MULTI-TASK crow swarm-intelligent algorithm for enhancing spectrum efficiency and energy conservation in cognitive radio ad-hoc networking

Abstract

Cognitive Radio Ad-hoc Networking (CRAHN) could unlicensed users access neglected spectrum assets. This research offers an innovative Multi-task Crow Swarm-Intelligent (MCSI) methodology for ensuring the effective usage of spectrum and the preservation of energy in CRAHN. The suggested method uses a crow-inspired swarm's collective cognition to maximize the distribution of spectrum assets, adjust to shifting network circumstances, and consume energy as possible. We covered the important factors that influence spectrum effectiveness, like the population of the swarm, the sensor's threshold measurement, and the optimum amount of iterations, underscoring the significance of finding the right mix of these factors. In comparison to previous algorithms, this work gives a thorough evaluation of MCSI using simulated tests, highlighting its outstanding results with regard to spectrum effectiveness and preserving energy. The results suggest that MCSI combines spectrum use and energy savings, making it a promising CRAHN performance technology.

Keywords

• Cognitive radio ad-hoc networking (CRAHNN)
• Multi-task crow swarm-intelligent (MCSI)
• Spectrum use
• Energy-saving
• Spectrum availability
• Network circumstances