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

Nowadays, there is an increasing demand to provide real-time environment information such as air quality, noise level, traffic condition, etc. to citizens in urban areas for various purposes. The proliferation of sensor-equipped smartphones and the mobility of people are making Mobile Crowdsensing (MCS) an effective way to sense and collect information at a low deployment cost. In MCS, instead of deploying static sensors in urban areas, people with mobile devices play the role of mobile sensors to sense the information of their surroundings and the communication network (3G, WiFi, etc.) is used to transfer data for MCS applications...

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

  1. 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. 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. 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. 4. Institut Mines-Télécom 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
  5. 5. Institut Mines-Télécom 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
  6. 6. Institut Mines-Télécom Mobile Phone-based Sensing GPS Sensor Temperature Sensor Air Quality Sensor Sensors Sensing Application Location Tracking Environment Sensing ……. 22 Jan 20156
  7. 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. 8. Institut Mines-Télécom Background and State of the Art ■ Two Major MCS Players ■ Four Steps of MCS Process ■ Five (Common) Research Issues 22 Jan 20158
  9. 9. 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
  10. 10. Institut Mines-Télécom Four Steps of MCS Process 22 Jan 201510
  11. 11. 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
  12. 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. 13. Institut Mines-Télécom 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
  14. 14. Institut Mines-Télécom EEMC Research Outline ■ Motivation & Assumption ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201514
  15. 15. Institut Mines-Télécom 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
  16. 16. Institut Mines-Télécom EEMC Motivation and 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. 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. 18. Institut Mines-Télécom 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
  19. 19. Institut Mines-Télécom 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
  20. 20. Institut Mines-Télécom ■ 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
  21. 21. Institut Mines-Télécom 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
  22. 22. Institut Mines-Télécom 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
  23. 23. Institut Mines-Télécom 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
  24. 24. Institut Mines-Télécom 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
  25. 25. Institut Mines-Télécom EMC3 Research Outline ■ Research Overview ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201525
  26. 26. Institut Mines-Télécom EMC3 Research Overview 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. 27. Institut Mines-Télécom 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
  28. 28. Institut Mines-Télécom 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
  29. 29. Institut Mines-Télécom 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
  30. 30. Institut Mines-Télécom Dataset and Evaluation Setups 22 Jan 201530 CBD Region Residential Region Statistics of CBD Call Trace Statistics of Residential Call Trace
  31. 31. Institut Mines-Télécom 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)
  32. 32. Institut Mines-Télécom 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
  33. 33. Institut Mines-Télécom 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
  34. 34. Institut Mines-Télécom CrowdRecruiter Research Outline ■ Research Overview ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201534
  35. 35. Institut Mines-Télécom CrowdRecruiter Research Overview 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. 36. Institut Mines-Télécom CrowdRecruiter Motivation and 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. 37. Institut Mines-Télécom 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
  38. 38. Institut Mines-Télécom 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
  39. 39. Institut Mines-Télécom 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
  40. 40. Institut Mines-Télécom 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
  41. 41. Institut Mines-Télécom 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
  42. 42. Institut Mines-Télécom Dataset and Evaluation 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. 43. Institut Mines-Télécom Results and Comparisons 22 Jan 201543 CR. : CrowdRecruiter
  44. 44. Institut Mines-Télécom Results and Comparisons 22 Jan 201544 CR. : CrowdRecruiter
  45. 45. Institut Mines-Télécom Temporal Coverage of CrowdRecruiter 22 Jan 201545
  46. 46. Institut Mines-Télécom 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
  47. 47. Institut Mines-Télécom CrowdTasker Research Outline ■ Research Overview ■ Research Problems ■ Technical Challenges ■ Framework and Algorithms ■ Evaluation and Summary 22 Jan 201547
  48. 48. Institut Mines-Télécom CrowdTasker Research Overview 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. 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. 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 readings4 quality, 7 readings 5 quality − Overall Estimation: sum of the coverage quality of each subarea as a whole. 22 Jan 201550
  51. 51. Institut Mines-Télécom 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
  52. 52. Institut Mines-Télécom 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
  53. 53. Institut Mines-Télécom 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
  54. 54. Institut Mines-Télécom 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.
  55. 55. Institut Mines-Télécom 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
  56. 56. Institut Mines-Télécom Results and Comparisons 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. 57. Institut Mines-Télécom Spatial Distribution of Sensor Readings 22 Jan 201557 Using CrowdTasker
  58. 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. 59. Institut Mines-Télécom 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
  60. 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. 61. Institut Mines-Télécom 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
  62. 62. Institut Mines-Télécom 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
  63. 63. Institut Mines-Télécom Two Involved Projects 22 Jan 201563 EU FP7 SOCIETIES Excellent project and Finalist of European Tech Cluster Leaders Awards EU FP7 MONICA
  64. 64. Institut Mines-Télécom Q&A ■Thanks! 22 Jan 201564
  65. 65. Institut Mines-Télécom Examples of Mobile/Wearable Sensors 22 Jan 201565 UCSD CITISENS Lapka Sensaris
  66. 66. Institut Mines-Télécom Case Study (Ne=250, 16h~18h, 14 Dec 2011, Residential Traces) 22 Jan 201566
  67. 67. Institut Mines-Télécom 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
  68. 68. Institut Mines-Télécom Progress of Participant Selection Each iteration means a new participant being selected Fastest Growth Fastest Convergence 22 Jan 2015
  69. 69. Institut Mines-Télécom Zoom-in the 65—100th iterations Turning Point 22 Jan 2015
  70. 70. Institut Mines-Télécom 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
  71. 71. Institut Mines-Télécom EMC3 Motivation and 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. 72. Institut Mines-Télécom EMC3 Contribution Summary 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. 73. Institut Mines-Télécom CrowdRecruiter Contribution Summary 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. 74. Institut Mines-Télécom CrowdTasker Contribution Summary 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. 75. Institut Mines-Télécom 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
  76. 76. Institut Mines-Télécom 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
  77. 77. Institut Mines-Télécom 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
  78. 78. 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

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