FuzzyGreen: A Context-Aware Fuzzy Inference Framework for Carbon Footprint Reduction in Next-Generation Wi-Fi Network Infrastructure
DOI:
https://doi.org/10.21015/vtse.v14i1.2332Abstract
The information and communication technology (ICT) sector contributes approximately 2--4\% of global carbon dioxide emissions, with wireless network infrastructure representing a significant yet underexplored opportunity for cleaner production interventions. Wi-Fi access points, operating continuously across billions of deployments worldwide, consume substantial energy while employing rigid power management mechanisms fundamentally inadequate for dynamic operational conditions. This paper introduces FuzzyGreen, a context-aware fuzzy logic-based power optimization framework designed to achieve measurable carbon footprint reductions in Wi-Fi network infrastructure. The proposed Mamdani-type fuzzy inference system processes five context variables—traffic load, channel utilization, user density, signal quality, and service requirements—to generate adaptive power scaling decisions. Comprehensive simulations across enterprise, high-density public venue, and residential deployment scenarios demonstrate that FuzzyGreen achieves 38–52% reduction in energy consumption compared to conventional approaches, translating to annual carbon dioxide equivalent savings of 47 kg per access point using global average emission factors. Scaled to the estimated 500 million global access points, the framework presents potential for avoiding 23.5 million tonnes of CO$_2$ emissions annually, contributing directly to Sustainable Development Goal~7 (Affordable and Clean Energy) and Goal~12 (Responsible Consumption and Production). The framework's computational efficiency (0.23 ~ms inference time) enables deployment on resource-constrained embedded hardware, positioning FuzzyGreen as a pragmatic cleaner production strategy for sustainable wireless network operations.
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