447 research outputs found
Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networks
This paper addresses the problem of Age-of-Information (AoI) in UAV-assisted
networks. Our objective is to minimize the expected AoI across devices by
optimizing UAVs' stopping locations and device selection probabilities. To
tackle this problem, we first derive a closed-form expression of the expected
AoI that involves the probabilities of selection of devices. Then, we formulate
the problem as a non-convex minimization subject to quality of service
constraints. Since the problem is challenging to solve, we propose an Ensemble
Deep Neural Network (EDNN) based approach which takes advantage of the dual
formulation of the studied problem. Specifically, the Deep Neural Networks
(DNNs) in the ensemble are trained in an unsupervised manner using the
Lagrangian function of the studied problem. Our experiments show that the
proposed EDNN method outperforms traditional DNNs in reducing the expected AoI,
achieving a remarkable reduction of .Comment: 6 pages, 3 figure
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
Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networks
Unmanned aerial vehicle (UAV)-assisted sensor networks (UASNets), which play
a crucial role in creating new opportunities, are experiencing significant
growth in civil applications worldwide. UASNets improve disaster management
through timely surveillance and advance precision agriculture with detailed
crop monitoring, thereby significantly transforming the commercial economy.
UASNets revolutionize the commercial sector by offering greater efficiency,
safety, and cost-effectiveness, highlighting their transformative impact. A
fundamental aspect of these new capabilities and changes is the collection of
data from rugged and remote areas. Due to their excellent mobility and
maneuverability, UAVs are employed to collect data from ground sensors in harsh
environments, such as natural disaster monitoring, border surveillance, and
emergency response monitoring. One major challenge in these scenarios is that
the movements of UAVs affect channel conditions and result in packet loss. Fast
movements of UAVs lead to poor channel conditions and rapid signal degradation,
resulting in packet loss. On the other hand, slow mobility of a UAV can cause
buffer overflows of the ground sensors, as newly arrived data is not promptly
collected by the UAV.
Our proposal to address this challenge is to minimize packet loss by jointly
optimizing the velocity controls and data collection schedules of multiple
UAVs.Furthermore, in UASNets, swift movements of UAVs result in poor channel
conditions and fast signal attenuation, leading to an extended age of
information (AoI). In contrast, slow movements of UAVs prolong flight time,
thereby extending the AoI of ground sensors.To address this challenge, we
propose a new mean-field flight resource allocation optimization to minimize
the AoI of sensory data
UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer
Maintaining freshness of data collection in Internet-of-Things (IoT) networks
has attracted increasing attention. By taking into account age-of-information
(AoI), we investigate the trajectory planning problem of an unmanned aerial
vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization
problem is formulated to minimize the total AoI of the collected data by the
UAV from the ground IoT network. Since the total AoI of the IoT network depends
on the flight time of the UAV and the data collection time at hovering points,
we jointly optimize the selection of hovering points and the visiting order to
these points. We exploit the state-of-the-art transformer and the weighted A*,
which is a path search algorithm, to design a machine learning algorithm to
solve the formulated problem. The whole UAV-IoT system is fed into the encoder
network of the proposed algorithm, and the algorithm's decoder network outputs
the visiting order to ground clusters. Then, the weighted A* is used to find
the hovering point for each cluster in the ground IoT network. Simulation
results show that the trained model by the proposed algorithm has a good
generalization ability to generate solutions for IoT networks with different
numbers of ground clusters, without the need to retrain the model. Furthermore,
results show that our proposed algorithm can find better UAV trajectories with
the minimum total AoI when compared to other algorithms
Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems
This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer non-convex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems
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