{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:16:41Z","timestamp":1760217401267,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2015,9,15]],"date-time":"2015-09-15T00:00:00Z","timestamp":1442275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period.<\/jats:p>","DOI":"10.3390\/s150923218","type":"journal-article","created":{"date-parts":[[2015,9,16]],"date-time":"2015-09-16T10:33:36Z","timestamp":1442399616000},"page":"23218-23248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2883-0781","authenticated-orcid":false,"given":"Yongxuan","family":"Lai","sequence":"first","affiliation":[{"name":"School of Software, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinshan","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Longyan University, Longyan 364000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Huaqiao University, Xiamen 360000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghong","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Software, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S1570-8705(03)00003-9","article-title":"Data mules: Modeling and analysis of a three-tier architecture for sparse sensor networks","volume":"1","author":"Shah","year":"2003","journal-title":"Ad Hoc Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, X.Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A., and Wu, M.Y. 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