Cargando…

Improving IoT Predictions through the Identification of Graphical Features

IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection method...

Descripción completa

Detalles Bibliográficos
Autores principales: Akter, Syeda, Holder, Lawrence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696358/
https://www.ncbi.nlm.nih.gov/pubmed/31344811
http://dx.doi.org/10.3390/s19153250
_version_ 1783444251855552512
author Akter, Syeda
Holder, Lawrence
author_facet Akter, Syeda
Holder, Lawrence
author_sort Akter, Syeda
collection PubMed
description IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features that are to be used by a classifier for prediction tasks. We show that a set of generic graph-based features can improve performance of sensor network predictions without the need for application-specific and task-specific feature engineering. We apply this approach to three different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone. Our approach produced comparable results with most of the state-of-the-art methods, while maintaining the additional advantage of general applicability to IoT sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. We further investigate the impact of using edge-transition times, categorical features, different sensor window sizes, and normalization in the smart home domain. We also consider deep learning approaches, including the Graph Convolutional Network (GCN), for the elimination of feature engineering in the smart home domain, but our approach provided better performance in most cases. We conclude that the graphical feature-based framework that is based on IoT sensor categorization, nodes and edges as features, and feature selection techniques provides superior results when compared to the non-graph-based features.
format Online
Article
Text
id pubmed-6696358
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-66963582019-09-05 Improving IoT Predictions through the Identification of Graphical Features Akter, Syeda Holder, Lawrence Sensors (Basel) Article IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features that are to be used by a classifier for prediction tasks. We show that a set of generic graph-based features can improve performance of sensor network predictions without the need for application-specific and task-specific feature engineering. We apply this approach to three different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone. Our approach produced comparable results with most of the state-of-the-art methods, while maintaining the additional advantage of general applicability to IoT sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. We further investigate the impact of using edge-transition times, categorical features, different sensor window sizes, and normalization in the smart home domain. We also consider deep learning approaches, including the Graph Convolutional Network (GCN), for the elimination of feature engineering in the smart home domain, but our approach provided better performance in most cases. We conclude that the graphical feature-based framework that is based on IoT sensor categorization, nodes and edges as features, and feature selection techniques provides superior results when compared to the non-graph-based features. MDPI 2019-07-24 /pmc/articles/PMC6696358/ /pubmed/31344811 http://dx.doi.org/10.3390/s19153250 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Akter, Syeda
Holder, Lawrence
Improving IoT Predictions through the Identification of Graphical Features
title Improving IoT Predictions through the Identification of Graphical Features
title_full Improving IoT Predictions through the Identification of Graphical Features
title_fullStr Improving IoT Predictions through the Identification of Graphical Features
title_full_unstemmed Improving IoT Predictions through the Identification of Graphical Features
title_short Improving IoT Predictions through the Identification of Graphical Features
title_sort improving iot predictions through the identification of graphical features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696358/
https://www.ncbi.nlm.nih.gov/pubmed/31344811
http://dx.doi.org/10.3390/s19153250
work_keys_str_mv AT aktersyeda improvingiotpredictionsthroughtheidentificationofgraphicalfeatures
AT holderlawrence improvingiotpredictionsthroughtheidentificationofgraphicalfeatures