Topology control flying backhaul networks

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TOPOLOGY CONTROL OF FLYING BACKHAUL

NETWORKS

EDUARDO NUNO MOREIRA SOARES DE ALMEIDA DISSERTAÇÃO DE MESTRADO APRESENTADA

À FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO EM

MESTRADO INTEGRADO EM ENGENHARIA ELECTROTÉCNICA E DE COMPUTADORES

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2015

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ACULDADE DE

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NGENHARIA DA

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NIVERSIDADE DO

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ORTO

Topology Control of Flying Backhaul

Networks

Eduardo Nuno Moreira Soares de Almeida

Mestrado Integrado em Engenharia Eletrotécnica e de Computadores Supervisor: Prof. Manuel Alberto Pereira Ricardo (PhD) Co-supervisor: Eng. José Carlos Marques Pedreira de Oliveira (MSc)

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Abstract

With the dissemination of the Internet-of-Things (IoT) paradigm, a rapidly increasing number of devices are connecting to the Internet, namely smartphones, tablets and wearable devices (e.g., smartwatches). Moreover, popularization of distributed systems and high-quality multimedia streaming services, along with the migration of data and services to the cloud, are contributing to an increase of the Internet traffic generated by these always-on devices. In order to provide a good Quality of Experience (QoE) to the user, these devices demand broadband Internet connec-tions.

As a result, during large public temporary crowded events (examples being music festivals, public demonstrations and sports events) users face problems accessing the Internet, whether they are using cellular networks or Wi-Fi Access Points (APs) installed on site. Solutions have been developed recently to overcome the cellular saturation problem. However, despite these efforts, cellular networks remain unable to handle the large amounts of data generated by user devices and, consequently, unable to provide the desired QoE demanded by the users. Current Wi-Fi based solutions, on the other hand, provide the desired broadband at the expense of a high deployment cost and inefficiency throughout large periods of time, due to a static planning prior to the event, which render these solutions inadequate for public temporary crowded events.

In fact, given that these events are extremely popular and frequent, the development of new so-lutions presents increased benefits to the users attending the events, reflected in the QoE provided. Moreover, the proposed solution can also be applied to slightly different scenarios, contributing to solve similar problems.

In this sense, in order to address this problem, the concept of a traffic-aware Flying Back-haul Network (FBN), based on Flying Ad Hoc Networks (FANETs), was introduced and explored in the scope of this Dissertation. The main objective of the work was to design a traffic-aware FBN that provides broadband Internet access to the users attending the public temporary crowded event, presenting multiple benefits relative to existing solutions, such as the ability to dynamically self-configure according to the users’ needs, thus providing them with the best possible QoE. To validate the developed solution, Matlab simulations were also developed which provided a graph-ical representation of the behavior of the algorithm, in response to different scenarios, common in these types of events. Moreover, a testbed capable of validating the developed solution on a real scenario and evaluate its performance was also designed.

The results obtained show that the concept developed in this Dissertation presents multiple benefits relative to the existing solutions, introducing the new traffic-aware FBN concept and the correspondent topology control algorithm, particularly adequate for public temporary crowded events.

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Resumo

Com a disseminação do paradigma Internet das coisas (IoT) verifica-se um aumento súbito de dispositivos com conectividade à Internet, nomeadamente smartphones, tablets e dispositivos

wearable(por exemplo, smartwatches). Por outro lado, a popularização de sistemas distribuí-dos e serviços de streaming multimédia de alta qualidade, juntamente com a migração de dadistribuí-dos e serviços para a nuvem, contribuem para um aumento do tráfego de Internet gerado por estes dispositivos permanentemente conectados. De forma a providenciar uma boa Qualidade de Ex-periência (QoE) ao utilizador, estes dispositivos requerem conexões de banda larga à Internet.

Como resultado, durante eventos públicos temporários com elevada densidade de pessoas (por exemplo, festivais de música, demonstrações públicas e eventos desportivos) os utilizadores deparam-se com problemas de acesso à Internet, quer através de redes celulares quer através de Pontos de Acesso (APs) Wi-Fi instalados no local. Recentemente, têm sido desenvolvidas soluções para superar o problema da saturação da rede celular. No entanto, apesar destes esforços, as redes celulares continuam a não ter a capacidade de suportar grandes quantidades de informação gerada pelos dispositivos dos utilizadores e, consequentemente, não oferecem a desejada QoE requerida pelos utilizadores. Por outro lado, as soluções atuais baseadas em Wi-Fi fornecem a banda larga desejada à custa de um elevado custo de instalação e ineficiência ao longo de grandes períodos de tempo, devido à necessidade de um planeamento estático prévio ao evento, que torna estas soluções inadequadas para os eventos públicos temporários com elevada densidade de pessoas.

De facto, uma vez que esses eventos são extremamente populares e frequentes, o desenvolvi-mento de novas soluções apresenta crescentes benefícios para os utilizadores que frequentam estes eventos, refletindo-se na QoE fornecida. Além disso, a solução proposta pode também ser aplicada a cenários semelhantes, contribuindo para resolver problemas com características similares.

Neste sentido, por forma a resolver este problema, o conceito de Flying Backhaul Networks (FBNs) sensíveis ao tráfego, baseado em redes ad hoc voadoras (FANETs), foi introduzido e ex-plorado no âmbito desta Dissertação. O principal objetivo do trabalho consistiu em projetar uma FBN sensível ao tráfego gerado pelos utilizadores, que forneça acesso a ligações de Internet de banda larga para os utilizadores presentes no evento público temporário com elevada densidade de pessoas, apresentando múltiplos benefícios relativamente às soluções existentes, tais como a capacidade de dinamicamente se auto-configurar de acordo com as necessidades dos utilizadores, oferecendo-lhes assim a melhor QoE possível. Para validar a solução desenvolvida, foram tam-bém desenvolvidas simulações em Matlab, que forneceram uma representação gráfica do compor-tamento do algoritmo em resposta a diferentes cenários comuns neste tipo de eventos. Além disso, foi também desenvolvido um testbed capaz de validar a solução desenvolvida e de avaliar o seu desempenho num cenário real.

Os resultados obtidos mostram que o conceito desenvolvido nesta Dissertação apresenta múlti-plos benefícios em relação às soluções existentes, introduzindo o novo conceito de FBNs sensíveis ao tráfego e o respectivo algoritmo de topologia de controlo, particularmente adequado para even-tos públicos temporários com elevada densidade de pessoas.

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Acknowledgments

To my supervisors, Professor Manuel Ricardo, Engineer José Oliveira and Professor Rui Cam-pos, I would like to express my profound admiration and gratitude for having me received and integrated in the Wireless Networks (WiN) research group, at the Center for Telecommunications and Multimedia (CTM) of INESC TEC. I would also like to thank for all the valuable knowledge transmitted and support given to me during this stimulating and exciting Dissertation.

To the WiN research group, my sincere gratitude for having welcomed me and provided addi-tional support during the last semester.

I would like to thank to the organizing committee of "CTM Open Day 2015" and "Eureka Effect - INESC TEC for Dummies" (Commemorative Event of the 30th INESC’s Anniversary), which gave me the opportunity to showcase my Dissertation to interested enterprises, press and general public.

Finally, I would also like to thank to my family and friends for their unconditional support, during the development of my Dissertation.

Eduardo Nuno Moreira Soares de Almeida

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"I found that I made more progress when I had obstacles than when things were easy."

Mildred Dresselhaus

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List of Figures

1.1 High density of mobile devices connected to the Internet during the event [6,7]. . 2

2.1 Cellular network performance during a public temporary crowded event. . . 6

2.2 Traffic offload from overloaded cells using UAVs [4]. . . 7

2.3 Unmanned Aerial Vehicles. . . 11

2.4 Ad hoc network types [19]. . . 13

2.5 Communication architectures between UAVs [20]. . . 16

2.6 Boids Flocking three steering rules [23]. . . 18

2.7 Potential field generated by one attractive (goal) and rejective (obstacle) forces [23]. 19 2.8 Illustration of the ranging process [35]. . . 22

3.1 Icons used to represent the network elements. . . 27

3.2 Proposed concept displaying UAV interactions and connections (adapted from [6]). 27 3.3 Event’s enclosure representation, identifying the map and constituting zones (adapted from [41]). . . 28

3.4 Calculation of the total resulting force applied on the UAV by the surrounding potential field generators (two attractive and one rejective). . . 30

3.5 User localization mechanism, depicting three UAVs’ area of possible user loca-tions and the final intersection zone. The user location is determined as the cen-troid of this area. . . 35

3.6 Calculation of the horizontal component of the distance to the UAV, based on the distance to the UAV and the UAV’s height. . . 36

3.7 Experimental model to estimate the distance between the UAV and the user, based on the frame’s RSSI. . . 37

3.8 Topology control protocol. . . 39

3.9 Routing protocol. . . 43

4.1 Homogeneous traffic generation in the entire map. . . 49

4.2 Users concentrated around the center area of the map. . . 50

4.3 Users concentrated around one area of the map, located on the top of the map. . . 51

4.4 Users concentrated around two areas of the map, located on the top-left and bottom-right corners of the map. . . 52

4.5 Random distribution of users / traffic generation on the map. . . 53

4.6 User localization simulation result, displaying the locations of the UAVs, the cor-respondent area of possible user localization and the actual and estimated location of the user. . . 55

4.7 Histogram with the distribution of the estimation error in the user localization sim-ulation, consisting in 100.000 executions for each value of the standard-deviation (σ ) of the normally-distributed noise generator. . . 56

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xii LIST OF FIGURES

5.1 Flying Mesh Access Point (FMAP) developed prototype. . . 58

5.2 DJI Phantom 2 [43]. . . 58

5.5 Hotspot 4G / LTE - ZTE MF910 [49]. . . 60

5.6 GPS Module BlueNEXT BN903S [50]. . . 61

5.7 Network Monitoring Application. . . 62

5.8 Testbed set-up of the Flying Backhaul Network (FBN). . . 64

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List of Tables

2.1 Summary of the centralized and distributed topology control algorithm character-istics. . . 18

2.2 Comparison between the three UAV swarm topology control algorithms [23]. . . 20

3.1 Table generated by every UAV, containing information regarding the frames cap-tured in the medium. . . 35

4.1 Simulation parameters. . . 47

4.2 Results of the user localization simulation, containing 100.000 executions for each value of the standard-deviation (σ ) of the normally-distributed noise generator. . 55

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List of Algorithms

1 Attractive potential field’s intensity - UAV coverage component . . . 31

2 Topology control algorithm - Main simulation script . . . 46

3 Topology control algorithm - Main Central Station script . . . 46

4 Topology control algorithm - Main UAV script . . . 47

5 User localization - Main simulation script . . . 54

6 User localization - Determine user localization script . . . 54

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Acronyms

AODV Ad hoc On-demand Distance Vector

AP Access Point

API Application Programming Interface BER Bit Error Rate

CS Central Station

DHCP Dynamic Host Configuration Protocol DNS Domain Name System

FANET Flying Ad hoc Network FBN Flying Backhaul Network FER Frame Error Rate

FMAP Flying Mesh Access Point GPS Global Positioning System GUI Graphical User Interface ID Identifier

IP Internet Protocol LAN Local Area Network LoS Line of Sight

LTE Long Term Evolution MAC Medium Access Control MANET Mobile Ad Hoc Network MAP Mesh Access Point

OFDM Orthogonal Frequency-Division Multiplex OLSR Optimized Link State Routing Protocol OS Operating System

PAN Personal Area Network PC Personal Computer PF Potential Field

RF Radio Frequency

RSSI Received Signal Strength Indication RTT Round-Trip Time

SaaS Software-as-a-Service

SIM Subscriber Identification Module SNR Signal-to-Noise Ration

SSID Service Set Identification TC Topology Control

TCP Transmission Control Protocol UAV Unmanned Aerial Vehicle

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xviii Acronyms

UDP User Datagram Protocol VANET Vehicular Ad Hoc Network QoE Quality of Experience QoS Quality of Service

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Chapter 1

Introduction

1.1 Context

With the dissemination of the Internet-of-Things (IoT) paradigm, a rapidly increasing number of devices are connecting to the Internet, namely smartphones, tablets and wearable devices (e.g., smartwatches). Moreover, popularization of distributed systems and high-quality multimedia streaming services, along with the migration of data and services to the cloud, are contributing to an increase of the Internet traffic generated by these always-on devices [1]. In order to pro-vide a good Quality of Experience (QoE) to the user, these devices demand broadband Internet connections.

As a result, during large public crowded events (examples being music festivals, public demon-strations and sports events, depicted in Fig.1.1) users face problems accessing the Internet, whether they are using cellular networks [2, 3] or Wi-Fi Access Points (APs) installed on site. This is caused by the high density of mobile terminals connected to the same cells, both cellular and Wi-Fi, which overloads the respective cell and, thus, causing it to be unable to provide the required QoE. In fact, as demonstrated by several studies performed during large crowded events [2, 3], cellular network’s performance is degraded when too much traffic is generated in the same cell, thus degrading the overall QoE of the users in the event.

Solutions have been developed recently to overcome the cellular saturation problem [4, 5]. However, despite these efforts, cellular networks remain unable to handle the large amounts of data generated by user devices and, consequently, unable to provide the desired QoE demanded by the users. Current Wi-Fi based solutions, on the other hand, provide the desired broadband at the expense of a high installation cost.

1.2 Motivation

As presented above, in order for current applications / systems to provide good QoE to users attending large public events, they require broadband Internet connections. One common

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2 Introduction

Figure 1.1: High density of mobile devices connected to the Internet during the event [6,7].

lem, though, to every solution relates to the inability of the infrastructure to automatically and dynamically self-configure to the variable environment. In fact, because of the variable traffic pat-tern generated by the users, and to provide the required broadband Inpat-ternet connection, networks should be planned to provide a given QoE to the users considering a worst case scenario in mind. This requirement, however, renders the solution highly inefficient as, throughout large periods of time, the amount of generated traffic will sit below the expected level, thus wasting resources; or above the expected level, being unable to provide the desired QoE.

In fact, cellular base-stations generating large cells are unable to provide the required QoE, due to the saturated capacity caused by the high density of users connected to the same cell. On the other hand, fixed solutions require a large number of fixed APs distributed throughout the event’s enclosure, which significantly increases the deployment cost. Furthermore, fixed APs cannot fol-low the users’ random movements throughout the event’s enclosure, thus lacking awareness of traffic generation and the ability to dynamically adjust itself accordingly.

Therefore, the broadband required by recent applications and devices, along with the lack of efficient solutions to provide it during public temporary crowded events, motivate the development of new solutions. In fact, given that these events are popular and frequent, the development of new solutions presents increased benefits to the users attending the events, reflected in the QoE provided. Moreover, the proposed solution can also be applied to slightly different scenarios, contributing to solve similar problems.

1.3 Objectives

In order to address this problem, the concept of a traffic-aware Flying Backhaul Network (FBN), based on Flying Ad Hoc Networks (FANETs), will be introduced and explored in the scope of this Dissertation. The main objective of the work is to design a traffic-aware FBN that provides broadband Internet access to the users attending the public temporary crowded event, presenting multiple benefits relative to existing solutions, such as the ability to dynamically self-configure according to the users’ needs, thus providing them with the best possible QoE.

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1.4 Contributions 3

Since this is a new concept, a topology control algorithm, providing the traffic-awareness ability to the FBN, will be designed and implemented, as, to the best of our knowledge, there are no algorithms developed for such application. Although this composes the main objective of the Dissertation, a complete solution will also be designed, where the topology control algorithm constitutes the fundamental part of the solution. To be able to validate the developed solution, Matlab simulations will also be developed, to provide a graphical representation of the behavior of the algorithm and collect results about its performance, in response to different scenarios, common in these types of events.

Moreover, a testbed capable of validating the developed solution and collect data about its performance will also be designed, allowing to demonstrate and prove the benefits relative to the existing solutions.

1.4 Contributions

The main contributions of this Dissertation include:

• Design and development of a novel traffic-aware topology control algorithm for FBNs that provides the optimal UAV location and Wi-Fi cell’s range for every UAV in the FBN. On the other hand, this algorithm can also be generalized to solve similar problems, involving FBNs. In fact, this algorithm can be adapted to provide awareness to the FBN, considering different metrics and objectives, such as the importance level of detected objects in object tracking and surveillance missions.

• Functional prototype of the developed solution, consisting in the Flying Mesh Access Point (FMAP) and the implementation of the developed solution.

• Testbed able to validate the developed solution and evaluate its performance on a real sce-nario.

1.5 Document Structure

The remainder of this document is organized as follows:

• Chapter 2 [State of the Art] - Background review of existing solutions, ad hoc networks, specifically Flying Ad Hoc Networks (FANETs), topology control algorithms for FANETs, as well as Flying Backhaul Networks (FBN).

• Chapter 3 [Proposed Solution] - Presentation in detail of the proposed solution developed to achieve the goals of this Dissertation.

• Chapter 4 [Matlab Simulations] - Matlab simulations developed, that allowed the validation and performance evaluation of the proposed solution in the previous chapter.

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4 Introduction

• Chapter 5 [Implementation and Testbed] - Details of the implementation of the proposed solution, along with the correspondent testbed developed to validate and evaluate the imple-mentation.

• Chapter 6 [Conclusion] - Conclusion of the Dissertation, regarding the work developed, results achieved and future work in line with this Dissertation.

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Chapter 2

State of the Art

This chapter presents a detailed review of the state of the art, mainly focusing on the most impor-tant topics related to this Dissertation. Section2.1starts with the analysis of the existing solutions to solve the problem presented in the previous chapter. Then, in Section2.2a brief review about ad hoc networks is presented, which leads to the review of an emerging type of ad hoc network based in Unmanned Aerial Vehicles (UAVs) - Flying Ad hoc Networks (FANETs) in Section2.3. Following that, topology control concepts related to FANETs are presented and discussed in Sec-tion2.4as well as the new concept of Flying Backhaul Networks (FBNs) in Section2.5. Lastly, in Section2.6the chapter finishes with a final discussion concerning the state of the art reviewed in the chapter.

2.1 Existing Solutions

As it has been mentioned in Section1.1, during public temporary crowded events there is a high density of mobile devices connected to the Internet, generating large amounts of Internet traffic. As a result, users face problems accessing broadband Internet connections, either Wi-Fi or cel-lular based solutions, degrading the overall Quality of Experience (QoE) provided. To solve this problem, several solutions have been developed recently, both in cellular networks and Wi-Fi.

2.1.1 Cellular networks

To analyze the performance of cellular networks during public temporary crowded events, several studies have been performed [2,3]. These results allow an understanding of the users’ behavior during these events.

In [2], a study is performed during two public temporary crowded events. Event A consists in a sporting event with two segments of activities separated by an intermission; whereas Event B consists in a conference event with multiple segments of activities separated by an intermission. The data performance results are depicted in Fig.2.1. The Radio Round-Trip Time (R-RTT) repre-sents the delay between the User Equipment (UE) and the Gateway GPRS Support Node (GGSN), whereas the Internet Round-Trip Time (I-RTT) represents the remaining path to the remote server

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6 State of the Art

(i.e., through the backbone Internet). The final RTT is, thus, given by:

RTT = R-RTT + I-RTT. It is shown that the Quality of Service (QoS) metrics, such as the packet loss ratio and the Round-Trip Time (RTT), become significantly degraded during the event’s du-ration, which translates to a degradation of the users’ QoE. In fact, in the worst-case peak traffic scenario, the packet loss ratio increases by a factor of 2 and 7 for events A and B, respectively, while the RTT increases by a factor of 3.5 and 1.5. This degradation in the QoE provided to the users are mainly caused by the exhaustion of the radio resources in the network cell, along with the consumption of the available radio resources by a large number of users at the same time.

(a) Cellular network data performance during public temporary crowded events [2].

(b) Cellular network architecture [2].

Figure 2.1: Cellular network performance during a public temporary crowded event.

To overcome the cellular saturation problem, solutions have been developed recently. [2] presents some solutions to reduce the radio resources saturation. For instance, since a large num-ber of users do not require all of the allocated radio resources, a more aggressive release of radio resources allows the achievement of a better traoff between the wasted resources, packet’s de-lay and energy consumption of the mobile devices. Another solution proposed takes advantage of opportunistic connection sharing between devices, which allows an aggregation of the Internet traffic from multiple devices into just one radio connection, thereby reducing the number of to-tal connections in the cell and, consequently, increasing the available bandwidth to the connected

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2.1 Existing Solutions 7

users. Although these solutions can improve the cellular network performance, their application for public temporary crowded events presents several issues. On the one hand, it is not possi-ble to dynamically and automatically adjust the macro-cells parameters according to the current users’ needs, such as their random movement and traffic pattern. On the other hand, the offload-ing of traffic through connections established with the neighboroffload-ing devices require their previous configuration, rendering this alternative complex to deploy and implement.

Offloading excessive traffic on a given cell to adjacent ones in 4G / LTE networks using UAVs has been presented as a simple and deployable solution, adequate for temporary situations [4], such as cell saturation and outage compensation, as presented in Fig. 2.2. UAVs are equipped with both an eNodeB and User Equipment (UE) technologies, so that it can generate a small 4G / LTE cell to the users and relay their traffic to adjacent cell’s base-stations. Although the UAVs’ positioning is carefully determined to provide the desired offload performance, they do not offer the ability to adapt their positions according to the random movement of the users at the event, neither to the traffic pattern currently being generated. Additionally, UAVs act independently, not taking advantage of the possibility to communicate and cooperate with each others. In [8,9] other solutions consisting in the deployment of UAVs to relay or offload traffic generated on a small area to cellular base-stations, for generic or specific applications (e.g., emergency traffic), are also analyzed. Furthermore, [10] presents field-test and simulation performance results in these scenarios, concluding that the gains obtained are sufficiently enough to consider the use of UAVs a reliable alternative to relay / offload traffic in various scenarios, including temporary events. However, these alternatives continue to share the same problems previously referred, such as the inability to follow users’ random movement and generated traffic pattern.

Figure 2.2: Traffic offload from overloaded cells using UAVs [4].

The offload of mobile data generated by the users attending the event through offloading tech-niques is yet another possible solution to solve the problem of network cell saturation. This can be achieved with the deployment of Wi-Fi Access Points (APs) throughout the area of interest [5], using ad hoc networks to connect devices [11] and opportunistic techniques [12,13]. In this regard, the use of small-cells is also a possibility, which reduces network cells’ size, so the number of devices covered by the cell is lower, thus reducing its load, increasing user-available bandwidth and, consequently, QoE [12,5]. Still, these solutions do not present the ability to dynamically and

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8 State of the Art

automatically self-configure according to the users’ needs, as it is required in this types of events. As such, despite these efforts, cellular networks remain unable to handle the large amounts of data generated by user devices and, consequently, unable to provide the desired QoE demanded by the users.

2.1.2 IEEE 802.11 (Wi-Fi)

Current Wi-Fi based solutions providing the desired broadband Internet connection to users on public temporary crowded events consist in the static deployment of multiple APs on site, present-ing, however, several disadvantages.

In order to provide a good coverage of the entire area, while also providing good QoE to the users, a large number of APs need to be installed on the event’s enclosure. In this scenario, each AP generates a small Wi-Fi cell to which users can connect to, being able to provide more bandwidth per user, since the total number of connected devices is lower. However, not only does this solution entail a high deployment cost of the infrastructure, which renders this solution as not being adequate for temporary events, as some APs may be forced to be installed in locations that affect users’ visibility, contributing to degrade the quality of the event.

More importantly, because of the variable traffic pattern observed during the event, and to be able to always provide the required broadband Internet connection, networks should be previously planned with a worst case scenario in mind. In fact, throughout the event’s duration there are times when the traffic generated by the users is lower than the available capacity of the network, thus wasting its resources. Other times, on the other hand, the generated traffic is superior to the planned capacity of the network. Therefore, to provide broadband Internet connection to all users attending the event during the entire duration of the event, the capacity of the network should be planned according to the expected peak traffic generated. This, however, constitutes a significantly inefficient solution, rendering it inadequate for public temporary crowded events, in which the users’ movement and traffic patterns are random and unpredictable. Indeed, since the APs mobility is null, they can not follow the users’ movement throughout the event’s enclosure, thus not being able to dynamically and automatically self-configure according to the users’ needs. In addition to the solutions presented above, some proprietary solutions have also been devel-oped. SingTel, for instance, developed a solution consisting in a Self-Organizing Network (SON) mechanism allied with small-cells technologies, particularly aimed at crowded events [14]. De-spite the network capacity improvements achieved, this alternative is not able to follow the users’ movements throughout the event’s enclosure.

Analyzing all solutions presented in this section, there is one major problem common to all of them: the inability of the network infrastructure to dynamically and automatically self-configure according to the variable conditions of the event (i.e., the mobility of the users and the variable traffic pattern generated).

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2.2 Ad Hoc Networks 9

2.2 Ad Hoc Networks

Ad hoc networks are a decentralized network where nodes connect to each other directly instead of relying on an access point (AP) to relay frames between them. Hence, if a node decides to send a frame to a non-adjacent node (i.e., with more than one hop between them), the frame has to be routed through intermediate nodes in a hop-by-hop basis until the final destination is reached [15]. The most common underlying communication protocols used in this kind of networks are the IEEE 802.11 (Wi-Fi), IEEE 802.15 family (wireless PANs) and IEEE 802.16 (WiMAX).

2.2.1 Ad hoc routing protocols

Due to decentralized and multi-hopped transmissions (motivated by the absence of a dedicated centralized equipment responsible for data routing), optimized routing protocols should be used in order to efficiently route data throughout the network. These protocols can be categorized in 4 main classes: static, reactive (on-demand), proactive (table-driven) and hybrid [15].

2.2.1.1 Static protocols

Static protocols are the simplest to implement, due to the fact that a routing table is previously computed and deployed on the nodes, thus avoiding the need to develop a routing table update mechanism, providing no overhead. However, for the same reason, these protocols are not fault tolerant, as a failure in some node may cause other nodes have an invalid routing table, leading to a communication failure of those nodes. For these reasons, static protocols are best suited for networks with a fixed topology [15].

2.2.1.2 Reactive protocols

Reactive (on-demand) protocols do not maintain a routing table information with the next-hop address required to reach each destination address. Instead, whenever a packet has to be sent, the protocol floods the network with Route Request packets searching for the optimum route to the referred destination. For that reason, as routes are calculated on-demand, there is only consumed bandwidth when transmissions occur, proving this type of protocols to be bandwidth-efficient. However, for that same reason, the latency inherit to transmissions turns out to be one major dis-advantage of reactive protocols, along with the flooding of Route Request packets in the network. Therefore, these features render reactive protocols the most adequate when networks are highly dynamic (i.e., when node movement is very frequent) and the amount of traffic is relatively low, as the probability of the calculated route being incorrect is very small. Examples of reactive routing protocols are: Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Flow State in the Dynamic Source Routing (FSDSR) [15].

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10 State of the Art

2.2.1.3 Proactive protocols

Contrasting with reactive routing protocols, proactive (table-driven) ones have internal mecha-nisms to maintain the routing table always updated. Such mechamecha-nisms include the broadcasting of HELLO messages for neighbor discovery and the flooding of defined messages announcing routes to predefined nodes. Although these mechanisms reduce latency when transmitting packets, spe-cially when large amounts of data are sent, they also lead to a higher bandwidth consumption, compared to reactive protocols, even in transmission-idle periods. Furthermore, proactive proto-cols have slow reaction to changes in the network topology, because the routing table only gets updated at predefined moments (whenever internal updating mechanisms are triggered). A solu-tion to overcome this limitasolu-tion requires a more frequent routing table update, causing an increase of bandwidth consumption, though. For these reasons, proactive protocols are best suited for rela-tively stable networks (where nodes’ movement is relarela-tively slow) or when the network generates large amounts of traffic. Examples of proactive routing protocols include: Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector (DSDV) and Babel [15].

2.2.1.4 Hybrid protocols

Lastly, combining the advantages of both paradigms, hybrid protocols extend reactive ones by caching calculated routes for a given period of time or until that route becomes invalid. Hybrid protocols are the most adequate protocols when neither reactive nor proactive ones are an optimum solution. Examples of hybrid protocols are: Zone Routing Protocol (ZRP), Temporarily Ordered Routing Algorithm (TORA) [15].

2.2.2 Distributed elections algorithms

In the context of distributed systems, some algorithms have been developed to dynamically elect a leader of the entire system with the consensus of all nodes [16,17,18]. These algorithms can also be applied in the context of ad hoc networks, to elect the optimum gateway to relay the traffic from the network to a backbone Internet gateway.

2.3 Flying Ad Hoc Networks (FANETs)

Flying Ad hoc Networks (FANET), also known as UAV ad hoc networks, are a new family of ad hoc networks composed by flying nodes, which are implemented using Unmanned Aerial Vehicles (UAVs). This network is considered a subset of Mobile Ad Hoc Networks (MANETs), having its own set of distinctive characteristics, that sets it apart of the traditional MANET model [19].

In fact, in FANETs, node’s mobility can be much faster than traditional MANET’s, causing even more frequent changes in link-state and, hence, in network topology. On the other hand, by the fact that nodes fly at a relatively high altitude, generally there is a permanent line-of-sight (LoS) between the nodes.

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2.3 Flying Ad Hoc Networks (FANETs) 11

2.3.1 Unmanned Aerial Vehicles (UAVs)

An Unmanned Aerial Vehicle (UAV), commonly known as a drone, is any kind of aircraft that doesn’t require a human pilot on-board nor a direct human intervention for flying. UAVs can be classified according to several parameters, including the controlling mechanism, flying mechanics and their size / weight.

As far as controlling mechanisms are concerned, there are essentially two types: autonomous and remotely piloted. Autonomous UAVs are automatically controlled by on-board computers with the use of sensors and actuators, whereas remotely piloted UAVs, on the other hand, are controlled with the aid of human pilots on ground or in another vehicle. Regarding flying mechan-ics, UAVs can be categorized as either a "Fixed Wing Vehicle" (FWV) or "Rotor Wing Vehicle" (RWV). FWVs generate lift through movement and, for that reason, they can not maintain their exact position (as they need to be constantly moving) and a take-off launch system is also required [Fig.2.3a]. RWVs, on the contrary, have a rotor wing, allowing them to maintain their position throughout long periods of time. Additionally, vertical take-offs and landings are also possible, hence eliminating the need of take-off launch systems [Fig.2.3b]. Finally, according to UAVs’ size and weight, they can be categorized as "mini", "small" or "large" [15].

Additionally, several UAVs can fly together in order to execute a mission, forming a swarm. If they maintain permanent connectivity with the entire swarm (single or multi-hopped) and ground devices, they are considered flying in a formation.

Comparing mission performance, UAV swarms feature several advantages compared to a sin-gle UAV, namely increased fault tolerance, extended coverage and the ability to distribute tasks across different UAVs (including assigning specific tasks to specialized UAVs), thus reducing mission-completion time [19,15].

UAVs’ main advantages consist in the ability to replace manned aircrafts performing danger-ous missions in harmful environments [20].

(a) Fixed-Wing Vehicle [21].

(b) Rotor-Wing Vehicle [22].

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12 State of the Art

2.3.2 FANET’s design considerations

A Mobile Ad Hoc Network (MANET) is an infrastructure-less dynamic self-organizing network, composed by mobile devices, such as laptops, smartphones, tablets or walkie-talkies [15, 23]. These devices can move freely at possibly different speeds, causing frequent changes in node’s link-state and, consequently, network topology. Usually, these devices are small and have a lim-ited processing capacity as well as a constricted energy supply and available bandwidth. As a re-sult, MANET’s protocols are designed to be energy and bandwidth-efficient and high-processing algorithms are normally avoided [15].

A Vehicular Ad Hoc Network (VANET), considered as a subset of MANETs, specializes the concept to moving vehicles. As such, both network types share common features but there are also some differences between them. Just as MANETs, VANETs have limited processing, en-ergy and available bandwidth capacity. On the other hand, VANET’s nodes move faster than MANET’s, causing even more changes in node’s link-state and network topology. However, con-trary to MANET’s, VANET’s mobility model is simpler and highly predictable due to the fact that VANET node’s movement is always restricted to roads and highways.

As a subset of MANETs and VANETs, Flying Ad Hoc Networks (FANETs), also known as UAV ad hoc networks, is a new family of ad hoc networks, composed by flying nodes, which are implemented using UAVs.

FANETs share common features with MANETs and VANETs, yet they own distinctive char-acteristics that set them apart from the existing ad hoc networks. In fact, FANET node’s mobility can be much faster than traditional MANET’s and VANET’s, causing even more frequent changes in link-state and, hence, in network’s topology. Doppler effects and changes in the distances be-tween UAVs also contribute to the variation of the received signal [23]. On the other hand, by the fact that nodes fly at a relatively high altitude, generally there is a permanent line-of-sight (LoS) between the nodes. This feature results in simpler propagation models compared to MANETs or VANETs.

As in MANETs, FANET’s greatest limitations are energy supply and available bandwidth. However, contrary to MANET’s nodes, UAVs can carry small and powerful microprocessors to enable them to process larger amounts of information. Still, due to the limited payload capacity, these microprocessors must be very light and have enough energy to match UAV’s autonomy [19,15].

The comparison between MANETs, VANETs and FANETs is summarized in the following figure.

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2.3 Flying Ad Hoc Networks (FANETs) 13

(a) Relationship between MANETs, VANETs and FANETs.

(b) Main differences between MANETs, VANETs and FANETs.

Figure 2.4: Ad hoc network types [19].

2.3.3 FANET’s routing protocols

Although FANETs are a ad hoc network, they present distinctive characteristics that differentiate them from regular ad hoc networks. As such, existing ad hoc routing protocols reveal themselves inadequate to accommodate FANET-specific issues, namely high-mobility of UAVs, leading to frequent link-state changes and, thus, topology changes. Therefore, FANET’s routing protocols consist in new or adapted protocols from regular ad hoc networks [15].

2.3.4 Communication architectures

Since UAVs communicate with each other through a shared medium (air), one very important aspect is the communication architecture employed in the network. In fact, there are essentially four types of communication architectures, namely (i) centralized, (ii) ad hoc architecture, (iii) multi-group UAV and (iv) multi-layer UAV architecture [20]. In what follows, we refer to each type in more detail.

2.3.4.1 Centralized architecture

A centralized architecture relies on a central station to relay all data communications, either UAV to UAV or UAV to central station. The main advantage of this architecture consists in its simple implementation. However, not only data routing performed by the central station increase the

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14 State of the Art

latency of all communications, but it may also require a high-power radio transmitter to cover the long distance of the UAV relative to the central station, which may not be adequate in swarms of small UAVs. Hence, this communication architecture doesn’t prove to be robust as it presents a single point of failure, located at the central station, where its failure may disrupt the entire network [20] [Fig.2.5a].

2.3.4.2 Ad hoc architecture

Ad hoc networks, on the other hand (as it’s previously explained in Section 2.2), provide a de-centralized architecture characterized by the ability of every UAV to participate in data routing. Therefore, all communications between UAVs will be routed through UAVs in the swarm until the final destination is reached. However, only one of the swarm’s UAVs is designated as the backbone UAV (i.e., gateway), relaying all communications between the central station and the swarm. In order to accomplish that, the gateway UAV requires two radio transceivers: one to communicate with the central station and another to communicate with the swarm.

Since UAVs only need one UAV in range to forward its communications, not only can they be equipped with low-cost and light-weight transceivers, but also the swarm may fly in a wider formation, thus increasing the range covered by the entire swarm, as long as the gateway UAV remains in range of the central station. If the number of UAVs in the swarm is sufficiently high, however, the latency of the multi-hop routing may be higher than the latency of the centralized architecture, thus rendering the architecture inefficient for large swarms. The same effect may also happen when the swarm is composed by different types of UAVs, which may generate different traffic patterns.

All these features make this architecture robust, as it doesn’t present a single point of failure, and the most suitable for small to medium-sized UAV swarms of the same type. One example of such use are operations of persistent surveillance [20] [Fig.2.5b].

2.3.4.3 Multi-group UAV architecture

A multi-group UAV architecture results of the combination between the centralized and the ad hoc network architectures, in the sense that small groups of UAVs of the same type form ad hoc networks (where intra-group communication is conducted as explained in Section2.3.4.2) whereas gateway UAVs provide connectivity between different ad hoc networks and the central station using the latter as the relay point. This classifies the presented architecture as a semi-centralized one, containing the advantages of both architectures, such as extended range and less-demanding transceivers, yet still lacking robustness, since the failure of the central station disables inter-group communication.

However, this architecture proves to be better suited when accommodating multiple UAV types than the simple ad hoc architecture, because similar UAVs can be grouped together in an ad hoc network [20] [Fig.2.5c].

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2.4 FANET’s Topology Control 15

2.3.4.4 Multi-layer UAV architecture

To overcome the main disadvantage of the architecture depicted above, gateway UAVs may be grouped together to form themselves another ad hoc network. This architecture is thereby com-posed by 2 layers of ad hoc networks: similar UAVs form ad hoc network (explained in Sec-tion 2.3.4.2) and gateway UAVs form a backbone ad hoc network, responsible for inter-group communication. However, just as the ad hoc architecture relies on a single UAV to relay com-munications of the swarm to the central station, the multi-layer UAV architecture also designates one of the backbone’s UAV to relay all communications of every UAV in the entire swarm to the central station.

This architecture enhances some of the advantages of the above architectures, such as the large coverage of the swarm (as long as the backbone gateway UAV remains in range of the central sta-tion), the efficient management of large-sized swarms, containing multiple UAV types, and the robustness provided by the alternative routes for inter-group communication. Also, the capacity of the central station to efficiently multicast data to the entire swarm, along with the reduced work-load and required bandwidth imposed on the central station (since it only processes information destined to itself), makes this architecture the most adequate when one-to-many communication is frequent. Yet, the complexity of the implementation and the latency inherit to the multi-hop transmissions constitute the major disadvantages of this architecture.

Therefore, the multi-layer UAV architecture forms an extension of the ad hoc architecture that efficiently supports a large and heterogeneous UAV swarm, one-to-many communications between the central station and the swarm, and a robust communication architecture, as there isn’t a single point of failure of the network [20] [Fig.2.5d].

2.3.5 Current Applications of FANETs

Current FANET applications include surveillance and reconnaissance missions [24], cooperative search, acquisition and tracking [25], forest fire monitoring [26], load sharing [4, 11], disaster recovery and outage compensation, [4,27,28] and military applications.

2.4 FANET’s Topology Control

Topology control of Flying Ad Hoc Networks (FANETs) consists in the development of algorithms aiming at dynamically adjusting UAV’s position according to the mission objectives, considering, at the same time, possible changes in the environment.

There are several solutions addressing FANETs static planning, allowing the development and deployment of a static flight plan to the UAV’s memory [29, 30, 31, 32]. However, this methodology can not be considered topology control, as this concept relies on a previous static planning of the entire mission and doesn’t take into account variable conditions imposed by the environment or changes in the mission’s objectives.

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(a) Centralized Architecture. (b) Ad hoc architecture.

(c) Multi-group UAV architecture. _{(d) Multi-layer UAV ad hoc architecture.}

Figure 2.5: Communication architectures between UAVs [20].

Current topology control algorithms for wireless ad hoc networks are based on the control and adaptation of the node’s transmission power and the use of directional antennas [23]. However, these mechanisms do not provide an efficient and adequate topology control for FANETs, due to the high mobility of UAVs, responsible for frequent changes in their link-state. For this reason, centralized algorithms are usually avoided because of the challenges in maintaining a continuous connectivity with all UAVs and ground devices, while decentralized UAV-based motion planning algorithms are generally applied to the topology control of FANETs. This decision improves the robustness and reliability of communications.

Therefore, FANET’s topology control algorithms should regard some important aspects, in-cluding connectivity and coverage. Connectivity allows real-time communications between UAVs and ground devices (single or multi-hopped), although they require UAVs to fly in a formation (as it was previously explained in Section2.3.1). Coverage, on the other hand, aims at increasing sensing coverage to rapidly identify and reach goal targets in the area of interest. Although ideal topology control algorithms should satisfy both requirements, in reality they constitute opposing objectives. In fact, on one hand spatial coverage is required to gather pertinent informations from multiple perspectives, resulting in UAV scattering around a large area of interest. On the other

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hand, permanent connectivity is also required to enable reliable communications, causing UAVs to fly closer to each other. In order to fulfill both requirements, a compromise solution must be achieved, so that both connectivity and coverage are accomplished. Therefore, UAV’s positioning should comply to certain restrictions, including receiving signals above the receiver’s minimum sensitivity and Signal-to-Noise Ratio (SNR).

Due to the above reasons, allied with the limitations of UAVs (presented in Section 2.3.2), FANETs should employ custom topology control algorithms, as it will be explained in the follow-ing sections.

2.4.1 Centralized vs. Distributed topology control

Topology control algorithms may be centralized or distributed, having both advantages and disad-vantages.

Centralized algorithms are simpler to implement, because they rely on a single node respon-sible for the determination and dissemination of every decision concerning the topology control of the FANET. However, due to the same reason, this class of algorithms present a single point of failure, which may result in a complete breakdown of the entire network. One way to compensate possible failures consists in the replication of the central station, thus reducing the probability of the network failure. This can be achieved through the deployment of additional central stations on the field or by allocating a server cluster using cloud computing concepts. Although this solution might cover most of the possible faults in the network, the implementation cost and complexity become higher.

Additionally, permanent connectivity between UAVs and the central station is also required, so that UAVs have information up-to-date. This restriction presents several challenges due to the high mobility of FANETs and, consequently, frequent changes in link-state.

On the other hand, distributed algorithms ensure greater robustness and reliability of the FANET at the cost of greater complexity. In fact, since every UAV within the FANET decides its next actions / movements by itself, they can still continue their mission regardless of temporary link failures with their neighbors. However, since the majority of these algorithms require some form of information related to the UAV’s neighbors, link failures should not exceed a determined amount of time as the information collected by the UAV may rapidly become outdated.

The main challenge of distributed algorithms, though, is the coordination and consensus re-garding distributed decision-making across the entire network. In fact, there may exist some de-cisions that must be taken by the entire network. Since each UAV decides locally which action to take, it is important to ensure that all reach the same decision. To accomplish this goal, specific consensus protocols should be used, providing fault-tolerance mechanisms, among other features. One example of such protocol is the Paxos designed by Leslie Lamport [18].

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Table 2.1: Summary of the centralized and distributed topology control algorithm characteristics. Centralized Distributed

Complexity +

-Robustness - +

2.4.2 Topology control algorithms for UAV swarms

There are essentially two models for the topology control of UAV swarms, which are explained in the following subsections.

2.4.2.1 Boids flocking

The boids flocking is a topology control model inspired by biological cooperative movements of agents. This model is a decentralized topology control algorithm describing the individual agent’s movement based on neighbors’ position and velocity. The main objective is to maintain a permanent connection inside a UAV formation, thus allowing every UAV to communicate with its neighbors. The agent / UAV is commonly referred to as a boid.

This algorithm is composed by three steering rules describing the basic mechanisms of the UAV’s movement control: separation, alignment and cohesion. The separation rule provides the collision / object avoidance component to the algorithm, allowing boids to move away from crowded and unwanted areas, while also preventing collisions inside the swarm. Cohesion, on the contrary, steers boids to move towards the average position of their neighbors, allowing them to concentrate on a specific position. Lastly, alignment steering rule is intended to align boid’s velocity (both speed and direction) with the neighbors’ average, thus keeping the boid integrated in the formation. These rules are depicted in Fig.2.6.

By using all of these steering rules in a proper way, it is possible to maintain the formation aligned, while, at the same time, allowing new UAVs to join the formation, yet preventing colli-sions between them [23,33].

Figure 2.6: Boids Flocking three steering rules [23].

2.4.2.2 Potential field

Potential field is a technique first described in [34] for robotic applications, such as goal seeking and obstacle avoidance, and has been widely used by the mobile robotic community. The main objective of potential field algorithm is to provide both connectivity and coverage.

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In this decentralized algorithm, goal and avoiding areas as well as targets and obstacles are represented by artificial or virtual potential field generators. As such, they will generate a field of forces in the neighboring area, either attractive or rejective, according to the application parame-ters. Thereby, attractive fields can be used to model the seek-goal behavior, forcing UAVs to head towards the field generator, whereas rejective (repulsive) fields can model the obstacle / collision avoidance behavior, where UAVs should move away from the field generator, as depicted by the example given in Fig.2.7.

The calculation of the type and strength of the potential field produced by the generators (e.g., obstacles, goal targets or other UAVs) is based on a mathematical model derived from the mis-sion’s objectives and on the information gathered by the UAV’s sensors. Therefore, for attractive generators the field is inwards and its strength is generally proportional to the distance to the generator, whereas rejective generator’s field is outwards with an inversely proportional strength relative to the generator distance. This allows UAVs to be attracted to attractive potential fields with greater intensity when they are located further away while also being quickly repelled from obstacles. Additionally, other parameters may influence the calculation of the model, such as the priority of the targets as well as restrictions imposed by the mission.

This model can be applied to maintain both UAV formation (i.e., maintain connectivity with the other UAVs) and coverage of a given area of interest [23,34].

Figure 2.7: Potential field generated by one attractive (goal) and rejective (obstacle) forces [23].

2.4.2.3 Comparison

The following table summarizes the advantages / disadvantages between the two algorithms, in-cluding the main applications for each one.

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Table 2.2: Comparison between the three UAV swarm topology control algorithms [23].

Mechanism Pros and cons Applications

Boids flocking Cons: Mostly for computer animation. Connectivity

Potential field Pros: Both distance and RSSI are used. Coverage and connectivity

2.4.3 FANET’s topology control using UAV swarm algorithms

Since connectivity losses between UAVs within a FANET are inevitable and unavoidable, it be-comes necessary to compensate such events. Hence, UAVs should be designed as autonomous agents capable of cognitively react on environmental changes, adapting their movements to pro-vide better network connectivity or coverage.

In [23], a new topology control algorithm is proposed, based on the potential field model (as previously explained in Section2.4.2.2). The main objective of this algorithm is the maintenance of a UAV formation, keeping all UAVs within the FANET reachable through multi-hop routing protocols. One assumption of this algorithm, though, is that every UAV is equipped with a GPS module, capable of broadcasting its position to the rest of the swarm informing UAV’s neighbors about their distance. However, if the UAVs do not possess a GPS module, other protocols can be used instead to measure and broadcast the distances between the UAVs, as it will be briefly explained in the following section (Section2.4.3.1).

Therefore, the algorithm represents every UAV as a virtual potential field generator, which will produce an attractive or rejective field whose strength is dependent on both the RSSI and distance measured between the two UAVs. Then, a lower / upper distance bound between both UAVs will be defined, using RSSI signal to adapt UAV movements within these bounds. In fact, the measured RSSI value of a neighbor UAV is inversely proportional to the distance between the two UAVs. Thus, if the measured RSSI is low, the distance separating both UAVs is high; on the contrary, if the RSSI is high, the distance between them is low. As such, if the measured RSSI drops below a defined threshold, the probability of a link loss is very high. Hence, UAVs should approach themselves in order to increase RSSI and, consequently, link quality. To do so, each UAV should produce an attractive field with a strength inversely proportional to the measured RSSI. Similarly, if the measured RSSI increases above a defined threshold, the probability of a collision between the two UAVs is very high, since the distance between them is small. Therefore, UAVs should distance themselves, thus reducing the probability of collisions, while still maintaining link quality. In order to do that, a rejective field should be generated by each UAV with a strength proportional to the measured RSSI.

Although this method is complete, there may be times where the RSSI signal retrieved is invalid. In such cases, GPS information might still be available and can be used to compensate the lack of valid RSSI measures. UAVs can use this information (i.e., neighbors’ GPS coordinates) to control their movements and maintain formation while simultaneously avoiding collisions with the rest of the swarm. Moreover, since some obstacles don’t produce any RSSI signal, an alternative is also required. In that sense, UAV’s sensors (i.e., cameras) can be used to detect obstacles and

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send that information to the remaining UAVs in the swarm.

According to the algorithm previously described, the inter-reactions between UAVs of a swarm only affect their relative positions and can not alter the movement of the swarm itself. Thus, the swarm’s motion will keep its initial state, which, in most cases, is unknown. Therefore, to control the movement of the entire swarm it is necessary to define and adopt a "leader-follower" strategy, in which commands are given to a certain UAV (i.e., leader) with the remaining (i.e., followers) adjusting themselves to the changes induced by the leader. Examples of such commands include moving the UAV to a given location, changing its velocity (speed and direction), among others [23].

2.4.3.1 Alternatives to the GPS module assumption

The FANET topology control algorithm presented above is based on the assumption that every UAV is equipped with a GPS module. However, there may be cases in which that assumption may not be accomplished and other protocols should be used instead to measure and broadcast the distances between the UAVs.

In fact, in [35] a new synchronization protocol for the relative localization of the mobile ad hoc network nodes is proposed, whose objective is to measure the distance between nodes and share that information across the network.

The protocol is based on the Reconfigurable and Adaptive Time Division Multiple Access (RA-TDMA) approach proposed in [36], and uses an RF ranging method to calculate the distances to the neighboring nodes using the time of flight (ToF) concept, described in [37], instead of the simple distance estimation through the received strength of the RF signal. This mechanism provides better results than the simple RF signal since it’s independent of signal amplitude and is less affected by many phenomena that hinder its relationship with distance. However, it’s more complex to use and takes substantially longer to measure.

The nanoLoc development kit [38] is then used to measure the Time-of-Flight (ToF) in three phases, as depicted in Fig.2.8. The first phase measures r1 while the second phase measures r2,

where V is the propagation speed of the RF signal.

r1= V × t1− t2 2 (2.1) r2= V × t3− t4 2 (2.2)

Lastly, the whole ranging process yields the final value of the distance between both nodes as the mean of both phases, as shown in Eq. (2.3).

D=r1+ r2

2 (2.3)

Once the distances are determined, an extended connectivity matrix is filled in with the mea-surements, similarly to the one presented in [39]. Then, using a novel broadcast protocol for

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22 State of the Art

Figure 2.8: Illustration of the ranging process [35].

wireless ad hoc networks, the matrix is broadcast to the remaining nodes. This dissemination can be done with or without synchronization of transmissions in order to reduce collisions in the medium.

The main advantages of this protocol relies on the full distribution of the algorithm, supporting dynamic ad hoc networks and also working without global knowledge of the network, particularly clock synchronization. However, due to the high mobility of FANET nodes, this protocol may not work properly when UAVs are constantly moving very quickly.

2.5 Flying Backhaul Networks (FBNs)

Recently, new and innovative applications of FANETs have been proposed and developed, as it was exposed in Section2.3.5. The concept of Flying Backhaul Networks (FBN) represents the application of FANETs to route Internet traffic generated by ground devices to a backbone gateway [23]. It consists in a swarm of UAVs carrying mobile APs, forming small and temporary network cells that provide access to WLANs, for instance IEEE 802.11 (Wi-Fi). Moreover, FBNs can then route the traffic generated in the WLAN to a backbone Internet gateway.

This new concept presents significant advantages in a variety of scenarios where traditional WLAN access methods are too expensive to deploy or cellular networks are saturated or inoper-able. Also, to provide broadband Internet access to a high-density of ground users, the size of network cells need to be as small as possible. In fact, QoE is inversely proportional to the size of network cells, since the number of users linked to that cell is smaller, which, in effect, leads to a higher bandwidth allocated to each user and reduces collisions when accessing the shared medium.

As already mentioned in Section2.1, existing solutions do not provide efficient methods to address this problem. In fact, small-cells creation using static Wi-Fi APs needs a very large number of APs deployed around the area of interest. This solution is highly inefficient, due to the high costs of AP’s installation, allied with the inability to adjust themselves according to the current traffic patterns and also possibly obstructing user’s line of sight. Cellular networks, on the other hand, saturate rapidly, decreasing substantially user’s QoE.

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2.6 Discussion 23

FBNs, on the other hand, have major benefits relative to existing solutions. In fact, due to the FANET’s enhanced mobility, FBNs can be deployed on any situation, providing Internet access to ground-based terminals with wireless connectivity. Additionally, the number of UAVs (i.e., APs) can be dynamically adjusted, according to the current needs of the mission, contributing to an optimized and efficient solution contrasting with the existing static solutions. In fact, this number can be dynamically adjusted in response to the variation of the traffic patterns, which can lead to a reduction of deployment costs in such applications. This introduces the concept of traffic-aware FBNs, which are explored in the following section.

Some applications of this concept have already been proposed. One example relates to the use of FANETs for cellular network outage compensation [4].

2.5.1 Traffic-aware FBN

One interesting application of FBNs relates to the ability to adapt itself according to current user needs and traffic patterns, that is, forming a traffic-aware FBNs. This is a new concept introduced and explored in the scope of this Dissertation. Its main objective is to provide broadband Internet access to the users attending the crowded event, presenting multiple benefits relative to current solutions, depicted in Section2.1.

To the best of our knowledge, there isn’t any record of a FBN whose main objective is to pro-vide broadband Internet access, considering the variable traffic patterns during a public crowded event. Similarly, specific topology control algorithms to support the above concept have never been designed, presenting an increased difficulty in the design of the solution presented in this Dissertation.

2.6 Discussion

In this chapter, the state of the art related with the Dissertation was presented and discussed. The main conclusions about such topics are presented below.

• Public crowded events are becoming increasingly popular around the world, specifically temporary events. However, it is difficult to provide broadband Internet access to the users attending these events. The reason behind it relates to the high density of devices connected to the Internet in the small geographical area of the event, allied with large amounts of traffic generated per device.

To solve the stated problem, several solutions have been proposed in recent years using both cellular and Wi-Fi technology. However, none of them proposes a simple, deployable and cost-efficient solution adequate for short duration events. Moreover, the ability of the infrastructure to dynamically and automatically self-configure according to the users’ needs is also a problem left unresolved.

Topology control flying backhaul networks

TOPOLOGY CONTROL OF FLYING BACKHAUL

NETWORKS

M

F

E

U

P

Topology Control of Flying Backhaul

Networks

Eduardo Nuno Moreira Soares de Almeida

Abstract

Resumo

Acknowledgments

Contents

List of Figures

List of Tables

List of Algorithms

Acronyms

Chapter 1

Introduction

1.1

Context

1.2

Motivation

1.3

Objectives

1.4

Contributions

1.5

Document Structure

Chapter 2

State of the Art

2.1

Existing Solutions

2.2

Ad Hoc Networks

2.3

Flying Ad Hoc Networks (FANETs)

2.4

FANET’s Topology Control

2.5

Flying Backhaul Networks (FBNs)

2.6

Discussion