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

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.