Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.

Explainability of artificial intelligence is an essential factor, which is required especially in critical domains e.g. legal requirements. The following research project focuses on an HR process, in which the quality and stability of several local XAI algorithms was tested. By increasing the number of repetitions of the algorithms, the results revealed that a more stable output with respect to the length of the confidence interval from feature importances could be achieved. In terms of the feature importance, by comparing the different algorithms, features importances could be strengthened/weakened and thus the respective relevances could be obtained.