Abstract
<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">This paper aims to enhance the positioning accuracy of wireless sensor network (WSN) nodes. For this purpose, a WSN node positioning algorithm was proposed based on artificial bee colony (ABC) algorithm and the neural network (NN). First, the parameters between three anchor nodes and the target node were measured. Then, the ABC and NN were introduced to simulate and predict the ranging error, and the weight was determined according to the results. In the proposed algorithm, the cluster structure was effectively combined with the NN model. The weight of backpropagation NN was optimized by the ant colony optimization (ACO) algorithm. Then, the ACO-optimized NN was used to fuse the data collected by WSN nodes. The simulation results show that the proposed algorithm can improve the positioning accuracy of WSN nodes and reduce the time of the search. The research findings shed new light on the positioning of WSN nodes.</span>
Highlights
1.1 Wireless sensor network (WSN)The WSN has been successfully applied in such field as smart home, indoor positioning and environment monitoring, due to its integration of computer, radio and communication technologies
The effect of WSN applications lies in node positioning accuracy, which is affected by many external factors, such as walls, walking bodies and other obstacles
The WSN node positioning through artificial bee colony (ABC) optimized-neural network (NN) is implemented in four steps: first, the values of A and n are estimated; second, the NN is introduced to correct the estimated distances; third, the connection weights and thresholds of the NN are optimized by the ABC algorithm to obtain the distance weights; fourth, the location of the target node is calculated by the trilateration algorithm
Summary
The WSN has been successfully applied in such field as smart home, indoor positioning and environment monitoring, due to its integration of computer, radio and communication technologies. The effect of WSN applications lies in node positioning accuracy, which is affected by many external factors, such as walls, walking bodies and other obstacles. Compared with range-based algorithms, the range-free algorithms are simple, accuracy and widely used [8] This type of algorithms estimate internodal distance against the intensity of the transmitted signal and calculate the target node position by trilateration. The intensity of the received signal is usually determined by range-based algorithms. Some experts have improved the conventional received signal intensity positioning algorithm, aiming to eliminate the external noises and enhance the positioning accuracy [10]. The data fusion technique should be introduced to the WSN to process and retransmit the original data as necessary. Reference [15] developed an improved power-efficient gathering in sensor information system (PEGASIS) based on the NN and the ACO algorithm
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