Fitness Landscape Analysis for Scalable Multicast RRM Problem in Cellular Network

International audienceThis paper aims to solve the Radio Resource Management (RRM) problem for Multimedia Broadcast Multicast Service (MBMS) system in cellular network. We develop a flexible model to perform dynamic radio resource allocation for MBMS service by using metaheuristic approach. We conduct fitness landscape analysis to study the characteristics of the proposed model, which helps us to select appropriate search strategy. Simulation results show that the proposed algorithm provides better performance than existing algorithms. Keywords: fitness landscape, metaheuristic approach, multimedia multicast, radio resource management

Géolocalisation et prédiction dans les réseaux Wi-Fi en intérieur

Democratization of mobile terminals and bandwidth capability allow to consider new applications, particularly context-aware applications. Such applications require service continuity and mobile terminal positioning. We propose on one hand to locate mobile terminaux and, on the other hand, mobility prediction.Navigation Satellites Systems are not working indoors. Thus we base our work on IEEE 802.11 networks. Two methods exist to locate a Wi-Fi terminal : the first one is based on a signal strength map. The other one is based on computation of distances between the mobile terminal and points whose coordinates are known. Each method having his own drawbacks, we merge both of them to improve positioning accuracy.We propose a first model, which computes distances between the terminal and the access points, based on the SS received. Terminal's location is inferred by calculation. The second model we propose restricts the positioning to an area through an SS map. Then, in this area, the first model is applied to determine the terminal's location.We tested our models and some models we studied, varying the tests conditions. Distance computation-based systems achieve an accuracy from 9 to 15 meters. The SS map-based ones reach an accuracy from 3 to 7 meters.Locations history allows a learning system to build a mobile terminals mobility model, allowing to predict further moves by comparing new moves to the model. We propose to model mobility through Markov models and bayesian networks. We add a threshold to these models to determine a mobility-related policy to the terminal. Accuracy of the models vary according to the threshold value and the order of the Markov model. However, the models reach 75\% good guesses when trying to predict a terminal's move. Such accuracy allows to consider handover anticipation by applying an adequate policy.La démocratisation des terminaux mobiles et l'accroissement des débits disponibles permettent d'envisager de nouvelles applications, en particulier relatives au contextes. Celles-ci nécessitent d'assurer la continuité des services et la détection de la position du terminal mobile. Nous proposons d'une part la géolocalisation des terminaux et, d'autre part, la prédiction de la mobilité.Les systèmes satellites ne fonctionnant pas à l'intérieur des bâtiments, nous basons nos travaux sur les réseaux Wi-Fi. Deux méthodologies se démarquent pour localiser un terminal Wi-Fi : l'une repose sur une cartographie des puissances, l'autre repose sur le calcul des distances entre le terminal et des points dont les coordonnées sont connues. Chaque modèle ayant ses points faibles, nous les avons combinés pour améliorer la précision finale.Nous proposons un premier modèle qui calcule les distances entre le terminal mobile et des points d'accès en se basant sur la puissance du signal reçu. Il en déduit la position du terminal par calcul. Le second modèle proposé restreint la recherche à une zone homogène grâce à la cartographie des puissances avant d'utiliser le premier modèle.Nous avons expérimenté nos modèles ainsi que les modèles fondamentaux de l'état de l'art en étendant leurs conditions d'application. Les résultats des systèmes basés sur la propagation des ondes sont de l'ordre de 9 à 15 mètres d'erreur. Les modèles basés sur une cartographie permettent quant-à-eux d'atteindre une précision de l'ordre de 3 à 7 mètres selon les conditions.L'historique des positions permet à un système d'apprentissage d'acquérir un modèle des déplacements des terminaux puis de prédire les déplacements futurs par l'étude et la comparaison du modèle obtenu à des déplacements ultérieurs. Nous avons proposé en particulier d'employer les chaînes de Markov et les réseaux bayésiens pour effectuer l'apprentissage et la prédiction de la mobilité. Nous avons enrichi ces modèles d'un seuil qui détermine le choix des politiques à appliquer en fonction des déplacements du terminal. La précision de nos modèles est variable en fonction des paramètres d'ordre et de seuil mais permet d'atteindre des taux de réussite de la prédiction de 75%. Cette précision permet d'envisager l'anticipation des handovers et l'application d'une politique appropriée

Enhancing Data Collection in Vehicular Network Through Clustering Optimization

International audienceIn this paper, we present a novel approach to enhance data collection in Vehicular Ad-Hoc NETworks (VANETs). VANETs are a growing area of interest due to their unique characteristics and challenges, such as rapidly changing topology and frequent network disruptions. Efficient data collection is a critical issue in vehicular networks and has therefore become a focus of research. To address this challenge, we propose a stable clustering optimization solution based on adaptive multiple metrics. The cluster head selection is done based on both mobility metrics, such as position and relative speed, and Quality of Service (QoS) metrics, such as neighborhood degree and link quality. The proposed solution has been tested and evaluated through simulations using a vehicular mobility simulator in a realistic urban environment. The results show that the proposed approach provides more stable clusters with higher QoS, and allows for the selection of the appropriate cluster head to collect data from the vehicles and forward it to the destination

DisCo: A Multiagent 3D Coordinate System for Lattice Based Modular Self-Reconfigurable Robots

International audienceLocalizing each module in a modular self-reconfigurable robot (MSR) is of paramount importance. In MSR, the communication graph is directly mapped to the real topology which makes the localization problem easy to solve. However, some types of connectors can lose the orientation of the modules, making the problem intractable. In this work, we propose to build a coordinate system for 3D lattice-based modular robots using a multiagent system.We present DisCo algorithm, that uses one agent per module which can only communicate with its connected neighbors and that does not need a central coordination system. We show that the agents can tackle any kinds of 3D lattice and we illustrate it with a Face Centered Cubic lattice (12 neighbors) and a cubic lattice (6 neighbors). Using communications and only four states, DisCo can also deduce the orientation of modules if the connectors do not provide this information

Scaffold-based Asynchronous Distributed Self-reconfiguration by Continuous Module Flow

International audienceDistributed self-reconfiguration in large-scale modular robots is a slow process and increasing its speed a major challenge. In this article, we propose an improved and asynchronous version of a previously proposed distributed self-reconfiguration algorithm to build a parametric scaffolding structure. This scaffold can then be coated to form the desired final object. The scaffolding is built through a continuous feeding of modules into the growing shape from an underneath reserve of modules which shows a reconfiguration time improved by a factor of $\sqrt[3]{N}$ compared to the previous and synchronous version of the algorithm, therefore attaining an $O(N^{1/3})$ reconfiguration time, with $N$ the number of modules in the system.Our algorithm uses a local motion coordination algorithm and pipelining techniques to ensure that modules can traverse the structure without collisions or creating deadlocks.Last but not least, our algorithm manages uncertainty in the motion duration of modules without negatively impacting reconfiguration time

Solving {MBMS RRM} problem by metaheuristics

International audienceMultimedia Broadcast Multicast Service system supports efficient diffusion of multicast multimedia services in cellular networks. Our previous work shows that the radio resource management problem for MBMS can be modeled as a combinatorial optimization problem which tries to find optimal assignment of power and channel codes [1]. In this paper, we propose to solve such problem by using metaheuristic algorithm: Tabu Search (TS). In our work, we modify the general TS algorithm and map it onto our model. We also extend the classic TS procedure by proposing a tabu repair mechanism, which helps to explore new candidate solutions. The proposed algorithm is compared with two other metaheuristics: Greedy Local Search (GLS) and Simulated Annealing (SA). Simulations show that, within acceptable amount of time, TS can find better solution than GLS and SA

Modeling and Fitness Landscape Analysis for Flexible MBMS Radio Resource Allocation

International audienceMultimedia traffic is constantly increasing and will soon dominate traffic flows in radio networks. The Multimedia Broadcast Multicast Service (MBMS) system provides efficient mechanisms for multimedia multicast services in mobile networks. We develop a flexible model to perform dynamic radio resource allocation for MBMS service by using metaheuristics approach. We conduct fitness landscape analysis to study the characteristics of the proposed problem, which helps us to select appropriate search strategy. Simulation results show that the proposed algorithm provides better performance than existing algorithms

Deterministic Scaffold Assembly By Self-Reconfiguring Micro-Robotic Swarms

International audienceThe self-reconfiguration of large swarms of modular robotic units from one object into another is an intricate problem whose critical parameter that must be optimized is the time required to perform a transformation. Various optimizations methods have been proposed to accelerate transformations, as well as techniques to engineer the shape itself, such as scaffolding which creates an internal object structure filled with holes for easing the motion of modules. In this paper, we propose a novel deterministic and distributed method for rapidly constructing the scaffold of an object from an organized reserve of modules placed underneath the reconfiguration scene. This innovative scaffold design is parameterizable and has a face-centered-cubic lattice structure made from our rotating-only micro-modules. Our method operates at two levels of planning, scheduling the construction of components of the scaffold to avoid deadlocks at one level, and handling the navigation of modules and their coordination to avoid collisions in the other. We provide an analysis of the method and perform simulations on shapes with an increasing level of intricacy to show that our method has a reconfiguration time complexity of time steps for a subclass of convex shapes, with N the number of modules in the shape. We then proceed to explain how our solution can be further extended to any shape

Optimization of Radio Resource Allocation for Multimedia Multicast in Mobile Networks

International audienceIn this paper we present a mathematical modeling of Radio Resource Management (RRM) for multicast service diffusion based on Multimedia Broadcast Multicast Service (MBMS) standard. In this model, a flexible allocation approach named F2R2M is proposed, combining three candidate transport channels with scalable video transmission technology. The allocation procedure is implemented based on simulated annealing algorithm with a two-dimensional optimization objective and a lexicographic order evaluation criteria. Experiments prove that, comparing with existing channel allocation approaches, F2R2M obtains allocation solution with equal QoS and lower transmission power consumption. Moreover, it reduces the possibility of achieving saturation of power or channelization codes when simulation scenarios have more users and heavy traffic load