AI for Sustainability: Bibliometric Review of Power-Saving Algorithms in IoT and Edge Systems

Abstract

Objective: This research aims to explore the global trends, challenges, and future opportunities in the development of energy-efficient AI technology in the context of IoT, edge computing, and its potential to synergize with quantum computing.
Research Design & Methods: This study uses a bibliometric-based systematic literature review approach to 113 documents from the Scopus database published in the 2021-2025 period. The analysis used VOSviewer to map keyword co-occurrence, collaboration between countries, topic trends, and citations.
Findings: The research focus is expanding from basic topics of power efficiency to integrating AI with edge systems, adaptive communication protocols (such as BLE and LoRaWAN), and ML-based predictive models for smart home and agriculture. Countries like China, the United States, and India are central to global collaboration. The research also revealed the lack of studies on integrating quantum-inspired optimization in energy-efficient AI edge architectures.
Implications & Recommendations: The findings indicate that further research directions are needed in the development of adaptive, efficient, and environmentally friendly AI architectures and that strengthening collaboration between countries is important. This research also supports strategies for developing low-emission and power-efficient digital technologies to support the global sustainability agenda.
Contribution & Value Added: This research provides an important contribution in the form of current literature mapping and a scientific synthesis framework to formulate a research agenda and policy for sustainable AI technology in IoT and edge computing systems.