Unmanned aerial vehicle–assisted node localization for wireless sensor networks. (December 2017)

• Record Type: Journal Article
• Authors: Yang, Xu; Gao, Zhenguo; Niu, Qiang
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 12(2017)
• Publication Date: 2017-12
• Subjects: Wireless sensor network -- node identification and localization -- Bayesian model averaging; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717749818
• Languages: English
• ISSNs: 1550-1329

Abstract

Wireless sensor networks have been proposed for many location-dependent monitoring applications. Existing localization methods often suffer low accuracy and high energy consumption. In response to the above limitations, this article introduces a novel localization approach by virtue of a miniature unmanned aerial vehicle based on our previous IEEE 1851 project, which is named as unmanned aerial vehicle–assisted node localization. The localization process contains three stages, which are node image collection, non-occluded node localization, and occluded node localization, respectively. In the first stage, a unmanned aerial vehicle carrying Global Positioning System shoots the deployment area to collect node images. Then, in the non-occluded node localization phase, the convolutional neural network technique is employed to identify the nodes. And, their positions are determined through the graph matching or Bayesian model averaging approach. Finally, the localized non-occluded nodes start to act as the role of anchors. And, an improved received signal strength indicator–based algorithm is further designed to localize the occluded nodes. Extensive simulations are conducted to verify the effectiveness, in which the accuracy increases by 10%–30% compared with the state of the art methods. Moreover, we implement a prototype localization system with 49 CC2430 nodes and a miniature unmanned aerial vehicle. Results confirm its effectiveness for outdoor sensor networks.