Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction

Abstract

Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses significant challenges to processing and analysis. While machine learning (ML) algorithms have emerged as powerful tools for handling these large datasets and providing accurate predictions, their environmental impact, particularly in terms of energy consumption, is often overlooked in favor of their predictive capabilities. This study investigates the potential of two bio-inspired models: Spiking Neural Networks (SNNs) and Reservoir Computing through Echo State Networks (ESNs) for cellular traffic forecasting. The evaluation focuses on both their predictive performance and energy efficiency. These models are implemented in both centralized and federated settings to analyze their effectiveness and energy consumption in decentralized systems. Additionally, we compare bio-inspired models with traditional architectures, such as Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs), to provide a comprehensive evaluation. Using data collected from three diverse locations in Barcelona, Spain, we examine the trade-offs between predictive accuracy and energy demands across these approaches. The results indicate that bio-inspired models, such as SNNs and ESNs, can achieve significant energy savings while maintaining predictive accuracy comparable to traditional architectures. Furthermore, federated implementations were tested to evaluate their energy efficiency in decentralized settings compared to centralized systems, particularly in combination with bio-inspired models. These findings offer valuable insights into the potential of bio-inspired models for sustainable and privacy-preserving cellular traffic forecasting.