Information Fusion in Smart Environments: Key hole in Industry 5.0

Information fusion is a multidisciplinary field that focuses on combining and integrating information from diverse sources to improve the accuracy, completeness, and reliability of the resulting information. It involves the process of merging data or knowledge from multiple sensors, databases, or information systems to generate a unified and coherent representation of the underlying reality. The main goal of information fusion is to extract meaningful and actionable insights by leveraging the strengths of individual information sources while compensating for their limitations, uncertainties, or redundancies. It aims to provide a more comprehensive and accurate understanding of a given situation or phenomenon than what can be achieved by using individual sources in isolation. Information fusion techniques typically involve various processes, including data preprocessing, feature extraction, data association, probabilistic modeling, decision-making, and knowledge representation. These processes may utilize methods from diverse disciplines such as statistics, signal processing, pattern recognition, artificial intelligence, machine learning, and cognitive science. Applications of information fusion are widespread and can be found in fields such as surveillance and intelligence, remote sensing, robotics, autonomous systems, medical diagnosis, weather forecasting, transportation systems, and cybersecurity. By integrating and interpreting information from multiple sources, information fusion enables improved situational awareness, decision-making, and prediction capabilities, leading to enhanced performance, efficiency, and reliability in complex and uncertain environments. Several Latin American problems could be solved by Information Fusion. We are looking to form a Forum to debate the usage of Information Fusion to produce solutions for the challenges in the region. The topics of Interest are: Theory and Representation Probability theory, Bayesian inference, argumentation, Dempster-Shafer theory, possibility and fuzzy set theory, rough sets, logic fusion, preference aggregation, decision theory, random sets, finite point processes and others. Algorithms Cognitive methods, signal processing and localisation, recognition, classification, identification, nonlinear filtering, data association, tracking, prediction, situation/impact assessment, alignment and registration, pattern/behavioural analysis, image fusion, fusion architectures, resource management, machine learning and artificial intelligence, topic modelling, natural language processing, contextual adaptation, anomaly/change detection. Application Soft-hard fusion, autonomous systems, defence/security, robotics, intelligent transportation, mining/manufacturing, wireless sensor networks, economics, finance, fintech, environmental monitoring medical care/e-health, bioinformatics, radio astronomy, critical infrastructure protection, condition monitoring precision agriculture, video streaming, streaming and sketching and other emerging applications. Methods/Tools Sequential inference, data mining. graph analysis, ontologies/semantics, modelling/realisation/evaluation, target/sensor modelling, benchmarks/testbeds, trust in fusion systems, computational methods, cloud/edge computing/fusion, fusion performance. This special issue aims to extend the contributions presented in the FUSION 2025 conference as well as bring new contributions on the use of Information Fusion techniques applied to the context of Industry 5.0. Industry 5.0 levereages the use of Cyber-phisical Systems, but in a human-centric and sustainable way. We will search for contributions of the use of Information Fusion (specially context-based Information Fusion) in this scenario.