Estimating Environmental Variables in Smart Sensor Networks with Faulty Nodes

Nicoleta Stroia, Daniel Moga, Vlad Muresan, Alexandru Lodin

Abstract

Estimation of missing sensor data is an important issue in control systems that are based on smart sensor networks, since it can support an adaptive functionality of the control network. The paper investigates the extension of a low cost sensor network with a smart emulator module, able to act as a virtual sensor node on the network. The embedded emulator module should allow running of several pre-trained neural networks for estimating the values of faulty sensors. Training of the neural networks is made on a PC based on the records available at the level of the gateway module interfacing the control network. The proposed approach is exemplified for the case of a distributed control network system applied to smart homes.

Download


Paper Citation


in Harvard Style

Stroia N., Moga D., Muresan V. and Lodin A. (2020). Estimating Environmental Variables in Smart Sensor Networks with Faulty Nodes.In Proceedings of the 9th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-418-3, pages 67-73. DOI: 10.5220/0009394500670073


in Bibtex Style

@conference{smartgreens20,
author={Nicoleta Stroia and Daniel Moga and Vlad Muresan and Alexandru Lodin},
title={Estimating Environmental Variables in Smart Sensor Networks with Faulty Nodes},
booktitle={Proceedings of the 9th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2020},
pages={67-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009394500670073},
isbn={978-989-758-418-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Estimating Environmental Variables in Smart Sensor Networks with Faulty Nodes
SN - 978-989-758-418-3
AU - Stroia N.
AU - Moga D.
AU - Muresan V.
AU - Lodin A.
PY - 2020
SP - 67
EP - 73
DO - 10.5220/0009394500670073