Hybrid moth flame-shark smell optimization-based secure wireless communications for IoT applications

Internet of Things (IoT) is a more eminent paradigm for wireless communication as it acts as a crucial part of data collection from the nodes. The computational complexity can be handled with the support of existing physical layered security approaches and higher layered cryptographic approaches that provides promising solutions. Secure communication can be ensured through the physical layered security methods to utilize the uniqueness of the wireless channel. This problem motivates the researchers to develop a 5G-based wireless IoT network for providing secured transmission. The developed IoT-based models include more devices having low complexity under harsh energy constraints. These challenges can be solved by using the physical layered security strategies together with energy harvesting which has arisen as a promising solution to solve these problems. This paper plans to introduce a secure wireless communication system considering the malicious behaviors of eavesdroppers. Here, a secure optimization problem is indulged to ensure communication security and the effective transfer of wireless energy. The core aim of this work is the optimization of the “transmit beamforming matrix and power splitting ratios” for enlarging the secrecy rate. The experimentation is carried out under imperfect and perfect Channel State Information (CSI) scenarios. The above-mentioned problem can be solved by promoting the latest hybridized optimization algorithm with the integration of Moth-Flame Optimization (MFO) and Shark Smell Optimization (SSO), which is termed Hybridized Moth Flame-Shark Smell Optimization (HMF-SSO). The proposed hybrid algorithm gains lower computational complexity and the highest secrecy rates when compared to traditional techniques. From the experiment findings, while taking the total to transmit power over secrecy rate, the effectiveness of the implemented HMF-SSO is correspondingly secured 4.6, 5.8, 7, and 9.75% advanced performance than PSO, GWO, MFO, and SSO algorithms, respectively for total transmit power at 7 dB. The empirical results reveal that the recommended method outperforms baseline algorithms significantly.