Suresense: sustainable wireless rechargeable sensor networks for the smart grid

The electrical power grid has recently been embracing the advances in Information and Communication Technologies (ICT) for the sake of improving efficiency, safety, reliability and sustainability of electrical services. For a reliable smart grid, accurate, robust monitoring and diagnosis tools are e...

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Bibliographic Details
Published in:IEEE wireless communications 2012-06, Vol.19 (3), p.30-36
Main Authors: Erol-Kantarci, M., Mouftah, H. T.
Format: Article
Language:English
Subjects:
Batteries
Electricity supply industry
Energy exchange
Information exchange
Power system reliability
Radio frequency
Robot sensing systems
Smart grids
Studies
Wireless communication
Wireless sensor networks
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Summary:The electrical power grid has recently been embracing the advances in Information and Communication Technologies (ICT) for the sake of improving efficiency, safety, reliability and sustainability of electrical services. For a reliable smart grid, accurate, robust monitoring and diagnosis tools are essential. Wireless Sensor Networks (WSNs) are promising candidates for monitoring the smart grid, given their capability to cover large geographic regions at low-cost. On the other hand, limited battery lifetime of the conventional WSNs may create a performance bottleneck for the long-lasting smart grid monitoring tasks, especially considering that the sensor nodes may be deployed in hard to reach, harsh environments. In this context, recent advances in Radio Frequency (RF)-based wireless energy transfer can increase sustainability of WSNs and make them operationally ready for smart grid monitoring missions. RF-based wireless energy transfer uses Electromagnetic (EM) waves and it operates in the same medium as the data communication protocols. In order to achieve timely and efficient charging of the sensor nodes, we propose the Sustainable wireless Rechargeable Sensor network (SuReSense). SuReSense employs mobile chargers that charge multiple sensors from several landmark locations. We propose an optimization model to select the minimum number of landmarks according to the locations and energy replenishment requirements of the sensors.
ISSN:1536-1284
1558-0687
DOI:10.1109/MWC.2012.6231157