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International Journal of Wireless and Microwave Technologies(IJWMT)

ISSN: 2076-1449 (Print), ISSN: 2076-9539 (Online)

Published By: MECS Press

IJWMT Vol.11, No.3, Jun. 2021

A Node Localization Algorithm based on Woa-Bp Optimization

Full Text (PDF, 594KB), PP.30-39


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Author(s)

Lang Fenghao, Sun Yun, Su Jun, Song Wenguang

Index Terms

Internet of things; indoor positioning; whale optimization algorithm; BP neural network.

Abstract

With the rapid development of 5G technology, the era of interconnection of all things has arrived. At the same time, a variety of hardware and software are getting more and more location information through sensors, and the accuracy of location information is increasingly important. Because traditional positioning relies on satellite signals, it achieves good results outdoors without obstruction, but indoors, due to the obstruction of various walls, such as Beidou satellite navigation system and U.S. Global Positioning System, it is difficult to meet the accuracy requirements for indoor positioning. Therefore, how to improve the positioning accuracy of indoor nodes has become a research hotspot in the field of wireless sensor. In order to improve the indoor positioning accuracy, this paper combines artificial neural network, intelligent optimization algorithm and node positioning to improve the accuracy of indoor positioning. One of the essences of the neural network is to solve the regression problem. Through the analysis of indoor node positioning, it can be concluded that the accuracy of distance-based positioning method lies in finding the relationship between signal strength and distance value. Therefore, the neural network can be used to regression analysis of signal strength and distance value and generate related models. In order to further improve the accuracy and stability of indoor node positioning, a method combining whale optimization algorithm with neural network is proposed. By using the whale optimization algorithm to find the optimal parameters of the neural network model, the training accuracy and speed of the neural network are improved. Then, using the excellent fitting ability of the neural network, the mapping relationship between RSSI value and distance value of indoor nodes is fitted, and the corresponding regression analysis model is generated, which can minimize the noise problem caused by abnormal signal attenuation and reduce the indoor positioning error. Finally, the data is processed by the neural network to get the parameters needed in the positioning algorithm. The experimental results show that the node positioning model based on the optimized neural network and the single optimization algorithm has significantly improved the positioning accuracy and stability.

Cite This Paper

Lang Fenghao, Sun Yun, Su Jun, Song Wenguang, " A Node Localization Algorithm based on Woa-Bp Optimization", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.11, No.3, pp. 30-39, 2021.DOI: 10.5815/ijwmt.2021.03.05

Reference

[1]Chen H , Guan W , Li S , et al. Indoor high precision three-dimensional positioning system based on visible light communication using modified genetic algorithm[J]. Optics Communications, 2018, 413:103-120.

[2]Lv Y , Liu W , Wang Z , et al. WSN Localization Technology Based on Hybrid GA-PSO-BP Algorithm for Indoor Three-Dimensional Space[J]. Wireless Personal Communications, 2020(2).

[3]Caso G, Nardis L D, Lemic F, et al. ViFi: Virtual Fingerprinting WiFi-based Indoor Positioning via Multi-Wall Multi-Floor Propagation Model[J]. IEEE Transactions on Mobile Computing, 2019, 19(6):1478-1491.

[4]Boney A. Labinghisa, Dong Myung Lee. Neural network‑based indoor localization system with enhanced virtual access points [J]. Journal of supercomputing, 2020.

[5]Afuosi M B, Zoghi M R. Indoor positioning based on improved weighted KNN for energy management in smart buildings[J]. Energy and Buildings, 212.

[6]Horn B K P. Doubling the Accuracy of Indoor Positioning: Frequency Diversity[J]. Sensors, 2020, 20(5):1489.

[7]Karakaya S, Ocak H. Low Cost Easy-to-Install Indoor Positioning System[J]. Journal of Intelligent and Robotic Systems, 2020(1).

[8]Sotenga P Z, Djouani K, Kurien A M, et al. Implementation of an indoor localisation algorithm for Internet of Things[J]. Future Generation Computer Systems, 2018: 107:1037-1046.

[9]S.Mirjalili, A.Lewis. The Whale Optimization Algorithm. Advances in Engineering Software, 2016, 95:51-67.

[10]Wentao F, Bing D. A Cauchy inverse whale optimization algorithm based on cross selection [J]. Journal of Ordnance Engineering, 2020(8).

[11]Guo H ,  Li M . Indoor Positioning Optimization Based on Genetic Algorithm and RBF Neural Network[C] 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2020.

[12]Cerar G , Vigelj A , Mohori M , et al. Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network. 2021.

[13]Xu Y , Sun Y,Neural Network-Based Accuracy Enhancement Method for WLAN Indoor Positioning[C] Vehicular Technology Conference. IEEE, 2012.

[14]Labinghisa B A , Dong M L . Neural network-based indoor localization system with enhanced virtual access points[J]. The Journal of Supercomputing, 2020(2).

[15]Wang X ,  Mao S . CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi[C] IEEE International Conference on Communications. IEEE, 2017:1-6.

[16]Zhang Y ,  Qu C ,  Wang Y . An Indoor Positioning Method Based on CSI by Using Features Optimization Mechanism With LSTM[J]. IEEE Sensors Journal, 2020, PP(99):1-1.

[17]Careem A A ,  Ali W H ,  Jasim M H . Wirelessly Indoor Positioning System based on RSS Signal[C] 2020 International Conference on Computer Science and Software Engineering (CSASE). 2020.

[18]Song X ,  Fan X ,  Xiang C , et al. A Novel Convolutional Neural Network Based Indoor Localization Framework with WiFi Fingerprinting[J]. IEEE Access, 2019, PP(99):1-1.

[19]Huang L ,  Gan X ,  Yu B , et al. An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite[J]. Sensors (Basel, Switzerland), 2019, 19(20).

[20]Dong L ,  Lv J . Research on Indoor Patrol Robot Location based on BP Neural Network[J]. IOP Conference Series: Earth and Environmental Science, 2020, 546(5):052035 (8p).

[21]Zhao B ,  Zhu D ,  Xi T , et al. Convolutional Neural Network and Dual-factor Enhanced Variational Bayes Adaptive Kalman Filter based Indoor localization with Wi-Fi[J]. Computer Networks, 2019, 162:106864.

[22]Adege A B ,  Lei Y ,  Lin H P , et al. Applying Deep Neural Network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm[C] 2018 IEEE International Conference on Applied System Invention (ICASI). IEEE, 2018.

[23]Choi E J , Yoo Y , Bo R P , et al. Development of Occupant Pose Classification Model Using Deep Neural Network for Personalized Thermal Conditioning[J]. Energies, 2019, 13(1):45.

[24]Bai J , Sun Y , Meng W , et al. Wi-Fi Fingerprint-Based Indoor Mobile User Localization Using Deep Learning[J]. Wireless Communications and Mobile Computing, 2021, 2021(7):1-12.

[25]Fernandes L , Santos S , Barandas M , et al. An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning[J]. Sensors, 2020.

[26]Zheng L , Hu B J , Qiu J , et al. A Deep Learning Based Self-Calibration Time-Reversal Fingerprinting Localization Approach on Wi-Fi Platform[J]. IEEE Internet of Things Journal, 2020, PP(99):1-1.