{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:44:59Z","timestamp":1760708699983,"version":"3.37.3"},"reference-count":20,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2014,5,1]],"date-time":"2014-05-01T00:00:00Z","timestamp":1398902400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61004112","CDJZR12180006"],"award-info":[{"award-number":["61004112","CDJZR12180006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["61004112","CDJZR12180006"],"award-info":[{"award-number":["61004112","CDJZR12180006"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2014,5,1]]},"abstract":"<jats:p> The abnormal activity detection in smart environments has experienced increasing attention over years, due to its usefulness in pervasive applications. In order to meet the real-time needs and overcome the high costs and privacy issues, this paper proposes distributed abnormal activity detection approach ( DetectingAct), which employs the computing and storage resources of simple and ubiquitous sensor nodes, to detect abnormal activity in smart environments equipped with wireless sensor networks (WSN). In DetectingAct, activity is defined as the combination of trajectory and duration, and abnormal activity is defined as the activity which deviates greater enough from those normal activities. DetectingAct works as follows. Firstly, DetectingAct finds the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopts unsupervised learning instead of supervised learning. Secondly, the distributed knowledge storage mechanism (DKSM) is introduced to store the mined patterns in each node. Then, the current triggered sensor adopts distributed abnormal activity detection algorithm (DAADA), in which the clustering analysis plays a critical role, to compare the present activity with normal activity patterns, by calculating the similarity between them. The feasibility, real-time property, and accuracy of the DetectingAct algorithm are evaluated using both simulation and real experiments case studies. <\/jats:p>","DOI":"10.1155\/2014\/283197","type":"journal-article","created":{"date-parts":[[2014,5,6]],"date-time":"2014-05-06T01:13:08Z","timestamp":1399338788000},"page":"283197","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Distributed Abnormal Activity Detection in Smart Environments"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0877-1064","authenticated-orcid":false,"given":"Chengliang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"},{"name":"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yayun","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3363-0020","authenticated-orcid":false,"given":"Debraj","family":"De","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Zhan","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2014,5,5]]},"reference":[{"key":"B1-2014-283197","doi-asserted-by":"publisher","DOI":"10.1007\/11752967_12"},{"first-page":"280","volume-title":"Proceedings of the International Conference on Artificial Intelligence (ICAI '08) and Proceedings of the International Conference on Machine Learning, Models, Technologies and Applications (MLMTA '08)","author":"Hasan M. 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