CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Communication

Wireless sensor networks (WSNs) and their applications are on an increasing trend owing to the users’ demands with interest in environmental activities, intelligent cities, and medical assistance. However, because of the inefficiency and limitations of wireless sensor networks, it is still very difficult to optimize the network parameters and performance (energy consumption, area coverage and reliable information transfer). In this paper optimization algorithms including Ant Colony Optimization (ACO), Moth Flame Optimization (MFO) and Whale Optimization Algorithm (WOA) to enhance the effectiveness of wireless sensor systems are presented. In this approach, ACO captures and explores functional properties, MFO coordinates the optimization and exploration processes, and WOA concentrates on global exploration. Wide-ranging simulations demonstrate that the recommended approach is more effective than the existing approaches in enhancing energy efficiency, network lifetime and effectiveness of packet delivery. The findings in this paper indicate that the proposed hybrid algorithms support the various requirements of the WSN, thus promoting development of networks that are secure and dependable.