skip to main content
research-article

Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model

Published:09 November 2021Publication History
Skip Abstract Section

Abstract

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

References

  1. Menatalla Abououf, Rabeb Mizouni, Shakti Singh, Hadi Otrok, and Anis Ouali. 2019. Multi-worker multi-task selection framework in mobile crowd sourcing. Journal of Network and Computer Applications 130 (2019), 52–62.Google ScholarGoogle ScholarCross RefCross Ref
  2. Menatalla Abououf, Hadi Otrok, Shakti Singh, Rabeb Mizouni, and Anis Ouali. 2020. A misbehaving-proof game theoretical selection approach for mobile crowd sourcing. IEEE Access 8 (2020), 58730–58741.Google ScholarGoogle ScholarCross RefCross Ref
  3. Menatalla Abououf, Shakti Singh, Hadi Otrok, Rabeb Mizouni, and Anis Ouali. 2019. Gale-Shapley matching game selection—A framework for user satisfaction. IEEE Access 7 (2019), 3694–3703.Google ScholarGoogle ScholarCross RefCross Ref
  4. Marco Anisetti, Claudio Agostino Ardagna, Ernesto Damiani, and Alessandro Sala. 2019. A trust assurance technique for Internet of Things based on human behavior compliance. Concurrency and Computation: Practice and Experience 33, 4 (2019), e5355.Google ScholarGoogle Scholar
  5. Rana Azzam, Rabeb Mizouni, Hadi Otrok, Anis Ouali, and Shakti Singh. 2016. GRS: A group-based recruitment system for mobile crowd sensing. Journal of Network and Computer Applications 72 (2016), 38–50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Rana Azzam, Rabeb Mizouni, Hadi Otrok, Shakti Singh, and Anis Ouali. 2018. A stability-based group recruitment system for continuous mobile crowd sensing. Computer Communications 119 (2018), 1–14. DOI:https://doi.org/10.1016/j.comcom.2018.01.012Google ScholarGoogle ScholarCross RefCross Ref
  7. Zhendong Bei, Zhibin Yu, Ni Luo, Chuntao Jiang, Chengzhong Xu, and Shengzhong Feng. 2018. Configuring in-memory cluster computing using random forest. Future Generation Computer Systems 79 (2018), 1–15.Google ScholarGoogle ScholarCross RefCross Ref
  8. Paolo Bellavista, Antonio Corradi, Luca Foschini, Asma Noor, and Alessandro Zanni. 2018. ParticipAct for smart and connected communities: Exploiting social networks with profile extension in crowdsensing systems. In Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking. 1–8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. David M. Blei and Peter I. Frazier. 2011. Distance dependent Chinese restaurant processes. Journal of Machine Learning Research 12 (Aug. 2011), 2461–2488. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Maja Bott and Gregor Young. 2012. The role of crowdsourcing for better governance in international development. Praxis: The Fletcher Journal of Human Security 27, 1 (2012), 47–70.Google ScholarGoogle Scholar
  11. Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16 (2002), 321–357. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Stefano Chessa, Michele Girolami, Luca Foschini, Raffaele Ianniello, Antonio Corradi, and Paolo Bellavista. 2017. Mobile crowd sensing management with the ParticipAct living lab. Pervasive and Mobile Computing 38 (2017), 200–214.Google ScholarGoogle ScholarCross RefCross Ref
  13. Carolina Crisci, Badih Ghattas, and Ghattas Perera. 2012. A review of supervised machine learning algorithms and their applications to ecological data. Ecological Modelling 240 (2012), 113–122.Google ScholarGoogle ScholarCross RefCross Ref
  14. David C. Funder and C. Randall Colvin. 1991. Explorations in behavioral consistency: Properties of persons, situations, and behaviors.Journal of Personality and Social Psychology 60, 5 (1991), 773.Google ScholarGoogle Scholar
  15. R. Michael Furr and David C. Funder. 2004. Situational similarity and behavioral consistency: Subjective, objective, variable-centered, and person-centered approaches. Journal of Research in Personality 38, 5 (2004), 421–447.Google ScholarGoogle ScholarCross RefCross Ref
  16. Xiaofeng Gao, Shenwei Chen, and Guihai Chen. 2020. MAB-based reinforced worker selection framework for budgeted spatial crowdsensing. IEEE Transactions on Knowledge and Data Engineering. Early access, May 4, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  17. Bin Guo, Yan Liu, Wenle Wu, Zhiwen Yu, and Qi Han. 2017. ActiveCrowd: A framework for optimized multitask allocation in mobile crowdsensing systems. IEEE Transactions on Human-Machine Systems 47, 3 (2017), 392–403.Google ScholarGoogle ScholarCross RefCross Ref
  18. Aymen Hamrouni, Hakim Ghazzai, Mounir Frikha, and Yehia Massoud. 2020. A spatial mobile crowdsourcing framework for event reporting. IEEE Transactions on Computational Social Systems 7, 2 (2020), 477–491.Google ScholarGoogle ScholarCross RefCross Ref
  19. Te Han, Dongxiang Jiang, Qi Zhao, Lei Wang, and Kai Yin. 2018. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control 40, 8 (2018), 2681–2693.Google ScholarGoogle ScholarCross RefCross Ref
  20. Y. Hou, N. Wu, M. Zhou, and Z. Li. 2017. Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 3 (2017), 517–530.Google ScholarGoogle ScholarCross RefCross Ref
  21. Upul Jayasinghe, Abayomi Otebolaku, Tai-Won Um, and Gyu Myoung Lee. 2017. Data centric trust evaluation and prediction framework for IoT. In Proceedings of 2017 ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K’17). IEEE, Los Alamitos, CA, 1–7.Google ScholarGoogle ScholarCross RefCross Ref
  22. Xing Jin, Mingchu Li, Xiaomei Sun, Cheng Guo, and Jia Liu. 2018. Reputation-based multi-auditing algorithmic mechanism for reliable mobile crowdsensing. Pervasive and Mobile Computing 51 (2018), 73–87.Google ScholarGoogle ScholarCross RefCross Ref
  23. Roswell H. Johnson and A. L. Bollens. 1927. The Loss Ratio method of extrapolating oil well decline curves. Transactions of the AIME 77, 01 (1927), 771–778.Google ScholarGoogle ScholarCross RefCross Ref
  24. Audun Josang and Roslan Ismail. 2002. The beta reputation system. In Proceedings of the 15th Bled Electronic Commerce Conference, Vol. 5. 2502–2511.Google ScholarGoogle Scholar
  25. Jiawen Kang, Zehui Xiong, Dusit Niyato, Shengli Xie, and Junshan Zhang. 2019. Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE Internet of Things Journal 6, 6 (2019), 10700–10714.Google ScholarGoogle ScholarCross RefCross Ref
  26. Qi Kang, Lei Shi, MengChu Zhou, XueSong Wang, QiDi Wu, and Zhi Wei. 2017. A distance-based weighted undersampling scheme for support vector machines and its application to imbalanced classification. IEEE Transactions on Neural Networks and Learning Systems 29, 9 (2017), 4152–4165.Google ScholarGoogle ScholarCross RefCross Ref
  27. Justin J. Levandoski, Mohamed Sarwat, Ahmed Eldawy, and Mohamed F. Mokbel. 2012. LARS: A location-aware recommender system. In Proceedings of the 2012 IEEE 28th International Conference on Data Engineering (ICDE’12). IEEE, Los Alamitos, CA, 450–461. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Haoyue Liu, MengChu Zhou, and Qing Liu. 2019. An embedded feature selection method for imbalanced data classification. IEEE/CAA Journal of Automatica Sinica 6, 3 (2019), 703–715.Google ScholarGoogle ScholarCross RefCross Ref
  29. Yan Liu, Bin Guo, Yang Wang, Wenle Wu, Zhiwen Yu, and Daqing Zhang. 2016. TaskMe: Multi-task allocation in mobile crowd sensing. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, New York, NY, 403–414. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Tagaram Soni Madhulatha. 2011. Comparison between k-means and k-medoids clustering algorithms. In Advances in Computing and Information Technology. Communications in Computer and Information Science, Vol. 198. Springer, 472–481.Google ScholarGoogle Scholar
  31. Tim Mareda, Ludovic Gaudard, and Franco Romerio. 2017. A parametric genetic algorithm approach to assess complementary options of large scale windsolar coupling. IEEE/CAA Journal of Automatica Sinica 4, 2 (2017), 260–272.Google ScholarGoogle ScholarCross RefCross Ref
  32. Suman Nath, Michel Goraczko, Jie Liu, and Azalia Mirhoseini. 2018. Optimizing task recommendations in context-aware mobile crowdsourcing. US Patent 9,911,088.Google ScholarGoogle Scholar
  33. Maryam Pouryazdan, Burak Kantarci, Tolga Soyata, Luca Foschini, and Houbing Song. 2017. Quantifying user reputation scores, data trustworthiness, and user incentives in mobile crowd-sensing. IEEE Access 5 (2017), 1382–1397.Google ScholarGoogle ScholarCross RefCross Ref
  34. Lingjun Pu, Xu Chen, Jingdong Xu, and Xiaoming Fu. 2017. Crowd foraging: A QoS-oriented self-organized mobile crowdsourcing framework over opportunistic networks. IEEE Journal on Selected Areas in Communications 35, 4 (2017), 848–862.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Sarvapali D. Ramchurn, Nicholas R. Jennings, Carles Sierra, and Lluis Godo. 2004. Devising a trust model for multi-agent interactions using confidence and reputation. Applied Artificial Intelligence 18, 9–10 (2004), 833–852.Google ScholarGoogle ScholarCross RefCross Ref
  36. Peter J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20 (1987), 53–65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Ahmed Ben Said, Abdelkarim Erradi, Azadeh Ghari Neiat, and Athman Bouguettaya. 2019. A deep learning spatiotemporal prediction framework for mobile crowdsourced services. Mobile Networks and Applications 24, 3 (2019), 1120–1133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Athanasios Salamanis, Dionysios D. Kehagias, Dimitrios Tsoukalas, and Dimitrios Tzovaras. 2019. Reputation assessment mechanism for carpooling applications based on clustering user travel preferences. International Journal of Transportation Science and Technology 8, 1 (2019), 68–81.Google ScholarGoogle ScholarCross RefCross Ref
  39. Sujan Sarker, Md Abdur Razzaque, Mohammad Mehedi Hassan, Ahmad Almogren, Giancarlo Fortino, and Mengchu Zhou. 2019. Optimal selection of crowdsourcing workers balancing their utilities and platform profit. IEEE Internet of Things Journal 6, 5 (2019), 8602–8614.Google ScholarGoogle ScholarCross RefCross Ref
  40. Mohamed Sarwat, Justin J. Levandoski, Ahmed Eldawy, and Mohamed F. Mokbel. 2014. LARS*: An efficient and scalable location-aware recommender system. IEEE Transactions on Knowledge and Data Engineering 26, 6 (2014), 1384–1399. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Ognjen Scekic, Mirela Riveni, Hong-Linh Truong, and Schahram Dustdar. 2018. Social Interaction Analysis for Team Collaboration. Springer, New York, NY, 2607–2621. DOI:https://doi.org/10.1007/978-1-4939-7131-2_257Google ScholarGoogle Scholar
  42. Veronique Sels, José Coelho, António Manuel Dias, and Mario Vanhoucke. 2015. Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem. Computers & Operations Research 53 (2015), 107–117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Abdulhadi Shoufan, Haitham M. Al-Angari, Muhammad Faraz Afzal Sheikh, and Ernesto Damiani. 2018. Drone pilot identification by classifying radio-control signals. IEEE Transactions on Information Forensics and Security 13, 10 (2018), 2439–2447.Google ScholarGoogle ScholarCross RefCross Ref
  44. Nguyen Binh Truong, Hyunwoo Lee, Bob Askwith, and Gyu Myoung Lee. 2017. Toward a trust evaluation mechanism in the social Internet of Things. Sensors 17, 6 (2017), 1346.Google ScholarGoogle ScholarCross RefCross Ref
  45. Eirini Eleni Tsiropoulou, Giorgos Mitsis, and Symeon Papavassiliou. 2018. Interest-aware energy collection and resource management in machine to machine communications. Ad Hoc Networks 68 (2018), 48–57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Jiangtao Wang, Feng Wang, Yasha Wang, Leye Wang, Zhaopeng Qiu, Daqing Zhang, Bin Guo, and Qin Lv. 2019. HyTasker: Hybrid task allocation in mobile crowd sensing. IEEE Transactions on Mobile Computing 19, 3 (2019), 598–611.Google ScholarGoogle ScholarCross RefCross Ref
  47. Jiangtao Wang, Feng Wang, Yasha Wang, Daqing Zhang, Brian Lim, and Leye Wang. 2018. Allocating heterogeneous tasks in participatory sensing with diverse participant-side factors. IEEE Transactions on Mobile Computing 18, 9 (2018), 1979–1991.Google ScholarGoogle ScholarCross RefCross Ref
  48. Jiangtao Wang, Yasha Wang, Leye Wang, and Yuanduo He. 2018. GP-selector: A generic participant selection framework for mobile crowdsourcing systems. World Wide Web 21, 3 (2018), 759–782. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Jiangtao Wang, Yasha Wang, Daqing Zhang, Jorge Goncalves, Denzil Ferreira, Aku Visuri, and Sen Ma. 2018. Learning-assisted optimization in mobile crowd sensing: A survey. IEEE Transactions on Industrial Informatics 15, 1 (2018), 15–22.Google ScholarGoogle ScholarCross RefCross Ref
  50. Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Haoyi Xiong, Chao Chen, Qin Lv, and Zhaopeng Qiu. 2018. Multi-task allocation in mobile crowd sensing with individual task quality assurance. IEEE Transactions on Mobile Computing 17, 9 (2018), 2101–2113.Google ScholarGoogle ScholarCross RefCross Ref
  51. Bing Xue, Mengjie Zhang, Will N. Browne, and Xin Yao. 2015. A survey on evolutionary computation approaches to feature selection. IEEE Transactions on Evolutionary Computation 20, 4 (2015), 606–626.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Harry Zhang. 2004. The optimality of naive Bayes. American Association for Artificial Intelligence 1, 2 (2004), 3.Google ScholarGoogle Scholar
  53. PeiYun Zhang, MengChu Zhou, and Giancarlo Fortino. 2018. Security and trust issues in Fog computing: A survey. Future Generation Computer Systems 88 (2018), 16–27.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!