Auction Approach Research Articles

Optimizing social welfare: A double-sided auction approach for low-carbon emission power systems with renewable energy certificates

Decreasing carbon emissions becomes essential for maximizing social welfare in power systems. This study investigates the market clearing strategy for maximizing participants' benefits in both economic and environmental power systems, considering renewable energy certificates (RECs). The proposed problem formulation is solved by a particle swarm optimization algorithm and applied to a modified IEEE 30-bus system. The study shows that a combined supply offer that includes supply costs, carbon emission costs (CEC), renewable energy (RE) costs, and REC pricing resulted in the greatest cost savings. This paper demonstrates the efficiency of thorough optimization approaches. In addition, a more effective model is obtained by including demand-sided bidding in the optimization framework in addition to CEC, RE costs, and REC prices, leading to higher social welfare and encouraging the adoption of sustainable energy utilization. These results emphasize the importance of incorporating various environmental and economic factors into optimization frameworks for low-carbon power systems. Implementing this comprehensive strategy promotes substantial enhancements in social welfare and the progression of sustainable energy methodologies.

An intelligent resource allocation strategy with slicing and auction for private edge cloud systems

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge of inequitable resource allocation in the private edge cloud, this work proposes an intelligent resource allocation strategy with a slicing and auction approach. By formalizing the resource allocation problem as a Mixed Integer Nonlinear Programming (MINLP) puzzle, the method transforms it into a hierarchical allocation challenge for Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and power terminals. The proposed Multi-hop Progressive Auction Algorithm (MPAA) addresses the sliced resource allocation problem between MNOs and MVNOs. Furthermore, a Terminal Resource Allocation Strategy (TRAS) based on improved particle swarm optimization is proposed to solve the spectrum resource allocation problem between MVNOs and power terminals. Extensive simulation results show that the bidding overhead of MPAA is reduced by 6.12% and the average terminal satisfaction of TRAS is improved by about 1.3% compared to conventional methods, thus improving the utilization of wireless resources within the power AIoT.

An auction approach to aircraft bandwidth scheduling in non-terrestrial networks

Internet Service has witnessed only limited deployment on commercial flights, where network infrastructure is lacking and subscription fees are high. It is natural to design an efficient scheduling strategy for Internet service access on flights, while maximizing social welfare. There are challenges to use either antennas on the ground or geostationary (GEO) satellites in space as Internet Service Provider (ISP) equipment. First, preemptive-purchase and resource-allocation in post-paid plans may not satisfy the dynamic demand for large amounts of flight-ISP machine links. Second, finding an optimal, dynamic, online solution based on flight demands is NP-hard. Third, existing solutions often fail to maximize either seller profit or social welfare. To address the aforementioned challenges, we formulate an online bandwidth scheduling integer linear program (ILP) among flights, antennas, and GEO satellites in a Non-Terrestrial Network (NTN). We transform the ILP into an exponential optimization problem, which is then relaxed, with its dual formulated. We iteratively solve a series of dual subproblems in polynomial time, towards social welfare maximization with a good competitive ratio in typical network settings. In empirical studies, our algorithm achieved a much better competitive ratio than the theoretical worst-case guarantee, and clearly outperforms alternative algorithms.

On the Robustness of Channel Allocation in Joint Radar and Communication Systems: An Auction Approach

Joint radar and communication (JRC) is a promising technique for spectrum re-utilization, which enables radar sensing and data transmission to operate on the same frequencies and the same devices. However, due to the multi-objective property of JRC systems, channel allocation to JRC nodes should be carefully designed to maximize system performance. Additionally, because of the broadcast nature of wireless signals, a watchful adversary, i.e., a warden, can detect ongoing transmissions and attack the system. Thus, we develop a covert JRC system that minimizes the detection probability by wardens, in which friendly jammers are deployed to improve the covertness of the JRC nodes during radar sensing and data transmission operations. Furthermore, we propose a robust multi-item auction design for channel allocation for such a JRC system that considers the uncertainty in bids. The proposed auction mechanism achieves the properties of truthfulness, individual rationality, budget feasibility, and computational efficiency. The simulations clearly show the benefits of our design to support covert JRC systems and to provide incentive to the JRC nodes in obtaining spectrum, in which the auction-based channel allocation mechanism is robust against perturbations in the bids, which is highly effective for JRC nodes working in uncertain environments.

An Improved Reeds–Shepp and Distributed Auction Algorithm for Task Allocation in Multi-AUV System with Both Specific Positional and Directional Requirements

Task assignment is of paramount importance in multi-AUV systems, particularly in applications such as bridge inspection where task execution is direction-specific. In such scenarios, the underactuation of AUVs is a critical factor that cannot be ignored. Therefore, it is essential to consider the AUV’s kinematic model comprehensively to ensure minimal energy consumption during task execution. In this paper, we introduce an improved Reeds–Shepp algorithm in conjunction with a distributed auction approach. We treat AUVs as car-like models in our approach, paying meticulous attention to their operational characteristics during path planning. Importantly, we effectively utilize their backward driving capabilities. Our analysis reveals that this model successfully fulfills the directional requirements of detection tasks. Furthermore, the distributed auction approach optimizes the overall task distribution in the multi-AUV system. We support our method with simulation results that underscore its effectiveness.

Mechanism Design for Semantic Communication in UAV-Assisted Metaverse: A Combinatorial Auction Approach

Mechanism Design for Semantic Communication in UAV-Assisted Metaverse: A Combinatorial Auction Approach

TiFLCS-MARP: Client selection and model pricing for federated learning in data markets

In recent years, the burgeoning data market has witnessed a surge in data exchange, playing a pivotal role in augmenting the predictive and decision-making capabilities of machine learning. Despite these advancements, persistent concerns surrounding data privacy have resulted in stringent limitations on data sharing and trading. Consequently, the data market is undergoing a transformative shift from pricing individual datasets to pricing the models themselves. The challenge of training high-performance machine learning models with restricted data from a single client is substantial. Federated learning has emerged as a popular solution, allowing collaborative model training without the need to transfer client data beyond local environments. However, training federated models within a data market introduces several challenges, including the effective selection of clients for model training and the optimization of utility through model pricing. In response to these challenges, we propose the Tiered Federated Learning Client Selection Algorithm (TiFLCS-MAR), employing a multi-attribute reverse auction approach. Integrated into the federated learning framework, TiFLCS-MAR excels at the comprehensive evaluation of client attributes, employing a tiered strategy to mitigate issues arising from client heterogeneity. Additionally, we introduce the TiFLCS-MAR Pricing Framework (TiFLCS-MARP), leveraging Nash equilibrium principles to maximize profitability for both clients and servers. Our framework accommodates the heterogeneity of diverse clients, efficiently selecting suitable candidates from a large pool, thereby boosting training efficiency and curbing model pricing costs. Empirical evidence showcases the efficacy of federated training with TiFLCS-MAR, demonstrating nearly double the convergence speed and a 5-10 percentage point improvement in accuracy across real and synthetic datasets. Furthermore, when compared to three baseline algorithms, TiFLCS-MARP substantially increases central server revenue by factors of 1.99, 27.05, and 1.78, highlighting its superior performance in the data market context.

Pre-auction optimization for the selection of shared customers in the last-mile delivery

AbstractCompanies are constantly looking for new strategies to improve their logistics performance and ensure their competitiveness in the global market. This article provides a new scheme for managing the selection of shared customers for a logistics company. The new mechanism proposes the use of the auction as a tool to manage the selection of shared clients through the coalition pool. Thus, all unprofitable shared customers will be pushed to the pool for outsourcing by the other collaborating carriers. Then, some profitable auctioned ones will be selected. The selection system is designed based on solving a vehicle routing problem that aims to maximize the carrier's profit in a decentralized context. At first, a mixed integer linear programing model is derived to solve the deterministic version of the problem. Then in order to efficiently address the stochastic version of the problem, a simulation-based optimization model is developed. This model is employed to solve a real case study of a parcel delivery company, considering the travel times as a bimodal distribution. A comparative study is conducted to demonstrate the effectiveness of the auction approach in managing shared customers. The results of our study reveal that the proposed auction approach efficiently manages the shared customers which leads to the substantial increase of 22.65% in profits for the delivery company. These findings have significant implications for logistics companies seeking to improve their performance and competitiveness in the global market.

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.

Comparative Double Auction Approach for Peer-to-Peer Energy Trading on Multiple microgrids

Peer-to-peer (P2P) energy trading is one of the most effective methods to increase the usage of Renewable Energy (RE) resources in the distribution network and reduce losses by eliminating long transmission and distribution lines. This research aims to enhance the efficiency of P2P energy trading by examining the suitability of four distinct double auction mechanisms: Average, McAfee, Trade Reduction and Vickrey-Clarke-Groves (VCG). We conducted a systematic evaluation of these mechanisms across various microgrid (MG) types. The study algorithm integrates user preferences, bidding strategies and time-of-use tariffs, allowing participants to indicate their willingness to pay for different energy qualities and specific time periods. Notably, both the Average and VCG mechanisms emerged as the most effective across a majority of MG setups. Specifically, the average mechanism was found to be optimal for a consumer-centric MG, while the VCG mechanism was predominantly advantageous during non-peak hours trading. However, it was observed that P2P energy trading from MG to MG was inefficient due to the lesser number of peers. In conclusion, this work offers a comprehensive solution that adeptly identifies and recommends the most fitting auction mechanisms for diverse MG configurations and usage timings, paving the way for more efficient P2P energy trading.

Preallocation-Based Combinatorial Auction for Efficient Fair Channel Assignments in Multi-Connectivity Networks

We consider a general multi-connectivity framework, intended for ultra-reliable low-latency communications (URLLC) services, and propose a novel, preallocation-based combinatorial auction approach for the efficient allocation of channels. We compare the performance of the proposed method with several other state-of-the-art and alternative channel-allocation algorithms. The two proposed performance metrics are the capacity-based and the utility-based context. In the first case, every unit of additional capacity is regarded as beneficial for any tenant, independent of the already allocated quantity, and the main measure is the total throughput of the system. In the second case, we assume a minimal and maximal required capacity value for each tenant, and consider the implied utility values accordingly. In addition to the total system performance, we also analyze fairness and computational requirements in both contexts. We conclude that at the cost of higher but still plausible computational time, the fairness-enhanced version of the proposed preallocation based combinatorial auction algorithm outperforms every other considered method when one considers total system performance and fairness simultaneously, and performs especially well in the utility context. Therefore, the proposed algorithm may be regarded as candidate scheme for URLLC channel allocation problems, where minimal and maximal capacity requirements have to be considered.

A Decentralized Auction Model for Sustainable Housing Rental Market

The Internet of Things has driven the transformation of the traditional offline rental model to online rental platforms such as Zillow, Zigbang, and Lianjia. These platforms provide abundant housing options, detailed information, convenient communication, and greater price transparency. However, existing online rental platforms are still centralized and rely on third-party intermediaries for settlement, which raises concerns about the integrity of real estate data, transaction security, and price transparency. To improve the real estate leasing business, we propose leveraging the decentralized nature of blockchain technology. By eliminating intermediaries, we can ensure customer privacy and reduce transaction costs. This strategy revolves around smart contracts as a core component, automatically matching landlords and tenants based on their offers, achieving transparency, and maintaining anonymity for both parties. We introduce a novel auction system that combines the features of interactive auctions and sealed-bid auctions, enabling landlords and tenants to engage in range bidding. In this study, we developed a detailed algorithm to define how smart contracts function during the auction process. Through a cost analysis, we demonstrate the economic feasibility and provide a secure, transparent, and reliable approach for online auctions.

Economics of Semantic Communication System: An Auction Approach

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meanings of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called “semantic model trading”. Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called “semantic information trading”. To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them to achieve higher utilities than the baseline method.

Multi-Domain Network Slicing in Satellite–Terrestrial Integrated Networks: A Multi-Sided Ascending-Price Auction Approach

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.

A latent auction model of firm establishment location choice in the Greater Toronto Area

We present a maximum bid model for business establishments in the Greater Toronto Area (GTA). The model is based on a latent auction approach, wherein price and utility are jointly modeled in a random utility-maximizing framework. Structural relationships between establishments by their industry classification and type (i.e., headquarters, subsidiary, branch, or single location) are explored through a generalized nesting correlation structure. This structure provides a mechanism for differentiating between branch and single-location establishments, having seemingly similar functions, but differing in their allocation between industry and type nests. Maximum bid models are estimated for the full establishment population in the GTA at the level of individual buildings. The model scope allows us to provide a range of insights into establishment location choice behavior, which was previously infeasible due to data limitations. We find that professional service establishments tend to locate near passenger rail stations (i.e., subway and LRT), while industrial establishments tend to locate near major highway interchanges. We confirm the finding by Kenworthy and Laube (1999) that Canadian cities are less auto-dependent than US cities, albeit from the perspective of establishment location rather than individual vehicle travel data.

Low complexity joint spectrum resource and power allocation for ultra dense networks

In this paper, we propose a low complexity spectrum resource allocation scheme cross the access points (APs) for the ultra dense networks (UDNs), in which all the APs are divided into several AP groups (APGs) and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells. Furthermore, we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput. The problem is formulated as a mixed-integer nonconvex optimization (MINCP) problem which is difficult to solve in general. The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively. For the spectrum allocation, we model it as a auction problem and a combinatorial auction approach is proposed to tackle it. In addition, the DC programming method is adopted to optimize the power allocation subproblem. To decrease the signaling and computational overhead, we propose a distributed algorithm based on the Lagrangian dual method. Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.

Pricing Model for Dynamic Resource Overbooking in Edge Computing

Edge Computing (EC) with cloud-like Quality of Service (QoS) can find its wide applications in various resource-constrained smart cities where the resource requirements can be different during peak and off-peak periods. During off-peak periods, there are often many resources that have been requested but not used, which can be reused to obtain higher profit. However, to the best of our knowledge, there is no effective pricing model or overbooking mechanism in EC. To fill in this gap, a novel pricing model for dynamic resource overbooking is proposed in this paper, specifically: 1) To meet the needs of different users in EC, methods of on-demand, daily, auction, and the new spot billing are designed, in which resources can be overbooked. 2) An auction approach with pricing rule and winner determination rule is designed for auction billing, which is proved to guarantee individual rationality, computational efficiency, and truthfulness. 3) To make more use of the auction approach to utilize idle resources, a dynamic resource overbooking mechanism is introduced, including a cancellation policy and a resource prediction method. The mechanism is validated with real-world data-trace. Experimental results show that the dynamic resource overbooking mechanism maximizes the profit of edge nodes with a high QoS Satisfaction ratio of on-demand and daily billing.

Toward resilient cloud warehousing via a blockchain-enabled auction approach

Cloud warehousing service (CWS) has emerged as a promising third-party logistics service paradigm driven by the widespread use of e-commerce. The current CWS billing method is typically based on a fixed rate in a coarse-grained manner. This method cannot reflect the true service value under the fluctuating e-commerce logistics demand and is not conducive to CWS resilience management. Accordingly, a floating mechanism can be considered to introduce more flexible billing. A CWS provider lacks sufficient credibility to implement floating mechanisms because it has vested interests in terms of fictitious demand. To address this concern, this report proposes a blockchain-enabled floating billing management system as an overall solution for CWS providers to enhance the security, credibility, and transparency of CWS. A one-sided Vickrey–Clarke–Groves (O-VCG) auction mechanism model is designed as the underlying floating billing mechanism to reflect the real-time market value of fine-grained CWS resources. A blockchain-based floating billing prototype system is built as an experimental environment. Our results show that the O-VCG mechanism can effectively reflect the real-time market value of CWSs and increase the revenue of CWS providers. When the supply of CWS providers remains unchanged, allocation efficiency increases when demand increases. By analyzing the performance of the O-VCG auction and comparing it with that of the fixed-rate billing model, the proposed mechanism has more advantages. Moreover, our work provides novel managerial insights for CWS market stakeholders in terms of practical applications.

Responsible antibiotic use labeling and consumers' willingness to buy and pay for fluid milk.

Concerns about antibiotic resistant infections in the United States have called for reduction of antibiotic use in livestock, including dairy cattle. Although effective in curbing antibiotic use, universal organic dairy farming would be impractical and unattainable due to its high land and premium demands. The US Department of Agriculture's organic certification, which completely eliminates antibiotic use in milk production, also raises animal welfare concerns, as it could discourage the use of antibiotics even to treat indicated diseases. Therefore, a proposed alternative for US consumers is a label indicating the responsible antibiotic use (RAU) - not complete elimination - that would minimize antibiotics more than conventional (unlabeled) milk and maximize animal welfare more than organic milk. Our goal was to determine consumers' (1) self-reported preference and (2) willingness to pay for this hypothetical RAU label of milk relative to existing substitutes in organic and unlabeled fluid milk. We conducted (1) a nationally representative survey of US adults and (2) a randomized non-hypothetical experimental Becker-Degroot-Marschak auction with real money and real milk. Although almost half of the survey participants (48.5%) responded that they would buy a RAU-labeled milk, consumers in the experimental auction refused to pay a significant premium for the milk compared with unlabeled milk (mean willingness to pay (95% confidence interval) per half-gallon: $1.92 ($1.65-$2.19) for RAU-labeled milk versus $1.86 ($1.58-$2.13) for unlabeled milk). These results suggest that consumers' survey-identified preferences for RAU-labeled milk could reflect either social desirability bias or a genuine preference for which, however, consumers simply will not pay a significant premium. The study provides preliminary data for future exploration of marketability of the proposed RAU label in the United States and demonstrates the benefits of using complementary survey and experimental auction approaches to understand the potential market for a new dairy product.

Resource-Efficient Federated Learning With Non-IID Data: An Auction Theoretic Approach

Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not identically and independently distributed (IID) across different data sources and locations. This distribution-skewness leads to significant quality degradation. Moreover, an intrinsic consequence of using such non-IID data in decentralized learning is increasing costs that would be mitigated if using IID data. As a remedy, we propose a resource-efficient method for training an FL-based application with non-IID data, effectively minimizing cost through an auction approach and mitigating quality degradation through data sharing. In an experimental evaluation, we investigate the FL performance using real-world non-IID data and use the resulting ground-truth outputs to develop functions for estimating the utility of non-IID data, computational resource costs, and data generation costs. These functions are used to optimize the costs of model training, ensuring resource efficiency. It is further demonstrated that using shared-IID data significantly increases the resource efficiency of FL with local non-IID data. This holds true even when the shared IID data size is less than 1% of the size of the local non-IID data. Moreover, this work demonstrates that the profitability of the stakeholders can be maximized using the proposed auction procedure. The integration of the auction procedure and a resource-efficient training strategy allows FL service providers to create practical trading strategies by minimizing the FL clients’ resources and payments in a machine learning marketplace.