A Novel Wireless Sensor Node Positioning Algorithm Based on Ant Colony Optimization Algorithm and Neural Network

Beichen Chen

Abstract


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.

Keywords


Wireless sensor network (WSN); neural network (NN); ant colony optimization (ACO) algorithm; data fusion; feature extraction

Full Text:

PDF



International Journal of Online Engineering (iJOE).ISSN: 1861-2121
Creative Commons License
Indexing:
Web of Science ESCI logo Engineering Information logo INSPEC logo DBLP logo ELSEVIER Scopus logo EBSCO logo Ulrich's logoGoogle Scholar logo Microsoft® Academic Search