PhD Defense Talk - Near-Optimal Mobile Crowdsensing: Design Framework and Algorithms

Institut Mines-Télécom
Near-Optimal Mobile
Crowdsensing: Design
Framework and
Algorithms
PhD Student: Haoyi Xiong
Director of Thesis: Prof. Monique Becker
Advisors: Dr. Daqing Zhang,
Dr. Vincent Gauthier
22 Jan 2015
xhyccc@gmail.com, http://fr.linkedin.com/in/haoyixiong/1

Outline
■ Introduction
• Motivation
• Background & State-of-the Art
■ Technical Contribution
• EEMC
• EMC3
• CrowdRecruiter
• CrowdTasker
■ Conclusion
• Summary
• Future Work
22 Jan 20152

Outline
■ Introduction
• Motivation
• Background & State-of-the Art
• EEMC
• EMC3
• CrowdRecruiter
• CrowdTasker
■ Conclusion
• Summary
• Future Work
22 Jan 20153

Large-scale Air Pollution Monitoring
World-wide Air Pollution Crisis
• 7—8 million deaths a year (WHO statistics,
2012)
• Increasing risk of lung/bladder cancers
• Even worse in developing countries…
22 Jan 20154

Air Pollution Monitoring using
Traditional Sensor Network
■ Need to deploy expensive
sensors and network
22 Jan 20155
■ Few sensors are deployed
■ Many areas are not covered
16 sensors deployed in Ile-de-France area*
*http://www.eea.europa.eu/themes/air/air-quality/map/real-time-map

Mobile Phone-based Sensing
GPS Sensor
Temperature
Sensor
Air Quality
Sensor
Sensors
Sensing Application
Location Tracking
Environment Sensing
…….
22 Jan 20156

Mobile Crowdsensing (MCS)
Crowd Sensing
Collecting Sensed Results and
locations from Mobile Users
Executing Mobile sensing on each Mobile Phone
Fine-grained Air Quality Map
(e.g., UrbanAir, MSRA)
22 Jan 20157

Background and State of the Art
■ Two Major MCS Players
■ Four Steps of MCS Process
■ Five (Common) Research Issues
22 Jan 20158

Two Major MCS Players [Zhang et al.’14]
■ MCS Participants
• The mobile users receiving/performing sensing tasks, and
returning sensed results.
■ MCS Organizers
• The entity that recruit participants for MCS tasks, assign MCS
task to each participant, and collect sensed results.
*Zhang et al. 4W1H of Mobile Crowdsensing, IEEE Communication Magazine
22 Jan 20159

Four Steps of MCS Process
22 Jan 201510

Five Research Issues [Ganti et al. 2011]
■ From MCS Participants Perspectives
• Energy consumption caused by e.g., MCS data transfer, computing,
and sensing on mobile phones
• Individual Incentive Payment e.g., money paid for each user’s
participation
• Privacy e.g., protecting user’s location information (not included in this
thesis)
■ From MCS Organizers Perspectives
• MCS Data Quality e.g., accuracy, coverage of sensor readings
• Total incentive payment e.g., total money paid for all user’s
participation
*Ganti, et al. "Mobile crowdsensing: current state and future challenges."
Communications Magazine, IEEE 49.11 (2011): 32-39.
22 Jan 201511

Outline
■ Introduction
• Motivation & Background
• State-of-the Art
• EEMC
• EMC3
• CrowdTasker
• CrowdRecruiter
■ Conclusion
• Summary
• Future Works
22 Jan 201512

Our Four Technical Contributions
■ With above research issues in mind
• EEMC
− Enabling Energy-efficient Mobile Crowdsensing with Anonymous Participants
(Energy + Data Quality)
• EMC3
− Energy-efficient Data Transfer for Mobile Crowdsensing under Full Coverage
Constraint. (Energy + Data Quality)
• CrowdRecruiter
− Selecting Participants for Piggyback CrowdSensing Under Probabilistic
Coverage Constraint. (Energy + Incentive + Data Quality)
• CrowdTasker
− Allocating MCS task to Participants in order to Maximize Coverage Quality
Under Budget Constraint (Energy + Incentive + Data Quality)
22 Jan 201513

EEMC Research Outline
■ Motivation & Assumption
■ Research Problems
■ Technical Challenges
■ Framework and Algorithms
■ Evaluation and Summary
22 Jan 201514

EEMC (Energy-Efficient Mobile Crowdsensing)
Motivation and Assumption
■ Motivations
• Reducing Individual Energy Consumption
− Two-way Piggyback Crowdsensing using call opportunities
75% energy reduction in MCS data transfer, Numerinen et al. 2010
*Nurminen, Jukka K. "Parallel connections and their effect on the battery
consumption of a mobile phone.“,CCNC, 2010. IEEE, 2010.
22 Jan 201515

EEMC Motivation and Assumption
■ Motivations
• Minimizing #task assignments in order to:
− Reduce Total incentive payment, while
− Meeting MCS Data quality requirement
■ Assumptions
• Individual Incentive Mechanism
− Pay per task assignment
• MCS Data Quality Requirement
− Dividing MCS process  sensing cycles (e.g., two hours)
− Collecting at least a predefined number of sensed from the
target region every sensing cycle
22 Jan 201516

EEMC Example
■ Please note:
• future calls are not known in advance.
• Only accumulated Call Traces are Accessible.
− CBD—central business district
22 Jan 201517

EEMC Technical Challenges
– Online Next-Call-Prediction
• Predicting if the new-arriving caller/callee would place
another call in the current sensing cycle, using
accumulated call traces
– Pause or Continue?—Pace Control
• Deciding if tasks already assigned could ensure the
expected number of returning…
– Current User or Future users?—Optimal Task
Assignment Decision Making
• Deciding if assigning the task to current caller/callee or left
the task for future callers/callees
22 Jan 201518

Framework of EEMC
A new phone call comes
Predicting next calls
If a further task
assignment is needed?
Current user or
future user?
22 Jan 201519

■ Next-n-Call Probability
• Probability of user i placing n calls from time t of cycle k to the
end of cycle k
■ Already-Assigned-Fulfilling Probability
• P{Xk,t(Ak-Rk)≥Ne-|Rk|}—probability of participants already assigned
returning (at least) the expected number of results
■ Future-surer-Fulfilling Probability
• P{X*k,t(FSui⋃(Ak-Rk)≥Ne-|Rk|}—probability of users having not
placed calls but having higher probability of placing two calls in
the future of the cycle fulfilling the task
Core Algorithm of EEMC
22 Jan 201520

Dataset and Evaluation Setups
22 Jan 201521
Statistics of D4D Call Traces for Evaluation
Evaluation Region in Cote d'Ivoire
From D4D Data Set
Two Baseline Algorithms
• Greedy
• Keep assigning tasks to new calling users, until an
expected number of participants have returned their
sensed results
• Pace
• Consisting of the fist two steps of EEMC
• Keep assigning tasks to new calling users, until pace
control decides to stop assigning any new tasks

Results and Comparison
22 Jan 201522
Ne: the expected number of sensor readings
EEMC vs
• Pace- 6%--23% fewer task assignment
• Greedy- 27%--63% fewer task assignment

Summary of EEMC Contribution
Individual Energy Individual
Incentive
Overall
Incentive
MCS Data
Quality
Task Creation Pay per task
assignment
#result/cycle
Task Assignment Minimize Incentives under Specific
MCS Data Quality Constraint
Individual Task
Execution
Two-way Piggyback
Sensing using Calls
Data Collection
and Aggregation
Nothing here
Individual Participants’ concerns.. MCS Organizer’ concerns..
22 Jan 201523

Open Issues of EEMC
• MCS Data Quality:
− Data may be collected from some dense area only
− No coverage guarantee
• Individual Incentives:
− Using other (e.g., pay per participant) or multiple (e.g., both
pay per task/participant) incentive payment mechanisms
22 Jan 201524

EMC3 Research Outline
■ Research Overview
22 Jan 201525

EMC3 Research Overview
Incentive
Overall
Incentive
MCS Data
Quality
assignment
#result/cycle
Full coverage
Individual Task
Execution
Two-way Piggyback
Sensing using Calls
Data Collection
and Aggregation
Nothing here
Beyond EEMC
22 Jan 201526

EMC3 Research Problem
– Input
• Given the target region as a set of cell towers,
• Given a series of sensing cycles,
• Given the accumulated call traces and mobility traces of all participants;
– Problem
• When a participant places a call in the target region, Deciding if we need to
assign a task to the participant, in order to:
• minimize the total number of task assignments while
• Ensure a given number of participants returning their sensed
results in each sensing cycle AND each cell tower in the target
region being covered by at least one participant. (full
coverage constraint.)
22 Jan 201527

EMC3 Research Challenges (Beyond
EEMC)
■ Online Mobility Prediction
• Given a new-arriving user, predicting in which cell tower the
user will place next calls
■ Pause or Continue? Coverage-based Pace Control
• Given participants already assigned, Predicting if they will fully
cover the target region
■ Current user or Future users? Coverage-based Optimal
Task Assignment Decision Making
• Given participants already assigned and users having higher
probability placing two calls in future, predicting if they can
fully cover the target region
22 Jan 201528

EMC3 Framework and Algorithms
■ Mobility Prediction
• The probability of user i being in cell tower cj
when he/she placing a call
■ Coverage Prediction
• The probability of cell tower cl being covered by
user ui. (cassign is the cell tower of cl being
assigned with the task)
■ Covering Probability
• The probability of cell tower cl being covered by
users in Ak-Rk i.e., the participants already
assigned but having not yet returned
22 Jan 201529
EMC3 Framework

22 Jan 201530
CBD Region
Residential Region
Statistics of CBD Call Trace
Statistics of Residential Call Trace

Results and Comparison (CBD Traces)
22 Jan 201531
Number of Task Assignment and Returned Results using CDB Call Traces
Ne: the expected number of returned sensed results
Coverage using CDB Call Traces (on each cell tower)

Results and Comparison (Residential
Traces)
22 Jan 201532
Number of task assignments and Returned Results
Coverage of Returned Results (on each cell tower)
Average response time and
estimated maximal throughput

Summary and Open Issues of EMC3
• MCS Data Quality:
− Unnecessary to cover all cell towers in the target region
(85% might be enough)
• Task Assignment Mechanism, Can we
− Select a set of participants before the MCS process, and
− Allow selected participant performing PCS task and returning
results autonomously
• Individual Incentives:
− Using Pay per Participant settings?
22 Jan 201533

CrowdRecruiter Research Outline
22 Jan 201534

CrowdRecruiter Research Overview
Incentive
Overall
Incentive
MCS Data
Quality
Task Creation Pay per
Participant
Partial
Coverage
Individual Task
Execution
One-way Piggyback
Sensing using Calls
Data Collection
and Aggregation
The same task assignment objectives
With different energy-saving, incentive, and data quality assumptions/settings
22 Jan 201535

CrowdRecruiter Motivation and
Assumptions
■ Motivations
− One-way Piggyback Crowdsensing using call opportunities
• Minimizing #selected participants (Offline) in order to:
− Reduce overall incentive payment, while
− Meeting MCS Data quality requirement (partial coverage)
■ Assumptions
− Pay per participant
• MCS Data Quality Requirement
− Ensuring a predefined percentage of subareas (cell tower)
being covered per sensing cycle.
22 Jan 201536

CrowdRecruiter Research Objectives
– Input:
• Given the target region as a set of cell towers,
• Given a series of sensing cycles,
• Given the historical call/mobility traces of all volunteers;
– Problem
• Selecting a minimal subset of participants from all volunteers, in
order to:
• Ensure a predefined percentage of cell towers being covered by the
selected participants.
22 Jan 201537

CrowdRecruiter Research Challenges
• Call/Mobility Prediction using Historical Call/Mobility
Traces. Estimating the coverage achieved by a given set of
participants
• Lowering the complexity of participant set Search:
− NP-hardness of selecting the best participant set
meeting the probabilistic coverage goal
− Using local search algorithm (e.g., Adaptive Greedy) to
approximate the near-optimal participant set
• Proposing the appropriate participant selection metrics
and stopping criteria.
22 Jan 201538

CrowdRecruiter Framework
Each iteration
• Selecting an unselected user having the maximal utility (aka utility
function) when combing with users already selected.
Estimating the covering
probability of selected users
Returning if meeting
the coverage goal
22 Jan 201539

CrowdRecruiter Core Algorithms
■ Call/Mobility Prediction
• The probability of user u placing at least one call in cell tower t at sensing
cycle i.
■ Utility Calculation
• Utility is calculated as the expectation of number of cell towers being
covered by users in combined set S⋃{U}, where S is set of participants
already selected and U is an unselected user
■ Covering Probability Calculation
• The probability of a predefined number (i.e., T ) of cell towers
being covered by selected users in cycle i (NP-hardness)
22 Jan 201540
and

Near-Optimality of CrowdRecruiter
■ The Utility function i.e., Utility(S) is an submodular set
function
■ According to Nemhauser et al. 1978, The greedy-based
participant search process could achieve (1-e-1)
approximation of Utility maximization.
■ For example
• Supposing the greedy process runs 10 iterations and selects 10
users, and these 10 users could cover 63 cell towers in expectation.
• The best 10-user combination (through enumeration) can cover no
more than 100 cell towers in maximal.
22 Jan 201541
*Nemhauser et al. "Best algorithms for approximating the maximum of a submodular
set function." Mathematics of operations research. 1978

■ Baseline Algorithms (leveraging
the same adaptive greedy local
search process)
1. MaxMin
• Selecting the user having maximal
minimum of covering probabilities
among all cycles in each iteration
2. MaxCom
• Selecting the user having maximal
complementary with participants
already selected in each iteration
3. MaxCov
• Selecting the user covering the most
of cell towers in each iteration
22 Jan 201542
Evaluation Regions
• Business Region
• 45 cell towers
• Residential Region
• 86 cell towers
• Merged Region
• 131 Cell towers

Results and Comparisons
22 Jan 201543
CR. : CrowdRecruiter

22 Jan 201544
CR. : CrowdRecruiter

Temporal Coverage of CrowdRecruiter
22 Jan 201545

Open Issues of CrowdRecruiter
• MCS Data Quality
− Considering both the number of subareas being
covered and the number of sensor readings obtained
in each subarea
• Individual Incentive Model
− Considering both the payment to each participant and
the payment to each task assignment
• Task Assignment Objectives
− Maximizing Overall Data Quality under Incentive Budget
Constraint?
22 Jan 201546

CrowdTasker Research Outline
22 Jan 201547

CrowdTasker Research Overview
Individual
Energy
Individual
Incentive
Overall
Incentive
MCS Data Quality
Participant +
Task
Budget Partial Coverage +
#sensor readings
Task Assignment
Maximize MCS Data Quality
under Incentive Budget Constraint
Individual Task
Execution
One-way Piggyback
Sensing using Calls
Data Collection
and Aggregation
Compared to Crowd Recruiter, new MCS data quality metrics, over incentive
budget, individual incentive models, and new task assignment objectives
22 Jan 201548

CrowdTasker Motivation
■ Motivations
− One-way Piggyback Crowdsensing using call opportunities
• Selecting a set of users and determining in which cycle each
user should participate in the MCS task, in order to:
− Maximize overall MCS Data Quality, while
− Ensuring the total incentive payment not exceeding the
given budget
22 Jan 201549

CrowdTasker Assumptiom
• Base/Bonus Incentive Mechanism
− Base: Pay per participant (e.g., Ba = $50/user)
− Bonus: Pay per task assignment (e.g., Bo = $ 5/ task)
– E.g., for a user with 3 assigned tasks, 50+3*5= $65
• MCS Coverage Quality Metrics
− Threshold: each subarea is given a threshold of sensor
readings E, e.g.,5 readings
− Saturation: for each subarea, supposing x sensor readings
collected in the task, the quality of this area is min{x,E},
– 4 readings4 quality, 7 readings 5 quality
− Overall Estimation: sum of the coverage quality of each
subarea as a whole.
22 Jan 201550

CrowdTasker Research Challenges
• Lowering the complexity of user-cycle combination
set Search using the local search process
− A user-cycle combination identifying assign a task to the user
in the sensing cycle
• Designing a task allocation process which can
− Approximate the “real cost” of each participant and,
− Search the near-optimal set of user-cycle combinations
according to the estimated coverage quality and cost.
22 Jan 201551

CrowdTasker Framework
In the nth iteration, Using adaptive Greedy
• Selecting a set of user-cycle combinations
Xn maximizing utilityn(X) while ensuring
the budget cost C(Xn)≤ budget
Return Xn-1, if CQE (Xn) ≤ CQE(Xn-1), otherwise
update Utilityn+1 using Xn, go to next iteration
Estimating coverage
quality CQE(Xn)
22 Jan 201552

CrowdTasker Core Algorithms
■ Call/Mobility Prediction (same as CrowdRecruier)
■ Coverage Quality Estimation
• The expectation of coverage quality achieved by user-cycle combinations
selected in X, where A(Cu, i), identifies if user u is assigned a task in sensing
cycle i.
■ Utility Calculation
• For the first outer-loop iteration, the utility function is the margin of coverage
quality improved by selecting a new user-cycle combination <v,j>
• For the rest outer-loop iteration (nth, n>1), the Utility function ratio of coverage
improvement versus the cost of adding a new user-cycle combination, where
costn(v,j) is the “modular approximation” of the real cost C(X).
22 Jan 201553

Near Optimality of CrowdTasker
■ The Coverage Quality function i.e., CQE(X) and Overall
Incentive Cost function i.e., C(X) are submodular
■ According to lyer et al. 2013, the proposed nested-loop greedy
search process could achieve (α, 1-e-1) approximation of CQE
maximization under budget constraint i.e.,
• Max CQE(X) s.t. C(X) ≤ Budget
■ For example
• Given the settings of 10 euro for Base, 1 euro for bonus, supposing the tasks
allocated by CrowdTasker can acheve 630 overall coverage with 10000 euros
budget
• Then the optimal solution achieved by brute-force enumeration no more 1000
coverage quality with 10000*(10+1)/10= 11000 euros.
22 Jan 201554
*Iyer, Rishabh K., and Jeff A. Bilmes. "Submodular optimization with submodular
cover and submodular knapsack constraints." NIPS. 2013.

Evaluation Dataset and Setups
22 Jan 201555
■ Baseline Algorithms (leveraging the
single-loop adaptive greedy local
search, like CrowdRecruiter)
1. MaxCQE
• Selecting the user-cycle combination having the
maximal coverage quality improvement in each
iteration
2. MaxUtils
• Selecting the user-cycle combination having
maximal coverage quality improvement/cost ratio
(using “real cost”)
3. MaxEnum
• Enumerating all possible cycle combination for each
user, Selecting the cycle combination of an
unselected user having the maximal coverage
quality improvement/cost ratio
Evaluation Regions
• Business Region
• 45 cell towers
• Residential Region
• 86 cell towers
• Merged Region
• 131 Cell towers

22 Jan 201556
Incentives Settings
• Bo= 1, Ba = 10, 30, 50 and 70
Budget Settings
• B=10000,20000,30000
Coverage Quality Threshold
• E= 1, 3, and 5
Computation Time Comparison
Coverage Quality under budget constraints

Spatial Distribution of Sensor Readings
22 Jan 201557
Using CrowdTasker

Outline
■ Introduction
• Motivation & Background
• State-of-the Art
• EEMC
• EMC3
• CrowdRecruiter
• CrowdTasker
■ Conclusion
• Summary
• Future Work
22 Jan 201558

Summary of Thesis
■ Our research
• Studying four optimization problems in Mobile
Crowdsensing, addressing energy, incentives and data
quality issues
• Proposing a unified design framework (4 step approach)
and four optimization algorithms (EEMC, EMC3,
CrowdRecruiter and CrowdTasker), addressing four
different optimization objectives.
• Evaluating proposed framework/algorithms using large-
scale real-world mobility dataset, and verifying
effectiveness of our algorithms
22 Jan 201559

Future Work
■ Data Fusion and Processing
• E.g., inferring the sensor readings of uncovered areas using the
sensor readings obtained. (Compressive Crowdsensing!)
■ Considering Privacy in Mobile Crowdsensing
• Investigating different privacy preserved strategies
22 Jan 201560

List of Publications I (Crowdsensing)
■ Haoyi Xiong, Daqing Zhang, Guanling Chen, Leye Wang and Vincent
Gauthier, CrowdTasker: Maximizing Coverage Quality in Piggyback Crowdsensing under
Budget Constraint, In Proc. of 13th IEEE International Conference on Pervasive
Computing and Communications (PerCom'15), accepted, 2015. (AR: 15%)
■ Haoyi Xiong, Daqing Zhang, Leye Wang, Hakima Chaouchi, EMC3: Energy-efficient
Data Transfer in Mobile Crowdsensing under Full Coverage Constraint, IEEE
Transactions on Mobile Computing (TMC), preprinted online, 2014. (IF:2.912)
■ Haoyi Xiong, Daqing Zhang, Leye Wang, J.Paul Gibson and Jie Zhu, EEMC: Enabling
Energy-efficient Mobile Crowd-sensing with Anonymous Participants, ACM Transactions
on Intelligent Systems and Technology (TIST), in press, 2014. (IF: 9.39)
■ Daqing Zhang*, Haoyi Xiong*, Leye Wang and Guanling Chen, CrowdRecruiter:
Selecting Participants for Piggyback Crowdsensing under Probabilistic Coverage
Constraint, In Proc. of the 2014 ACM International Joint Conference on Pervasive
and Ubiquitous Computing (UbiComp'14), Seattle, WA. (*co-primary, AR: 12%)
■ Daqing Zhang, Leye Wang, Haoyi Xiong and Bin Guo. 4W1H in Mobile Crowd
Sensing. IEEE Communications Magazine (ComMag), 2014. (IF: 4.46)
■ Leye Wang, Daqing Zhang and Haoyi Xiong, effSense: Energy-Efficient and Cost-
Effective Data Uploading in Mobile Crowdsensing , PUCAA'13 with Ubicomp'13.
■ 22 Jan 201561

List of Publications II (Mobility Prediction)
■ Haoyi Xiong, Daqing Zhang, Daqiang Zhang, Vincent Gauthier, Kun Yang and
Monique Becker, MPaaS: Mobility Prediction as a Service in Telecom Cloud,
Information Systems Frontiers (ISF), 2014, Springer. (IF: 0.73)
■ Haoyi Xiong, Daqing Zhang, Daqiang Zhang and Vincent Gauthier, Predicting Mobile
Phone User Locations by Exploiting Collective Behavioral Patterns, In Proc. of the 9th
IEEE Conference on Ubiquitous Intelligence and Computing (UIC'12), Fukuoka,
Japan, 2012. (Best Paper Award, AR: 25%)
■ Daqiang Zhang, Daqing Zhang, Haoyi Xiong, Laurence T. Yang and Vincent
Gauthier, NextCell: Predicting Location Using Social Interplay from Cell Phone
Traces, IEEE Transactions on Computers (TC), preprinted, 2014. (IF: 1.473)
■ Daqiang Zhang, Daqing Zhang, Haoyi Xiong, Ching-Hsien Hsu and Athanasios
Vasilakos, BASA: Building Mobile Ad-Hoc Social Networks on Top of Android, IEEE
Network Magazine, 2014. (IF: 3.72)
■ Daqiang Zhang, Min Chen, Mohsen Guizani, Haoyi Xiong and Daqing Zhang, Mobility
Prediction in Telecom Cloud Using Mobile Calls, IEEE Wireless Communication
Magazine, 2014. (IF: 6.524)
22 Jan 201562

Two Involved Projects
22 Jan 201563
EU FP7 SOCIETIES
Excellent project and Finalist of European
Tech Cluster Leaders Awards
EU FP7 MONICA

Q&A
■Thanks!
22 Jan 201564

Examples of Mobile/Wearable Sensors
22 Jan 201565
UCSD CITISENS Lapka Sensaris

Case Study (Ne=250, 16h~18h, 14 Dec
2011, Residential Traces)
22 Jan 201566

Near-Optimality of CrowdRecruiter
■ The Utility function of CrowdRecruiter is an submodular
set function
■ The greedy-based participant search process could
achieve (1-e-1) approximation of Utility maximization.
■ For example
• Supposing the greedy process runs 10 iterations and selects 10
users, and these 10 users could cover 63 cell towers in
expectation.
• The best 10-user combination (through enumeration) can cover
no more than 100 cell towers in maximal.
22 Jan 201567

Progress of Participant Selection
Each iteration means a new participant being selected
Fastest Growth Fastest Convergence
22 Jan 2015

Zoom-in the 65—100th iterations
Turning Point
22 Jan 2015

Near Optimality of CrowdTasker
■ The Coverage Quality function and Overall Incentive Cost
function of CrowdTasker are submodular
■ The nested-loop greedy search process could achieve (α,
1-e-1) approximation of coverage quality maximization.
■ For example
• Given the settings of 10 euro for Base, 1 euro for bonus’
• Supposing the tasks allocated by CrowdTasker can acheve 630
overall coverage with 10000 euros budget
• Then the optimal solution achieve by brute-force enumeration
achieve no more 1000 coverage quality with 10000*(10+1)/10=
11000 euros.
22 Jan 201570

EMC3 Motivation and Assumption
■ Motivations
• Minimizing #task assignments in order to:
− Reduce Overall incentive payment, while
− Meeting MCS Data quality requirement (Full Coverage)
■ Assumptions
− Pay per task assignment
• MCS Data Quality Requirement (beyond EEMC)
− Splitting target region  cell towers (subareas)
− Ensuring each cell tower being covered by at least one sensed
result every sensing cycle.
22 Jan 201571

EMC3 Contribution Summary
Incentive
Overall
Incentive
MCS Data
Quality
assignment
#result/cycle
Full coverage
Individual Task
Execution
Two-way Piggyback
Sensing using Calls
Data Collection
and Aggregation
22 Jan 201572

CrowdRecruiter Contribution Summary
Incentive
Overall
Incentive
MCS Data
Quality
Participant
Partial
Coverage
Individual Task
Execution
One-way Piggyback
Sensing using Calls
Data Collection
and Aggregation
22 Jan 201573

CrowdTasker Contribution Summary
Individual
Energy
Individual
Incentive
Overall
Incentive
MCS Data Quality
Participant +
Task
Budget Partial Coverage +
#sensor readings
Task Assignment
Maximize MCS Data Quality
under Incentive Budget Constraint
Individual Task
Execution
One-way Piggyback
Sensing using Calls
Data Collection
and Aggregation
22 Jan 201574

Core Algorithms of EEMC I
■ Next-n-Call Probability Estimation
• Probability of user i placing n calls from time t of cycle k to the
end of cycle k
■ Online Poisson Intensity Estimation
• The Poisson intensity of user i’s calls in cycle k, based on the call
traces up to time t
22 Jan 201575

Core Algorithms of EEMC II
■ Already-Assigned-Fulfiling Probability Estimation
• Probability of N users already assigned tasks but having not yet
returned sensed results (at time t) returning their sensed results
before the end of cycle k (NP-hardness in Calculation)
• Note: when P{Xk,t(Ak-Rk)≥Ne-|Rk|} is lower than a given threshold,
EEMC decides that users already assigned tasks cannot guarantee
to return an expected number (Ne) of results then continues
assigning new tasks.
22 Jan 201576

Core Algorithms of EEMC III
■ Future-Surer-Fulfilling Probability Estimation
• Probability of N users from those, who have already assigned tasks
but having not yet returned sensed results (at time t) or who haven’t
placed any calls yet but have higher probability of placing at least two
calls (i.e., FSui), returning their sensed results before the end of cycle k
• Note: When this probability is higher than a threshold, EEMC decides
that sufficient number of better users will place at least two calls in the
future of cycle k, then drops current user and lefts tasks for future.
22 Jan 201577

Four Steps of MCS Process
• MCS Process [Zhang et al. ’14]
− Step 1. an MCS organizer proposes an MCS task in order to collect
sensed results in the given target region and time-frame
− Step 2. Given the mobile users who are willing to participate in the
MCS tasks, the MCS organizer selects a group of mobile users as
MCS participants.
− Step 3. During the MCS task timeframe, the MCS participants (a)
receive and perform the MCS tasks, and (b) return the sensed
results.
− Step 4. The MCS organizer collects/aggregates sensed results from
large crowds…analyzes…
*Zhang et al. 4W1H of Mobile Crowdsensing, IEEE Communication Magazine
22 Jan 201578

PhD Defense Talk - Near-Optimal Mobile Crowdsensing: Design Framework and Algorithms

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