By connecting a vast array of objects and enabling remote as well as global control over them, wireless sensor networks (WSN) and internet of things (IoT) have drastically altered the world in an advanced and effective manner. Making high precision on the real-time observations is the primary goal of implementing WSN-based applications, but it is very difficult to achieve due to the sensors’ limited computing power when operating in restricted environments, a large volume of high-speed, heterogeneous, and resource-limited data, including energy, memory, computation speed, quickly-changing data, and bandwidth. The IoT is a cutting-edge technology that is now a necessary component of smart cities. Sensor nodes must overcome resource constraints in order to carry out the improved routing procedure. Therefore, a hybrid heuristic method utilizing the osprey optimization algorithm (OOA) and barnacles mating optimizer (BMO) is suggested in this work in order to improve the processing of IoT-based WSN nodes. This algorithm is termed the hybrid barnacles mating with osprey optimization algorithm (Hy-BMOO) algorithm. In order to choose the best path for network transmission, this hybrid method examines the node properties. Consequently, the multi-objective function, which includes some constraints to guarantee system efficiency, is formulated with the aid of the optimum routing. Lastly, a comparison with certain current techniques and an analysis of the suggested model using specific metrics are conducted. As a result, thorough research reveals that it obtains encouraging outcomes in determining the best course for improved communication.
S. Kannan, Jafar A. Alzubi, Punithavathi Rasappan et al.· Scientific Reports· 0 citations
The Millimeter-wave massive Multiple-Input Multiple-Output (MIMO) is a core mechanism for the Sixth-Generation (6G) wireless communication networks. By including numerous antennas in the compact model of advanced smartphones, the MIMO enhances the network capacity and the Spectral Efficiency (SE). The growth of 6G technology is significant for the future and provided evolutionary and revolutionary solutions. The resource allocation in the MIMO-based wireless networks is selected for various users, aiming to optimize the network resource distribution. But the high increase in the antennas and users poses complexities for the resource allocation and interference suppression for the MIMO systems. In this research, an advanced Deep Reinforcement Learning (DRL)-based approach is proposed for efficient dynamic spectrum allocation in 6G MIMO systems. To perform spectrum allocation in 6G MIMO systems, an Adaptive Deep Multiagent Reinforcement Learning with Co-ordinate Attention (ADMRL-CA) model is developed. The DMRL mechanism is capable of handling varying traffic and channel conditions. The incorporation of the CA mechanism enhances the policy learning process for accurate spectrum allocation. The parameters of the ADMRL-CA are fine-tuned using the Flying workers phase Modified Termite Queen Algorithm (FMTQA). Finally, the model performance is analyzed with various existing models. The SE of the recommended FMTQA-ADMRL-CA is increased by 4.44% of DRL, 6.66% of SAC, 2.77% of DDPG and 10.88% of DMRL-CA when system uses as 32
nd
batch size. Hence, it is guaranteed that the recommended FMTQA-ADMRL-CA can allocate the spectrum efficiently and robustly in 6G MIMO systems than the existing methods.
Asha Aiyappan, Jafar A. Alzubi, M. P. Rajakumar et al.· Scientific Reports· 0 citations
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