Use of wireless sensor networks for distributed event detection in disaster management applications

Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity

Annual Report 2019 of the Institute for Nuclear and Energy Technologies (KIT Scientific Reports ; 7759)

The annual report of the Institute for Nuclear and Energy Technologies of KIT summarizes its research activities and provides some highlights of each working group, like thermal-hydraulic analyses for fusion reactors, accident analyses for light water reactors, and research on innovative energy technologies: liquid metal technologies for energy conversion, hydrogen technologies and geothermal power plants. The institute has been engaged in education and training in energy technologies