An enhanced energy-efficient fuzzy-based cognitive radio scheme for IoT
Neural Computing and Applications
Energy is a critical factor to be considered in electrical and electronic systems. With the advent of technology, numerous techniques have been developed in communication systems to make the systems reliable, durable, and economic. In modern communication systems, the major requirements of an efficient radio model are to improve the delay, and throughput, reduce the energy consumption, and extend the network lifetime. So, there is a need to design a radio model to improve the quality of service (QoS) parameters. From the limitations identified in the wireless communication networks, the authors proposed an Enhanced Energy-Efficient Fuzzy-based Cognitive Radio scheme for Internet of things (IoT) networks. The proposed protocol is compared with the conventional method, Cognitive Radio-based Heterogeneous Wireless Sensor Area Network. The test-bed results show that the EEFCR protocol has achieved a significant gain on sum goodput versus a number of secondary radio users, average probability of bit error, computational time vs. sensor nodes, delay vs. sensing time. The computational time of the EEFCR protocol is shown to be 5% to 7% and 15% to 21% faster while comparing to CoRHAN and conventional methods. The EEFCR sensing time is reduced up to 80%. The average computational time for 500 nodes is reduced up to 40%. Also, 53% increment is achieved in spectrum utilization. The average bit error is reduced up to 5%.
Open Access Status
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