Reverse Auction Mechanism Research Articles

A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients’ volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets.

Reverse double auction mechanism: An efficient algorithm for E-commerce platform operations

Transaction efficiency is critical to the success of e-commerce platforms. We propose a Reverse Double Auction Mechanism (RDouMech), an innovative auction-based algorithm, to revolutionize the transaction process in e-commerce operations. It incorporates unique features of reverse auction, multi-bids, and multi-constraints (i.e., market clearing, individual rationality, and incentive compatibility). This operational innovation facilitates market clearing by precisely matching buyers with sellers, leading to a balance between demand and supply, improved matching rates between buyers and sellers, and a significant increase in transaction prices on the platform. We further validate our algorithm using proprietary data from an online automobile transaction platform. Our results show that RDouMech raises the transaction revenues by 19.73% and doubles social welfare compared to the human expert-assisted artificial intelligence (AI) algorithm that is currently employed by the platform.

Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things

Federated learning, a promising distributed machine learning paradigm, has been used in various Internet of Things (IoT) environments to solve isolated data island issues and protect data privacy. However, since the central server in federated learning cannot detect the local training process of the client, it is vulnerable to adversarial attacks against its security and privacy by malicious clients during the training process. To address this problem, this work proposes a federated learning system model based on dual-reputation reverse auction in IoT. Specifically, due to resource constraints, not all clients participate in the federated learning process, so we propose the reputation-bid ratio based greedy half-splitting algorithm to select some clients to participate in the federated learning, which can guarantee that each client has the chance to be selected while selecting as many honest and high-quality clients as possible. Then, we propose an adaptive dropout aggregation method based on a training quality score, which can effectively defend against malicious workers’ attacks. After the completion of federated learning, we put forward a subjective evaluation incentive mechanism based on a second reputation to realize the fair incentive. Furthermore, we store and manage reputations through blockchain in our model to ensure their availability. Theoretical analysis deduces the complexity and security of our proposed model. Finally, simulation results indicate that our proposed model can achieve the highest accuracy across all test datasets compared to the baseline. Particularly, on the CIFAR10 dataset, the accuracy of our proposed model surpasses the baseline by 5% to 30%. In responding to sudden attacks initiated by normally participating workers, our model exhibits the fastest reaction time, with accuracy surpassing the baseline by 8% to 40%.

Proof of Fairness: Dynamic and Secure Consensus Protocol for Blockchain

Blockchain technology is a decentralized and secure paradigm for data processing, sharing, and storing. It relies on consensus protocol for all decisions, which focuses on computational and resource capability. For example, proof of work (PoW) and proof of stake (PoS) are the most famous consensus protocols that are currently used. However, these current consensus protocols are required to recruit a node with a high computational or a large amount of cryptocurrency to act as a miner node and to generate a new block. Unfortunately, these PoW and PoS protocols could be impractical for adoption in today’s technological fields, such as the Internet of Things and healthcare. In addition, these protocols are susceptible to flexibility, security, and fairness issues, as they are discussed in detail in this work. Therefore, this paper introduces a proof of fairness (PoF) as a dynamic and secure consensus protocol for enhancing the mining selection process. The selection of the miner node is influenced by numerous factors, including the time required to generate a block based on the transaction’s sensitivity. Firstly, a reverse auction mechanism is designed as an incentive mechanism to encourage all nodes to participate in the miner selection process. In a reverse auction, each node will draw its strategy based on its computational capability and claimed cost. Secondly, an expressive language is developed to categorize transaction types based on their sensitivity to processing time, ensuring compatibility with our miner selection process. Thirdly, a homomorphic concept is designed as a security and privacy scheme to protect the bidder’s data confidentiality. Finally, an extensive evaluation involving numerical analysis was carried out to assess the efficiency of the suggested PoF protocol, which confirms that the proposed PoF is dynamic and more efficient than current PoW and PoS consensus protocols.

Procurement of last-mile delivery capacity: a reverse auction mechanism considering logistics service quality

ABSTRACT The increasing demand for last-mile delivery has created a challenge for urban consolidation centers (UCCs). Due to UCC’s limited delivery capacity, it has to purchase spare capacity from carriers. In the procurement, the UCC cannot observe the carriers’ efforts, resulting in low-quality logistics services. Thus, we propose a reverse auction mechanism considering logistics service quality (LSQ) for the UCC’s procurement of last-mile delivery capacity. First, we construct the benchmark of the mechanism by introducing the LSQ in last-mile delivery and presenting a reverse auction. Then, we develop an auction model with ideas of ensuring the UCC’s incentive and awarding business to the carriers who deliver high-quality services. The case study shows that the proposed mechanism is an efficient procurement tool for the UCC with a guarantee of LSQ, and the UCC will benefit from the proposed mechanism by significant total cost saving as well as effective use of resources.

UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

Federated learning is a promising technique in cloud computing and edge computing environments, and designing a reasonable resource allocation scheme for federated learning is particularly important. In this paper, we propose an auction mechanism for federated learning resource allocation in the edge–cloud collaborative environment, which can motivate data owners to participate in federated learning and effectively utilize the resources and computing power of edge servers, thereby reducing the pressure on cloud services. Specifically, we formulate the federated learning platform data value maximization problem as an integer programming model with multiple constraints, develop a resource allocation algorithm based on the monotone submodular value function, devise a payment algorithm based on critical price theory and demonstrate that the mechanism satisfies truthfulness and individual rationality.

A Truthful Combinatorial Reverse Auction Mechanism for Crowdshipping

Crowdshipping is an emerging logistics paradigm that leverages excess capacities of occasional couriers to transport goods. This paper investigates the problem that a crowdshipping platform recruits occasional couriers to serve the parcel pickup and delivery requests. A combinatorial reverse auction framework is introduced to model the interactions between the platform and couriers. We design an auction mechanism that implements in polynomial time, which satisfies the computational requirements of crowdshipping. It can also achieve near-optimal social cost with the approximation ratio rigidly proved. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed auction mechanism is truthful, individual rational and has high computation efficiency.

FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit

Federated Learning (FL) emerges as a new distributed machine learning (ML) paradigm that enables thousands of mobile devices to collaboratively train ML models using local data without compromising user privacy. However, the FL learning quality highly relies on the data contribution from the distributed mobile devices. Therefore, a well-designed incentive mechanism with effectiveness, fairness, and reciprocity is in urgent need to guarantee the stable participation of users. In this paper, we propose FedAB, an incentive and client selection strategy based on a novel multi-attribute reverse auction mechanism and a combinatorial multi-armed bandit (CMAB) algorithm. First, we develop a local contribution evaluation method based on importance sampling in the FL context. We then design a novel payment mechanism that is able to preserve individual rationality and incentive compatibility (truthfulness). At last, we design a UCB-based winner selection algorithm that is proven to achieve the server’s utility maximization with fairness and reciprocity. We have conducted extensive experiments on real datasets. The results demonstrate the superiority of FedAB, with a 10-50% improvement in total reward, final accuracy, and convergence speed compared to state-of-the-art solutions.

SEAL: A Strategy-Proof and Privacy-Preserving UAV Computation Offloading Framework

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.

An efficient multi-attribute multi-item auction mechanism with ex-ante and ex-post satisfaction for 4PL transportation service procurement

Reverse auction is an effective tool for a 4PL to purchase transportation services. This paper investigated a new transportation services procurement problem for 4PL, which involves three features: the 4PL’s loss-averse behavior, price and non-price attributes, and multiple transportation requests. An efficient multi-attribute multi-item reverse auction mechanism considering the 4PL ex-ante and ex-post satisfaction (EES-MMRA) is proposed to purchase transportation services for the 4PL. In the EES-MMRA, integrating the allocation rule with the 4PL ex-ante satisfaction, a 0-1 programming model is constructed to determine winning 3PLs and obtain efficient allocations. Then, a payment rule considering the 4PL ex-post satisfaction is established to ensure truthful bidding of 3PLs. And we discuss some desirable properties (e.g., incentive compatibility, individual rationality, efficiency, and budget balance properties) to justify the EES-MMRA mechanism, subsequently. Next, several numerical experiments are conducted to demonstrate the effectiveness and applicability of the EES-MMRA mechanism. Furthermore, sensitivity analysis presents the influences of the weights of the non-price attributes, risk attitude coefficients, and loss aversion coefficients. Finally, we conduct comparison analysis to show the advantages of the EES-MMRA mechanism over the known Vickrey–Clark–Groves (P-VCG) mechanism.

A Reverse Auction Mechanism for Time-Varying Multidimensional Resource Allocation in Vehicular Fog Computing with Cloud and Edge Collaboration

A Reverse Auction Mechanism for Time-Varying Multidimensional Resource Allocation in Vehicular Fog Computing with Cloud and Edge Collaboration

Transportation Service Procurement Auctions in Cyber-Physical Internet

This study is the first to propose auction models for transportation service procurement (TSP) in Cyber-Physical Internet (CPI). In the TSP problem, shippers are the service buyers who generate a large number of orders, and carriers are the service sellers who can deliver these orders. CPI is a novel concept aiming to completely alter freight transportation to develop a new generation of global logistics network (GLN). The introduction of CPI prompts the rethinking of several TSP basic issues, such as route planning, service mode selection, and real-time pricing based on spatial-temporal information sharing in CPI. Therefore, we first detail two key components of CPI, i.e., the router and the routing table and then describe the GLN in CPI through a typical example. We further establish a one-sided Vickrey-Clarke-Groves (O-VCG) online reverse auction mechanism to maximize social welfare. This auction mechanism is built to make decisions according to multidimensional information provided by the routing table about the transport services. Moreover, we further analyze the four important attributes of the auction mechanism, including allocative efficiency, incentive compatibility, individual rationality and budget balance. We finally conclude with critical challenges and future research directions, including potential TSP auctions in CPI and the applicability of the underlying model to general logistics systems based on spatial-temporal considerations.

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after realizing the importance of digital twin technology in building the metaverse. However, when utilizing digital twin technology to construct a metaverse, there remains limited research on how to allocate multibase station resources. This research translates a multibase station wireless resource allocation problem into an integer linear programming constraint model when virtual service providers construct a metaverse. In addition, the optimal VCG reverse auction (OPT-VCGRA) mechanism is designed to maximize social welfare and solve the problem of IoT devices competing for base station wireless resources. Specifically, the problem of the optimal allocation of wireless channel resources and payment rule based on the Vickrey–Clarke–Groves mechanism is solved to achieve optimal allocation and calculation of payment prices. Since the optimal allocation problem is NP-hard, this paper also designs a metaverse digital twin resource allocation and pricing (MDTRAP) mechanism based on monotonic allocation and key value theory. The mechanism sends the resource allocation results of multiple base stations to IoT devices and calculates the price payment when building a metaverse in the real world. This paper shows that both auction mechanisms have incentive compatibility and individual rationality properties. Through experiments, this paper compares the two mechanisms in terms of social welfare, the number of winners, and the overall payment. The MDTRAP mechanism performs similarly to the OPT-VCGRA mechanism in terms of social welfare, the number of winners, and channel utilization but is far superior to the OPT-VCGRA mechanism in terms of execution time and total payment. The trustful experiment also verified the truthfulness of the MDTRAP mechanism. The experimental results show that the MDTRAP mechanism can be used to solve the resource allocation problem of multiple base stations to IoT devices when building a metaverse in the real world and can effectively maximize social welfare.

Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage with multiple energy-limited UAVs in dynamic environments, which we call MCTA (Multi-UAV Coverage through Two-step Auction). Specifically, the online two-step auction mechanism is proposed to select the optimal action. Then, an obstacle avoidance mechanism is designed by defining several heuristic rules. After that, considering energy constraints, we develop the reverse auction mechanism to balance workload between multiple UAVs. Comprehensive experiments demonstrate that MCTA can achieve a high coverage rate while ensuring a low repeated coverage rate and average step deviation in most circumstances.

Truthful resource trading for dependent task offloading in heterogeneous edge computing

Various Internet of Things (IoT) applications that comprise computation-intensive dependent tasks have been widely popularized. Since these applications have real-time requirements, offloading computation-intensive tasks to edge server devices (ESDs) for expediting the execution of applications has become an important technology. ESDs may incur extra costs when they share computing resources to execute tasks for others. To motivate ESDs to execute IoT applications, this paper designs a reverse auction mechanism that charges the users of applications and rewards the ESDs. Moreover, it can ensure the respective benefits of IoT users and ESDs in resource trading. In the designed auction mechanism, each of the tasks ready for execution in applications counts as an auctioneer, while all ESDs act as the bidder competing for executing the task. Meanwhile, charging IoT users for expediting the execution of applications is formulated as the problem of minimizing the money cost. It is proved to be NP-hard. We propose a greedy strategy for selecting a winning computation resource from the bidder with the polynomial time. Built upon the proposed strategy, all tasks of applications are assigned to the winning ESD with the minimum money cost, and the utility of the winning ESD is maximized as much as possible. Extensive simulations validate that the proposed resource allocation strategy is superior to existing strategies in terms of total money cost, average makespan, and deadline violation rate reduction.

Iterative Multi-Attribute Procurement Auction with Decision Support for Bid Formulation

Iterative multi-attribute reverse auctions in practice create certain difficulties for both the buyer and participating bidders. While the buyer faces the problem of creating the right attribute weights, the bidders have difficulty in adjusting the attribute values in each round. In this paper, we present an iterative multi-attribute reverse auction mechanism based on integrated data envelopment analysis (DEA) and best–worst method (BWM) with an objective of reducing the intervention of the buyer in the determination of the winner and also easing up the preference elicitation process. Unlike the typical scoring auctions, the proposed mechanism does not require the buyer to estimate the characteristics of the participating sellers in order to determine the optimal scoring function. As there will be no other intervention from the buyer during the winner determination process, the proposed method makes the procurement process impartial and corruption-free. Besides solving the buyer’s problem, the proposed mechanism is also associated with an optimal bid determination method (OBDM) to assist the sellers in formulating improvised bids in iterative rounds of the auction. Simulation experiments show that the proposed OBDM benefits both the buyer and sellers. For the buyer, it provides higher expected utility and attribute values as per his preferences; for the seller, it gives a better expected profit and a higher probability of winning.

Migrating to reverse Auction mechanisms in wind energy sector: Status and challenges

India, with a total installed wind power capacity of 39.25 GW, is fourth largest wind producer in the world. The government aims to achieve 60 GW capacity of wind energy by 2022. Accelerated Depreciation (AD) and Generation Based Incentives (GBI) as policy mechanisms supported the initial growth, but the sudden transition to reverse auction mechanism in 2016 brought a big disruption in the market and the regime is yet to be stabilized. Till now 9 tranches of auctions have been conducted by Solar Energy Corporation of India (SECI) and 06 by various states. However, most of the projects allocated through auctions are behind the schedule and many tenders have gone undersubscribed. At present limited literature is available on the status and challenges arisen due to wind energy reverse auctions in India and this paper through systematic literature review does a comprehensive study on the same . Land availability, transmission and evacuation infrastructure bottlenecks, tariff revisions and renegotiation of PPAs by states, poor financial health of state discoms, financing problems, curtailment risk and tariff caps are identified as major challenges faced by wind power developers (WPDs) and these are required to be addressed by the government on priority.

A multi-objective robust possibilistic programming approach to coordinating procurement operations in the disaster supply chain using a multi-attribute reverse auction mechanism

This paper presents a multi-attribute reverse auction framework to facilitate the purchase operations between suppliers and a relief organization. The proposed auction framework consists of announcement, construction, and evaluation phases. In the announcement phase, the relief organization provides suppliers with information about demand and required relief items. In the construction phase, a fuzzy bi-objective mathematical model is developed from a supplier’s perspective; by solving the model, the suppliers offer their bids, each of which stipulating price, quantity, and delivery time for each item. The evaluation phase is formulated from the relief organization’s perspective; suppliers are first ranked based on their proposed bids and other qualitative criteria by the fuzzy PROMETHEE method. Then, a fuzzy tri-objective mathematical model is solved to determine winner(s) and allocate orders. For the first time, this paper develops a multi-objective robust possibilistic programming (MORPP) approach to cope with the uncertainty of parameters in the relief chain. Furthermore, multi-objective models are solved by an augmented ∊-constraint method. Finally, applicability and validity of the proposed auctioning framework are tested through a real case study in Iran and the managerial insights are provided.

Diffusion Analysis and Incentive Method for Mobile Crowdsensing User Based on Knowledge Graph Reasoning

Aiming at the problem that the mobile crowdsensing (MCS) system relies on a specific platform with a large user group presupposed, this paper proposes a sensing user diffusion analysis and incentive method based on knowledge graph reasoning. We consider motivating users to participate under the constraint of limited budget so that the platform and users can get the most benefits. In this paper, we focus on socially aware users represented by self-organizing social networks, combine the knowledge graph to establish a knowledge graph for the crowdsensing system, use rules to derive user influence, and optimize user contributions. With the goal of maximizing social welfare, we propose a social awareness reverse auction (SARA) mechanism, in which the total contribution of users is the key to select winners, and the winners are paid based on critical prices. Through experimental simulations, we verify that SARA is close to the optimal social welfare under budget constraints.