{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:54:20Z","timestamp":1771037660734,"version":"3.50.1"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2015,4,24]],"date-time":"2015-04-24T00:00:00Z","timestamp":1429833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61300179, 61271041, and 61202436"],"award-info":[{"award-number":["61300179, 61271041, and 61202436"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2015,5,20]]},"abstract":"<jats:p>\n            Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most\n            <jats:italic>appropriate<\/jats:italic>\n            participants to collect the sensing data. On the one hand, participants\u2019 handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users\u2019 incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users\u2019 incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes.\n          <\/jats:p>","DOI":"10.1145\/2630074","type":"journal-article","created":{"date-parts":[[2015,4,28]],"date-time":"2015-04-28T12:43:57Z","timestamp":1430225037000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints"],"prefix":"10.1145","volume":"6","author":[{"given":"Bo","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing China"}]},{"given":"Zheng","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing China"}]},{"given":"Chi Harold","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Beijing Institute of Technology, Beijing China"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing China"}]},{"given":"Wendong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing China"}]}],"member":"320","published-online":{"date-parts":[[2015,4,24]]},"reference":[{"key":"e_1_2_1_1_1","series-title":"Lecture Notes in Computer Science","volume-title":"Efficient Global Minimization Methods for Variational Problems in Imaging and Vision","author":"Bae Egil","year":"2011","unstructured":"Egil Bae , Jing Yuan , Xue-Cheng Tai , and Yuri Boykov . 2011. 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