La pénétration du marché des appareils mobiles a connu une croissance impressionnante ces dernières années. Smartphones, tablettes et ordinateurs portables sont devenus soit producteurs soit consommateurs de contenus générés par les utilisateurs. Les communications opportunistes permettent une couverture étendue dans les endroits où il n'existe aucune infrastructure réseau disponible et des stratégies de délestage de données pour aider les opérateurs à soulager la charge de leurs infrastructures. Dans cette thèse, nous considérons le cas de la diffusion opportuniste de plusieurs grands contenus d'un point de vue expérimental. Dans la première partie nous commençons par implémenter EPICS, un protocole réseau conçu pour l'échange opportuniste de grands contenus, dans des terminaux Android. Après sa évaluation nous proposons DAD, un nouveau protocole, qui envoie une rafale de paquets de données de façon adaptative. Nous comparons les deux protocoles expérimentalement et, à l'aide des traces de contacts, soit réelles, soit synthétiques, nous obtenons des gains importants avec cette nouvelle approche. La deuxième partie est dédiée au passage à l'échelle des systèmes de surveillance passive. Nous proposons deux approches. La première est basée sur la similarité des traces et des algorithmes de détection de communautés. La deuxième est basée sur des mesures collaboratives. / The market penetration of mobile devices has experienced an impressive growth. Smartphones, tablets, and laptops have become both producers and consumers of user-generated contents. They also motivate novel communication paradigms such as the possibility to establish, in an opportunistic fashion, direct device-to-device links whenever two mobile nodes enter within the wireless range of each other. In this thesis, we consider the case of opportunistic dissemination of multiple large contents from an experimental point of view. This implies revisiting, among others, the common assumption that contacts have enough capacity to transfer any amount of data.In the first part of this thesis, we start from an Android implementation of EPICS, a network protocol designed for exchanging large contents in opportunistic networks, on off-the-shelf devices. After an deep analysis of application-level logs and captured wireless traces we found out limitations and uncovered improving possibilities. We then propose DAD, a new content dissemination protocol that adaptively sends bursts of data instead of the per-fragment transmission strategy of EPICS.The second part of this thesis deals with the scalability of legacy WLAN monitoring systems. We propose two original approaches. With the first one, based on trace similarity and community detection algorithms, we are able to identify how many monitor we need in a target area and where to place them. The second approach in based on collaborative measurements. In this case we face the risk of biased measures due attacks of malicious users generating adulterated traces. We then propose a method to detect such malicious behaviors.

La programmation bi-niveau désigne une classe de problèmes d'optimisation emboîtés impliquant deux joueurs.Un joueur meneur annonce une décision à un joueur suiveur qui détermine sa réponse parmi l'ensemble des solutions d'un problème d'optimisation dont les données dépendent de la décision du meneur (problème de niveau bas).La décision optimale du meneur est la solution d'un autre problème d'optimisation dont les données dépendent de la réponse du suiveur (problème de niveau haut).Lorsque la réponse du suiveur n'est pas unique, on distingue les problèmes bi-niveaux optimistes et pessimistes,suivant que la réponse du suiveur soit respectivement la meilleure ou la pire possible pour le meneur.Les problèmes bi-niveaux sont souvent utilisés pour modéliser des problèmes de tarification. Dans les applications étudiées ici, le meneur est un vendeur qui fixe un prix, et le suiveur modélise le comportement d'un grand nombre de clients qui déterminent leur consommation en fonction de ce prix. Le problème de niveau bas est donc de grande dimension.Cependant, la plupart des problèmes bi-niveaux sont NP-difficiles, et en pratique, il n'existe pas de méthodes générales pour résoudre efficacement les problèmes bi-niveaux de grande dimension.Nous introduisons ici une nouvelle approche pour aborder la programmation bi-niveau.Nous supposons que le problème de niveau bas est un programme linéaire, en variables continues ou discrètes,dont la fonction de coût est déterminée par la décision du meneur.Ainsi, la réponse du suiveur correspond aux cellules d'un complexe polyédral particulier,associé à une hypersurface tropicale.Cette interprétation est motivée par des applications récentes de la géométrie tropicale à la modélisation du comportement d'agents économiques.Nous utilisons la dualité entre ce complexe polyédral et une subdivision régulière d'un polytope de Newton associé pour introduire une méthode dedécomposition qui résout une série de sous-problèmes associés aux différentes cellules du complexe.En utilisant des résultats portant sur la combinatoire des subdivisions, nous montrons que cette décomposition mène à un algorithme permettant de résoudre une grande classe de problèmes bi-niveaux en temps polynomial en la dimension du problème de niveau bas lorsque la dimension du problème de niveau haut est fixée.Nous identifions ensuite des structures spéciales de problèmes bi-niveaux pour lesquelles la borne de complexité peut être améliorée.C'est en particulier le cas lorsque la fonction coût du meneur ne dépend que de la réponse du suiveur.Ainsi, nous montrons que la version optimiste du problème bi-niveau peut être résolue en temps polynomial, notammentpour des instancesdans lesquelles les données satisfont certaines propriétés de convexité discrète.Nous montrons également que les solutions de tels problèmes sont des limites d'équilibres compétitifs.Dans la seconde partie de la thèse, nous appliquons cette approche à un problème d'incitation tarifaire dans les réseaux mobiles de télécommunication.Les opérateurs de données mobiles souhaitent utiliser des schémas de tarification pour encourager les différents utilisateurs à décaler leur consommation de données mobiles dans le temps, et par conséquent dans l'espace (à cause de leur mobilité), afin de limiter les pics de congestion.Nous modélisons cela par un problème bi-niveau de grande taille.Nous montrons qu'un cas simplifié peut être résolu en temps polynomial en utilisant la décomposition précédente,ainsi que des résultats de convexité discrète et de théorie des graphes.Nous utilisons ces idées pour développer une heuristique s'appliquant au cas général.Nous implémentons et validons cette méthode sur des données réelles fournies par Orange. / Bilevel programming deals with nested optimization problems involving two players. A leader annouces a decision to a follower, who responds by selecting a solution of an optimization problem whose data depend on this decision (low level problem). The optimal decision of the leader is the solution of another optimization problem whose data depend on the follower's response (high level problem). When the follower's response is not unique, one distinguishes between optimistic and pessimistic bilevel problems, in which the leader takes into account the best or worst possible response of the follower.Bilevel problems are often used to model pricing problems.We are interested in applications in which the leader is a seller who announces a price, and the follower models the behavior of a large number of customers who determine their consumptions depending on this price.Hence, the dimension of the low-level is large. However, most bilevel problems are NP-hard, and in practice, there is no general method to solve efficiently large-scale bilevel problems.In this thesis, we introduce a new approach to tackle bilevel programming. We assume that the low level problem is a linear program, in continuous or discrete variables, whose cost function is determined by the leader. Then, the follower responses correspond to the cells of a special polyhedral complex, associated to a tropical hypersurface. This is motivated by recent applications of tropical geometry to model the behavior of economic agents.We use the duality between this polyhedral complex and a regular subdivision of an associated Newton polytope to introduce a decomposition method, in which one solves a series of subproblems associated to the different cells of the complex. Using results about the combinatorics of subdivisions, we show thatthis leads to an algorithm to solve a wide class of bilevel problemsin a time that is polynomial in the dimension of the low-level problem when the dimension of the high-level problem is fixed.Then, we identify special structures of bilevel problems forwhich this complexity bound can be improved.This is the case when the leader's cost function depends only on the follower's response. Then, we showthe optimistic bilevel problem can be solved in polynomial time.This applies in particular to high dimensional instances in which the datasatisfy certain discrete convexity properties. We also show that the solutions of such bilevel problems are limits of competitive equilibria.In the second part of this thesis, we apply this approach to a price incentive problem in mobile telecommunication networks.The aim for Internet service providers is to use pricing schemes to encourage the different users to shift their data consumption in time(and so, also in space owing to their mobility),in order to reduce the congestion peaks.This can be modeled by a large-scale bilevel problem.We show that a simplified case can be solved in polynomial time by applying the previous decomposition approach together with graph theory and discrete convexity results. We use these ideas to develop an heuristic method which applies to the general case. We implemented and validated this method on real data provided by Orange.

La densification de réseau à l'aide de petites cellules massivement déployées sur les zones macro-cellules, représente une solution prometteuse pour les réseaux mobiles 5G avenir pour faire face à l'augmentation du trafic mobile. Afin de simplifier la gestion de l'hétérogène du réseau d'accès radio (Radio Access Network RAN) qui résulte du déploiement massif de petites cellules, des recherches récentes et des études industrielles ont favorisé la conception de nouvelles architectures de RAN centralisés appelés comme Cloud-RAN (C-RAN), ou RAN virtuel (V-RAN), en incorporant les avantages du cloud computing et Network Functions Virtualization (NFV). Le projet de DynaRoC vise l'élaboration d'un cadre théorique de l'orchestration de ressources pour les C-RAN et dériver les limites de performance fondamentaux ainsi que les arbitrages entre les différents paramètres du système, et la conception de mécanismes d'orchestration de ressources dynamiques sur la base des conclusions théoriques à atteindre un équilibre de performance souhaité, en tenant compte des différents défis de conception. Le doctorant va étudier les mécanismes d'optimisation des ressources novatrices pour favoriser le déploiement de C-RAN, améliorer leur performance exploitant la technologie Network Functions Virtualization / Network densification using small cells massively deployed over the macro-cell areas, represents a promising solution for future 5G mobile networks to cope with mobile traffic increase. In order to simplify the management of the heterogeneous Radio Access Network (RAN) that results from the massive deployment of small cells, recent research and industrial studies have promoted the design of novel centralized RAN architectures termed as Cloud-RAN (C-RAN), or Virtual RAN (V-RAN), by incorporating the benefits of cloud computing and Network Functions Virtualization (NFV). The DynaRoC project aims at (1) developing a theoretical framework of resource orchestration for C-RAN and deriving the fundamental performance limits as well as the tradeoffs among various system parameters, and (2) designing dynamic resource orchestration mechanisms based on the theoretical findings to achieve a desired performance balance, by taking into account various design challenges. The PhD student will investigate innovative resource optimization mechanisms to foster the deployment of C-RANs, improving their performance exploiting the enabling Network Functions Virtualization technology

Long time ago, there were developed methods which can allow fast exchange of information at the longest distance possible. Until recent time, the possibilities of long way communications were very limited. There were technological and financial limitations mainly. With telegraph, telephone and the newest – computer networks invention, the telecommunication services became cheaper and much more comprehensive. With accession and high scale spread of internet, the role of communications is much more important. The most actual trend is mobile internet and connected multimedia networks and their instant accessibility from anywhere. On the first side there are classical telecommunication networks as GSM, UMTS, on the other side there is very strong alternative in shape of WiMAX and WiFi networks combination. For the usage of multimedia services in that networks is very important to handle their prefferization over other traffic, which is very difficult task even on wireless media. For the finding of new processes and methods, which can allow it, there is, at fist, the need for highly accurate and authentic mathematical models. In this doctoral thesis is mapped actual state of the art and proposed the new mathematical model of Distribution Coordination Function, which is much-frequent used as access method in 802.11 networks, Wi-Fi.

D. Tech. (Department of Process Control and Computer Systems, Faculty of Engineering and Technology), Vaal University of Technology. / In this research study, a scheme to minimise interference in converged mobile cellular networks (MCNs) and wireless sensor networks (WSNs) was designed and implemented. The focus was the mitigation of interference that arises when proximity service (ProSe)-enabled sensors engage in a device-to device (D2D) communication to alert smartphone users upon the detection of explosives at highly crowded areas like shopping malls. D2D is a technology that academia and industry experts believe will play a prominent role in the implementation of the next generation of mobile networks, specifically, the fifth generation (5G). However, the full roll out of D2D is being impeded by the interference that the technology introduces to the cellular network. D2D devices cause a significant amount of interference to the primary cellular network especially when radio resources are shared. In the downlink phase, primary user equipment is likely to suffer from interference emanating from a D2D transmitter. On the other hand, the immobile base station is affected by interference caused by the D2D transmitter in the uplink phase. This type of interference can be avoided or reduced if radio resources are allocated intelligently under strict coordination of the base station. An NP-hard optimisation problem was formulated and finding a solution to this problem in 1 ms is not possible. 5G has a frame structure duration of 10 ms with 10 subframes of 1 ms each. Heuristic algorithms were then developed to mitigate the interference affecting the primary network that could carry out resource allocation within the fast-scheduling period of 1 ms. Smartphones have progressed into devices capable of generating massive volumes of data. The challenge is that battery technology is not keeping up with the pace of smartphone technology, so any additional feature that designers want to add, is met with a lot of contempt from customers who are concerned about their smartphone batteries depleting rapidly. In this context, the strategy must be energy-efficient for smartphone users to embrace it. A system level simulator was developed using MATLAB to evaluate the efficacy of the proposed design. Extensive simulation results showed that ProSe-enabled sensors can safely be integrated into cellular networks participating in D2D communication with smart phones, without introducing significant harm to the primary cellular network. The results showed that after implementing the proposed strategy, overall user throughput decreased by 3.63 %. In cellular networks, this is a modest figure since a reduction of up to 5% is acceptable to both users and network providers. The figure generally capped in service level agreements signed between network providers and users is 5%. The proposed technique also resulted in a 0 % reduction in SINR of CUEs in a cellular network, according to the findings. In terms of D2D link throughput for different D2D transmit levels, the method proposed in this research work surpassed a similar scheme proposed in literature by an average of 18.3%.

With an increasing demand for bandwidth and lower latency in mobile communication networks it becomes gradually more important to improve current mobile network management solutions using available network data. To improve the network management it can for instance be of interest to infer future available bandwidth to the end user of the network. This can be done by utilizing the current knowledge of real-time user equipment (UE) behaviour in the network. In the scope of this thesis interest lies in, given a set of visited radio access points (cells), to predict what the next one is going to be. For this reason the aim is to investigate the prediction performance when utilizing the All-K-Order Markov (AKOM) model, with some added variations, on collected data generated from train trajectories. Moreover a method for testing the suitability of modeling the sequence of cells as a time-homogeneous Markov chain is proposed, in order to determine the goodness-of- t with the available data. Lastly, the elapsed time in each cell is attempted to be predicted using linear regression given the prior history window of previous cell and elapsed times pairs. The results show that moderate to good prediction accuracy on the upcoming cell can be achieved with AKOM and associated variations. For predicting the upcoming sojourn time in future cells the results reveal that linear regression does not yield satisfactory results and possibly another regression model should be utilized. / Med en ökande efterfrågan på banbredd och kortare latens i mobila nätverk har det gradvis blivit viktigare att förbättra nuvarande lösningar för hantering av nätverk genom att använda tillgänglig nätverksdata. Specifikt är det av intresse att kunna dra slutsatser kring vad framtida bandbredsförhållanden kommer vara, samt övriga parametrar av intresse genom att använda tillgänglig information om aktuell mobil användarutrustnings (UE) beteende i det mobila nätverket. Inom ramen av detta masterarbete ligger fokus på att, givet tidigare besökta radio accesspunkter (celler), kunna förutspå vilken nästkommande besökta cell kommer att vara. Av denna anledning är målet att undersöka vilken prestanda som kan uppnås när All-$K$-Order Markov (AKOM) modellen, med associerade varianter av denna, används på samlad data från tågfärder. Dessutom ges det förslag på test som avgör hur lämpligt det är att modelera observerade sekvenser av celler som en homogen Markovkedja med tillgänglig data. Slutligen undersöks även om besökstiden i en framtida cell kan förutspås med linjär regression givet ett historiskt fönster av tidigare cell och besökstids par. Erhållna resultat visar att måttlig till bra prestanda kan uppnås när kommande celler förutspås med AKOM modellen och associerade variationer. För prediktering av besökstid i kommande cell med linjär regression erhålles det däremot inte tillfredsställande resultat, vilket tyder på att en alternativ regressionsmetod antagligen är bättre lämpad för denna data.

At present, the current 4G systems provide a universal platform for broadband mobile services; however, mobile traffic is still growing at an unprecedented rate and the need for more sophisticated broadband services is pushing the limits on current standards to provide even tighter integration between wireless technologies and higher speeds. This has led to the need for a new generation of mobile communications: the so-called 5G. Although 5G systems are not expected to penetrate the market until 2020, the evolution towards 5G is widely accepted to be the logical convergence of internet services with existing mobile networking standards leading to the commonly used term “mobile internet” over heterogeneous networks, with several Gbits/s data rate and very high connectivity speeds. Therefore, to support highly increasing traffic capacity and high data rates, the next generation mobile network (5G) should extend the range of frequency spectrum for mobile communication that is yet to be identified by the ITU-R. The mm-wave spectrum is the key enabling feature of the next-generation cellular system, for which the propagation channel models need to be predicted to enhance the design guidance and the practicality of the whole design transceiver system. The present work addresses the main concepts of the propagation channel behaviour using ray tracing software package for simulation and then results were tested and compared against practical analysis in a real-time environment. The characteristics of Indoor-Indoor (LOS and NLOS), and indoor-outdoor (NLOS) propagations channels are intensively investigated at four different frequencies; 5.8 GHz, 26GHz, 28GHz and 60GHz for vertical polarized directional, omnidirectional and isotropic antennas patterns. The computed data achieved from the 3-D Shooting and Bouncing Ray (SBR) Wireless Insite based on the effect of frequency dependent electrical properties of building materials. Ray tracing technique has been utilized to predict multipath propagation characteristics in mm-wave bands at different propagation environments. Finally, the received signal power and delay spread were computed for outdoor-outdoor complex propagation channel model at 26 GHz, 28 GHz and 60GHz frequencies and results were compared to the theoretical models.

Group communication, security and 5G technology present a unique dimension of challenges and security remains crucial in the successful deployment of 5G technology across different industry. Group key management plays a vital role in secure group communication. This research work studies various group key management schemes for mobile wireless technology and then a new scheme is proposed and evaluated. The main architecture is analysed, while the components and their roles are established, trust and keying relationships are evaluated, as well as detailed functional requirements. A detailed description of the main protocols required within the scheme is also described. A numerical and simulation analysis is employed to assess the proposed scheme with regards to fulfilling the security requirement and performance requirements. The impact of group size variation, the impact of mobility rate variation are studied with regards to the average rekeying messages induced by each event and 1-affects-n phenomenon. The results obtained from the simulation experiments show that the proposed scheme outperformed other solutions with a minimal number of rekeying messages sent and less number of affected members on each event. The security requirements demonstrate that backward and forward secrecy is preserved and maintained during mobility between areas. Finally, the research work also proposes a 5G-enabled software-defined multicast network (5G-SDMNs), where software-defined networking (SDN) is exploited to dynamically manage multicast groups in 5G and mobile multicast environment. Also, mobile edge computing (MEC) is exploited to strengthen network control of 5G-SDMN. / National Open University of Nigeria

With the rapid evolution in mobile telecommunications, there is a significant need for more accurate and efficient management of resources such as CPU, RAM, and bandwidth. This thesis utilizes customer usage data alongside machine learning algorithms to predict resource demands, enabling telecommunications service providers to optimize service quality and reduce unnecessary costs. This thesis investigates enhancing mobile network cost prediction and resource utilization by integrating customer behavior analysis using machine learning models. As a predictive model we employed various machine learning techniques, including Random Forest Regressor and Recurrent Neural Networks (LSTM and GRU), and can effectively predict resource needs based on user events. Among these models, the Random Forest Regressor performed the best. This model enhances operational efficiency by providing precise resource predictions within the dataset ranges. / Med den snabba utvecklingen inom mobiltelekommunikation finns det ett betydande behov av mer exakt och effektiv hantering av resurser som CPU, RAM och bandbred. Rapporten använder data om kundanvändning tillsammans med maskininlärningsalgoritmer för att förutsäga resursbehov, vilket möjliggör att telekommunikationsleverantörer kan optimera tjänstekvalitet och minska onödiga kostnader. Detta examensarbete undersöker hur förutsägelser av kostnader och resursanvändning i mobila nätverk kan förbättras genom att integrera analys av kundbeteende med maskininlärningsmodeller. Som en prediktiv modell använde vi olika maskininlärningstekniker, inklusive Random Forest Regressor och Recurrent Neural Networks (LSTM och GRU), effektivt kan förutsäga resursbehov baserat på användarhändelser. Bland dessa modeller presterade Random Forest Regressor bäst. Denna modell förbättrar den operativa effektiviteten genom att ge mer precisa resurs prediktion inom datamängdens intervaller.

Yes / One of the main features of the third generation (3G) mobile networks is their capability to provide different classes of services; especially multimedia and real-time services in addition to the traditional telephony and data services. These new services, however, will require higher Quality of Service (QoS) constraints on the network mainly regarding delay, delay variation and packet loss. Additionally, the overall traffic profile in both the air interface and inside the network will be rather different than used to be in today's mobile networks. Therefore, providing QoS for the new services will require more than what a call admission control algorithm can achieve at the border of the network, but also continuous buffer control in both the wireless and the fixed part of the network to ensure that higher priority traffic is treated in the proper way. This paper proposes and analytically evaluates a buffer management scheme that is based on multi-level priority and Complete Buffer Sharing (CBS) policy for all buffers at the border and inside the wireless network. The analytical model is based on the G/G/1/N censored queue with single server and R (R¿2) priority classes under the Head of Line (HoL) service rule for the CBS scheme. The traffic is modelled using the Generalised Exponential distribution. The paper presents an analytical solution based on the approximation using the Maximum Entropy (ME) principle. The numerical results show the capability of the buffer management scheme to provide higher QoS for the higher priority service classes.