Auction Game Research Articles

Trust Based Incentive Scheme to Allocate Big Data Tasks with Mobile Social Cloud

Recently, mobile social cloud (MSC), formed by mobile users with social ties, has been advocated to allocate tasks of big data applications instead of relying on the conventional cloud systems. However, due to the dynamic topology of networks and social features of users, how to optimally allocate tasks to mobile users based on the trust becomes a new challenge. Therefore, this paper proposes a novel incentive scheme based on the trust of mobile users in the MSC to allocate the tasks of big data. First, a social trust degree is defined according to the social tie among users, the importance of task, and the available resources of networks. With the social trust degree, the task owner can select a group of mobile users as the candidates for task allocation. Second, a reverse auction game model is developed to study the interactions among the task owner and the candidates. With the reverse auction game model, the optimal strategy of task allocation can be obtained with a low cost for the task owner where the selected candidate of mobile users can also obtain the high profit. Finally, simulation experiments are carried out to prove that the proposal can outperform other existing methods with a low delay and a high efficiency to allocate tasks in the MSC.

Distributed Auction-Based Incentive Mechanism for Energy Trading Between Electric Vehicles and Mobile Charging Stations

With the increasing number of electric vehicles, deploying fixed charging stations (FCSs) has been a widely adopted solution for providing charging services to EVs. However, the charging requirement of EVs being near overload FCSs and/or in areas of inadequate charging infrastructures such as highways and rural areas will surpass the capabilities of FCSs. To address this challenge, the realized peer-to-peer (P2P) energy trading between electric vehicles (EVs) and mobile charging stations (MCSs) can be utilized to relieve the overload on FCSs, leverage under-utilized energy resources among cities and achieve trading benefits. By deploying this energy trading model, EVs purchase available energy from MCSs instead of FCSs to fulfill their charging demands. However, such an energy trade requires an incentive mechanism to ensure fair trading and prevent personal gain, which motivates us to study the incentive mechanism design for the energy trading model. In this paper, first, we consider an energy trading system involving multiple MCSs and EVs. Then, we formulate the incentive mechanism between MCSs and EVs as an auction game, in which the MCSs are auctioneers and EVs are bidders. In the formulated problem, each EV secretly submits its bid to MCSs, and each MCS distributively decides the winners without the knowledge of other MCSs. To achieve this, we design a distributed action-based energy trading mechanism that ensures fairness of providing charging service, validity in trading price’s resource determination, and nothing incentivizing the EVs to cheat on payment decisions. The proposed energy trading scheme achieves certain critical properties, including truthfulness, individual rationality, budget balance, and computational efficiency for both buyers and sellers. Finally, we perform simulation experiments to verify the proposed scheme’s effectiveness compared with the baseline in the literature.

Coalitional Formation-Based Group-Buying for UAV-Enabled Data Collection: An Auction Game Approach

Unmanned aerial vehicles (UAVs) enable promising solutions in assisting data collection in wide-area distributed sensor networks, leveraging their advanced properties of high mobility and line-of-sight communication links. However, existing UAV-assisted data collection methods mainly focus on unilaterally maximizing the utility of UAVs or sensors. Unfortunately, the problem driven by the market economy is ignored, namely the game between buyer and seller, in the process of sensors competing for UAV services. To address this problem, we propose a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV data collection services. Then, a parallel variable neighborhood ascent search algorithm is designed to quickly search the approximately optimal group-buying coalition structure. We further propose a novel group-buying coalition auction method, named TRUST, which can ensure the economical properties, i.e., truthfulness, individual rationality, and maximization of social welfare. Numerical results show that the sensors' average age of information (AoI) under the proposed method is reduced by 16.7% and 44.5% compared with the coalition formation game (CFG) and joint trajectory design-task scheduling (TDTS) UAV-to-community methods. To our best knowledge, this is the first effort on truthful coalition formation-based group-buying auction.

A Game-Theoretical Learning Approach for Spectrum Trading in Cognitive Satellite-Terrestrial Networks

This letter investigates the bandwidth allocation problem for discontinuous frequency bands in cognitive satellite-terrestrial networks (CSTNs). Different from existing studies, we take the realistic assumption that the available idle spectrum bands are disconnected. Most of the existing works focused on the continuous bandwidth allocation problem and proposed corresponding algorithms optimizing continuous variables, which cannot directly solve the bandwidth allocation problem for discontinuous frequency bands. Besides, we also take the individual bandwidth requirement, the priority of terrestrial cognitive users, and the limitation on the total available bandwidth into consideration. The bandwidth allocation for discontinuous frequency bands is formulated as a bandwidth auction game, which is proven to have at least one pure strategy Nash Equalization (NE) solution. Furthermore, an improved best response learning algorithm based on exploration and exploitation (BREE) is proposed to achieve satisfying performance. Finally, simulations are conducted to verify the convergence and superiority of the proposed BREE algorithm.

An Incentive Mechanism for Federated Learning in Wireless Cellular Networks: An Auction Approach

Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collects local models and constructs the global model. Then, we formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers. In the proposed game, each mobile user submits its bids according to the minimal energy cost that the mobile users experiences in participating in FL. To decide winners in the auction and maximize social welfare, we propose the primal-dual greedy auction mechanism. The proposed mechanism can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency. Finally, numerical results are shown to demonstrate the performance effectiveness of our proposed mechanism.

Learning equilibria in symmetric auction games using artificial neural networks

Learning equilibria in symmetric auction games using artificial neural networks

Evolution Strategies for Approximate Solution of Bayesian Games

We address the problem of solving complex Bayesian games, characterized by high-dimensional type and action spaces, many (> 2) players, and general-sum payoffs. Our approach applies to symmetric one-shot Bayesian games, with no given analytic structure. We represent agent strategies in parametric form as neural networks, and apply natural evolution strategies (NES) [wierstra2014natural] for deep model optimization. For pure equilibrium computation, we formulate the problem as bi-level optimization, and employ NES in an iterative algorithm to implement both inner-loop best response optimization and outer-loop regret minimization. In simple games including first- and second-price auctions, it is capable of recovering known analytic solutions. For mixed equilibrium computation, we adopt an incremental strategy generation framework, with NES as strategy generator producing a finite sequence of approximate best-response strategies. We then calculate equilibria over this finite strategy set via a model-based optimization process. Both our pure and mixed equilibrium computation methods employ NES to efficiently search for strategies over the functional space, given only black-box simulation access to noisy payoff samples. We experimentally demonstrate the efficacy of all methods on two simultaneous sealed-bid auction games with distinct type distributions, and observe that the solutions exhibit qualitatively different behavior in these two environments.

SFPAG-R: A Reliable Routing Algorithm Based on Sealed First-Price Auction Games for Industrial Internet of Things Networks

Due to the harsh deployment environment of the Industrial Internet of Things, the reliability of data transmission has always been an important challenge and a hot topic in Industrial Internet of Things (IIoT). Based on a multiple sink node network model, we propose a reliable routing algorithm called SFPAG-R, which is based on sealed first-price auction games. The algorithm offers a route discovery process and data transmission mechanism using the multi-sink network structure. The classic sealed first-price auction game is improved, and we propose a sealed first-price auction game model based on standard normal distribution, in order to ensure that the auction nodes can obtain higher returns. Based on an analysis of the factors affecting reliable data transmission in a large-scale, industrial IoT system, a data forwarding auction game model is established. Finally, to address the issue of selfish nodes, a dynamic revenue control method is proposed to promote cooperation between nodes. The simulation results indicate that the SFPAG-R algorithm proposed in this paper can reliably reduce the average amount of hops that occur during data transmission, ensure a high rate of successful data transmission, balance network energy consumption, and prolong the network's life cycle.

An Agile and Distributed Mechanism for Inter-Domain Network Slicing in Next Generation Mobile Networks

Network slicing is emerging as a promising method to provide sought after versatility and flexibility to cope with ever-increasing demands. To realize such potential advantages and to meet the challenging requirements of various network slices in an on-demand fashion, we need to develop an agile and distributed mechanism for resource provisioning to different network slices in a heterogeneous multi-resource multi-domain mobile network environment. We formulate inter-domain resource provisioning to network slices in such an environment as an optimization problem which maximizes social welfare among network slice tenants (so that maximizing tenants’ satisfaction), while minimizing operational expenditures for infrastructure service providers at the same time. To solve the envisioned problem, we implement an iterative auction game among network slice tenants, on one hand, and a plurality of price-taking subnet service providers, on the other hand. We show that the proposed solution method results in a distributed privacy-saving mechanism which converges to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal solution</i> of the described optimization problem. In addition to providing analytical results to characterize the performance of the proposed mechanism, we also employ numerical evaluations to validate the results, demonstrate convergence of the presented algorithm, and show the enhanced performance of the proposed approach (in terms of resource utilization, fairness and operational costs) against the existing solutions.

Unmanned Era: A Service Response Framework in Smart City

The autonomous vehicles (AVs) in smart city, as intelligent mobile robots, are expected to provide diversified services to facilitate the life of citizens. However, the attributes of the services requested by users are different and the statuses of the AVs managed by different central servers are dynamically changed. To execute the services with the minimum cost based on the requirements of users and the statuses of AVs therefore becomes a challenge. In this article, we establish an intelligent multi-attribute service response framework in smart city based on the request of users and the response of AVs. In the first phase of the framework, each central server decides the minimum service execution cost (SEC) to respond to the user’s service by considering the available resources of its AVs, where the minimization problems are formulated for the services with one attribute and the services with multiple attributes, respectively. To address the problems, the optimal AV selection (OAVS) algorithm for the services with one attribute and the OAVS-M algorithm for the services with multiple attributes are designed. In the second phase, based on the SEC of each central server, an auction game is developed to model the competition among the central servers to help the user select the optimal one to execute the service with the lowest service transaction price (STP). By achieving the Nash equilibrium of the game, the optimal strategy of each central server to win the chance for executing the service is obtained. The simulation results show that the designed framework can reduce the STP compared with the conventional schemes.

A reputation-based cooperative content delivery with parking vehicles in vehicular ad-hoc networks

In recent years, vehicular ad-hoc network (VANET) is getting a growing interest due to its significant function in terms of vehicle information services and vehicular entertainment applications. In particular, VANET enables parking vehicles (PVs) and roadside units (RSUs) to share their contents with mobile vehicles (MVs), which improves the efficiency of content delivery. However, content delivery in VANET still confronts with several challenges. Due to the selfishness of the PVs, an incentive mechanism is needed to motivate them to contribute their cached contents. Moreover, MVs may be threatened by the trustless or even malicious PVs. In this paper, we propose a reputation-based cooperative content delivery mechanism to improve the efficiency and security of content delivery. We formulate the relationships among MVs, RSUs, and PVs as the two-layer auction game. With the auction game, MVs find the optimal PV and RSU for delivering contents and offering the optimized rewards. In addition, we present a dynamic reputation evaluation model. Based on the feature of content delivery, this model incentivizes these honest PVs and isolates malicious PVs, which improves the security of content delivery. Finally, simulation results show that the proposed mechanism can not only improve the effectiveness of content delivery in VANET, but also avoid the attacks of malicious PVs.

Strategic Overbidding in Dynamic Auctions: Structural Estimation of Value of Synergy

In this study, we formulate and estimate a dynamic procurement auction game where bidder asymmetry due to synergy is endogenous. The seller sells multiple goods via a sequence of first‐price auctions. While bidders are ex‐ante symmetric, the first‐period winner has a valuation advantage as a result of such synergy in the second‐period bidding game and becomes a strong bidder. This endogenous synergy creation leads to overbidding in the first‐period auction relative to a static bidding game. We characterize the equilibrium in terms of the observed bid distribution and suggest a two‐step estimation procedure. We apply our method to data on OCS oil‐tract auctions. We find that the federal government recovers 52% of the “strong” buyers’ willingness to pay in the second period. Bidders perceive the value of synergy to be about 5% of their first period’s informational rent. Counterfactual experiments show that government revenue could have increased by about 27% under alternative selling mechanisms.

Simultaneous auctions with budgets: Equilibrium existence and characterization

We study the sale of two units of a good through simultaneous sealed bid first-price auctions to bidders who have private types and multi-unit demand. Bidders are differentiated based on their valuations and budgets. Under a parametric restriction, we show that this auction game is better-reply secure and thus possesses a Nash equilibrium. In equilibrium, bidders expend their budgets. The equilibrium strategies are essentially pure in the sense that each bidder-type has a unique split (up to a permutation) of her budget between the two auctions. In equilibrium, there always exist bidder-types who submit unequal bids in equilibrium even though they have the same valuations for the units. In addition, the equilibrium is monotone in valuations and budgets: bidders with higher valuations (lower budgets) prefer more unequal splits of their budgets than bidders with lower valuations (higher budgets) and the same budget (valuation).

A Multi-Objective Routing Algorithm Based on Auction Game for Space Information Network

A Multi-Objective Routing Algorithm Based on Auction Game for Space Information Network

Elektrik Piyasasında Satın Alma Sürecinin İhale Oyunları İle İncelenmesi

In this study, it is aimed to examine the purchasing processes in an integrated manner with the knowledge and knowledge within the scope of auction games. The procedures and principles for purchasing process and the requirements that are needed for issues such as problems may occur in the process. Design and execution of the purchasing process are discussed and examined. In this process, a study is examined to ensure the optimum benefit of the participants, and integrated with the of auction games. The savings of the two players operating in the electricity markets are studied based on the changes in the market demand and the amount of production based on their competitors' strategies. Nash equilibrium and methods are used to reach the most appropriate strategy.

Task Allocation With Unmanned Surface Vehicles in Smart Ocean IoT

The unmanned surface vehicles (USVs) have been regarded as a promising paradigm to automatically perform emergency tasks in a dynamic maritime traffic environment. However, the performance of maritime communication between USVs and offshore platforms becomes a critical challenge, and the efficiency of task allocation for USVs in the smart ocean is low. In this article, a novel task allocation scheme for USVs in the smart ocean Internet of Things (IoT) is proposed to improve the efficiency of task allocation. First, the offshore platform is developed to provide maritime communication for USVs in the smart ocean IoT. Second, the network resource allocation process between USVs and offshore platforms is modeled as the second price sealed auction game, where the optimal bidding strategy of USV is derived by the $Q$ -learning to maximize the utilities of USVs and offshore platforms. Third, the task allocation scheme is proposed to improve the number of allocated tasks. Finally, the performance of the proposed scheme is conducted based on extensive simulations. The simulation results show that the proposed scheme can significantly improve the number of allocated tasks compared with the conventional schemes.

Blockchain Meets Edge Computing: Stackelberg Game and Double Auction Based Task Offloading for Mobile Blockchain

Blockchain technology is developing rapidly and has been applied in various aspects, among which there are broad prospects in Internet of Things (IoT). However, IoT mobile devices are restricted in communication and computation due to mobility and portability, so that they can’t afford the high computing cost for blockchain mining process. To solve it, the free resources displayed on non-mining-devices and edge cloud are selected to construct collaborative mining network(CMN) to execute mining tasks for mobile blockchain. Miners can offload their mining tasks to non-mining-devices within a CMN or the edge cloud when there are insufficient resources. Considering competition for resource of non-mining-devices, resource allocation problem in a CMN is formulated as a double auction game, among which Bayes-Nash Equilibrium (BNE) is analyzed to figure out the optimal auction price. When offloading to edge cloud, Stackelberg game is adopted to model interactions between edge cloud operator and different CMNs to obtain the optimal resource price and devices’ resource demands. The mechanism realizes improving the mining utility in mining networks while ensuring the maximum profit of edge cloud operator. Finally, profits of mining networks are compared with an existing mode which only considers offloading to edge cloud. Under the proposed mechanism, mining networks obtain 6.86% more profits on average.

Collaborative Content Delivery in Software-Defined Heterogeneous Vehicular Networks

The software defined heterogeneous vehicular networks (SD-HetVNETs), which consist of cellular base stations (CBSs) and roadside units (RSUs), have emerged as a promising solution to address the fundamental problems imposed by the surge increase of vehicular content demand. However, due to the ever increasing requirement of the vehicles' quality of experience (QoE) and the network vendors' utilities, there come new challenges to motivate CBS to cooperate with RSU for content delivery in order to maximize their utilities and improve the efficiency of the networks. Therefore, in this paper, we propose a collaborative content delivery scheme to improve the utilities of the participants (i.e., CBS, RSU and vehicles) in the SD-HeVNETs, where the CBS can cooperate with RSUs by serving a group of vehicles with multicast technology. We first define the utility models to map the profits of the participants in the networks and formulate the utilities of CBS and RSU as two optimization problems. Then, we exploit the double auction game to motivate CBS to cooperate with RSU for the multicast assisted content delivery to address the two maximization problems. Next, the optimal bidding strategies of CBS and RSU in the game are analyzed when the Bayesian Nash equilibrium is achieved. With the optimal bidding strategies, both CBS and RSU can bid for the multicast assisted content delivery services to maximize their utilities based on the network status. Finally, the performance of the proposed cooperative scheme is evaluated by using simulations. The simulation results demonstrate that the utilities of all the participants in the networks can be enhanced and the efficiency of the networks can be improved.

The Effect of Auction Game on Students’ Understanding of Simple Past Tense

The aim of this research was to investigate the effect of auction game on students’ understanding of simple past tense at the eighth grade of MTs. Jabal Nur Kandis. The population of this research was the second year students of MTs. Jabal Nur Kandis consisting of two classes which had 48 students. The sample of this research were VIII A (24 students) as experimental class 1 and VIII B (24 students) as experimental class 2. The researchers applied auction game variation 1 in experimental class 1 while; in experimental class 2 the researcher applied auction game variation 2. The data was the students’ scores in simple past tense taken from the post test. The data was analyzed by using Normality-test, Homogeneity-test, T-test and N-gain. After conducting this research, it was found that the result of n-gain average score in experimental class is 0.39. The Assimp.Sig (2-tailed) is 0.195. So the hyphothesis H01 was accepted and Ha1 is rejected. With the Assimp.Sig (2-tailed) &gt;0.05 it meant there was no any significant effect of the implementation of Auction game (variation 1 and variation 2) on students’ understanding of simple past tense. In conclusion, there was no any significant effect of the implementation of auction Game on students understanding of simple past tense at the eighth grade of MTs. Jabal Nur Kandis. But, auction game can improve students’ understanding on simple past tense.

Dynamic QoS-Aware Scheduling for Concurrent Traffic in Smart Home

Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their Quality-of-Service (QoS) requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in smart home networks do not consider concurrent traffic in their scheduling solutions. This article presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffic in smart home network, propose an innovative queuing design based on the auction economic model of the game theory to provide a fair multiple access over different communication channels/ports, and design an applicable model to implement auction game on both sides; traffic sources and the home gateway, without changing the structure of the IEEE 802.11 standard. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.