Table Of ContentCheating-Resilient Incentive Scheme for Mobile
Crowdsensing Systems
Cong Zhao∗, Xinyu Yang∗, Wei Yu†, Xianghua Yao∗, Jie Lin∗ and Xin Li∗
∗Xi’an Jiaotong University
†Towson University
Abstract—Mobile Crowdsensing is a promising paradigm for this paper), even when it pays fairly for data acquisition.
ubiquitous sensing, which explores the tremendous data col- Intuitively, we can see that: (i) the MCS server needs to
lected by mobile smart devices with prominent spatial-temporal
7 recruitcredibleusersinsensingtasks,and(ii)honestresponses
coverage. As a fundamental property of Mobile Crowdsensing
1 deserve substantial reward while dishonest reporting requires
Systems, temporally recruited mobile users can provide agile,
0
fine-grained, and economical sensing labors, however their self- reprimand. A mechanism satisfying these two requirements
2
interest cannot guarantee the quality of the sensing data, even simultaneously is necessary for MCS implementations.
n when there is a fair return. Therefore, a mechanism is required User Incentive schemes [5] aim at encouraging self-
a for the system server to recruit well-behaving users for credible interested users to participate in system tasks by rewarding
J sensing, and to stimulate and reward more contributive users
monetary or tradable paybacks. Generally speaking, existing
8 based on sensing truth discovery to further increase credible
schemes in participatory systems model the user incentive
reporting. In this paper, we develop a novel Cheating-Resilient
] Incentive(CRI)schemeforMobileCrowdsensingSystems,which process as an optimization problem for either the system
I achieves credibility-driven user recruitment and payback max- server [6], [7] or users [8], [9] by designing mechanisms
N
imization for honest users with quality data. Via theoretical based on auction or game theory. Nonetheless, research on
. analysis, we demonstrate the correctness of our design. The
s incentivemechanismsthatconsidersactivecheatingbehaviors
c performance of ourschemeisevaluated based onextensive real-
from self-interested users is relatively limited. Schemes in
[ worldtrace-drivensimulations.Ourevaluationresultsshowthat
ourschemeisproventobeeffectiveintermsofbothguaranteeing [10] and [11] aim to guarantee the ‘bidder’s truthfulness’ in
1 sensing accuracy and resisting potential cheating behaviors, as designed auctions. Also, incentive schemes were proposed to
v demonstrated in practical scenarios, as well as those that are evaluate the quality of user reports[12], [13]. However,these
8 intentionally harsher.
2 schemes cannot be directly deployed in highly dynamic and
IndexTerms—MobileCrowdsensing;Cheating-ResilientIncen-
9 opportunistic MCSs. To stimulate the service time of MCS
tive Scheme; Mobile Applications
1 participators, a Stackelberg game-based incentive mechanism
0
is proposed in [14], which maximizes the utility of the MCS
. I. INTRODUCTION
1 platform and proves that a best strategy for all self-interested
0 The proliferation of mobile smart devices has promoted participators can be centrally determined. However, since the
7
the development of Mobile Crowdsensing Systems (MCSs), purposeof this work is to stimulate user participation,and no
1
a promising paradigm for agile, fine-grained, and economical sensingdataqualityrelatedfactorisconsidered,itcannotsolve
:
v sensing with prominent spatial-temporal coverage [1]. Ex- the dishonest user reporting issue. An incentive mechanism
i
X ploring people-centric data collected by smart devices with thatencouragesqualitydatareportingisnecessarytoguarantee
enriched sensors (e.g. Global Positioning System, gyroscope the usability of MCSs.
r
a and microphone), a growing number of MCS prototypes In this paper,we developa novelCheating-ResilientIncen-
have been developed to support applications, including urban tive(CRI)schemeforMCSs,whichguaranteestheaccuracyof
sensing [2], environmental monitoring [3] and mobile social crowdsensingtaskswhileencouragingmobileuserstoprovide
networking [4]. quality data without cheating for maximum paybacks. Our
ObservingthatthedatasourceofMCSsisasetofpersonal contributions are summarized as follows:
mobile devices temporallyrecruited, the self-interested nature • Based on the participation-drivenincentivization in [14],
ofmobileusersneedstobetakenintoaccountforMCSimple- we develop a reputation-driven method for the MCS
mentations. From the perspective of mobile users considering server to recruit the most credible users autonomously
the potential costs (e.g. physical labor, device battery life, according to their historical behaviors. Meanwhile, re-
and network bandage usage), participation is unlikely in an cruited users can obtain maximum paybacks only when
MCS sensing task unless there was a considerable payback they contribute no less than expected. We demonstrate
under the rational person hypothesis. From the perspective of the correctness of our design with theoretical analysis.
the MCS server, the credibility of reported observations from • We introduce the truth discovery technique [15] into the
temporally recruited users is not guaranteed (i.e. users may user incentive issue to evaluate the actual contribution
cheat in sensing tasks just for the payback without reporting of recruited users in MCS tasks. The adaptive truth
quality data, which is referred to as Cheating Behavior in discoveryguaranteestheaccuracyofcrowdsensingwhile
To guarantee the quality of sensing reports, s needs to
considertwo issues: (i) theselection ofemployeesfora given
taskshouldbebasedontheapplicant’sreputation,and(ii)the
reputation adjustment and payback to an employee should be
based on their contribution in the currenttask. Therefore,our
user incentive scheme should solve the following problems:
1) How to define and manage the user reputation?
2) How to recruit employees based on their reputations?
3) How to evaluate an employee’s contribution?
4) How to quantify and maximize an employee’s payback
according to its contribution?
III. THE CHEATING-RESILIENTINCENTIVESCHEME
Fig. 1: System Architecture
Inthis section,we developthe Cheating-ResilientIncentive
(CRI) scheme to address the four problems outlined in Sub-
providing a baseline for user contribution evaluation.
section II-B. Before the construction of CRI, we first provide
• Through extensive trace-driven simulations, we evaluate the formal definitions of both user reputation and employee
the performance of CRI. The simulation results validate
payback.Then,we presenta mechanism for reputation-driven
the effectiveness of CRI with respect to both quality-
employeerecruitment.Afterthat,weaddresstheissue ofhow
driven user stimulation and cheating behavior resistance.
toevaluateanemployee’scontributionandhowtoquantifyand
The remainder of the paper is organized as follows: In maximizeanemployee’spaybackaccordingtoitscontribution.
Section II, we present the MCS model and the description
of the user incentive problem. In Section III, we present the A. Definitions
CRIschemeindetail.InSectionIV,wepresenttheevaluation In this paper, we formally define user reputation and em-
results of our designed scheme via extensive trace-driven ployee payback as follows.
simulations. Finally, we conclude this paper in Section V. User Reputation: Intuitively, we treat the reputation as a
user’s credibility for offering quality reports. In fact, once
II. SYSTEMMODEL AND PROBLEMDEFINITION recruited, the user is responsible for providing an actual
contributiontothe sensingtask inproportiontoits reputation.
A. MCS Architecture
For any mobile user i ∈P, the reputation of i, denoted as
As shown in Figure 1, a general MCS consists of a
r (06r 61), will be adjusted by s wheneveri participates
i i
cloud server s and a set of registered mobile users P =
in a sensing task τ by
{1,2,...,M}, where M > 2. Any user i ∈ P can com-
c
municate with s via either cellular or WiFi access points. r =αr′ +(1−α) i, (1)
i i c¯
For a specific sensing target, s can announce a task τ to i
all users in P. Receiving the announcement, any user i ∈ P where r′ denotes the reputation of i before it participates in
i
who is interested in τ can reply with its reputation r as an τ, c¯ denotes i’s expected contribution to τ (estimated by s
i i
application,which reflectsits behaviorin historicaltasks. Ac- considering r′, discussed latter), c denotes i’s actual contri-
i i
cording to all applications received, s can determine the total bution to τ (evaluated by s, discussed latter), and 06α61
reward R and the set of applicants E = {1,2,...,N} ⊆ P determines the sensitivity of i’s reputation adjustment. After
to be the final employees of τ. All employees ∈ E then the user’s registration, s issues any i ∈ P a value r as its
0
conduct sensing obligations required by τ and report their initial reputation.
observations O ={o ,o ,...,o } to s. Based on all reports Employee Payback: Intuitively, it is natural to determine
1 2 N
received,s can discoverτ’s sensing truth o , and evaluatethe an employee’s payback according to its contribution. Also,
τ
contributionC ={c ,c ,...,c }ofallemployeesseparately, considering that the reputation of an employee only denotes
1 2 N
which determines their paybacks G = {g ,g ,...,g } for τ the probability of its good behavior, we take the potential
1 2 N
and corresponding reputation adjustments. quality risk into account for the payback determination.
Inspired by the sensing time-driven model in [14], for an
B. User Incentive Problem employee i ∈ E of task τ, we define its reputation-driven
payback g as
The purposeof incentivemechanismsin MCSs is to stimu- i
late mobile user activity throughpaybacksfor participatingin c 1−r
g = i R− ic , (2)
sensing tasks. As the decision maker, s prefers to maximize i c r i
paybacks of the most contributive users as stimulation, while Pj∈E j i
minimizing the effect of potential cheating behaviors (e.g. where R > 0 denotes the total reward of τ. For a fixed R,
intentionally reporting random data just for the payback), to gi is subjectedto boththe ratio of i’s contribution, Pjc∈iEcjR,
achieve accurate sensing. and the potential quality risk for recruiting i, 1−ririci.
Algorithm 1: Reputation-Driven Employee Recruitment
Input:
ROu:trpeuput:tations of ∀i∈P; cg¯¯ii==qPjr∈c¯iiERc¯Pj1Rj−∈Er−i\{i1}−rc¯irjic¯−i P>j0∈.E\{i}c¯j >0; (4)
E: recruited employees;
C¯: expected contributions of ∀i∈E. To allow s to autonomously recruit as many credible users
as possible with a fixed total reward, we developAlgorithm1
1 put ∀ri ∈R in descending order; basedontheNEcomputationalgorithmintheSTDgame[14]
2 C¯←∅, E ←{1,2}, i←3; to determine final employees just based on their reputations.
3 while i6|P| && ri > P |1E−|r−j1+|E|−1 do Here,allemployeesrecruitedwillhavethemaximumpayback
j∈E rj only if they contribute as expected.
4 E ←E ∪{i};
According to [14], we have Propositions 1 and 2 for
5 i++;
Algorithm 1 as follows.
6 for ∀i∈P do Proposition1.Anyi∈P thatisnotrecruitedbyAlgorithm
7 if i∈E then 1 gets the maximum payback by not participating in τ.
8 c¯i = P(|jE∈|E−11−)rRjrj (1− (P|Ej|−∈E1)11−r−rjrirji); 1 cParnopoobstiatiinonth2e.mAanxyimiu∈mEpatyhbaatckisornelcyruifiteitdcboyntArilbguotreisthams
9 else expected in τ.
10 c¯i =0; Because of the page limitation, please refer to Theorem 1
11 C¯←C¯∪c¯i; and 2 in [14] for specific proofs.
Until now, s can recruit the most credible employees E
12 return E, C¯; and compute their expected contributionsC¯only based on all
receivedapplicationsR,andC¯istreatedasoneofthemetrics
for the payback determination.
Based on the definitions above, we develop CRI, which
C. Contribution Evaluation
consists of three components: (i) Employee Recruitment, (ii)
Contribution Evaluation, and (iii) Payback Determination. In the following, we address the issue of how to evaluate
an employee’s contribution. After employee recruitment, s
B. Employee Recruitment announces a detailed task description of τ to all i ∈ E. The
employees then reply corresponding reports containing their
We now address the issue of how to recruit employees
observationsO ={o ,o ,...,o }1. Based on O, s discovers
based on their reputations. After the announcementof task τ, 1 2 N
the sensing truth o , and evaluates the actual contributions
receiving all applications R = {r ,r ,...,r } (see Subsec- τ
1 2 M C ={c ,c ,...,c } of all i∈E.
tion II-A),spreferstorecruitemployeeswithtopreputations. 1 2 N
Because there is no ground truth in our MCS scenario, s
Nonetheless, according to Equation (2), it is possible that an
needs to discover the sensing truth o , based on O, as the
applicant could obtain no payback if the total reward budget τ
baseline to evaluate employees’ actual contributions. Consid-
is low. Therefore, s needs to determine the final employees
considering their expected paybacks G¯ = {g¯ ,g¯ ,...,g¯ }, ering the potential conflict in reported observations and the
which are determined by their expected cont1ribu2tions C¯M= difference in employee reputations, we develop Algorithm 2
based on the general truth discovery framework in [15] to
{c¯ ,c¯ ,...,c¯ }.
1 2 M allow s to compute o and to evaluate actual employee
For effectivestimulations,it is necessaryfor s to maximize τ
contributions C at the same time.
theexpectedpaybackthatawell-behavingemployeecanhave.
AccordingtoAlgorithm2,eachactualcontributionc ∈C is
According to Equation (2), it is easy to get that g is second- i
i subjected to both the distance between o and o , and i’s rep-
ordercontinuousdifferentiableonc¯,andthesecondderivative i τ
i utation r . We treat observations from employees with higher
of g on c¯ is: i
i i reputations as more credible, and an employee’s contribution
will be higher if its observationis closer to o . C is treated as
2R c¯ τ
g¨(c¯)=− Pj∈P\{i} j. (3) another metric for the payback determination.
i i ( c¯ )3
Pj∈P j
D. Payback Determination
When c¯ > 0, there is g¨(c¯) < 0, and g (c¯) is a concave
i i i i i We now answer the question about how to quantify and
function.Therefore,g (c¯)hasauniquemaximumvaluewhen
i i maximize an employee’s payback according to its contri-
|P| > 2. We call such a value g¯ as i’s expected payback,
i bution. Based on the expected contributions C¯ and the ac-
which can be calculated whenever g˙ (c¯) = 0,c¯ > 0 has a
i i i tual contributions C, s can determine the final paybacks
solution.
Therefore, from the perspective of s, any i ∈ P that is 1Wetreateachemployee’staskobservationasasingle-dimensionvaluefor
finally recruited should satisfy following restrictions: concision, whichcanbeexpanded indifferent sensingscenarios.
Algorithm 2: Weighted Sensing Truth Discovery [16]. In the following,we firstpresentthesimulation settings,
Input: and then show the evaluation results.
O: observations of ∀i∈E;
R : reputations of ∀i∈E; A. Simulation Settings
E
ǫ: convergencethreshold;
According to Rometrace, we constructed an MCS with a
Output:
cloud server and 366 registered users. All users possessed
o : discovered sensing truth;
τ outdoor temperature data opportunistically collected within
C: actual contributions of ∀i∈E.
24 hours. The server spontaneously announced temperature
1 compute the standard deviation of O: stdO; sensing tasks to the users. After receiving an announcement,
2 initialize oτ as a random value; a user who possessed data collected within ±60 seconds
3 initialize ∀ci ∈C as 0; autonomously applied for the task, and then uploaded cor-
4 repeat responding report if it was recruited. The server provided
5 for ∀i∈E do paybacks and updated employee reputations based on CRI
(oj−oτ)2 during the simulation.
6 ci =log(Pj∈(oEi−osτtd)2Orj ); Fortheparametersettings,wesettheinitialreputationr0 =
stdOri 0.5 and α = 0.5 in Equation (1) for reasonable reputation
7 o′τ =oτ; bootstrapping and management. In addition, we set ǫ = 0.1
c o in Algorithm 2 as the truth discovery convergence threshold.
8 oτ = Pj∈E jc j; Again,accordingtoRometrace,eachroundofsimulationlasts
9 until |oτ −Poj′τ∈|E<jǫ; for 86400 simulation seconds.
We collected the following four metrics to evaluate the
10 for ∀ci ∈C do
ci impact of cheating behaviors2 on the MCS performance:
11 ci = ;
Pcj∈Ccj • DiscoveredTruth(DT)referstothesensingtruthdiscov-
12 return oτ, C; ered in a task, whose cumulative distribution reflects the
sensing accuracy. Ideally, CRI should be able to prevent
cheating behaviors from disrupting DT;
• Reputation (REP) refers to the user reputation, whose
G = {g ,g ,...,g } for all i ∈ E, and update employees’
1 2 N cumulative distribution reflects the user’s behavior in
reputations R according to their behaviors in τ.
E historicaltasks.Ideally,CRIshouldbeabletodowngrade
For all i∈E, to compare c¯ and c , we set the total reward
i i a cheater’s REP in proportion to its cheating intensity;
R = P(j|∈EE|−11−r)jrj to guarantee that c¯i is within the range of • Payback(PB)3 refersto whata userreceivesforaccom-
[0,1]. plishing sensing tasks, which reflects the motivation of
According to Equation (2), we set the user participatingin future tasks. Ideally,CRI should
be able to reduce the PB that a user can get if it cheats;
c¯ 1−r
i R− ic¯i, if ci >c¯i, • Task Count (TC) refers to the number of sensing tasks
c¯ r
gi =Pjc∈E j 1−ir (5) accomplished by a user, which reflects the popularity
i R− ic , if c <c¯. of the user. Ideally, CRI should be able to limit the
i i i
c¯ r
Pj∈E j i probability of a cheater participating in MCS tasks.
Also, according to Equation (1), s updates all ri ∈RE as: For comparison, we ran a round of simulation without
any cheating behavior as the baseline (i.e. the no cheating
αr′ +(1−α), if c >c¯,
i i i scenario).Then,weanalyzedtheimpactofcheatingbehaviors
ri =αri′ +(1−α)cc¯i, if ci <c¯i. (6) introduced by users with different properties. In following
i subsections, we depict the simulation results using either the
As demonstrated, CRI guarantees that (i) s can recruit a Cumulative Distribution Figure (CDF) or the Time-Variance
proper number of the most credible applicants for sensing Figure (TVF) for a distinct demonstration.
tasks, and (ii) recruited users can only obtain the maximum
paybackswhentheycontributenolessthanexpected.Cheating B. Impact of General Cheating Intensity
behaviors will reduce their paybacks and opportunities of
In this set of simulations, to study the impact of general
being recruited in future tasks.
cheating behaviors with different intensities, we set all users
IV. EVALUATION in the MCS to introduce cheating behaviors with different
To validate the performance of CRI in real-world MCSs,
we conducted extensive trace-driven simulations based on 2Weconsidereduser’scheatingbehaviorinsimulationsasreportingaran-
domobservation withintherangeof[2◦C,24◦C](according toRometrace).
OMNeT++4.6,usingreal-worldoutdoortemperaturedatacol-
3The payback of each task was normalized within the range of [0,1] for
lectedbytaxisinRome(hereinafterreferredtoasRometrace) effective comparisons.
1 1 1 1
No Cheating No Cheating No Cheating No Cheating
10% Cheating 10% Cheating Consistent Cheating Consistent Cheating
0.8 15% Cheating 0.8 15% Cheating 0.8 Intermittent Cheating 0.8 Intermittent Cheating
20% Cheating 20% Cheating
Probability00..46 Probability00..46 Probability00..46 Probability00..46
0.2 0.2 0.2 0.2
00 5 10 15 20 25 00 0.2 0.4 0.6 0.8 1 0 5 10 15 20 00 0.2 0.4 0.6 0.8 1
Temperature / oC Reputation Temperature / oC Reputation
(a)Discovered Truth(CDF) (b)Reputation (CDF) (a)Discovered Truth(CDF) (b)Reputation (CDF)
1 1 35 50
No Cheating No Cheating
30 Consistent Cheating Consistent Cheating
0.8 0.8 Intermittent Cheating 40 Intermittent Cheating
25
Probability00..46 Probability00..46 Payback1250 Task Count2300
No Cheating No Cheating 10
0.2 10% Cheating 0.2 10% Cheating 10
15% Cheating 15% Cheating 5
20% Cheating 20% Cheating
00 5 10 15 20 25 30 00 10 20 30 40 01 2 3 4 5 6 0 2 3 4 5
Payback Task Count Time / simulation second x 104 Time / simulation second x 104
(c)Payback(CDF) (d)TaskCount(CDF) (c)Payback(TVF) (d)TaskCount(TVF)
Fig. 2: Impact of General Cheating Intensity Fig. 4: Impact of Cheating Behavior of TopP User
1 No Cheating 1 No Cheating 1 No Cheating 1 No Cheating
Consistent Cheating Consistent Cheating Consistent Cheating Consistent Cheating
0.8 Intermittent Cheating 0.8 Intermittent Cheating 0.8 Intermittent Cheating 0.8 Intermittent Cheating
Probability00..46 Probability00..46 Probability00..46 Probability00..46
0.2 0.2 0.2 0.2
0 5 Temp1e0rature / oC15 20 00 0.2 R0.e4putati0o.6n 0.8 1 0 5 Temp1e0rature / oC15 20 00 0.2 R0.e4putati0o.6n 0.8 1
(a)Discovered Truth(CDF) (b)Reputation (CDF) (a)Discovered Truth(CDF) (b)Reputation (CDF)
1102 NCInoote nCrsmhiseittetaentnint tgC Chheeaatitningg 1102 NCInoote nCrsmhiseittetaentnint tgC Chheeaatitningg 3305 NCInoote nCrsmhiseittetaentnint tgC Chheeaatitningg 50 NCInoote nCrsmhiseittetaentnint tgC Chheeaatitningg
Payback 468 Task Count 468 Payback122505 Task Count234000
10
2 2 10
5
00 Tim2e / simul4ation seco6nd x 1048 00 Tim2e / simul4ation seco6nd x 1048 0 2 Time / 3simulatio4n secon5d x 104 02 Tim3e / simulat4ion secon5d x 104
(c)Payback (TVF) (d)TaskCount(TVF) (c)Payback(TVF) (d)TaskCount(TVF)
Fig. 3: Impact of Cheating Behavior of TopR User Fig. 5: Impact of Cheating Behavior of TopC User
probabilities (i.e. 10%, 15% and 20%)4 in all sensing tasks. on the average DT no more than 3.65% (i.e. 0.4◦C) is
The simulation result is illustrated in Figure 2.
introduced by general cheating behaviors with an intensity
According to Figure 2(a), compared with the baseline sce- up to 20%. Such an impact is nearly negligible considering
nario, the cumulative distribution of DT remains almost the the practical temperature sensing requirement. CRI manages
same in all cheating scenarios. We can see that a disturbance to effectivelyrestrictthe impactofgeneralcheatingbehaviors
on DT in both realistic and even harsher scenarios.
4Thesettingofthesecheatingintensities shouldbereasonableconsidering
According to Figure 2(b), (c), and (d), when the cheating
the well accepted fact that the MCS is a relatively good community with a
limited ratioofmalicious behaviors (e.g.4%in[17],or10%in[18]). intensity increases, user’s reputation, payback, and task count
are correspondingly downgraded for at least 1.64%, 3.44% analysis, we demonstrated the feasibility and correctness of
and 2.20%, respectively.CRI managesto reducethe cheater’s our design. To evaluate the performance of CRI in practical
probability of being recruited in future tasks by reducing its MCSs, we conducted extensive simulations based on real-
reputation and payback autonomously, which will inherently world crowdsensingdata. The results show that CRI manages
restrict user’s cheating intention. to guarantee the sensing accuracy under realistic cheating
intensities (up to 20% of total reports). Meanwhile, cheating
C. Impact of Cheaters with Different Properties
behaviors from users with selected advantages (i.e. higher
Inreal-worldMCSs,cheatingbehaviorsofmoretrustworthy reputations, more received paybacks, and more accomplished
oractiveusersmayposedeeperimpactsontheMCS’sperfor- tasks) can be effectively resisted as well. Our future work is
mance.Inthissetofsimulations,dependingonthesimulation todevelopaprivacy-preservingCRI,whichencourageshonest
resultofthebaselinescenario,weseparatelysetauserthat(i) userbehaviorswithoutjeopardizingsensitiveuserinformation
hadthehighestreputation(referredtoastheTopRcheater),(ii) including identities, locations, and living patterns.
received the most paybacks (referred to as the TopP cheater),
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