Optimized UAV-Based Connectivity Solutions for Urban IoT Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149778" target="_blank" >RIV/00216305:26220/23:PU149778 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10333303" target="_blank" >https://ieeexplore.ieee.org/document/10333303</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT61075.2023.10333303" target="_blank" >10.1109/ICUMT61075.2023.10333303</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimized UAV-Based Connectivity Solutions for Urban IoT Networks

Popis výsledku v původním jazyce

The rapid growth of the Internet of Things (IoT) in cities has resulted in a dense deployment of devices such as sensors and smart meters. These devices necessitate a steady and reliable internet connection. Traditional cellular networks, such as NB-IoT and LTE Cat-M, need help to meet these demands where the performance of current infrastructure has severely degraded due to high device density. As an alternative to existing cell towers, Unmanned Aerial Vehicle Base Stations (UAV-BSs) are proposed in this paper to address this challenge. These UAV-BSs act as mobile cell towers and are strategically placed in regions with a high concentration of IoT devices to improve network coverage and capacity. Furthermore, Tethered UAVs (TUAVs) powered by robotic Cellular-on-Wheels (COW) units support a robust backhaul link between the UAV-BSs and the network core. The study used the K-means algorithm, a machine-learning technique that clusters IoT devices according to their spatial distribution, to select the optimal places for the UAV-BSs. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are then used to refine the positions, systematically investigating multiple UAV-BSs placements to discover the most effective arrangement. Finally, the paper utilizes a new data transmission technique, the 'Mini slots' scheduling algorithm, to improve data exchange efficiency between IoT devices and UAV-BSs.

Název v anglickém jazyce

Optimized UAV-Based Connectivity Solutions for Urban IoT Networks

Popis výsledku anglicky

The rapid growth of the Internet of Things (IoT) in cities has resulted in a dense deployment of devices such as sensors and smart meters. These devices necessitate a steady and reliable internet connection. Traditional cellular networks, such as NB-IoT and LTE Cat-M, need help to meet these demands where the performance of current infrastructure has severely degraded due to high device density. As an alternative to existing cell towers, Unmanned Aerial Vehicle Base Stations (UAV-BSs) are proposed in this paper to address this challenge. These UAV-BSs act as mobile cell towers and are strategically placed in regions with a high concentration of IoT devices to improve network coverage and capacity. Furthermore, Tethered UAVs (TUAVs) powered by robotic Cellular-on-Wheels (COW) units support a robust backhaul link between the UAV-BSs and the network core. The study used the K-means algorithm, a machine-learning technique that clusters IoT devices according to their spatial distribution, to select the optimal places for the UAV-BSs. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are then used to refine the positions, systematically investigating multiple UAV-BSs placements to discover the most effective arrangement. Finally, the paper utilizes a new data transmission technique, the 'Mini slots' scheduling algorithm, to improve data exchange efficiency between IoT devices and UAV-BSs.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

181-186

Název nakladatele

IEEE Computer Society

Místo vydání

neuveden

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—