Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Wang, Fangxin; Fan, Xiaoyi; Wang, Feng; Liu, Jiangchuan

doi:10.1109/tmc.2018.2842733

Cited by 31 publications

(14 citation statements)

References 21 publications

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“…The recent breakthroughs in deep learning and hardware design have enabled researchers to train much more powerful models, which highly empower many applications such as crowdsourced delivery [11], network caching [12], energy management [13] and edge computing [14]. In the following we highlight the advantages of deep learning as compared with traditional machine learning methods, which demonstrates the benefits to apply deep learning in IoT applications.…”

Section: Introductionmentioning

confidence: 93%

A Survey on Deep Learning Empowered IoT Applications

Yao

et al. 2019

IEEE Access

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The Internet of Things (IoT) is widely regarded as a key component of the Internet of the future and thereby has drawn significant interests in recent years. IoT consists of billions of intelligent and communicating ''things'', which further extend borders of the world with physical and virtual entities. Such ubiquitous smart things produce massive data every day, posing urgent demands on quick data analysis on various smart mobile devices. Fortunately, the recent breakthroughs in deep learning have enabled us to address the problem in an elegant way. Deep models can be exported to process massive sensor data and learn underlying features quickly and efficiently for various IoT applications on smart devices. In this article, we survey the literature on leveraging deep learning to various IoT applications. We aim to give insights on how deep learning tools can be applied from diverse perspectives to empower IoT applications in four representative domains, including smart healthcare, smart home, smart transportation, and smart industry. A main thrust is to seamlessly merge the two disciplines of deep learning and IoT, resulting in a wide-range of new designs in IoT applications, such as health monitoring, disease analysis, indoor localization, intelligent control, home robotics, traffic prediction, traffic monitoring, autonomous driving, and manufacture inspection. We also discuss a set of issues, challenges, and future research directions that leverage deep learning to empower IoT applications, which may motivate and inspire further developments in this promising field.

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Section: Introductionmentioning

confidence: 93%

A Survey on Deep Learning Empowered IoT Applications

Yao

et al. 2019

IEEE Access

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“…To solve the above‐mentioned problem keeping in mind the benefit of using DL, Ref. 119 proposed a DL‐based battery profiling strategy. Appliance‐level power profiling can be a handy tool for making some decisions 120 .…”

Section: Applications Of DL Techniques For Power Load Datamentioning

confidence: 99%

“…It is mostly used for backup purposes. The base station also uses battery backup system 119 . For the backup system, it is needed to predict future situations.…”

Section: Applications Of DL Techniques For Power Load Datamentioning

confidence: 99%

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Elahe

Jin

Zeng

2021

Intl J of Energy Research

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The collection and storage of large-scale load data in a smart grid provide new approaches for the efficient, economical, and safe operation of power systems.Deep Learning (DL) has become increasingly popular for large-scale load data analytics in recent years because of its ability to extract latent features and discovering complex relationships. This paper first overviews eight typical open load datasets of the grid and smart meter collected worldwide, the challenges faced by conventional machine learning, and the DL techniques applied to these challenges. A comprehensive review of the applications of DL techniques is then conducted from the perspective of analysis, forecast, management, and presented observation on each application. Critical points of DL models for improving performance are further discussed. In conclusion, several pressing problems of DL in load data analytics are identified, such as the accuracy gap between the actual and the expected, the generalization of hyperparameter setting, and the interpretation mechanism of DL output, which need special attention.

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“…The recent advances of edge computing migrate the massive computation from the remote cloud to the local edge, enabling more low-latency and secure manufacturing [129]. And deep learning further empowers more effective local analysis and prediction [130] at the edge node of industry instead of the cloud. We summarize them in the next two parts as illustrated in Tab.…”

Section: E Smart Industrymentioning

confidence: 99%

“…Wu et al [125] focused on the remaining useful life estimation of the engineered system and proposed to use vanilla LSTM neural networks to get good remaining lifetime prediction accuracy. Wang et al [126] focused on the remaining lifetime analysis of backup batteries in the wireless base stations. They proposed to use DNN-based architecture to accurately predict the remaining energy and remaining lifetime of batteries, which further enables an informed power configuration among base stations.…”

Section: ) Smart Industrial Analysismentioning

confidence: 99%

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

et al. 2020

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With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field. INDEX TERMS Internet of Things, edge computing, deep learning, intelligent edge applications.

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Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Cited by 31 publications

References 21 publications

A Survey on Deep Learning Empowered IoT Applications

A Survey on Deep Learning Empowered IoT Applications

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

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