RT Journal Article
T1 CRDT-based knowledge synchronisation in an Internet of Robotics Things ecosystem for Ambient Assisted Living
A1 Galeas-Merchán, José Miguel
A1 Tudela, Alberto
A1 Pons, Óscar
A1 Bandera-Rubio, Juan Pedro
A1 Bandera-Rubio, Antonio Jesús
A1 Bustos, Pablo
K1 Proceso electrónico de datos
K1 Internet de los objetos
K1 Robótica
AB Integrating IoT and assistive robots in the design of Ambient Assisted Living (AAL) frameworks has proven to be a useful solution for monitoring and assisting elderly people at home. As a way to manage the information captured and assess the person’s condition, respond to emergencies, promote physical or cognitive exercises, etc., these systems can also integrate a Virtual Caregiver (VC). Given the diversity of technologies deployed in such an AAL framework, deciding how to manage knowledge appropriately can be complex. This paper proposes to organise the AAL framework as a distributed system, i.e., as a collection of autonomous software agents that provide users with a single coherent response. In this distributed system, agents are deployed locally and handle replicas of the knowledge model. The problem of merging these replicas into a consistent representation, therefore arises.The -CRDT (Conflict-free Replicated Data Type) synchronisation mechanism is employed to ensure the eventual consistency with low communication overhead. To manage the dynamics of the AAL ecosystem, the -CRDT is combined with the publish/subscribe interaction protocol. In this way, the performance of the IoT, the robot and the VC, through the functionalities that depend on them, is efficiently adapted to changes in the context. To demonstrate the validity of the proposal, two use cases have been designed in which a collaborative response from the system is required. The first one deals with a possible fall of the user at home, while the second one deals with the problem of helping the person move small objects around the flat. The measured values of latency or consistency in the data show that the proposal works satisfactorily.
PB Elsevier
SN 1077-3142
YR 2025
FD 2025-06-28
LK https://hdl.handle.net/10630/39174
UL https://hdl.handle.net/10630/39174
LA eng
NO Formoso, M. A., Arco, J. E., Ortiz, A., Gan, J. Q., & Rodréguez-Rodríguez, I. (2025). Cerebral lateralization assessment: an explainable deep learning approach with channel attention mechanism. IEEE Journal of Biomedical and Health Informatics, 1–14.
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 13 abr 2026