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![Institut Mines-Télécom
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](https://image.slidesharecdn.com/thesis-150305112041-conversion-gate01/85/PhD-Defense-Talk-Near-Optimal-Mobile-Crowdsensing-Design-Framework-and-Algorithms-9-320.jpg)
![Institut Mines-Télécom
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](https://image.slidesharecdn.com/thesis-150305112041-conversion-gate01/85/PhD-Defense-Talk-Near-Optimal-Mobile-Crowdsensing-Design-Framework-and-Algorithms-11-320.jpg)
![Institut Mines-Télécom
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](https://image.slidesharecdn.com/thesis-150305112041-conversion-gate01/85/PhD-Defense-Talk-Near-Optimal-Mobile-Crowdsensing-Design-Framework-and-Algorithms-78-320.jpg)
PhD Defense Talk - Near-Optimal Mobile Crowdsensing: Design Framework and Algorithms
The document discusses a PhD research project by Haoyi Xiong focused on mobile crowdsensing (MCS) to address air pollution monitoring through a framework and several algorithms that enhance energy efficiency, data quality, and participant incentives. It outlines technical contributions including energy-efficient mobile crowdsensing (EEMC), energy-efficient data transfer (EMC3), participant selection (CrowdRecruiter), and task allocation (CrowdTasker). The thesis aims to optimize the use of mobile phone sensors while improving the overall effectiveness of crowdsourced environmental monitoring.
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PhD Defense Talk - Near-Optimal Mobile Crowdsensing: Design Framework and Algorithms
- 1. 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
- 2. Institut Mines-Télécom Outline ■ Introduction •Motivation • Background & State-of-the Art ■ Technical Contribution • EEMC • EMC3 • CrowdRecruiter • CrowdTasker ■ Conclusion • Summary • Future Work 22 Jan 20152
- 3. Institut Mines-Télécom Outline ■ Introduction •Motivation • Background & State-of-the Art ■ Technical Contribution • EEMC • EMC3 • CrowdRecruiter • CrowdTasker ■ Conclusion • Summary • Future Work 22 Jan 20153
- 4. Institut Mines-Télécom Large-scale AirPollution 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
- 5. Institut Mines-Télécom Air PollutionMonitoring 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
- 6. Institut Mines-Télécom Mobile Phone-basedSensing GPS Sensor Temperature Sensor Air Quality Sensor Sensors Sensing Application Location Tracking Environment Sensing ……. 22 Jan 20156
- 7. Institut Mines-Télécom 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
- 8. Institut Mines-Télécom Background andState of the Art ■ Two Major MCS Players ■ Four Steps of MCS Process ■ Five (Common) Research Issues 22 Jan 20158
- 9. Institut Mines-Télécom Two MajorMCS 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
- 10. Institut Mines-Télécom Four Stepsof MCS Process 22 Jan 201510
- 11. Institut Mines-Télécom Five ResearchIssues [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
- 12. Institut Mines-Télécom Outline ■ Introduction •Motivation & Background • State-of-the Art ■ Technical Contribution • EEMC • EMC3 • CrowdTasker • CrowdRecruiter ■ Conclusion • Summary • Future Works 22 Jan 201512
- 13. Institut Mines-Télécom Our FourTechnical 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
- 14. Institut Mines-Télécom EEMC ResearchOutline ■ Motivation & Assumption ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201514
- 15. Institut Mines-Télécom EEMC (Energy-EfficientMobile 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
- 16. Institut Mines-Télécom EEMC Motivationand Assumption ■ Motivations • Reducing Individual Energy Consumption − Two-way Piggyback Crowdsensing using call opportunities • 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
- 17. Institut Mines-Télécom EEMC Example ■Please note: • future calls are not known in advance. • Only accumulated Call Traces are Accessible. − CBD—central business district 22 Jan 201517
- 18. Institut Mines-Télécom EEMC TechnicalChallenges – 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
- 19. Institut Mines-Télécom Framework ofEEMC A new phone call comes Predicting next calls If a further task assignment is needed? Current user or future user? 22 Jan 201519
- 20. Institut Mines-Télécom ■ Next-n-CallProbability • 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
- 21. Institut Mines-Télécom Dataset andEvaluation 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
- 22. Institut Mines-Télécom Results andComparison 22 Jan 201522 Ne: the expected number of sensor readings EEMC vs • Pace- 6%--23% fewer task assignment • Greedy- 27%--63% fewer task assignment
- 23. Institut Mines-Télécom Summary ofEEMC 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
- 24. Institut Mines-Télécom Open Issuesof 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
- 25. Institut Mines-Télécom EMC3 ResearchOutline ■ Research Overview ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201525
- 26. Institut Mines-Télécom EMC3 ResearchOverview Individual Energy Individual Incentive Overall Incentive MCS Data Quality Task Creation Pay per task assignment #result/cycle Full coverage 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.. Beyond EEMC 22 Jan 201526
- 27. Institut Mines-Télécom EMC3 ResearchProblem – 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
- 28. Institut Mines-Télécom EMC3 ResearchChallenges (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
- 29. Institut Mines-Télécom EMC3 Frameworkand 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
- 30. Institut Mines-Télécom Dataset andEvaluation Setups 22 Jan 201530 CBD Region Residential Region Statistics of CBD Call Trace Statistics of Residential Call Trace
- 31. Institut Mines-Télécom Results andComparison (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)
- 32. Institut Mines-Télécom Results andComparison (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
- 33. Institut Mines-Télécom Summary andOpen 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
- 34. Institut Mines-Télécom CrowdRecruiter ResearchOutline ■ Research Overview ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201534
- 35. Institut Mines-Télécom CrowdRecruiter ResearchOverview Individual Energy Individual Incentive Overall Incentive MCS Data Quality Task Creation Pay per Participant Partial Coverage Task Assignment Minimize Incentives under Specific MCS Data Quality Constraint Individual Task Execution One-way Piggyback Sensing using Calls Data Collection and Aggregation Individual Participants’ concerns.. MCS Organizer’ concerns.. The same task assignment objectives With different energy-saving, incentive, and data quality assumptions/settings 22 Jan 201535
- 36. Institut Mines-Télécom CrowdRecruiter Motivationand Assumptions ■ Motivations • Reducing Individual Energy Consumption − 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 • Individual Incentive Mechanism − Pay per participant • MCS Data Quality Requirement − Ensuring a predefined percentage of subareas (cell tower) being covered per sensing cycle. 22 Jan 201536
- 37. Institut Mines-Télécom CrowdRecruiter ResearchObjectives – 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
- 38. Institut Mines-Télécom CrowdRecruiter ResearchChallenges • 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
- 39. Institut Mines-Télécom CrowdRecruiter Framework Eachiteration • 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
- 40. Institut Mines-Télécom CrowdRecruiter CoreAlgorithms ■ 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
- 41. Institut Mines-Télécom Near-Optimality ofCrowdRecruiter ■ 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
- 42. Institut Mines-Télécom Dataset andEvaluation Setups ■ 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
- 43. Institut Mines-Télécom Results andComparisons 22 Jan 201543 CR. : CrowdRecruiter
- 44. Institut Mines-Télécom Results andComparisons 22 Jan 201544 CR. : CrowdRecruiter
- 45. Institut Mines-Télécom Temporal Coverageof CrowdRecruiter 22 Jan 201545
- 46. Institut Mines-Télécom Open Issuesof 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
- 47. Institut Mines-Télécom CrowdTasker ResearchOutline ■ Research Overview ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201547
- 48. Institut Mines-Télécom CrowdTasker ResearchOverview Individual Energy Individual Incentive Overall Incentive MCS Data Quality Task Creation Pay per 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 Individual Participants’ concerns.. MCS Organizer’ concerns.. Compared to Crowd Recruiter, new MCS data quality metrics, over incentive budget, individual incentive models, and new task assignment objectives 22 Jan 201548
- 49. Institut Mines-Télécom CrowdTasker Motivation ■Motivations • Reducing Individual Energy Consumption − 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
- 50. Institut Mines-Télécom 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 readings4 quality, 7 readings 5 quality − Overall Estimation: sum of the coverage quality of each subarea as a whole. 22 Jan 201550
- 51. Institut Mines-Télécom CrowdTasker ResearchChallenges • 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
- 52. Institut Mines-Télécom CrowdTasker Framework Inthe 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
- 53. Institut Mines-Télécom CrowdTasker CoreAlgorithms ■ 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
- 54. Institut Mines-Télécom Near Optimalityof 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.
- 55. Institut Mines-Télécom Evaluation Datasetand 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
- 56. Institut Mines-Télécom Results andComparisons 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
- 57. Institut Mines-Télécom Spatial Distributionof Sensor Readings 22 Jan 201557 Using CrowdTasker
- 58. Institut Mines-Télécom Outline ■ Introduction •Motivation & Background • State-of-the Art ■ Technical Contribution • EEMC • EMC3 • CrowdRecruiter • CrowdTasker ■ Conclusion • Summary • Future Work 22 Jan 201558
- 59. Institut Mines-Télécom Summary ofThesis ■ 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
- 60. Institut Mines-Télécom 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
- 61. Institut Mines-Télécom List ofPublications 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
- 62. Institut Mines-Télécom List ofPublications 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
- 63. Institut Mines-Télécom Two InvolvedProjects 22 Jan 201563 EU FP7 SOCIETIES Excellent project and Finalist of European Tech Cluster Leaders Awards EU FP7 MONICA
- 64.
- 65. Institut Mines-Télécom Examples ofMobile/Wearable Sensors 22 Jan 201565 UCSD CITISENS Lapka Sensaris
- 66. Institut Mines-Télécom Case Study(Ne=250, 16h~18h, 14 Dec 2011, Residential Traces) 22 Jan 201566
- 67. Institut Mines-Télécom Near-Optimality ofCrowdRecruiter ■ 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
- 68. Institut Mines-Télécom Progress ofParticipant Selection Each iteration means a new participant being selected Fastest Growth Fastest Convergence 22 Jan 2015
- 69. Institut Mines-Télécom Zoom-in the65—100th iterations Turning Point 22 Jan 2015
- 70. Institut Mines-Télécom Near Optimalityof 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
- 71. Institut Mines-Télécom EMC3 Motivationand Assumption ■ Motivations • Reducing Individual Energy Consumption − Two-way Piggyback Crowdsensing using call opportunities • Minimizing #task assignments in order to: − Reduce Overall incentive payment, while − Meeting MCS Data quality requirement (Full Coverage) ■ Assumptions • Individual Incentive Mechanism − 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
- 72. Institut Mines-Télécom EMC3 ContributionSummary Individual Energy Individual Incentive Overall Incentive MCS Data Quality Task Creation Pay per task assignment #result/cycle Full coverage Task Assignment Minimize Incentives under Specific MCS Data Quality Constraint Individual Task Execution Two-way Piggyback Sensing using Calls Data Collection and Aggregation Individual Participants’ concerns.. MCS Organizer’ concerns.. 22 Jan 201572
- 73. Institut Mines-Télécom CrowdRecruiter ContributionSummary Individual Energy Individual Incentive Overall Incentive MCS Data Quality Task Creation Pay per Participant Partial Coverage Task Assignment Minimize Incentives under Specific MCS Data Quality Constraint Individual Task Execution One-way Piggyback Sensing using Calls Data Collection and Aggregation Individual Participants’ concerns.. MCS Organizer’ concerns.. 22 Jan 201573
- 74. Institut Mines-Télécom CrowdTasker ContributionSummary Individual Energy Individual Incentive Overall Incentive MCS Data Quality Task Creation Pay per 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 Individual Participants’ concerns.. MCS Organizer’ concerns.. 22 Jan 201574
- 75. Institut Mines-Télécom Core Algorithmsof 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
- 76. Institut Mines-Télécom Core Algorithmsof 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
- 77. Institut Mines-Télécom Core Algorithmsof 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
- 78. Institut Mines-Télécom Four Stepsof 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