Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks

Wireless sensor networks (WSNs) are generically self-configuring and organizing networks with constrained communicational ability and energy supply. One of the crucial crises in WSN is the employment of energy effectual routing and load balancing protocol to improve network lifetime. Therefore, this work anticipated an effectual load balancing and routing strategies using the Glowworm swarm optimization approach (LBR-GSO). This LBR-GSO employs a pseudo-random route discovery algorithm and an enhanced pheromone trail-based updating strategy to handle the energy consumption of sensor nodes. It utilizes an effectual heuristic updating algorithm based on cost effectual energy measure to optimize route establishment. At last, to eliminate energy consumption that causes due to control overhead, LBR-GSO cast-off energy-based broadcasting strategy has been proposed. Here, WSNs environment is simulated in MATLAB for various application scenarios to compute LBR-GSO along with metrics such as energy efficiency, energy consumption and prolonging network lifetime. Outcomes derived from this comprehensive analysis determine that LBR-GSO offers an effectual enhancement in contrary to prevailing approaches like ACO, EE-ACO and s-Ant approaches.