LAUSR

Ant colony optimization (ACO) is a well-applied technique to solve the real-time problem of discovering the energy-efficient routes to transmit the sensing information to the base station (BS). Traditionally, ACO incorporated wireless sensor networks used only one pheromone, i.e., minimum distance between the sensor nodes to discover the optimum route to the BS. The authors illustrated a multiple pheromone-based ACO technique known as multiple pheromone ant colony optimization (MPACO), for instance, distance between sensing nodes, their residual energy and number of neighbor nodes to ascertain an efficient route. MPACO enables the sensing nodes to transmit the sensing data to BS over optimal routes with economical energy consumption to achieve a prolonged network life span. The comprehensive evaluation reveals that MPACO proffers 20% more network lifetime than the current existing ACO technique, i.e., improved ACO. Moreover, MPACO shows a significant improvement of 300% in network life span than another existing fuzzy-based strategy, i.e., multi-objective fuzzy clustering algorithm.