{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:25:46Z","timestamp":1760243146235,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2015,12,3]],"date-time":"2015-12-03T00:00:00Z","timestamp":1449100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41174158"],"award-info":[{"award-number":["41174158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SinoProbe-09-04","award":["201011081-4"],"award-info":[{"award-number":["201011081-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than                                        95               %                                  of the theoretical optimal throughput and                                        0               .               97                                  of fairness index especially in dynamic and dense networks.<\/jats:p>","DOI":"10.3390\/s151229800","type":"journal-article","created":{"date-parts":[[2015,12,3]],"date-time":"2015-12-03T11:12:09Z","timestamp":1449141129000},"page":"30221-30239","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6663-6576","authenticated-orcid":false,"given":"Chunyang","family":"Lei","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxia","family":"Bie","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gengfa","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Engineering, Macquarie University, Sydney 2109, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuekun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lopez-Iturri, P., Aguirre, E., Azpilicueta, L., Fernandez-Valdivielso, C., Matias, I., and Falcone, F. 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