Auction-based Approach Research Articles

Two-Stage Client Selection for Federated Learning Against Free-Riding Attack: A Multiarmed Bandits and Auction-Based Approach

Two-Stage Client Selection for Federated Learning Against Free-Riding Attack: A Multiarmed Bandits and Auction-Based Approach

An Auction-Based Approach for Multi-Agent Uniform Parallel Machine Scheduling with Dynamic Jobs Arrival

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents. All agents are self-interested and rational with the aim of maximizing their own objectives, resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information. Within the context, a centralized scheduling approach is unfeasible, and a decentralized approach is considered to deal with the targeted problem. This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’ preferences under incomplete information. For this purpose, a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed. In the proposed approach, a dynamic auction procedure is established for dynamic jobs participating in a real-time auction, and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination. In addition, an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently. A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids. Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on large-scale problems with numerous consumer agents and jobs. A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

Fairness Guaranteed and Auction-Based x-Haul and Cloud Resource Allocation in Multi-Tenant O-RANs

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min-max fairness</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vickrey-Clarke-Groves (VCG) auction</i> -based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimizes the maximum OPEX of RUs</i> through cost-sharing proportional to their demands, whereas the VCG auction-based approach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs</i> . We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

Dynamic traffic-aware auction-based signal control under vehicle to infrastructure communication

The advent of connected vehicle technology brings forth a new set of possibilities for more efficient traffic control. Indeed, the ability to consider driver-specific attributes, such as value of time or willingness to pay, in intersection management through advanced communication protocols allows for reducing individual delay costs. In this context, this study presents an auction-based approach for signalized intersection control under a connected vehicle setting. A second-price sealed-bid auction is employed to assign green time to conflicting movements, while driver bids and bidding distance per lane vary based on delay accumulation. The proposed auction-based control is evaluated using various demand levels and flow patterns and compared against pre-timed signal control. Results show that the proposed mechanism may be a promising alternative for several flow patterns, achieving lower average delays while maintaining fairness.

Trustworthy handover in LEO satellite mobile networks

Recently, a low earth orbit (LEO) satellite network is one of major systems to provide seamless access for terrestrial network systems. In order to provide robust access, efficient handover mechanisms are essential. However, conventional mechanisms may introduce frequent handovers due to the rapid movement of satellites. To deal with this problem, this paper proposes a learning-based auction handover under the consideration of received signal strength and service time between terrestrial users and satellites. The reason why auction-based approach is utilized is that it is generally considered as trustworthy. Our experiment results verify the proposed algorithm achieves desired performance.

Auction-based approach with improved disjunctive graph model for job shop scheduling problem with parallel batch processing

The job-shop scheduling problem (JSSP) is encountered in several industries, including the military where heat treatment is applied prior to the machining process in production. This study aims to minimize the overall make-span of a JSSP with parallel batch processing. The problem is formulated as a mixed-integer linear programming model. Feasible solutions are derived from an auction-based approach for forming batches, allocating operation machines, and scheduling. An improved disjunctive graph model is further developed to search for better solutions. We conduct numerical experiments to test a set of benchmark instances. A comparison of the results with those obtained applying other existing algorithms and CPLEX demonstrates the effectiveness and stability of the proposed auction-based approach and improved graph model. Furthermore, a statistical analysis using IBM SPSS shows that the proposed auction-based approach has an absolute advantage in solving medium-scale and large-scale instances of JSSP with batch processing.

Auction-based approach to the job-shop problem with parallel batch processing and a machine availability constraint

In military manufacturing enterprises, exceptionally high product performance standards are generally met by using heat treatment followed by machining. These procedures are typically parallel batch-processing (BP) scheduling problems and job-shop problems, respectively. A mixed-integer nonlinear programming model is created to describe the problem with a machine availability constraint, which is divided into two stages: BP and machine processing (MP). Furthermore, an auction-based approach is developed in which jobs are categorized into batches during the BP stage and resources are allocated to operating machines during the MP stage. A local search operator is then applied to optimize the obtained feasible solutions. Benchmark instances are enlarged to adapt to the proposed problem. The approach is tested and compared to existing algorithms, and statistical analysis is performed using SPSS Statistics. The results show that the auction-based approach is effective and stable, and has absolute advantages in solving large-scale instances.

Auction-Based Multichannel Cooperative Spectrum Sharing in Hybrid Satellite-Terrestrial IoT Networks

In this article, we investigate the multichannel cooperative spectrum sharing in hybrid satellite-terrestrial Internet of Things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellite-based IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each T-IoT network. Specifically, the one-shot Vickrey-Clarke-Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the nonorthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail.

Market Driven Multidomain Network Service Orchestration in 5G Networks

The advent of a new breed of enhanced multimedia services has put network operators into a position where they must support innovative services while ensuring both end-to-end Quality of Service requirements and profitability. Recently, Network Function Virtualization (NFV) has been touted as a cost-effective underlying technology in 5G networks to efficiently provision novel services. These NFV-based services have been increasingly associated with multi-domain networks. However, several orchestration issues, linked to cross-domain interactions and emphasized by the heterogeneity of underlying technologies and administrative authorities, present an important challenge. In this paper, we tackle the cross-domain interaction issue by proposing an intelligent and profitable auction-based approach to allow inter-domains resource allocation.

Vickrey Auction-Based Secondary Relay Selection in Cognitive Hybrid Satellite-Terrestrial Overlay Networks With Non-Orthogonal Multiple Access

In this letter, we investigate the Vickrey auction-based secondary relay selection in cognitive hybrid satellite-terrestrial overlay networks (CHSTONs) with non-orthogonal multiple access (NOMA). We analyze a cooperative spectrum sharing scheme for a multi-potential secondary relay selection scenario, in which the Vickrey auction is introduced to achieve an efficient secondary relay selection by one shot within the distributed computation. Both the decode-and-forward (DF) and amplify-and-forward (AF) protocols are considered for the relay to retransmit the primary user's message, separately. Besides, the NOMA scheme is implemented to increase the total capacity in the second temporal phase. Finally, simulation results validate that the proposed auction-based approach is more effective than conventional approaches in secondary relay selection. Also, the influence of key factors on the performance of the auction mechanism is analyzed by the simulations.

Multi-Robot Dynamic Task Allocation for Exploration and Destruction

Environmental exploration is one of the common tasks in the robotic domain which is also known as foraging. In comparison with the typical foraging tasks, our work focuses on the Multi-Robot Task Allocation (MRTA) problem in the exploration and destruction domain, where a team of robots is required to cooperatively search for targets hidden in the environment and attempt to destroy them. As usual, robots have the prior knowledge about the suspicious locations they need to explore but they don’t know the distribution of interested targets. So the destruction task is dynamically generated along with the execution of exploration task. Each robot has different strike ability and each target has uncertain anti-strike ability, which means either the robot or target is likely to be damaged in the destruction task according to that whose ability is higher. The above setting significantly increases the complexity of exploration and destruction problem. The auction-based approach, vacancy chain approach and a deep Q-learning approach based on strategy-level selection are employed in this paper to deal with this problem. A new simulation system based on Robot Operating System and Gazebo is specially built for this MRTA problem. Subsequently, extensive simulation results are provided to show that all proposed approaches are able to solve the MRTA problem in exploration and destruction domain. In addition, experimental results are further analyzed to show that each method has its own advantages and disadvantages.

An auction-based approach for the re-optimization shortest path tree problem

The shortest path tree problem is one of the most studied problems in network optimization. Given a directed weighted graph, the aim is to find a shortest path from a given origin node to any other node of the graph. When any change occurs (i.e., the origin node is changed, some nodes/arcs are added/removed to/from the graph, the cost of a subset of arcs is increased/decreased), in order to determine a (still) optimal solution, two different strategies can be followed: a re-optimization algorithm is applied starting from the current optimal solution or a new optimal solution is built from scratch. Generally speaking, the Re-optimization Shortest Path Tree Problem (R-SPTP) consists in solving a sequence of shortest path problems, where the kth problem differs only slightly from the $$(k-1){th}$$ one, by exploiting the useful information available after each shortest path tree computation. In this paper, we propose an exact algorithm for the R-SPTP, in the case of origin node change. The proposed strategy is based on a dual approach, which adopts a strongly polynomial auction algorithm to extend the solution under construction. The approach is evaluated on a large set of test problems. The computational results underline that it is very promising and outperforms or at least is not worse than the solution approaches reported in the literature.

DeepAuC: Joint deep learning and auction for congestion-aware caching in Named Data Networking

Over the last few decades, the Internet has experienced tremendous growth in data traffic. This continuous growth due to the increase in the number of connected devices and platforms has dramatically boosted content consumption. However, retrieving content from the servers of Content Providers (CPs) can increase network traffic and incur high network delay and congestion. To address these challenges, we propose a joint deep learning and auction-based approach for congestion-aware caching in Named Data Networking (NDN), which aims to prevent congestion and minimize the content downloading delays. First, using recorded network traffic data on the Internet Service Provider (ISP) network, we propose a deep learning model to predict future traffic over transit links. Second, to prevent congestion and avoid high latency on transit links, which may experience congestion in the future; we propose a caching model that helps the ISP to cache content that has a high predicted future demand. Paid-content requires payment to be downloaded and cached. Therefore, we propose an auction mechanism to obtain paid-content at an optimal price. The simulation results show that our proposal prevents congestion and increases the profits of both ISPs and CPs.

An auction-based approach to control automated warehouses using smart vehicles

In modern automated warehouses smart vehicles are becoming popular, allowing flexible adaptation to changing requirements. Thus, encouraged by their considerable abilities, this paper proposes an auction-based approach for the control of automated warehouse systems with smart vehicles to provide modularity, near-realtime capabilities, scalability, robustness and services orientation to the whole system. In particular, the approach focuses on the mission assignment problem. Moreover, to overcome the difficulties in design and development of the employed approach and to size and configure the system to be controlled, a hybrid model with an automatic model synthesis procedure to dynamically add/remove resources is presented.

Design of auction‐based approach for market clearing in peer‐to‐peer market platform

This study designs a market platform for peer-to-peer (P2P) energy trading in transactive energy (TE) systems, where prosumers and consumers actively participate in the market as seller or buyer to trade energy. An auction-based approach is used for market clearing in the proposed platform and a review of different types of auctions is performed. The appropriate auction approach for market clearing in the proposed platform is designed. The proposed auction mechanism is implemented in three steps namely determination, allocation and payment. This study identifies important P2P market clearing performance indices, which are used to compare and contrast the designed auction with different types of auction mechanisms. Comparative studies demonstrate the efficacy of the proposed auction mechanism for market clearing in the P2P platform.

Auction-based approach for a flexible job-shop scheduling problem with multiple process plans

ABSTRACTThis study addresses the flexible job-shop scheduling problem with multiple process plans with the objective of minimizing the overall makespan. A nonlinear programming model is formulated to allocate machines and schedule jobs. An auction-based approach is proposed to address the integrated production route selection and resource allocation problem and focus on improving resource utilization and productive efficiency to reduce the makespan. The approach consists of an auction for process plans and an auction for machines. The auctions are evaluated to select a more suitable route for production and allocate resources to a more desirable job. Numerical experiments are conducted by testing new large benchmark instances. A comparison of Lingo and other existing algorithms demonstrates the effectiveness and stability of the proposed auction-based approach. Furthermore, SPSS is used to prove that the proposed method exhibits an absolute advantage, particularly for medium-scale or large-scale instances.

Dynamic Spectrum Sharing for Hybrid Access in OFDMA-Based Cognitive Femtocell Networks

Cognitive femtocell has emerged as one of the promising ways to solve the indoor coverage and spectrum shortage problems in cellular networks. However, it is a challenge to motivate both macro base station ( $\text{MBS}$ ) and femtocell access points ( $\text{FAPs}$ ) to adopt this hybrid access policy. In this paper, we propose a systematic dynamic spectrum sharing framework for hybrid access in orthogonal frequency-division multiple-access-based cognitive macro-femtocell networks. In this framework, the $\text{MBS}$ offloads some macro user equipments ( $\text{MUEs}$ ) to $\text{FAP}$ to improve the transmission performance and save energy. In return, the MBS leases a portion of its spectrum to $\text{FAP}$ . Then, the $\text{FAP}$ can use a part of spectrum for its own transmission and use another part to serve $\text{MUEs}$ . We design an auction-based approach where each $\text{FAP}$ receives additional $\text{MUE}$ as bidders, while MBS acts as auctioneer. We formulate the dynamic resource sharing as a sum utility maximization problem. Numerical results demonstrate that both the macro and femtocell could get benefit from our framework.

An auction-based approach for prebooked urban logistics facilities

The proper management of urban loading/unloading facilities is crucial for both the efficiency of the logistic services and urban traffic systems. Thanks to the advances in technologies, such management can be achieved through prebooked systems. In this paper, we propose a comprehensive and unified auction-based approach to optimize the use of prebooked loading and unloading facilities, based on a previous work of the authors. The new proposed approach assigns parking spots to companies and computes the price of the service, to maximize the social welfare of the system. It has the following advantages over the previous system: (i) by employing an auction’s formulation based on the Vickrey–Clarke–Groves mechanism, the proposed system explicitly considers the heterogeneity in the penalty functions; (ii) uncertainty in the carrier arrivals due to traffic condition is handled explicitly with an online readjustment and two robust formulations.The proposed auction system is tested on instances inspired by the logistic system in the city of Barcelona. Results show that compared to the previous work, the new approach exploiting the use of new information improves the social welfare by up to 22% with heterogeneous carriers. Results further show that the scenarios where demand is uniformly distributed over the time horizon present better utilities than the scenarios where demand is concentrated following a peak hour behavior. It is also shown that the proposed online readjustment formulation performs similar to the global offline optimum, and significantly improves the social welfare of the system when carriers fail to arrive at the allocated time due to traffic uncertainties.

Auction-based cooperation mechanism for cell part scheduling with transportation capacity constraint

This paper addresses cell part scheduling (CPS) problem with transportation capacity constraint. In the problem, parts may need to visit different cells, they have to be transferred by automated guided vehicle (AGV). The objective is to minimise the over-all process make-span. An integer nonlinear programming (INLP) model is formulated to allocate the machines, AGVs and schedule all parts. A reasonable transportation mode is presented, and an auction-based heuristic approach is proposed to solve the problem, which focuses on dealing with cooperation between machines and AGVs during processing and transferring parts. The auction consists of two aspects: auction for AGV and auction for machine. In both auctions, AGVs and machines act as auctioneers respectively, and parts act as bidders. A new improved disjunctive graph model is developed to optimise the feasible solutions obtained by auction-based approach. Numerical experiments were conducted to test the auction-based approach and improved disjunctive graph model. The results demonstrate the effectiveness of proposed auction-based approach and improved disjunctive graph model, also indicate influence of the capacity of AGV on scheduling parts.

Using a combinatorial auction-based approach for simulation of cooperative rescue operations in disaster relief

In practice, we experience low efficiency of search and rescue (SAR) frequently in disaster relief. Here, we will optimize the SAR through agent-based simulation. In the kind of cases described here, rescue teams are characterized by different capabilities, and the tasks often require different capabilities to complete. To this end, a combinatorial auction-based task allocation scheme is used to develop a cooperative rescue plan for the heterogeneous rescue teams. Then, we illustrate the proposed cooperative rescue plan in different scenarios with the case of landslide disaster relief. The simulation results indicate that the combinatorial auction-based cooperative rescue plan would increase victims’ relative survival probability by 13.8–16.3%, increase the ratio of survivors getting rescued by 10.7–12.7%, and decrease the average elapsed time for one site getting rescued by 19.0–26.6%. The proposed rescue plan outperforms the rescue plan based on the F-Max-Sum a little bit. The robustness analysis shows that the proposed rescue plan is relatively reliable on condition that both the search radius and scope of cooperation are larger than thresholds. Furthermore, we have investigated how the number of rescue teams influences the rescue efficiency.