An incentive mechanism design for mobile crowdsensing with demand uncertainties

doi:10.1016/j.ins.2020.03.109

Information Sciences

Volume 528, August 2020, Pages 1-16

https://doi.org/10.1016/j.ins.2020.03.109 Get rights and content

Abstract

Mobile crowdsensing (MCS) has shown great potential in addressing large-scale data sensing problem by allocating sensing tasks to pervasive mobile users (MU). The MUs will participate in the MCS if they can receive sufficient compensation. Existing work has designed lots of incentive mechanisms for MCS, but ignores the MUs’ resource demand uncertainties that is critical for resource-constrained mobile devices. In this paper, we propose to design an incentive mechanism for MCS by taking the MUs’ own resource demand into the economic model. As different MUs will have different behavior, they will participate in the MCS with different levels. Based on this idea, we formulate the incentive mechanism by using the Stackelberg game theory. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach.

Introduction

With the ubiquity of mobile devices such as smartphones and tablets that are equipped with multiple powerful built-in sensors including GPS, accelerometer, gyroscope, camera, etc., the mobile crowdsensing (MCS) applications which provide location based services [1] become possible. Currently, various of MCS systems [2], [3], [4], [5] have been deployed that cover almost every aspect of our lives, including healthcare, intelligent transportation, environmental monitoring, etc.

In the MCS system that offers crowdsensing applications, the sensing-platform (SP) will recruit mobile users (MUs) at locations of interest to report sensing data. Many of existing MCS systems [6], [7] are based on the voluntary participation from MUs. However, to perform the sensing tasks, the participating MUs have to consume their own resources such as computing and communicating energy. Moreover, the MUs may face the potential privacy threats when the sensing data is submitted with own sensitive information (e.g. location tags and visiting patterns). For these reasons, the MUs would not be interested in participating in the sensing tasks unless they receive a satisfying reward to compensate their resources consumption and potential privacy breach. Therefore, it is necessary to design an effective incentive mechanism that can stimulate the MUs to participate in the crowdsensing applications. In order to achieve the maximum user participation level, large quantities of incentive-aware mechanisms [8], [9], [10], [11], [12] have been proposed by research community for the MCS systems. Notably, in real practice, the smart devices’ resources such as energy are limited, and these resources need to satisfy MUs’ varying demand caused by their uncertain behavior (e.g., when MUs are busy at work, their smart devices may be free. When MUs want to have entertainments, their smart devices may be occupied with few resources left). However, few of these aforementioned works take MUs uncertain behavior into consideration. Therefore, the design of incentive mechanism for MCS game with demand uncertainties is still an open problem.

To deal with this problem, in this paper, the interaction between SP and MUs is formulated into a two-stage Stackelberg game. As shown in Fig. 1, in Stage I, the SP as the leader of the Stackelberg game first determines and broadcasts its pricing policy. In Stage II, each MU as a follower computes his or her sensing effort based on the price offered by the SP, his or her resources constraints and demand uncertainties. The analysis in this two-stage problem is particularly challenging, as we need to characterize the SP’s profit by first computing the MUs’ sensing effort with demand uncertainties. Through mathematical analysis, the existence and uniqueness of the Stackelberg Equilibrium (SE) in this MCS game is proven and the expressions for computing the SE is derived. That is, the SP in Stage I has an optimal pricing strategy and the MUs in Stage II also have optimal decisions under their own demand uncertainties.

However, in order to compute the SE of the above static MCS game, the SP needs to know the private information of the MUs, which is impossible in lots of practical situations. To protect MUs’ private information, the dynamic MCS game is modeled and dynamic incentive mechanism based on deep reinforcement learning (DRL) approaches are employed, which enable the sensing platform to learn the optimal pricing strategy directly from game experience (the past game records). Since the game experience of the SP can be regarded as a motivation for its future pricing strategy, the dynamic MCS game can be formulated into a Markov Decision Process (MDP) problem. Thus, it can be addressed by DRL algorithms effectively [13].

Overall, the main contributions of this paper can be summarized as follows:

1.
A novel economic model for the MCS game with MUs’ resources constraints and demand uncertainties is formulated and an incentive mechanism based on a two-stage Stackelberg game is designed.
2.
The existence and uniqueness of the SE in the proposed MCS game is proven and its computing procedure is provided, revealing the feasibility of allowing MCS game to cope with MUs’ uncertain demand and limited resources.
3.
A dynamic incentive mechanism (DIM) based on DRL approach for the dynamic MCS game is proposed, which enables the SP to learn the optimal pricing strategy directly from game experience without any prior knowledge about MUs’ private information.
4.
Numerical simulation results demonstrate the effectiveness of the proposed incentive mechanisms for both of the static MCS game and the dynamic MCS game. It is also derived that the demand uncertainties have a significant impact on MCS system performance.

The rest of the paper is organized as follows. Section 2 provides a literature review. Section 3 presents the network economics model of the crowdsensing system. The incentive mechanism based on a two-stage Stackelberg game for the static MCS game is designed in Section 4 and the DRL-based dynamic incentive mechanism for the dynamic MCS game is designed in Section 5. In Section 6, the numerical simulations are conducted to evaluate the performance of the proposed incentive mechanisms, followed by conclusions of this paper in Section 8.

Section snippets

Literature review

MCS has been widely studied in recent years [1]. For example, Reddy et al. [14] developed an application to enable sensing platform employ well-suited participants to complete sensing tasks. Xiao et al. [6] and Li et al. [15] both studied the task allocation and participants selection problem in MCS. However, these works only focus on the user selection, task assignment or sensing data collection. They do not consider the design of incentive mechanism, which has been widely studied in lots of

System model and problem formulation

We consider a single SP which resides in the cloud and consists of some servers. A set $N = {1, 2, \dots, N}$ of MUs that connect to the sensing-platform via the Internet. The sensing-platform will stimulate the mobile users to participate in the MCS tasks via rewards. More specifically, the system model is described in Section 3.1.1, followed by the problem formulation in Section 3.2 finally.

Incentive mechanism for static MCS game

In this section, how to design the incentive mechanism for the static MCS game by solving the Stackelberg game defined in Section 3.2 is demonstrated. In the static MCS game, the main challenges are (a) how to develop the resource allocation strategy for the MUs and (b) how to develop a pricing strategy for the SP. In the following, it is firstly proven that for any feasible $p = {[p_{1}, p_{2}, \dots, p_{N}]}^{T},$ each MU has a unique optimal resource allocation strategy in the second stage (Section 4.1). Afterwards,

Dynamic incentive mechanism for MCS with deep reinforcement learning approach

In this section, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is designed for MCS. Obviously, in order to derive the optimal pricing strategy, the SP needs to solve Eq. (9) by knowing the private information such as τ_n, δ_n, c_n of the MUs. However, it is unrealistic to obtain these information in practice because MUs may refuse to expose their utility parameters due to privacy concerns. Hence, a DRL approach is designed to learn the optimal strategy

Performance evaluation

In this section, numerical simulations are conducted. Specifically, 5 MUs are randomly generated. For each MU, c_n and δ_n are randomly from [0,1] while guaranteeing δ_n > c_n. We set the total available resources τ_n of each MU to 20 units, and randomly drawn the own resources demand ξ_n from a uniform distribution in [0,25].

Improvement of DIM

Although the proposed DIM can effectively learn the SE in MCS game, it requires large quantities of interactions between SP and MUs, leading to relatively high time cost to reach the optimal pricing strategy. Moreover, in practice, the MCS system may not allow the SP to interact constantly with MUs and limit the number of learning episodes. Therefore, it is significant to improve the learning efficiency of DIM, especially when the training data is limited. One promising direction is to leverage

Conclusion

In this paper, the static MCS game with MUs’ resources constraints and demand uncertainties is formulated firstly, the incentive mechanism is then considered based on a Stackelberg game. The existence of the unique SE is proved and the expressions for calculating the SE are provided. By analyzing the SE, it is found that the MUs’ demand uncertainties have evident impacts on the performance of the MCS system. Moreover, considering that the SP requires the MUs’ private information to achieve the

CRediT authorship contribution statement

Yufeng Zhan: Writing - original draft. Yuanqing Xia: Conceptualization. Jiang Zhang: Investigation. Ting Li: Validation. Yu Wang: Conceptualization.

Declaration of Competing Interest

None.

Acknowledgment

This work was supported by National Natural Science Foundation under Grant 61836001, the National Natural Science Foundation Projects of International Cooperation and Exchanges under Grant 61720106010, the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621063, the National Natural Science Foundation of China 61572347, the US National Science Foundation (CNS-1319915 and CNS-134335), and the U.S. Department of Transportation Center for

References (42)

M. Xiao et al.
Online task assignment for crowdsensing in predictable mobile social networks
IEEE Trans. Mob. Comput.
(2016)
X. Duan et al.
Distributed algorithms to compute Walrasian equilibrium in mobile crowdsensing
IEEE Trans. Ind. Electron.
(2016)
L. Gao et al.
Spectrum trading in cognitive radio networks: acontract-theoretic modeling approach
IEEE J. Sel. Areas Commun.
(2011)
M.J. Osborne et al.
A Course in Game Theory
(1994)
B. Guo et al.
Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm
ACM Computing Surveys
(2015)
P. Mohan et al.
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proc. of ACM SenSys
(2008)
A. Thiagarajan et al.
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proc. of ACM SenSys
(2009)
Y. Cheng et al.
AirCloud: a cloud-based air-quality monitoring system for everyone
Proc. of ACM SenSys
(2014)
F. Wang et al.
SRMCS: a semantic-aware recommendation framework for mobile crowd sensing
Inf. Sci.
(2018)
R.K. Rana et al.
Ear-phone: an end-to-end participatory urban noise mapping system
Proc. of ACM IPSN
(2010)

Y. Zhan et al.

Incentive mechanism in platform-centric mobile crowdsensing: a one-to-many bargaining approach

Comput. Netw.

(2018)

S. He et al.

An exchange market approach to mobile crowdsensing: pricing, task allocation, and Walrasian equilibrium

IEEE J. Sel. Areas Commun.

(2017)

D. Yang et al.

Incentive mechanisms for crowdsensing: crowdsourcing with smartphones

IEEE/ACM Trans. Netw.

(2016)

X. Zhang et al.

Free market of crowdsourcing: incentive mechanism design for mobile sensing

IEEE Trans. Parallel Distrib. Syst.

(2014)

J. Schulman et al.

Trust region policy optimization

Proc. of ICML

(2015)

S. Reddy et al.

Examining micro-payments for participatory sensing data collections

Proc. of ACM UbiComp

(2010)

H. Li et al.

Dynamic participant recruitment of mobile crowd sensing for heterogeneous sensing tasks

Proc. of IEEE MASS

(2015)

L. Gao et al.

Map: multiauctioneer progressive auction for dynamic spectrum access

IEEE Trans. Mob. Comput.

(2010)

T. Ning et al.

Self-interest-driven incentives for ad dissemination in autonomous mobile social networks

Proc. of IEEE INFOCOM

(2013)

W. Wang et al.

OURS: optimal unicast routing systems in non-cooperative wireless networks

Proc. of ACM MobiCom

(2006)

L. Chen et al.

Conflicts and incentives in wireless cooperative relaying: a distributed market pricing framework

IEEE Trans. Parallel Distrib. Syst.

(2010)

Cited by (29)

DLFTI: A deep learning based fast truth inference mechanism for distributed spatiotemporal data in mobile crowd sensing
2023, Information Sciences
The paradigm of Mobile Crowd Sensing (MCS) allows for numerous applications with distributed spatiotemporal data, where great attention is drawn to the fundamental problems for truth inference. Existing works all suffer from poor accuracy and slow start, due to the lack of valid information for Ground Truth Data (GTD) and workers. These problems make the MCS system vulnerable to fraudulent attacks by malicious gangs, causing the Estimated Truth Data (ETD) to deviate significantly from GTD. In this paper, we propose a Deep Learning based Fast Truth Inference mechanism, called DLFTI, to achieve fast trust computing and accurate truth discovery in MCS. First, we introduce the Degrees-Of-Trust (DOT) to characterize the sensing ability of workers and establish worker profiles based on DOT to recognize workers’ trustworthiness dynamically. Then, we abandon the unrealistic assumption of priori GTD in previous studies and instead utilize the Unmanned Aerial Vehicles (UAVs), recognized trustworthy workers and the Deep Matrix Factorization (DMF) method to construct three-level GTD and three-level ETD, which are used for fast trust computing of workers and accurate truth discovery of tasks respectively. Finally, we conduct extensive simulations on a real-world dataset to corroborate the significant performance of DLFTI.
Identifying influential users in unknown social networks for adaptive incentive allocation under budget restriction
2023, Information Sciences
In recent years, recommenze the social influence among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world applications, identifying influential users can be challenging because of the unknown network topology. In this paper, we propose a novel algorithm for exploring influential users in unknown networks, estimating the influential relationships among users based on their historical behaviors without knowing the network topology. In addition, we design an adaptive incentive allocation approach that determines incentive values based on each user’s preferences and influence ability. We evaluate the performance of the proposed approaches by conducting experiments on synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.
SCTD: A spatiotemporal correlation truth discovery scheme for security management of data platform
2023, Future Generation Computer Systems
Citation Excerpt :
The second is how to identify the trust of workers [6,13,17]. The third is how to recruit trustworthy workers while minimizing system costs, thereby minimizing the risks and potential attacks suffered by the data platform [9,15]. The first challenge in identifying the truth of the reported data is the most critical issue.
The continuous development of mobile sensing devices enables them to quickly perceive a large amount of data, forming a promising mobile crowd sensing (MCS) platform for large-scale data collection. The raw data stored in the data platform is further synthesized into a variety of Internet of Things (IoT) services to data consumers through the processing of the data platform. However, due to the wide range of data sources, sensing devices may contribute corrupted data, or maliciously spread forged data, causing the data platform to make wrong decisions and damage the quality of service. Therefore, collecting high-quality data is critical for the security of the data platform and the quality of IoT applications. In this paper, a novel Spatiotemporal Correlation Truth Discovery (SCTD) scheme is proposed, which adopts historical data as verifiable evidence to identify the truth of reported data and gain the trust of workers, consequently recruiting high-trust devices to collect data. First, Unmanned Aerial Vehicles (UAVs) are sent to collect Gold Ground Truth Data (GGTD), which is used as the benchmark to verify the data truth of the minority sensing devices. Then a trust evaluation method is proposed to calculate the trust of devices. Second, the data reported by trusted devices as Silver Ground Truth Data (SGTD) is utilized to verify the trust of most devices, so the method proposed in this paper can discover the truth of massive data. Third, to reduce the cost of truth discovery, a low-cost method of data fitting is proposed to collect massive historical data of the trusted device, thereby verifying the truth of data in the same time and space. Since historical data contributes little value to IoT services, the platform can obtain a large amount of historical data by paying low rewards to the devices. Finally, we propose to select mobile sensing devices to collect truthful data in different spaces, which can effectively cover the spatiotemporal correlation data truth discovery in time and space, thereby verifying as much data submitted to the platform as possible. Based on the trust relationships constructed in this paper, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy workers to participate in data-sensing tasks. The experimental results show that our solution can accurately identify the trust of more workers and verify the truth of data in a wider range while minimizing the cost of the data platform.
A collaboration-driven mechanism for AI diagnose with multiple requesters under incomplete information
2023, Computer Networks
In this paper, we design a collaboration-driven mechanism with multiple requesters cooperating together to collect massive accurate data and minimize costs in AI diagnose. Under the process of this incentive mechanism, two issues, the accuracy level of data and how to encourage requesters to cooperate, has been solved. A novel payment policy, with which the most accurate data gets the highest payment, is presented to assure the accuracy level of data. A Stackelberg game, which describes the interactions between requesters and workers, is put forward to stimulate multiple requesters jointly collect data. Considering requesters’ selfishness, Stackelberg equilibrium with multiple leaders are presented to maximize both requesters’ and workers’ utilities simultaneously. A cost allocation method inspired by separable costs remaining benefits method is proposed to ensure the increase of requesters’ utilities when collecting data as a team. However, since the worker’s (mobile device users’) privacy information is unknown, the game mentioned above is incomplete information dynamic MCS game. Hence, Q-learning is used to learn from historical data to estimate the unknown private data of workers. Several simulations are presented to prove that Q-learning can obtain theoretical solutions independent of parameters in the case of incomplete information.
Privacy preserving IoT-based crowd-sensing network with comparable homomorphic encryption and its application in combating COVID19
2022, Internet of Things (Netherlands)
IoT-based crowd-sensing network, which aims to achieve data collection and task allocation to mobile users, become more and more popular in recent years. This data collected by IoT devices may be private and directly transmission of these data maybe incur privacy leakage. With the help of homomorphic encryption (HE), which supports the additive and/or multiplicative operations over the encrypted data, privacy preserving crowd-sensing network is now possible. Until now several such secure data aggregation schemes based on HE have been proposed. In many cases, ciphertext comparison is an important step for further secure data processing. However efficient ciphertext comparison is not supported by most such schemes. In this paper, aiming at enabling ciphertext comparison among multiple users in crowd-sensing network, with Lagrange’s interpolation technique we propose comparable homomorphic encryption (CompHE) schemes. We also prove our schemes’ security, and the performance analysis show our schemes are practical. We also discuss the applications of our IoT based crowd-sensing network with comparable homomorphic encryption for combatting COVID19, including the first example of privacy preserving close contact determination based on the spatial distance, and the second example of privacy preserving social distance controlling based on the spatial difference of lockdown zones, controlled zones and precautionary zones. From the analysis we see our IoT based crowd-sensing network can be used for contact tracing without worrying about the privacy leakage. Compared with the existing CompHE schemes, our proposals can be collusion resistance or secure in the semi-honest model while the previous schemes cannot achieve this easily. Our schemes only need 4 or 5 modular exponentiation when implementing the most important comparison algorithm, which are better than the existing closely related scheme with advantage of 50% or 37.5%.
Crowdsensing based missing data inference algorithm considering outlier data and GPS errors
2022, Information Sciences
Citation Excerpt :
A mobile crowdsensing system (MCS) is a system that uses mobile devices to effectively collect data, so as to provide a variety of novel Internet of Things applications [18], as shown in Fig. 1. An MCS needs to recruit a large number of participants by utilizing efficient incentive mechanisms [35]. The mobile devices of participants automatically collect the data required by the MCS and then transmit these data to the server of the MCS through various communication methods, such as 4G, 5G, or WiFi.
Crowdsensing is a mode of using mobile devices to collect data. With the development of smartphones, crowdsensing-based urban environmental monitoring systems have become a promising research topic. In this system, because the distribution of participants is uneven, a lack of measured data will exist in the times and locations where there are no participants. These missing data need to be inferred. However, the existing inference algorithms do not consider GPS location errors and outlier data. To solve these problems, a grid distance-based tensor completion missing data inference algorithm considering the local outlier factor (LOF) and GPS location error processing is proposed, called the LGGDTC algorithm. In the algorithm, the LOF is used to detect and eliminate the outlier data. On this basis, the probabilities that each environmental data may be collected in different locations are calculated, and then the environmental data of each location are calculated. Then, the similarity distance between two different locations is calculated by the geographic and feature distance, and the objective function is constructed. Finally, the block coordinate descent method is used to obtain the solution of the objective function. The simulation results show that the LGGDTC algorithm can obtain good performance.

View all citing articles on Scopus

View full text

An incentive mechanism design for mobile crowdsensing with demand uncertainties

Abstract

Introduction

Section snippets

Literature review

System model and problem formulation

Incentive mechanism for static MCS game

Dynamic incentive mechanism for MCS with deep reinforcement learning approach

Performance evaluation

Improvement of DIM

Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgment

IEEE Trans. Mob. Comput.

IEEE Trans. Ind. Electron.

IEEE J. Sel. Areas Commun.

Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm

ACM Computing Surveys

Nericell: rich monitoring of road and traffic conditions using mobile smartphones

Proc. of ACM SenSys

VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

Proc. of ACM SenSys

AirCloud: a cloud-based air-quality monitoring system for everyone

Proc. of ACM SenSys

SRMCS: a semantic-aware recommendation framework for mobile crowd sensing

Inf. Sci.

Ear-phone: an end-to-end participatory urban noise mapping system

Proc. of ACM IPSN

Incentive mechanism in platform-centric mobile crowdsensing: a one-to-many bargaining approach

Comput. Netw.

An exchange market approach to mobile crowdsensing: pricing, task allocation, and Walrasian equilibrium

IEEE J. Sel. Areas Commun.

Incentive mechanisms for crowdsensing: crowdsourcing with smartphones

IEEE/ACM Trans. Netw.

Free market of crowdsourcing: incentive mechanism design for mobile sensing

IEEE Trans. Parallel Distrib. Syst.

Trust region policy optimization

Proc. of ICML

Examining micro-payments for participatory sensing data collections

Proc. of ACM UbiComp

Dynamic participant recruitment of mobile crowd sensing for heterogeneous sensing tasks

Proc. of IEEE MASS

Map: multiauctioneer progressive auction for dynamic spectrum access

IEEE Trans. Mob. Comput.

Self-interest-driven incentives for ad dissemination in autonomous mobile social networks

Proc. of IEEE INFOCOM

OURS: optimal unicast routing systems in non-cooperative wireless networks

Proc. of ACM MobiCom

Conflicts and incentives in wireless cooperative relaying: a distributed market pricing framework

IEEE Trans. Parallel Distrib. Syst.