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

Abstract : 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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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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