An Energy-Efficient Quantum Sunflower Optimization Based Clustering Algorithm for Internet of Things Environment

Abstract

Internet of Things (IoT) is a network of Internet enabled gadgets which has the ability of sensing, communicating, and reacting to the changes occurred in the target region. It finds use in several application areas ranging from civilian to military application. The collaboration amongst the IoT gadgets in several applications in the real time environment poses a challenging issue. In addition, energy constrained characteristics of the IoT devices also encounters a major design issue and can be resolved by the use of clustering techniques.  This paper presents an energy-efficient quantum sunflower optimization based clustering (EEQSFOC) technique for IoT environments. The sunflower optimization (SFO) algorithm is based on the movement of sunflowers to observe solar radiations. In addition, quantum computing concept is incorporated into the SFO algorithm to improve the searching process, and obtain better tradeoff among the exploration and exploitation capabilities. For validating the effectual energy efficient performance of the EEQSFOC technique, a set of simulations were performed and the results are examined interms of distinct measures. The experimental outcome ensured the betterment of the EEQSFOC technique over the existing clustering techniques.