RT doctoral thesis
T1 Análisis en Tiempo Real para el Internet Industrial de las Cosas
T2 Real-time Analytics for Industrial Internet of Things
A1 Zouai, Meftah
A2 Ingeniería Informática
AB The rapid expansion of the Industrial Internet of Things (IIoT) has led to the largescale deployment of interconnected sensors, machines, and cyber-physical systemscapable of generating continuous streams of heterogeneous data. While this transformation enables intelligent automation, predictive maintenance, and data-drivenoptimization, traditional centralized cloud-based architectures are unable to satisfy the strict latency, scalability, and contextual-awareness requirements of modern Industry 4.0 environments. Addressing these challenges requires distributed,adaptive, and real-time data processing mechanisms capable of operating acrossmultiple computational layers.This doctoral research proposes a context- and situational-aware collaborativearchitecture for real-time heterogeneous data processing in IIoT ecosystems. Theprimary contribution is the design and implementation of Atmosphere, a unifiededge–fog–cloud framework that integrates multi-agent systems, Complex EventProcessing (CEP), and Event-Driven Service-Oriented Architecture (ED-SOA). Inthe edge layer, autonomous software agents perform local filtering, rule-based reasoning, and immediate decision-making, reducing communication overhead andlatency. The fog layer incorporates MQTT-based messaging and CEP enginesto aggregate, correlate, and analyze distributed event streams in near real-time,enabling coordinated responses among heterogeneous nodes. The cloud layer provides large-scale analytics, long-term knowledge extraction, global orchestration,and strategic optimization. The architecture ensures bidirectional communication,interoperability, and dynamic adaptation based on contextual information.The second contribution validates the proposed architecture through the design and development of an IoT-based, solar-powered camouflaged robotic systeminspired by the Fennec desert fox. The robotic platform operates as an intelligentedge node capable of autonomous data acquisition, context-aware decision-making, and energy-efficient operation in harsh and isolated environments. Experimentalevaluations demonstrate improved responsiveness, scalability, and operational efficiency compared to traditional centralized monitoring approaches.Overall, this research advances the state of the art in real-time IIoT analyticsby delivering a scalable, distributed, and context-aware framework that bridgesheterogeneous data sources, intelligent coordination mechanisms, and practicaldeployment. The proposed solutions contribute to the realization of resilient,adaptive, and intelligent industrial ecosystems aligned with the principles of Industry 4.0.
YR 2026
FD 2026
LK http://hdl.handle.net/10498/39800
UL http://hdl.handle.net/10498/39800
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026