746 research outputs found
Blockchain-Based Security Architecture for Unmanned Aerial Vehicles in B5G/6G Services and Beyond: A Comprehensive Approach
Unmanned Aerial Vehicles (UAVs), previously favored by enthusiasts, have
evolved into indispensable tools for effectively managing disasters and
responding to emergencies. For example, one of their most critical applications
is to provide seamless wireless communication services in remote rural areas.
Thus, it is substantial to identify and consider the different security
challenges in the research and development associated with advanced UAV-based
B5G/6G architectures. Following this requirement, the present study thoroughly
examines the security considerations about UAVs in relation to the
architectural framework of the 5G/6G system, the technologies that facilitate
its operation, and the concerns surrounding privacy. It exhibits security
integration at all the protocol stack layers and analyzes the existing
mechanisms to secure UAV-based B5G/6G communications and its energy and power
optimization factors. Last, this article also summarizes modern technological
trends for establishing security and protecting UAV-based systems, along with
the open challenges and strategies for future research work.Comment: 25 pages, 6 figures, 3 table
Intelligent-Reflecting-Surface-Assisted UAV Communications for 6G Networks
In 6th-Generation (6G) mobile networks, Intelligent Reflective Surfaces
(IRSs) and Unmanned Aerial Vehicles (UAVs) have emerged as promising
technologies to address the coverage difficulties and resource constraints
faced by terrestrial networks. UAVs, with their mobility and low costs, offer
diverse connectivity options for mobile users and a novel deployment paradigm
for 6G networks. However, the limited battery capacity of UAVs, dynamic and
unpredictable channel environments, and communication resource constraints
result in poor performance of traditional UAV-based networks. IRSs can not only
reconstruct the wireless environment in a unique way, but also achieve wireless
network relay in a cost-effective manner. Hence, it receives significant
attention as a promising solution to solve the above challenges. In this
article, we conduct a comprehensive survey on IRS-assisted UAV communications
for 6G networks. First, primary issues, key technologies, and application
scenarios of IRS-assisted UAV communications for 6G networks are introduced.
Then, we put forward specific solutions to the issues of IRS-assisted UAV
communications. Finally, we discuss some open issues and future research
directions to guide researchers in related fields
UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization
In this paper, a UAV is deployed as a flying base station to collect data
from time-constrained IoT devices and then transfer the data to a ground
gateway (GW). In general, the latency constraint at IoT users and the limited
storage capacity of UAV highly hinder practical applications of UAV-assisted
IoT networks. In this paper, full-duplex (FD) technique is adopted at the UAV
to overcome these challenges. In addition, half-duplex (HD) scheme for
UAV-based relaying is also considered to provide a comparative study between
two modes. In this context, we aim at maximizing the number of served IoT
devices by jointly optimizing bandwidth and power allocation, as well as the
UAV trajectory, while satisfying the requested timeout (RT) requirement of each
device and the UAV's limited storage capacity. The formulated optimization
problem is troublesome to solve due to its non-convexity and combinatorial
nature. Toward appealing applications, we first relax binary variables into
continuous values and transform the original problem into a more
computationally tractable form. By leveraging inner approximation framework, we
derive newly approximated functions for non-convex parts and then develop a
simple yet efficient iterative algorithm for its solutions. Next, we attempt to
maximize the total throughput subject to the number of served IoT devices.
Finally, numerical results show that the proposed algorithms significantly
outperform benchmark approaches in terms of the number of served IoT devices
and the amount of collected data.Comment: 30 pages, 11 figure
A Survey on Cellular-connected UAVs: Design Challenges, Enabling 5G/B5G Innovations, and Experimental Advancements
As an emerging field of aerial robotics, Unmanned Aerial Vehicles (UAVs) have
gained significant research interest within the wireless networking research
community. As soon as national legislations allow UAVs to fly autonomously, we
will see swarms of UAV populating the sky of our smart cities to accomplish
different missions: parcel delivery, infrastructure monitoring, event filming,
surveillance, tracking, etc. The UAV ecosystem can benefit from existing 5G/B5G
cellular networks, which can be exploited in different ways to enhance UAV
communications. Because of the inherent characteristics of UAV pertaining to
flexible mobility in 3D space, autonomous operation and intelligent placement,
these smart devices cater to wide range of wireless applications and use cases.
This work aims at presenting an in-depth exploration of integration synergies
between 5G/B5G cellular systems and UAV technology, where the UAV is integrated
as a new aerial User Equipment (UE) to existing cellular networks. In this
integration, the UAVs perform the role of flying users within cellular
coverage, thus they are termed as cellular-connected UAVs (a.k.a. UAV-UE,
drone-UE, 5G-connected drone, or aerial user). The main focus of this work is
to present an extensive study of integration challenges along with key 5G/B5G
technological innovations and ongoing efforts in design prototyping and field
trials corroborating cellular-connected UAVs. This study highlights recent
progress updates with respect to 3GPP standardization and emphasizes
socio-economic concerns that must be accounted before successful adoption of
this promising technology. Various open problems paving the path to future
research opportunities are also discussed.Comment: 30 pages, 18 figures, 9 tables, 102 references, journal submissio
Relaying in the Internet of Things (IoT): A Survey
The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions
AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty
In this paper, we develop a hierarchical aerial computing framework composed
of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute
the fully offloaded tasks of terrestrial mobile users which are connected
through an uplink non-orthogonal multiple access (UL-NOMA). To better assess
the freshness of information in computation-intensive applications the
criterion of age of information (AoI) is considered. In particular, the problem
is formulated to minimize the average AoI of users with elastic tasks, by
adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which
is restricted by the channel state information (CSI) uncertainty and multiple
resource constraints of UAVs and HAP. In order to solve this non-convex
optimization problem, two methods of multi-agent deep deterministic policy
gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to
design the UAVs trajectory, and obtain channel, power, and CPU allocations. It
is shown that task scheduling significantly reduces the average AoI. This
improvement is more pronounced for larger task sizes. On one hand, it is shown
that power allocation has a marginal effect on the average AoI compared to
using full transmission power for all users. Compared with traditional
transmission schemes, the simulation results show our scheduling scheme results
in a substantial improvement in average AoI
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure
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