Auction Scheme Research Articles

Zk‐STARKs based scheme for sealed auctions in chains

AbstractOn‐chain sealed auctions represent a novel approach to electronic bidding auctions, wherein the introduction of zero‐knowledge proof technology has significantly enhanced the security of auctions. However, most mainstream on‐chain sealed auction schemes currently employ Bulletproofs to prove auction correctness, which leaves room for optimization in terms of verification time and inherent security. Addressing these issues, an on‐chain sealed auction scheme based on zero‐knowledge succinct non‐interactive argument of knowledge (zk‐STARK) is proposed. This scheme leverages the decentralization and immutability of blockchain and smart contracts to eliminate third‐party involvement while ensuring the security of the auction process. The Inter Planetary File System is utilized to provide a qualification review mechanism for the auctioneer, enabling the screening of unqualified bidders before the auction. Additionally, the scheme employs RSA encryption to conceal bidders' bids, Pedersen commitments to ensure the consistency of bidding information, and zk‐STARKs to verify the correctness of the winning bid. Security analysis and experimental results demonstrate that the proposed scheme meets the required security standards, with time consumption at various stages of the auction being within acceptable limits, and effectively reduces the time required for proof verification.

Potential and goal conflicts in reverse auction design for bioenergy with carbon capture and storage (BECCS)

Bioenergy with carbon capture and storage (BECCS) is considered as a future key technology to provide baseload electricity, heat, pulp, paper, and biofuels, while also enabling atmospheric carbon dioxide removal (CDR). Sweden seeks to lead the way in bringing this technology up to scale, introducing a EUR 3.6 billion reverse auction scheme to facilitate market entry of companies producing BECCS. We explore instrument design preferences among politicians, regulators, and prospective BECCS operators to identify trade-offs and explore feasible policy design. Based on 35 interviews with experts in the latent BECCS sector in Sweden, we identify under which circumstances prospective operators would be willing to place bids and discuss how actor preferences both align with and challenge auction theory. The analysis concludes that at least four dilemmas need attention. These concerns how to: (1) balance the state’s demand for BECCS to be implemented already in 2030 against the prospective BECCS operators’ fear of the winner’s curse, i.e., a fear of bidding for a contract that turns out to be too costly to implement; (2) allocate contracts at the margin of the auctioneer’s demand for BECCS without driving up costs; (3) design compliance mechanism to achieve effectiveness without undermining efficiency, and; 4) integrate the auction with the voluntary carbon market—if at all—in a manner that safeguards the environmental integrity of the auctions.

Secure blockchain bidding auction protocol against malicious adversaries

In recent years, with the development of blockchain, electronic bidding auction has received more and more attention. Aiming at the possible problems of privacy leakage in the current electronic bidding and auction, this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries, which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party. The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography. It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants, which can solve the problem of resisting malicious adversary attacks. The security of the protocol is proved by the real/ideal model paradigm, and the efficiency of the protocol is analyzed. The efficiency of the protocol is verified by simulating experiments, and the protocol has practical value.

How does carbon emission trading scheme affect enterprise market value? A roadmap towards natural resources sustainability

China has committed to establishing a unified national carbon emissions market to achieve carbon reduction goals. The Carbon Emission Trading Scheme (CETS) internalizes emission reduction costs, which may affect enterprise market value (EMV), but this has seldom received attention. Therefore, based on A-share listed enterprises data from 2009 to 2019, this study not only focuses on investigating the direct and heterogeneous impact of CETS on EMV but also identifies the effective market mechanism design by constructing a unified research framework including carbon emission quota allocation schemes, penalty mechanisms, and EMV performance. The empirical results are as follows: (1) The CETS can significantly enhance the EMV of covered enterprises, indicating that China's CETS supports the Porter hypothesis. Compared with state-owned and small enterprises, the CETS has a more significant effect on promoting the EMV of non-state-owned and large enterprises. (2) The mixed carbon quota allocation scheme (the combination of free and auction schemes) is more effective in improving EMV than the pure free quota allocation system. Meanwhile, the mixed penalties combining economic and non-economic measures can effectively encourage CETS-covered enterprises to participate in carbon trading activities, thus enhancing overall EMV. (3) The results of the mediation effect test indicate that the mixed schemes improve EMV by promoting low-carbon technology innovation, and mixed carbon quota allocation schemes have a more significant effect through this channel. These findings have important reference value for improving EMV under the CTEs, achieving the carbon peak and carbon neutrality goals, and promoting sustainable economic development.

Optimal energy procurement with long-term photovoltaic energy contracts considering generation uncertainty: A two-dimensional auction approach

The procurement auction scheme for long-term photovoltaic (PV) energy contracts is being implemented in various countries to ensure stable profits for potential PV generators. However, in most of these auction formats, there is a deficiency in that they consider only the contract price and capacity, neglecting to account for the uncertainty of generation efficiency. In this regard, this study proposes a procurement auction scheme for long-term photovoltaic (PV) energy contracts based on mechanism design theory. We developed a two-dimensional auction model in which PV generators bid their cost and capacity. The energy buyer then determines the winners and enters into contracts with them for a fixed period. We incorporated the capacity factor into the payoff functions of both the buyer and the sellers to reflect different generation efficiencies of generators. Following the revelation principle, we characterized the incentive-compatible, individually rational direct mechanism that maximizes the buyer’s expected payoff during the contract period. We also proposed a computation algorithm to implement the auction. Numerical analysis using data from the Korean PV auction market suggested that the proposed model demonstrates results similar to the uniform price auction in terms of the levelized cost of electricity and contract price, and these results are lower than those of the Vickrey auction. Furthermore, despite the fact that the proposed auction results in a slight increase in social costs (approximately 1% more than the Vickrey auction), it maximizes the expected procured electricity and the auctioneer’s payoff.

A blockchain-based privacy-preserving and collusion-resistant scheme (PPCR) for double auctions

Electronic auctions (e-auctions) remove the physical limitations of traditional auctions and bring this mechanism to the general public. However, most e-auction schemes involve a trusted auctioneer, which is not always credible in practice. Some studies have applied cryptography tools to solve this problem by distributing trust, but they ignore the existence of collusion. In this paper, a blockchain-based Privacy-Preserving and Collusion-Resistant scheme (PPCR) for double auctions is proposed by employing both cryptography and blockchain technology, which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy. A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance. A Dispute Resolution agreement (DR) is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct. In addition, a Concise Dispute Resolution protocol (CDR) is designed to handle situations where the number of accused winners is small, significantly reducing the computation cost of dispute resolution. Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.

Measuring the benefits of cross-border renewable auctions in Central and Eastern Europe — The theoretical case of Hungary

Economic theory suggests that significant benefits can be realized, if two or more countries integrate their renewable support schemes and capitalize on the lowest cost renewable energy projects in merged markets. Hungary is one of the Central and Eastern European countries having a legal obligation to open their renewable auction schemes to other EU member states. This paper provides a model-based assessment of what such a cross-border cooperation could mean for Hungary and its neighbouring countries. Economic benefits are calculated and presented for the participating countries according to three pairwise cases for cooperation. Based on these outcomes, the paper concludes with the policy considerations and recommendations to shape the design of the opened auction scheme.

End-to-End Service Auction: A General Double Auction Mechanism for Edge Computing Services

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

Evaluating the German onshore wind auction programme: An analysis based on individual bids

Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals, but create incentives for large actors to game the system.

Optimized channel prediction and auction‐based channel allocation for personal cognitive networks

SummaryCognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bio‐inspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy‐based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm.

Renewable Energy (Solar and Wind) Generation and its Effect on some Variables for Selected EU Countries with Panel VAR Model

Keeping the world livable, the policy makers are giving their extended focus on green energy. They are trying to move on hand-on-hand with rising green energy and lessening carbon dioxide (CO2) emission steading the economic activities specially for the open and developing countries. This can be helpful to provide a more lucrative economic environment to meet the extended demand of the society. The paper highlights to find out whether the economic growth, renewable energy consumption, fossil fuel-based energy generation and CO2 emission are significantly impactful on renewable energy generation or not? Is there any mutual, bi-directorial, unidirectional relationship with each other? For this, the Vector Autoregression (VAR) model is run. The paper focuses on six EU countries who are practicing auction scheme for deploying robust green energy (especially solar and wind) by reducing dependency on fossil fuel and expanding their economy with less CO2 emission. The result of the analysis shows that a positive influence of each concern variable on renewable energy (RE) generation and auction scheme might have a significant impact if robustness of the generation occurs.

Policy implications of spatially differentiated renewable energy promotion: A multi-level scenario analysis of onshore wind auctioning in Germany

Auctions have been introduced in many countries as a useful alternative for renewable energy support. However, they often lead to a high concentration of renewable energy power plants at productive sites, at the expense of other, less favorable sites. This paper studies the impact of alternative renewable energy auction schemes on the promotion of renewable energy in Germany, using a novel multi-level approach—analyzing auctioning on the national, north/south, and federal states level, respectively. First, using a GIS tool, the onshore wind potential is studied at a high regional and temporal resolution. The results of this analysis show a considerable untapped onshore wind potential in the southern federal states of Germany. Second, an onshore wind power auction model is developed by using a system dynamics approach. Finally, the policy support payments are compared for different auction designs by using an electricity market simulation model. The findings suggest that more bidders from the southern federal states could win in the regional auctions. Detailed spatial analysis reveals a trade-off between balanced diversity of bidders and the average auction price. We conclude that regional auctioning can indeed lead to significant support payment savings, and should thus be considered in renewable energy support policy design.

Effective Consensus-Based Distributed Auction Scheme for Secure Data Sharing in Internet of Things

In a traditional electronic auction, the centralized auctioneer and decentralized bidders are in an asymmetric structure, where the auctioneer has more ability to decide the auction result. This asymmetric auction structure is not fair to the participants and not suitable for data auctions in the Internet of Things (IoT). The blockchain-based auction system, with participant equality and fairness, is typically symmetrical and particularly suitable for IoT data sharing. However, when applied to IoT data sharing in reality, it faces privacy and efficiency problems. In this context, how to guarantee privacy and break the inherent performance bottleneck of blockchain is still a major challenge. In this paper, a consensus-based distributed auction scheme is proposed for data sharing, which enforces privacy preservation and collusion resistance. A reverse auction-based decentralized data trading model is introduced to solve the trust problem without a centralized auctioneer, where bidders reach consensus on the auction result. Specifically, we devise a differentially private auction mechanism to incentivize data owners to participate in data sharing. An effective hybrid consensus algorithm is constructed among bidders to reach consensus on the auction result with improved security and efficiency. Theoretical analysis shows that the proposed scheme ensures the properties of privacy preservation, incentive compatibility and collusion resistance. Experimental results reveal that the proposed mechanism guarantees the data sharing efficiency and has certain scalability.

Three‐stage auction scheme for computation offloading on mobile blockchain with edge computing

SummaryBlockchain has been applied in wide range of fields to guarantee security. However, it has been very challenging for blockchain to flourish in mobile environment with limited resources. Existing studies mainly assume that single mobile user can buy the whole resources from edge servers in mobile blockchain. This paper formulates the problem of maximizing the social welfare for computation offloading in mobile blockchain. A three‐stage auction scheme with approximation ratio of based on group‐buying mechanism is proposed to allocate edge server resources for mobile blockchain applications. In the first stage, the miners are divided into groups, and a Vickrey–Clarke–Groves based auction is proposed to determine the bid of each group for each edge server. In the second stage, a matching algorithm is proposed to match edge servers and Access Points for maximizing the profit of edge servers. In the third stage, the edge server resources are allocated to mobile users for mining base on the results in the above stages. We prove that our auction scheme guarantees truthfulness, individual rationality and budget balance. Simulation results show that, the social welfare of our scheme is improved by 33.78%, 21.84%, 19.69%, and 6.69% for 1000 miners, compared with the existing works.

Secure and Reliable Transfer Learning Framework for 6G-Enabled Internet of Vehicles

In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Artificial Intelligence (AI). Transfer Learning (TL) has great potential to empower promising 6G-enabled IoV, such as smart driving assistance, with its outstanding features including enhancing the quality and quantity of training data, speeding up learning processes, and reducing computing demands. Although TL had been widely adopted in wireless applications (e.g., spectrum management and caching), its reliability and security in 6G-enabled IoV were still not well investigated. For instance, malicious vehicles in source domains may transfer and share untrustworthy models (i.e., knowledge) about connection availability to target domains, thus adversely affecting the performance of learning processes. Therefore, it is important to select and also incentivize trustworthy vehicles to participate in TL. In this article, we first introduce the integration of TL and 6G-enabled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to evaluate the reliability of pre-trained models and utilizing the consortium blockchain to achieve secure and efficient decentralized reputation management. Moreover, a deep learning-based auction scheme for the TL model market is designed to motivate high-reputation vehicles to participate in model sharing. Finally, the simulation results demonstrate that the proposed framework is secure and reliable with well-designed incentives for TL in 6G-enabled IoV.

Toward Privacy-Preserving Blockchain-Based Electricity Auction for V2G Networks in the Smart Grid

With the development of electric vehicle (EV) technology, EV has become a key component in the future smart grid. Due to the sheer large number of EVs on the road, the emerging vehicle-to-grid (V2G) technology, which allows for two-way electrical flows between EVs and the power grid, is gaining traction. However, establishing a fair and private electricity exchange scheme has gradually become a critical challenge. The emergence of blockchain technology offers a novel approach for resolving this issue. In this study, we overview the opportunities and challenges of blockchain in the smart grid. Then, we provide a privacy-preserving blockchain-based electricity auction scheme for V2G networks in smart grid. In particular, we exploit PS group signatures to keep the privacy of EVs or charging stations and leverage blockchain to provide automated auction execution. With our mechanism, the identity of EV/charging station is conditionally protected. In case of an emergency, the trusted authority (i.e., the group manager) can open the identity. Meanwhile, we present the security analysis to prove our scheme’s security. Finally, we implement the experiment to evaluate efficiency. The experimental results show that our proposal is efficient and suitable for V2G networks.

Applications of Auction and Mechanism Design in Edge Computing: A Survey

Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption, security and privacy issues, etc. Auction mechanisms can well characterize bidirectional interactions between edge servers and user devices under the above constraints in edge computing. As demonstrated by the existing works, auction and mechanism design approaches are outstanding on achieving optimal allocation strategy while guaranteeing mutual satisfaction among edge servers and user devices, especially for scenarios with scarce resources. In this paper, we introduce a comprehensive survey of recent researches that apply auction approaches in edge computing. Firstly, a brief overview of edge computing including three common edge computing paradigms, i.e., cloudlet, fog computing and mobile edge computing, is presented. Then, we introduce fundamentals and backgrounds of auction schemes commonly used in edge computing systems. After then, a comprehensive survey of applications of auction-based approaches applied for edge computing is provided, which is categorized by different auction approaches. Finally, several open challenges and promising research directions are discussed.

Policy choices and outcomes for offshore wind auctions globally

Offshore wind energy is rapidly expanding, facilitated largely through auctions run by governments. We provide a detailed quantified overview of utilised auction schemes, including geographical spread, volumes, results, and design specifications. Our comprehensive global dataset reveals heterogeneous designs. Although most auction designs provide some form of revenue stabilisation, their specific instrument choices vary and include feed-in tariffs, one-sided and two-sided contracts for difference, mandated power purchase agreements, and mandated renewable energy certificates.We review the schemes used in all eight major offshore wind jurisdictions across Europe, Asia, and North America and evaluate bids in their jurisdictional context. We analyse cost competitiveness, likelihood of timely construction, occurrence of strategic bidding, and identify jurisdictional aspects that might have influenced auction results. We find that auctions are embedded within their respective regulatory and market design context, and are remarkably diverse, though with regional similarities. Auctions in each jurisdiction have evolved and tend to become more exposed to market price risks over time. Less mature markets are more prone to make use of lower-risk designs. Still, some form of revenue stabilisation is employed for all auctioned offshore wind energy farms analysed here, regardless of the specific policy choices. Our data confirm a coincidence of declining costs and growing diffusion of auction regimes.

Methodology for obtaining simplified models for the long-term energy management of renewable assets under a high degree of uncertainty

This paper addresses the attainment of a methodology aimed at obtaining simplified models embedding the regulatory constraints imposed by the country-specific remuneration mechanisms in the energy management system of long operating life renewable assets under a high degree of uncertainty. This methodology, composed of different steps in which sensitivity analysis as well as Monte Carlo simulation play a key role, is focused on a significant case study that has implemented two of the most widely used worldwide remuneration mechanisms in the promotion of renewable energies, i.e., feed-in tariffs and auctions. The earnings before interest, tax, depreciation and amortization have been used as the output variable of the energy management model, as it is essential to take into account both revenues and operating costs of these renewable assets to manage them optimally. Some valid simplified models have been achieved by applying the proposed methodology to the case study with generalized errors below 5%. Specifically, one simplified energy management system model has been obtained under the feed-in tariff scheme, which involves acting on almost 40% of the equations of the original model and reducing the initial input parameters by 22%. Meanwhile, two simplified energy management system models have been obtained under the auction scheme. The most conservative simplified model involves acting on almost 50% of the equations of the original model and reducing the initial input parameters by 35%, while in the less conservative case it involves acting on more than 50% of the equations of the original model and reducing the initial input parameters by 42%. In short, although the uncertainty on the energy assets cannot be completely eliminated, it can be considerably reduced by facilitating the assessment of its prospective financial results. The validity of the achieved simplified models demonstrates the suitability and usefulness of the proposed simplifying methodology, providing a touch of quality in the long-term judgement and decision-making of the stakeholders when optimally managing renewable energy facilities under any type of remuneration scheme.

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

This paper studies a federated learning (FL) system, where <i>multiple</i> FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising <i>intra-service</i> resource allocation and <i>inter-service</i> resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile catering it to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service providers as the bidders and the network operator as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.