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UNIVERSITÉ MOHAMMED V

FACULTÉ DES SCIENCES

Rabat

Faculté des Sciences, 4 Avenue Ibn Battouta B.P. 1014 RP, Rabat – Maroc Tel +212 (0) 37 77 18 34/35/38, Fax : +212 (0) 37 77 42 61, http://www.fsr.ac.ma

Thèse de Doctorat

En cotutelle avec l’Université de Reims Champagne Ardenne

Présentée par

Ismail BENNIS

Titre

Contribution aux protocoles de routage dans les réseaux de

capteurs sans fil : Application à la supervision agricole

Discipline : Sciences de l’ingénieur

Spécialité : Informatique et Télécommunications

Soutenue le 19/10/2015, devant le jury composé de :

Président :

Rachid OULAD HAJ

THAMI

PES, École nationale supérieure d'informatique et

d'analyse des systèmes, Maroc

Examinateurs :

Driss ABOUTAJDINE

PES, Faculté des Sciences de Rabat, Maroc

Jalel BEN OTHMAN

Pr. des universités, Université Paris 13, France

Hacène FOUCHAL

Pr. des universités, Université de Reims

Champagne Ardenne, France

Sanaa EL FKIHI

PH, École nationale supérieure d'informatique et

d'analyse des systèmes, Maroc

Tarik TALEB

Professeur, Université Aalto, Finlande

Ouadoudi ZYTOUNE

PH, Université Ibn Tofail, Maroc

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Je dédie ce travail

A

Ma chère mère

...

A

La mémoire de mon père

...

A

Mon cher frère et mes chères sœurs

...

A toute la famille

...

A

A mes amis et collègues

Avec tous mes souhaits de bonheur, de santé et de réussite.

Et enfin à toute personne ayant contribué de près ou de loin à la

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AVANT-PROPOS 4

Avant-propos

Les travaux présentés dans ce mémoire ont été effectués au Laboratoire de Recherche en In-formatique et Télécommunications (LRIT), à la Faculté des Sciences de Rabat (FSR) sous la direction de Mr. Driss ABOUTAJDINE et le co-encadrement de Monsieur Ouadoudi ZYTOUNE. Ces travaux ont été réalisés aussi au sein du département Mathématique-Informatique-Mécanique, à l’université de Reims Champagne Ardenne (URCA) sous la direction et l’encadrement de Mr. Hacène FOUCHAL dans le cadre d’une thèse en co-tutelle pour l’obtention du grade de Docteur au Maroc ainsi qu’en France.

C’est avec la plus profonde gratitude que je souhaite remercier mon directeur de thèse Mr. Driss ABOUTAJDINE, Professeur d’enseignement supérieur à la Faculté des Sciences de Rabat, directeur du laboratoire LRIT et directeur du Centre National pour la Recherche Scientifique et Technique (CNRST). Je lui exprime ma profonde gratitude d’avoir accepté de diriger mes travaux de recherche au sein du laboratoire LRIT et de m’avoir accordé sa confiance pour entamer ce projet de cotutelle. Je le remercie infiniment pour tout l’intérêt, le suivi et l’implication inconditionnels portés à cette thèse durant toutes ces années et malgré son emploi du temps chargé. J’exprime ici ma profonde gratitude à son égard et l’estime respectueuse que je lui porte.

Mes remerciements vont aussi à mon co-encadrant, Mr. Ouadoudi ZYTOUNE, Professeur habilité à l’université Ibn Tofail. Je tiens à le remercier pour ces années de soutien, pour ses précieux conseils scientifiques. De plus, je le remercie pour sa modestie, son optimisme ainsi que son intérêt porté à mes travaux. Je le remercie infiniment pour sa compréhen-sion dans toutes les circonstances et pour son encouragement, qui ne manquaient pas d’augmenter ma motivation.

Je veux également exprimer toute ma reconnaissance à Mr. Hacène FOUCHAL, Professeur des universités à l’URCA, mon deuxième directeur de thèse et chef du département Maths-Informatique à l’URCA. Je le remercie de m’avoir accueilli au sein du département, de m’avoir consacré son temps et son énergie et cela toujours dans la bonne humeur. Je le remercie aussi pour son aide et sa capacité à simplifier les problèmes rencontrés dans le cadre du travail. J’ai ainsi largement pu profiter de sa grande acuité scientifique, de ses remarques pertinentes et de sa clairvoyance pour le l’avancement du travail. Je le remercie également de son soutien moral qui m’a permis d’avancer et de fournir plus d’effort. Je lui suis donc très reconnaissant de m’avoir permis de poursuivre cette thèse dans de bonnes conditions.

Je tiens à remercier vivement Mr. Rachid OULAD HAJ THAMI, Professeur d’enseignement supérieur à l’Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes (EN-SIAS), d’avoir accepté de présider le jury de ma thèse.

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AVANT-PROPOS 5

Nationale Supérieure d’Informatique et d’Analyse des Systèmes (ENSIAS), d’avoir accepté de juger la qualité de mon travail en tant que rapporteur et examinateur de participer au membre de jury.

Je tiens également à exprimer ma gratitude à Mr. Jalel BEN OTHMAN, Professeur des universités à l’Université Paris 13, d’avoir accepté d’évaluer ce travail en qualité de rapporteur et d’examinateur ainsi que pour sa participation au jury final.

Je tiens aussi à remercier Mr. Tarik TALEB, Professeur à l’Université Aalto en Finlande, d’avoir accepté d’examiner ce travail.

Au cour de cette thèse, j’ai bénéficié d’une bourse d’excellence octroyée par le CNRST dans le cadre du programme des bourses de recherche initié par le ministère de l’éducation national de l’enseignement supérieur, de la recherche scientifique et de la formation des cadres. Aussi, dans le cadre de la cotutelle et durant mes séjours en France, j’ai bénéficié d’une bourse de mobilité dans le cadre du programme de coopération franco-marocain STIC. Je tiens ainsi à exprimer toute ma gratitude à ces deux institutions ainsi qu’à Mr. El Houssain BENCHATER, chargé de la gestion des bourses missions et invitations au niveau du service SCAC de l’ambassade de France à Rabat, pour sa bonne humeur, sa gentillesse et sa compréhension lors de la mise en place de chaque séjour.

Au cours de ces dernières années, j’ai eu l’occasion de rencontrer des personnes toutes aussi intéressantes les unes que les autres. A leur façon, ils ont tous contribué à mon apprentissage. Bien que je sois reconnaissant envers chacune de ces personnes, certaines d’entre elles méritent un merci tout particulier. Je tiens à remercier tous mes collègues du laboratoire LRIT à la faculté des sciences de Rabat. Je remercie également mon ami Marwane Ayaida, MCF à l’URCA, de tous ses conseils, de son temps et son aide précieuse lors des moments difficiles que j’ai rencontré.

Je termine par les personnes que je ne saurais jamais remercier assez. Milles merci à ma chère maman pour son soutien constant et sa confiance qu’elle m’a accordé durant toutes ces années d’études. Merci à mes sœurs et mon frère et l’ensemble de ma grande famille. Merci à mes amis et collègues pour m’avoir soutenu pendant tout ce temps, s’ils ne peuvent pas être tous cités ici ils se reconnaîtront.

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RÉSUMÉ 7

Résumé

Les réseaux de capteurs sans fil (WSN : Wireless Sensor Netwroks) ont suscités un grand intérêt scientifique durant cette dernière décennie. Ces réseaux promettent de nouvelles applications dans les domaines militaire et civil, notamment la supervision agricole, la surveillance des frontières, les systèmes de contrôle routier, et bien d’autres. Un des grands défis des WSNs est d’assurer une communication avec la Qualité de Service (QoS) exigée par l’application tout en prenant en considération les contraintes intrinsèques des capteurs. L’engouement pour les applications de ces réseaux a donné lieu à un nombre très important de travaux de recherche visant à améliorer les performances à travers les différentes couches de communication.

Un autre défi est relatif à la génération des trafics hétérogènes avec des priorités diverses. Cette diversité implique des exigences spécifiques pour chaque type de trafic, ce qui im-pose des contraintes supplémentaires aux différents protocoles de communication. En plus, dans le cas où un grand nombre de capteurs est déployé sur de vastes terrains, le trafic généré devient assez conséquent, impliquant ainsi un besoin important en termes de délai de transmission et de fiabilité. Dans cet ordre d’idées, nous nous intéressons dans cette thèse particulièrement aux protocoles de routage dédiés aux WSNs. Dans un premier temps, nous proposons des améliorations de deux protocoles appartenant à deux catégories différentes de routage. Le premier est un protocole qui se base sur la technique d’optimisation par colonies de fourmis. Le deuxième est un protocole qui se base sur les positions géographiques des nœuds. L’objectif est de surmonter les contraintes liées aux caractéristiques des capteurs sans fil et d’assurer de meilleure performance.

Dans un deuxième temps, nous proposons une solution pour remédier à la vulnérabilité de la technique des chemins multiples aussi bien dans le cas d’une seule source ou que dans le cas de plusieurs sources dans le réseau. Ainsi, notre principale contribution est de proposer un protocole de routage à chemins multiples, capable de créer des chemins tout en évitant l’effet du rayon de détection de porteuse. Ce protocole que nous avons nommé « Carrier Sense Aware Multipath Geographic Routing (CSA-MGR) », satisfait la qualité de service exigée par les réseaux de capteurs sans fil. Comme application directe de notre solution, nous avons étudié un scénario d’irrigation par goutte-à-goutte en utilisant les WSNs. Principalement, nous nous sommes intéressé au cas où un dysfonctionnement du système se produit, tel que la rupture des tuyaux d’irrigation ou bien le blocage des émetteurs dans les tuyaux. Ainsi, nous distinguons deux niveaux de priorité pour les informations transmises par le réseau, et en utilisant le protocole que nous avons proposé, le CSA-MGR, nous concevons un routage selon la priorité exigée.

Notre travail dans cette thèse a été validé en deux-temps. Premièrement, par le biais des simulateurs NS2 et TOSSIM afin de pouvoir tester diffèrent cas de figure. Deuxièmement,

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RÉSUMÉ 8

par le biais d’une implémentation réelle sur des nœuds capteurs TelosB. Les résultats des simulations numériques et des tests expérimentaux montrent l’apport de nos contributions par rapport aux solutions existantes en termes de délai, de taux de paquets reçus (PDR : Packet Delivery Ratio), de la surcharge du réseau par des paquets de contrôle (overhead) et de la bande passante.

Mots-clé:

Protocoles de routage; Routage multi-chemins; Effet d’interférences; Réseaux de Capteurs Sans Fil; Qualité de Service; Agriculture de précision; Simulation NS-2 et TOSSIM ; TinyOS.

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Contribution to routing protocol

for wireless sensor networks:

Application to agricultural

monitoring

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ABSTRACT 10

Abstract

Wireless Sensor Networks (WSNs) have aroused great scientific interest during the last decade. These networks open new applications in military and civilian fields including agricultural monitoring, border surveillance, traffic control systems, and many others. However, one of the greatest challenge of WSNs is to ensure communication with the Quality of Service (QoS) required by the application while taking into account the inherent constraints of the sensor nodes. This enthusiasm has given a large number of researches aimed at improving performances across the different communication layers. Another challenge is related to the generation of heterogeneous traffic with different priorities. This diversity implies specific requirements for each type of traffic which imposes additional constraints on different communication protocols. Also, in the case where a great number of sensors are deployed over large terrain, traffic induced is very important involving a great need in terms of transmission delay and reliability.

In this context, we are interested in this thesis specifically to routing protocols dedicated to WSNs. First, we propose improvements of protocols based on combinatorial optimiza-tion techniques and those based on nodes geographic posioptimiza-tions to overcome the related constraints of wireless sensors. Secondly, we propose a solution to address the vulnera-bility of the multiple paths technique, whether for the case of a single source or several sources in the network. Thus, our main contribution is to provide a multi-path routing protocol, able to creating paths while avoiding the carrier sense range effect. This protocol that we have denoted “Carrier Sense Aware Multipath Geographic Routing (CSA-MGR)” meets the QoS required by WSNs.

As direct application of our solution, we studied a drip irrigation scenario using WSNs. Mainly, we studied the case where a system dysfunctioning occurs, such as irrigation pipe rupture or the emitters blocking. Also, we distinguish two priority levels for the data transmitted over the network, and based on our protocol CSA-MGR we design routing according to the required priority.

Our work in this thesis has been validated in two steps. In the first step, it has achieved through NS2 and TOSSIM simulators in order to test different scenarios. In the second step, it has been done through a real implementation over the TelosB motes. The results of numerical simulations and experimental results show the advantage of our contributions compared to existing solutions in terms of delay, Packet Delivery Ratio (PDR), overhead and bandwidth.

Keywords:

Routing protocols; Multi-paths routing; Interference effects; Wireless Sensor Networks; Quality of Service; Monitoring Agriculture; NS-2 and TOSSIM simulation; TinyOS.

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ABSTRACT 12

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Contents

1 Introduction 21

1.1 Context . . . 21

1.2 Motivations and Issues . . . 22

1.3 Contributions . . . 23

1.4 Structure of the thesis . . . 25

2 Background and Context 26 2.1 Wireless sensor networks . . . 27

2.2 Quality of service . . . 30

2.3 Challenges and classification of routing protocols . . . 31

2.3.1 Network level . . . 31

2.3.2 Operation level . . . 32

2.3.3 Data delivery level . . . 34

2.3.4 Objective level . . . 35

3 Swarm Intelligence Routing Approach 37 3.1 Introduction . . . 39

3.2 Ant colony Optimization routing . . . 40

3.3 Enhanced AntNet protocol . . . 42

3.3.1 Adaptation for wireless communications . . . 42

3.3.2 Proposed improvements . . . 44

3.4 Simulation results and discussion . . . 47

3.4.1 Simulation environment . . . 48

3.4.2 Results discussion . . . 49

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CONTENTS 14 4 Location-based Routing Approach 55

4.1 Introduction . . . 57

4.2 Background and Related work . . . 58

4.3 Evaluation and improvement of the TPGF protocol . . . 60

4.3.1 TPGF description . . . 60

4.3.2 Evaluation and improvement . . . 61

4.3.2.1 Multi-source case . . . 61

4.3.2.2 Energy study . . . 64

4.4 Simulation results and discussion . . . 65

4.4.1 Simulation environment . . . 66

4.4.2 Results analysis . . . 69

4.5 Conclusion . . . 71

5 CSA-MGR Routing protocol 72 5.1 Introduction . . . 74

5.2 Related work . . . 75

5.3 Carrier sense effect model . . . 78

5.3.1 Connectivity graph . . . 79 5.3.2 Conflict graph . . . 81 5.3.3 Multipath case . . . 82 5.3.4 Non-interfering paths . . . 83 5.4 CSA-MGR . . . 83 5.4.1 Protocol description . . . 83 5.4.2 Protocol components . . . 84 5.4.2.1 Banish state . . . 84

5.4.2.2 Number of Common Neighbors (N CN ) metrics . . . 86

5.4.2.3 Zigzag avoidance mechanism . . . 87

5.4.2.4 Routing and neighboring tables . . . 89

5.4.2.5 Hello mechanism . . . 90

5.4.2.6 The next hop selection . . . 90

5.4.3 Multi-source case with differentiated service . . . 90

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CONTENTS 15

5.5.1 One source case . . . 93

5.5.1.1 Working environment . . . 94

5.5.1.1 Working environment . . . 94

5.5.1.1 Working environment . . . 94

5.5.1.2 Result analysis . . . 95

5.5.2 Multi-source case with heterogeneous traffic . . . 100

5.5.2.1 Working environment . . . 101

5.5.2.2 Result analysis . . . 102

5.6 Conclusion . . . 104

6 Control DIS using WSNs 106 6.1 Introduction . . . 108

6.2 DIS: Drip irrigation system . . . 110

6.2.1 Deployment strategy . . . 111

6.2.2 Communication strategy . . . 112

6.3 Priority-based DIS . . . 114

6.3.1 Priority-based protocol . . . 114

6.3.2 Protocol description . . . 115

6.4 Simulation and result analysis . . . 116

6.4.1 NS2 simulator . . . 116 6.4.2 TOSSIM simulator . . . 119 6.5 Experimentation . . . 123 6.5.1 Experimental setup . . . 123 6.5.2 Experimental scenario . . . 125 6.5.3 Experimental results . . . 126 6.6 Conclusion . . . 127

7 Conclusions and future work 128 7.1 Summary of the thesis contributions . . . 128

7.2 Limitations of the proposed solutions . . . 129

7.3 Outcomes . . . 130

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List of Figures

2 Background and Related Works 26

2.1 Reference architecture of WMSNs [6] . . . 27

2.2 Sensing coverage of scalar sensors (a) and multimedia sensor (b) . . . 28

2.3 Wireless Underground Sensor Networks (WUSNs) [8] . . . 28

2.4 Wireless Actuator and Sensor Networks (WASNs) . . . 29

3 Swarm intelligence Routing Approach 37 3.1 Unoptimized and optimized path . . . 45

3.2 Forward and backward ant mechanism using the ancestor list . . . 46

3.3 Simulation results for the one source case . . . 50

3.4 Simulation results for the many sources case . . . 53

4 Location-based Routing Approach 55 4.1 Greedy forwarding strategies . . . 59

4.2 multi-source case problem . . . 61

4.3 Forward agent process . . . 62

4.4 Backward agent process . . . 63

4.5 Decision process at the source node . . . 64

4.6 Neighbours scores computation . . . 65

4.7 Network density variation with 10% of nodes are sources . . . 68

4.8 Percentage sources variation with 100 nodes topology . . . 68

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LIST OF FIGURES 17 5 Carrier Sense Aware Multipath Geographic Routing protocol 72

5.1 The different ranges for the node nk. . . 78

5.2 Potential interference zone. . . 80

5.3 Multipath study under the three ranges. . . 82

5.4 Example of a twice banish node. . . 86

5.5 Example of useless neighbors. . . 87

5.6 Example of reducing the banish node. . . 88

5.7 The carrier sense range effect in the case of one path. . . 88

5.8 Carrier sense effect in the case of multi-sources . . . 92

5.9 Delay and PDR results . . . 96

5.10 Average Nbr path vs network size . . . 97

5.11 Average Nbr hop vs network size . . . 98

5.12 Average overhead vs network size . . . 98

5.13 Average throughput vs network size . . . 99

5.14 Average Delay vs packet per second . . . 102

5.15 Average PDR vs packet per second . . . 103

5.16 Average QoE vs packet per second . . . 104

6 Control Drip Irrigation System using Wireless Sensor Networks 106 6.1 Drip Irrigation System layout . . . 110

6.2 Drip Irrigation System communication . . . 112

6.3 Node’s workflow . . . 114

6.4 Average PDR of normal traffic vs packet per second . . . 117

6.5 Average PDR of priority traffic vs packet per second . . . 118

6.6 Average delay of priority traffic vs packet per second . . . 118

6.7 Average delay vs PPS . . . 121

6.8 Zoom average delay vs PPS . . . 122

6.9 Average pdr vs PPS . . . 122

6.10 Experimentation topology . . . 124

6.11 The motes coordinates . . . 124

6.12 Average delay vs PPS . . . 126

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List of Tables

2 Background and Related Works 26 3 Swarm intelligence Routing Approach 37

3.1 Routing table at node k . . . 43

3.2 Main configuration parameters . . . 49

4 Location-based Routing Approach 55 4.1 Main configuration parameters . . . 67

5 Carrier Sense Aware Multipath Geographic Routing protocol 72 5.1 Summary of notations . . . 81

5.2 Neighbor table at node i . . . 89

5.3 Routing table at node i . . . 89

5.4 Main configuration parameters . . . 95

5.5 Main configuration parameters . . . 101

6 Control Drip Irrigation System using Wireless Sensor Networks 106 6.1 Main configuration parameters for NS2 simulation . . . 117

6.2 Main configuration parameters for TOSSIM simulation . . . 121

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Acronym List

ACO Ant Colony Optimization

AODV Ad hoc On-demand Distance Vector

AOMDV Ad hoc On-demand Multipath Distance Vector CMOS Complementary Metal Oxide Semiconductor

CSA-MGR Carrier Sense Aware - Multipath Geographic Routing DIS Drip irrigation system

LQI Link quality Indicator MCU Micro-controller Unit

NS Network Simulator

PA Precision Agriculture PDR Packet Delivery Ratio PIZ Potential Interference Zone

PPS Packet Per Second

PSQA Pseudo-Subjective Quality Assessment QoE Quality of Experience

QoS Quality of Service

RAM Random Access Memory

RSSI Received Signal Strength Indication SINR Signal to Interference and Noise Ratio SPI Serial Peripheral Interface

TPGF Two-Phase geographic Greedy Forwarding

TRA-MGR Transmission Range Aware - Multipath Geographic Routing WSN Wireless Sensor Network

WASN Wireless Actuator and Sensor network WMSN Wireless Multimedia Sensors Network WUSN Wireless Underground Sensor Network

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Chapter 1

Introduction

Contents

1.1 Context . . . 21

1.2 Motivations and Issues . . . 22

1.3 Contributions . . . 23

1.4 Structure of the thesis . . . 25

1.1

Context

The latest technological advances have fostered in remarkable way the development of wire-less sensor networks (WSNs). A wirewire-less sensor network is a distributed system consisting of several autonomous entities able to communicate with each other without infrastruc-ture. These nodes have very limited coverage area and are typically deployed in large density in hostile environments. The nodes communicate via radio frequencies and can self-organize and cooperate to meet needs and provide services. Each node must be able to capture the surrounding physical parameters, to process the received data, to take a local decision and to communicate independently with its neighbours. This cooperation is intended to ensure the best possible decision taken despite the limitations of these nodes in terms of energy consumption and processing power. Indeed, WSNs are subject to strong constraints with multiple natures (computational and energy), limiting the processing and communication capabilities of network nodes.

The WSNs have been initially developed for military applications, but their properties make them as practical solutions in many areas of our daily life. In particular, the ap-plication studied in our thesis, namely the agricultural monitoring through the WSNs is a perfect example of a promising application of this type of network. Indeed, the use of WSNs allows having an efficient precision agriculture with low cost. Before this con-cept, farmers had to rely on satellite imagery or aircraft to supervise their growing areas.

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1.2 MOTIVATIONS AND ISSUES 22

Thanks to the wireless sensor technology, farmers can now monitor and maintain in auto-mated way a zone of dozens or hundreds of square meters, which means a higher yield and lower cost, with less impact on the environment. Thus, each plot receives exactly what it needs at the desired time and for the appropriate duration. The WSNs allow a contin-uous monitoring of the condition of the environment (air temperature, air humidity, soil moisture, solar radiation) and condition of plants (leaf temperature), helping to optimize resources used in water, fertilizers and pesticides, improve production in terms of quantity and quality, or even predict the plantation diseases.

1.2

Motivations and Issues

Like most African countries, agriculture in Morocco is an important economic sector, with 40 % of the population living from this sector. Thus agriculture has always been a strate-gic point for socio-economic development of the country. In recent years an agricultural development strategy named ’Green Morocco Plan’ was developed to promote the agri-culture sector. Integrating in the same axis, the renewal of the farming operating tools can be an important means of this promotion.

It is through the use of new technologies such as embedded systems and WSNs, that modern agriculture can be addressed. Thus, optimization of crop inputs depending on soil characteristics, parasitic attacks, topologies or climate can be conducted to improve performance and reduce adverse effects on the environment. Agriculture is a sector where WSNs are increasingly used. They enable remote monitoring broad geographical areas without infrastructure. Different types of data are collected in the network such as scalar data (temperature, humidity ...), and also vectorial data (video and image). The collected data are sent to a decision center which can act in an automated manner by sending commands remotely. However, the biggest challenge of WSNs is to ensure communication with the Quality of Service (QoS) required by the application taking into account the intrinsic limitations of the sensors. This enthusiasm has given a very significant number of research works to improve communication across the different communication layers, namely routing, MAC and physical layers.

Another challenge relates to the generation of heterogeneous traffic with different prior-ities. This diversity implies specific requirements for each type of traffic which imposes additional constraints to the different communication protocols. Also, in the case where a large number of sensors are deployed over large fields, the traffic generation becomes very important involving a great need in terms of bandwidth with a strong constraint in terms of transmission delay.

One of the flagship solutions in routing protocols allowing better use of network resources is the use of multiple paths from a source to a destination. However, this solution hides

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1.3 CONTRIBUTIONS 23

a serious drawback. In fact, since a single transmission channel is used in the wireless network, the communication medium is shared by all the nodes in the same area. In addition, due to the nature of radio waves that are broadcast, the interference level is more pronounced. Thus, when multiple paths; close enough; are used simultaneously, there is a significant risk of collision which results in a high packet loss rate. This problem known in the literature by the interference problem is not the only disadvantage of the multiple paths solution. In fact, the carrier sense range effect must also be taken into account in the design of multiple paths. However, until now, few research studies have focused on this problem. In this context, we are interested in this thesis specifically to routing protocols dedicated to WSNs. First, we propose improvements of protocols based on combinatorial optimization techniques and those based on nodes geographical positions to overcome the constraints related to the characteristics of wireless sensors. Secondly, we propose a solution to address the vulnerability of the multiple paths technique, whether for the case of a single source or several sources in the network.

1.3

Contributions

Our first contribution of this thesis is to propose improvements of two routing protocols for WSNs from two different routing classes. First, we extended a routing protocol based on swarm intelligence technique, especially the ant colony optimization technique (ACO). Indeed, this type of protocols shows desirable properties for WSNs in terms of adaptability, scalability and robustness. However, the ACO has two major drawbacks, especially the convergence time required to find a path and the network overhead by the control packets. Our goal was to deal with these disadvantages to provide the best possible QoS in terms of delay and Packet Delivery Ratio (PDR). Thus, we proposed an improvement of the AntNet protocol by optimizing the paths found, changing the way of updating the pheromone, and introducing new concepts such as the list of ancestors. We have named our proposal Enhanced-AntNet.

Secondly, we have improved the Two-Phase geographic Greedy Forwarding protocol (TPGF) which is known as a protocol that meets the constraints of the wireless multimedia sensors networks (WMSNs). Our study allowed us to reveal two drawbacks of this Protocol, par-ticularly the fact that it always uses the same paths discovered for future transmissions, which reduces the network lifetime. Also, in a multi-source environment, where several sources try to achieve one or more destinations at the same time, the TPGF encounters limitations due to the concept of ” disjoint nodes ”. This concept is that each node can be involved only in one path at each time. The objective was therefore to resolve these problems by proposing an improvement of the TPGF protocol that we named EA-TPGF. The improvement involves extending the network lifetime and avoiding that the protocol still uses the same paths discovered for future transmissions. For this, we have introduced

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1.3 CONTRIBUTIONS 24

the nodes residual energy as an additional parameter to choose the paths. Thus, the deci-sion of the next hop is based on a compromise between the residual energy and distance. Afterwards, in a multi source context, we have modified the concept of ”disjoint nodes” so as to enable the sharing of occupied nodes. The assessment of our two improvements is performed under the NS-2 simulator. The results of several simulations show a significant contribution of our proposals compared to the initial version of the AntNet and TPGF protocols. Different metrics were used as the delay, PDR and overhead.

The second contribution of this thesis is to identify the carrier sense range effect on the performance of routing protocols, especially in the case where the multi-path solution is considered. We hear through the carrier sense range effect, the fact that a given node cannot transmit when a node in its carrier sense range is already in the transmission phase. In this sense, our main contribution is to provide a multi-path routing protocol, capable of creating paths while avoiding the carrier sense range effect. The protocol that we have named ”Carrier Sense Aware Multipath Geographic Routing (CSA-MGR)” meets the QoS required by the WSNs. Our approach differs from existing studies that address the interference phenomenon in the multi paths routing. The difference lies in the consideration of the carrier sense range, instead of the transmission range or the interference range, in the paths construction process. The CSA-MGR uses a new metric which we named number of common neighbours (NCN) to ensure rapid and efficient construction of paths. The simulations conducted under the NS2 simulator show promising results in terms of delay, PDR and overhead.

Our third contribution of this thesis is to study a drip irrigation scenario using WSNs. In-deed, the adoption of an optimized irrigation system has become a necessity due to the lack of water resources. Therefore, many researchers have addressed this problem by merging the new information and communications technologies with agricultural practices. Wire-less Sensor-Actuator Networks (WASNs) are an excellent example of this cooperation. In a first stage, we presented a conceptual model for a drip irrigation system using WASNs. Our model includes soil moisture, temperature and pressure sensors to monitor the irriga-tion operairriga-tions. We studied the case where a system malfuncirriga-tion occurs, such as irrigairriga-tion pipe rupture or the blocking of the emitters. Also, we distinguish two priority levels for the information transmitted by the network, and based on our protocol CSA-MGR we design routing according to the required priority. Our solution is validated in two steps. First, through NS2 and TOSSIM simulators in order to test different scenarios. Secondly through a real implementation on TelosB motes. The results of numerical simulations and experimental results show the advantage of our solution.

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1.4 STRUCTURE OF THE THESIS 25

1.4

Structure of the thesis

This thesis is composed of six chapters. After the general introduction, Chapter 2 presents basic notions about the characteristics of WSNs and its different variants. Also, the con-cept of QoS is detailed by reviewing the different metrics to measure network performance. Then, a classification of routing protocols is performed according to four levels, namely the network level, the operation level, the data level and the objective level. In the four chapters that follow, we present a study and analysis of existing solutions and describe in detail our contributions, followed by a validation and a comparative analysis with other similar approaches.

Chapter 3 is devoted mainly to the routing protocols family based on the Ant Colony Optimisation approach. First of all, we reviewed some routing protocols proposed in the literature. Then, we offer our improvements of the AntNet protocol that we have named Enhanced-AntNet. These improvements have enabled us to cope with two major disadvantages of Ant Colony approach, namely the convergence time needed to find a path and the overload of the network by control packets.

Chapter 4 provides an analysis and improvement of the two-phase geographic Greedy Forwarding (TPGF) routing protocol. An overview of geographic routing techniques is presented with an analysis of some related work. Afterwards, the TPGF protocol analysis has allowed us to reveal two disadvantages of this protocol, in particular the fact that it always uses the same paths for subsequent transmissions, which reduces the network lifetime

In Chapter 5 we address the multiple paths routing problem. We focus first on the impact of the carrier sense range on the performance of routing protocols. Next, we study the case where a source uses two paths at once or more, and the case where many sources communicate simultaneously. Afterwards, we propose a multipath routing protocol that we named ”Carrier Sense Aware Multipath Geographic Routing (CSA-MGR)”, which is able to create paths while avoiding the effect of carrier sense range. Also, we present a new metric that we named number of common neighbours (NCN) to ensure fast and efficient path construction

In Chapter 6 we propose our solution to enhance the reliability of drip irrigation system. Particularly we investigate the case where a system malfunction occurs, such as the break-ing of irrigation pipes or the blockbreak-ing of emitters. Thus, we distbreak-inguish two priority levels for the information transmitted and based on our protocol CSA-MGR we design rout-ing in the required priority. Finally, we analyse the results obtained firstly through NS2 simulator and TOSSIM and secondly through a real implementation over TelosB motes. The last chapter is the conclusion of this thesis. We offer an overview of our contributions, and we refer potential prospects of our work.

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Chapter 2

Background and Context

Contents

2.1 Wireless sensor networks . . . 27 2.2 Quality of service . . . 30 2.3 Challenges and classification of routing protocols . . . 31

2.3.1 Network level . . . 31 2.3.2 Operation level . . . 32 2.3.3 Data delivery level . . . 34 2.3.4 Objective level . . . 35

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2.1 WIRELESS SENSOR NETWORKS 27

2.1

Wireless sensor networks

Wireless sensor networks (WSNs) consist of an amount of independent nodes equipped with sensing capabilities, wireless communication interfaces, limited processing and energy resources. The objective of the deployed wireless sensor networks is to measure physical phenomena like temperature, pressure, humidity, and also to estimate location of objects [6]. Regarding the WSNs applications, they are characterized in general, with low band-width demand and are usually delay tolerant. Many variants of the WSNs have been emerged by the research community to satisfy the specific needs in some application field. The availability of inexpensive hardware such as CMOS cameras and microphones that are able to capture multimedia content from the environment, has allowed the arrival of new network type called Wireless Multmedia sensor networks (WMSN) [7]. This novel network will not only enhance existing sensor network applications such as tracking, home automa-tion, and environmental monitoring, but they will also enable several new applications such as multimedia surveillance sensor networks, storage of potentially relevant activities, traffic monitoring, enforcement and control systems, advanced health care delivery[7].

Figure 2.1: Reference architecture of WMSNs [6]

In the WMSNs, different types of traffic can be generated by the sensors. To address these various types, flexible and heterogeneous architecture must be designed. There are two types of basic architecture [7]:

• Single-Tier: In this architecture we can have either a set of homogeneous sensors with distributed processing and centralized storage as depicted in part a of Figure 2.1, or a set of heterogeneous sensors with distributed processing and storage as depicted in part b of Figure 2.1.

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2.1 WIRELESS SENSOR NETWORKS 28

• Multi-Tier: This hierarchical structure provides the flexibility to use network re-sources adaptively, it offers considerable advantages in terms of scalability, cost minimization, better coverage and better reliability. An illustrative example of this architecture is depicted in part c of Figure 2.1.

Figure 2.2: Sensing coverage of scalar sensors (a) and multimedia sensor (b) Defining an architecture for WMSNs also depends on the sensing coverage. An important challenge in this area comes from the different coverage properties of multimedia sensors, such as cameras and microphones, against to the scalar sensors such as temperature sensors and humidity. As depicted in Figure, the sensing coverage of multimedia sensors, also known as Field of View (FoV), is much more lesser than that of scalar sensor.

Figure 2.3: Wireless Underground Sensor Networks (WUSNs) [8]

The Wireless Underground Sensor Networks (WUSNs) is another variant of the WSNs which can be used to monitor a variety of conditions, such as soil properties for agricultural applications and toxic substances for environmental monitoring [8]. In addition to the constraints of classical WSNs, the WUSNs impose a high challenges to ensure a reliable wireless underground communication due to the hostile environment of such networks. An example of this network is illustrated in Figure 2.3.

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2.1 WIRELESS SENSOR NETWORKS 29

Finally, Wireless Actuator and Sensor networks (WASNs) [96] refer to a group of sen-sors and actors linked by wireless medium to perform distributed sensing and actuation tasks. The physical architecture of the WSAN is given in Figure 2.4. In such a network, sensors gather information about the physical world, while actuators take decisions and then perform appropriate actions upon the environment, which allows remote, automated interaction with the environment.

Figure 2.4: Wireless Actuator and Sensor Networks (WASNs)

There are several criteria that influence the design of WSNs and their different variant, among them we cite:

• Multimedia source coding

Production, processing and transmission of multimedia traffic such as audio, video, or simple images is a great challenge. Therefore, the raw data should be compressed by sophisticated coding exploiting the redundancy in images or video.

• The high demand for bandwidth

Although multimedia coding techniques significantly reduce the transmitted infor-mation, the information compressed still exceeds the current capacity of wireless sensor nodes. More specifically, the video stream requires a transmission bandwidth that is higher than what is supported by currently available sensors.

• Application requiring specific QoS

The routing and communication techniques proposed so far for sensor networks typ-ically follow a ”best effort” service approach. In other words, no guarantees in terms of energy consumption, delay, jitter, or bandwith are provided. On the other hand, multimedia applications have special needs for these types of guarantees for efficient transmission of the detected phenomenon.

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2.2 QUALITY OF SERVICE 30

• Energy consumption

Energy consumption is a major concern in traditional sensor networks. This is even more pronounced in WMSNs, given that the sensors have batteries with a limited capacity, whereas multimedia applications produce high volumes of data that solicit high transmission rates and thus intensive energy consumption.

2.2 Quality of service

The Quality of Service (QoS) notion in WSN refers to the capability of a network to deliver a service needed by a specific network traffic from a provider to the end user with specified limits. It can be regarded also as how can the different communication layer (physic, MAC, network and application) satisfy the applications requirements. Historically, there are three basic levels of end-to-end QoS in communication net-works [75, qos]:

– Best-effort service - Also known as lack of QoS, it refers to a basic connectivity

with no guarantees and no differentiation between flows. In this category, a minimization and a maximization approach of the QoS metrics is adopted.

– Differentiated service (DiffServ) - Also known as soft QoS, this class refers to

the fact that some traffic is treated better than the rest. To do so, a traffic pri-oritization mechanism is used to classify a network traffic into different classes of service, and assign preferential treatment to each class according to their requirements (delay within a certain limit, specific bandwidth, or lower average loss rate).

– Integrated service (IntServ) - Also known as hard QoS, this class provides

per-flow end-to-end guarantees through resource reservation mechanisms. IntServ is achieved via a framework composed of four components: a packet scheduler; an admission control routine; a classifier and a reservation protocol. However, this category cannot be suitable for WSNs since it is hardly scalable [82]. In WSNs, most of the proposed routing protocols belong to the best-effort or DiffServ category. To evaluate the satisfaction degree of the proposed services, some QoS metrics may be used, such as delay, jitter, throughput, and packet delivery ratio [17, 95]. A definition of each metrics is given as bellow:

– Throughput: is the effective number of data flow transported within a certain

period of time, also specified as bandwidth in some situations. In general, the higher the network throughput is, better the system performances are. The nodes that generate high-speed data streams, such as a camera sensor node used to transmit images for target tracking, often requires high throughput. In

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2.3 CHALLENGES AND CLASSIFICATION OF ROUTING PROTOCOLS 31

order to improve the resource efficiency, the throughput of WSN should often be maximized.

– Delay: is the time elapsed from the departure of a data packet from the source

node to the arrival at the destination node, including queuing delay, switching delay, propagation delay, etc. Delay sensitive applications usually require a network able to deliver data packets in real-time. The real-time notion means that the flow needs to be treated at a speed that fulfils the timing requirements.

– Jitter: is generally referred to as variations in delay. It is often caused by the

difference in queuing delays experienced by consecutive packets.

– Packet loss rate: is the percentage of data packets that are lost during the

process of transmission. It can be used to represent the probability of packets being lost. A packet may be lost due to e.g. congestion, bit error, or bad connectivity. This parameter is closely related to the network reliability. In the literature, the proposed routing protocols for WSNs can be classified as based protocol or non based protocol. A routing protocol is considered as QoS-based if it can guarantee one or more performance metrics for a specific application. Our approach in this thesis falls in the second class. However, our proposed solution can be integrated into the Diffserv category as it will be discussed in Chapter 5.

2.3 Challenges and classification of routing protocols

The routing layer plays a crucial role to establish communication between the source and the destination node. In the literature many works as in [5, 9, 23, 24, 106], have been proposed to provide a global view of routing protocols design and challenges. Furthermore, the routing protocols are generally classified according to the network structure and to the protocol operations. In the following, we summarize the different levels that impact the routing protocols design.

2.3.1 Network level

The network parameters such as the topology structure and the nodes character-istics can impact significantly the routing protocols performances in WSNs. The node deployment and mobility are also important elements to be considered. In the following we discuss each parameter.

– Network structure: Two main structures are defined in the literature. The first

one is a flat network where all nodes typically play the same role and collaborate with each other to carry out the sensing task. In such structure, all nodes are

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2.3 CHALLENGES AND CLASSIFICATION OF ROUTING PROTOCOLS 32

supposed to be homogeneous, having equal capacity in terms of computation, communication and power. The second structure is the hierarchical network, also called cluster-based network. In this structure, the nodes are grouped in clusters and will play different roles in the network. The clusters are formed dy-namically and a node in each cluster is elected as a leader. The main advantage of such architecture is to ensure the scalability and the efficient-energy commu-nication. In such structure, the nodes are generally heterogeneous making the routing more challenging.

– Node deployment: the sensor nodes can be deployed in either a deterministic or

a random fashion. When the nodes are deployed with deterministic positions, the routing protocol should be adapted to the fixed network topology. In the random case, the sensor nodes are scattered randomly creating an infrastructure in an ad-hoc manner. In both infrastructures, the sink position is crucial in terms of energy efficiency and performance.

– Node mobility: In most application cases, the sensor nodes in WSNs are

sup-posed to be fixed nodes. However, the mobility of both sink and sensor nodes is sometimes necessary in many applications. Routing in a dynamic network is more challenging since route stability becomes an important issue.

– Node characteristics: It is another parameter that impacts the routing

perfor-mance. The node characteristics encompass all the capabilities that a node can have either on hardware or on software part. In fact, the nodes in WSNs have a limited storage, processing and computational capacities. Such constraints, alongside the limited energy, impose high challenges to the routing protocols design. Another point to be considered is the node coverage and connectivity which are specially governed by the node transmission power. The transmis-sion power can be either fixed or dynamically adjustable. In the former case, using a large transmission power leads to ensure a fully connected network and to have high neighbouring level which improves the routing process. However, such case is only profitable in small size networks due to the large energy con-sumption and interference problem. In the latter case, each node can compute the energy transmission level that it should be used to transmit a message to a neighbouring node. Finally, the node can be equipped with additional module like the GPS. In such case, the routing process can be significantly improved by means of location information provided by the nodes.

2.3.2 Operation level

Each routing protocol performs different operations to achieve the communication between the sender and the receiver part. Applying one operation or not depends

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2.3 CHALLENGES AND CLASSIFICATION OF ROUTING PROTOCOLS 33

to the application goals and to the network parameters discussed above. In the following, we summarize the most known operations.

– Data-centric: A routing protocol can be designed to be a data-centric protocol

if the traditional address-based routing cannot be applied. In fact, it is not practicable to assign global identifiers to each node, mainly in the case of large network. Therefore, to overtake this constraint the sink node sends queries to certain regions and waits for data from the sensors located in the selected regions. The data-centric protocols, also known as content-based protocols need the aggregation operation to avoid significant redundancy. In fact, when several sensor nodes in the same region replay to the sink quires, a redundant response can arrive at the sink node, which leads also to less efficiency in terms of energy consumption.

– Location based: The routing protocols are location-based if they need the

po-sition node information to forward data to the base station. In this category of routing protocol, also known as geographic routing protocols, each node is assumed to know its geographical position, its neighbours positions and the base station position. The node may get its position information by estimating the incoming signal strength using as example the triangulation system. Al-ternatively, the location of nodes may be available directly by communicating with a satellite, using GPS (Global Positioning System), if nodes are equipped with a small low power GPS receiver [97]. The neighbourhood information can be obtained by exchanging ”Hello” messages between neighbouring nodes.

– Swarm intelligence: The routing protocol can be designed to mimic the

col-lective behaviour of biological species such as ants or bees. The aim of this category, which belongs to the meta-heuristic protocols, is to provide a so-lution for NP-complete problems such as routing problem, without any extra central control or coordination. The swarm-based protocols are self-organizing, which include positive feedback, negative feedback, fluctuation, amplification and multiple interactions [106]. To illustrate these concepts, we consider the ant colony optimization (ACO) as an example. When the ants go through a path to the sink, they increase a local node value called pheromone. This action is a positive feedback mechanism to incite other ants to choose the same path. Contrary, the pheromone evaporation is a negative feedback with the aim to reduce the pheromone level. In this way, the shortest paths to the sink can be found accordingly.

– Data aggregation: Since the sensor nodes may generate significant redundant

data, similar data packets from multiple sources can be aggregated to reduce the number of transmissions and thus minimizing the energy consumption. Data

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2.3 CHALLENGES AND CLASSIFICATION OF ROUTING PROTOCOLS 34

aggregation is the combination of data from different sources by using different functions such as suppression, min, max and average [47]. Some of these func-tions can be performed either partially or fully in each sensor node, by allowing sensor nodes to conduct in-network data reduction [39].

– Multipath routing: The routing protocols can use multiple paths rather than a

single path in order to enhance the routing performance. There are two options for using the multipath approach. First, the alternative paths can be used as a backup. The aim is to enhance the routing fault tolerance which is measured by the likelihood that an alternate path exists between a source and a destination when the primary path fails. However, this option requires additional overhead to keep alive the alternative paths. The second option is using several paths at the same time for load balancing purpose which can reduce the end-to-end delay. Also, this option may be used to improve the communication reliability by sending multiple copies of the same data on multiple paths.

– Starting routing process: The routing protocols can be designed to be either

proactive, reactive or hybrid protocols. For the proactive case, the routes are computed before they are actually needed. To do so, each node stores infor-mation on routes to every other node in the network. The proactive protocols are only beneficial for small network since the exchanged messages to main-tain route information grow with alarming rate. For the reactive protocols, the source nodes compute routes only when they are needed. In this case, each node stores routes only to its immediate neighbours which reduces significantly the generated overhead. Finally, in the hybrid case, the protocols use a combi-nation of reactive and proactive schemes. The proactive system is used within a given radius, while the reactive system is used to establish routes to nodes out side the defined radius.

2.3.3 Data delivery level

The data delivery model, also known as reporting model, describes what initiates the data reporting process. It depends closely to the application goals. The routing protocol can be highly affected by the data delivery scheme, especially regarding the minimization of energy consumption and route stability [5]. Three delivery models are described in the literature:

– time-driven: In this model each sensor sends data periodically. The time-driven

delivery model is suitable for applications that require periodic data monitoring. As such, sensor nodes will periodically switch on their sensors and transmitters, sense the environment and transmit the data of interest at constant periodic

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2.3 CHALLENGES AND CLASSIFICATION OF ROUTING PROTOCOLS 35

time intervals [9]. However, determining the accurate periodic interval is a crucial task. In fact, short periods may cause more traffic in the network and thereby more energy consumption, when long periods can lead to loosing information since the nodes are kept sleeping between two periods.

– event-driven: In this model, the nodes report their data when an event occurs

as exceeding some thresholds when detecting actions in the sensed area. In such case, the source node must react immediately by establishing path toward the sink node.

– query-driven: For this model, the data delivery starts once the concerned nodes

receive a query from the sink node. In this case, the sink disseminates its query in the network, while the sensor nodes try to resolve this query, and they may send a response back to the base station.

We notice that a hybrid model can be applied by using a combination of two or all discussed models.

2.3.4 Objective level

Generally, the routing protocols are designed to achieve the application goals. Some sensor applications only require the successful delivery of messages between a source and a destination. While other applications require more guarantees in terms of real-time message delivery, network lifetime maximization or ensuring QoS commu-nication. The objective level of routing protocols impacts directly how the next hop will be chosen. In most protocols for WSNs, the next hop selection is a trade-off between different parameters to satisfy different objectives. In the following, we discuss the main objectives associated to routing protocols.

– Network lifetime: The routing protocols with the aim of maximizing the

net-work lifetime must ensure a total exploitation of sensor nodes as long as possible. To do so, the protocols try to balance the energy consumption equally among nodes considering their residual energy levels. Hence, the next hop selection must be a compromise of energy consumption with other metrics.

– Scalability: Some applications need the deployment of a large number of nodes

which can reach more than thousands nodes. In such case, the routing protocol must be able to work with this huge number of sensor nodes and to respond quickly and reliably to events in the environment. In addition, the routing protocols should be able to work correctly when the number of nodes in the network is increased.

– Fault Tolerance: In real conditions, some nodes may be out of service due

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2.3 CHALLENGES AND CLASSIFICATION OF ROUTING PROTOCOLS 36

failure should not affect the overall network performance or task handling. So, the routing protocols can be designed to be fault-tolerance by performing some maintenance operations such as a local repair of the routing path or finding some routing backup. Also, the routing protocols can adjust the transmit powers and signalling rates on the existing links to reduce energy consumption, or re-routing packets through regions of the network where more energy is available.

– Real-time delivery: Some applications require that a message must be delivered

within a specified time, otherwise the message becomes useless or its informa-tion content is decreasing after the time bound. The messages can be delayed due to a high number of hops relays or due to in-processing operations at the node such as data aggregation or data fusion. Therefore, the main objective of these protocols is to completely control the network delay. The average case performance of these protocols can be evaluated by measuring the message delivery ratio with time constraints.

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Chapter 3

Swarm Intelligence Routing

Approach

Contents

3.1 Introduction . . . 39 3.2 Ant colony Optimization routing . . . 40 3.3 Enhanced AntNet protocol . . . 42

3.3.1 Adaptation for wireless communications . . . 42 3.3.2 Proposed improvements . . . 44

3.4 Simulation results and discussion . . . 47

3.4.1 Simulation environment . . . 48 3.4.2 Results discussion . . . 49

3.5 Conclusion . . . 54

Dans ce chapitre, nous étendons un protocole de routage basé sur les méta-heuristiques, particulièrement la technique d’optimisation par colonies de fourmis (ACO). Ce protocole montre des propriétés souhaita-bles pour les RCSFs en termes d’adaptabilité, de scalabilité et de ro-bustesse. Cependant, l’utilisation des techniques d’ACO dans les RCSFs présente deux inconvénients majeurs, notamment le temps de conver-gence nécessaire pour trouver un chemin et la surcharge du réseau par les paquets de routage.

Notre objectif dans ce chapitre est donc de faire face à ces incon-vénients afin de fournir la meilleure QoS possible en termes de délai et

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CHAPTER 3: SWARM INTELLIGENCE ROUTING APPROACH 38 de taux de paquets reçus(PDR). Ainsi, nous avons proposé une améliora-tion du protocole AntNet en optimisant les chemins trouvés, changeant la façon de mettre à jour le phéromone, et en introduisant de nouveaux con-cepts tel que la liste des ancêtres. Les résultats de plusieurs simulations sur NS2 montrent que notre proposition a de meilleures performances par rapport à la version initiale du protocole AntNet et par rapport à l’AODV.

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3.1 INTRODUCTION 39

3.1 Introduction

Transmitting the collected information through WSNs imposes many challenges. In fact, satisfying the need for high QoS metrics performance such as delay, packet delivery ratio or overhead requires dedicated solution at each layer of the commu-nication stack. In addition, due to the limited energy resources of the WSNs, an efficient energy policy must be designed to extend the network lifetime.

Furthermore, routing with QoS constraints has been shown to be NP-complete prob-lem [31, 32]. Among the solutions addressed to tackle such probprob-lem are those based on the meta-heuristic approach, specially the Ant Colony Optimization (ACO) [36, 107]. In fact, by using ants and other social swarms as a model, software agents can be launched to solve many complex problems such as traffic routing in busy communication networks. However, the ACO solution incurs two main disadvan-tages. In fact, in the case of high density network , the convergence time needed to reach the destination becomes very significant. Also, the generated overhead grows dramatically which leads to more consumption energy.

In this chapter, we will discuss an enhancement of the AntNet routing protocol [26] which is based on the stigmergy driven shortest path following the biological ant’s behaviour. According to the AntNet analysis given in [25], the robustness and near optimal performance of the AntNet algorithm makes it an attractive solution for routing in communication networks. In addition, AntNet algorithm performs better than shortest path routing under varying traffic loads. Our main goal is to reduce the inherent AntNet disadvantages effects, namely the convergence time and the overhead cost, while keeping an acceptable QoS level. We achieve our goal by optimizing the found paths, modifying the updating pheromone policy and introducing the Ancestor list approach. We have called our solution Enhanced-AntNet protocol. Simulation scenarios conducted over the NS2 simulator have shown promising results in terms of delay, PDR, overhead and remaining energy. These results show the efficiency of our contributions.

The remain of this chapter is organized as follows: in Section 3.2, we give an overview of the ACO functionality with a discussion of some related works. In Section 3.3, we present our enhancement of the AntNet protocol. In Section 3.4, we present the studied scenarios and we discuss the simulation results. Finally, Section 3.5 concludes this chapter.

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3.2 ANT COLONY OPTIMIZATION ROUTING 40

3.2 Ant colony Optimization routing

One of the most successful swarm intelligence techniques employed in routing prob-lem is the ACO [28]. This optimization algorithm is used to find approximate solu-tions for difficult combinatorial optimization problems such as Travelling Salesman Problem (TSP). We can say that during the last two decades, ACO has served as an important source of inspiration for the design of novel algorithms and systems [70]. As described in [29], when ants are out searching for food, they leave their nest and walk toward the food. When an ant reaches a crossroad, it must decide which way to follow. While walking, ants deposit pheromones, leaving behind tracks of the route taken. Ants can smell pheromone and they are more likely to follow paths characterized by strong pheromone concentrations. The pheromone trails allow ants to find their way to the food source, or back to the nest. The same pheromone can be used by other ants to find the location of the food sources discovered by their mates.

The routing protocols based on the meta-heuristic approach, especially the ant colony optimization, show desirable properties of being adaptive, scalable, and robust. Such characteristics make this kind of protocols more suitable for QoS-constrained appli-cations.

According to [22], the ACO routing displays several features making it particularly suitable for WSNs, these features are summarized as follows:

– the algorithm is fully distributed; there is no single point of failure. – at every node, the operations to be performed are very simple.

– the algorithm is based on agent’s asynchronous and autonomous interactions. – the algorithm is characterized by its self-organization capacity and robustness;

so we do not need to define a path recovery algorithm.

– it inherently adapted to all kinds of long-term variations in topology and traffic

demand, which are difficult to take into account by deterministic approaches.

Related work

Here, we review some routing protocols proposed in the literature that are designed for WSNs and based on the ant colony optimization.

– ASAR (an Ant-based Service-Aware Routing algorithm for multimedia

sen-sor networks) [80]: this protocol defines three different services for sensen-sor net-works, namely, event-driven, data query and stream query services. The ASAR chooses suitable paths to meet diverse QoS requirements from different kinds of

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3.2 ANT COLONY OPTIMIZATION ROUTING 41

services in order to improve network performances and maximize network uti-lization . Compared to the typical routing algorithm in sensor networks and the traditional ant-based algorithm, ASAR algorithm has better convergence and provides better QoS for multiple service types in multimedia sensor networks.

– EEABR (Energy-Efficient Ant-Based Routing Algorithm) [21]: it uses ant

agents that travel through the network looking for paths between the source node and the destination node in such a way to maximize the WSNs lifetime. The constructed path must be short in terms of number of hop and energy-efficient. Each ant chooses the next hop node with a probability depending on the node energy and the pheromone amount of the node’s connections. The simulation results show that the EEABR protocol minimizes communication load and maximizes energy saving.

– AntHocNet [27]: this protocol can be described as a hybrid algorithm, since it

combines a reactive path set-up with a proactive path probing, maintenance and improvement. The protocol aims to design an algorithm which works effi-ciently in MANETs while maintaining the properties which make ACO routing algorithms so appealing. Simulation shows that the AntHocNet outperforms AODV compared to different evaluation metrics like delay and overhead. The performance advantage is visible over a broad range of possible network sce-narios.

– M-IAR (Multimedia-enabled Improved Adaptive Routing) [65]: it is a flat

multi-hop routing protocol that exploits the sensor nodes location in order to select the best routing path. The author relies on the point that finding the shortest path with the least number of forwarding nodes will help achieving the least end-to-end delay and the best jitter conditions. So the M-IAR protocol finds the shortest path between the source and the destination node, which leads it to satisfy the communication constraint and the multimedia processing cost.

AntNet description

Here, we will present an overview of the AntNet algorithm that was the starting for our work. The algorithm works as follows:

– The protocol is proactive, therefore, during each time interval, a forward ant

(FANT) Fs→d is launched from every network node s towards a randomly se-lected destination node d.

– The forward ants memorize the address of each visited node and the departure

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3.3 ENHANCED ANTNET PROTOCOL 42

as data packets, so they experience the same traffic delays.

– The ants, once generated, are considered as autonomous agents. They act

concurrently, independently and asynchronously. They communicate in an in-direct, stigmergic way, through the information they locally read from and write to the nodes.

– The forward ant migrates from a node to an adjacent one towards its

desti-nation. At each intermediate node, a stochastic decision policy is applied to select the next node to move to. The parameters of the local policy are: (i) the local pheromone variables, (ii) the status of the local link queues, and (iii) the information carried into the ant memory (to avoid cycles). The decision is the results of some tradeoff among all these components.

– Once arrived at destination, the forward ant becomes a backward ant (BANT) Bd→sand goes back to its source node by moving along the same path as before

but in the opposite direction. For its return trip the ant uses higher priority queues than those used by data packets in order to quickly retrace the path.

– Arriving at a node k coming from a neighbour node h, the backward ant updates

the routing table Tkfor all the entries corresponding to the destination node d.

– Once they have returned to their source node, the agent is removed from the

network.

3.3 Enhanced AntNet protocol

Now we will discuss our optimizations that we have made over the initial version of the AntNet protocol. These optimizations can be classified into two categories:

3.3.1 Adaptation for wireless communications

This first category summarises some required modification to the basic version of the AntNet protocol. First of all, we need to adapt the AntNet protocol to the wireless environment since it has been designed for fixed networks. After that, we add a priority queuing mechanism allowing traffic classification at the network layer. Also, we add a neighbour discovery mechanism to detect the wireless neighbourhood nodes. Some of these modifications were inspired from others works like [27] and [21]. Also, we were led to make some changes to the packet classes and to the node structure as we describe in what follows:

(a) Packet Classes

Figure

Figure 2.1: Reference architecture of WMSNs [6]
Figure 3.1: Unoptimized and optimized path
Figure 3.2: Forward and backward ant mechanism using the ancestor list
Table 3.2 summarizes the parameters used for simulation.
+7

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