A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

Li Cao, Yong Cai, Yinggao Yue, Shaotang Cai, Bo Hang
2020 IEEE Access  
In order to effectively reduce the redundant information transmission in the network, a data fusion algorithm based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks is proposed. In this paper, the data fusion process of mobile heterogeneous wireless sensor networks is mainly studied, and regards the nodes of wireless sensor networks as neurons in the neural network of extreme learning machines. The neural network of the extreme learning
more » ... hine extracts the sensory data collected by mobile heterogeneous wireless sensor network and combines the collected sensor data with the clustering route to greatly reduce the amount of network data sent to the sink node. Aiming at the problem that the extreme learning machine randomly generates the input layer weight and the hidden layer threshold before training, the output result is unstable, affecting the data fusion efficiency and the long delay, a new method of data fusion for mobile heterogeneous wireless sensor networks based on extreme learning machine optimized by bat algorithm is proposed. Simulation experiments are carried out from two aspects: mobile heterogeneous wireless sensor networks and heterogeneous mobile heterogeneous wireless sensor networks. The simulation results show that compared with the traditional SEP algorithm, BP neural network algorithm and ELM algorithm, the proposed BAT-ELM-based data fusion algorithm can effectively reduce network traffic, save network energy, improve network work efficiency, and significantly prolong network's lifetime. INDEX TERMS Mobile heterogeneous wireless sensor networks, data fusion, extreme learning machine, bat algorithm, energy efficient, reliability. LI CAO received the B.S. degree in electrical automation from the North University of China, in 2009, and the M.S. degree in pattern recognition and intelligent systems from the Sichuan University of Science and Engineering, China, in 2013. She is currently pursuing the Ph.D. degree with the School of Information Engineering, Southwest University of Science and Technology. Her research interests include wireless sensor networks and signal processing. YONG CAI received the Ph.D. degree in electrical system control and information control from Southwest Jiaotong University, China, in 2006. Since 2005, he has been a Professor and the Dean of the School of Manufacturing Science and Engineering, Southwest University of Science and Technology. His research interests include mechanical engineering, rapid manufacturing, control information technology, and signal processing. YINGGAO YUE received the B.S. degree in automation from the Wuhan Institute of Technology, in 2009, the M.S. degree in pattern recognition and intelligent systems from the
doi:10.1109/access.2020.2967118 fatcat:y7oh5r23dngxxorrhxccmkc3au