A Distributed Unmanned Aerial Vehicles Traffic Management System

The rapid adoption of Internet of Things (IoT) has encouraged the integration of new
connected platforms such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous
network. UAVs promise a pragmatic solution to the limitations of existing terrestrial
IoT infrastructure as well as they bring new means of delivering services through a
wide range of applications ranging from monitoring and surveillance to on-demand
last-mile delivery and people transport. Owning to their potential, UAVs are expected
to soon dominate the low-altitude airspace over populated cities. This introduces
new research challenges such as the safe management of UAVs operation
under high traffic demands. In response to this, industry proposed a handful of
constructs for UAV Traffic Management (UTM), however due to their centralised
approaches, they will inevitably face limitations in scalability and resilience with
predicted traffic demands and advancement in UAV autonomy.
In this context, the main objective of this work is to address the aforementioned
problem by proposing a distributed UAV Traffic Management system (dUTM). This
thesis, hence, investigates the validity of the above hypothesis by:
(i) showing the performance insufficiency of centralised systems due to their inadequacy
in efficiently optimising large UAV traffic,
(ii) showing why a distributed system is favourable due to its characteristics of scalability
and resilience,
(iii) proposing a novel dUTM framework consisting of an airspace structure model,
information exchange model and a traffic optimisation model that rely on distributed
methods and approaches to intelligently handle highly dynamic and challenging
traffic conditions.
To this end, this manuscript contributes to scientific literature by proposing a novel
way of structuring the uncontrolled, low-altitude airspace and introduces a model of
the Class G airspace as a multi-weighted multilayer network of nodes and airways.
Additionally the work presents a novel distributed multiobjective path planning algorithm
incorporating a dynamic multi-criteria decision matrix allowing each UAV
or agent to plan their path relying on local knowledge gained via digital stigmergy.
The PhD thesis additionally contributes to existing state of the art by exploring the
technical standardisation landscape and investigating synergies between research
directions and standards developments, taking into consideration pressing inherit
challenges of UAVs within IoT such as security, data protection and privacy.