In recent years, with the growth in Unmanned Aerial Vehicles (UAVs), UAV-based systems have become popular in both military and civil applications. In these scenarios, the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes, also known as Flying Ad Hoc Networks (FANETs). However, in FANETs, the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology, making end-to-end connections in FANETs challenging. Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability. We thus develop a Software Defined Network (SDN)-based heterogeneous architecture for reliable communication in FANETs. In this architecture, we apply an Extended Kalman Filter (EKF) for accurate mobility estimation and prediction of UAVs. In particular, we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem. As the problem is NP-hard, we further propose a Directional Particle Swarming Optimization (DPSO) approach to solve it. The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput, packet delivery ratio, and delay.
All Science Journal Classification (ASJC) codes