Flying Ad-hoc Networks (FANETs) being the most popular wing of the research in recent time had many evolutions to cope up with the growing demands in defense and other civil applications. Limited number of nodes, limited source of energy and increased mobility of the drones were the major problems to be addressed while building an effective FANET. Drones were aerial-nodes that are driver-less; their actions were prescribed by means of algorithms devoid of some human relations and could be easily deployed in the system. Hence this article aims to propose a novel framework to improvise the energy efficiency of FANET through Correlation and Confidence based Gradient Clustering Metric (CC-GCM) approach in the clustering algorithm.The results obtained through the proposed model are proven to be outstanding by producing an improvised performance efficiency and augmented energy efficiency. This article also involves a comparative study of the performance attributes with the existing models with tabulated values and graphs in terms of energy consumption, average end-to-end delay, cluster building time, average cluster life time, and Packet Delivery Ratio (PDR).

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Summary Flying adhoc network (FANET) is a rapidly flourishing technology in recent years which has an excellent scope of growth in the to the improvised mobility and the frequency changing topology FANETs are considered as a challenging technology to design an optimal network model. This problem is fixed with the topology based routing technique. Hence a novel hybrid clustering algorithm where fuzzy based Markov chain cluster (FMC2) is integrated with the optimized novel ant bee colony (ONABC) optimization algorithm (IoFMC2‐ONABC). This algorithm demonstrates an improvised efficiency with the novel ant bee colony algorithm. The proposed model simulation is done in an NS‐2 simulator and results are compared against the existing conventional approaches like adhoc on‐demand distance vector, efficient intelligent packet broadcasting protocol, energy‐efficient routing in flying ad hoc networks, and energy‐efficient gray wolf. This method aims at achieving improvised connectivity, optimizing the power consumption thereby increasing the residual energy augmenting the performance efficiency of the network through selection of the proper cluster center during each transmission. Thus the energy consumption of the node was optimized to 7.6 J with the highest packet delivery ratio of about 85.15 ensuring minimal packet losses during huge data transmission.