502 research outputs found
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be
integrated into future cellular networks as new aerial mobile users. Providing
cellular connectivity to UAVs will enable a myriad of applications ranging from
online video streaming to medical delivery. However, to enable a reliable
wireless connectivity for the UAVs as well as a secure operation, various
challenges need to be addressed such as interference management, mobility
management and handover, cyber-physical attacks, and authentication. In this
paper, the goal is to expose the wireless and security challenges that arise in
the context of UAV-based delivery systems, UAV-based real-time multimedia
streaming, and UAV-enabled intelligent transportation systems. To address such
challenges, artificial neural network (ANN) based solution schemes are
introduced. The introduced approaches enable the UAVs to adaptively exploit the
wireless system resources while guaranteeing a secure operation, in real-time.
Preliminary simulation results show the benefits of the introduced solutions
for each of the aforementioned cellular-connected UAV application use case.Comment: This manuscript has been accepted for publication in IEEE Wireless
Communication
Applications, Challenges, and Research Issues for Enabling a UAV Swarm
Unmanned aerial vehicle (UAV) swarms have the potential to be useful in numerous applications due to their versatility and ability to operate without human intervention. However, this promising technology still requires further investigation, research, and testing before UAV swarms can be implemented extensively. The level of human intervention needed to control the system determines the differing levels of autonomy for UAV swarms. For swarms to become more independent, efficient algorithms for task and path planning are essential. In addition, accurate communication is essential for swarms to be able to coordinate and accomplish tasks successfully. This paper seeks to provide a review on the architecture, communication, applications, and challenges associated with UAV swarms. Furthermore, this paper discusses the types of communication that have been used or proposed for UAV swarms. Lastly, this paper provides a review of the potential applications of UAV swarms, as well as the research issues which still exist surrounding this technology
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
Communication Technologies Enabling Effective UAV Networks: A Standards Perspective
[EN] Recent developments in the unmanned aerial
vehicles (UAVs) field have made evident the need
for a standardization process of the communication technologies supporting direct information
exchange, thus enabling UAV-to-UAV networking. We consider this is necessary to achieve all
sorts of cooperative tasks requiring real-time (or
near-real-time) synchronization, including swarm
formation and collision avoidance. In this article,
we therefore argue in favor of introducing a new
standard that would address this specific area,
highlighting why current technologies are not
adequate, what the different steps toward rapid
standardization are, and which lessons have been
learned from related fields, namely the vehicular
and robotic environments, in the past few years.This work is derived from R&D project
RTI2018-096384-B-I00 funded by MCIN/
AEI/10.13039/501100011033 and ERDF A way
of making Europe.Vegni, AM.; Loscri, V.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2021). Communication Technologies Enabling Effective UAV Networks: A Standards Perspective. IEEE Communications Standards Magazine. 5(4):33-40. https://doi.org/10.1109/MCOMSTD.0001.2000074S33405
Hybrid LoRa-IEEE 802.11s Opportunistic Mesh Networking for Flexible UAV Swarming
Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key "ingredient" to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases
Machine Learning for Unmanned Aerial System (UAS) Networking
Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale.
With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring.
This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS
- …