Proyecto de Investigación del Plan Nacional del Gobierno de España. Convocatoria 2018 – Proyectos I+D+I – Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad

Reference: RTI2018-098371-B-I00
Principal investigator (PI): Dr. Joaquín Olivares, Universidad de Córdoba
Principal investigator (PI): Dr. José Manuel Palomares, Universidad de Córdoba
2019/2022

The convergence of Big Data and IoT has been structured in a three-level strategy: Edge, Fog, and Cloud. This convergence requires a very efficient combination of computer networks and distributed computing for the sensing/control/actuation loop, usually included in Edge level of the IoT vision, and the high computational algorithms and platforms required by Big Data techniques, which are commonly placed in the Cloud level. Thus, the proposed Smart-Fog must provide distributed support to both levels from different views: network hardware, software services, computational platforms, efficient distributed data processing.

Moreover, it is expected that the number of IoT devices will increase largely in the next few years, thus Smart-Fog will be designed to handle many simultaneous agents, in a scalable fashion. Besides, Big Data is requesting more data and more complex algorithms that are requiring larger computational capabilities. Therefore, Smart-Fog will execute some services and part of the algorithms that currently are executed in the Cloud level.

The general scenario, which Smart-Fog will have to deal with, can be summarized as massive dataflows coming from a large number of heterogeneous sources, routed into a processing-capable network able to compute, as close as possible to the generating sources, those dataflows, in their way to the high-level services.

The main goal of the Smart-Fog project is to advance in the development of the processing and data management technology for IoT. And, to achieve it, the general objectives of the Smart-Fog can be summarized in the following items:
G1: Reduce the gap between Cloud and Edge levels, providing more computational power to Fog, to process data with high-demanding services.
G2: Include distributed intelligence in the Fog level to take decisions closer to the generating sources.
G3: Create a distributed, scalable, dependable, and intelligent infrastructure, with communication capabilities to take data from lower levels (Edge) to servers (Cloud).

Publications:

Distributed Fog Computing System for Weapon Detection and Face Recognition
H Martínez, FJ Rodriguez, F León-Garcia, JM Palomares, J Olivares
Journal of Network and Computer Applications. Vol 232, 104026. 2024
DOI https://doi.org/10.1016/j.jnca.2024.104026

GS3: A Lightweight Method of Generating Data Blocks With Shuffling, Scrambling, and Substituting Data for Constrained IoT Devices
F Alcaraz, JM Palomares, Joaquín Olivares
IEEE Internet of Things Journal. Vol 11 (15) 25782-25801, 2024

3D reconstruction system and multiobject local tracking algorithm designed for billiards
FJ Rodriguez-Lozano, JC Gámez-Granados, H Martínez, JM Palomares, Joaquín Olivares
Applied Intelligence. https://doi.org/10.1007/s10489-023-04542-3 2023

Lightweight method of shuffling overlapped data-blocks for data integrity and security in WSNs
F Alcaraz, J Olivares, JM Palomares
Computer Networks. Vol 199, 108470. 2021

Data Communication Optimization for the Evaluation of Multivariate Conditions in Distributed Scenarios
F León-Garcia, FJ Rodriguez-Lozano, J Olivares, JM Palomares-Muñoz
IEEE Access. 7 (1), 123473-123489. 2019

SysGpr: System of Generation of Pseudo-realistic Synthetic Signals
F Leon, FJ Rodriguez-Lozano, A Cubero-Fernandez, JM Palomares, J Olivares
Revista Iberoamericana de Automática e Informática Industrial 16 (3), 369-379. 2019

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML
Rey-Merchán, M.d.C.; López-Arquillos, A.; Soto-Hidalgo, J.M.
Appl. Sci. 2022, 12, 6057. https://doi.org/10.3390/app12126057

Conferences:

A Preliminary Fuzzy Markup Language based Approach for the Queue Buffer Size Optimization in Fog Nodes for Stream Processing
G. Corpas-Prieto, F. Leon-Garcia, J. C. Gámez-Granados, J. M. Palomares, J. Olivares and J. M. Soto-Hidalgo
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy, 2022, pp. 1-8
doi: 10.1109/FUZZ-IEEE55066.2022.9882741.

Analysis of the random shuffling of message blocks as a low-cost integrity and security measure
F. Alcaraz-Velasco, J. M. Palomares and J. Olivares
2022 17th Iberian Conference on Information Systems and Technologies (CISTI), Madrid, Spain, 2022, pp. 1-6,
doi: 10.23919/CISTI54924.2022.9820101.

PhD Thesis:

Modelling efficient data transmission for multivariate threshold-based events
F León
2019