trading markets.The proposed mechanism is embedded into blockchain consensus protocols for high consensus efficiency,and the budget surplus of the CPA mechanism motivates the prosumers to manage the blockchain.Case studies are carried out to show the effectiveness of the proposed method.

Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing

Cloud manufacturing is a new kind of networked manufacturing model.In this model,manufacturing resources are organized and used on demand as marketoriented 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.

Agents learned,but do we?Knowledge discovery using the agent-based double auction markets

This paper demonstrates the potential role of autonomous agents in economic theory.We first dispatch autonomous agents,built by genetic programming,to double auction markets.We then study the bargaining strategies,discovered by them,and from there,an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

Strategic Peer-to-peer Energy Trading Framework Considering Distribution Network Constraints

With the development of smart home energy management technology,prosumers are endowed with increased initiative in peer-to-peer(P2P)transactions,bringing new potential for cost savings.In this study,a novel strategic P2P energy trading framework is proposed considering the impact of network constraints on personal transaction strategies.Prosumers can estimate the allowed power injection before engaging in the P2P energy trading,which is solved in a distributed manner based on the sharing form alternating direction method of multipliers(ADMM)algorithm.To quantify the network usage cost for each prosumer and promote local transactions among prosumers at the same bus,a modified continuous double auction(CDA)matching algorithm is proposed including a transaction fee.An adaptive aggressiveness-based bidding strategy is generated considering the risk of uncertainty in real-time energy delivery amount under the limitations of the distribution network.The proposed strategic P2P energy trading framework is tested with the IEEE 37-bus distribution network and it is effective in creating profits for prosumers and supporting distribution network operations.