scholarly journals Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1908 ◽  
Author(s):  
Bingjie Liu ◽  
Haitao Xu ◽  
Xianwei Zhou
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Information Transmission ◽  
Unmanned Aerial Vehicle ◽  
Power Level ◽  
Dynamic Game ◽  
Proposed Model ◽  
Wireless Nodes ◽  
Optimal Resource ◽  
Aerial Vehicle

Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) to power the wireless nodes in the IoT and an investigation on the optimal resource allocation approach based on dynamic game theory. This IoT system consists of one UAV as the power source and information receiver. The wireless nodes can be powered and collected by the UAV. In order to distinguish the wireless nodes, the wireless nodes are divided into two categories based on the energy consumption. The UAV tries to power these two categories of nodes using a different power level based on the proposed approach, where the wireless nodes control the resources for information transmission. Based on Bellman dynamic programming, the optimal allocated resources for power transfer and information transmission are obtained for both the UAV and wireless nodes, respectively. In order to show the effectiveness of the proposed model and approach, we present numerical simulations.

scholarly journals Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986588 ◽  
Author(s):  
Shan Meng ◽  
Xin Dai ◽  
Bicheng Xiao ◽  
Yimin Zhou ◽  
Yumei Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicle ◽  
Millimeter Wave ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Base Stations ◽  
Communication Link ◽  
Multiple Sensors ◽  
Channel Tracking ◽  
Aerial Vehicle

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

Multi-Economic Agent Grid Resource Allocation Strategies in the Signaling Game Theory Analysis Model

2010 ◽  
Vol 29-32 ◽  
pp. 1093-1099 ◽  
Author(s):  
Jun Xie ◽  
Ji Guang Li
Keyword(s):  
Resource Allocation ◽  
Analysis Model ◽  
Theory Analysis ◽  
Grid Resource ◽  
Trading Agents ◽  
Proposed Model ◽  
Finite Set ◽  
Optimal Resource ◽  
Resource Allocation Strategy ◽  
Allocation Strategies

The paper presents a market oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This paper is target to solve above issues by using utility-based optimization scheme. We firstly point out the factors that influence the resources’ prices; then make out the trading flow for resource consumer agents and provider agents. By doing these, the two trading agents can decide their price due to the dynamic changes of the Grid environment without any manmade interferences. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the consumer’s market optimization and provider’s market optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation.

scholarly journals UAV-Enabled Data Collection: Multiple Access, Trajectory Optimization, and Energy Trade-Off

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Lin Xiao ◽  
Yipeng Liang ◽  
Chenfan Weng ◽  
Dingcheng Yang ◽  
Qingmin Zhao
Keyword(s):  
Resource Allocation ◽  
Data Collection ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Optimization ◽  
Minimum Energy ◽  
Trajectory Design ◽  
Energy Trade ◽  
Significant Performance ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

Grid Resource Discovery Strategies Base on Multi-Economic Agent

2010 ◽  
Vol 29-32 ◽  
pp. 1100-1108
Author(s):  
Jun Xie ◽  
Ji Guang Li
Keyword(s):  
Resource Allocation ◽  
Resource Discovery ◽  
Optimal Resource Allocation ◽  
Grid Resource ◽  
Trading Agents ◽  
Proposed Model ◽  
Finite Set ◽  
Optimal Resource ◽  
Resource Allocation Strategy ◽  
Grid Resource Discovery

The paper presents a market oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This paper is target to solve above issues by using utility-based optimization scheme. We firstly point out the factors that influence the resources’ prices; then make out the trading flow for resource consumer agents and provider agents. By doing these, the two trading agents can decide their price due to the dynamic changes of the Grid environment without any manmade interferences. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the consumer’s market optimization and provider’s market optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation.

scholarly journals Distributed Training and Inference of Deep Learning Models for Multi-Modal Land Cover Classification

2020 ◽  
Vol 12 (17) ◽  
pp. 2670
Author(s):  
Maria Aspri ◽  
Grigorios Tsagkatakis ◽  
Panagiotis Tsakalides
Keyword(s):  
Resource Allocation ◽  
Land Cover ◽  
Network Architecture ◽  
Large Scale ◽  
Land Cover Classification ◽  
Processing Power ◽  
Proposed Model ◽  
Efficient Resource ◽  
Optimal Resource ◽  
Machine Communication

Deep Neural Networks (DNNs) have established themselves as a fundamental tool in numerous computational modeling applications, overcoming the challenge of defining use-case-specific feature extraction processing by incorporating this stage into unified end-to-end trainable models. Despite their capabilities in modeling, training large-scale DNN models is a very computation-intensive task that most single machines are often incapable of accomplishing. To address this issue, different parallelization schemes were proposed. Nevertheless, network overheads as well as optimal resource allocation pose as major challenges, since network communication is generally slower than intra-machine communication while some layers are more computationally expensive than others. In this work, we consider a novel multimodal DNN based on the Convolutional Neural Network architecture and explore several different ways to optimize its performance when training is executed on an Apache Spark Cluster. We evaluate the performance of different architectures via the metrics of network traffic and processing power, considering the case of land cover classification from remote sensing observations. Furthermore, we compare our architectures with an identical DNN architecture modeled after a data parallelization approach by using the metrics of classification accuracy and inference execution time. The experiments show that the way a model is parallelized has tremendous effect on resource allocation and hyperparameter tuning can reduce network overheads. Experimental results also demonstrate that proposed model parallelization schemes achieve more efficient resource use and more accurate predictions compared to data parallelization approaches.

scholarly journals An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 451
Author(s):  
Shahzad Latif ◽  
Suhail Akraam ◽  
Tehmina Karamat ◽  
Muhammad Attique Khan ◽  
Chadi Altrjman ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cognitive Radio ◽  
Internet Of Things ◽  
Pareto Optimality ◽  
Research Work ◽  
High Data ◽  
Data Rates ◽  
Conflicting Objectives ◽  
Optimal Resource ◽  
Resource Allocation Scheme

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

Internet of Things (IoT) for Monitoring Air Pollutants with an Unmanned Aerial Vehicle (UAV) in a Smart City

2018 ◽  
pp. 108-120 ◽  
Author(s):  
José-Isidro Hernández-Vega ◽  
Elda Reyes Varela ◽  
Natividad Hernández Romero ◽  
Carlos Hernández-Santos ◽  
Jonam Leonel Sánchez Cuevas ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicle ◽  
Smart City ◽  
Air Pollutants ◽  
Aerial Vehicle
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