Smart Unmanned Aerial Vehicles as base stations placement to improve the mobile network operations

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site’s environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight.

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Efficient Deployment of Small Cell Base Stations Mounted on Unmanned Aerial Vehicles for the Internet of Things Infrastructure

IEEE Sensors Journal ◽

10.1109/jsen.2020.2973320 ◽

2020 ◽

Vol 20 (13) ◽

pp. 7460-7471 ◽

Cited By ~ 2

Author(s):

Mohammad Javad Sobouti ◽

Zahra Rahimi ◽

Amir Hossein Mohajerzadeh ◽

Seyed Amin Hosseini Seno ◽

Reza Ghanbari ◽

...

Keyword(s):

Internet Of Things ◽

Unmanned Aerial Vehicles ◽

Small Cell ◽

Base Stations ◽

The Internet ◽

Aerial Vehicles ◽

The Internet Of Things ◽

Cell Base

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Towards a Predictive Analysis of UAV-Based Flying Base Station Decisions

International Journal of Business Data Communications and Networking ◽

10.4018/ijbdcn.2020070102 ◽

2020 ◽

Vol 16 (2) ◽

pp. 20-52 ◽

Cited By ~ 1

Author(s):

Hamid Garmani ◽

Driss Ait Omar ◽

Mohamed El Amrani ◽

Mohamed Baslam ◽

Mostafa Jourhmane

Keyword(s):

Nash Equilibrium ◽

Unmanned Aerial Vehicles ◽

Base Station ◽

Base Stations ◽

Practical Significance ◽

Game Model ◽

Activity Scheduling ◽

Area Of Interest ◽

Aerial Vehicles ◽

Fairness Concern

The use of unmanned aerial vehicles (UAVs) as a communication platform is of great practical significance in the wireless communications field. This paper studies the activity scheduling of unmanned aerial vehicles acting as aerial base stations in an area of interest for a specific period. Specifically, competition among multiple UAVs is explored, and a game model for the competition is developed. The Nash equilibrium of the game model is then analyzed. Based on the analysis, an algorithm for Nash equilibrium computation is proposed. Then, a game model with fairness concern is established, and its equilibrium price is also analyzed. In addition, numerical examples are conducted to determine the factors that affect the strategies (price, quality of service, and beaconing duration) of the UAV and to show how the expected profits of UAVs change with that fairness concern point. The authors believe that this research paper will shed light on the application of UAV as a flying base station.

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Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Sensors ◽

10.3390/s21030830 ◽

2021 ◽

Vol 21 (3) ◽

pp. 830

Author(s):

Guilherme Marcel Dias Santana ◽

Rogers Silva de Cristo ◽

Kalinka Regina Lucas Jaquie Castelo Branco

Keyword(s):

Cognitive Radio ◽

Unmanned Aerial Vehicles ◽

Smart Cities ◽

Low Cost ◽

Real Data ◽

Base Stations ◽

Communication Performance ◽

Transmission Frequency ◽

Aerial Vehicles ◽

Future Challenges

Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application’s requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

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Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance

10.20944/preprints201809.0487.v1 ◽

2018 ◽

Author(s):

Yao Liu ◽

Zhong Liu ◽

Jianmai Shi ◽

Guohua Wu ◽

Chao Chen

Keyword(s):

Unmanned Aerial Vehicles ◽

Heuristic Algorithms ◽

Programming Model ◽

Base Station ◽

Base Stations ◽

Location Routing Problem ◽

Routing Problem ◽

Solution Approach ◽

Location Routing ◽

Aerial Vehicles

The location routing problem of unmanned aerial vehicles (UAV) in border patrol for intelligence, surveillance and reconnaissance is investigated, where the location of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the Sino-Vietnamese border is presented to illustrate the problem and the solution approach. The performance of the two algorithms are analyzed and compared through randomly generated instances.

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On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network

Sensors ◽

10.3390/s21020399 ◽

2021 ◽

Vol 21 (2) ◽

pp. 399

Author(s):

Jordi Mongay Batalla ◽

Constandinos X. Mavromoustakis ◽

George Mastorakis ◽

Evangelos K. Markakis ◽

Evangelos Pallis ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Autonomous Vehicles ◽

Mobile Network ◽

Radio Link ◽

Learning Tools ◽

Two Phase ◽

Flight Duration ◽

Aerial Vehicles ◽

Mobile Network Operators ◽

Selection Of

This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.

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Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance

10.20944/preprints201809.0487.v2 ◽

2019 ◽

Author(s):

Yao Liu ◽

Zhong Liu ◽

Jianmai Shi ◽

Guohua Wu ◽

Chao Chen

Keyword(s):

Unmanned Aerial Vehicles ◽

Heuristic Algorithms ◽

Programming Model ◽

Base Station ◽

Base Stations ◽

Location Routing Problem ◽

Routing Problem ◽

Solution Approach ◽

Location Routing ◽

Aerial Vehicles

The location routing problem of unmanned aerial vehicles (UAV) in border patrol for intelligence, surveillance and reconnaissance is investigated, where the location of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the Sino-Vietnamese border is presented to illustrate the problem and the solution approach. The performance of the two algorithms are analyzed and compared through randomly generated instances.

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Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Nigerian Journal of Technology ◽

10.4314/njt.v39i2.22 ◽

2020 ◽

Vol 39 (2) ◽

pp. 514-527

Author(s):

A.A. Periola ◽

E. Obayiuwana

Keyword(s):

Unmanned Aerial Vehicles ◽

Path Loss ◽

Training Data ◽

Base Stations ◽

Network Access ◽

Learning Mechanisms ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Flight Parameters ◽

Low Altitude

The increased use of drones and aerial vehicles in applications poses challenges of airspace safety for aviation organizations. It is important to ensure the safety of the airspace when a significant number of unmanned aerial vehicles are deployed by civilian users. A solution that meets this requirement is important to promote innovation in the commercialization of air space for civilian users deploying unmanned aerial vehicle. The discussion in this paper proposes a mechanism that uses artificial intelligence to address this challenge. The proposed mechanism utilizes a low altitude platform (LAP) and entities in terrestrial wireless networks. The low altitude platform (LAP) observes, develops insights and training data (with human aid). The training data is used to develop learning mechanisms which determine the suitable unmanned aerial vehicles flight parameters in different scenarios. The use of the LAP reduces the burden of communicating with terrestrial base stations. The unmanned aerial vehicles have a reduced altitude between the LAPs in comparison to terrestrial base stations. This reduces the free space path loss and rain-induced attenuation. The performance benefit of the proposed mechanism in comparison to existing solution is examined via MATLAB simulations. Evaluation shows that the proposed mechanism reduces the network access costs by up to 90% on average. The proposed mechanism also increases available flight power and improves airspace safety by 37.3% and up to 53.2% on average respectively. Keywords: Autonomous unmanned aerial vehicles, Intelligence Paradigm; Aviation Safety, Capital Constrained Aviation Organizations.

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Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance, and Reconnaissance

Journal of Advanced Transportation ◽

10.1155/2019/9063232 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 7

Author(s):

Yao Liu ◽

Zhong Liu ◽

Jianmai Shi ◽

Guohua Wu ◽

Chao Chen

Keyword(s):

Unmanned Aerial Vehicles ◽

Heuristic Algorithms ◽

Programming Model ◽

Base Station ◽

Base Stations ◽

Location Routing Problem ◽

Routing Problem ◽

Solution Approach ◽

Location Routing ◽

Aerial Vehicles

The location-routing problem (LRP) of unmanned aerial vehicles (UAV) in border patrol for Intelligence, Surveillance, and Reconnaissance is investigated, where the locations of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the practical border in Guangxi is presented to illustrate the problem and the solution approach. The performance of the two algorithms is analysed and compared through randomly generated instances.

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Flying Height Optimization for Unmanned Aerial Vehicles in Cellular - Flying Adhoc Network

Journal of Advanced Engineering and Computation ◽

10.25073/jaec.201824.210 ◽

2018 ◽

Vol 2 (4) ◽

pp. 216

Author(s):

Quynh Ngo ◽

Duc Ngoc Minh Dang ◽

Khoa Anh Tran

Keyword(s):

Unmanned Aerial Vehicles ◽

Cellular Network ◽

Base Stations ◽

Flying Height ◽

Medium Access ◽

Creative Commons ◽

Aerial Vehicles ◽

Adhoc Network ◽

Geometry Analysis ◽

Design Factors

In Flying Adhoc NETwork (FANET), the communications between Unmanned Aerial Vehicles (UAV), UAVs to infrastructure, and UAVs to wireless sensors are crucial design factors. With strict energy constrain during flying operation, the allocation of power and resources, the flying strategy, and the medium access mechanism shall be effectively used. When employing cellular network as backhaul for UAVs, the flying height of UAVs not only affects the communication between UAVs and end users on the ground, but also determines the reception between infrastructure (base stations from the cellular network) to UAVs. In this paper, we optimize the flying height of UAVs for better reception from the cellular network by using stochastic geometry analysis to model the aggregated interference at the UAV side. The network system performance is also examined under the effect of fading-less channel and Rayleigh fading channel.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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