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User Equipment (UE) is equipped with limited cache resources that can be utilized to offload data traffic through device-to-device (D2D) communications. Data caching at a UE level has the potential to significantly alleviate data traffic burden from the backhaul link. Moreover, in wireless networks, users exhibit mobility that poses serious challenges to successful data transmission via D2D communications due to intermittent connectivity among users. Users’ mobility can be exploited to efficiently cache contents by observing connectivity patterns among users. Therefore, it is crucial to develop an efficient data caching mechanism for UE while taking into account users’ mobility patterns. In this work, we propose a mobility-aware data caching approach to enhance data offloading via D2D communication. First, we model users’ connectivity patterns. Then, contents are cached in UE’ cache resources based on users’ data preferences. In addition, we also take into account signal-to-interference and noise ratio (SINR) requirements of the users. Hence, our proposed caching mechanism exploits connectivity patterns of users to perform data placement based on users’ own demands and neighboring users to enhance data offloading via cache resources. We performed extensive simulations to investigate the performance of our proposed mobility-aware data caching mechanism. The performance of our proposed caching mechanism is compared to most deployed data caching mechanisms, while taking into account the dynamic nature of the wireless channel and the interference experienced by the users. From the obtained results, it is evident that our proposed approach achieves 14%, 16%, and 11% higher data offloading gain than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of users, cache capacity, and number of contents, respectively. Moreover, we also analyzed cache hit rates, and our proposed scheme achieves 8% and 5% higher cache hit rate than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of contents and cache capacity, respectively. Hence, our proposed caching mechanism brings significant improvement in data sharing via D2D communications.

Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.(c) 2022 Elsevier B.V. All rights reserved.

With the emergence of affordable smart mobile devices (such as smartphones and tablets) running innovative applications have severely overloaded the cellular network. To cope with this issue, there have been many efforts to offload the traffic from the cellular network to other complement networks, for instance, Wi-Fi and device-to-device (D2D) communications. In this paper, we formulate the traffic offloading issue as a link prediction problem in opportunistic D2D network, which is targeted to alleviate the overburdened cellular network traffic and reduce the delay time. Considering the complexity of realistic networks, we employ three indexes of link prediction: common neighbors, Katz, and LRW index. To measure the performance of our proposed algorithm, we analyze it is offloading traffic capacity along with delay minimization among users in different networks. It is demonstrated that our proposed link prediction solution can efficiently offload up to 80% of the cellular traffic.

Animal monitoring in natural habitats provides significant insights into the animals��� behavior, interactions, health, or external influences. However, the sizes of monitoring devices attachable to animals strongly depends on the animals��� sizes, and thus the range of possible sensors including batteries is severely limited. Gathered data can be offloaded from monitoring devices to data sinks in a wireless sensor network using available radio access technologies, but this process also needs to be as energy-efficient as possible. This paper presents an approach to combine the benefits of high-throughput WiFi and robust low-power LoRa communication for energy-efficient data offloading. WiFi is only used when connectivity between mobile devices and data sinks is available, which is determined by LoRa-based distance estimations without the need for additional GPS sensors. A prototypical implementation on low-end commodity-off-the-shelf hardware is used to evaluate the proposed approach in a German mixed forest using a simple path loss model for distance estimation. The system provides an offloading success rate of 87%, which is similar to that of a GPS-based approach, but with around 37% less power consumption.

Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers&rsquo, computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers&rsquo, computation resources. In order to properly model the users&rsquo, risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user&rsquo, s prospect-theoretic utility function is formulated by quantifying the user&rsquo, s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users&rsquo, pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user&rsquo, s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.

The advent of 5G has sparked interest in Wi-Fi offloading techniques that enable efficient resource sharing and congestion management of wireless communication spectrum. However, offloading data between multiple networks (i.e. service providers) requires costly inter-provider communication which has a substantial overhead as well as high offloading latency. Moreover, involvement of the profit-oriented decision making of service providers has an inherent weakness of unfair scheduling among users and networks. To overcome those problems, this research work proposes a holistic framework similar to an online data market place where existing infrastructure can be used to set up Wi-Fi zones that everyone can use from their own data plan irrespective of the network operators they belong to. First, our proposed architecture improves the efficacy of offloading by using decentralized nature of the emerging Software-Defined Networking (SDN) to set up an operator-assisted data offloading platform, resulting in efficient inter-provider communication. Second, our proposal strengthens the fair scheduling of offloading resources by using blockchain technology to initiate unbiased and independent decision making. The resulting service is a rating system for the sellers to make reliable transactions for payments.

Rapid developments in network technologies and mobile devices improve our lives, but the resultant fast-growing traffic leads to a lack of cellular network resources and breaks the balance between profit and costs for network providers. Data offloading can relieve the loads on network infrastructure and shift data to a complementary wireless technology, thereby increasing maximum throughput and extending network coverage. In this paper, we propose a terminal-to-terminal-based offloading method called Virtual Bank with movement prediction (VBMP), which uses an opportunistic network to achieve offloading. We establish a virtual trading system to manage information transmission. Meanwhile, node movement prediction and node subset selection via a submodular function are used to select the relay nodes and to discover the dynamic topology of mobile nodes. Theoretical analysis and simulation results demonstrate that VBMP effectively improves data offloading.

Recently, for satisfying users’ various mobile Internet service requests for data exchange anytime and anywhere even in their moving vehicles, the generated mobile data traffic has been rapidly increasing and has become a serious burden on current cellular networks. To partially address such a serve challenge, vehicular ad hoc networks (VANETs) have emerged as an effective approach for enhancing vehicular services and applications by equipping vehicles with wireless and processing capabilities. In this paper, we survey the recent advances in the data offloading techniques through VANETs. Particularly, based on the communication patterns among vehicles and infrastructures, we classify these techniques into three categories, i.e., data offloading through vehicle-to-vehicle communications, vehicle-to-infrastructure communications, and vehicle-to-everything communications. Besides, we present a detailed taxonomy of the related techniques by discussing the pros and cons for various offloading techniques for different problems in VANETs. Finally, some opening research issues and challenges are outlined to provide guidelines for future research work.

International audience; In vehicular networks, vehicles may carry various types of data that need to be offloaded to the RoadSide Units (RSUs) through Vehicle-to-Infrastructure (V2I) communications when vehicles come into their coverage. RSUs are not widely deployed everywhere, which causes intermittent connectivity between vehicles and RSUs. In this paper, we propose DIVINE, a Data offloading In VehIcular NEtworks scheme with QoS provisioning, which enables a vehicle to offload its data to RSU directly through V2I communications or using other neighboring vehicles through Vehicle-to-Vehicle (V2V) communications. DIVINE considers the connectivity time of an offloading vehicle with the RSU, with other vehicles heading either on the same or opposite direction, offloading capacity, expected time to reach RSU and contact duration with neighboring vehicles. Additionally, the Quality of Service (QoS) is an important consideration for data offloading in vehicular networks due to the coexistence of urgent data to offload (e.g., accident or emergency data). Therefore, for QoS provisioning, DIVINE uses three QoS functions: traffic classification, overload control and admission control. DIVINE is presented with algorithms and procedures, as well as with illustrative examples. The performance evaluation in network simulator OMNeT++ with Veins and SUMO frameworks shows that DIVINE outperforms other schemes in terms of average offloading delay, maximum offloading delay and running time for a varying number of vehicles, maximum speed values, number of RSUs and RSUs' capacity. It also best behaves in terms of amount of offloaded important data.

Future Connected and Autonomous Vehicles (CAVs) will be equipped with a large set of sensors. The large amount of generated sensor data is expected to be exchanged with other CAVs and the road-side infrastructure. Both in Europe and the US, Dedicated Short Range Communications (DSRC) systems, based on the IEEE 802.11p Physical Layer, are key enabler for the communication among vehicles. Given the expected market penetration of connected vehicles, the licensed band of 75 MHz, dedicated to DSRC communications, is expected to become increasingly congested. In this paper, we investigate the performance of a vehicular communication system, operated over the unlicensed bands 2.4 GHz - 2.5 GHz and 5.725 GHz - 5.875 GHz. Our experimental evaluation was carried out in a testing track in the centre of Bristol, UK and our system is a full-stack ETSI ITS-G5 implementation. Our performance investigation compares key communication metrics (e.g., packet delivery rate, received signal strength indicator) measured by operating our system over the licensed DSRC and the considered unlicensed bands. In particular, when operated over the 2.4 GHz - 2.5 GHz band, our system achieves comparable performance to the case when the DSRC band is used. On the other hand, as soon as the system, is operated over the 5.725 GHz - 5.875 GHz band, the packet delivery rate is 30% smaller compared to the case when the DSRC band is employed. These findings prove that operating our system over unlicensed ISM bands is a viable option. During our experimental evaluation, we recorded all the generated network interactions and the complete data set has been publicly available.

Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

Future Connected and Automated Vehicles (CAVs) will be supervised by cloud-based systems overseeing the overall security and orchestrating traffic flows. Such systems rely on data collected from CAVs across the whole city operational area. This paper develops a Fog Computing-based infrastructure for future Intelligent Transportation Systems (ITSs) enabling an agile and reliable off-load of CAV data. Since CAVs are expected to generate large quantities of data, it is not feasible to assume data off-loading to be completed while a CAV is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be in the range of an RSU only for a limited amount of time, necessitating data reconciliation across different RSUs, if traditional approaches to data off-load were to be used. To this end, this paper proposes an agile Fog Computing infrastructure, which interconnects all the RSUs so that the data reconciliation is solved efficiently as a by-product of deploying the Random Linear Network Coding (RLNC) technique. Our numerical results confirm the feasibility of our solution and show its effectiveness when operated in a large-scale urban testbed., To appear in IEEE VTC-Spring 2019

Connected and Automated Vehicles (CAVs) are expected to constantly interact with a network of processing nodes installed in secure cabinets located at the side of the road -- thus, forming Fog Computing-based infrastructure for Intelligent Transportation Systems (ITSs). Future city-scale ITS services will heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog Computing network. Due to the broadcast nature of the medium, CAVs' communications can be vulnerable to eavesdropping. This paper proposes a novel data offloading approach where the Random Linear Network Coding (RLNC) principle is used to ensure the probability of an eavesdropper to recover relevant portions of sensor data is minimized. Our preliminary results confirm the effectiveness of our approach when operated in a large-scale ITS networks., Comment: To appear in IEEE VTC-Spring 2019

Recently, Device-to-Device (D2D) has been brought inside mobile (cellular) networks with the introduction of the LTE-D2D standard into the 5G ecosystem. This cellular D2D operates in the same operator's frequencies used for regular communications with access points (i.e., base stations). In D2D mode, terminals can communicate directly and do not need to go through a base station. However, D2D communications are authorized and controlled by operators to implement their requirements and policies. A notable example of D2D is data offloading, which helps in reducing traffic congestion in mobile networks. In this scenario, terminals collaborate using their D2D connections to carry data, usually over multiple D2D hops, using other terminals as relays and avoiding base stations. However, the latter still must decide on routing (e.g., which devices should be part of the path) and wireless resource allocation (which frequencies to use by devices). Also, base stations must manage interferences between D2D and cellular communication since they all share the same spectrum. Besides, there is also the energy issue in employing battery-constrained terminals as relays. Another concern, in offloading designs, is how they scale when terminals density increases, such as in crowded-platform scenarios. These scenarios include mobile users in waiting halls of airports and train stations, or stadiums. In such situations, the decision problems mentioned before must be solved rapidly. Doing so avoids long delays in communications that can affect user experience or limit responsiveness. In this thesis, we address the problem of optimizing routing and wireless resource allocation in multihop D2D systems with a focus on data offloading. Our proposals to solve the problem consider practical aspects of the LTE-D2D standard. Moreover, we also address the mentioned energy and scalability concerns. We propose three contributions to deal with these problems. In the first contribution, we propose a novel method (JRW-D2D) to solve jointly routing and resource allocation in the aim of offloading unicast flows inside one cell over the LTE-D2D relaying system. The proposal JRW-D2D is based on Integer Linear Programming (ILP) and gives good results in terms of reliability, latency, and acceptance ratio. In the second contribution, we present two methods to solve the same problem for both unicast and multicast traffic. In the first step, we introduce an optimal ILP-based method (JRW-D2D-MC) to solve routing and resource allocation jointly. Next, to address the scalability issue in JRW-D2D-MC, we propose another scalable method (JRW-D2D-CG) based on the Column-Generation technique. Finally, our third contribution considers the energy issue, where we put forward two energy-aware schemes to solve routing and resource allocation. Initially, we propose an ILP-based method for Energy-Efficient Joint Routing and Resource Allocation (JRRA-EE). In the next step, we highlight the non-scalability of JRRA-EE and introduce a novel parametric three-stage method called Heuristic Energy-aware Routing and Resource Allocation (HERRA). Both JRRA-EE and HERRA consider energy consumption using a state-of-the-art empirical model for LTE-D2D terminals. Moreover, we evaluate the performance of our contributions based on network simulations in NS-3, which we have extended to support the LTE-D2D standard., Récemment, D2D (Device-to-Device) a été intégré aux réseaux mobiles avec l'introduction de la norme LTE-D2D dans l'écosystème 5G. Ce D2D cellulaire fonctionne aux mêmes fréquences que l’opérateur utilisé pour les communications régulières avec les points d’accès (c.à.d. les stations de base). En mode D2D, les terminaux peuvent communiquer directement et n’ont pas besoin de passer par une station de base. Cependant, les communications D2D sont autorisées et contrôlées par les opérateurs pour mettre en œuvre leurs exigences et leurs politiques. Le délestage de données est un exemple notable de D2D, qui aide à réduire la congestion du trafic dans les réseaux mobiles. Dans ce scénario, les terminaux collaborent en utilisant leurs connexions D2D pour transporter les données, généralement sur plusieurs sauts D2D, en utilisant d'autres terminaux comme relais et en évitant les stations de base. Toutefois, ces derniers doivent décider du routage (par exemple, quels terminaux devraient faire partie du chemin) et de l’allocation de ressources sans fil (quelles fréquences à utiliser par les terminaux). De plus, les stations de base doivent gérer les interférences entre les communications, D2D et cellulaires, car elles partagent toutes le même spectre. En outre, il y a aussi le problème énergétique lié à l'utilisation de relais soumis aux contraintes de batterie. Un autre enjeu, dans ces conceptions de délestage, concerne la manière dont elles évoluent lorsque la densité des terminaux augmente (p. ex., dans les scénarios de plate-forme surpeuplée). Ces scénarios incluent des utilisateurs mobiles dans les halls d’attente des aéroports et des gares, ou des stades. Dans de telles situations, les problèmes de décision susmentionnés doivent être résolus rapidement. Cela évite de longs délais dans les communications qui peuvent affecter l'expérience utilisateur ou limiter la réactivité. Dans cette thèse, nous abordons le problème de l'optimisation du routage et de l'allocation de ressources sans fil dans les systèmes D2D multi-sauts en mettant l'accent sur le délestage de données. Nos propositions pour résoudre le problème prennent en compte les aspects pratiques de la norme LTE-D2D. De plus, nous répondons également aux enjeux mentionnés en matière d’énergie et d’évolutivité. Nous proposons trois contributions pour traiter ces problèmes. Dans la première contribution, nous proposons une nouvelle méthode (JRW-D2D) pour résoudre conjointement le routage et l’allocation de ressources afin de délester des flux unicast sur un système de relais LTE-D2D. La proposition JRW-D2D est basée sur la programmation linéaire en nombres entiers (ILP) et donne de bons résultats en termes de fiabilité, de latence et de taux d’acceptation. Dans la deuxième contribution, nous présentons deux méthodes pour résoudre le même problème pour les trafics unicast et multicast. Dans la première étape, nous présentons une méthode optimale basée sur ILP (JRW-D2D-MC) pour résoudre conjointement le routage et l’allocation de ressources. Ensuite, pour résoudre le problème de non-évolutivité de JRW-D2D-MC, nous proposons une autre méthode évolutive (JRW-D2D-CG) basée sur la génération de colonnes. Enfin, notre troisième contribution aborde la question de l’énergie, dans laquelle nous avons présenté deux systèmes axés sur l’énergie pour résoudre les problèmes de routage et d’allocation de ressources. Dans un premier temps, nous proposons une méthode (JRRA-EE) basée sur ILP. Dans l'étape suivante, nous mettons en évidence la non-évolutivité de JRRA-EE et présentons une nouvelle méthode paramétrique à trois étapes appelée (HERRA) et basée sur l’heuristique. Les deux méthodes JRRA-EE et HERRA considèrent la consommation d'énergie à l'aide d'un modèle empirique de pointe pour les terminaux LTE-D2D. De plus, nous évaluons la performance de nos contributions sur la base de simulations de réseau dans NS-3 que nous avons étendu pour prendre en charge la norme LTE-D2D.

The emerging smart city paradigm e.g., intelligent transport, smart grid and participatory sensing etc. is to advance the quality, performance and experience of urban citizten services through greater connectivity. This paradigm needs to collect data from citizens, various devices and assets that could be monitored, processed and analysed for the city governers to make better decision and also more efficiently manage those assests and resources. While the telecommunication and Internet are progressively being over-burdened and congested by the growing data transmission demands. To keep expanding the telecommunications and Internet infrastructures to accomodate these intensive data demands is costly and also the associated energy consumptions and carbon emissions could at long last wind up genuinely hurting the environment. To face this issue in the coming era of big data, we envision it will be best to utilize the established urban transport and road infrastructure and existing daily massive vehicular trips, to complement traditional option for data transmission. After detailing the current state-of-the-art, we consider the main challenges that need to be faced. Moreover, we define the main pillars to integrate the telecommunications and transport infrastructures, and also a proposal for the future urban network architecture.

A. Serdar Tan (MEF Author) Mobile data traffic inside mobile operator's infrastructure is increasing exponentially every year. This increasing demand forces mobile network operators (MNOs) to seek for alternative communication methods in order to relieve the traffic load in base stations, especially in highly populated and crowded environments. Network assisted data offload and Device-to-Device(D2D) communications are two prominent methods to help MNOs solve this problem. In this study, a data offload framework is developed that incorporates network assisted multiple attribute decision making (MADM) for best network selection and D2D communications for exploiting user proximity in crowded environments. The performance of the framework is evaluated with simulation results as well as analytic solutions and performance bounds. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions and demonstrates the significant benefits of D2D communications when the density of D2D users is properly adjusted. The simulation results depict that up to 168% and 200% increase in user satisfaction and throughput can be achieved under high network load scenarios at optimal D2D density. (C) 2018 Elsevier B.V. All rights reserved. WOS:000441117100003 2-s2.0-85047401058 Science Citation Index Expanded Q2 Article Uluslararası işbirliği ile yapılmayan - HAYIR Ağustos 2018 YÖK - 2017-18

Due to the explosive growth of mobile data traffic and the shortage of spectral resources, 5G networks are envisioned to have a densified heterogeneous network (HetNet) architecture, combining multiple radio access technologies (multi-RATs) into a single holistic network. The co-existing of multi-tier architectures bring new challenges, especially on resource management and security provisioning, due to the lack of common interface and consistent policy across HetNets. In this thesis, we aim to address the technical challenges of data traffic management, coordinated spectrum sharing and security provisioning in 5G HetNets through the introduction of a programmable management platform based on Software-defined networking (SDN). To address the spectrum shortage problem in cellular networks, cellular data traffic is efficiently offloaded to the Wi-Fi network, and the quality of service of user applications is guaranteed with the proposed delay tolerance based partial data offloading algorithm. A two-layered information collection is also applied to best load balancing decision-making. Numerical results show that the proposed schemes exploit an SDN controller's global view of the HetNets and take optimized resource allocation decisions. To support growing vehicle-generated data traffic in 5G-vehicle ad hoc networks (VANET), SDN-enabled adaptive vehicle clustering algorithm is proposed based on the real-time road traffic condition collected from HetNet infrastructure. Traffic offloading is achieved within each cluster and dynamic beamformed transmission is also applied to improve trunk link communication quality. To further achieve a coordinated spectrum sharing across HetNets, an SDN enabled orchestrated spectrum sharing scheme that integrates participating HetNets into an amalgamated network through a common configuration interface and real-time information exchange is proposed. In order to effectively protect incumbent users, a real-time 3D interference map is developed to guide the spectrum access based on the SDN global view. MATLAB simulations confirm that average interference at incumbents is reduced as well as the average number of denied access. Moreover, to tackle the contradiction between more stringent latency requirement of 5G and the potential delay induced by frequent authentications in 5G small cells and HetNets, an SDN-enabled fast authentication scheme is proposed in this thesis to simplify authentication handover, through sharing of user-dependent secure context information (SCI) among related access points. The proposed SCI is a weighted combination of user-specific attributes, which provides unique fingerprint of the specific device without additional hardware and computation cost. Numerical results show that the proposed non-cryptographic authentication scheme achieves comparable security with traditional cryptographic algorithms, while reduces authentication complexity and latency especially when network load is high.

This paper presents an operator-assisted data offloading platform for 5G mobile networks by using Software Defined Networking (SDN). By enabling lateral communication between multiple SDN controllers, operators are able to perform the offloading process without the intervention of the user. Moreover, the offloading decision of proposed platform is based on accurate real time network conditions. The proposed mechanism is implemented on a testbed to verify feasibility and performance.

International audience; With increasing usage of mobile devices, we are witnessing an explosion of mobile Internet traffic. In order to cope with recent traffic growth, current mobile network architectures are being flattened, and IP mobility support protocols need to be adopted in the evolution of mobile network architectures. Existing IP mobility support protocols rely on centralized mobility anchors that suffer from inefficient routing and scalability issues due to rapidly increasing traffic volumes. In that vein, this article introduces a new IP mobility support protocol that does not rely on a single-handed mobility anchor, but utilizes distributed mobility anchors to overcome the limitations of the existing protocols while providing selective data offloading. We highlight the main features and strengths of the new protocol through qualitative comparisons and simulations.

Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users’ computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users’ data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers’ profit is formulated and solved to determine the MEC servers’ optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users.

In this thesis, we exploit the daily mobility of vehicles to create an alternative transmission medium. Our objective is to draw on the many vehicular trips taken by cars or public transports to overcome the limitations of conventional data networks such as the Internet. In the first part, we take advantage of the bandwidth resulting from the mobility of vehicles equipped with storage capabilities to offload large amounts of delay-tolerant traffic from the Internet. Data is transloaded to data storage devices we refer to as offloading spots, located where vehicles stop often and long enough to transfer large amounts of data. Those devices act as data relays, i.e., they store data it is until loaded on and carried by a vehicle to the next offloading spot where it can be dropped off for later pick-up and delivery by another vehicle. We further extend the concept of offloading spots according to two directions in the context of vehicular cloud services. In the first extension, we exploit the storage capabilities of the offloading spots to design a cloud-like storage and sharing system for vehicle passengers. In the second extension, we dematerialize the offloading spots into pre-defined areas with high densities of vehicles that meet long enough to transfer large amounts of data. The performance evaluation of the various works conducted in this thesis shows that everyday mobility of entities surrounding us enables innovative services with limited reliance on conventional data networks., Dans cette thèse, nous exploitons la mobilité des véhicules pour créer un médium de communication ad hoc utile pour déployer des services connectés. Notre objectif est de tirer partie des trajets quotidiens effectués en voiture ou en transport en commun pour surmonter les limitations des réseaux de données tels que l’Internet. Dans une première partie, nous profitons de la bande passante que génèrent les déplacements de véhicules équipés de capacités de stockage pour délester en masse l’Internet d’une partie de son trafic. Les données sont détournées vers des équipements de stockage appelés points de délestage installés aux abords de zones où les véhicules s’arrêtent habituellement, permettant ainsi de relayer les données entre véhicules jusqu'au point de délestage suivant où elles pourront éventuellement être déchargées. Nous proposons ensuite deux extensions étendant le concept de point de délestage selon deux directions dans le contexte de services reposant toujours la mobilité des véhicules. Dans la première extension, nous exploitons les capacités de stockage des points de délestage pour concevoir un service de stockage et partage de fichiers offert aux passagers de véhicules. Dans la seconde extension, nous dématérialisons les points de délestage en zones géographiques pré-définies où un grand nombre de véhicules se rencontrent suffisamment longtemps pour transférer de grandes quantités de données. L’évaluation des performances des différents travaux menés au cours de cette thèse montrent que la mobilité inhérente aux entités du quotidien permet la fourniture de services innovants avec une dépendance limitée vis-à-vis des réseaux de données traditionnels.

International audience; Traffic offloading using opportunistic device-to-device (D2D) communications is a new and exciting opportunity for cellular operators to cope with the unprecedented mobile data growth. A limitation of existing proposals is that they assume that all terminals are, by default, involved in the D2D forwarding process. In particular, they do not capture the need to reward seed users. For this reason, we include a rewarding cost in the design of the opportunistic offloading strategy. In our solution, we make the difference between nodes that receive content through the cellular channel only (leechers) and nodes that take part in the forwarding process (seeders). The key point for an operator is to design a global strategy to select which nodes act as seeders and which ones as leechers, in order to reduce the total dissemination cost. We formulate this question as a stochastic control problem that we solve using an application of Pontryagin's Maximum Principle. We provide a mathematical framework to devise the optimal strategy for opportunistic offloading under a generic cost model. First, we show that an optimal solution exists; then, from this policy, we extract some insights to develop heuristics. Finally, we discuss the advantages of the proposed model compared to the classic seeder-only model. We demonstrate that separating seeders/leechers leads to better incentive strategies in the most demanding cases of content with a large span of delivery delays.

International audience; In this article we explore one of the most promising technologies for 5G wireless networks using an underlay small cell network, namely proactive caching. Using the increase in storage technologies and through studying the users behavior, peak traffic can be reduced through proactive caching of the content that is most probable to be requested. We propose a new method, in which, instead of caching the most popular content, the users within the network are clustered according to their content popularity and the caching is done accordingly. We present also a method for estimating the number of clusters within the network based on the Akaike information criterion. We analytically derive a closed form expression of the hit probability and we propose an optimization problem in which the small base stations association with clusters is optimized.

International audience; Mobile devices today are constantly generating and consuming a tremendous amount of content on the internet. Caching of such " massive " data is beyond the capacity of existing cellular networks both in terms of cost and bandwidth due to its connection-centric nature. The increasing demand for content poses fundamental questions like, where, what, how to cache and how to retrieve cached content? Leveraging the shift towards content-centric networking paradigm, we propose to cache content close to the mobile user to avoid wasting resources and decrease access delays. Therefore, we present SAVING, a Socially Aware Vehicular Information-centric Networking system for content storage and sharing over vehicles due to their Computing, Caching, and Communication (3Cs) capabilities. The encapsulated 3Cs are exploited first to identify the potential candidates, socially important to cache in the fleet of vehicles. To achieve this, we propose a novel vehicle ranking system allowing a smart vehicle to autonomously " Compute " its eligibility to address the question, where to cache? The identified vehicles then collaborate to efficiently " Cache " content between them based on the content popularity and availability to decide what and how to cache? Finally, to facilitate efficient content distribution, we present a socially-aware content distribution protocol allowing vehicles to " Communicate " to address the question, how to retrieve cached content? Implementation results for SAVING on 2986 vehicles with realistic mobility traces suggests it as an efficient and scalable computing, caching and communication system.

In this paper we present the initial implementation of an integrated optical and acoustic system that can enable large data transfer between mobile and static nodes in Underwater Wireless Sensor Networks (UWSNs). The proposed system is based on the OptoCOMM optical modem and on the SUNSET Software Defined Communication Stack (S-SDCS) framework. The OptoCOMM modem allows to overcome the limits of maximum data rate and bandwidth imposed by the use of acoustic communication by providing a data rate of 10Mbps. SUNSET SDCS instead has been used to provide networking and fragmentation capabilities to efficiently offload large data in UWSNs. The performance of the proposed approach has been evaluated through in lab experiments where large files with arbitrary sizes have been optically transferred. The results achieved show that our system is able to transfer up to 1.5 GBytes of data in short time.