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

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).


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

Data Communication Optimization for the Evaluation of Multivariate Conditions in Distributed Scenarios
F León-Garcia, FJ Rodriguez-Lozano, J Olivares, JM Palomares
IEEE Access. 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

PhD Thesis:

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