Joint User Mobility and Traffic Characterization in Temporary Crowded Events

Em eventos temporários com elevada densidade de pessoas (Temporary Crowded Events – TCEs), por exemplo, festivais de música, os utilizadores deparam-se com problemas no acesso a ligações `a Internet. TCEs são eventos de duração limitada com elevada concentração de pessoas que se movimentam dentro de um recinto, enquanto acedem `a Internet. Contrariamente a outro tipo de eventos onde as localizações dos utilizadores são constantes e conhecidas `a partida (ex. estádios), a geração de tráfego e a movimentação dos utilizadores em TCEs ´e variável e influenciada pela dinâmica do evento. A movimentação dos utilizadores pode levar a sobrecargas em APs (Access Point) no caso de estes serem fixos. De modo a minimizar este fenómeno, têm vindo a ser exploradas novas técnicas que recorrem ao posicionamento ajustável de APs integrados em UAVs (Unmanned Aerial Vehicles). Nestes cenários, a dinâmica da localização dos APs requer que ferramentas de previsão do movimento dos utilizadores e, por sua vez, das fontes de tráfego ganhem particular expressão ao serem relacionadas com os algoritmos de posicionamento dos referidos APs. De modo a permitir o desenvolvimento e análise de novas soluções de planeamento de rede para TCEs, ´e necessário recriar estes cenários em simulação, o que, por sua vez, exige uma caracterização detalhada deste tipo de eventos. Esta Dissertação tem como objetivo caracterizar e modelizar conjuntamente a mobilidade e o tráfego gerado pelos utilizadores em TCEs. Esta caracterização possibilitará o desenvolvimento de novos modelos estatísticos de geração de tráfego e mobilidade dos utilizadores em TCEs.At Temporary Crowded Events (TCEs), for example, music festivals, users are faced with problems accessing the Internet. TCEs are limited time events with a high concentration of people moving within the event enclosure while accessing the Internet. Unlike other events where the user locations are constant and known at the start (e.g. stadiums), the traffic generation and the user movement in TCEs is variable and influenced by the dynamics of the event. The movement of users can lead to overloads in APs (Access Point) in case they are fixed. In order to minimize this phenomenon, new techniques have been explored that resort to the adjustable positioning of APs integrated into UAVs (Unmanned Aerial Vehicles). In these scenarios, the dynamic of the location of the APs requires that tools of prediction of the movement of the users and, in turn, of the sources of traffic gain particular expression when being related to the algorithms of positioning of the referred APs. In order to allow the development and analysis of new network planning solutions for TCEs, it is necessary to recreate these scenarios in simulation, which, in turn, requires a detailed characterization of this type of events. This dissertation aims to characterize and model the mobility and traffic generated by users in TCEs. This characterization will enable the development of new statistical models of traffic generation and user mobility in TCEs

Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

Adaptive Network Densification with Small Cell Mobile Base Stations Carried by Vehicles

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnopartially_openembargoed_20211014Mohammadnia, Foroog

Mobility Models for Vehicular Communications

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-15497-8_11The experimental evaluation of vehicular ad hoc networks (VANETs) implies elevate economic cost and organizational complexity, especially in presence of solutions that target large-scale deployments. As performance evaluation is however mandatory prior to the actual implementation of VANETs, simulation has established as the de-facto standard for the analysis of dedicated network protocols and architectures. The vehicular environment makes network simulation particularly challenging, as it requires the faithful modelling not only of the network stack, but also of all phenomena linked to road traffic dynamics and radio-frequency signal propagation in highly mobile environments. In this chapter, we will focus on the first aspect, and discuss the representation of mobility in VANET simulations. Specifically, we will present the requirements of a dependable simulation, and introduce models of the road infrastructure, of the driver’s behaviour, and of the traffic dynamics. We will also outline the evolution of simulation tools implementing such models, and provide a hands-on example of reliable vehicular mobility modelling for VANET simulation.Manzoni, P.; Fiore, M.; Uppoor, S.; Martínez Domínguez, FJ.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC. (2015). Mobility Models for Vehicular Communications. En Vehicular ad hoc Networks. Standards, Solutions, and Research. Springer. 309-333. doi:10.1007/978-3-319-15497-8_11S309333Bai F, Sadagopan N, Helmy A (2003) The IMPORTANT framework for analyzing the impact of mobility on performance of routing protocols for adhoc networks. Elsevier Ad Hoc Netw1:383–403Baumann R, Legendre F, Sommer P (2008) Generic mobility simulation framework (GMSF). In: ACM mobility modelsBononi L, Di Felice M, D’Angelo G, Bracuto M, Donatiello L (2008) MoVES: A framework for parallel and distributed simulation of wireless vehicular ad hoc networks. Comput Netw 52(1):155–179Cabspotting Project (2006) San Francisco exploratorium’s invisible dynamics initiative. http://cabspotting.org/index.htmlCamp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wirel Commun Mobile Comput 2(5):483–502. Special issue on Mobile Ad Hoc Networking: Research, Trends and ApplicationsCavin D, Sasson Y, Schiper A (2002) On the accuracy of MANET simulators. In: Proceedings of the second ACM international workshop on principles of mobile computing. ACM, New York, pp 38–43Choffnes D, Bustamante F (2005) An integrated mobility and traffic model for vehicular wireless networks. In: ACM VANETDavies V (2000) Evaluating mobility models within an ad hoc network. Master’s thesis, Colorado School of Mines, Boulder, Etats-UnisEhling M, Bihler W (1996) Zeit im Blickfeld. Ergebnisse einer repräsentativen Zeitbudgeterhebung. In: Blanke K, Ehling M, Schwarz N (eds) Schriftenreihe des Bundesministeriums für Familie, Senioren, Frauen und Jugend, vol 121. W. Kohlhammer, Stuttgart, pp 237–274ETH Laboratory for Software Technology (2009) K. Nagel. http://www.lst.inf.ethz.ch/research/ad-hoc/car-traces/Fiore M, Härri J (2008) The networking shape of vehicular mobility. In: ACM MobiHoc, Hong Kong, ChinaFiore M, Haerri J, Filali F, Bonnet C (2007) Vehicular mobility simulation for VANETS. In: Proceedings of the 40th annual simulation symposium (ANSS 2007), Norfolk, VAFleetnet Project - Internet on the Road (2000) NEC Laboratories Europe. http://www.neclab.eu/Projects/fleetnet.htmGawron C (1998) An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. Int J Mod Phys C 9(3):393–407Haerri J, Filali F, Bonnet C (2009) Mobility models for vehicular ad hoc networks: a survey and taxonomy. IEEE Commun Surv Tutorials 11(4):19–41. doi: 10.1109/SURV.2009.090403 . http://dx.doi.org/10.1109/SURV.2009.090403Härri J, Fiore M, Filali F, Bonnet C (2011) Vehicular mobility simulation with VanetMobiSim. Simulation 87(4):275–300. doi: 10.1177/0037549709345997 . http://dx.doi.org/10.1177/0037549709345997Hertkorn G, Wagner P (2004) The application of microscopic activity based travel demand modelling in large scale simulations. In: World conference on transport researchHuang E, Hu W, Crowcroft J, Wassell I (2005) Towards commercial mobile ad hoc network applications: a radio dispatch system. In: Sixth ACM international symposium on mobile ad hoc networking and computing (MobiHoc 2005), Urbana-Champaign, ILJaap S, Bechler M, Wolf L (2005) Evaluation of routing protocols for vehicular ad hoc networks in city traffic scenarios. In: ITSTJardosh A, Belding-Royer E, Almeroth K, Suri S (2003) Towards realistic mobility models for mobile ad hoc networks. In: ACM/IEEE international conference on mobile computing and networking (MobiCom 2003), San Diego, CAKim J, Sridhara V, Bohacek S (2009) Realistic mobility simulation of urban mesh networks. Ad Hoc Netw 7(2):411–430Krajzewicz D (2009) Kombination von taktischen und strategischen Einflüssen in einer mikroskopischen Verkehrsflusssimulation. In: Jürgensohn T, Kolrep H (eds) Fahrermodellierung in Wissenschaft und Wirtschaft. VDI-Verlag, Düsseldorf, pp 104–115Krajzewicz D, Blokpoel RJ, Cartolano F, Cataldi P, Gonzalez A, Lazaro O, Leguay J, Lin L, Maneros J, Rondinone M (2010) iTETRIS - a system for the evaluation of cooperative traffic management solutions. In: Advanced microsystems for automotive applications 2010, VDI-Buch. Springer, Berlin, pp 399–410Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of SUMO—simulation of urban mobility. Int J Adv Syst Measur 5(3/4):128–138Krauss S (1998) Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics. Ph.D. thesis, Universität zu KölnKrauss S, Wagner P, Gawron C (1997) Metastable states in a microscopic model of traffic flow. Phys Rev E 55(304):55–97Legendre F, Borrel V, Dias de Amorim M, Fdida S (2006) Reconsidering microscopic mobility modeling for self-organizing networks. Network IEEE 20(6):4–12. doi: 10.1109/MNET.2006.273114Mangharam R, Weller D, Rajkumar R, Mudalige P (2006) GrooveNet: a hybrid simulator for vehicle-to-vehicle networks. In: IEEE MobiquitousMartinez FJ, Cano JC, Calafate CT, Manzoni P (2008) Citymob: a mobility model pattern generator for VANETs. In: IEEE vehicular networks and applications workshop (Vehi-Mobi, held with ICC), BeijingMiller J, Horowitz E (2007) FreeSim: a free real-time freeway traffic simulator. In: IEEE ITSCNagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I 2(12):2221–2229Nagel K, Wolf D, Wagner P, Simon P (1998) Two-lane traffic rules for cellular automata: a systematic approach. Phys Rev E 58:1425–1437NOW - Network on Wheels Project (2008) Hartenstein H, Härri J, Torrent-Moreno M. https://dsn.tm.kit.edu/english/projects_now-project.phpPiorkowski M, Raya M, Lugo A, Papadimitratos P, Grossglauser M, Hubaux JP (2008) TraNS: realistic joint traffic and network simulator for VANETs. ACM Mobile Comput Commun Rev 12(1):31–33Rindsfüser G, Ansorge J, Mühlhans H (2002) Aktivitätenvorhaben. In: Beckmann K (ed) SimVV Mobilität verstehen und lenken—zu einer integrierten quantitativen Gesamtsicht und Mikrosimulation von Verkehr, Ministry of School, Science and Research of Nordrhein-WestfalenSaha A, Johnson D (2004) Modeling mobility for vehicular ad hoc networks. In: ACM VANETSeskar I, Maric S, Holtzman J, Wasserman J (1992) Rate of location area updates in cellular systems. In: IEEE 42nd vehicular technology conference, 1992, vol 2, pp 694–697. doi: 10.1109/VETEC.1992.245478Sommer C, German R, Dressler F (2011) Bidirectionally coupled network and road traffic simulation for improved ivc analysis. IEEE Trans Mobile Comput 10(1):3–15Tian J, Haehner J, Becker C, Stepanov I, Rothermel K (2002) Graph-based mobility model for mobile ad hoc network simulation. In: SCS ANSS, San DiegoTreiber M, Helbing D (2002) Realistische mikrosimulation von strassenverkehr mit einem einfachen modell. In: ASIM, Rostock, AllemagneTreiber M, Hennecke A, Helbing D (2000) Congested traffic states in empirical observations and microscopic simulations. Phys Rev E 62(2):1805–1824UDel Models for Simulation of Urban Mobile Wireless Networks (2009) Stephan Bohacek. http://www.udelmodels.eecis.udel.eduUMass DieselNet Project (2009) UMass diverse outdoor mobile environment (DOME). https://dome.cs.umass.edu/umassdieselnetUppoor S, Trullols-Cruces O, Fiore M, Barcelo-Ordinas JM (2015) Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Trans Mobile Comput 1:1. PrePrints. doi: 10.1109/TMC.2013.27Varschen C, Wagner P (2006) Mikroskopische Modellierung der Personenverkehrsnachfrage auf Basis von Zeitverwendungstagebuchern. Stadt Region Land 81:63–69Yoon J, Liu M, Noble B (2003) Random waypoint considered harmful. In: Proceedings of IEEE INFOCOMM 2003, San Francisco, CAZheng Q, Hong X, Liu J (2006) An agenda-based mobility model. In: 39th IEEE annual simulation symposium (ANSS-39-2006), Huntsville, A

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis