1,312 research outputs found
Lightweight Simulation of Hybrid Aerial- and Ground-based Vehicular Communication Networks
Cooperating small-scale Unmanned Aerial Vehicles (UAVs) will open up new
application fields within next-generation Intelligent Transportation Sytems
(ITSs), e.g., airborne near field delivery. In order to allow the exploitation
of the potentials of hybrid vehicular scenarios, reliable and efficient
bidirectional communication has to be guaranteed in highly dynamic
environments. For addressing these novel challenges, we present a lightweight
framework for integrated simulation of aerial and ground-based vehicular
networks. Mobility and communication are natively brought together using a
shared codebase coupling approach, which catalyzes the development of novel
context-aware optimization methods that exploit interdependencies between both
domains. In a proof-of-concept evaluation, we analyze the exploitation of UAVs
as local aerial sensors as well as aerial base stations. In addition, we
compare the performance of Long Term Evolution (LTE) and Cellular
Vehicle-to-Everything (C-V2X) for connecting the ground- and air-based
vehicles
IRS-assisted UAV Communications: A Comprehensive Review
Intelligent reflecting surface (IRS) can smartly adjust the wavefronts in
terms of phase, frequency, amplitude and polarization via passive reflections
and without any need of radio frequency (RF) chains. It is envisaged as an
emerging technology which can change wireless communication to improve both
energy and spectrum efficiencies with low energy consumption and low cost. It
can intelligently configure the wireless channels through a massive number of
cost effective passive reflecting elements to improve the system performance.
Similarly, unmanned aerial vehicle (UAV) communication has gained a viable
attention due to flexible deployment, high mobility and ease of integration
with several technologies. However, UAV communication is prone to security
issues and obstructions in real-time applications. Recently, it is foreseen
that UAV and IRS both can integrate together to attain unparalleled
capabilities in difficult scenarios. Both technologies can ensure improved
performance through proactively altering the wireless propagation using smart
signal reflections and maneuver control in three dimensional (3D) space. IRS
can be integrated in both aerial and terrene environments to reap the benefits
of smart reflections. This study briefly discusses UAV communication, IRS and
focuses on IRS-assisted UAC communications. It surveys the existing literature
on this emerging research topic and highlights several promising technologies
which can be implemented in IRS-assisted UAV communication. This study also
presents several application scenarios and open research challenges. This study
goes one step further to elaborate research opportunities to design and
optimize wireless systems with low energy footprint and at low cost. Finally,
we shed some light on future research aspects for IRS-assisted UAV
communication
A Holistic Investigation on Terahertz Propagation and Channel Modeling Toward Vertical Heterogeneous Networks
User-centric and low latency communications can be enabled not only by small
cells but also through ubiquitous connectivity. Recently, the vertical
heterogeneous network (V-HetNet) architecture is proposed to backhaul/fronthaul
a large number of small cells. Like an orchestra, the V-HetNet is a polyphony
of different communication ensembles, including geostationary orbit (GEO), and
low-earth orbit (LEO) satellites (e.g., CubeSats), and networked flying
platforms (NFPs) along with terrestrial communication links. In this study, we
propose the Terahertz (THz) communications to enable the elements of V-HetNets
to function in harmony. As THz links offer a large bandwidth, leading to
ultra-high data rates, it is suitable for backhauling and fronthauling small
cells. Furthermore, THz communications can support numerous applications from
inter-satellite links to in-vivo nanonetworks. However, to savor this harmony,
we need accurate channel models. In this paper, the insights obtained through
our measurement campaigns are highlighted, to reveal the true potential of THz
communications in V-HetNets.Comment: It has been accepted for the publication in IEEE Communications
Magazin
Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networks
In this paper, we study a digital twin (DT)-empowered integrated sensing,
communication, and computation network. Specifically, the users perform radar
sensing and computation offloading on the same spectrum, while unmanned aerial
vehicles (UAVs) are deployed to provide edge computing service. We first
formulate a multi-objective optimization problem to minimize the beampattern
performance of multi-input multi-output (MIMO) radars and the computation
offloading energy consumption simultaneously. Then, we explore the prediction
capability of DT to provide intelligent offloading decision, where the DT
estimation deviation is considered. To track this challenge, we reformulate the
original problem as a multi-agent Markov decision process and design a
multi-agent proximal policy optimization (MAPPO) framework to achieve a
flexible learning policy. Furthermore, the Beta-policy and attention mechanism
are used to improve the training performance. Numerical results show that the
proposed method is able to balance the performance tradeoff between sensing and
computation functions, while reducing the energy consumption compared with the
existing studies.Comment: 14 pages, 11 figures
Software-Driven and Virtualized Architectures for Scalable 5G Networks
In this dissertation, we argue that it is essential to rearchitect 4G cellular core networks–sitting between the Internet and the radio access network–to meet the scalability, performance, and flexibility requirements of 5G networks. Today, there is a growing consensus among operators and research community that software-defined networking (SDN), network function virtualization (NFV), and mobile edge computing (MEC) paradigms will be the key ingredients of the next-generation cellular networks. Motivated by these trends, we design and optimize three core network architectures, SoftMoW, SoftBox, and SkyCore, for different network scales, objectives, and conditions. SoftMoW provides global control over nationwide core networks with the ultimate goal of enabling new routing and mobility optimizations. SoftBox attempts to enhance policy enforcement in statewide core networks to enable low-latency, signaling-efficient, and customized services for mobile devices. Sky- Core is aimed at realizing a compact core network for citywide UAV-based radio networks that are going to serve first responders in the future. Network slicing techniques make it possible to deploy these solutions on the same infrastructure in parallel. To better support mobility and provide verifiable security, these architectures can use an addressing scheme that separates network locations and identities with self-certifying, flat and non-aggregatable address components. To benefit the proposed architectures, we designed a high-speed and memory-efficient router, called Caesar, for this type of addressing schemePHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146130/1/moradi_1.pd
A review of relay network on UAVS for enhanced connectivity
One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented
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Mobile robotics in agricultural operations: A narrative review on planning aspects
The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p
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
An Open Source and Open Hardware Deep Learning-Powered Visual Navigation Engine for Autonomous Nano-UAVs
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful external computation servers. In this work, we present what is, to the best of our knowledge, the first 27g nano-UAV system able to run aboard an end-to-end, closed-loop visual pipeline for autonomous navigation based on a state-of-the-art deep-learning algorithm, built upon the open-source CrazyFlie 2.0 nano-quadrotor. Our visual navigation engine is enabled by the combination of an ultra-low power computing device (the GAP8 system-on-chip) with a novel methodology for the deployment of deep convolutional neural networks (CNNs). We enable onboard real-time execution of a state-of-the-art deep CNN at up to 18Hz. Field experiments demonstrate that the system's high responsiveness prevents collisions with unexpected dynamic obstacles up to a flight speed of 1.5m/s. In addition, we also demonstrate the capability of our visual navigation engine of fully autonomous indoor navigation on a 113m previously unseen path. To share our key findings with the embedded and robotics communities and foster further developments in autonomous nano-UAVs, we publicly release all our code, datasets, and trained networks
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