Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities

By Yi Liu, Chao Yang, Li Jiang, Shengli Xie, Yan Zhang. IEEE Network, 2019

437 citations recorded by the source corpus.

In recent years, green energy management systems (smart grid, smart buildings, and so on) have received huge research and industrial attention with the explosive development of smart cities. By introducing Internet of Things (IoT) technology, smart cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in smart cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.

Subfields: Building and Construction, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/114a3bc81f451c7363d137e88b78dc2e32dfc2c6

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Citations in the corpus

Tackling Climate Change with Machine Learning (2019, methodology): “Edge computing can be used to process data from distributed sensors and other Internet of Things devices, and deep reinforcement learning can then use this data to efficiently schedule energy use [300].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, positive): “Recent work in [165] studied the energy management aspect in smart city where the deep reinforcement learning methods were employed into MEC-enabled IoT system to manage the energy grid efficiently.”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “Due to the efﬁcient data sharing and communication technologies, the concept of the IoT has been adopted by almost every ﬁeld of research, i.e., smart networking [144], energy management [145], business [146], healthcare [147], and medical [148].”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “In response to this problem, Liu et al. [140] The authors prove by experiment that cloud-edge collaboration works best in terms of energy consumption, followed by the method of deploying AI algorithms only on the edge side, and the worst is the method of deploying AI algorithms only on the cloud…”
Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks (2019, background): “Numerous literatures are devoted to design the optimal strategy to solve this problem under different performance requirements [18]–[21].”
Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends (2023, background): “Analyzing large amounts of complex data can be challenging to accurately determine the most precise and effective course of action [24,25].”