An Efficient Framework for Data Dissemination in Multi-UAV Ad hoc Networks

Abstract

Unmanned aerial vehicles (UAVs) are self-abundant flying robots, which provide efficient, low-complex, critical connectivity and concordance coverage. Multiple UAVs form an autonomous connected communication network which has revolutionized both civilian and military aviation and paved in towards unprecedented innovations in the area of infrastructure less connectivity. UAVs are sometimes interpreted as and are equated to mobile connected devices, however, UAVs demand extra features and advance planning towards network formations, transmission scheduling, data dissemination, trajectory and velocity planning and Quality of Service (QoS) requirements.

It is copiously acknowledged and proved with simulations, real time research and industry modelings that aerial and ground networks can come together and perform complex tasks uplifting the barriers of energy, environment, geography and trajectory. The works presented in this thesis target data dissemination is multi UAV ad hoc networks. Both transmission scheduling and aerial mobility aspects of data dissemination are presented. Initially, an efficient data dissemination scheme for multi-UAV assisted Wireless Sensor Networks (WSN) is presented. The WSN network formations constantly suffer from depleting charge forcing the topology into constant reconfiguration. On account of rapidly depleting and re-configuring ground nodes a data dissemination framework is presented. In order to facilitate data aggregation and transmission a virtual topology is constructed by exploiting aerial network formation and Software Defined Networks (SDN). The topology is constantly monitored and reconfigured when required. An effective and efficient sleep timer and back-off counter is also proposed on accord of depleting energy.

Building upon the transmission scheduling framework, two UAV Mobility/Trajectory Frameworks for enhanced coverage and data dissemination in multi-UAV ad hoc networks are presented. First, a novel mobility framework for multi-UAV assisted WSNs is proposed which takes into account average transmission densities of the underlying topology/geography. The underlying network is classified dense or scarce based on the derived attraction factor, the classification is further processed to generate UAV waypoints. Second, an SDN based mobility framework for communication and coordination among aerial and ground nodes is introduced. The SDN controller provides the opportunity to update flows on the move, thus, adapting to the dynamic topology. This helps updating the legal moves through reconfigurable flow tables.

A QoS enhancement mechanism for multi UAV ground ad hoc networks is presented in form of a throughput maximization approach which involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance.

The coordination between aerial and ground nodes has enhanced the versatility and quality of the traditional networks but if unaddressed, it also exposes the overall networked infrastructure. The conceptualization of attacks on UAV systems is as inherent as the exciting possibilities and future that comes along Unmanned Aerial Systems (UAS). The thesis presents a study of possible threats, vulnerabilities and attacks mounted on the connected UAS and presents a framework for safeguarding UAS against malicious attackers and recovering the rogue UAVs. Recurrent neural networks and multivariate component analysis is used for detecting outliers and abnormalities in the aerial networked environment