Mobile crowdsensing services are divided into two categories: opportunistic and participatory. In opportunistic mobile crowdsensing services, users do not need to specify the crowdsensing tasks to be completed. Compared with participatory crowdsensing services, the application scope is wider and more user-friendly. In participatory crowdsensing, the service provider assumes that the user can successfully complete the data collection task. However, such an approach cannot work in an opportunistic crowdsensing service because in opportunistic crowdsensing, the user’s execution of the task is uncertain, which brings great challenges to the quality of the crowdsensing service. This article is based on the assumption of the user coverage probability model and transforms the opportunistic mobile crowdsensing value maximization problem into an ordered submodularity value function model with budget constraints. This model is also good at representing participatory crowdsourcing problems. To the best of our knowledge, this is the first study to apply the ordered submodularity feature to a mobile crowdsensing service. Furthermore, we combine the properties of ordered submodular and auction models and propose an ordered submodularity-proportional share mechanism (O-PSM) to solve the allocation and payment problems in opportunistic mobile crowdsensing services. Specifically, in the allocation stage, the winning users are selected based on the proportional share threshold, and in the payment stage, the payment price for the winning users is designed based on critical value theory. We prove that the mechanism satisfies the economic characteristics of individual rationality, truthfulness, and budget feasibility. In the experimental section, the mechanism design based on ordered submodularity is shown to enable the service provider to obtain a higher value and a lower payment.

Mobile crowdsensing service (MCS) providers recruit users to complete data collection tasks with an incentive mechanism. How to maximize the utility of service providers has long been a popular topic in MCS research. Applying the existing reverse auction mechanism to an MCS may result in excessively high payments, thereby reducing the utility of the MCS provider. The affine maximizer auction (AMA) mechanism increases the revenue of service providers and meets dominant-strategy incentive-compatible (DSIC) characteristics. However, the AMA mechanism is a forward auction mechanism and cannot be applied to MCSs. Inspired by the AMA mechanism, this paper innovatively proposes a reverse affine maximizer auction (RAMA) mechanism to solve the task allocation problem of MCSs, effectively improving the MCS provider utility. Specifically, we construct a RAMA theoretical model and prove that the mechanism satisfies DSIC characteristics. For the discrete MCS task allocation problem, we use the reverse virtual valuation combinatorial auction (RVVCA) mechanism, a subclass of RAMA, to design a random mechanism RVVCA$^{t}$ and prove that the RVVCA$^{t}$ has a logarithmic approximate ratio. For the differentiable MCS task allocation problem, we use the deep learning transformer framework to design RAMANet, which can fit an exponential number of allocation solutions and output the optimal allocation and payment. We experimentally compare the algorithms of the RAMA family we propose, which use affine maximization, with existing state-of-the-art algorithms, demonstrating that the proposed algorithms significantly improve MCS provider utility.

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.