Rapid advances in technology have endowed intelligent vehicles with increasing computing power and rich perception abilities. In this paper, we address the problem of computation offloading and resource sharing in vehicle computing, where the vehicle provides computing and sensing resources to users. Since sensing devices capture the same data at any given moment, multiple users can simultaneously share the sensing resources of the same vehicle. Building on this, we propose a novel resource-sharing model that, while allocating computing resources only to users, allows users to monopolize or share sensing resources. By serving more users, this resource-sharing model results in savings on vehicle costs. The proposed mechanism comprises three types of auctions: one-to-many, many-to-one, and one-to-one. While the one-to-many auction allocates resources of one vehicle to multiple users, the many-to-one auction allocates resources of multiple vehicles to one user. The one-to-one auction, on the contrary, restricts allocation of resources of one vehicle to one user. While being truthful for both users and vehicles, this mechanism also contributes to individual rationality, budget balance, and consumer sovereignty. Finally, simulation results confirm the efficiency of the mechanism, which achieves high performance while facilitating additional utility.

Vehicle computing has emerged as a promising paradigm for delivering time-sensitive computing services to Internet of Things applications. Intelligent vehicles (IVs) offer onboard computing and sensing capabilities for delivering a wide range of services. In this paper, we propose a dynamic adaptability service model that leverages the swift mobility of vehicles to adjust the distribution of IVs to users’ dynamically changing locations. There are two types of areas in our model: the user area and the parking area. The former is where services are provided, while the latter serves as the preparation zone for backup IVs. IVs in the parking area are dispatched to service areas, where existing vehicle resources cannot meet user demand, and they return to the parking area after delivering the service. Multiple users share sensing resources, and our model allocates the costs among them. To ensure strategy-proofness, we introduce the concepts of no additional cost and allocation stability. We propose a strategy-proof cost-sharing mechanism for dynamic adaptability service. The proposed mechanism achieves no positive transfers, voluntary participation, individual rationality, consumer sovereignty, budget balance, no additional costs, and allocation stability. Moreover, the proposed mechanism’s approximation performance is analyzed. We further use comprehensive simulations to verify the effectiveness and efficiency of the proposed mechanism.

Mobile edge computing (MEC) affords high computing power but lacks sensing capability. Furthermore, intelligent vehicles, which possess rich sensing resources, consume limited energy. Motivated by this, we propose edge-vehicle collaborative computing and investigate the task offloading and interaction problem (TOIP), in which MEC servers and vehicles collaborate to leverage their strengths and mitigate their weaknesses. Motivated by practical application requirements, we propose a task interaction model where a user’s computing and sensing subtasks are respectively offloaded onto the MEC servers and vehicles, which then collaborate to complete the tasks. Aiming to maximize group efficiency, we formulate the TOIP in an auction-based setting. To motivate MEC servers and vehicles, we propose a reverse auction where each user is an auctioneer, while MEC servers and vehicles are the bidders. Our reverse auction mechanism achieves budget feasibility, where the rewards received by MEC servers and vehicles cannot exceed the budget. The proposed mechanism proves to be truthful; that is, MEC servers or vehicles cannot obtain higher utility by declaring untrue values. We also demonstrate how to make the mechanism meet the truthfulness requirement in TOIP. In addition, the proposed mechanism achieves individual rationality, consumer sovereignty, and computation efficiency. We also theoretically analyze the approximate ratio. The simulation results show that the proposed mechanism exhibits exceptional performance in all the scenarios.

We consider the edge-vehicle computing system (EVCS), where the combination of edge computing and vehicle computing takes respective advantages to provide various services. We address the problem of computation offloading in EVSC, where the computing tasks and the sensing tasks with limited budgets are offloaded to edge servers and vehicles. The resource-sharing model is proposed, where sensing resources of one vehicle are shared by multiple tasks. We consider the vehicle hierarchy, where vehicles with different equipment accuracy are classified into different hierarchies. A sensing task has different values and different demands for different hierarchies. A budget-feasible mechanism based on the clock auction is proposed. We show our proposed mechanism is strategy-proof and group strategy-proof, this drives the system into an equilibrium. In addition, the proposed mechanism achieves individual rationality, budget balance, and consumer sovereignty. The proposed mechanism consists of two algorithms that are based on the idea of dominant resource and iteration to improve resource utilization and reduce costs. Furthermore, the approximate ratios of the two allocation algorithms are analyzed. Experimental results demonstrate that the proposed mechanism achieves the near-optimal value and brings higher utility for participants.

In the context of mobile edge computing (MEC), it can be quite challenging to provide and allocate multiple resources from heterogeneous MEC servers for remote execution of tasks of mobile devices (MDs). However, obtaining more resources for tasks can save time and effort, and MDs are willing to pay higher prices for more resources. Motivated by these challenges, this study addresses the problem of heterogeneous resource allocation with multi-minded (HRAM) in MEC. Unlike other studies that have focused on MDs with single-minded demands, this study considers the multi-attribute demands of MDs. It considers cases where an MD declares multiple demands, each with a different bid attached to it. This multi-minded approach gives MDs more flexibility and control over the resources they receive, leading to increased satisfaction and better outcomes. However, MDs are self-interested and can misreport their preferences, which results in a low utilization rate of heterogeneous resources. Therefore, we have formulated this problem in an auction-based setting, and our objective is to allocate heterogeneous resources of heterogeneous MEC servers to maximize social welfare, which is the sum of MDs’ valuations. We demonstrate that the HRAM problem is NP-hard, proposing a randomized mechanism consisting of a second-price auction and a fixed-price auction. This study claims that the proposed randomized mechanism is universally truthful and that the MDs have no desire to misreport their demands. Additionally, we analyze the randomized mechanism’s time complexity and approximation ratio and present the experimental results to support our claim. We demonstrate that the randomized mechanism performs excellently in different environments, benefiting MDs and edge cloud providers.

As a novel sensing paradigm, crowd sensing systems have gained great attention and been widely adopted in the environmental monitoring and calculation areas. In crowd sensing systems, mobile users provide their multiple resources to the requesters to execute tasks. Existing studies focus on the divisible task or one-to-one mapping for single resource allocation. However, this assumption does not hold for crowd sensing systems. Owing to the task attribute, some tasks cannot be divided into multiple parts to run on different devices. In addition, a high performance mobile device can execute multiple tasks simultaneously. We address the problem of multi-resource allocation in crowd sensing systems for the auction-based model considering many-to-one mapping for indivisible tasks, where many-to-one mapping allows one mobile device to provide multiple resources to execute one or more tasks. In this article, we study, for the first time to the best of our knowledge, a truthful mechanism that stimulates mobile users and requesters to declare their true values. We design a truthful double auction mechanism together with a payment scheme tailored to fit it that would help researchers understand how a truthful double auction mechanism can be designed. In addition, we prove that our proposed mechanism maintains budget-balance, individual rationality, and computational tractability. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. Experimental results demonstrate that our proposed mechanism has high computation efficiency and good performance.

In this paper, we address the problem of heterogeneous physical machines resource management (HPMRM); that is, providing and allocating multiple virtual machine (VM) instances from heterogeneous physical machines to maximize social welfare. Although existing allocation mechanisms allocate VMs to users through the single-mapping mechanism, such allocations cannot guarantee maximum social welfare or efficient utilization of multiple types of resources for cloud providers. Thus, we consider the multi-mapping mechanism, which permits mapping VMs allocated to one user to physical machines for VM provisioning and allocation. This can result in improved social welfare and lead to less resource fragmentation. We formulate the HPMRM problem in an auction-based setting, and design optimal and approximate mechanisms to solve it. In addition, we show that our proposed mechanism is strategy-proof; that is, our proposed mechanism drives the system into an equilibrium where no users have incentives to maximize their own profit by untruthfully reporting their requests. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. We also perform experiments to investigate the performance of our proposed approximation mechanism compared to the optimal mechanism. Experimental results demonstrate that our proposed approximation mechanism can obtain near optimal solutions and significantly improve allocation efficiency, while generating greater social welfare.

In this paper, we design an efficient algorithm for the energy-aware profit maximizing scheduling problem, where the high performance computing system administrator is to maximize the profit per unit time. The running time of the proposed algorithm is depending on the number of task types, while the running time of the previous algorithm is depending on the number of tasks. Moreover, we prove that the worst-case performance ratio is close to 2, which maybe the best result. Simulation experiments show that the proposed algorithm is more accurate than the previous method.