Work

Real-Time Edge Deployment of ANFIS for IoT Energy Optimization

Year: 2026

Type: article

Abstract: This work presents the real-world deployment of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent energy control in resource-constrained IoT devices. The proposed system employs a first-order Takagi–Sugeno fuzzy model with three Gaussian membership functions per input: ambient temperature, light intensity, and battery voltage. The model was trained offline using augmented environmental datasets and subsequently translated into optimized embedded C code for execution on an ESP32 microcontroller. The controller dynamically adjusts the node’s deep sleep duration according to environmental conditions, enabling adaptive behavior based solely on local environmental conditions without requiring external connectivity. A 10-day field deployment compared the ANFIS controller with conventional fixed and rule-based strategies. Results show that the ANFIS-based strategy reduced energy consumption by 31.1% relative to the fixed approach while maintaining accurate adaptation to environmental conditions (RMSE = 9.6 s). The inference process required less than 2.5 ms and used under 30 KB of RAM, confirming the feasibility of real-time fuzzy inference on resource-constrained embedded platforms.

Source: Processes

DOI: 10.3390/pr14061004

Authors: Daniel Teso-Fz-Betoño, Iñigo Aramendia, Jose Antonio Ramos-Hernanz, Koldo Portal-Porras, Daniel Caballero-Martin, Jose Manuel Lopez-Guede

Institution: University of the Basque Country

Language: English

Topic: Energy Efficiency in Computing

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,896