Next Article in Journal
Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA)
Next Article in Special Issue
Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining
Previous Article in Journal
Exploiting Inter- and Intra-Base Crossing with Multi-Mappings: Application to Environmental Data
Previous Article in Special Issue
The Rise of Big Data Science: A Survey of Techniques, Methods and Approaches in the Field of Natural Language Processing and Network Theory
Open AccessArticle

Edge Machine Learning: Enabling Smart Internet of Things Applications

School of Computing and Digital Technology, Birmingham City University, Birmingham B5 5JU, UK
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2018, 2(3), 26; https://doi.org/10.3390/bdcc2030026
Received: 11 July 2018 / Revised: 14 August 2018 / Accepted: 17 August 2018 / Published: 3 September 2018
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details. View Full-Text
Keywords: machine learning; edge computing; Internet of Things; IoT machine learning; edge computing; Internet of Things; IoT
Show Figures

Figure 1

MDPI and ACS Style

Yazici, M.T.; Basurra, S.; Gaber, M.M. Edge Machine Learning: Enabling Smart Internet of Things Applications. Big Data Cogn. Comput. 2018, 2, 26.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop