Distributed decision making policy for frequency band selection boosting RF energy harvesting rate in wireless sensor nodes

Abstract

Emerging paradigms such as smart cities and Internet of Things are expected to be an intrinsic part of next generation communication standards. To bring these paradigms to life, self-sustainable wireless sensor network (WSN) nodes capable of seamless and maintenance free operation at remote locations are desired. Recently, radio frequency energy harvesting (RFEH) circuits capable of harvesting RF power transmitted by base stations, TV towers and other ambient RF sources have been developed. Low power requirements and architectural compatibility between WSN nodes and RFEH circuits make RFEH a promising and feasible solution for WSN nodes. In this paper, a novel multi-stage decision-making policy (DMP) for RFEH enabled WSN nodes has been proposed. It offers an intelligence, via online learning algorithm, for characterization and selection of frequency bands based on their RF potential especially in the dynamic spectrum environment. Furthermore, proposed DMP supports multiantenna multi-band harvesting capabilities of the RFEH circuits. The final contribution includes tunable RFEH duration that leads to significant improvement in the harvested energy and fewer number of frequency band switchings (FBS). Derived theoretical performance bounds and simulation results validate the superiority of proposed DMP in terms of the harvested RF energy and

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