SWARM communication - scalability and resiliency

SWARM technology can coordinate large numbers of small drones to perform collective tasks, including but not limited to surveillance, search and rescue, and cargo transport. SWARM nodes connect wirelessly to each other, coordinating communication and sensing. Different from traditional mobile/wir...

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Main Author: Tao, Zhengyi
Other Authors: Erry Gunawan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/170603
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1706032023-09-22T15:44:04Z SWARM communication - scalability and resiliency Tao, Zhengyi Erry Gunawan School of Electrical and Electronic Engineering EGUNAWAN@ntu.edu.sg Engineering::Electrical and electronic engineering::Wireless communication systems SWARM technology can coordinate large numbers of small drones to perform collective tasks, including but not limited to surveillance, search and rescue, and cargo transport. SWARM nodes connect wirelessly to each other, coordinating communication and sensing. Different from traditional mobile/wireless communications, SWARM communication operates as an ad-hoc self-organizing mesh network. In addition, SWARM nodes can communicate with each other through multi-hop links. Benefit from the characteristics of low power consumption and strong mobility of each node, SWARM network can dynamically change and cover a large area. In this project we use Raspberry Pi platform to configure mesh nodes and build mesh networks, wireless communication technology and protocols are utilized to realize SWARM communication in Ad-hoc mode. More specifically, we use the BATMAN-adv protocol at the data link layer to communicate. Master of Science (Communications Engineering) 2023-09-20T08:28:08Z 2023-09-20T08:28:08Z 2023 Thesis-Master by Coursework Tao, Z. (2023). SWARM communication - scalability and resiliency. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/170603 https://hdl.handle.net/10356/170603 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Wireless communication systems
spellingShingle Engineering::Electrical and electronic engineering::Wireless communication systems
Tao, Zhengyi
SWARM communication - scalability and resiliency
description SWARM technology can coordinate large numbers of small drones to perform collective tasks, including but not limited to surveillance, search and rescue, and cargo transport. SWARM nodes connect wirelessly to each other, coordinating communication and sensing. Different from traditional mobile/wireless communications, SWARM communication operates as an ad-hoc self-organizing mesh network. In addition, SWARM nodes can communicate with each other through multi-hop links. Benefit from the characteristics of low power consumption and strong mobility of each node, SWARM network can dynamically change and cover a large area. In this project we use Raspberry Pi platform to configure mesh nodes and build mesh networks, wireless communication technology and protocols are utilized to realize SWARM communication in Ad-hoc mode. More specifically, we use the BATMAN-adv protocol at the data link layer to communicate.
author2 Erry Gunawan
author_facet Erry Gunawan
Tao, Zhengyi
format Thesis-Master by Coursework
author Tao, Zhengyi
author_sort Tao, Zhengyi
title SWARM communication - scalability and resiliency
title_short SWARM communication - scalability and resiliency
title_full SWARM communication - scalability and resiliency
title_fullStr SWARM communication - scalability and resiliency
title_full_unstemmed SWARM communication - scalability and resiliency
title_sort swarm communication - scalability and resiliency
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/170603
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