Applications of AI and ML in IoT

Badar, Mohammad Sufian; Shamsi, Shazmeen; Haque, Mohammad Maksuf Ul; Aldalbahi, Adel

doi:10.1201/9781003107521-13

Cited by 7 publications

(2 citation statements)

References 1 publication

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“…On another aspect, cloud computing servers are considered as powerful computational platforms that can perform cost-efficient and scalable Machine Learning (ML) and Deep Learning (DL) algorithms [ 4 , 5 ]. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, networking latency, and energy efficiency [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Gramian Angular Field classification for edge internet of energy applications

Alsalemi

Amira²,

Malekmohamadi³

et al. 2022

Cluster Comput

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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 ~ 90% accuracy in less than 30 s, paving the path towards industrial adoption of edge IoE. Supplementary Information The online version contains supplementary material available at 10.1007/s10586-022-03704-1.

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Section: Introductionmentioning

confidence: 99%

Lightweight Gramian Angular Field classification for edge internet of energy applications

Alsalemi

Amira²,

Malekmohamadi³

et al. 2022

Cluster Comput

View full text Add to dashboard Cite

show abstract

“…Deep Learning (DL) is empowering technology in a plethora of ways, especially when big data analytics is a core process [1]. For instance, cloud computing servers are considered as powerful computational platforms that can perform cost-efficient and scalable Machine Learning (ML) and DL algorithms [2], [3]. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification

Alsalemi

Amira

Malekmohamadi

et al. 2022

2022 IEEE Conference on Dependable and Secure Computing (DSC)

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Deep Learning (DL) is empowering technology in a plethora of ways, especially when big data processing is a core requirements. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite increasing popularity of edge computing, the overarching issue in this scope is lack of a comprehensive documentation on how to setup a given edge computing device to run DL algorithms. Due to its specialized nature, installing the full version of TensorFlow DL library on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel guide on how to setup the TensorFlow Lite software library and outline a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloudbased ML.

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Energy data classification at the edge: a comparative study for energy efficiency applications

Alsalemi,

Amira,

Malekmohamadi

et al. 2023

Cluster Comput

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Applications of AI and ML in IoT

Cited by 7 publications

References 1 publication

Lightweight Gramian Angular Field classification for edge internet of energy applications

Lightweight Gramian Angular Field classification for edge internet of energy applications

Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification

Energy data classification at the edge: a comparative study for energy efficiency applications

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