Lightweight Gramian Angular Field classification for edge internet of energy applications

被引：8

作者：

Alsalemi, Abdullah ^{[1
]}

Amira, Abbes ^{[1
,2
]}

Malekmohamadi, Hossein ^{[1
]}

Diao, Kegong ^{[3
]}

机构：

[1] De Montfort Univ, Inst Artificial Intelligence, Leicester, Leics, England

[2] Univ Sharjah, Dept Comp Sci, Sharjah, U Arab Emirates

[3] De Montfort Univ, Inst Energy & Sustainable Dev, Leicester, Leics, England

来源：

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2023年 / 26卷 / 02期

关键词：

Edge computing; Energy efficiency; Artificial intelligence; Deep learning; Gramian angular fields; Internet of energy; ZERO;

D O I：

10.1007/s10586-022-03704-1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With adverse industrial effects on the global landscape, climate change is imploring the global economy to adopt sustainable solutions. The ongoing evolution of energy efficiency targets massive data collection and Artificial Intelligence (AI) for big data analytics. Besides, emerging on the Internet of Energy (IoE) paradigm, edge computing is playing a rising role in liberating private data from cloud centralization. In this direction, a creative visual approach to understanding energy data is introduced. Building upon micro-moments, which are timeseries of small contextual data points, the power of pictorial representations to encapsulate rich information in a small two-dimensional (2D) space is harnessed through a novel Gramian Angular Fields (GAF) classifier for energy micro-moments. Designed with edge computing efficiency in mind, current testing results on the ODROID-XU4 can classify up to 7 million GAF-converted datapoints with similar to 90% accuracy in less than 30 s, paving the path towards industrial adoption of edge IoE.

引用

页码：1375 / 1387

页数：13

共 37 条

[1]

Al-Kababji A, 2020, 2020 IEEE 2ND GLOBAL POWER, ENERGY AND COMMUNICATION CONFERENCE (IEEE GPECOM2020), P312, DOI [10.1109/gpecom49333.2020.9247901, 10.1109/GPECOM49333.2020.9247901]

[2]

Alsalemi A., 2021, INT C APPL ENERGY

[3] The Role of Micro-Moments: A Survey of Habitua Behavior Change and Recommender Systems for Energy Saving [J].

Alsalemi, Abdullah ;

Sardianos, Christos ;

Bensaali, Faycal ;

Varlamis, Iraklis ;

Amira, Abbes ;

Dimitrakopoulos, George .

IEEE SYSTEMS JOURNAL, 2019, 13 (03) :3376-3387

[4]

[Anonymous], TensorFlow Lite Model Analyzer

[5]

[Anonymous], MICR AR CHANG RUL

[6] Resource Characterisation of Personal-Scale Sensing Models on Edge Accelerators [J].

Antonini, Mattia ;

Vu, Tran Huy ;

Min, Chulhong ;

Montanari, Alessandro ;

Mathur, Akhil ;

Kawsar, Fahim .

PROCEEDINGS OF THE 2019 INTERNATIONAL WORKSHOP ON CHALLENGES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR INTERNET OF THINGS (AICHALLENGEIOT '19), 2019, :49-55

[7]

Badar MS., 2021, INTEGRATION WSNS INT

[8] The Internet of Energy: A Web-Enabled Smart Grid System [J].

Bui, Nicola ;

Castellani, Angelo P. ;

Casari, Paolo ;

Zorzi, Michele .

IEEE NETWORK, 2012, 26 (04) :39-45

[9] Privacy Partition: A Privacy-preserving Framework for Deep Neural Networks in Edge Networks [J].

Chi, Jianfeng ;

Owusu, Emmanuel ;

Yin, Xuwang ;

Yu, Tong ;

Chan, William ;

Liu, Yiming ;

Liu, Haodong ;

Chen, Jiasen ;

Sim, Swee ;

Iyengar, Vibha ;

Tague, Patrick ;

Tian, Yuan .

2018 THIRD IEEE/ACM SYMPOSIUM ON EDGE COMPUTING (SEC), 2018, :378-380

[10] How to evaluate performance of net zero energy building - A literature research [J].

Deng, S. ;

Wang, R. Z. ;

Dai, Y. J. .

ENERGY, 2014, 71 :1-16

← 1 2 3 4 →