Machine Learning on the Edge for Sustainable IoT Networks: A Systematic Literature Review

TL;DR

The paper investigates how machine learning deployed at the edge can improve IoT sustainability, emphasizing actual hardware testing to validate energy and resource benefits. Using a PRISMA-guided systematic review of SCOPUS-indexed studies from 2013–2023, it analyzes 10 hardware-evaluated works that differentiate between communication- and computation-focused energy efficiency and between full-network versus prototype deployments. Key findings show substantial energy savings from both data transmission reductions and compute offloading, with DL and RL/DRL methods increasingly used, yet many studies rely on simulations or small prototypes and report heterogeneous metrics. The work identifies critical gaps—notably scarce open datasets/testbeds and limited anomaly handling—and proposes future directions such as advanced DL architectures, energy-cost modeling, and shared hardware testbeds to advance practical, scalable sustainable IoT systems.

Abstract

The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long-term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real-time decision-making, and improving data privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real-world conditions, as it captures the complexities and constraints of real-world IoT deployments. Combining ML at the edge and actual hardware testing, therefore, increases the reliability of ML models to effectively improve the sustainability of IoT systems. The present systematic literature review explores how ML can be utilized to enhance the sustainability of IoT networks, examining current methodologies, benefits, challenges, and future opportunities. Through our analysis, we aim to provide insights that will drive future innovations in making IoT networks more sustainable.

Figures (6)

• Figure 1: PRISMA Methodology followed in the systematic review process SLRigSLRuv
• Figure 2: Flow Diagram for the selection of the literature reviewed.
• Figure 3: Layout of the findings section.
• Figure 4: iot applications in the included papers.
• Figure 5: Different communication technologies and their usage count in the selected papers.