Proposed Auction Mechanism Research Articles

Revenue-Optimal Auction For Resource Allocation in Wireless Virtualization: A Deep Learning Approach

Wireless virtualization has become a key concept in future cellular networks which can provide multiple virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation is a main challenging issue in wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare (i.e., the sum of all valuations of winning bidders) due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues (i.e., received payments from auction winners). However, the revenue-optimal auction problem is much more complex since the payment price is unknown before calculation. In this paper, we aim to design a revenue-optimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity, deep learning techniques are applied. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users’ bids as the input and the allocation rule and conditional payment rule for the users as the output. The proposed auction mechanism possesses several desirable properties, e.g., individual rationality, incentive compatibility and budget constraint. Finally, simulation results demonstrate the effectiveness of the proposed scheme. Comparing with second-price auction and optimization-based schemes, the proposed scheme can increase the revenue by 10 and 30 percent on average, for single MVNO and multi-MVNO cases, respectively.

Toward an Automated Auction Framework for Wireless Federated Learning Services Market

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

A Montero Auction Mechanism to Regulate Antimicrobial Consumption in Agriculture

Livestock production accounts for the lion's share of antibiotic consumption worldwide, and the sector is a significant contributor to antimicrobial resistance (AMR). Because farmers do not consider the contribution of their individual consumption of antibiotics to AMR, regulations are necessary to ensure optimal use. However, previously suggested regulatory instruments are either inefficient or nearly impossible to implement, mainly because they fail to address the information asymmetry that exists between farmers and regulators. In this article, we customize and suggest an efficient and incentive‐compatible regulatory instrument for antibiotic use based on the Montero auction mechanism (2008). We analytically and empirically demonstrate that the proposed auction mechanism is welfare improving as compared to current regulations. For the empirical analysis, we calibrate a simulation model for the Danish livestock sector based on farm accounts data for different types of livestock farms and combine it with calibrated demand functions for antibiotic allowances and a calibrated damage function. Based on the simulation results and the implementation of systems such as the Danish VETSTAT (a detailed accounting of farmers' antibiotic use), we anticipate that the proposed mechanism can be effectively used in the future.

Trusted UAV Network Coverage Using Blockchain, Machine Learning, and Auction Mechanisms

The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

Parallel demand side auction mechanism for dynamic and efficient resource allocation

Effective allocation mechanisms are required for infinitesimally divisible demand side resources capturing self-interest and non-cooperation among the users who consume the resources, in order to achieve efficient resource allocation maximising the social welfare with decentralisation of information sharing and decision making. The authors study an auction based mechanism to allocate the demand side resource, where a set of users who consume resources game with each other by bidding. The proposed auction mechanism fits into the progressive second price (PSP) mechanism, which follows a Vickrey-Clarke-Groves type payment. These kind of mechanisms admit incentive compatibility and existence of the efficient Nash equilibrium (NE). Furthermore, considering the fairness and privacy of the bidders as well as limitation of sequential algorithms in practical applications, the authors present a parallel updating algorithm in which all the bidders synchronously update their bids to achieve the efficient NE. By rigorous theoretical analyses, the proposed algorithm is guaranteed to converge to a bid profile which is shown to be the efficient NE. Numerical simulations demonstrate the performance of the proposed auction mechanism and parallel algorithm.

Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks

As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of "block mining" by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.

Design of auction‐based approach for market clearing in peer‐to‐peer market platform

This study designs a market platform for peer-to-peer (P2P) energy trading in transactive energy (TE) systems, where prosumers and consumers actively participate in the market as seller or buyer to trade energy. An auction-based approach is used for market clearing in the proposed platform and a review of different types of auctions is performed. The appropriate auction approach for market clearing in the proposed platform is designed. The proposed auction mechanism is implemented in three steps namely determination, allocation and payment. This study identifies important P2P market clearing performance indices, which are used to compare and contrast the designed auction with different types of auction mechanisms. Comparative studies demonstrate the efficacy of the proposed auction mechanism for market clearing in the P2P platform.

Joint Geometric Unsupervised Learning and Truthful Auction for Local Energy Market

Development of smart grid technologies has created a promising atmosphere for smart cities and energy trading markets. Especially, traditional electricity consumers evolve into prosumers who produce as well as consume electricity in modern power electric systems. In this evolution, the electric power industry has tried to introduce the notion of local energy markets for prosumers. In the local energy market, prosumers purchase electricity from distributed energy generators or the other prosumers with surplus electricity via a local power exchange center. For this purpose, this paper proposes joint geometric clustering and truthful auction schemes in the local energy markets. The proposed clustering scheme is designed for distribution fairness of the distributed energy generator for serving prosumers, where the scheme is inspired by expectation and maximization based unsupervised learning. Moreover, this paper proposes an auction mechanism for truthful electricity trading in a local energy market. In order to guarantee truthful electricity trading, the proposed auction mechanism is constructed based on the Vickrey–Clarke–Groves auction, which was proven to guarantee truthful operations. The Hungarian method is also considered in addition to the auction. The simulation results for the auction verify that the utilities of local market energy entities are maximized when the prosumers are truthful.

Adaptive Task Offloading Auction for Industrial CPS in Mobile Edge Computing

The emerging intelligent applications in Industrial Cyber-Physical Systems (ICPS), such as product inspection by deep-learning-based image recognition technology, are highly computation-consuming. However, the smart devices without sufficient computing resources fail to handle this kind of applications. Moreover, the Internet has very high latency compared with the local network which fails to meet the requirements of time-sensitive tasks, therefore we can not offload these tasks over the cloud. Mobile Edge Computing (MEC) brings the opportunities to offload the tasks of ICPS to the MEC servers to satisfy strict latency requirements, as well as to meet the demand for security requirements. Considering MEC servers owned by the third parties, resource allocation in MEC should be solved jointly with network economics to maximize the utility of system. In this paper, we investigate the task offloading problem under the access capability, latency and security constraints. Specifically, we present a novel Adaptive Task Offloading (ATO) auction mechanism to determine which MEC server to offload with access capability and security constraints, and how to schedule tasks with various deadline constraints, which incentives the third party of MEC providers to share their computing resources with the maximum profit. According to our theoretical analysis, the proposed auction mechanism has the properties of individual rationality, computational efficiency and truthfulness. Extensive simulations have been conducted to evaluate the performance of ATO auction and the experimental results show our method provides better solutions with the classic greedy algorithms in terms of maximizing the utility of the MEC server.

Blockchain-Based Distributive Auction for Relay-Assisted Secure Communications

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

Multi-Dimensional Auction Mechanisms for Crowdsourced Mobile Video Streaming

Crowdsourced mobile video streaming enables nearby mobile video users to aggregate network resources to improve their video streaming performances. However, users are often selfish and may not be willing to cooperate without proper incentives. Designing an incentive mechanism for such a scenario is challenging due to the users’ asynchronous downloading behaviors and their private valuations for multi-bitrate encoded videos. In this paper, we propose both the single-object and multi-object multi-dimensional auction mechanisms, through which users sell the opportunities for downloading single and multiple video segments with multiple bitrates, respectively. Both the auction mechanisms can achieve truthfulness (i.e., truthful private information revelation) and efficiency (i.e., social welfare maximization). Simulations with real traces show that crowdsourced mobile streaming facilitated by the auction mechanisms outperforms noncooperative streaming by 48.6% (on average) in terms of social welfare. To evaluate the real-world performance, we also construct a demo system for crowdsourced mobile streaming and implement our proposed auction mechanism. Experiments over the demo show that those users who provide resources to others and those users who receive help can increase their welfares by 15.5% and 35.4% (on average) via cooperation, respectively.

Truthful incentive mechanisms for mobile crowd sensing with dynamic smartphones

The emergence of ubiquitous mobile devices has given rise to mobile crowd sensing, as a new data collection paradigm to potentially produce enormous economic value. Fully aware of the paramount importance to incentivize smartphone users’ participation, a wide variety of incentive mechanisms have been proposed, however, most of which have made the impractical assumption that smartphones remain static in the system and sensing tasks are known in advance. Designing truthful incentive mechanisms for mobile crowd sensing system has to address four major challenges, i.e., dynamic smartphones, uncertain arrivals of tasks, strategic behaviors, and private information of smartphones. To jointly address these four challenges, we propose two truthful auction mechanisms, OT-OFMCS and NOT-ONMCS, with respect to the offline and online case of mobile crowd sensing, aiming at selecting an optimal set of winning bids with low costs for maximizing the social welfare. The OT-OFMCS mechanism features an optimal task allocation algorithm with the polynomial-time computational complexity where the information of all smartphones and tasks are known a priori. The NOT-ONMCS mechanism is comprised of a critical payment scheme and an online allocation algorithm with a 12-competitive ratio, where the real-time allocation decisions are made based on current active smartphones. To improve the theoretical competitive ratio, we investigate a modified online approximation algorithm RWBD with the ratio of (1−1e). Rigorous theoretical analysis and extensive simulations have been performed, and the results demonstrate our proposed auction mechanisms achieve truthfulness, individual rationality and computational efficiency.

A Spectrum Auction Enables Spectrum Spatial Reuse Among SUs in Cognitive Networks

Spectrum auction has been considered as an effective way for dynamic spectrum access. In this paper, we propose a novel spectrum auction framework which enables spectrum spatial reuse among secondary users (SUs) and the payment rule is optimized here. There are two stages during the framework: auction and payment. Auction phase, the winner determination problem (WDP) is modeled as a 0/1 knapsack problem, and the Genetic Algorithm is selected to solve it. Payment phase, a payoff function similar to the Second-Price Auction is made, to ensure that all the SUs can get non-negative utilities only with their truthful information. Finally, simulation experiments show that the proposed auction mechanism has a better performance in solving the problem of WDP.

Auction-Based Mechanism for Dynamic and Efficient Resource Allocation

We study how to efficiently allocate the infinitesimal divisible resource under auction mechanism in a dynamic way. We propose a Vickrey–Clarke–Groves-type auction mechanism with a 2-D bid which specifies a per unit price and a maximum of the demand. Due to the absence of enough information related to the infinite dimensional valuations of individual players in a single-bid strategy, it is challenging to implement the efficient Nash equilibrium (NE) in a dynamic way. In this paper, we introduce a pair of parameters related to players’ valuations, and design a decentralized dynamic process assisted with this pair of values, such that at each iteration, a single player updates its best bid under a constrained set of demand. Under the proposed auction mechanism, we show the incentive compatibility, efficiency, and uniqueness of the NE. Furthermore, our method is guaranteed to converge to the efficient NE, and it presents the enhanced convergence performance compared with those methods proposed in the literature. Case studies are given to demonstrate the results developed in this paper.

Selecting Efficient Service-providers in Electric Power Distribution Industry Using Combinatorial Reverse Auction

In this paper, a combinatorial reverse auction mechanism is proposed for selecting the most efficient service-providers for resolving sustained power interruptions in multiple regions of an electric power distribution company’s responsibility area. Through this mechanism, supplying the required service in each region is assigned to only one potential service-provider considering two criteria including cost and service time. So, the corresponding winner determination problem of the proposed auction mechanism is formulated as a bi-objective combinatorial optimization problem. However, finding a feasible solution for the formulated problem as well as its solving is NP-complete. Since exact optimization algorithms are failed in solving this kind of problems in a reasonable time, a problem specific metaheuristic called NSGA-II that is an evolutionary algorithm for solving multi-objective optimization problems is developed to estimate the set of Pareto optimal solutions of the formulated bi-objective winner determination problem. In our developed NSGA-II, two problem-specific operators are proposed for creating initial feasible solutions and converting infeasible solutions to feasible ones. Furthermore, a new method for determining the population size based on the size of problem instance is proposed. We conduct a computational experiment in which several randomly generated problem instances are solved using the proposed NSGA-II in different settings. Computational results of proposed algorithm in different settings are compared using a quality measure and statistical hypothesis tests. The results of performance comparison show that the proposed NSGA-II with a population size determined by proposed method and a different form of binary tournament method performs better in finding non-dominated solutions for different instances of formulated bi-objective optimization problem.

Improving Auction Mechanisms for Online Real-Time Bidding Advertising with a Two-stage Resale Model

Real Time Bidding (RTB), widely publicized as one of the most promising big-data-driven business models for online computational advertising, has the potential of effectively monetizing user clicks and impressions via two-stage auctions with resale. Demand side platforms (DSPs) play a key role as intermediators in this auction process. Typically, the first-stage auctions will be conducted separately in each DSP to determine one winning advertiser registered on it, and in the second-stage auction, each DSP submits a bid based on its winning advertiser’s bid to the ad exchange platform (AdX). The highest-bid DSP wins the ad impression from AdX and resells it to its winning advertiser in pursuit of the intermediate fee. This two-stage resale auction is a critical component in maintaining the effectiveness and efficiency of RTB ecosystems. In this paper, we strive to identify and design potential improvements for this auction mechanism with the aim of enhancing the total revenue of both advertisers and DSPs. We also validated our proposed auction mechanisms using the computational experiment approach. The experimental results indicate that our proposed mechanisms can make both advertisers and DSPs better off. Our work represents the first step towards a new research area of optimal mechanism design for RTB auctions, and is expected to provide useful managerial insights in RTB market practice.

Spectrum Auction for Differential Secondary Wireless Service Provisioning With Time-Dependent Valuation Information

In this paper, we propose a spectrum auction mechanism for secondary spectrum access in cognitive radio networks. Different from existing works in the literature, the time-dependent buyer valuation information is employed in the proposed mechanism so that the primary spectrum owner (PO) can determine more favorable spectrum allocations and pricing functions in order to maximize the expected auction revenue. In addition, to exploit the temporal spectrum reusability, the proposed mechanism allows each secondary wireless user to declare its specific time preferences, including service starting time, delay tolerance, and service length. By further considering the heterogeneities in secondary wireless service provisioning, the proposed mechanism is able to support heterogeneous forms (continuous or disjointed spectrum usages) of secondary spectrum requests. Specifically, at the beginning of the auction frame, secondary wireless users report their different spectrum usage requests along with the bidding prices, while the PO decides a single-step spectrum allocation and calculates the payment for each winner based on not only the received bids but also the known time-dependent valuation information. Theoretical analyses and simulation results show that the proposed auction mechanism can satisfy all desired economic properties, and can improve the spectrum allocation efficiency and auction revenue compared with counterparts.

A Truthful Incentive Mechanism for Emergency Demand Response in Geo-Distributed Colocation Data Centers

Data centers are key participants in demand response programs, including emergency demand response (EDR), in which the grid coordinates consumers of large amounts of electricity for demand reduction in emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers, this work studies EDR in geo-distributed multitenant colocation data centers in which servers are owned and managed by individual tenants. EDR in colocation data centers is significantly more challenging due to lack of incentives to reduce energy consumption by tenants who control their servers and are typically on fixed power contracts with the colocation operator. Consequently, to achieve demand reduction goals set by the EDR program, the operator has to rely on the highly expensive and/or environmentally unfriendly on-site energy backup/generation. To reduce cost and environmental impact, an efficient incentive mechanism is therefore needed, motivating tenants’ voluntary energy reduction in the case of EDR. This work proposes a novel incentive mechanism, Truth-DR, which leverages a reverse auction to provide monetary remuneration to tenants according to their agreed energy reduction. Truth-DR is computationally efficient, truthful, and achieves 2-approximation in colocation-wide social cost. Trace-driven simulations verify the efficacy of the proposed auction mechanism.

Reconsidering Carbon Permits Auction Mechanism: An Efficient Dynamic Model

AbstractOne of major concerns for China and developing countries is the new international trade rule concerning carbon emissions, which one needs to know its suitable mechanism and the possible implication on China's carbon trading market. Precisely, in an emission‐and‐trade scheme, an important question is whether the auction outcome is efficient, since this is closely related with the economic costs of emissions reduction. Due to the unique features of carbon permits, existing auction mechanisms do not seem to be suitable for their distribution. Inspired by the tâtonnement process, this paper proposes a new auction mechanism for them. For both practical application and theoretical extension, the discrete and continuous situations are examined, respectively. The result shows that the proposed auction converges to Pareto optimal equilibria, and that without ties, the equilibrium is unique. For bidders, the straightforward bidding strategy is the most preferred one, in the sense that the strategy makes them avoid the risk of loss. Intuitively, the auctioneer's utility is increasing in the number of bidders, while bidders’ pay‐off is increasing in permits. To a certain extent, the proposed auction mechanism resolves the confliction between efficiency and revenue in the multi‐item auction theory, while it increases people's understanding for carbon emission auction mechanism design.

A Variable bandwidth spectrum auction mechanism with performance guarantee

Spectrum resource is experiencing a rapid growth, which cannot meet the demand of the ever-increasing wireless communications technologies in recent years. Spectrum auctions in the secondary market have been considered as a prominent way to solve this challenge due to its fairness and effectiveness. However, most of the existing studies mainly focus on allocating spectrum in units of channels without considering allocate spectrum with variable bandwidths to the secondary users, which has been supported by the software-defined radio technologies. Variable bandwidth trading can make the usage of spectrum more flexible and efficient. Thus, we study the spectrum auction problem where the primary user wants to share a continuous spectrum with the secondary users, and each secondary user has a fixed transmission demand. The target of this work is to design a truthful auction mechanism, which can allocate spectrum with variable bandwidths to the secondary users and maximize the social efficiency at the same time. We first propose a bid-monotone winner determination mechanism to decide the winning secondary users in the auction. Since the optimal winner determination problem is NP-hard, we prove that the proposed mechanism has an approximation factor of 10. Then, a channel allocation mechanism is proposed, which can allocate spectrum to winners without interference. Finally, we compute the critical value for each winner to ensure truthfulness. We can demonstrate that the proposed auction mechanism is truthful through theoretical analysis. We also perform extensive simulations to study the performance of the proposed auction mechanism, and the simulation results corroborate our theoretical analysis.