Next Article in Journal
Epitaxial Growth of Sc0.09Al0.91N and Sc0.18Al0.82N Thin Films on Sapphire Substrates by Magnetron Sputtering for Surface Acoustic Waves Applications
Next Article in Special Issue
Fusion of Environmental Sensing on PM2.5 and Deep Learning on Vehicle Detecting for Acquiring Roadside PM2.5 Concentration Increments
Previous Article in Journal
Real-Time Impedance Detection of Intra-Articular Space in a Porcine Model Using a Monopolar Injection Needle
Previous Article in Special Issue
Architecture for Trajectory-Based Fishing Ship Classification with AIS Data
Open AccessArticle

Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks

Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4628; https://doi.org/10.3390/s20164628
Received: 16 July 2020 / Revised: 11 August 2020 / Accepted: 12 August 2020 / Published: 17 August 2020
(This article belongs to the Special Issue Information Fusion and Machine Learning for Sensors)
Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge networks can be used to improve the quality of life of the humanity by continuously monitoring many useful indicators, like the health of the users, the air quality or the population movements. Nevertheless, in this scalable context, a percentage of the sensor data readings can fail due to several reasons like sensor reliabilities, network quality of service or extreme weather conditions, among others. Moreover, sensors are not homogeneously replaced and readings from some areas can be more precise than others. In order to address this problem, in this paper we propose to use collaborative filtering techniques to predict missing readings, by making use of the whole set of collected data from the IoT network. State of the art recommender systems methods have been chosen to accomplish this task, and two real sensor array datasets and a synthetic dataset have been used to test this idea. Experiments have been carried out varying the percentage of failed sensors. Results show a good level of prediction accuracy which, as expected, decreases as the failure rate increases. Results also point out a failure rate threshold below which is better to make use of memory-based approaches, and above which is better to choose model-based methods. View Full-Text
Keywords: collaborative filtering; sensor arrays; IoT; matrix factorization collaborative filtering; sensor arrays; IoT; matrix factorization
Show Figures

Figure 1

MDPI and ACS Style

Ortega, F.; González-Prieto, Á.; Bobadilla, J.; Gutiérrez, A. Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks. Sensors 2020, 20, 4628.

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
Search more from Scilit
 
Search
Back to TopTop