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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 km2 basin and are deployed in a complex environment, with large variations of terrain attributes and vegetation coverage. Four candidate machine-learning algorithms...
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