Auction-based Mechanism Research Articles

An online auction-based mechanism for pricing and allocation of instant delivery services

Recently, instant delivery has been growing rapidly, with numerous platforms emerging to offer such services. Requestors dynamically arrive at the platform to place delivery service requests that detail their pickup locations, recipient locations, package weights, departure times, and willingness-to-pay (WTP). The platform then uses its dedicated riders, scattered in different places, to fulfill these requests. Given the dynamic and fluctuating characteristics of the demand, coupled with limited rider resources and heterogenous pickup costs, the platform faces the critical problem of dynamically pricing the requests and assigning the riders to maximize social welfare. To address this problem, we propose an online auction-based transaction mechanism. Specifically, we first propose a scoring function to evaluate the values of the requests over multi-period operations taking into account the requests’ attributes, riders’ delivery costs, and resource availability. Based on the scoring function, we design a time-varying Vickrey–Clarke–Groves (VCG)-like payment rule that can reflect the impacts of fluctuating supply-demand imbalances. Under this rule, a requestor will pay more during undersupply periods than during oversupply periods. To carve out the different impact degrees of the supply-demand imbalances, we further consider the linear, quadratic, and exponential time-varying resource parameters to construct the payment rule. In addition, we develop an online instant delivery resource allocation model to efficiently assign the riders to fulfill the accepted requests. We show that the proposed mechanism has desirable properties (individual rationality, budget balance, and incentive compatibility) and is computationally efficient. Furthermore, we give a lower bound for the mechanism efficiency. To validate the practicality of our mechanism and get some managerial insights into the operations of the instant delivery platform, we conduct numerical studies to compare the performance of our mechanism to the First-in, first-out (FIFO) allocation mechanism and to investigate the impacts of pricing functions, rolling horizon configurations, and rider numbers on the mechanism's performance.

When Should Prices Stay Fixed? On the Chances and Limitations of Spot Pricing in Larger Markets

Selling resources via auctions often seems profit-optimal in theory. Yet in practice, providers most often choose to sell homogeneous resources such as cloud computing instances at fixed prices. While it has been argued that this is explained by relatively non-volatile demand distributions and highly competitive market environments, these arguments only paint a partial picture. Through a formal game theoretic analysis, we show that the relative profit increase of offering resources through a spot pricing mechanism instead of at a fixed price is unbounded as long as a sufficiently competitive outside option exists, even if demand is very non-volatile. To explain the lack of spot pricing in practice, we consider that users might be biased against more complex auction-based mechanisms. We find that in large, non-volatile markets even a very small user bias will turn fixed-pricing profit-optimal if demand is sufficiently large and non-volatile. We derive a sufficient condition under which fixed prices are profit-optimal and illustrate the observed effects through a numerical example.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

Truthful VNFI Procurement Mechanisms With Flexible Resource Provisioning in NFV Markets

With the rapid development of network function virtualization (NFV), more and more enterprises and operators are seeking network service provisioning via service chains of virtual network functions (VNFs), instead of depending on proprietary hardware appliances. Following this trend, an NFV market is emerging, where users can procure different VNF instances (VNFIs) and their combinations from multiple network service providers (NSPs) in a pay-as-you-go way. In such a procurement process, how to guarantee truthfulness while enabling flexible resource provisioning in form of VNFIs is a significant challenge. In this paper, we propose a truthful reverse combinatorial auction-based mechanism to solve the combinatorial VNFI procurement problem. To support flexible resource provisioning, this mechanism allows NSPs to be multi-minded, and determine the provisioning VNFIs according to the auction results. Specifically, we design a heuristic algorithm to determine the winning bids in polynomial time. Furthermore, we devise a critical-payment-based pricing algorithm to induce NSPs to disclose their real costs, aiming to achieve truthfulness. Rigorous theoretical analysis shows the proposed mechanism can guarantee truthfulness, individual rationality and computational efficiency. Simulation results also verify the effectiveness and efficiency of the proposed mechanism.

Priority-Aware Resource Allocation for 5G mmWave Multicast Broadcast Services

5G Multicast Broadcast Services (MBS) are viewed as a promising 5G New Radio (NR) application, as standardization begins in 3GPP Release 17. With MBS, one next generation Node B (gNB) delivers data to multiple user equipments (UE) simultaneously, thus improving spectrum efficiency. Millimeter wave (mmWave) beamforming further enhances system performance by focusing signals in a dedicated direction. However, despite the advantages, we identify three issues of multicast with beamforming techniques. First, link directionality causes the gNB to transmit data over the beams sequentially, resulting in a combinatorial resource allocation problem. Confronting this beam scheduling issue, we develop an optimization algorithm that obtains an optimal solution in polynomial time. Second, UEs may falsely report their valuations over beam resources to gain more utility. Under this scenario, the gNB cannot allocate the resources to those in need due to the lack of accurate UE information. Therefore, we propose a Vickrey–Clarke–Groves (VCG) auction-based mechanism to incentivize the UEs to reveal their valuations over resources truthfully. This mechanism guarantees solution efficiency and maximizes social welfare. Third, as 3GPP standards allow for different priorities for different multicast flows, and video content providers distinguish between ordinary and premium UEs, we take UE priority into account. In this regard, we extend the valuation-based mechanism to a multi-priority one. Finally, the mathematical analysis validates some desirable properties of the proposed scheme, such as incentive-compatibility. Simulation results also justify our superior performance in the 5G MBS system compared with other resource allocation schemes.

Incentive-compatible mechanisms for online resource allocation in Mobility-as-a-Service systems

In the context of Mobility-as-a-Service (MaaS), the transportation sector has been evolving towards user-centric business models, which put the user experience and tailored mobility solutions at the center of the offer. The emerging concept of MaaS emphasizes that users value experience-relevant factors, e.g., service time, inconvenience cost, and travel delay, over segmented travel modes choices. This study proposes an auction-based mechanism and tractable optimization models for the demand-side management of MaaS systems wherein users’ trip requests are represented as mode-agnostic mobility resources. Users’ requests arrive dynamically in the MaaS system and users compete for mobility resources by bidding for mobility services based on their willingness to pay and experience-relevant preferences. We take the perspective of a MaaS platform regulator who aims to maximize social welfare by optimally allocating mobility resources to users in real-time. The MaaS regulator first decides whether to offer each user a MaaS bundle and identifies the optimal allocation of mobility resources for the selected users. Users have the possibility to accept or reject offered MaaS bundles by comparing the associated utility obtained from MaaS with a reserve utility obtained from other travel options. We introduce mixed-integer programming formulations for this online mobility resource allocation problem. We show that the proposed MaaS mechanism is incentive-compatible, individually rational, budget balanced, and computationally efficient. We propose a polynomial-time online algorithm and derive its competitive ratio relative to an offline algorithm. We also explore rolling horizon configurations with varying look-ahead policies to implement the proposed mechanism. Extensive numerical simulations conducted on large-scale instances generated from realistic mobility data highlight the benefits of the proposed mechanism.

Core-selecting auction-based mechanisms for service function chain provisioning and pricing in NFV markets

With the rapid development of network function virtualization (NFV), an ever-increasing number of enterprises, businesses and operators are resorting to network service provisioning by means of the service function chains (SFCs). Toward this trend, NFV markets are emerging, where users rent SFCs from the network service provider (NSP) to execute their tasks, and the NSP uses the computation resources across geo-distributed clouds to construct various SFCs. Such NFV markets need well-designed pricing mechanisms for SFCs since the users have motivations to strategize around their payments. The Vickrey–Clarke–Groves (VCG) mechanism is a natural fit here, which can ensure that truthful bidding is a dominant strategy. However, VCG mechanism is susceptible to the problems of collusion and shill bidding. To address these problems, in this paper, we design a core-selecting auction-based mechanism (CSAM) for SFC provisioning and pricing in the NFV market. This mechanism considers dynamic provisioning, flexible deployment and reservation prices across geo-distributed clouds. Specifically, we first formulate the core of NFV auction, and prove that an in-core outcome can prevent collusion and shill bidding. Next, we use a couple of correlated linear program (LP) and quadratic program (QP) to design an in-core VCG-nearest payment algorithm, aiming to acquire an in-core outcome and minimize users’ incentives to bid untruthfully. Simulation results verify the strict theoretical analysis, and validate the effectiveness and efficiency of our proposed CSAM.

Trust as a metric for auction-based task assignment in a cooperative team of robots with heterogeneous capabilities

As robots become part of our everyday lives, they may be required to cooperate without being aware of each other’s capabilities (e.g., because different teams have developed them), and therefore will have to trust each other to work together safely and efficiently. Starting from this premise, this work identifies trust as an essential metric to assign tasks to robots using auction-based mechanisms. We model trust by taking inspiration from popular models in the literature and adapting them to an open environment in which heterogeneous robots may dynamically enter or exit, execute assigned tasks, or verify the correct execution of tasks by other robots. Robots are considered to be heterogeneous in the sense that they may have different capabilities in executing and verifying the execution of actions. In the proposed model, “doing an action” and “verifying the execution of an action” are distinct, not necessarily overlapping, capabilities. Some robots may be able to do an action, whereas some robots may not be able to do it but only to observe and judge the ability of other robots to do it. After introducing the relevant formalism, the article describes the system’s architecture implemented in ROS and multiple experiments performed in simulation and with real robots (one NAO and two Pepper robots by SoftBank Robotics), providing a proof-of-concept for broader utilization of the system in cooperative robotic scenarios.

A truthful subsidy scheme for a peer-to-peer ridesharing market with incomplete information

Traffic congestion during peak periods has become a serious issue around the globe, partly due to the high number of single-occupancy commuter trips. Peer-to-peer (P2P) ridesharing platforms can present a suitable alternative for serving commuter trips. However, they face a major obstacle that prevents them from being a viable mode of transportation in practice: ridesharing users often provide tight time windows, which ultimately leads to a low matching rate. This study addresses this issue by introducing a subsidy scheme that allocates incentives to encourage a few carefully selected set of travelers to change their desired departure or arrival times, and thereby form successful matches. In order to implement this scheme for a ridesharing platform in the existence of private information, we propose an auction-based mechanism that guarantees truthfulness, individual rationality, budget-balance, and computational efficiency. Using numerical experiments, we show the merits of the proposed subsidy scheme when compared to its no-deficit variant as well as the conventional VCG mechanism.

Mechanisms for Resource Allocation and Pricing in Mobile Edge Computing Systems

In this article, we address the resource allocation and monetization challenges in Mobile Edge Computing (MEC) systems, where users have heterogeneous demands and compete for high quality services. We formulate the Edge Resource Allocation Problem (ERAP) as a Mixed-Integer Linear Program (MILP) and prove that ERAP is NP-hard. To solve the problem efficiently, we propose two resource allocation mechanisms. First, we develop an auction-based mechanism and prove that the proposed mechanism is individually-rational and produces envy-free allocations. We also propose an LP-based approximation mechanism that does not guarantee envy-freeness, but it provides solutions that are guaranteed to be within a given distance from the optimal solution. We evaluate the performance of the proposed mechanisms by conducting an extensive experimental analysis on ERAP instances of various sizes. We use the optimal solutions obtained by solving the MILP model using a commercial solver as benchmarks to evaluate thequality of solutions. Our analysis shows that the proposed mechanisms obtain near optimal solutions for fairly large size instances of the problem in a reasonable amount of time.

Energy-Efficient Resource Allocation for Parked-Cars-Based Cellular-V2V Heterogeneous Networks

As the fast development of vehicular network, the layout of the roadside unit (RSU) is indispensable. Due to the shackles of factors, such as coverage and cost, there is an urgent need for effective solution to solve the contradiction that RSU cannot be deployed on large scale. Parked cars provide a feasible solution for replacing RSUs and effectively reducing the arrangement of edge nodes. Inspired by this, parked cars as RSUs (P-RSUs) are leveraged to support cities’ vehicular network in this article. We first construct the P-RSU-based cellular-V2V heterogeneous networks (C-V2V HetNets) system model, and then formulate an optimization problem to maximize the energy efficiency (EE) of C-V2V HetNets with parked cars. Since the proposed issue is an NP-hard mixed-integer nonlinear programming (MINLP) problem coupled with P-RSU incentive, we reformulate it into two subproblems, which are the P-RSU recruitment and the joint resource allocation. For the first subproblem, an effective reverse auction-based mechanism is given to encourage parked cars participate and become P-RSUs. For the second subproblem, nonlinear fractional programming is used to optimize transmission power, and many-to-one matching is utilized to effectively obtain channel reusing scheme constrained by QoS. Moreover, a multihop-based transmission strategy is given to further expand vehicular network coverage. Algorithms are evaluated based on real-world scenarios using SUMO. Numerical results demonstrate that the proposed approach can both effectively recruit P-RSUs with low cost and achieve excellent system performance in terms of EE, spectrum efficiency, and network coverage compared to other benchmark algorithms.

Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization

The sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city.

A Two-Dimensional Sybil-Proof Mechanism for Dynamic Spectrum Access

Achieving higher spectrum utilization, auction-based mechanisms has been regarded as a popular tool in dynamic spectrum access (DSA). Recently, Sybil attacks in auction-based DSA mechanisms have been investigated, where a cheating bidder can manipulate an auction by submitting bids under multiple fake identities. Existing Sybil-proof mechanisms in DSA are limited to prevent Sybil attacks from primary users (PUs) or secondary users (SUs). However, both of PUs and SUs may perform Sybil attacks in DSA, i.e., double Sybil attacks. The challenge of solving the double Sybil attacks is that fictitious identities and fake bids can directly affect allocation results, but the malicious bidders cannot be straightforwardly distinguished from all bidders. To resist the double Sybil attacks, we propose STEAM, the first double Sybil-proof and two-dimensional Truthful spEctrum Auction Mechanism for DSA. Specifically, STEAM merges suspicious buyers based on geographic characteristics and sorts sellers by a bid-independent sorting method to minimize the impact of untruthful bids and Sybil attacks on the allocation results. Theoretical analysis and extensive evaluations prove that STEAM is double Sybil-proof, two-dimensional truthful, individual rational and budget-balanced, while the performance loss in various metrics within 8% compared to the existing auction-based mechanisms.

Distributed Resource Allocation for Collaborative Data Uploading in Body-to-Body Networks

In this paper, we study a body-to-body network (BBN) framework, which enables wireless body area network (WBAN) users located in close proximity to cooperate and share their network resources to improve the overall network performance. Our main aim is to design a distributed resource allocation mechanism that encourages each participating WBAN user to participate and upload each other’s data collaboratively. We propose an auction-based mechanism that optimizes data uploading for all participating users and the corresponding reimbursement. In the proposed auction mechanism, each user acts as both auctioneer and bidder. Fist, the auctioneer initiates the auction by announcing the amount of resource it wants to share and its price and each bidder submits their bid to each auctioneer based on its demand. We further propose a distributed algorithm for the auction mechanism that jointly solves both the auctioneers’ and the bidders’ optimization problems and determines the optimal amount of resource users should reserve for their own and the portion they should share. Our theoretical analysis demonstrates that the proposed distributed algorithm converges to the solution that maximizes the aggregated benefit of the users. Finally, the simulation results exhibit that the proposed algorithm always improves WBAN user’s individual performance together with overall BBN performance.

Evaluating Auction Mechanisms for the Preservation of Cost-Aware Digital Objects under Constrained Digital Preservation Budgets

Digital preservation is a field of research focused on designing strategies for maintaining digital objects accessible for general use in the coming years. Out of the many approaches to digital preservation, the present research article is a continuation work of a previously published article containing a proposal for a novel object-centered paradigm to address the digital preservation problem where digital objects share part of the responsibility for self-preservation. In the new framework, the behavior of digital objects is modeled to find the best preservation strategy. The results presented in the current article add a new economic constraint to the object behavior. Now, differently from the previous paper, migrations, copies and updates are not free to use, but subject to budget limitations to ensure the economic sustainability of the whole preservation system, forcing the now-called cost-aware digital objects for efficient management of available budget. The presented approach compares two auction-based mechanisms, a multi-unit auction and a combinatorial auction, with a simple direct purchase strategy as possible efficient behaviors for budget management. TiM, a simulated environment for running distributed digital ecosystems, is used to perform the experiments. The simulated results map the relation between the studied purchase models with the sustained quality level of digital objects, as a measure of its accessibility, together with its budget management capabilities. About the results, the best performance corresponds to the combinatorial auction model. The results are a good approach to deal with the digital preservation problem from a sustainable point of view and open the door to future implementations with other purchase strategies.

A Consortium Blockchain-Based Energy Trading for Demand Response Management in Vehicle-to-Grid

In this paper, we propose a Peer-to-Peer (P2P) energy trading scheme between EVs and the SPs to manage the demand response in V2G environment. Unlike the traditional schemes, which consists of complex energy-transportation meshes, the proposed scheme achieves a balance between demand and response by providing incentives to EVs out of their self-interests. However, the online transactions security and privacy protection of EVs poses challenges with respect to confidentiality and integrity preservation. To cope up with the issues, we design a consortium blockchain-based scheme to ensure secure energy transactions between EVs and the SPs without trusted third-party intervention. Moreover, the energy pricing and the amount of traded energy problems for demand response are solved by a double auction mechanism to maximize the social welfare. Numerical results based on a real-time implementation on a private Ethereum network having smart contract demonstrated that the auction-based mechanism achieves social welfare maximization with privacy protection of online transactions between EVs and the SPs. The results obtained show that the rate of convergence, scalability metric average latency, and standard deviation of the energy transactions have been improved by approximately 18.18%, 15%, and 28.1%, respectively in comparison to the existing schemes. Moreover, security and privacy analysis of the proposed scheme shows that it improves the transaction security substantially in comparison to the existing state-of-the-art proposals.

About auction strategies for intersection management when human-driven and autonomous vehicles coexist

Autonomous vehicles are appearing in our streets, and will soon populate our transportation infrastructures, which must be equipped with appropriate sensors and actuators in order to manage vehicles in a fruitful way. Besides the infrastructures, appropriate algorithms must be defined in order to coordinate the vehicles and to enable them to exploit the resources in a fair yet effective way. In the immediate future, autonomous vehicles must coexist human-driven vehicles, and this transitory scenario poses several challenges in coordinating both kinds to exploit street resources. One of these resources, whose management is quite challenging, is represented by intersections: vehicles come and aim at passing the intersection, often as soon as possible, but they must compete with other vehicles having the same aim. A possible approach that has been used in literature to this problem uses auction based mechanisms. In this paper, we place ourselves in the above-mentioned transitory scenario in which both human-driven and autonomous vehicles will compete to cross intersections, and we investigate the effectiveness of auction-based mechanism to coordinate vehicles at intersections. We devise some simple auction policies, and assume vehicle coordination strategies that are suitable also for human drivers. Our results lead us to believe that, under these assumptions, simple auction mechanisms do not introduce advantages for what concern traveling times as they do in the case of exclusively autonomous vehicles.

The Prisoners\u2019 Dilemma in collaborative carriers\u2019 request selection

Logistics providers have to utilize available capacities efficiently in order to cope with increasing competition and desired quality of service. One possibility to reduce idle capacity is to build coalitions with other players on the market. While the willingness to enter such coalitions does exist in the logistics industry, the success of collaborations strongly depends on mutual trust and behavior of participants. Hence, a proper mechanism design, where carriers do not have incentives to deviate from jointly established rules, is needed. We propose to use a combinatorial auction system, for which several properties are already well researched but little is known about the auction’s first phase, where carriers have to decide on the set of requests offered to the auction. Profitable selection strategies, aiming at maximization of total collaboration gains, do exist. However, the impact on individual outcomes, if one or more players deviate from jointly agreed selection rules is yet to be researched. We analyze whether participants in an auction-based transport collaboration face a Prisoners’ Dilemma. While it is possible to construct such a setting, our computational study reveals that carriers do not profit from declining the cooperative strategy. This is an important and insightful finding, since it further strengthens the practical applicability of auction-based trading mechanisms in collaborative transportation.

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resource management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids—forward, reverse, and two-sided auctions, (2) the heterogeneity of resources—combinatorial auctions, (3) the number and types of adopted attributes in winner determination—multi-attribute auctions, (4) the setting of auction—offline, online, or sequential auctions, and (5) the interoperability among cloud providers—inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented. Our comprehensive survey shows that although significant progress has been achieved in this field, there still exist key challenges that need to be investigated, as discussed here to provide future research directions.

Optimal Mobile Crowdsensing Incentive Under Sensing Inaccuracy

Due to the pervasive adoption of sensor-embedded mobile devices yet increasing demand on data and computing resources, mobile crowdsensing is a promising paradigm with rapid growth. One of the most challenging issues is how to maximize the utilities of crowdsensing platforms under inaccurate distributed sensing. The nature of such inaccuracy is due to the fact that energy-based sensing can be greatly impacted by thermal and environmental noise, which significantly affects task allocation strategies of crowdsensing platforms. Because of the allocation efficiency and fairness concerns, auction-based mechanisms have been extensively used in crowdsensing systems. However, the existing auction-based mechanisms for crowdsensing do not take sensing inaccuracy into consideration, while guaranteeing that each participator obtains her maximal utility by bidding with her true cost for tasks. To tackle this issue, in this article, we propose OSIER, an optimal mobile crowdsensing incentive under sensing inaccuracy. Specifically, a quantitative analytical framework on characterizing the impact of sensing inaccuracy on a crowdsensing platform is presented, and an optimization problem involving sensing inaccuracy is solved to achieve a maximum utility of the platform. Furthermore, depending on whether a user needs to perform all tasks simultaneously or not, indivisible tasks and divisible tasks are discussed, and OSIER-I and OSIER-D are presented for these two kinds of tasks. Simulation results verify the truthfulness of OSIER, and given a sample set with 5%-20% noise in spectrum sensing, OSIER can achieve 10% higher utilities than the existing crowdsensing mechanisms on average.