{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T23:18:12Z","timestamp":1771715892623,"version":"3.50.1"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,1,18]],"date-time":"2018-01-18T00:00:00Z","timestamp":1516233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1320149, CNS-1461963"],"award-info":[{"award-number":["IIS-1320149, CNS-1461963"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,5,31]]},"abstract":"<jats:p>\n            <jats:italic>Spatial Crowdsourcing (SC)<\/jats:italic>\n            is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task (e.g., reporting the precipitation level at their area and time). In this setting, there is often a\n            <jats:italic>budget<\/jats:italic>\n            constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as\n            <jats:italic>budget-per-time-period<\/jats:italic>\n            vs.\n            <jats:italic>budget-per-campaign<\/jats:italic>\n            and\n            <jats:italic>binary-utility<\/jats:italic>\n            vs.\n            <jats:italic>distance-based-utility<\/jats:italic>\n            . We study the hardness of the task assignment problem in the\n            <jats:italic>offline<\/jats:italic>\n            setting and propose\n            <jats:italic>online<\/jats:italic>\n            heuristics which exploit the spatial and temporal knowledge acquired over time. Our experiments are conducted with spatial crowdsourcing workloads generated by the SCAWG tool, and extensive results show the effectiveness and efficiency of our proposed solutions.\n          <\/jats:p>","DOI":"10.1145\/3078853","type":"journal-article","created":{"date-parts":[[2018,1,18]],"date-time":"2018-01-18T13:13:36Z","timestamp":1516281216000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":59,"title":["A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing"],"prefix":"10.1145","volume":"9","author":[{"given":"Luan","family":"Tran","sequence":"first","affiliation":[{"name":"University of Southern California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hien","family":"To","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3819-1098","authenticated-orcid":false,"given":"Liyue","family":"Fan","sequence":"additional","affiliation":[{"name":"University at Albany, SUNY, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cyrus","family":"Shahabi","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,1,18]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2016. iRain: new mobile App to promote citizen-science and support water management. Retrieved from http:\/\/en.unesco.org\/news\/irain-new-mobile-app-promote-citizen-science-and-support-water-management.  2016. iRain: new mobile App to promote citizen-science and support water management. Retrieved from http:\/\/en.unesco.org\/news\/irain-new-mobile-app-promote-citizen-science-and-support-water-management."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2015.55"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","volume-title":"Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques","author":"Chekuri Chandra","DOI":"10.1007\/11538462"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2550041"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/2794367.2794372"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864349.1864380"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2539150.2539243"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-016-0251-4"},{"key":"e_1_2_1_10_1","unstructured":"Bruce Dorminey. 2014. Crowdsourcing the weather. Retrieved from http:\/\/www.forbes.com\/sites\/brucedorminey\/2014\/02\/26\/crowdsourcing-as-the-future-of-weather-forecasting\/.  Bruce Dorminey. 2014. Crowdsourcing the weather. Retrieved from http:\/\/www.forbes.com\/sites\/brucedorminey\/2014\/02\/26\/crowdsourcing-as-the-future-of-weather-forecasting\/."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/285055.285059"},{"key":"e_1_2_1_12_1","volume-title":"Top-k team recommendation and its variants in spatial crowdsourcing. 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