Efficient support of the upcoming massive number of IoT devices

National audienc

A bridging-based solution for efficient multicast support in wireless mesh networks

Proceedings of: The 34th Annual IEEE Conference on Local Computer Networks (LCN 2009), October 20-23, 2009, Zurich, SwitzerlandWireless mesh networking is a promising, cost effective and efficient technology for realizing backhaul networks supporting high quality services. In such networks, multicast data are transmitted blindly without any mechanism protecting data from loss, ensuring data reception, and optimizing channel allocation. The multicast services may undergo, then, very high data loss ratio which is exacerbated with the number of hops. In this paper, we propose a Reliable Multicast Distribution System (RMDS) to optimize multicast packets transmission in bridged networks. Relying on a modification of the IGMP snooping protocol, RMDS enables reliable services provisioning support in common wireless mesh networks. In particular, RMDS only exploits the local knowledge of a particular node to compute the multicast tree, which significantly reduces the signalling overhead in comparison with network layer and overlay solutions. Simulation results elucidate that RMDS optimizes resources’ allocation by reducing significantly the network load, the media access delay and the data drop rate compared to the classical approach, which is based on the combination of spanning tree algorithm and IGMP snooping protocol.European Community's Seventh Framework ProgramPublicad

DVB-T2 Simulation Model for OPNET

DVB-T2 is offering a new way for broadcasting value-added services to end users, such as High Denition (HD) TV and 3D TV. Thanks to the advances made in digital signal processing, and specically in channel coding, DVB- T2 brings an increased transfer capacity of 50% and a new exibility in services' broadcasting in contrast with the rst generation DVB-T standard. As DVB-T2 is still in deployment's test, simulation model could be an interesting way to evaluate the performance of this network in supporting new value-added services. In this paper, we describe the new features and enhancements we have integrated within the DVB-T2 module in OPNET, and in particular: (i) a realistic physical model;(ii) an MPEG-TS layer with an IP encapsulator;(iii) hierarchical application layer ables to use pcap traces to simulate real video traces. Also, we include an extensive simulation campaign in order to well understand the performance of DVB-T2 networks

DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs

In addition to General Purpose Processors (GPP), Multicore SoCs equipping modern mobile devices contain specialized Digital Signal Processor designed with the aim to provide better performance and low energy consumption properties. However, the experimental measurements we have achieved revealed that system overhead, in case of DSP video decoding, causes drastic performances drop and energy efficiency as compared to the GPP decoding. This paper describes DyPS, a new approach for energy-aware processor switching (GPP or DSP) according to the video quality . We show the pertinence of our solution in the context of adaptive video decoding and describe an implementation on an embedded Linux operating system with the help of the GStreamer framework. A simple case study showed that DyPS achieves 30% energy saving while sustaining the decoding performanc

Gestion des accès massifs des équipements dans les réseaux NB-IoT : une stratégie basée sur l'apprentissage par renforcement

International audienc

On Combining Reinforcement Learning and Monte Carlo for Dynamic Virtual Network Embedding

Demo paperInternational audienceNetwork slicing is one of the key building blocks in the evolution towards "zero touch networks". Indeed, this will allow 5G and beyond 5G networks to deploy services dynamically, on the same substrate network, regardless of their constraints. In this demo, we introduced a platform for dynamic virtual network embedding, a problem class known to be NP-hard. The proposed solution is based on a combination of a deep reinforcement learning strategy and a Monte Carlo (MC) approach. The idea here is to learn to generate, using a Deep Q-Network (DQN), a distribution of the placement solution, on which a MC-based search technique is applied. This makes the agent's exploration of the solution space more efficient

An Advanced Coordination Protocol for Safer and more Efficient Lane Change for Connected and Autonomous Vehicles

In this paper we will explore novel ways of utilizing inter-vehicle and vehicle to infrastructure communication technology to achieve a safe and efficient lane change manoeuvre for Connected and Autonomous Vehicles (CAVs). The need for such new protocols is due to the risk that every lane change manoeuvre brings to drivers and passengers lives in addition to its negative impact on congestion level and resulting air pollution, if not performed at the right time and using the appropriate speed. To avoid this risk, we design two new protocols, one is built upon and extends an existing protocol, and it aims to ensure safe and efficient lane change manoeuvre, while the second is an original lane change permission management solution inspired from mutual exclusion concept used in operating systems. This latter complements the former by exclusively granting lane change permissions in a way that avoids any risk of collision. Both protocols are being implemented using computer simulation and the results will be reported in a future work

How to Accommodate Network Slicing & Network Neutrality?: A View from ERMINE Team

Slicing is seen as one of the key characteristics of 5G and beyond networks but seems in apparent contradiction with neutrality principles promoted worldwide. We discuss the two contradictory but considered compulsory notions notions and propose how they can be accommodated

Quality of Experience estimation for Adaptive HTTP/TCP video streaming using H.264/AVC

International audienceVideo services are being adopted widely in both mobile and fixed networks. For their successful deployment, the content providers are increasingly becoming interested in evaluating the performance of such traffic from the final users' perspective, that is, their Quality of Experience (QoE). For this purpose, subjective quality assessmentmethods are costly and can not be used in real time. Therefore, automatic estimation of QoE is highly desired. In this paper, we propose a noreference QoE monitoringmodule for adaptive HTTP streaming using TCP and the H.264 video codec. HTTP streaming using TCP is the popular choice of many web based and IPTV applications due to the intrinsic advantages of the protocol. Moreover, these applications do not suffer from video data loss due to the reliable nature of the transport layer. However, there can be playout interruptions and if adaptive bitrate video streaming is used then the quality of video can vary due to lossy compression. Our QoE estimation module, based on Random Neural Networks, models the impact of both factors. The results presented in this paper show that our model accurately captures the relation between them and QoE