ARTÍCULO
TITULO

Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints

Thomas Janssen    
Maarten Weyn and Rafael Berkvens    

Resumen

Supply chain management requires regular updates of the location of assets, which can be enabled by low power wide area networks, such as Sigfox. While it is useful to localize a device simply by its communication signals, this is very difficult to do with Sigfox because of wide area and ultra narrowband nature. On the other hand, installing a satellite localization element on the device greatly increases its power consumption. We investigated using information about nearby Wi-Fi access points as a way to localize the asset over the Sigfox network, so without connecting to those Wi-Fi networks. This paper reports the location error that can be achieved by this type of outdoor localization. By using a combination of two databases, we could localize the device on all 36 test locations with a median location error of 39 m . This shows that the localization accuracy of this method is promising enough to warrant further study, most specifically the minimal power consumption.

 Artículos similares

       
 
Suleiman Abu Kharmeh, Emad Natsheh, Batoul Sulaiman, Mohammad Abuabiah and Saed Tarapiah    
Datasets used for artificial-neural-network and machine-learning applications play a vital role in the research and application of such techniques in solving real-life problems. The construction and availability of large datasets to be used in the off-li... ver más
Revista: Applied Sciences

 
Jin Peng, Chengming Liu, Haibo Pang, Xiaomeng Gao, Guozhen Cheng and Bing Hao    
With the rise of image manipulation techniques, an increasing number of individuals find it easy to manipulate image content. Undoubtedly, this presents a significant challenge to the integrity of multimedia data, thereby fueling the advancement of image... ver más
Revista: Applied Sciences

 
Liang Chen, Yuyi Yang, Zhenheng Wang, Jian Zhang, Shaowu Zhou and Lianghong Wu    
Underwater robot perception is a critical task. Due to the complex underwater environment and low quality of optical images, it is difficult to obtain accurate and stable target position information using traditional methods, making it unable to meet pra... ver más

 
Shenghan Zhou, Tianhuai Wang, Linchao Yang, Zhao He and Siting Cao    
This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as... ver más
Revista: Aerospace

 
Xinjian Xiang, Haibin Hu, Yi Ding, Yongping Zheng and Shanbao Wu    
This study proposes a GC-YOLOv5s crack-detection network of UAVs to work out several issues, such as the low efficiency, low detection accuracy caused by shadows, occlusions and low contrast, and influences due to road noise in the classic crack-detectio... ver más
Revista: Applied Sciences