Enhanced Cluster Head Selection Based Resource Allocation with Hybrid PSO and Modified Moth Flame Optimization in Cognitive Radio Networks for IoT Applications

Abstract

The enhanced cluster head selection-based resource allocation with hybrid PSO and modified moth flame optimization in cognitive radio networks for IoT applications (ECHRAC) approach proposed by IBM aims to reduce power consumption and increase energy efficiency in Cognitive Radio Network (CRN) nodes through an advanced method, which is highly sophisticated. ECHRAC employs a dual-phase approach that efficiently selects the cluster head (CH) and uses specialized algorithms in conjunction with inverse optimization methods. Spectrum sensing plays a crucial role in the selection of CH. Using primary user (PU) channels by clusters of secondary users will enhance spectrum access and decrease interference during this phase. ECHRAC employs a delicate approach to the probabilistic framework that manages false alarms, setting up high detection thresholds in such synchronization to prevent interference with PU. The ECHRAC's cluster formation and path selection phase is given significant attention. Nodes in the CRN are dynamically clustered according to the availability of the spectrum and their proximity to nodes. A complex selection procedure is involved in this stage, which identifies nodes with optimal energy and connectivity attributes for CH roles. ECHRAC employs a unique energy state function that utilizes Energy Harvesting (EH) to determine the cluster's CH status. This encompasses energy harvested, battery status, and energy consumption for data forwarding and control signaling. By selecting CHs through a competitive process, nodes that meet these energy requirements are selected to maintain varying amounts of energy across the network. ECHRAC employs an energy-based control system that divides nodes into active, sleep and dead states based on their remaining energy to improve the reliability of data transmission. This mechanism minimizes energy depletion risk, which permits nodes in low-power states to prioritize crucial tasks or switch to sleep mode to conserve energy. A hybrid approach is employed during the optimization phase, which involves combining an Improved Particle Swarm Optimization (PSO) algorithm with the Modified Moth Flame Optimization (MFO) Algorithm. ECHRAC's PSO algorithm utilizes a particle-based representation scheme to represent potential solutions, while also considering the optimization of node parameters for efficient clustering and route selection. By using a logarithmic spiral function, the MFO algorithm dynamically alters the paths of CH nodes to achieve optimal convergence towards high-fitness node convection, while minimizing the need for flames in each iteration. By utilizing dual optimization techniques, network longevity, and throughput are improved, and data transmission reliability is enhanced by prioritizing routes with energy-efficient CH nodes. Simulation results show that ECHRAC has a significant impact on several performance metrics of CRN. This model is implemented using MATLAB software, focusing on parameters such as network throughput, power usage, energy efficiency, data delivery ratio, and average delay for performance analysis.