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Universidade de Aveiro Departamento de Eletrónica,Telecomunicações e Informática 2019

André Filipe

Nascimento Rodrigues

Controlo e Comunicação num Sistema com Múltiplos

Drones Aéreos Interligados

Control and Communication in a System with

Multiple Interconnected Aerial Drones

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“Não ter já mais nada para dizer e continuar a escrever é um crime. Porque não tem o direito de continuar a escrever se não tem nada para dizer.”

— José Saramago Universidade de Aveiro Departamento de Eletrónica,Telecomunicações e Informática

2019

André Filipe

Nascimento Rodrigues

Controlo e Comunicação num Sistema com Múltiplos

Drones Aéreos Interligados

Control and Communication in a System with

Multiple Interconnected Aerial Drones

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Universidade de Aveiro Departamento de Eletrónica,Telecomunicações e Informática 2019

André Filipe

Nascimento Rodrigues

Controlo e Comunicação num Sistema com Múltiplos

Drones Aéreos Interligados

Control and Communication in a System with

Multiple Interconnected Aerial Drones

Dissertação apresentada à Universidade de Aveiro para cumprimento dos requisi-tos necessários à obtenção do grau de Mestre em Engenharia de Computadores e Telemática, realizada sob a orientação científica da Doutora Susana Sargento, Professora Catedrática do Departamento de Eletrónica, Telecomunicações e Infor-mática da Universidade de Aveiro, e do Doutor André Braga Reis, Investigador da Universidade de Aveiro

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o júri / the jury

presidente / president Prof. Dr. António José Neves

Professor Auxiliar do Departamento de Eletrónica, Telecomunicações e Informática da Universi-dade de Aveiro

vogais / examiners committee Prof. Dr. António Manuel Raminhos Cordeiro Grilo

Professor Auxiliar do Instituto Superior Técnico de Lisboa

Prof. Dra. Susana Sargento

Professora Catedrática do Departamento de Eletrónica, Telecomunicações e Informática da Uni-versidade de Aveiro

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agradecimentos / acknowledgements

Em primeiro lugar quero agradecer à minha mãe, ao meu pai e à Judite, pelo incansável apoio desde o primeiro dia nesta grande luta, e por estarem sempre por perto, ainda que distantes.

Obrigado a todos os meus amigos madeirenses pelo apoio e por estarem sempre presentes nos bons e maus momentos. Um grande obrigado a todos os colegas e amigos com quem partilhei e cooperei nesta caminhada.

Um especial agradecimento à professora Susana Sargento e ao André Reis pela integração num excelente grupo de investigação, NAP, e pelo tempo dedicado na orientação e correção deste documento. Um último obrigado ao Rui Lopes, André Martins e Bruno Areias pela partilha de conhecimentos e constante motivação até à conclusão da dissertação.

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Palavras Chave Encaminhamento, UAVs, GPSR, FANET, WMN, Ad Hoc, Predição de Posição, RSSI.

Resumo Redes de drones aéreos, Unmanned Aerial Vehicles (UAVs), estão cada vez mais presentes nas áreas de investigação em Tecnologias de Informação. Com o au-mento das áreas em que estes UAVs se encaixam, a planificação de missões cada vez mais complexas permitem gerir e controlar autonomamente vários drones que pertencem a missões distintas. Para garantir a comunicação entre os drones é necessária uma Rede de Malha Sem Fios, Wireless Mesh Networks (WMN), capaz de se adaptar às alterações instáveis da topologia das Redes Ad-Hoc Voadoras, Flying Ad Hoc Networks (FANETs), e garantir a comunicação dentro da malha sem o auxílio de uma estação base externa estacionária. O objetivo desta Disser-tação é projetar, implementar e testar uma plataforma de encaminhamento capaz de interligar todos os drones presentes numa FANET. Para tal, a Dissertação pos-sui quatro objetivos principais definidos: (1) a análise de diferentes algoritmos de encaminhamento que se encaixam nas metodologias de Redes Ad-Hoc Móveis (MANET); (2) o estudo teórico, testes de implementação, e simulação de uma versão melhorada do algoritmo de encaminhamento escolhido para se enquadrar no ambiente de FANETs; (3) o planeamento da estrutura, implementação e teste de uma plataforma de encaminhamento capaz de gerir as comunicações drone a drone, cujas rotas são decididas pelo algoritmo de encaminhamento também ele desenvolvido, e esta estrutura também lida com as trocas de comunicação entre drones; e, por último, (4) a integração e teste da plataforma de encaminhamento desenvolvida com um mecanismo de missões autónomas existente que cria missões e controla autonomamente as ações de cada drone.

A versão base do algoritmo de encaminhamento escolhido foi testada em cenários de simulação e mostra que o desempenho da rede pode ser melhorado com a in-clusão de predição de posição, prevendo quais os vizinhos que são mais benéficos para a escolha do próximo salto que conduz para um destino específico. A versão proposta revela melhoramentos na seleção de um próximo salto para um destino num ambiente móvel e instável presente numa FANET. Através de um conjunto de simulações, a versão proposta revela uma diminuição na taxa de perda de pa-cotes em 33%, de overhead/sobrecarga na rede de 13%, e no jitter de 46%. Esta versão optimizada é também integrada com um planeador de missões autónomo. O desempenho desta integração é validado em cenário reais.

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Keywords Routing, UAVs, GPSR, FANET, WMN, Ad Hoc, Position Prediction, RSSI.

Abstract Flying ad-hoc networks (FANETs) that are comprised of swarms of drones have be-come an important field of research in the area of Information Technologies (IT). With the increase of areas where these small UAVs fit, complex mission planners are being developed in order to autonomously manage and control multiple drones that belong to distinct missions. To be able to guarantee the communication be-tween drones, a Wireless Mesh Network (WMN) with the ability to adapt to the FANETs unstable topology changes is necessary, ensuring communications within the mesh without the aid of an external, stationary base station.

The purpose of this Dissertation is to design, implement and test a routing plat-form capable of interconnecting all drones present on a FANET. To do that, the Dissertation has four main goals: (1) the analysis of different routing algorithms that fit Mobile Ad-Hoc Networks (MANETs) methodologies; (2) the theoretical study, implementation and simulation tests of an improved version of the chosen routing algorithm to fit FANET environments; (3) the design, implementation and testing of a routing platform capable of managing drone-to-drone communications were routes are decided by the developed routing algorithm, while also dealing with communication handover; and finally, (4) the integration and testing of the routing platform with an existing autonomous mission planner which assembles missions and autonomously controls drones’ actions.

By predicting the future positions of the drones in the network, and foretelling which neighbors are the best next-hop candidates for specific destinations, we show sub-stantial improvements in network performance over the base version of the chosen routing algorithm. The enhanced routing protocol improves the selection of the next-hop node in the highly-mobile and noisy FANET environments, and a thorough set of simulations shows improvements in packet loss by 33%, in routing overhead by 13% and in jitter by 46%. This enhanced version is also integrated with a mission planner that controls drones autonomously on predefined missions, which performance is evaluated in real-life scenarios, which are successfully validated.

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Contents

Contents i List of Figures v List of Tables ix Glossary xi 1 Introduction 1

1.1 Context and Motivation . . . 1

1.2 Objectives . . . 2

1.3 Contributions . . . 2

1.4 Document Structure . . . 3

2 State of the Art 5 2.1 Overview . . . 5

2.2 Unmanned Aerial Vehicle . . . 6

2.2.1 History . . . 6 2.2.2 Applications . . . 6 2.2.3 Communication . . . 8 2.3 Ad-Hoc Networks . . . 8 2.3.1 Topology . . . 8 2.3.2 Issues . . . 9 2.4 Wireless Technologies . . . 10 2.4.1 ZigBee . . . 12 2.4.2 Bluetooth . . . 13 2.4.3 IEEE 802.11 . . . 14

2.5 Wireless Mesh Networks . . . 15

2.5.1 Architecture . . . 16

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2.5.3 Mobility Classification . . . 18

2.5.4 Routing . . . 19

2.6 Related Work . . . 24

2.6.1 Mesh Topologies & Applications . . . 24

2.6.2 Routing Protocols & Adaptations . . . 28

2.7 Discussion . . . 32

2.8 Summary . . . 32

3 A FANET Routing Approach with Position Prediction and Uncertainty 33 3.1 Overview . . . 33

3.2 Analysis . . . 34

3.2.1 Minimum Angle . . . 34

3.2.2 Position & Velocity Vector . . . 37

3.2.3 Next Position & Time Travel Delay . . . 38

3.2.4 Lifetime . . . 39

3.2.5 Uncertainty . . . 40

3.3 Summary . . . 42

4 Integration and Implementation 43 4.1 Overview . . . 43

4.2 Setup . . . 44

4.3 Network Discovery Service . . . 45

4.4 Routing Platform . . . 47 4.4.1 Attributes . . . 47 4.4.2 Structures . . . 48 4.4.3 Processes . . . 49 4.5 Summary . . . 54 5 Experimental Results 57 5.1 Overview . . . 57

5.2 Metrics and Methodologies . . . 58

5.3 Simulation . . . 59

5.4 Real Experiments . . . 70

5.4.1 Indoor Testbed . . . 70

5.4.2 Outdoor Testbed . . . 76

5.4.3 Firefighting Integrated Testbed . . . 78

5.5 Discussion . . . 87

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6 Conclusion and Future Work 91 6.1 Future Work . . . 92

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List of Figures

2.1 Remote UAV prototype . . . 6

2.2 A Drone’s Point Of View [2]. . . 7

2.3 Precision Agriculture Sensor for Unmanned Aerial Vehicle (UAV) Multispectral Imaging 7 2.4 Ad Hoc Network Example. . . 8

2.5 Hidden Node Scenario. . . 9

2.6 RTS-CTS Handshake. . . 9

2.7 Hidden Node IP Conflict Scenario. . . 10

2.8 Wireless Technologies Ranges. . . 11

2.9 ZigBee Topologies. . . 12

2.10 Bluetooth Architecture, Scatternet & Piconets. . . 13

2.11 Predicted radio range of the various 802.11 standards. . . 15

2.12 Wireless Mesh Networks (WMNs) Classification. . . 16

2.13 WMN Architecture Types. . . 17

2.14 Mobile Ad Hoc Network Derivatives. . . 18

2.15 WMN Routing Classification. . . 19

2.16 Ad-hoc On-Demand Distance Vector (AODV) Route Request broadcast & Route Reply answer. . . 21

2.17 Destination-Sequenced Distance Vector (DSDV) Topology Example. . . 21

2.18 Zone Routing Protocol Example. . . 22

2.19 Greedy Perimeter Stateless Routing (GPSR) data forwarding methodology. . . 23

2.20 Hierarchical Topology Construction. . . 24

2.21 Mesh deployment on a military scenario . . . 25

2.22 One-hop, Two-hop & Mesh Scenarios. . . 26

2.23 Proposed Traffic-Aware Multi-Tier Flying Network (TMFN) topology . . . 26

2.24 Control Base Station Communication Example. . . 27

3.1 GPSR default selection in Perimeter Mode . . . 34

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3.4 GPSR Path selection using Minimum Angle approach . . . 36

3.5 GPSR Path topology prediction after t seconds . . . . 37

3.6 GPSR Path prediction on a predicted scenario (after t seconds) . . . . 38

3.7 GPSR Path selection with t seconds prediction . . . . 38

3.8 GPSR Path selection with t seconds prediction with route changing after time travel delay 39 3.9 Uncertainty evolution . . . 41

4.1 Hardware Setup. . . 44

4.2 Acquiring destination’s position (Simulation versus Real-world). . . 45

4.3 Multicast on a WiFi Ad Hoc Cell. . . 46

4.4 Wireless Point-to-point experiment results. . . 47

4.5 Connection Classification based on Received Signal Strength Indicator (RSSI) levels. . . . 47

4.6 Neighbour Relationship Establishment . . . 49

4.7 HELLO Message . . . 50

4.8 HELLO Message Flow Chart . . . 50

4.9 HELLO-BACK Message . . . 51

4.10 HELLO-BACK Message Flow Chart . . . 51

4.11 HELLO-BACK-ACK Message . . . 52

4.12 HELLO-BACK-ACK Message Flow Chart . . . 53

4.13 Path Building Process (1 Iteration) . . . 53

4.14 RSSI Monitor Process (1 Iteration) . . . 54

5.1 Example of Random Rectangle Position Allocator configurations . . . 60

5.2 Simulation results of GPSR default version, AODV and Optimized Link State Routing (OLSR) 61 5.3 Simulation results of GPSR default version and GPSR minimum angle . . . 63

5.4 Simulation results of GPSR minimum angle with simple position prediction, given 1, 2 and 5 seconds of prediction input . . . 65

5.5 Simulation results of GPSR default version, minimum angle version, minimum angle with simple position prediction (given 1 second of prediction input) and final version without uncertainty . . . 67

5.6 Simulation results of GPSR default version, final version without uncertainty and final version with uncertainty . . . 69

5.7 Indoor testbed, nodes displacement . . . 70

5.8 Indoor test stages, node C initially side by side with node B, then moving to a new position, and finally the test ends with C moving to the initial position . . . 71

5.9 Indoor test routes: initially all nodes have a direct connection, when C moves away, C and A have to find a route to communicate with each other again, finally when C moves to its initial position, no routes are needed. . . 71

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5.10 Indoor 2.4GHz interferences . . . 72

5.11 Default (Without RSSI Monitor) test result example, jitter & average latency, loss percent-age & packets per second and transferred bytes & data rate . . . 73

5.12 Enhanced (With RSSI Monitor) test result example, jitter & average latency, loss percentage & packets per second and transferred bytes & data rate . . . 74

5.13 Two HELLO messages announcement per second (With RSSI Monitor) test result example, jitter & average latency, loss percentage & packets per second and transferred bytes & data rate . . . 75

5.14 Outdoor Testbed, nodes displacement. . . 76

5.15 Outdoor 2.4GHz interferences . . . 77

5.16 Expected scenarios (stages) on the firefighting integrated experiment. . . 80

5.17 Non-multi-hop scenario . . . 81

5.18 Multi-hop scenario . . . 82

5.19 Drones trajectories between multi-hop stage and non-multi-hop stage. . . 83

5.20 Drones trajectories between multi-hop zone and non-multi-hop zone, marking active routes. 84 5.21 Flying Ad-Hoc Network (FANET) behaviour, regarding bandwidth occupation between multi-hop stage to non-multi-hop stage. . . 85

5.22 Drones trajectories between non-multi-hop zone and multi-hop zone, marking active routes. 86 5.23 FANET behaviour, regarding bandwidth occupation between non-hop stage to multi-hop stage. . . 86

5.24 Performance comparison between AODV, OLSR, GPSR (default) and GPSR with Position Prediction . . . 89

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List of Tables

2.1 ZigBee Characteristics. . . 12 2.2 Bluetooth Characteristics. . . 13 2.3 Node F routing table. . . 22 2.4 Proactive, Reactive & Hybrid Comparison. . . 29

4.1 Architecture Setup. . . 44 4.2 802.11n Specifications. . . 44

5.1 Network Simulator 3 (NS3) configuration parameters used on simulation tests . . . 59 5.2 Simulation results of GPSR default version . . . 62 5.3 Simulation results of GPSR minimum angle version . . . 62 5.4 Improvement of GPSR minimum angle version in relation with GPSR default version,

regarding Packet Delivery Ratio (PDR), routing overhead and jitter . . . 62 5.5 PDR comparison between GPSR default version with position prediction (1 second) and

GPSR minimum angle . . . 64 5.6 Routing overhead comparison between GPSR default version with position prediction (1

second) and GPSR minimum angle . . . 64 5.7 Jitter comparison between GPSR default version with position prediction (1 second) and

GPSR minimum angle . . . 64 5.8 PDR results comparison between GPSR final developed version and GPSR default version 66 5.9 Routing overhead results comparison between GPSR final developed version and GPSR

default version . . . 66 5.10 Jitter results comparison between GPSR final developed version and GPSR default version 66 5.11 PDR results comparison between GPSR final developed version without uncertainty and

with uncertainty, compared with default version . . . 68 5.12 Delay results comparison between GPSR final developed version without uncertainty and

with uncertainty, compared with default version . . . 68 5.13 Network overall performance running default version (without RSSI monitor) . . . 73 5.14 Network peaks’ performance running default version (without RSSI monitor) . . . 73

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5.15 Network overall performance running enhanced version (with RSSI monitor) . . . 74 5.16 Network peaks’ performance running enhanced version (with RSSI monitor) . . . 74 5.17 Network overall performance running enhanced version (with RSSI monitor), 0.6% versus

2.4% routing overhead . . . 76 5.18 Network peaks performance running enhanced version (with RSSI monitor), 0.6% versus

2.4% routing overhead . . . 76 5.19 Network overall performance running enhanced version (with RSSI monitor) with maximum

2.4% routing overhead on outside environment . . . 77 5.20 Network peaks performance running enhanced version (with RSSI monitor) with maximum

2.4% routing overhead on outside environment . . . 77 5.21 Drone 1 (10.1.1.1) routing details on Stage 1 (non-multi-hop) . . . 80 5.22 Drone 2 (10.1.1.2) routing details on Stage 1 (non-multi-hop) . . . 81 5.23 Drone 3 (10.1.1.3) routing details on Stage 1 (non-multi-hop) . . . 81 5.24 Drone 1 (10.1.1.1) routing details on Stage 2 (multi-hop) . . . 82 5.25 Drone 2 (10.1.1.2) routing details on Stage 2 (multi-hop) . . . 82 5.26 Drone 3 (10.1.1.3) routing details on Stage 2 (multi-hop) . . . 83 5.27 Comparison between GPSR default and final developed version results in min-max intervals 87 5.28 Evolution of PDR on each iteration compared with GPSR default version . . . 87 5.29 Evolution of routing overhead (%) on each iteration compared with GPSR default version 88 5.30 Evolution of jitter on each iteration compared with GPSR default version . . . 88 5.31 Results from network overall and peaks’ performance in intervals at indoor tests . . . 88 5.32 Results from network overall and peaks’ performance in intervals at outdoor tests . . . . 90

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Glossary

5G Fifth Generation Cellular Network Technology

AMMNET Autonomous Mobile Mesh Network

AODV Ad-hoc On-Demand Distance Vector

AOMDV An Optimized Ad-hoc On-Demand Multipath Distance Vector

AP Access Point

BATMAN Better Approach To Mobile Ad-hoc Networking

BLE Bluetooth Low Energy

CBR Constant Bit Rate

CM-AODV Cross-layered Multipath Ad-hoc On-Demand Distance Vector

CTS Clear-To-Send

DSDV Destination-Sequenced Distance Vector

DSR Dynamic Source Routing

DTN Delay Tolerant Network

FANET Flying Ad-Hoc Network

FMAP Flying Mesh Access Point

G-DSDV Geography aided Destination-Sequenced Distance Vector

GPS Global Positioning System

GPSR Greedy Perimeter Stateless Routing

GPSR-L Greedy Perimeter Stateless Routing with Lifetime

HaLow IEEE 802.11ah

HWMP Hybrid Wireless Mesh Protocol

IARP Intra-zone Routing Protocol

IERP Inter-zone Routing Protocol

IEEE Institute of Electrical and Electronics Engineers

IoT Internet of Things

IP Internet Protocol

IT Instituto de Telecomunicações

LAN Local Area Network

LSP Label Switch Path

LoRa Long Range

M-AODV Multipath Ad-hoc On-Demand Distance Vector

MAC Media Access Control

MANET Mobile Ad-Hoc Network

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MTM Multipoint-to-Multipoint

MPLS Multi-Protocol Label Switching

MPLS-OLSR Multi Protocol Label Switching Optimized Link State Routing

NetPlan Network Planning

NS2 Network Simulator 2

NS3 Network Simulator 3

NTP Network Time Protocol

OLSR Optimized Link State Routing

OSPF Open Shortest Path First

PA-GPSR Path Aware Greedy Perimeter Stateless Routing

PDR Packet Delivery Ratio

PTP Point-to-Point

PTM Point-to-Multipoint

QoS Quality of Service

RIP Routing Information Protocol

ROS2 Robot Operating System 2

RPI Raspberry Pi

RREQ Route Request

RREP Route Reply

RRER Route Error

RSSI Received Signal Strength Indicator

RTS Request-To-Send

SSID Service Set Identifier

TCP Transmission Control Protocol

TCE Temporary Crowded Event

TMFN Traffic-Aware Multi-Tier Flying Network

TORA Temporally Ordered Routing Algorithm

TTL Time To Live

UAV Unmanned Aerial Vehicle

UDP User Datagram Protocol

V2V Vehicle-to-Vehicle

VANET Vehicular Ad-Hoc Network

VoIP Voice over Internet Protocol

WAVE IEEE 802.11p

WiFi IEEE 802.11

White-Fi IEEE 802.11af

WSN Wireless Sensor Network

WMN Wireless Mesh Network

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CHAPTER

1

Introduction

1.1 Context and Motivation

Commonly known as drones, UAVs have been through a significant technological advancement in the last decade that contributed to the evolution of several areas. Their functionalities led to the development of more sophisticated solutions for scientific purposes such as data gathering to predict farming yields, oceanographic and topographic monitoring, and through 3D image reconstruction, make possible to inspect building and even reconstruct unreachable places for humans on a real scale, such as places with higher and difficult access and also blocked access, as e.g. an area surrounded by fire. Drones have been improving not only for scientific purposes, but also for entertainment and casual usage such as racing events and photography, among others.

On scientific areas the advancements on drone mission planning platforms show that drones could co-operate with each other and complete missions together; thus, the idea of assigning multiple missions to multiple drones to achieve organized swarms of drones starts by making possible for these flying units to interact with each other directly.

In the context of research projects, within Instituto de Telecomunicações (IT), a platform capable of planning and supporting multiple-drone missions was developed. The current platform keeps a persistent connection with every drone on a mission, and information between drones is always delivered to the base station instead of delivering drone-to-drone by resorting to multi-hop communications. To decrease the dependency of drones to the platform, and make drones fully autonomous, drones will interact directly with each other in a peer-to-peer approach.

Swarms of flying drones can form wireless mesh networks between the drones, which, through multi-hop communications, can be made to be highly resilient, provide widespread coverage, and enable drones to talk with one another without any infrastructure in place. Multiple paths between any two drones may exist at once, and due to the drones’ high mobility and terrain obstructions, those paths may change frequently.

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The present work proposes a wireless mesh network to improve the network’s flexibility and resilience to topology changes, and overall performance, by introducing drone-to-drone communications without the aid of a centralized unit, thus, upgrading the existing platform. 1.2 Objectives

The main goal of this dissertation is the specification, implementation and test of a routing platform able to intelligently interconnect all existing UAVs on the network. This routing platform will integrate an existing mission manager platform.

With this main goal in mind, the present dissertation has the following objectives: • Evaluate the existing wireless technologies that form mesh type networks to build up

the physical layer of communication that will be used by autonomous UAVs;

• Among the several routing algorithms available, choose the one that fits the UAVs’ nature and provides a reliable and resilient routing layer for FANETs;

• Improve and include intelligence on the decision core of the chosen routing algorithm; • Design the overall elements of the architecture required to include the developed routing

module into the routing platform that will integrate on the existing platform;

• Evaluate the overall performance of the proposed algorithm modifications in simulation scenarios and its integration with the routing platform in real world scenarios.

1.3 Contributions

The work developed in this dissertation led to the following contributions:

• Evaluation and comparison of the GPSR base version with several modifications under simulation scenarios resorting to NS3;

• Development, under the NS3 simulation environment, of a routing proposal for FANETs, based on GPSR, that includes position prediction by taking into account the current speed and heading of each drone, predicting whether a given path may still exist once the network packets are in transit, or whether a better path may appear in the meantime; • Creation of a routing platform that extends auto-discoverability, allowing the network to work seamlessly with any number of drones, and a communication handover mechanism which deals with path switching without having to wait for connection tear down to establish a new one;

• Integration and experimentation of the developed routing platform with a multiple autonomous drones mission-planner platform.

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1.4 Document Structure

This section outlines the structure of this Dissertation.

• Chapter 1 - Presents a brief description of this Dissertation’s work along with the context and motivation, objectives and contributions;

• Chapter 2 - Provides an analysis of the advancements in areas relevant to this Dis-sertation, covering wireless technologies which form wireless mesh networks, routing algorithms that take a part on Mobile Ad-Hoc Networks (MANETs), and changes to the base version of these routing algorithms;

• Chapter 3 - Analyses the chosen algorithm to be improved, focuses on its weaknesses and proposes modifications to add sophisticated functionalities to the algorithm such as the inclusion of position prediction;

• Chapter 4 - Describes the designed architecture and its implementation, which includes a routing decision module that uses the developed routing algorithm;

• Chapter 5 - Evaluates the results obtained regarding the performance of the routing algorithm and its modifications until its last version under simulation, and tests and discusses the results of the implementation of the routing platform along with indoor and outdoor real testbeds;

• Chapter 6 - Concludes the Dissertation and proposes additional improvements that can be implemented in order to enhance the overall performance of the proposed routing algorithm and the routing platform.

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CHAPTER

2

State of the Art

2.1 Overview

This chapter focuses on providing an overview of crucial concepts needed to understand the whole dissertation’s work, and brings similar and helpful studies into the analysis towards the development of a FANET solution for drone-to-drone communication scenarios. Section 2.2 introduces the history of UAVs, often known as aerial drones, how and where they are useful nowadays, and ends by highlighting the need for drone-to-drone communications.

Section 2.3 discusses Ad Hoc networks, analyses their undefined structure, inherent issues, and solutions.

Section 2.4 lists the existing wireless technologies, analyses them and exposes their pros and cons, and main characteristics. Specifically, the Bluetooth, ZigBee, and IEEE 802.11 (WiFi) technologies have been considered.

Section 2.5 presents the concept of WMNs, and their helpfulness on the Internet of Things (IoT) on dynamic and unstable environments, such as the drone-to-drone communication scenario. We also describe the various architecture types of WMN as well as their components. Finally, this section ends by analyzing the routing protocols existent on mobile Ad Hoc networks, focusing on table-driven and on-demand methodologies.

Section 2.6 delves into related research articles, splitted between the mesh topologies, that is, strategies and hierarchies presented in research studies, and studies related to routing protocol performance analysis and comparison in Ad Hoc network scenarios. Finally, this section ends by presenting improvements to the existent protocols, and new solutions in order to optimize them to FANETs scenarios such as taking advantage of position-based routing principles.

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2.2 Unmanned Aerial Vehicle 2.2.1 History

The first use of an UAV, commonly known as a drone, occurred in the time of war in 1849, while Austrian soldiers attacked the city of Venice with balloons filled with explosives. Some of these balloons did hit their target, but most of the balloons were hit and eventually wounded the Austrians themselves, ending the possibility of reusing this strategy in war scenarios.

Later, the idea of controlling an unmanned aerial object remotely led Great Britain to develop the first ’pilotless’ winged aircraft, the Ruston Proctor, named Aerial Target. A drone prototype example is shown in Figure 2.1.

The Aerial Target was based on Nikola Tesla’s designs and was controlled by radio, and we can see the same methodology on the drones of nowadays.

Figure 2.1: Remote UAV prototype [1].

After the World War II, the UAV technology seemed to be improved, but on the other side, it was still seen as an unreliable and expensive technology on the military scenario.

Afterwards, during the Cold War, leaked classified information tells that there were UAVs used to spy between US and USSR.

Modern military drones are designed to scan enemies, combat, tactical reconnaissance, and tactical decoy.

2.2.2 Applications

Since it appeared on the market, the commonly used drone was adopted by the community as a flight toy, thanks to its portability, price and being radio-controlled.

Despite this naive thought of drones’ usability, research made drones able to join and help on human needs, armed to do new tasks.

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More than just a toy, drones joined on a new era where they can be useful to humans on several professional areas.

One of the most-watched usages of drones is on aerial photography. On almost every musical event or firework show there is at least one drone recording it on angles that were previously unreachable by humans without significant cost and effort, an example of a dron point of view is illustrated in Figure 2.2.

Figure 2.2: A Drone’s Point Of View [2].

Monitoring, data collection, recognition and image processing are some of the main features that are being developed on drones and can fit important areas such as the search and rescue operations, firefighting support by finding humans surrounded by the fire, firefighters that are having difficulties and cannot return safely, and measuring air gases and temperature.

On non-emergency environments, drones equipped with thermal cameras can help in agriculture development and monitoring [3]. These are designed to collect data, combined with ground sensor units, on the way to monitor nutrients„ humidity, identifying crop diseases, pest problems, and water-stress, depicted in Figure 2.3. Collected data can be successfully used to estimate and predict yields.

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2.2.3 Communication

On large missions, a single drone cannot handle complex tasks alone, so multiple drones are used to cooperate on each scenario. To deal with the missions’ inherent scalability problem, drones have to keep a communication between each other, resorting for the most valuable routing paths to achieve time, power consumption and task efficiency.

Traditional mission planner architectures are designed so that every drone keeps a perma-nent connection to a base station that takes control over the planned mission. However, a permanent connection to each drone on a mission is not efficient compared with peer-to-peer cooperation approach.

Battery powered drones are built to support specified weights; battery size and duration is crucial during eventual missions.

To reduce power consumption, this dissertation proposes a WMN to help drones to communicate between each other, increasing power efficiency and communication on and between each group of drones that were assigned to a particular task.

Before introducing WMNs, it is essential to review some of the existing wireless technologies that allow us to build this type of ad-hoc network topology.

2.3 Ad-Hoc Networks

This section discusses Ad-Hoc Networks, their structure and design, and describes some issues with this type of networks and solutions to those issues.

2.3.1 Topology

Ad-hoc networks, as illustrated on Figure 2.4, consist of networks without a well-defined structure. They can take different forms due to the mobility of their nodes, being flexible and considered highly adaptive.

Nodes are either mobile hosts or mobile routers, forwarding traffic between each other within the network.

Once ad-hoc networks are formed in an on-demand strategy, the network starts to grow and connect the nodes as soon as they come closer to each other; thus, this property eliminates

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the need of wired stationary equipment like routers, base stations and also avoids the need for a central network administration.

The topology of these networks is dynamic and is subject to frequent changes due to the nodes’ mobility and environment changes; thus, traditional routing protocols such as Open Shortest Path First (OSPF) and Routing Information Protocol (RIP) will not work as well as on wired infrastructures, and are likely to fail to adapt to a dynamic topology due to the fact that they were not designed for frequent topology changes. Therefore, Ad Hoc networks need a new class of routing protocols able to deal with topology changes.

2.3.2 Issues

Since MANETs use wireless medium access for data transmission, the access must be controlled to prevent collisions, so that nodes do not send data simultaneously.

A common problem is the so-called hidden node problem, as shown in Figure 2.5. It occurs when two nodes that are out of their radio range send data, at the same time, to an intermediate node which is in the radio range of both, causing a collision at the receiver node.

Figure 2.5: Hidden Node Scenario.

To minimize this problem, an handshake protocol such as Request-To-Send (RTS)-Clear-To-Send (CTS), shown on Figure 2.6, may be used.

When a node wants to transmit some data, it sends an RTS beforehand and waits for the CTS so that the ’path is clear’ and the data will not collide on the receiver. Nodes that listen to the RTS, if they are the receiver, wait and send a CTS. When listening to CTS, surrounding nodes stay quiet to avoid collisions.

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Another problem, shown on Figure 2.7, happens when a node is sending data to another, and due to the fact that ad-hoc networks do not have a centralized solution to assign Internet Protocol (IP) addresses. When an outer node appears with the same IP as the receiver, the data transmission flow is sent down and the sender node starts to send to the new node, believing that it is the legitimate receiver.

Figure 2.7: Hidden Node IP Conflict Scenario.

2.4 Wireless Technologies

Nowadays, the existence of several wireless technologies allows users to create different projects according to the benefits offered by each one; however, there is not a perfect technology that can cover all applications, and therefore, it is necessary to make a selection of the technologies that are the most suitable in terms of the equipment available, its monetary cost, energy consumption, weight of the equipment, range of wireless networks supported, among other characteristics.

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The IoT encompasses many applications and equipment types, being WiFi and its variants one of the most widely used and adopted technologies.

WiFi is heavily accessed and acquired in various areas and equipment, such as in homes, restaurants, cinemas, shopping centers, schools, mobile phones, laptops and others. It has become the keyword when it comes to providing Internet connectivity, and is the most common choice for IoT applications due to its ease of purchase, installation and use.

However, despite its usefulness, it is not possible to fulfill all the requirements of the different IoT applications using WiFi, since one of the main objectives of these applications is the reduced use of resources, and WiFi equipment tend to waste more energy than other wireless technologies.

There are many other factors that can limit the use of wireless technologies, such as hardware compatibility, frequency interference, and even their monetary cost. Reduced power consumption is also a limiting factor and dominant in IoT applications; however, with the reduction of energy consumption, we consequently suffer a change in other factors such as signal range, frequency and bandwidth.

When long range is desired, there are technologies such as Long Range (LoRa), IEEE 802.11ah (HaLow) and IEEE 802.11af (White-Fi), which combine the range and data rate. The longer the range, the higher the power consumption; however, in some instances the bandwidth is not crucial and can be reduced, allowing a long-range signal coverage at a reduced energy consumption. On the other hand, technologies like Bluetooth and ZigBee, where high range signal coverage and data rate is not a requirement, have a significantly lower energy consumption when compared to WiFi.

Wireless technologies ranges comparison is shown in Figure 2.8.

Figure 2.8: Wireless Technologies Ranges.

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2.4.1 ZigBee

Based on the IEEE 802.15.4, ZigBee is a wireless technology comparable to WiFi and Bluetooth, distinguishing itself in its reduced power consumption and short range. It is typically used in areas such as home automation, industrial area and sensor networks. ZigBee devices typically must have a lifetime of two years, when battery powered, to pass ZigBee certification.

Frequency 2.4 GHz

Channel Bandwith 868 MHz (Europe)

Range 100 m

Data Rate 20 Kbps (@ 868 Mhz)

250 Kbps (@ 2.4 GHz)

Table 2.1: ZigBee Characteristics.

Architecture

ZigBee was designed with three types of topology available, as can be seen in Figure2.9: Tree, Star and Mesh. Mesh topology is the most flexible, ensuring a low cost and power consumption, durability and a mechanism of discovery of neighbouring nodes able to select the best path between them and helping in the self-healing of the network.

Figure 2.9: ZigBee Topologies.

On a ZigBee network, the constituent nodes can assume three roles. The first is an end device, a node that takes advantage of the network but does not contribute to its operation, its presence is not crucial and does not allow connection to the network through it. A node can also be a router, although not mandatory in all topologies when it exist. It is responsible for relaying the messages to the remaining nodes and allow connectivity to the network through the router. Finally, a node can be a coordinator, responsible for the initial configuration of the network, dealing with configurations such as frequency and the network identifier. This node has a higher degree of hierarchy related with the remaining routers, those being their children.

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2.4.2 Bluetooth

Bluetooth is mostly integrated into devices such as mobile phones, laptops, printers, video-game consoles, smart houses, and more. The Bluetooth Low Energy (BLE) version uses the same WiFi transmission frequency but with different multiplexing that allows maintaining a reasonable data rate with energy consumption, since BLE devices most of the time are in idle mode. These devices are known for having low energy consumption, reduced size, low monetary cost and compatibility with various devices present in the market.

One of the problems in using BLE is the in-compatibility with the previous Bluetooth versions. BLE devices can build a mesh network between them; however, an increase of nodes in the network leads to interference problems, and consequently the power consumption of the devices tend to increase.

Frequency 2.4 GHz

Channel Bandwidth 2 MHz

Range 100 m

Data Rate 1 Mbps

Table 2.2: Bluetooth Characteristics.

Architecture

On a Bluetooth architecture, shown in Figure 2.10, a node can either be a master or a slave. The communication always occurs between a master and a slave node and it is only initiated by the master; however, once established, a slave can request master’s power.

Figure 2.10: Bluetooth Architecture, Scatternet & Piconets.

When grouping more than two nodes, we get a structure called piconet. All units within a piconet share the same channel. Each piconet has one master, and up to seven active slaves at a time. Slaves do not communicate directly with each other, and inactive slaves

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and frequency hops with the master because each piconet uses a distinct frequency hopping sequence.

When grouping piconets, we get a scatternet: masters from each piconet act as slaves on neighbours piconets when they are the ones bridging; otherwise, slaves will do the job relaying the data.

2.4.3 IEEE 802.11

IEEE 802.11 is the primary technology when we talk about connecting to the Internet. Because it is important to have technologies able to cover a certain area focusing on the coverage and network performance, IEEE developed several IEEE 802.11 variants, where some of them are suitable to be a part in IoT networks. Apart from the most known variant, the IEEE 802.11b/g/n, we describe some of the promising variants which are the IEEE 802.11p (WAVE), HaLow, White-Fi and IEEE 802.11s; their coverage comparison is illustrated in Figure 2.11. • IEEE 802.11ah - HaLow is designed to increase the range of WiFi by reducing the data rate, which is helpful on sensor networks that do not require high data transfers, and because of the range offered by 802.11ah, a higher area can be covered by the signal. Because the 900 Mhz frequency can more easily penetrate walls, it is used for short data burst and does not have interference problems with the remaining wireless technologies that operate on the 2.4 and 5 GHz bands.

• IEEE 802.11af - IEEE 802.11af [4] operates on the unused television spectrum fre-quencies, known as white spaces to transmit information, and so it was called White-Fi. Also due to these frequencies being between 54 MHz and 790 MHz, 802.11af can be used for low power and wide-area range, like HaLow.

The downside of White-Fi is that, although it gives high data rates through very long ranges, the equipment is costly and the white spaces are not equally available around the world.

• IEEE 802.11s - The IEEE 802.11s [5] standard aims to improve mesh networking. Its architecture is formed by devices called Mesh Station (STA), typically on Ad Hoc mode. Mesh STAs form mesh links between them, and in the end, the network can establish mesh paths. This standard follows the mesh paradigm where the network is split into areas and takes advantage of wireless multi-hopping to obtain high area coverage and reducing power consumption by having a backbone structure and a client structure, forming a hybrid mesh network structure.

Its key to success is the usage of free bands 2.4GHz and 5GHz, and it is designed to fit mesh topology networks using WiFi.

• IEEE 802.11p - The IEEE 802.11p [6] WAVE is an amendment to the 802.11 standard to provide access in vehicular environments. It supports low latency data transfers at high mobility vehicles. WAVE implementation surged to get real time traffic analysis,

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increase security and minimize traffic congestion problems, providing robust vehicular connections.

Figure 2.11: Predicted radio range of the various 802.11 standards.

2.5 Wireless Mesh Networks

WMNs arose as a low-cost network optimized for area coverage and resilience. These networks are comprised of client nodes, routers typically in ad hoc mode, and also by gateway nodes. In other words, a mesh network is an overlay topology that takes advantage of the wireless links between the nodes that form the network.

WMNs are typically dynamic, self-configured and organized. They are also known as flexible and robust networks due to their backbone structure built with the existing routers, and, thanks to its flexibility, it can reach long distances reducing the cost of transmission through wireless multi-hop.

Based on the connectivity types of the network elements, WMNs can be classified as Point-to-Point (PTP), Point-to-Multipoint (PTM) or Multipoint-to-Multipoint (MTM), as shown in Figure 2.12.

PTP networks are considered reliable, but they have a low level of adaptability and are not scalable.

To pass through these limitations, MTM have surged to provide high reliability, adaptability and capacity to have a scalable network. Once the number of nodes increases, the transmission power needed per node will decrease, and thanks to wireless multi-hopping that also increases the network coverage, by splitting the network into areas, the main objective of energy

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Figure 2.12: WMNs Classification.

2.5.1 Architecture

The way nodes are displaced on a WMN can form three different architectures, each of them giving their own advantages and functionalities: a WMN can be seen as a Backbone WMN, Client WMN and as a Hybrid WMN, depicted on Figure 2.13.

• Backbone WMN - This is the most used structure; it integrates with other existing wireless networks and is based on a grid of mesh routers that are connected to different clients. These routers have gateways functionalities allowing the access to the Internet through them.

• Client WMN - On this type of networks, mesh client nodes provide routing functional-ities and also the role as end-users. The routing is based on the multi-hopping technique until the packet reaches the destination node. Connectivity is based on peer-to-peer links, labelling this network as a PTP.

• Hybrid WMN - a Hybrid WMN combines both structures; mesh clients are able to access gateway services, such as the Internet, through multi-hopping between the mesh routers, or by direct connection. A Hybrid WMN can interoperate with the Internet, WiFi networks and sensor networks, optimizing the connectivity cost and coverage area through the usage of multi-hopping techniques.

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Figure 2.13: WMN Architecture Types.

2.5.2 Components

WMNs were designed to provide high-bandwidth Internet access. They consist of Mesh Clients and Mesh Routers that relay packets in a multi-hop technique between them. Mesh Routers have low mobility due to their role of holding and being a part of the backbone structure. This structure is also formed by Gateway Nodes that give access to the Internet, and thanks to wireless multi-hopping clients, they can access the Internet.

In order to reach extended coverage areas, the mesh architecture breaks long distances into hops in order to increase the signal strength by the nodes that split the areas.

• Clients - Mesh Clients are usually mobile end-user devices; those devices can be laptops, smartphones, devices that access common applications such has Voice over Internet Protocol (VoIP), email clients and Global Positioning System (GPS) location. They have limited power, can be connected to the network and may have routing capabilities. Typically mesh clients are considered as mobile nodes, and in such cases, their network will be often focused on an on-demand methodology because of their mobility.

• Routers - Mesh Routers handle and route the network’s traffic. They have mobility limitations due to its role of making the backbone resilient, reliable and robust, so in this case, instead of mesh clients, the network methodology will focus on a table-driven perspective.

By taking advantage of multi-hopping techniques, the transmission power consump-tion is reduced, and the routers also help the mesh to be scalable due to their capability of supporting multiple interfaces and channels.

• Gateways - Gateways are routers that give access to the Internet and other structures. Due to their multiple interfaces, wired and wireless, they are expensive, and their

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position on the networks is crucial to the mesh performance: the more gateways and well positioned, the better is the network’s performance and resilience.

This crucial participant of a mesh is often embedded into the table-driven network perspective along with routers, forming most of the time a robust backbone structure.

2.5.3 Mobility Classification

A mesh network built over a wireless structure, with mobile wireless nodes, can be classified as a mobile network or the so-called MANET. Thus, MANET is a generic network classification that groups all Ad Hoc networks which includes mobile nodes.

Deep into MANETs, we can obtain another classification that is known for the vehicu-lar network scenario, known as Vehicuvehicu-lar Ad-Hoc Networks (VANETs), and also, through VANETs we can then obtain another derivative classification that involves nodes with flying capabilities, called FANETs, where the Drone-to-Drone scenario fits.

This classification is illustrated in Figure 2.14, where the deeper we go into MANETs, the higher is the mobility of the client nodes and consequently the network complexity and unpredictability.

Figure 2.14: Mobile Ad Hoc Network Derivatives.

VANETs are considered a 2D network model because vehicular clients will not be flying or changing their altitude level significantly. However in FANETs it is not expected to have stationary units levitating and predicting drone mobility behaviour, while in the case of vehicular scenarios, the traffic flow is expected to run on a specific direction; thus, it can be concluded that FANETs have an extra dimension (altitude), being considered a 3D network model.

MANETs are designed for devices with low mobility; the network topology is steady and changes slowly, the computational power is limited to the client device; thus, power consumption is also critical and in need of energy-efficient routing protocols.

Compared to VANETs, FANETs do have a higher node mobility factor: network topology changes faster than vehicular scenarios, power consumption is a crucial detail in order to

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extend network lifetime, and last but not least, FANETs, due to its flying movements and unstable nature, achieve a higher power consumption.

2.5.4 Routing

When addressing routing on a WMN, we must consider existing Ad Hoc routing protocols and their classifications: these routing protocols can be classified as hierarchical, flat and position-based also known as geographic position assisted routing, outlined in Figure 2.15.

Figure 2.15: WMN Routing Classification.

The main class that is often the subject of research is flat routing, from where we can divide it into Reactive, Proactive and Hybrid; some also include Position-based.

• Proactive or Table Driven protocols try to keep a predetermined number of routing tables on the network that store routes to each individual nodes/regions of the network and continuously send them along with the network’s exchange and update packets to neighbour nodes, flooding information. These protocols do not scale on large networks due to the amount of information collected concerning global routing.

Optimized Link State Routing (OLSR) and Destination-Sequenced Distance Vector (DSDV) are the most well-known proactive routing protocols on mesh networks. • Reactive or On Demand protocols do not generate flooding messages to neighbor

nodes. Every time a packet is sent, the sending node will receive information about the route to the destination unknown node, and although it does not generate network traffic, the time needed to get the route information increases as the network scales up. Examples of reactive routing protocols used on WMNs are the Ad-hoc On-Demand Distance Vector (AODV) and Temporally Ordered Routing Algorithm (TORA).

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• On a Hybrid WMN, since it is built by joining a backbone to a client structure, the same ideology should be approached, taking advantage of both routing protocols. Based on this approach, protocols like Zone Routing Protocol (ZRP) and Hybrid Wireless Mesh Protocol (HWMP) were developed. HWMP is used by IEEE 802.11s and its reactive nature is based on AODV, when, on the other hand, the existing proactive ideology on the backbone structure keeps the network information resorting to tree-based routing [7].

• Position-based routing protocols are often considered a subtype of hybrid routing pro-tocols and are present on MANETs. These routing propro-tocols use network’s participants location, often by resorting to Global Positioning System (GPS), to find the availability of routes. An example of a position-based routing protocol is Greedy Perimeter Stateless Routing (GPSR) which follows a greedy approach when analyzing the position of existent network members.

When choosing between proactive or reactive protocols, one must consider the type of WMN structure. In a backbone structure entirely made with mesh routers that are static and have a low or no mobility, a proactive protocol must be chosen due to low mobility of the mesh routers. The network traffic (discovery) will be almost null between mesh routers, thus, the client WMN usually picks reactive protocols.

WMNs lack routing protocols suitable for these mesh networks, since most metrics on Ad-Hoc networks cannot be applied to mesh networks. Some of the metrics that are being used in research areas are, for example, the number of hops (Hop Count), Quality of Service (QoS) and network load rate (Load-Dependent).

AODV

AODV is an on-demand routing protocol; it starts to find a route to a certain destination when a source node requests it. After requesting, AODV will keep the route while it is still being used.

When a source node wants to request a route, it sends a broadcast Route Request (RREQ) packet. The message is flooded until the destination is found, at which moment the destination node sends a Route Reply (RREP) packet back to the source node through the best-considered path. This is shown in Figure 2.16.

AODV nodes also send periodic broadcast Hello packets to observe the nearby nodes, and when a route is lost due to node mobility, a Route Error (RRER) is sent back.

During the broadcast RREQ, if a node receives duplicated requests they are discarded, and it is assumed that the request was already sent. This situation occurs on node D, as illustrated in Figure 2.16, when both paths are joining, because only one request is needed to define the optimal routing path from node D to node G.

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Figure 2.16: AODV Route Request broadcast & Route Reply answer.

DSDV

DSDV protocol is a table-driven routing protocol based on Routing Information Protocol (RIP). One of the limitations of DSDV is that it only provides one route per source-destination pair, using bidirectional links.

A DSDV node holds a routing table with the information regarding the global network structure, thus, gathering all possible route destinations and their hops. DSDV maintains two tables, one for forwarding packets and another for incremental advertising.

The table algorithm is based on the incremental sequence number, updating the table when a received advertisement sequence number is higher than the stored one. On the case that distinct nodes provide distinct routes to the same destination node, the route with the lowest cost is assumed to be the shortest.

Given the topology example shown in Figure 2.17, where each link is bidirectional, the routing table for the node F is shown in Table 2.3, where the sequence number is missing. The metric value expresses the number of hops to reach the destination node.

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Destination Node Next Hop Metric A D 3 B D 2 C D 3 D D 1 E G 3 F F 0 G G 1 H G 2

Table 2.3: Node F routing table.

ZRP

ZRP is considered a hybrid routing protocol; thus: it takes advantage of both on-demand and table-driven approaches, and combining these features, ZRP divides the network in zones, as shown in Figure 2.18.

Inside a zone, it uses Intra-zone Routing Protocol (IARP), a proactive approach. Any route request to a node within the zone is quickly established by checking the IARP routing table.

When communicating zone to zone, it uses Inter-zone Routing Protocol (IERP), a reactive approach. To find a route for a node out of its zone, a request to the neighbour zone has an on-demand request; however, establishing the route takes more effort than just checking its zone’s table, although keeping all the details and routes also wastes more resources.

IARP can be seen as a similar routing technique as DSDV, and IERP can also be associated with AODV.

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GPSR

GPSR is a position-based routing protocol which forwards data packets by evaluating the distance between the destination’s position and neighbours’ position, as illustrated in Figure 2.19, delivering to the closest neighbour to the destination, in case it exists, through greedy mode; otherwise, if there is not a closest neighbour and there is no direct connection to the destination, GPSR forwards data in recovery/perimeter, mode which chooses the first neighbour to appear in the right hand rule evaluation.

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2.6 Related Work

On this section there are presented related research studies, starting by wireless mesh projects and topologies, and then focusing on the routing protocols’ performance analysis and modifi-cations in order to fit mobile networks’ environment.

2.6.1 Mesh Topologies & Applications

Research on [8] explores the functionalities of MANETs and focuses on the network partitioning problem, which penalizes the military scenario where a large team works by dividing itself into groups. The communication between teams is not possible due to MANETs mobility limitations. Authors developed an Autonomous Mobile Mesh Network (AMMNET) that allows communication intra-group and inter-group. Each group’s mesh dynamically covers the nearby surrounding members, making it possible to communicate efficiently inside the mesh and between teams.

This implementation assumes that the application terrain is filled with mobile mesh units, instead of stationary units. When soldiers move together on a group, mobile mesh units manage the dynamic backbone structure. Because mobile mesh nodes have mobility and position mechanisms (GPS), they can follow the groups and allow this structure to be dynamic and mobile.

Figure 2.20: Hierarchical Topology Construction [8].

The networks hierarchy, shown in Figure 2.20, organizes itself in clusters of groups, and, due to mesh units movement intelligence, by following the soldiers’ group, the network adapts to topology changes with the focus of reducing network partitioning.

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In [9] mesh networks are also equationized on the military scenario, which requires the deployment of a wireless network to make possible teams and crucial departments to communicate. Authors assume a terrain where a natural disaster has occurred, or where there are terrorism disputes with a risk of buildings to collapse. In these situations, cellular networks and wireless networks are not available.

The strategy adopted was to deploy tactically positioned drones, so they can form a mesh network to cover an area of interest. Drones equipped with mesh routers align and cooperate in providing Internet connectivity through wireless multi-hopping along the covered area, shown in Figure 2.21. Each drone provides video feedback to the base station, monitoring and avoiding undesirable situations. The Internet connection and routing capabilities were successfully tested by using the Better Approach To Mobile Ad-hoc Networking (BATMAN) routing protocol [10] over IEEE 802.11n and IEEE 802.11ac.

Figure 2.21: Assumed Military Scenario on [9].

The authors in [11] made an experimental performance analysis on aerial WiFi networks. They considered three scenarios illustrated in Figure 2.22. The first one, a ground station was directly connected to a drone (one-hop). In the second scenario, there was an Access Point (AP) (another drone) between the ground station and the flying drone (at two-hops). The last and third scenario was designed exactly similar to the second, but all nodes were mesh points.

This study characterized the Packet Delivery Ratio (PDR) and Transmission Control Protocol (TCP) throughput performance. After the experiment, the authors justified that one-hop communications are path limited, so they did opt to use two-one-hop or mesh communications to reach more extensive paths. One-hop and mesh setup did show a high variance in throughput.

Authors on [12] explain that, due to the increasing availability of bandwidth-demanding online services and applications, traditional users of these services and applications are increasingly demanding always-on broadband Internet connectivity. These users expect that

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Figure 2.22: One-hop, Two-hop & Mesh Scenarios.

research, it is assumed crowed scenarios, denominated Temporary Crowded Events (TCEs), such as outdoor activities like music festivals, where due to the crowd, the network’s service quality is highly degraded. To avoid network degradation during the event and to provide a better on-demand service to users, it was designed a Traffic-Aware Multi-Tier Flying Network (TMFN). This network consists of a mobile and physically reconfigurable network of Flying Mesh Access Points (FMAPs), and Gateways UAVs organized on a two-layer architecture. TMFNs are able to readjust their topology according to the users’ traffic demands in order to provide better connectivity to the Internet.

Figure 2.23: Proposed TMFN’s Topology on [12].

A TMFN, depicted in Figure 2.23, is composed, on the first layer, by UAVs carrying FMAPs which can establish WiFi cells that are dynamically configured and positioned. These UAVs continuously seek for the users that generate a considerable amount of traffic in order to provide a privileged service. On the second layer, considered as a backhaul network, there can be found the Gateway UAVs that forward traffic to the Internet using dedicated broadband wireless links. In order to make a TMFN topology concept to work correctly, the authors proposed a traffic-aware Network Planning (NetPlan) algorithm, which is responsible for dynamically determining the network’s topology according to the users’ needs, improving the TMFN aggregated throughput.

Authors in [13], along with MITRE Corporation [14], explored the FireChat application [15] on Ad Hoc Networks by using monitor drones. FireChat is a mobile messaging application

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that uses a mesh network topology to make users able to chat through cellular, WiFi, or BLE connections. This application is designed to be used in crowded events such as protests. FireChat and these types of messaging applications that work through mesh networks topologies do relay messages from one device to another through each user’s WiFi or Bluetooth radios without the need of an Internet connection. This application was highly used on Hong Kong pro-democracy protests in 2014, where due to the crowded area, cellular networks on the city became overloaded. To complement the work in AirChat, authors combined a UAV to fly over the crowd, collecting WiFi packets that correspond to the FireChat application, relaying it to the servers’ application and database through a gateway connection.

Cases where real-time data is crucial, a reliable communication to the control base station is required. On the other hand, the mesh network can keep the gathered data and retrieve it when the mission is over or when a connection to the control base station is re-established. In both cases, one should take into consideration the concept of Delay Tolerant Networks (DTNs) [16], which are networks that use the store-and-forward methodology to preserve the collected data and retrieve it when the route to the base station is re-established.

Figure 2.24: Control Base Station Communication Example.

In Figure 2.24 a possible Hybrid WMN is exhibited, where drones acting as inter-group (bridge) routers form the backbone structure, because their mobility will not be high compared with the drones acting as clients (which are included on each illustrated team). Each team collects data, which, by multi-hopping, is sent to the control base station through a gateway, or

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to the nearest stationary base station (fire brigade) within the range of the drones responsible for the backbone structure.

2.6.2 Routing Protocols & Adaptations

To make the previous network approach to communicate, the backbone and each team must have a routing protocol that makes it possible to communicate intra-group and inter-group through the bridge routers. To do so, on to the network layer, [17] provides an overview of some of the used routing protocols in WMNs. In this paper, the author concludes that AODV was the one which produced the highest throughput, and OLSR achieved the lowest delay in wireless Local Area Network (LAN) on voice and audio streaming scenarios.

Both routing protocols could be used on this networks’ approach, placing the reactive one, AODV, responsible for the group/team communication (clients of the mesh), and the proactive one, OLSR, responsible for the backbone structure communication, following the methodology presented in Subsection 2.5.4, but due to high mobility of these devices, it is still tough to maintain an acceptable network performance while trying to minimize energy consumption and dealing with topology changes.

Wireless Sensor Networks (WSNs) have similarities with WMN, and some of the used protocols on WSN are often adapted to be used on WMN. Thus, in [18] the author provides an overview of some of the existing routing protocols on WSNs and provides a comparison, evaluating the network delay and energy consumption, which in the case of drone-to-drone it is a crucial detail. The author has chosen AODV, Dynamic Source Routing (DSR), and ZRP, being AODV and DSR reactive/on-demand protocols and ZRP hybrid. The evaluation measures the end-to-end delay and average network throughput. In the test scenario, it was considered a fixed set of 50 sensors where some of them were senders, and others were receptors. The results regarding energy consumption revealed that DSR was the one with the lowest energy consumed, followed by AODV and lastly ZRP. For the average delay, AODV was the best one, also with the highest average network throughput, followed by DSR and ZRP.

A study published in [19] addresses the existence of FANETs, being a subset of VANETs, which are also a subset of MANETs, and provide a study between each subtype. Afterward, it provides a comparison between routing protocols presented on FANETs, where authors divide them as being Static, Proactive, Reactive, Hybrid, Positional, and Hierarchical protocols. From this comparison, shown on Table 2.4, we can perceive that Proactive protocols (Table Driven) are useful for smaller networks: its memory usage is higher, the networks topology convergence is slow, and latency is low. On the other hand, Reactive protocols (On-Demand) are used for more extensive networks, require lower memory usage and network topology convergence is faster, but latency is higher. Finally, for Hybrid protocol, it takes advantage of both protocols dependent on the scenario.

On another research, the work in [20] provides a performance measurement of reactive versus proactive routing protocols on IEEE 802.11 Ad Hoc networks. The chosen reactive protocols were DSR and AODV, while OLSR was the only proactive protocol chosen for the

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Figure 2.1: Remote UAV prototype [1].
Figure 2.12: WMNs Classification.
Figure 2.14: Mobile Ad Hoc Network Derivatives.
Figure 2.16: AODV Route Request broadcast & Route Reply answer.
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