Raquel Caballero Aguila
Biography
Raquel Caballero-Aguila is a Professor in the Department of Statistics and Operations Research at the University of Jaen, Spain. She received her MSc and PhD degrees in Mathematics from the University of Granada, Spain, in 1997 and 1999, respectively. Her research interests include time-varying stochastic systems, complex networks and design of estimation al algorithms to address emerging challenges in networked systems.
Prof. Caballero-Aguila has authored numerous scientific papers in refereed international journals indexed in Journal Citation Reports and has been included in recent editions of the Spanish National Research Council ranking of most cited Spanish female researchers. She has participated in different research projects both as a research member and as a principal investigator.
She serves as a reviewer for Mathematical Reviews and is an academic editor for the journals Mathematical Problems in Engineering, Journal of Control Science and Engineering, and Systems Science and Control Engineering. In addition, she has reviewed for prestigious international journals, and has actively contributed to the organization and chairing of sessions at international conferences. She has also been invited to deliver keynote speeches at various international events. Her research has been further enriched through international collaborations, including research visits to Kagoshima
University (Japan) and Harbin University of Science and Technology (China).
Research Interest
Prof. Raquel Caballero-Aguila specializes in time-varying stochastic systems, complex networks, and estimation algorithms for intelligent, networked systems. Her work supports advancements in automation, robotics, and autonomous technologies through data-driven modeling and control in dynamic environments.
Abstract
Challenges and advances in distributed estimation for networked stochastic systems: Distributed estimation in networked stochastic systems has become a cornerstone of modern signal processing, control, and monitoring applications. Under the assumption of perfect communication links, centralized estimation approaches ?in which all sensor measurements are transmitted to a fusion center where a global estimator processes the complete dataset? can achieve optimal performance. However, in real-world networked systems, communication channels are often imperfect, and transmitting all data to a central node can be impractical or even infeasible, especially in large-scale or geographically distributed networks. By enabling individual sensor nodes to estimate global system parameters through localized computations and limited communication with neighboring nodes, distributed strategies overcome these limitations, offering scalable, robust, and energy-efficient alternatives to centralized methods. These advantages are particularly vital in resource-constrained environments such as wireless sensor networks, industrial automation systems, and autonomous multi-agent platforms.
This keynote presents a global overview of recent advances in distributed estimation, with an emphasis on addressing key practical challenges encountered in real-world
deployments. After introducing the fundamental principles of distributed estimation and its advantages over centralized schemes, we focus on four critical issues: (i) random variations in system parameters, (ii) quantization effects due to limited measurement resolution, (iii) temporal correlations in observation noise, and (iv) the presence of mixed random attacks. Recent theoretical developments addressing these challenges will be presented. The talk concludes with a discussion of promising future directions, including the analysis of communication delays and packet dropouts on estimator performance, the design of distributed estimation mechanisms under complex and dynamic attack scenarios, and the integration of random-access communication
protocols as an active defense mechanism against potential attacks. Key references will be provided for researchers interested in further exploring the mathematical foundations and engineering applications of distributed estimation.
