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Communication Dans Un Congrès Année : 2018

Feature Selection Framework for Multi-source Energy Harvesting Wireless Sensor Networks

Résumé

Energy harvesting technologies are constantly evolving to help power sensor network nodes. Ranging from miniature power solar panels to micro wind turbines, nodes still express a deep need to harvest energies in order to keep both good performance level and energy autonomy. Recently, the simultaneous use of multiple sources has been proposed to tackle the time-varying characteristics of certain sources that can induce energy scarcity period and thus alter the node performance. In this context, this paper presents a methodology aimed at classifying the energy sources to choose the most efficient energy manager. As sensor nodes are embedded devices, it is necessary to ensure a balance between computational effort and classification accuracy. Feature extraction and selection phases can be processed and analyzed offline before deployment, and only a subset of features will be needed by the nodes to achieve efficient energy management. Simulations on real energy traces show that the proposed approach achieves classification accuracy higher than 95% through the computation of 4 features only.
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Dates et versions

hal-01794094 , version 1 (17-05-2018)

Identifiants

  • HAL Id : hal-01794094 , version 1

Citer

Marwa Lagha Kazdoghli, Fayçal Ait Aoudia, Matthieu Gautier, Olivier Berder. Feature Selection Framework for Multi-source Energy Harvesting Wireless Sensor Networks. IEEE Vehicular Technology Conference (VTC-Spring), Jun 2018, Porto, Portugal. ⟨hal-01794094⟩
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