{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:13Z","timestamp":1740202033254,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>In wireless sensor networks (WSNs), the massive node density and the diverse sensory placement will produce huge amount of sensory traffic with redundancy. This will lead to the decline of scarce network resources and will decrease lifetime of WSNs. Data aggregation is a useful way to save energy consumption and prolong lifetime of sensor network. In this paper, we propose a radial basis function (RBF) neural network based data aggregation algorithm for wireless sensor networks. In this algorithm, an improved three-layer RBF neural network is firstly applied to each cluster where nodes in each cluster are used as input layer neuron to process the preliminary data. Secondly, processing results are transferred to the cluster head. Based on the function of hidden layer neuron and output layer neuron, cluster head waits further processing and finally sends the characteristic value to remote sink node. Extensive simulation results show that energy consumption is largely reduced, network lifetime is much prolonged, and the accuracy of data aggregation is improved in our proposed algorithm than some other popular algorithms.<\/jats:p>","DOI":"10.3233\/978-1-61499-828-0-428","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:26:53Z","timestamp":1740133613000},"source":"Crossref","is-referenced-by-count":0,"title":["A RBF Neural Network Based Data Aggregation Algorithm for Wireless Sensor Networks"],"prefix":"10.3233","author":[{"family":"Wang Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Wang Kai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Cao Yiquan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Younn Geumran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Kim Jeong-Uk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining III"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:08:36Z","timestamp":1740136116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-827-3&spage=428&doi=10.3233\/978-1-61499-828-0-428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-828-0-428","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}