Data fabric and digital twins: An integrated approach for data fusion design and evaluation of pervasive systems

Recent technological improvements have made it possible for pervasive computing intelligent environments, augmented by sensors and actuators, to offer services that support society's aims for a wide variety of appli-cations. This requires the fusion of data gathered from multiple sensors to convert them into information to obtain valuable knowledge. Poor implementation of data fusion hinders the appropriate actions from being taken and offering the appropriate support to users and environment needs, particularly relevant in the healthcare domain. Data fusion poses challenges that are mainly related to the quality of the data or data sources, the definition of a data fusion process and evaluating the data fusion carried out. There is also a lack of holistic engineering frameworks to address these challenges. These frameworks should be able to support automated methods of extracting knowledge from information, selecting algorithms and techniques, assessing information and evaluating information fusion systems in an automatic and standardized manner. This work proposes a holistic framework to improve data fusion in pervasive systems, addressing the issues identified by means of two processes: the first of which guides the design of the system architecture and focuses on data management. It is based on a previous proposal that integrated aspects of Data Fabric and Digital Twins to solve data management and data contextualization and representation issues, respectively. The extension of the previous proposal pre-sented here was mainly defined by integrating aspects and techniques from different well-known multi-sensor data fusion models. The previous proposal identified high-level data processing activities and was intended to facilitate their traceability to components in the system architecture. However, the previously defined stages are not completely adequate in a data fusion process and the data processing tasks to be performed in each stage are not described in detail, especially in the data fusion stages. The second process of the framework deals with evaluating data fusion systems and is based on international standards to ensure the quality of the data fusion tasks performed by such systems. This process also offers guidelines for designing the architecture of an eval-uation subsystem to automatically perform data fusion evaluation in runtime as part of the system. To illustrate the proposal, a system for preventing the spread of COVID-19 in nursing homes is described that was developed using the proposed guidelines It is also illustrated by a description of how the data fusion tasks it supports are evaluated by the proposed evaluation process. The overall evaluation of the data fusion performed by this system was considered satisfactory, which indicates that the proposal facilitates the design and development of data fusion systems and helps to achieve the necessary quality requirements.