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In the past decades, the unmanned aerial systems have been utilized only for military operations. However, recently, the potential uses and applicability of unmanned aerial vehicles (commonly known as drones) in civilian application domains are becoming a fast-growing phenomenon. A flying ad hoc network is a wireless ad hoc network specifically designed for the communication of unmanned aerial vehicles. Multicast routing is one of the vital aspects in wireless ad hoc networks. Using multicast transmission approaches, flying ad hoc network applications may need to send the same message to a specific group of flying nodes. The multicast communication approaches can benefit flying ad hoc network applications in conserving the scarce resources of flying nodes. Research works have been proposed to tackle the challenges in multicast routing with multi-hop communication in ad hoc network environments. Nevertheless, the conventional multicast routing mechanisms incur excessive control message overhead when a large number of nodes experience frequent topological changes. A scalable geographic multicast routing mechanism, which specially require localized operation and reduced control packet overhead, is necessary. Multicast routing in flying ad hoc networks is extremely challenging because of the dynamic topology changes and network disconnection resulted from frequent mobility of nodes. In this article, we present and implement a scalable and predictive geographic multicast routing mechanism in flying ad hoc networks. In uniform and random deployment scenarios, the MATLAB-based evaluation result has revealed that when the communication range increases, the probability of finding one-hop predicted forwarders to reach multicast destinations also increases. The implementation of scalable and predictive geographic multicast routing mechanism in flying ad hoc network is done using Optimizing Network Engineering Tools Modeler 16.0. We have added the scalable and predictive geographic multicast routing mechanism in flying ad hoc network as a new routing scheme in the Mobile Ad hoc Network routing protocol groups of the Optimizing Network Engineering Tools Modeler. Then, the performance of scalable and predictive geographic multicast routing mechanism in flying ad hoc network is compared with two of the existing Mobile Ad hoc Network routing protocols (Geographic Routing Protocol and Dynamic Source Routing). Eventually, we present two instance scenarios regarding the integration of scalable and predictive geographic multicast routing mechanism in flying ad hoc network scheme in the Internet of Things platform.

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