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              <p>For wireless sensor network nodes (WSNNs), whichare usually distributed over wide area and transmit intermittently,radio frequency energy harvesting (RFEH) from ambientsources such as base stations, TV towers, access points etc. seemto be practical and promising approach. Such RFEH enabledWSNNs need intelligence to choose optimal frequency band(s) for RFEH from wideband spectrum. Furthermore, for minimizing energy consumption, number of frequency band switching (FBS) should be as minimum as possible. To meet these requirements,a new multi-stage decision making policy (DMP) for RFEHenabled WSNNs has been proposed in this paper. The proposed DMP uses Bayes-UCB algorithm to identify the subbands with high RFEH potential. Another novelty of the proposed DMP is that RFEH duration is made tunable based on the RF potentialof the chosen subband(s) leading to significant improvement in harvested energy and number of FBS. Simulation results showthat the proposed DMP offer 10%–18% improvement in total harvested RF energy. Furthermore, the number of FBS in theproposed DMP are 25%–50% of that in existing DMPs making the proposed DMP a preferred choice for resource-constrainedWSNNs.</p>
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