Transmission power control of wireless sensor networks based on optimal connectivity. (August 2018)

• Record Type: Journal Article
• Authors: Han, Yefei; Bai, Guangwei; Zhang, Gongxuan
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 844
• Page End: 852
• Publication Date: 2018-08
• Subjects: Satellite networks -- Traffic prediction -- Spatiotemporal correlation -- Least square support vector machine; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.12.039
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: To develop a short-term traffic load prediction model for satellite networks, a prediction algorithm based on spatiotemporal correlation and least square support vector machine (STLS-SVM) is presented. The prediction model fully exploits the regularity and periodicity of satellite constellations and uses the lag correlation coefficients to determine which satellite pairs have the highest spatiotemporal correlation. Then, the traffic time sequences of the most highly correlated satellites are taken as input feature vectors for training the LS-SVM for short-term traffic prediction. A simulation test shows that the algorithm has higher network flow prediction accuracy and that using the spatiotemporal correlation improves the predictive performance.