cs.LGOct 9, 2026

Sustainable Cloud-Fog-Edge Systems for Smart Buildings: Algorithmic Adaptation under Memory Constraints and Dynamic Orchestration Strategies

Authors: Mamadou Sow, Christophe Cerin

Organizations: University Sorbonne Paris Nord LIPN, UMR CNRS 7030 Villetaneuse, France · University Sorbonne Paris Nord LIPN, UMR CNRS 7030 INRIA DATAMOVE Paris/Grenoble, France

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Abstract

The escalating environmental footprint of the Information and Communication Technol- ogy (ICT) sectorwith global Data Center electric- ity usage potentially reaching 3% by 2030 and the imperative for real-time responsiveness in smart buildings demand a foundational shift towards Edge Computing. This article proposes a holis- tic architectural and algorithmic approach for cre- ating autonomous, resilient, and privacy-preserving learning systems within smart buildings, advancing the core concept of the building itself as the data center. Our contributions, demonstrating originality compared to conventional centralized systems, are structured around three main pillars: 1) Validation of resilient data acquisition chains using the af- fordable Arduino ecosystem (Uno R4 WiFi/BME280) and secure persistence solutions including centralized archiving (Node-RED/MySQL) and secure remote transfer (Python/rsync/SSH). 2). The proposal of a lightweight, KOptim orchestration framework, inter- operable with the FIWARE standard, for the dynamic management of Service Level Agreements (SLA). 3) The adaptation and experimental valida- tion of the Generalized Hebbian Algorithm (GHA) for Principal Component Analysis (PCA) on low- power microcontrollers,We introduce and compare an iterative learning strategy using data buffers, featuring sorting and explicit preservation of extreme samples to maintain diversity. Experimental results on the high-dimensional Tour Perret sensor data confirm that this novel method, despite introducing a necessary temporal complexity of O(W log W ) due to sorting, achieves convergence and final result quality that is visually equivalent to processing the entire dataset in full memory, reliably identifying two distinct operational states.

Figures & tables

Fig. 1: Architectural concerns, illustrating the hierarchy of Cloud, Fog, and Edge levels for smart building management
Fig. 2: KOptim coupled with FIWARE
Fig. 3: Wiring diagram for the BME230 i2c 3.3V sensor on the Arduino Uno R4 Wifi MCU
Fig. 4: Content displayed in the Web Browser: The displayed URL is the IP address 10.10.4.108
Fig. 5: Electronic Circuit
Fig. 6: Scatter Plot illustrating Dimension reduction with GHA (Block Processing with Diversity Management)
Fig. 7: Probability density of principal components (KDE Plot)
Fig. 8: Message Flow, Processing Slice and Number of Possible Iterations
Fig. 9: Distribution Diagram ELSYS_ERS_CO2, ERS_CO2_48B30 W=512, Data= 5120, K=4
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