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Although synchronization and localization depend on each other and have the similar process, they have been usually handled separately. In this paper, we suggest time synchronization and localization based on the semiperiodic property of seawater movement, called SLSMP. Firstly, we analyze error factors in time synchronization and localization and then propose a method to handle those errors. For more accurate synchronization, SLSMP controls the transmission instant by exploiting the pattern of seawater movement and node deployment. Then SLSMP progressively decreases the localization errors by applying the Kalman filter or averaging filter. Finally, INS (inertial navigation system) is adopted to relieve localization error caused by node mobility and error propagation problem. The simulation results show that SLSMP reduces time synchronization error by 2.5\u2009ms and 0.56\u2009ms compared with TSHL and MU-Sync, respectively. Also localization error is lessened by 44.73% compared with the single multilateration. <\/jats:p>","DOI":"10.1155\/2014\/172043","type":"journal-article","created":{"date-parts":[[2014,1,9]],"date-time":"2014-01-09T10:22:15Z","timestamp":1389262935000},"page":"172043","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["SLSMP: Time Synchronization and Localization Using Seawater Movement Pattern in Underwater Wireless Networks"],"prefix":"10.1177","volume":"10","author":[{"given":"Sungryul","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Pusan National University, Busan, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Younghwan","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Pusan National University, Busan, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2014,1,2]]},"reference":[{"key":"B1-2014-172043","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/701834"},{"key":"B2-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2006.1683469"},{"key":"B3-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2006.161"},{"key":"B4-2014-172043","doi-asserted-by":"publisher","DOI":"10.1145\/1410107.1410115"},{"key":"B5-2014-172043","doi-asserted-by":"publisher","DOI":"10.1145\/1868812.1868815"},{"key":"B6-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2011.6134447"},{"key":"B7-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2013.13"},{"key":"B8-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/ISCIT.2012.6380854"},{"key":"B9-2014-172043","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2009.08.005"},{"key":"B10-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/OCEANS.2004.1406519"},{"key":"B11-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/ROBIO.2004.1521826"},{"key":"B12-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2007.912142"},{"key":"B13-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2007.56"},{"key":"B14-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2008.081209"},{"key":"B15-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2010.158"},{"key":"B16-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/SECON.2012.6275793"},{"key":"B17-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/WASA.2007.143"},{"key":"B18-2014-172043","doi-asserted-by":"publisher","DOI":"10.4304\/jnw.5.1.28-38"},{"key":"B19-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/WAINA.2011.90"},{"key":"B20-2014-172043","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2010.5673086"},{"key":"B21-2014-172043","series-title":"Wiley Series in Probability and Statistics","volume-title":"Introduction to Linear Regression Analysis","author":"Montgomery D. 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