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Authors: Marco Pellegrini 1 ; Renato De Leone 2 ; Pierluigi Maponi 2 and Maurizio Ferretti 3

Affiliations: 1 LIF srl, Italy ; 2 Università di Camerino, Italy ; 3 Regione Marche - Centro Funzionale Multirischi, Italy

Keyword(s): Adaptive Systems, Support Vector Machines, Environmental Engineering.

Related Ontology Subjects/Areas/Topics: Adaptive Systems ; Digital Signal Processing ; Embedded Communications Systems ; Sensors and Sensor Networks ; Telecommunications

Abstract: Environmental monitoring is a challeging task for both researchers and technical operators. Data loggers for ultrasonic hydrometric level sensors are compact devices equipped with microprocessor input channels and data storage. One of the critical issues that electronic engineers have to face in designing this kind of sensors is the energy consumption during the sensor startup phase preceding the level measurement. In this paper we propose a new methodology to reduce the power consumption by decreasing the sensor sampling rate when no flood events are occurring. This procedure allows the sampling rate to dynamically self-adapt based on the error between observed and predicted water level time-trend. Support Vector Machines are used to predict the hydrometric level given a limited number of previous samples. The method effectiveness has been tested on a real-world stage-discharge dataset.

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Paper citation in several formats:
Pellegrini, M.; De Leone, R.; Maponi, P. and Ferretti, M. (2013). Reducing Power Consumption in Hydrometric Level Sensor Networks using Support Vector Machines. In Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems - PECCS; ISBN 978-989-8565-43-3; ISSN 2184-2817, SciTePress, pages 229-232. DOI: 10.5220/0004312602290232

@conference{peccs13,
author={Marco Pellegrini. and Renato {De Leone}. and Pierluigi Maponi. and Maurizio Ferretti.},
title={Reducing Power Consumption in Hydrometric Level Sensor Networks using Support Vector Machines},
booktitle={Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems - PECCS},
year={2013},
pages={229-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004312602290232},
isbn={978-989-8565-43-3},
issn={2184-2817},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems - PECCS
TI - Reducing Power Consumption in Hydrometric Level Sensor Networks using Support Vector Machines
SN - 978-989-8565-43-3
IS - 2184-2817
AU - Pellegrini, M.
AU - De Leone, R.
AU - Maponi, P.
AU - Ferretti, M.
PY - 2013
SP - 229
EP - 232
DO - 10.5220/0004312602290232
PB - SciTePress