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Sci."],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In order to estimate the location of the user, the previous studies introduced many sensor deployment methods applying the sensor network. The important issues to consider when placing the sensors are a configuration cost and detection area of a sensor network. In other words, the sensors consisting the network should be optimally deployed by taking into account the coverage and connectivity of them. In this paper, a sensor signal is modeled as the Gaussian distribution based signal points group, and signal points in overlapping region between two different sensors are classified by <jats:italic>e<\/jats:italic>-SVM (support vector machine) method as each sensor group. In addition, a trilateration technique is used for estimating the position of a moving object. At this time, we efficiently deploy the sensors with <jats:italic>f<\/jats:italic>-Apriori method to maintain the connectivity between the sensors as well as to apply the trilateration. The proposed method can be utilized for optimal placement of sensors if we know a detection range of one sensor. In this paper, we introduce more effective and adaptive deployment method to consist the sensor network as taking into account the cost and the situation.<\/jats:p>","DOI":"10.1186\/s13673-017-0117-2","type":"journal-article","created":{"date-parts":[[2017,9,18]],"date-time":"2017-09-18T12:50:23Z","timestamp":1505739023000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["SensDeploy: efficient sensor deployment strategy for real-time localization"],"prefix":"10.1186","volume":"7","author":[{"given":"Jin-Hee","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byeong-Seok","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,11]]},"reference":[{"issue":"1","key":"117_CR1","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MPRV.2004.1269130","volume":"3","author":"J Burrell","year":"2004","unstructured":"Burrell J, Brooke T, Beckwith R (2004) Vineyard computing: sensor networks in agricultural production. 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