862 research outputs found
D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service
In this paper, we consider the use of UAVs to provide wireless connectivity
services, for example after failures of wireless network components or to
simply provide additional bandwidth on demand, and introduce the concept of
UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework,
dubbed D3S, which consists of four phases: demand, decision, deployment, and
service. The main objective of this framework is to develop efficient and
realistic solutions to implement these four phases. The technical problems
include determining the type and number of UAVs to be deployed, and also their
final locations (e.g., hovering or on-ground), which is important for serving
certain applications. These questions will be part of the decision phase. They
also include trajectory planning of UAVs when they have to travel between
charging stations and deployment locations and may have to do this several
times. These questions will be part of the deployment phase. The service phase
includes the implementation of the backbone communication and data routing
between UAVs and between UAVs and ground control stations
Design of Ad Hoc Wireless Mesh Networks Formed by Unmanned Aerial Vehicles with Advanced Mechanical Automation
Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs)
equipped with wireless transceivers (access points (APs)) are increasingly
being touted as being able to provide a flexible "on-the-fly" communications
infrastructure that can collect and transmit sensor data from sensors in
remote, wilderness, or disaster-hit areas. Recent advances in the mechanical
automation of UAVs have resulted in separable APs and replaceable batteries
that can be carried by UAVs and placed at arbitrary locations in the field.
These advanced mechanized UAV mesh networks pose interesting questions in terms
of the design of the network architecture and the optimal UAV scheduling
algorithms. This paper studies a range of network architectures that depend on
the mechanized automation (AP separation and battery replacement) capabilities
of UAVs and proposes heuristic UAV scheduling algorithms for each network
architecture, which are benchmarked against optimal designs.Comment: 12 page
Planning UAV Activities for Efficient User Coverage in Disaster Areas
Climate changes brought about by global warming as well as man-made
environmental changes are often the cause of sever natural disasters. ICT,
which is itself responsible for global warming due to its high carbon
footprint, can play a role in alleviating the consequences of such hazards by
providing reliable, resilient means of communication during a disaster crisis.
In this paper, we explore the provision of wireless coverage through UAVs
(Unmanned Aerial Vehicles) to complement, or replace, the traditional
communication infrastructure. The use of UAVs is indeed crucial in emergency
scenarios, as they allow for the quick and easy deployment of micro and pico
cellular base stations where needed. We characterize the movements of UAVs and
define an optimization problem to determine the best UAV coverage that
maximizes the user throughput, while maintaining fairness across the different
parts of the geographical area that has been affected by the disaster. To
evaluate our strategy, we simulate a flooding in San Francisco and the car
traffic resulting from people seeking safety on higher ground
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid
development of communication technologies, sensor technologies, and the
Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of
the vehicle networks, it is rather challenging to make timely and accurate
decisions of vehicle behaviors. Moreover, in the presence of mobile wireless
communications, the privacy and security of vehicle information are at constant
risk. In this context, a new paradigm is urgently needed for various
applications in dynamic vehicle environments. As a distributed machine learning
technology, federated learning (FL) has received extensive attention due to its
outstanding privacy protection properties and easy scalability. We conduct a
comprehensive survey of the latest developments in FL for ITS. Specifically, we
initially research the prevalent challenges in ITS and elucidate the
motivations for applying FL from various perspectives. Subsequently, we review
existing deployments of FL in ITS across various scenarios, and discuss
specific potential issues in object recognition, traffic management, and
service providing scenarios. Furthermore, we conduct a further analysis of the
new challenges introduced by FL deployment and the inherent limitations that FL
alone cannot fully address, including uneven data distribution, limited storage
and computing power, and potential privacy and security concerns. We then
examine the existing collaborative technologies that can help mitigate these
challenges. Lastly, we discuss the open challenges that remain to be addressed
in applying FL in ITS and propose several future research directions
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
ECaD: Energy‐efficient routing in flying ad hoc networks
Much progress can be expected in the domain of unmanned aerial vehicle (UAV) communication by the next decade. The cooperation between multiple UAVs in the air exchanging data among themselves can naturally form a flying ad hoc network (FANET). Such networks can be the key support to accomplish several kinds of missions while providing the required assistance to terrestrial networks. However, they are confronted with many challenges and difficulties, which are due to the high mobility of UAVs, the frequent packet losses, and the weak links between UAVs, all affecting the reliability of the data delivery. Furthermore, the unbalanced energy consumption may result in earlier UAV failure and consequently accelerate the decrease of the network lifetime, thus disrupting the overall network. This paper supports the use of the movement information and the residual energy level of each UAV to guarantee a high level of communication stability while predicting a sudden link breakage prior to its occurrence. A robust route discovery process is used to explore routing paths where the balanced energy consumption, the link breakage prediction, and the connectivity degree of the discovered paths are all considered. The performance of the scheme is evaluated through a series of simulations. The outcomes demonstrate the benefits of the proposed scheme in terms of increasing the lifetime of the network, minimizing the number of path failures, and decreasing the packet losses.Much progress can be expected in the domain of unmanned aerial vehicle (UAV) communication by the next decade. The cooperation between multiple UAVs in the air exchanging data among themselves can naturally form a flying ad hoc network (FANET). Such networks can be the key support to accomplish several kinds of missions while providing the required assistance to terrestrial networks. However, they are confronted with many challenges and difficulties, which are due to the high mobility of UAVs, the frequent packet losses, and the weak links between UAVs, all affecting the reliability of the data delivery. Furthermore, the unbalanced energy consumption may result in earlier UAV failure and consequently accelerate the decrease of the network lifetime, thus disrupting the overall network. This paper supports the use of the movement information and the residual energy level of each UAV to guarantee a high level of communication stability while predicting a sudden link breakage prior to its occurrence. A robust route discovery process is used to explore routing paths where the balanced energy consumption, the link breakage prediction, and the connectivity degree of the discovered paths are all considered. The performance of the scheme is evaluated through a series of simulations. The outcomes demonstrate the benefits of the proposed scheme in terms of increasing the lifetime of the network, minimizing the number of path failures, and decreasing the packet losses
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