Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network

被引:4
作者
Krishnamoorthy, Vinoth Kumar [1 ]
Duraisamy, Usha Nandini [2 ]
Jondhale, Amruta S. [3 ]
Lloret, Jaime [4 ]
Ramasamy, Balaji Venkatesalu [5 ]
机构
[1] New Horizon Coll Engn, Dept Elect & Elect Engn, Bengaluru 560103, Karnataka, India
[2] Sathyabama Inst Sci & Technol, Dept CSE, Chennai, India
[3] Pravara Rural Engn Coll, Dept Instrumentat & Control, Loni, India
[4] Univ Politecn Valencia, Valencia, Spain
[5] Sri Krishna Coll Engn & Technol, Dept ECE, Coimbatore, India
关键词
ALGORITHM; TRACKING;
D O I
10.1155/2023/1426430
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in the domain of location-based applications. Without the knowledge of location, the measurements obtained with WSN are of no use. The trilateration is a widely used technique to get location updates of target based on RSSI measurements from WSN. However, it suffers with high location estimation errors arising due to random variations in RSSI measurements. This paper presents a range-free radial basis function neural network (RBFN) and Kalman filtering- (KF-) based algorithm named RBFN+KF. The performance of the RBFN+KF algorithm is evaluated using simulated RSSIs and is compared against trilateration, multilayer perceptron (MLP), and RBFN-based estimations. The simulation results reveal that the proposed RBFN+KF algorithm shows very low location estimation errors compared to the rest of the three approaches. Additionally, it is also seen that RBFN-based approach is more energy efficient than trilateration and MLP-based localization approaches.
引用
收藏
页数:12
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