The Community for Technology Leaders
2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) (2018)
Chengdu, China
Oct 9, 2018 to Oct 12, 2018
ISSN: 2155-6814
ISBN: 978-1-5386-5580-1
pp: 380-387
Wireless sensor networks (WSN) are widely used in environmental applications where the aim is to sense a physical parameter such as temperature, humidity, air pollution, etc. Most existing WSN-based environmental monitoring systems use data interpolation based on sensor measurements in order to construct the spatiotemporal field of physical parameters. However, these fields can be also approximated using physical models which simulate the dynamics of physical phenomena. In this paper, we focus on the use of wireless sensor networks for the aim of correcting the physical model errors rather than interpolating sensor measurements. We tackle the activity scheduling problem and design an optimization model and a heuristic algorithm in order to select the sensor nodes that should be turned off to extend the lifetime of the network. Our approach is based on data assimilation which allows us to use both measurements and the physical model outputs in the estimation of the spatiotemporal field. We evaluate our approach in the context of air pollution monitoring while using a dataset from the Lyon city, France and considering the characteristics of a monitoring system developed in our lab. We analyze the impact of the nodes' characteristics on the network lifetime and derive guidelines on the optimal scheduling of air pollution sensors.
air pollution measurement, approximation theory, data assimilation, energy conservation, environmental monitoring (geophysics), interpolation, optimisation, telecommunication network reliability, telecommunication scheduling, wireless sensor networks

A. Boubrima, A. Boukerche, W. Bechkit and H. Rivano, "WSN Scheduling for Energy-Efficient Correction of Environmental Modelling," 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Chengdu, China, 2019, pp. 380-387.
158 ms
(Ver 3.3 (11022016))