Sensors for Smart Industry and Environment
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 943
Special Issue Editors
Interests: IoT; sensor data management; semantic web; multimodal machine learning on sensor data; resilient and trustworthy AI
Interests: ML-based virtual reality; explainable AI; personalization; sustainability; smart cities; human–computer interaction; ubiquitous computing
Special Issue Information
Dear Colleagues,
The focus of industry, research and even society as a whole is increasingly shifting from short-term economic interest towards sustainability. For example, the European Green Deal aims to boost the efficient use of resources by moving towards a clean, circular economy in order to stop climate change, revert biodiversity loss and reduce pollution. Therefore, measures need to be implemented in many areas, e.g., manufacturing, energy and mobility infrastructure, agriculture, and forestry, to name just a few. The adoption of digital technologies in these areas promises to be an accelerator. The guiding principle of the so-called twin transformation is that sustainability and digitalization complement each other.
This Special Issue is focused on new developments in the area of smart industry and environment through the use of sensor data, analytics and artificial intelligence (AI). It includes specific sensor technologies (e.g., electrochemical/odor sensors, hyper-/multispectral and satellite imaging), techniques and methods such as machine learning (ML), semantics/ontologies, digital twins, simulation, or virtual/augmented reality (VR/AR), as well as applications in industry (e.g., predictive maintenance, anomaly detection in SCADA networks), agriculture and forestry (e.g., plant health detection) or smart buildings/cities/regions (e.g., microclimate for health and well-being).
Dr. Torsten Priebe
Prof. Dr. Rui Neves Madeira
Guest Editors
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Keywords
- connectivity of IoT sensors, data collection from sensor systems
- semantics/ontology-based integration of (multi-) sensor data
- analytics, AI, (multimodal) machine learning on sensor data
- simulation and visualization with VR/AR and digital twins
- citizen science for sensor data in smart environments
- applications in smart industry and critical infrastructure
- applications in precision agriculture and forestry
- applications in smart buildings, cities and regions
- resilience, trustworthiness and explainability aspects
- sustainability effects of sensor-based systems and AI
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