A Distributed Double Auction for Shared Energy Storage: Differentiated Pricing and Welfare Maximization

An Online Double Auction Mechanism for Dynamic Resource Allocation in Maritime Networks

A Small-Scale Restricted Double Auction Mechanism Based on Local Differential Privacy

As electric vehicles (EVs) gain popularity, the existing public charging infrastructure is struggling to keep pace with the rapidly growing demand for the immediate charging needs of EVs. V2V power trading has gradually attracted widespread attention and development. EVs need to transmit sensitive information, such as transaction plans, through communication entities in the Internet of Vehicles (IoV). This could lead to leaks of sensitive information, thereby threatening the fairness of transactions. In addition, due to the differences in the cryptographic systems of entities, communication between entities faces challenges. Therefore, a privacy-preserving scheme for V2V double auction power trading based on heterogeneous signcryption and IoV is proposed. Firstly, a heterogeneous signcryption algorithm is designed to realize secure communication from certificateless cryptography to identity-based cryptography. Secondly, the scheme employs a pseudonym mechanism to protect the real identities of EVs. Furthermore, a verification algorithm is designed to verify the information sent by EVs and ensure the traceability and revocation of malicious EVs. The theoretical analysis shows that the proposed scheme could serve common security functions, and the experiment demonstrates that the proposed scheme reduces communication costs by about 14.56% and the computational cost of aggregate decryption by 80.51% compared with other schemes in recent years.

Cost-Efficient and Privacy-Preserving Distributed Learning: A Double Layer-Based Auction Design

Multi-attribute based double auction for virtual machine trading in federated cloud environment

With the growing demand for sustainable energy solutions, microgrids face the dual challenge of optimizing energy production and carbon credit trading to facilitate sustainable and efficient operations. This paper proposes a novel hybrid framework for carbon credit trading among microgrids, integrating Interval Type-2 Fuzzy Logic Controllers (IT2-FLC) and blockchain technology to enhance operational efficiency, security, and sustainability. The framework consists of three islanded microgrids, each characterized by a unique energy consumption profile and production capacity, with decision-making governed by IT2-FLCs. A continuous double auction mechanism facilitates decentralized energy and carbon trading, while smart contracts securely settle transactions, ensuring tamper-proof execution and transparent record-keeping. The IT2-FLCs provide robust, real-time control in the presence of uncertainty, enhancing system adaptability. Simulation results confirm the framework’s ability to maintain operational balance, reduce carbon emissions, and enable trustless, scalable coordination across microgrids. This study presents a notable advancement in integrating fuzzy logic control with blockchain-based market mechanisms for real-time, resilient carbon credit trading, offering a robust and scalable pathway for future smart grid implementations under uncertainty.

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms.

Truthful Double Auction for Multiple Secondary Operator Spectrum Sharing With Flexible Bidding

A multi-round combinatorial double auction for carrier collaboration with carbon emission permits trading in less than truckload transportation

P2P energy trading of multi-energy prosumers: An electricity-heat coupling double auction market

Double Auctions: Formalization and Automated Checkers

The proliferation of Internet of Things (IoT) devices has opened new roads for collaborative distributed applications, particularly in smart city environments, where a variety of resources, including sensing, actuation, computation, and storage, are essential for providing effective location-based services. This paper specifically focuses on the sharing of heterogeneous resources among IoT applications in smart cities. By leveraging game-theoretic principles, this study addresses resource allocation through a combinatorial double auction. The solution is rooted in the concept of Social IoT (SIoT), where Internet-connected objects create dynamic social networks based on rules set by their owners. Social relationships, such as ownership and co-location, are leveraged to form groups offering enhanced reliability resource bundles. The proposed solution offers several key economic properties, including incentive compatibility, individual rationality, and a balanced budget, while maintaining low computational complexity. Simulation results demonstrate that the proposed combinatorial double auction mechanism achieves over 70% successful resource allocation for up to 1000 requests, maintains computational efficiency with execution times under 30 s, and ensures economic properties such as incentive compatibility and individual rationality, making it a scalable and practical solution for large-scale smart city IoT applications.

A two-way dynamic adaptive pricing resource allocation model based on combinatorial double auctions in computational network

Reinforcement Learning-Based Dual-Identity Double Auction in Personalized Federated Learning

Helminthiasis is a significant public health problem in Indonesia, with an estimated prevalence ranging from 45 to 65% in 2013, with 19.7% being attributed to A. lumbricoides and 1.5% to T. trichiura. This makes it especially concerning for children of primary school age, as they are particularly at risk. To improve the detection of helminthiasis in children, a research study was conducted to construct an expert system based on four primary characteristics: body weight, eye color, stomach shape, and facial skin color. A questionnaire was created for specialists to fill out and the criteria were then weighed and ranked to identify inconsistencies before being used to detect helminthiasis. To further optimize resource allocation and health outcomes, the concept of double auctions was implemented. This approach suggests that innovative healthcare solutions can be used to increase the efficiency of disease identification and treatment, ultimately leading to improved health outcomes.

Designing a double auction mechanism for parallel machines scheduling with multiple consumer agents and resource agents

Combinatorial Group-Buying double auction for recycled remanufacturing products of construction waste

As an important market mechanism, double auctions are widely applied in complex markets such as finance and energy. The design of auction rules significantly impacts market efficiency, fairness, and resource allocation. This paper focuses on two typical auction rules: uniform pricing and discriminatory pricing. It compares and analyzes their characteristics and market performance from price discovery efficiency, resource allocation efficiency, and market transparency perspectives. This paper uses the German electricity market and the Shanghai license plate auction to validate the theoretical analysis as case studies. Through an in-depth analysis of these two markets, this study demonstrates the applicability and limitations of uniform pricing and discriminatory pricing under different market environments. The results show that uniform pricing, with its single equilibrium price, improves market transparency and fairness, making it suitable for markets that prioritize price stability and efficiency. In contrast, discriminatory pricing caters to individualized needs through differentiated pricing but may face challenges in transparency and fairness due to strategic behaviors and price dispersion. This study provides theoretical support and practical insights for optimizing rule design in double auction markets.

Truthful online bundle double auction mechanisms for a transport market with dynamic demands and supplies