An Overview of Machine Learning-Driven Resource Allocation in IoT Networks
Zhengdong Li
TL;DR
The paper addresses resource allocation in IoT networks under two regimes: Low-Power IoT and Mobile IoT. It surveys how ML, DL, and RL can optimize resource distribution (bandwidth, energy, compute) in these settings. Key contributions include mapping ML/DL/RL applications to LPWAN and mobile contexts, outlining challenges (accuracy, adaptability, computational cost), and proposing future research directions (edge computing, 6G integration). The work highlights the potential of AI-driven resource management to improve latency, reliability, and energy efficiency in large-scale IoT deployments.
Abstract
In the wake of disruptive IoT technologies generating massive amounts of diverse data, Machine Learning (ML) will play a crucial role in bringing intelligence to Internet of Things (IoT) networks. This paper provides a comprehensive analysis of the current state of resource allocation within IoT networks, focusing specifically on two key categories: Low-Power IoT Networks and Mobile IoT Networks. We delve into the resource allocation strategies that are crucial for optimizing network performance and energy efficiency in these environments. Furthermore, the paper explores the transformative role of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) in enhancing IoT functionalities. We highlight a range of applications and use cases where these advanced technologies can significantly improve decision-making and optimization processes. In addition to the opportunities presented by ML, DL, and RL, we also address the potential challenges that organizations may face when implementing these technologies in IoT settings. These challenges include crucial accuracy, low flexibility and adaptability, and high computational cost, etc. Finally, the paper identifies promising avenues for future research, emphasizing the need for innovative solutions to overcome existing hurdles and improve the integration of ML, DL, and RL into IoT networks. By providing this holistic perspective, we aim to contribute to the ongoing discourse on resource allocation strategies and the application of intelligent technologies in the IoT landscape.