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.