Integrating Routing Schemes and Platform Autonomy Algorithms for UAV Ad-hoc & Infrastructure based networks

This paper highlights the considerations for implementing autonomous or self-organising unmanned aerial vehicles (UAVs) for communications area coverage with particular emphasis on the impact of aerial vehicle autonomy algorithms on routing techniques for such networks. UAV networks can be deployed either as ad-hoc or infrastructure based solutions. The mobility of UAVs introduce periodic topology changes, impacting link availability and routing paths. This work examines the implications of autonomous coordination of multiple UAVs on routing techniques and network architecture stability. The paper proposes a solution where routing techniques and UAV autonomy algorithms are integrated for improved global network efficiency for both ad-hoc and infrastructure-based scenarios. Integrating UAV autonomy algorithms with routing schemes may be an efficient method to mitigate link/topology stability issues and improve inter-UAV communication and network throughput, a key requirement for UAV networks. The implementation of interUAV links using optical, microwave or mmWave transmission was examined as a critical element in the context of this work. The proposed integration may be crucial for communications coverage, where UAVs provide communications area coverage to community of mobile or fixed users in either ad-hoc or infrastructure based modes

Situation Awareness and Routing Challenges in Unmanned HAPS/UAV based Communications Networks

This paper examines Situation Awareness (SA) as a factor in addressing routing challenges in HAPS/UAV based networks especially in multi-HAPS/UAV implementations. Routing in UAV based networks is a critical element for successful transmission of data from source to destination node. However, in UAV based networks the concept of routing assumes a more challenging dimension mainly due to the mobility of the vehicles or platforms. This paper highlights how situation awareness can impact routing decisions and consequently improve network throughput. It is suggested that SA should be considered a factor in mitigating routing challenges in UAV based networks

Swarm of UAVs for Network Management in 6G: A Technical Review

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs. Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile networks. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.Comment: 19,

UTILIZING THE MESSAGING LAYER SECURITY PROTOCOL IN A LOSSY COMMUNICATIONS AERIAL SWARM

Recent advancements in unmanned aerial vehicle (UAV) capabilities have led to increasing research into swarming systems. Tactical employment of UAV swarms, however, will require secure communications. Unfortunately, efforts to date have not resulted in viable secure communications frameworks. Furthermore, the limited processing power and constrained networking environments that characterize these systems preclude the use of many existing secure group communications protocols. Recent research in secure group communications indicates that the Messaging Layer Security (MLS) protocol might provide an attractive option for these types of systems. This thesis documents the integration of MLS into the Advanced Robotic Systems Engineering Laboratory (ARSENL) UAV swarm system. The ARSENL implementation is intended as a proof-of-concept demonstration of the efficacy of MLS for secure swarm communications. Implementation test results are presented both for experiments conducted in a simulation environment and experiments with physical UAVs. These results indicate that MLS is suitable for a swarm, with the caveat that testing did not implement a delivery mechanism to ensure reliable packet delivery. For future work, mitigation of unreliable communications paths is required if a reliable MLS system is to be maintained.Civilian, CyberCorps: Scholarship for ServiceApproved for public release. Distribution is unlimited

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Using a Multiobjective Approach to Balance Mission and Network Goals within a Delay Tolerant Network Topology

This thesis investigates how to incorporate aspects of an Air Tasking Order (ATO), a Communications Tasking Order (CTO), and a Network Tasking Order (NTO) within a cognitive network framework. This was done in an effort to aid the commander and or network operator by providing automation for battlespace management to improve response time and potential inconsistent problem resolution. In particular, autonomous weapon systems such as unmanned aerial vehicles (UAVs) were the focus of this research This work implemented a simple cognitive process by incorporating aspects of behavior based robotic control principles to solve the multi-objective optimization problem of balancing both network and mission goals. The cognitive process consisted of both a multi-move look ahead component, in which the future outcomes of decisions were estimated, and a subsumption decision making architecture in which these decision-outcome pairs were selected so they co-optimized the dual goals. This was tested within a novel Air force mission scenario consisting of a UAV surveillance mission within a delay tolerant network (DTN) topology. This scenario used a team of small scale UAVs (operating as a team but each running the cognitive process independently) to balance the mission goal of maintaining maximum overall UAV time-on-target and the network goal of minimizing the packet end-to-end delays experienced within the DTN. The testing was accomplished within a MATLAB discrete event simulation. The results indicated that this proposed approach could successfully simultaneously improve both goals as the network goal improved 52% and the mission goal improved by approximately 6%

Comparative Study for Coordinating Multiple Unmanned HAPS for Communications Area Coverage.

This work compares the application of Reinforcement Learning (RL) and Swarm Intelligence (SI) based methods for resolving the problem of coordinating multiple High Altitude Platform Stations (HAPS) for communications area coverage. Swarm coordination techniques are essential for developing autonomous capabilities for multiple HAPS/UAS control and management. This paper examines the performance of artificial intelligence (AI) capabilities of RL and SI for autonomous swarm coordination. In this work, it was observed that the RL approach showed superior overall peak user coverage with unpredictable coverage dips; while the SI based approach demonstrated lower coverage peaks but better coverage stability and faster convergence rates