A Green Stackelberg-game Incentive Mechanism for Multi-service Exchange in Mobile Crowdsensing

Mobile crowd sensing (MCS), as a novel paradigm that coordinates a crowd of distributed devices to complete a whole sensing task, has attracted tremendous attention. While providing an effective and practical approach for sensing in largescale mobile scenes, the existing works on MCS suffer from a risk of privacy leakage because user data needs to be gathered in the cloud for processing and analysis. Federated Learning (FL) is a promising alternative as it can leverage mobile devices to accomplish a large learning task without centrally collecting the user data. However, incorporating FL into MCS is a non-trivial task due to the following reasons: 1) the data quality of mobile devices is often unreliable, especially in the context of crowd sensing; 2) the existing incentive mechanism in MCS may not work due to the lack of access to the user data. To address the problem, we propose a privacy-preserving mobile crowd sensing system based on Federated Learning with unreliable user data (called F-Sense). We analyze the key issues of sensing tasks, and further design an incentive mechanism to reward and motivate participants. Moreover, we explore to construct a federated quality model of user data in order to improve the data quality and obtain better training results for sensing tasks. Extensive simulation results show that F-Sense achieves privacy-preserving crowd sensing and the developed incentive mechanism can improve the task efficiency by encouraging local training at mobile devices.

In the user selection phase of mobile crowdsensing, most existing incentive mechanisms focus on either single-attribute selection or random selection, which possibly lead to serious consequences such as low user enthusiasm, decreased task completion rate, and increased cost of platform consumption. To tackle these issues, in this paper, we propose a novel incentive mechanism MAIM, which is based on multi-attribute user selection and participation intention analysis function in mobile crowdsensing. In this mechanism, the sensing platform employs the analytic hierarchy process to determine the weights of three attributes: participation threshold, cost, and reputation. The weight calculation results of each sensing user with respect to each attribute are then integrated to obtain the sorted weight of each user, with which the sensing platform will then obtain the optimal user set. From the users’ perspective, they can autonomously decide whether to accept task processing requests, as enabled by the participation intention analysis function, thereby voiding the absolute authority and control of the sensing platform over users and achieving a two-way selection between the sensing platform and the sensing users. Furthermore, the sensing platform establishes a score-based reputation reward to inspire active performers and utilizes a punishment mechanism to overawe malicious vandals, which substantially helps activize enthusiasm of user participation and improve sensing data quality. Simulation results indicate that the proposed MAIM has significantly improved the sensing task completion ratio and the budget surplus ratio compared with the existing incentive mechanisms in mobile crowdsensing.

In the mobile crowdsensing of vehicular ad hoc networks (VANETs), in order to improve the amount of data collection, an effective method to attract a large number of vehicles is needed. Therefore, the incentive mechanism plays a dominant role in the mobile crowdsensing of vehicular ad hoc networks. In addition, the behavior of providing malicious data by vehicles as data collectors will have a huge negative impact on the whole collection process. Therefore, participants need to be encouraged to provide data honestly to obtain more available data. In order to increase data collection and improve the availability of collected data, this paper proposes an incentive mechanism for mobile crowdsensing in vehicular ad hoc networks named V-IMCS. Specifically, the Stackelberg game model, Lloyd’s clustering algorithm and reputation management mechanism are used to balance the competitive relationship between participants and process the data according to the priority order, so as to improve the amount of data collection and encourage participants to honestly provide data to obtain more available data. In addition, the effectiveness of the proposed mechanism is verified by a series of simulations. The simulation results show that the amount of available data is significantly higher than the existing incentive mechanism while improving the amount of data collection.

Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure the stability of the sensing data quality. Most of the existing incentive mechanisms for data quality in crowdsensing are based on traditional economics, which believe that the decision of participants to complete a task depends on whether the benefit of the task is greater than the cost of completing the task. However, behavioral economics shows that people will be affected by the cost of investment in the past, resulting in decision-making bias. Therefore, different from the existing incentive mechanism researches, this paper considers the impact of sunk cost on user decision-making. An incentive mechanism based on sunk cost called IMBSC is proposed to motivate participants to improve data quality. The IMBSC mechanism stimulates the sunk cost effect of participants by designing effort sensing reference factor and withhold factor to improve their own data quality. The effectiveness of the IMBSC mechanism is verified from three aspects of platform utility, participant utility and the number of tasks completed through simulation experiments. The simulation results show that compared with the system without IMBSC mechanism, the platform utility is increased by more than 100%, the average utility of participants is increased by about 6%, and the task completion is increased by more than 50%.

Although mobile crowdsensing (MCS) has become a new paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, how to effectively incentivize users’ active participation and high quality data contribution in MCS has become a critical issue. Existing offline incentive mechanisms are inefficient to be applied to the online scenario, where a large number of heterogeneous users arrive and leave dynamically in a random manner, meanwhile the MCS platform has to make an irrevocable immediate decision on whether to accept or reject users’ requests, and how much a user should be paid under a strict budget constraint without knowing future information. Although there are a few online incentive mechanisms, these efforts tend to focus on achieving some desirable properties without addressing the issues of online incentive as a whole, and are not effective to motivate users’ active participation and high-quality sensory data contribution simultaneously. In this paper, we integrate the quality of sensing, rating update, user selection, and payment determination to develop the first online rating protocol for practical MCS to deal with adverse selection and moral hazard simultaneously. First, we design an endogenous learning approach to quantify the users’ ratings based on their historical contributions. Second, we exploit an incremental learning approach to compute the density threshold based on users’ historical information. Third, we theoretically prove that our online rating protocol achieves the desirable properties of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computationally efficiency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">budgetary feasibility</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual rationality</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">truthfulness</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constant competitiveness</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rating coverage</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incentive effectiveness</i> . Finally, extensive evaluation results support the performance of our protocol and validate the theoretical properties derived in this paper.

Smart consumer devices have become one of the fundamental communication and computing devices in people's everyday lives over the past decade. Their various sensors and wireless connectivity have paved the way for a new application area called mobile crowdsensing (MCS) where sensing services are provided by using the sensor outputs collected from smart consumer devices. MCS system's service quality heavily depends on the participation of smart device users who probably expect to be compensated in return for their participation. Therefore, MCS applications need incentive mechanisms to motivate such people into participating. In this work, we first defined a reverse auction based incentive mechanism for a representative MCS system. Then, we integrated the Vickrey-Clarke-Groves (VCG) mechanism into the initial incentive mechanism so that truthful bidding would become the dominant strategy in the resulting incentive mechanism. Finally, we conducted simulations of both incentive mechanisms in order to measure the fairness of service prices and the fairness of cumulative participant earnings using Jain's fairness index. We observed that both the fairness of service prices and the fairness of cumulative participant earnings were generally better in the derived incentive mechanism when the VCG mechanism was applicable. We also found that at least 70% of service requests had fair prices, while between 5% and 85% of participants had fair cumulative earnings in both incentive mechanisms.

Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.

Truth based three-tier Combinatorial Multi-Armed Bandit ecosystems for mobile crowdsensing

Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

Mobile crowdsensing (MCS) is a promising paradigm for large-scale sensing. A group of users are recruited as workers to accomplish various sensing tasks and provide data to the platform and requesters. A key problem in MCS is to design the incentive mechanism, which can attract enough workers to participate in sensing activities and maintain the truthfulness. As the main advantage of MCS, user mobility is a factor that must be considered. We make an attempt to build a technical framework for MCS, which is associated with a truthful incentive mechanism taking the movements of numerous workers into account. Our proposed framework contains two challenging problems: path planning and incentive mechanism design. In the path planning problem, every worker independently plans a tour to carry out the posted tasks according to its own strategy. A heuristic algorithm is proposed for the path planning problem, which is compared with two baseline algorithms and the optimal solution. In the incentive mechanism design, the platform develops a truthful mechanism to select the winners and determine their payments. The proposed mechanism is proved to be computationally efficient, individually rational, and truthful. In order to evaluate the performance of our proposed mechanism, the well-known Vickrey–Clarke–Groves (VCG) mechanism is considered as a baseline. Simulations are conducted to evaluate the performance of our proposed framework. The results show that the proposed heuristic algorithm for the path planning problem outperforms the baseline algorithms and approaches the optimal solution. Meanwhile, the proposed mechanism holds a smaller total payment compared with the VCG mechanism when both mechanisms achieve the same performance. Finally, the utility of a selected winner shows the truthfulness of proposed mechanism by changing its bid.

Mobile crowdsensing: A survey on privacy-preservation, task management, assignment models, and incentives mechanisms

Mobile crowdsensing is a novel paradigm to collect sensing data and extract useful information about regions of interest. It widely employs incentive mechanisms to recruit a number of mobile users to fulfill coverage requirement in the interested regions. In practice, sensing service providers face a pressing optimization problem: How to maximize the valuation of the covered interested regions under a limited budget? However, the relation between two important factors, i.e., Coverage Maximization and Budget Feasibility , has not been fully studied in existing incentive mechanisms for mobile crowdsensing. Furthermore, the existing approaches on coverage maximization in sensor networks can work, when mobile users are rational and selfish. In this paper, we present the first in-depth study on the coverage problem for incentive-compatible mobile crowdsensing, and propose BEACON, which is a B udget f EA sible and strategy-proof incentive mechanism for weighted CO verage maximizatio N in mobile crowdsensing. BEACON employs a novel monotonic and computationally tractable approximation algorithm for sensing task allocation, and adopts a newly designed proportional share rule based compensation determination scheme to guarantee strategy-proofness and budget feasibility. Our theoretical analysis shows that BEACON can achieve strategy-proofness, budget feasibility, and a constant-factor approximation. We deploy a noise map crowdsensing system to capture the noise level in a selected campus, and evaluate the system performance of BEACON on the collected sensory data. Our evaluation results demonstrate the efficacy of BEACON.

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users’ historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user’s historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user’s resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user’s optimal response strategy. Finally, two conversion methods of the user’s total payoff are proposed to ensure flexible application of the user’s payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility.

The designs of existing incentive mechanisms in mobile crowdsensing (MCS) are primarily platform-centered or user-centered, while overlooking the multidimensional consideration of sensing task requirements. Therefore, the user selection fails to effectively address the task requirements or the relevant maximization and diversification problems. To tackle these issues, in this paper, with the aid of edge computing, we propose a task-oriented user selection incentive mechanism (TRIM), in an effort toward a task-centered design framework in MCS. Initially, an edge node is deployed to publish the sensing task according to its requirements, and constructs a task vector from multiple dimensions to maximize the satisfaction of the task requirements. Meanwhile, a sensing user constructs a user vector to formalize the personalized preferences for participating in the task response. Furthermore, by introducing a privacy-preserving cosine similarity computing protocol, the similarity level between the task vector and the user vector can be calculated, and subsequently a target user candidate set can be obtained according to the similarity level. In addition, considering the constraint of the task budget, the edge node performs a secondary sensing user selection based on the ratio of the similarity level and the expected reward of the sensing user. By designing a secure multi-party sorting protocol, enhanced by fuzzy closeness and the fuzzy comprehensive evaluation method, the target user set is determined aiming at maximizing the similarity of the task requirements and the user's preferences, while minimizing the payment of the edge node, and ensuring the fairness of the sensing user being selected. The simulation results show that TRIM achieves feasible and efficient user selection while ensuring the privacy and security of the sensing user in MCS. Among the dynamic changes of task requirements, TRIM excels with user selections reaching nearly 90% on the data quality level compliance rate and 70% on the task budget consumption ratio, superior to the other incentive mechanisms.

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator’s utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS.