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Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long\u2010term participation of users and high quality of tasks. This paper designs a long\u2010term incentive mechanism based on game theory. The long\u2010term incentive mechanism is to encourage participants to provide long\u2010term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game\u2010based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long\u2010term participation and high\u2010quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long\u2010term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright\u2010Fisher model in biology are used to analyze the evolution of participants\u2019 strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long\u2010term incentive effect for participants in mobile crowdsourcing systems.<\/jats:p>","DOI":"10.1155\/2021\/6695696","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T06:05:06Z","timestamp":1611813906000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Multistrategy Repeated Game\u2010Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7352-3000","authenticated-orcid":false,"given":"Chuanxiu","family":"Chi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0920-9305","authenticated-orcid":false,"given":"Yingjie","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1906-7112","authenticated-orcid":false,"given":"Yingshu","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4855-3723","authenticated-orcid":false,"given":"Xiangrong","family":"Tong","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2959258"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3012157"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3019443"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.04.018"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2975587"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000089"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2018.02.008"},{"key":"e_1_2_11_8_2","unstructured":"https:\/\/answers.yahoo.com\/ 2020."},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896912"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2941735"},{"key":"e_1_2_11_11_2","doi-asserted-by":"crossref","unstructured":"LiJ. 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