A Machine-Learning Based Connectivity Model for ...

URL: http://ckan.iotlab.eu/dataset/3b96c98c-b5a4-4153-82ee-6c7cd6707232/resource/736b0a20-8c3a-430b-a028-03de93c45b75/download/amachine-learningbasedconnectivity.pdf

We evaluate the accuracy of a multivariate, non-parametric path loss model derived from 42,157,324 RSSI samples collected over one year from an environmental wireless sensor network. The 2218 links in the network span a 2000 km2 basin and are deployed in a complex environment, with large variations of terrain attributes and vegetation coverage. The model is based on an ensemble regression tree machine learning algorithm (Random Forest). We compare the accuracy of this model to several well-known canonical (Free Space, plane earth) and empirical propagation models (Weissberger, ITU-R, COST235). 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.

There are no views created for this resource yet.

Additional Information

Field Value
Last updated August 23, 2017
Created August 23, 2017
Format PDF
License Creative Commons Attribution Share-Alike
createdover 1 year ago
formatPDF
id736b0a20-8c3a-430b-a028-03de93c45b75
last modifiedover 1 year ago
on same domain1
package id3b96c98c-b5a4-4153-82ee-6c7cd6707232
position3
revision id8ce32e3c-2a80-488e-bb02-eb7d47058cdf
stateactive
url typeupload