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Cognitive radio-based wireless sensor network is the next-generation sensor network paradigm. Important to this emerging sensor network is the need to reduce energy consumption, paving way for 'green' communication among sensor nodes. Therefore, in this paper, we have proposed an energy-efficient, learning-inspired, adaptive and dynamic channel decision and access technique for cognitive radio-based wireless sensor networks. Using intelligent learning technique based on the previous experience, the cognitive radio-based wireless sensor network agent decides which available channel to access based on the energy-efficiency achievable by transmitting using the channel. From simulation results, we found that as the channel packet availability increases, the energyefficiency of the channel increase. This lends credence to the fact that the proposed learning-inspired algorithm is significantly energy-efficient for cognitive radio-based wireless sensor networks.

The Universal Software Radio Peripheral (USRP) and GNU Radio framework have a wide range of applications and are actively in use for teaching, research and for practical deployment of various trending wireless technologies. In this work, we explore the relationships between the power outputs of a USRP B200 device and its operating frequencies and USRP Hardware Driver (UHD) gains. These relationships are not precisely specified on the device datasheet, thereby hindering prompt design and experimentation decisions among Software Defined and Cognitive Radio (SDCR) researchers. A general purpose handheld spectrum analyser was engaged for recording the power outputs from the USRP B200 device within an experimental testbed that was setup for this study. The B200 device was driven by a GNU radio flowgraph designed for both wired and wireless modes and running on a general purpose host computer in the testbed. The results indicate that the output power decreases while the centre frequency increases, whereas the output power increases as the UHD gain increases from 65 dB until the device reaches the saturation point at 89.9 dB. These results provide a handy toolkit for SDCR researchers and practitioners.

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