Percorrer por autor "Sabiri, Khadija"
A mostrar 1 - 2 de 2
Resultados por página
Opções de ordenação
- Chain of Trust: Integrating IoT and Blockchain to Certify Wild Mushroom Growth QualityPublication . Rosse, Higor Vendramini; Camargo, Caio; Gonçalves, Estefânia; Sabiri, Khadija; Coelho, João PauloThis paper presents the preliminary results of the study developed within the scope of the ”Safe2Taste” project presenting the main concept of hardware and software architectures. The project aims to build a monitoring system supported by IoT technologies and covered by a security layer through interaction with a blockchain network to be applied in an edible mushroom production system. The hardware and software components were validated in the laboratory and taken to domestic production for the concept phase. the impacts caused by the inconsistent production management are in the analysis phase as the data collection is not finalized. At this stage, the implementation of low-cost IoT nodes is intended for collecting and monitoring parameters, these parameters are sent via the MQTT protocol to the cloud, starting the blockchain mining process and storing the mining result in an immutable database. The information from monitoring, as well as the keys generated in blockchain mining, will be presented in an intuitive way through a user interface built using the NODE-red framework.
- Fairness in Designing Decision-Making Processes with Multi-Agent Systems and Human FactorsPublication . Vieira, Gustavo; Zeiner, Herwig; Paletta, Lucas; Fernandes, Rui; Rosse, Higor; Sabiri, Khadija; Paz, Juan F De; Barbosa, JoséThis paper explores the integration of Human Factors (HF) into Multi-Agent Systems (MAS) to enhance fairness in decision-making processes in Industry 5.0 environments. We contribute with a human-centred perspective in the development of MAS by integrating the physiological aspects of workers in the manufacturing industry. This culminates in the measurement of human resilience. This paper presents an automotive manufacturing environment where wearable sensors and AI-driven analytics assess workers' physiological and psychological stress levels to calculate a human resilience score. This score, along with worker preferences, supports a dynamic worker allocation algorithm based on MAS that adapts to production demands. Our approach embodies the Industry 5.0 vision of technologies that support adaptive, transparent and, above all, fair human management. The system uses advanced technologies to meet business goals and employee needs and to promote a more inclusive, supportive and people-centred work environment.