Skip to Main content Skip to Navigation
New interface
Journal articles

A Machine-Learning Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments

Abstract : We evaluate the accuracy of a machine-learning-based path loss model trained on 42,157,324 RSSI samples collected over one year from an environmental wireless sensor network using 2.4 GHz radios. The 2218 links in the network span a 2000 km 2 basin and are deployed in a complex environment, with large variations of terrain attributes and vegetation coverage. Four candidate machine-learning algorithms were evaluated in order to find the one with lowest error: Random Forest, Adaboost, Neural Networks, and K-Neareast-Neighbors. Of the candidate models, Random Forest showed the lowest error. The independent variables used in the model include path distance, canopy coverage, terrain variability, and path angle. We compare the accuracy of this model to several well-known canonical (Free Space, plane earth) and empirical propagation models (Weissberger, ITU-R, COST235). Unlike canonical models, machine-learning algorithms are not problem-specific: they rely on an extensive dataset and a flexible model architecture to make predictions. We show how this model achieves a 37% reduction in the average prediction error compared to the canonical/empirical model with the best performance. The article presents a in-depth discussion on the strengths and limitations of the proposed approach as well as opportunities for further research.
Document type :
Journal articles
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download
Contributor : Thomas Watteyne Connect in order to contact the contributor
Submitted on : Saturday, February 16, 2019 - 10:51:12 PM
Last modification on : Tuesday, November 29, 2022 - 12:12:15 PM
Long-term archiving on: : Friday, May 17, 2019 - 2:09:27 PM


Files produced by the author(s)


  • HAL Id : hal-01571215, version 1



Carlos Oroza, Ziran Zhang, Thomas Watteyne, Steven D. Glaser. A Machine-Learning Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments. IEEE Transactions on Cognitive Communications and Networking, 2017. ⟨hal-01571215⟩



Record views


Files downloads