@inproceedings{Kappelt2025:MachineLearning,

title = {Machine Learning for In-Network Spatial Localization within Wireless Mesh Networks},

author = {Peter Kappelt and Christian Leonard Vielhaus and Justus Rischke and Marek Sobe and Frank H. P. Fitzek},

year  = {2025},

date = {2025-06-08},

urldate = {2025-06-01},

booktitle = {IEEE International Conference on Communications (ICC)},

pages = {5.97},

address = {Montreal, Canada},

abstract = {Wireless localization is a crucial technology for determining the spatial 

position of nodes within a wireless network, with applications ranging from 

indoor navigation to asset tracking and robotic movement. However, 

traditional localization methods often rely on specialized hardware, while 

other localization methods suitable for commodity hardware tend to have 

limited accuracy. To improve localization accuracy, we propose models that 

integrate machine learning (ML) techniques for distance and position 

estimation. We observe improved localization performance by incorporating 

not only signal strength indicators from the physical layer, but also 

features specific to the mesh link layer of wireless mesh networks as model 

inputs. Furthermore, a proposed end-to-end machine learning localization 

model demonstrates promising performance by leveraging diverse network 

features for the direct inference of node position estimates. 

We evaluate the models using experimental data from a measurement campaign 

we conducted in an office environment hosting a wireless mesh testbed. Our 

method achieves an absolute position error of less than two meters. Using 

both information specific to mesh networks and novel machine learning 

methods yields improved performance, making our approach a promising 

solution for industrial applications.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}