Thwarting Selfish Behavior in 802.11 WLANs

The 802.11e standard enables user configuration of several MAC parameters, making WLANs vulnerable to users that selfishly configure these parameters to gain throughput. In this paper we propose a novel distributed algorithm to thwart such selfish behavior. The key idea of the algorithm is for honest stations to react, upon detecting a selfish station, by using a more aggressive configuration that penalizes this station. We show that the proposed algorithm guarantees global stability while providing good response times. By conducting a game theoretic analysis of the algorithm based on repeated games, we also show its effectiveness against selfish stations. Simulation results confirm that the proposed algorithm optimizes throughput performance while discouraging selfish behavior. We also present an experimental prototype of the proposed algorithm demonstrating that it can be implemented on commodity hardware.Comment: 14 pages, 7 figures, journa

Mobility through Heterogeneous Networks in a 4G Environment

Serving and Managing users in a heterogeneous environment. 17th WWRF Meeting in Heidelberg, Germany, 15 - 17 November 2006. [Proceeding presented at WG3 - Co-operative and Ad-hoc Networks]The increase will of ubiquitous access of the users to the requested services points towards the integration of heterogeneous networks. In this sense, a user shall be able to access its services through different access technologies, such as WLAN, Wimax, UMTS and DVB technologies, from the same or different network operators, and to seamless move between different networks with active communications. In this paper we propose a mobility architecture able to support this users’ ubiquitous access and seamless movement, while simultaneously bringing a large flexibility to access network operators

Resource-on-demand schemes in 802.11 WLANs with non-zero start-up times

Increasing the density of access points is one of the most effective mechanisms to cope with the growing traffic demand in wireless networks. To prevent energy wastage at low loads, a resource-on-demand (RoD) scheme is required to opportunistically (de)activate access points as network traffic varies. While previous publications have analytically modeled these schemes in the past, they have assumed that resources are immediately available when activated, an assumption that leads to inaccurate results and might result in inappropriate configurations of the RoD scheme. In this paper, we analyze a general RoD scenario with N access points and non-zero start-up times. We first present an exact analytical model that accurately predicts performance but has a high computational complexity, and then derive a simplified analysis that sacrifices some accuracy in exchange for a much lower computational cost. To illustrate the practicality of this model, we present the design of a simple configuration algorithm for RoD. Simulation results confirm the validity of the analyses, and the effectiveness of the configuration algorithm

Phenotypic spectrum of MFN2 mutations in the Spanish population

INTRODUCTION: The most common form of axonal Charcot-Marie-Tooth (CMT) disease is type 2A, caused by mutations in the mitochondrial GTPase mitofusin 2 (MFN2). OBJECTIVE: The objective of our study is to establish the incidence of MFN2 mutations in a cohort of Spanish patients with axonal CMT neuropathy. MATERIAL AND METHODS: Eighty-five families with suspected axonal CMT were studied. All MFN2 exons were studied through direct sequencing. A bioenergetics study in fibroblasts was conducted using a skin biopsy taken from a patient with an Arg468His mutation. RESULTS: Twenty-four patients from 14 different families were identified with nine different MFN2 mutations (Arg94Trp, Arg94Gln, Ile203Met, Asn252Lys, Gln276His, Gly296Arg, Met376Val, Arg364Gln and Arg468His). All mutations were found in the heterozygous state and four of these mutations had not been described previously. MFN2 mutations were responsible for CMT2 in 16% +/- 7% of the families studied and in 30.8 +/- 14.2% (12/39) of families with known dominant inheritance. The bioenergetic studies in fibroblasts show typical results of MFN2 patients with a mitochondrial coupling defect (ATP/O) and an increase of the respiration rate linked to complex II. CONCLUSION: It is concluded that mutations in MFN2 are the most frequent cause of CMT2 in this region. The Arg468His mutation was the most prevalent (6/14 families), and our study confirms that it is pathological, presenting as a neuropathy in a mild to moderate degree. This study also demonstrates the value of MFN2 studies in cases of congenital axonal neuropathy, especially in cases of dominant inheritance, severe clinical symptoms or additional symptoms such as optic atrophy

Thwarting selfish behavior in 802.11 WLANs

The 802.11e standard enables user configuration of several MAC parameters, making WLANs vulnerable to users that selfishly configure these parameters to gain throughput. In this paper, we propose a novel distributed algorithm to thwart such selfish behavior. The key idea of the algorithm is for stations to react, upon detecting a misbehavior, by using a more aggressive configuration that penalizes the misbehaving station. We show that the proposed algorithm guarantees global stability while providing good response times. By conducting an analysis of the effectiveness of the algorithm against selfish behaviors, we also show that a misbehaving station cannot obtain any gain by deviating from the algorithm. Simulation results confirm that the proposed algorithm optimizes throughput performance while discouraging selfish behavior. We also present an experimental prototype of the proposed algorithm demonstrating that it can be implemented on commodity hardware.This work was supported by the European Community under the CROWD Project FP7-ICT-318115 and the Centro Universitario de la Defensa under Project CUD2013-05

Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions

Large-scale data resulting from users online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study emergence of the emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite network of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion-classifier developed for this type of texts. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore robustness of these critical states, we design a network automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states

vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

In Press / En PrensaThe virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complexrelationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resourceorchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data(traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithmbased on an actor-critic neural network structure and a classifier to map contexts into resource control decisions.We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over aproduction RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods;(ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning,vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To ourknowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-freesolution that does not need to assume any particular platform or context.This work was partially supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 856950 (5G-TOURS); by Science Foundation Ireland (SFI) through Grant No. 17/CDA/4760; and AEI/FEDER through project AIM under Grant No. TEC2016-76465-C2-1-R. Furthermore, the work is closely related to the EU project DAEMON (Grant No. 101017109)

Demo: vrAIn proof-of-concept: a deep learning approach for virtualized RAN resource control

Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.While the application of the NFV paradigm into the network is proceeding full steam ahead, there is still one last milestone to be achieved in this context: the virtualization of the radio access network (vRAN). Due to the very complex dependency between the radio conditions and the computing resources needed to provide the baseband processing functionality, attaining an efficient resource control is particularly challenging. In this demonstration, we will showcase vrAIn, a vRAN dynamic resource controller that employs deep reinforcement learning to perform resource assignment decisions. vrAIn, which is implemented using an open-source LTE stack over a Linux platform, can achieve substantial savings in the used CPU resources while maintaining the target QoS for the attached terminals and maximizing throughput when there is a deficit of computational capacity.The work of University Carlos III of Madrid was supported by H2020 5G-MoNArch project (grant agreement no. 761445) and H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agreement no. 761536) and 5GROWTH project (grant agreement no. 856709). The work of University of Cartagena was supported by Grant AEI/FEDER TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701