UAV‐SHUTTLE‐MMM: UAV‐shuttle‐enabled mobility management model in sparsely deployed flying ad hoc networks

Abstract

SummaryThe reliable surveillance of a large region typically needs a large number of unmanned aerial vehicles (UAVs), which leads to high operational costs in flying ad hoc networks (FANETs). Therefore, the UAV mobility model plays an important role that affecting the performance of FANET. This research paper comes up with a novel UAV‐shuttle‐enabled mobility management model (US‐MMM) that is a crucial attribute for the large and highly sparse network. The proposed mobility model uses UAV shuttles. A relatively lesser number of UAVs are used than comparative approaches to enhance the connectivity among the UAVs in a sparse network. UAV shuttle is the special self‐governing node, which traverses the network and collects the data to deliver to the base station (BS). The main strength of the proposed mobility model is the self‐governing ability of the UAV shuttle, which leads to high connectivity in the network. The UAV shuttle executes the proposed dynamic path planning algorithm and uses the adaptive ranking algorithm to determine its target. The main contribution of this research work is to develop a subnet scan and Number of Neighbor and Distance‐based Adaptive Ranking of Targets (NN‐DART) algorithm for dynamic route planning of UAV shuttle. The proposed mobility model is used with various routing schemes to check the difference in the performance of various routing schemes. The proposed mobility model is simulated in NS‐2.35, and the results have shown an improvement in the packet delivery ratio, routing overhead, and end‐to‐end delay.