Ankur Pandey's research while affiliated with Oriental Institute of Science and Technology of Rajiv Gandhi Proudyogiki Vishwavidyalaya and other places

Unmanned Aerial Vehicles (UAVs) are well appropriate devices for wireless communication in Flying Ad-hoc Networks deployed in several applications like disaster and rescue management. The challenging issues of UAVs such as tiny flight time and infertile routing in view of constrained battery ability and maximum movement are abridged by utilizing the Salp Swarm Optimization based Clustering Algorithm (SSOCA). The proficiency of SSOCA is analyzed in terms of the packet delivery ratio, cluster existence period, total clusters, throughput, delay, cluster construction time, and energy consumption and consequences clarify the preferable adeptness of SSOCA against several previous clustering approaches such as MOPSO, CLPSO, CACONET, and CAVDO.

Flying Ad-hoc Networks (FANETs) and Unmanned Aerial Vehicles (UAVs) are widely utilized in various rescues, disaster management and military operations nowadays. The limited battery power and high mobility of UAVs create problems like small flight duration and unproductive routing. In this paper, these problems will be reduced by using efficient hybrid K-Means-Fruit Fly Optimization Clustering Algorithm (KFFOCA). The performance and efficiency of K-Means clustering is improved by utilizing the Fruit Fly Optimization Algorithm (FFOA) and the results are analyzed against other optimization techniques like CLPSO, CACONET, GWOCNET and ECRNET on the basis of several performance parameters. The simulation results show that the KFFOCA has obtained better performance than CLPSO, CACONET, GWOCNET and ECRNET based on Packet Delivery Ratio (PDR), throughput, cluster building time, cluster head lifetime, number of clusters, end-to-end delay and consumed energy.

Unmanned aerial vehicles (UAVs) are broadly used for disaster and rescue operations in flying ad-hoc networks (FANETs). UAVs generate problems like small time for flying and infertile routing due to limited power of battery and high velocity. In this paper, t hese issues will be condensed by utilizing efficient Grasshopper Optimization based Clustering Algorithm (GOCA). The performance of clustering is enhanced by using the grasshopper optimization algorithm and outputs are analyzed in terms of several performance parameters and compared with other optimization approaches like CLPSO, CACONET, and GWOCNET. The experimental results represent that the GOCA has been generated better efficiency than CLPSO, CACONET, and GWOCNET on the basis of packet delivery ratio (PDR), cluster lifetime, end to end delay and consumed energy.