Energy‐Efficient M2M Scheduling Over Cellular Networks: A Survey

Abstract

The introduction of machine-to-machine communication (M2M), also known as machine-type communication (MTC), is a key enabler of the internet of things (IoT), facilitating large-scale connectivity among heterogeneous devices in cellular networks. Although this technological advancement has opened new revenue opportunities for service providers, the rapid growth in M2M devices introduces significant challenges related to energy consumption, spectrum utilisation, network scalability and heterogeneous quality of service (QoS) requirements. These challenges complicate the task of balancing energy efficiency (EE) with diverse QoS demands. Among the potential solutions, scheduling strategies have emerged as a critical technique for improving the EE problem in M2M communication. This paper presents a comprehensive survey of scheduling techniques for optimising energy consumption in M2M communication. This provides a comprehensive review of energy-efficient scheduling techniques for M2M communications. It further examines emerging technological trends that influence scheduling design, including artificial intelligence (AI), machine learning (ML), simultaneous wireless information and power transfer (SWIPT), renewable energy harvesting and digital twin technologies in 5G/6G networks. Finally, the paper identifies key research challenges and outlines future directions for the development of intelligent, energy-efficient scheduling frameworks in M2M and IoT ecosystems.