文章基本信息

标题：Conserving Energy Through Neural Prediction of Sensed Data
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作者：Siamak Aram ; Ikramullah Khosa ; Eros Pasero 等
期刊名称：Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
印刷版ISSN：2093-5374
电子版ISSN：2093-5382
出版年度：2015
卷号：6
期号：1
页码：74-97
出版社：Innovative Information Science & Technology Research Group
摘要：The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors' data during their idle periods. The success of this method relies heavily on a high correlation between the points mak- ing a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors' idle periods and save over 65 percent of energy
关键词：Wireless sensor networks; Neural networks; Data prediction; Power Consumption