Energy-Efficient Machine Learning Techniques: Advancing Edge Computing Capabilities for Sustainable IoT Network Operations

One of the most important ways that the Internet of Things (IoT) is facilitating decentralized decision-making and real-time data processing is through edge computing. However, energy consumption is a major concern because to the computational demands of ML models, which can make it difficult for IoT network operations to be sustainable. The goal of this project is to improve the capabilities of edge computing for sustainable IoT systems by investigating energy-efficient machine learning approaches. We investigate optimization strategies for data classification, anomaly detection, and predictive maintenance that reduce energy consumption without sacrificing performance. In order to decrease computational complexity, memory needs, and energy consumption at the edge, our study introduces new algorithms and model compression methodologies. Additionally, we discuss the possibilities of lightweight models, optimization that takes hardware into account, and adaptive learning processes while addressing the trade-offs between model accuracy, latency, and power economy. This study’s findings show that IoT networks can accomplish sustainable operations, increase device battery life, and decrease pervasive computing systems’ carbon footprint by using these energy-efficient strategies. Supporting the wider use of sustainable, scalable, and intelligent IoT networks, this work offers crucial insights into improving edge computing capabilities.