Predictive geographic multicast routing protocol in flying ad hoc networks

Abstract

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.

Review of Protocol Stack Development of Flying Ad-hoc Networks for Disaster Monitoring Applications

“…In order to do this, a geographical advance measure is calculated for each UAV neighbour, which makes it possible to calculate the optimal routes to the destination. Furthermore, each UAV has a buffer for sending the data packets, and the quantity of loading and sending the information is taken into consideration while selecting the perfect path [ 117 ]. MPGR [ 104 ] is a greedy forwarding-based FANET positional routing protocol.…”

LoCaL: Link‐optimized cone‐assisted location routing in flying ad hoc networks

“…However, UAVs' high 3D mobility and limited energy lead to new challenges in route finding between UAVs. Recently, several routing protocols for FANETs have been suggested 12,13 . In FANETs, compared to the topology‐based routing algorithms, excellent performance has been exhibited by location‐based routing algorithms concerned with the stable route and a minimum number of next hop.…”

“…Recently, several routing protocols for FANETs have been suggested. 12,13 In FANETs, compared to the topology-based routing algorithms, excellent performance has been exhibited by location-based routing algorithms concerned with the stable route and a minimum number of next hop. The location-based routing approaches are straightforward since they utilize Global Positioning System (GPS) to determine the exact locations of a UAV.…”

A Seamless and Efficient Transition Algorithm For Aerial Drone Multicasting

“…They however introduce considerable traffic overheads or complex maintenance for networks and their devices, making them unsuitable for drones that often have energy constraints and computation limitations. More popular studies develop geographic multicasting [3][4][5][6]. In general, geographic multicasting arranges group members into different physical zones based on their locations.…”