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Truth discovery (TD) has been developed as an effective method for reducing data inconsistency and as a validity assessment for conflicting data from various sources. In addition, MCS applications and services are moving beyond a single individual participant to community groups and are influenced by group behavior. To address these challenges in this paper, we propose a novel Fog-assisted Group-based Truth Discovery Framework over MCS Data Streams, an efficient TD system for real-time applications. Specifically, we first initialized the weights for the weight update process in TD with the participants\u2019 credibility level. Then, we developed a novel Two-layer Group-based Truth Discovery (TGTD) mechanism in which the first layer estimates the truth of the group\u2019s members and the second layer estimates the aggregated truth for the groups. We have conducted extensive experiments over synthetic and real-world datasets to prove the effectiveness and efficiency of our framework. The results indicate that TGTD achieves superior truth discovery accuracy compared to current streaming truth discovery approaches, while maintaining a reasonable running time. The organization of the streaming process within the fog architecture simulation is identified as an area for further investigation and future work.<\/jats:p>","DOI":"10.1371\/journal.pone.0330656","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T17:40:14Z","timestamp":1756230014000},"page":"e0330656","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["A fog-assisted group-based truth discovery framework over mobile crowdsensing data streams"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6514-692X","authenticated-orcid":true,"given":"Bayan Hashr Saeed","family":"Alamri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Mostafa","family":"Monowar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suhair","family":"Alshehri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5637-4798","authenticated-orcid":true,"given":"Mohammad Haseeb","family":"Zafar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"issue":"6","key":"pone.0330656.ref001","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/MNET.2018.1700374","article-title":"Querying in Internet of Things with privacy preserving: challenges, solutions and opportunities","volume":"32","author":"H Ren","year":"2018","journal-title":"IEEE Network"},{"issue":"6","key":"pone.0330656.ref002","doi-asserted-by":"crossref","first-page":"5076","DOI":"10.1109\/JIOT.2018.2867113","article-title":"Verifiable outsourcing computation for matrix multiplication with improved efficiency and applicability","volume":"5","author":"S Zhang","year":"2018","journal-title":"IEEE Internet Things J"},{"key":"pone.0330656.ref003","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.cose.2016.11.014","article-title":"Achieving efficient and privacy-preserving truth discovery in crowd sensing systems","volume":"69","author":"G Xu","year":"2017","journal-title":"Computers & Security"},{"key":"pone.0330656.ref004","doi-asserted-by":"crossref","unstructured":"Ghaffaripour S, Miri A. 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