Prasenjit Dhara is a Research Fellow at the Department of Information and Communication Technology within the University of Agder . He is affiliated with the CIEM - Centre for Integrated Emergency Management research group, focusing on ethical and safety challenges in technology-driven crisis scenarios. Research Focus : Prasenjit's work bridges Information and Communication Technology with Disaster Management , particularly investigating the implications of UAVs (Unmanned Aerial Vehicles) in emergency data collection. His research emphasizes ethical frameworks for drone usage in crises safety protocols for UAV-assisted operations data integrity in high-risk environments human-robot interaction during disasters Publications : His 2025 article at the International ISCRAM Conference highlights the intersection of robotics , emergency response , and data ethics . Contact : Email prasenjit.dhara@uia.no or call +47 37233179. Office located at A2122, Jon Lilletuns vei 9, 4879 Grimstad, Norway.
Professor John Markus Bjørndalen is affiliated with the Department of Informatics at UiT The Arctic University of Norway . He is a member of the Cyber Physical Systems (CPS) research group and actively contributes to the Distributed Arctic Observatory project. His work focuses on sustainable distributed systems for Arctic research. Research interests include: Cyber-Physical Systems (CPS) Internet of Things (IoT) for resource-constrained environments Machine learning applications in Arctic ecosystem monitoring Concurrency models like CSP Weather-aware energy optimization Distributed version control scalability Projects involve: Distributed Arctic Observatory (DAO) for sustainable research Improved resource control of fisheries using deep learning
Erik Næsset is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Science and Nature Management (MINA), affiliated with the Section for Renewable Energy and Forest Science. He leads research in forest inventory and monitoring through advanced remote sensing technologies, particularly airborne laser scanning (LiDAR). His work is central to the SkogRover research group, which develops innovative methods for assessing forest resources, biodiversity, and carbon storage. His research focuses on integrating three-dimensional LiDAR data with spectral information from aerial imagery and satellite sensors to improve forest characterization. Key areas include tree species classification, biomass estimation, change detection, and subalpine zone delineation. He emphasizes model-assisted and probability-based sampling approaches for robust forest assessments across scales. The available publications, including his supervision of Hans Ole Ørka’s 2011 PhD thesis, reveal a consistent trend toward enhancing forest inventory accuracy through multi-source remote sensing. His work spans applications from local forest management in Norway to international projects such as sample mapping in Tanzania, demonstrating both technical rigor and practical relevance. Scientific Awards: None listed in the provided text. Erik Næsset actively supervises PhD students and contributes to academic training through Nordic NOVA network courses. He has participated in numerous research projects and seminars related to forest planning, valuation, and decision support tools. His work often involves collaboration with national and international institutions, and he plays a key role in advancing methodological frameworks for national forest assessments. The SkogRover research group, which he is part of, operates under a dedicated website and engages in projects like BioDivAbove (biodiversity mapping from above) and REDD monitoring. The team utilizes UAVs, terrestrial laser scanning, and multispectral cameras to collect high-resolution data for ecological modeling and sustainable forest management.
Assistant Professor in the Faculty of Engineering and Science at University of Agder, Norway. Active researcher in machine learning, smart grids, and wireless communication systems with specific focus on energy applications and IoT technologies. Current institution: University of Agder Email: surender.redhu@uia.no Research Interests: Specializes in applying advanced machine learning techniques to energy systems and IoT networks. Works on electricity price forecasting models, smart home energy optimization with cognitive data fusion, and wireless sensor management in constrained environments. Recent Article Trends: 15 publications from 2021-2023 show consistent focus on temporal-spatial neural networks for energy prediction, UAV-based IoT powering strategies, and biomedical signal interpretation with deep learning systems.
