823 research outputs found
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications
In this paper, the efficient deployment and mobility of multiple unmanned
aerial vehicles (UAVs), used as aerial base stations to collect data from
ground Internet of Things (IoT) devices, is investigated. In particular, to
enable reliable uplink communications for IoT devices with a minimum total
transmit power, a novel framework is proposed for jointly optimizing the
three-dimensional (3D) placement and mobility of the UAVs, device-UAV
association, and uplink power control. First, given the locations of active IoT
devices at each time instant, the optimal UAVs' locations and associations are
determined. Next, to dynamically serve the IoT devices in a time-varying
network, the optimal mobility patterns of the UAVs are analyzed. To this end,
based on the activation process of the IoT devices, the time instances at which
the UAVs must update their locations are derived. Moreover, the optimal 3D
trajectory of each UAV is obtained in a way that the total energy used for the
mobility of the UAVs is minimized while serving the IoT devices. Simulation
results show that, using the proposed approach, the total transmit power of the
IoT devices is reduced by 45% compared to a case in which stationary aerial
base stations are deployed. In addition, the proposed approach can yield a
maximum of 28% enhanced system reliability compared to the stationary case. The
results also reveal an inherent tradeoff between the number of update times,
the mobility of the UAVs, and the transmit power of the IoT devices. In
essence, a higher number of updates can lead to lower transmit powers for the
IoT devices at the cost of an increased mobility for the UAVs.Comment: Accepted in IEEE Transactions on Wireless Communications, Sept. 201
Optimization and Communication in UAV Networks
UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects
Throughput Maximization for UAV-Aided Backscatter Communication Networks
This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks
UAV-Enabled SWIPT in IoT Networks for Emergency Communications
Energy-limited devices and connectivity in complicated environments are two main challenges for Internet of Things (IoT)-enabled mobile networks, especially when IoT devices are distributed in a disaster area. Unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) is emerging as a promising technique to tackle the above problems. In this article, we establish an emergency communications framework of UAV-enabled SWIPT for IoT networks, where the disaster scenarios are classified into three cases, namely, dense areas, wide areas and emergency areas. First, to realize wireless power transfer for IoT devices in dense areas, a UAV-enabled wireless power transfer system is considered where a UAV acts as a wireless charger and delivers energy to a set of energy receivers. Then, a joint trajectory planning and resource scheduling scheme for a multi-UAVs system is discussed to provide wireless services for IoT devices in wide areas. Furthermore, an intelligent prediction mechanism is designed to predict service requirements (i.e., data transmission and battery charging) of the devices in emergency areas, and accordingly, a dynamic path planning scheme is established to improve the energy efficiency (EE) of the system. Simulation results demonstrate the effectiveness of the above schemes. Finally, potential research directions and challenges are also discussed
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
Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT Networks
Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of
Things (IoT) networks in mission-critical data collection, thanks to their
flexibility and cost-effectiveness. However, challenges arise due to the UAV's
limited onboard energy and the unpredictable status updates from sensor nodes
(SNs), which impact the freshness of collected data. In this paper, we
investigate the energy-efficient and timely data collection in IoT networks
through the use of a solar-powered UAV. Each SN generates status updates at
stochastic intervals, while the UAV collects and subsequently transmits these
status updates to a central data center. Furthermore, the UAV harnesses solar
energy from the environment to maintain its energy level above a predetermined
threshold. To minimize both the average age of information (AoI) for SNs and
the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN
scheduling, and offloading strategy. Then, we formulate this problem as a
Markov decision process (MDP) and propose a meta-reinforcement learning
algorithm to enhance the generalization capability. Specifically, the
compound-action deep reinforcement learning (CADRL) algorithm is proposed to
handle the discrete decisions related to SN scheduling and the UAV's offloading
policy, as well as the continuous control of UAV flight. Moreover, we
incorporate meta-learning into CADRL to improve the adaptability of the learned
policy to new tasks. To validate the effectiveness of our proposed algorithms,
we conduct extensive simulations and demonstrate their superiority over other
baseline algorithms
Multi-Objective Optimization for UAV-Assisted Wireless Powered IoT Networks Based on Extended DDPG Algorithm
This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives
Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System
Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things
(IoT) systems, e.g., smart farms, as a data collection platform. However, the
UAV-IoT wireless channels may be occasionally blocked by trees or high-rise
buildings. An intelligent reflecting surface (IRS) can be applied to improve
the wireless channel quality by smartly reflecting the signal via a large
number of low-cost passive reflective elements. This article aims to minimize
the energy consumption of the system by jointly optimizing the deployment and
trajectory of the UAV. The problem is formulated as a
mixed-integer-and-nonlinear programming (MINLP), which is challenging to
address by the traditional solution, because the solution may easily fall into
the local optimal. To address this issue, we propose a joint optimization
framework of deployment and trajectory (JOLT), where an adaptive whale
optimization algorithm (AWOA) is applied to optimize the deployment of the UAV,
and an elastic ring self-organizing map (ERSOM) is introduced to optimize the
trajectory of the UAV. Specifically, in AWOA, a variable-length population
strategy is applied to find the optimal number of stop points, and a nonlinear
parameter a and a partial mutation rule are introduced to balance the
exploration and exploitation. In ERSOM, a competitive neural network is also
introduced to learn the trajectory of the UAV by competitive learning, and a
ring structure is presented to avoid the trajectory intersection. Extensive
experiments are carried out to show the effectiveness of the proposed JOLT
framework.Comment: 11 pages, 7 figures, 4 table
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