Alternative paths computation for congestion mitigation in segment-routing networks

In backbone networks, it is fundamental to quickly protect traffic against any unexpected event, such as failures or congestions, which may impact Quality of Service (QoS). Standard solutions based on Segment Routing (SR), such as Topology-Independent Loop-Free Alternate (TI-LFA), are used in practice to handle failures, but no distributed solutions exist for distributed and tactical congestion mitigation. A promising approach leveraging SR has been recently proposed to quickly steer traffic away from congested links over alternative paths. As the pre-computation of alternative paths plays a paramount role to efficiently mitigating congestions, we investigate the associated path computation problem aiming at maximizing the amount of traffic that can be rerouted as well as the resilience against any 1-link failure. In particular, we focus on two variants of this problem. First, we maximize the residual flow after all possible failures. We show that the problem is NP-Hard, and we solve it via a Benders decomposition algorithm. Then, to provide a practical and scalable solution, we solve a relaxed variant problem, that maximizes, instead of flow, the number of surviving alternative paths after all possible failures. We provide a polynomial algorithm. Through numerical experiments, we compare the two variants and show that they allow to increase the amount of rerouted traffic and the resiliency of the network after any 1-link failure.Comment: 6 page

Optimisation de la qualité de service par l'utilisation de mémoire cache

International audienceNous nous intéresserons dans cet article à l'utilisation de mémoire cache pour améliorer l'utilisation des ressources dans un réseau de capteurs. Plusieurs clients souhaitent consulter la même information au sein d'un réseau contraint en énergie. Afin d'éviter les requêtes redondantes, nous utiliserons un serveur proxy intelligent capable de décider quand transférer la requête au réseau de capteurs ou d'y répondre directement avec des informations mémorisées pour une durée de validité donnée. Nous verrons d'abord comment l'information est mise à la disposition de ce serveur, puis comment les informations venant d'autres couches logiques peuvent contribuer à prendre des décisions de communica- tions et de configuration plus intelligentes, par exemple comment mettre en place une stratégie d'optimisation qui nous permettra d'augmenter la durée de vie du réseau ou la satisfaction utilisateur, exprimée par la fraicheur des informa- tions disponibles. Nous verrons aussi comment maximiser les deux en utilisant des solutions optimales non dominées de Pareto

Deep Reinforcement Learning for Smart Queue Management

With the goal of meeting the stringent throughput and delay requirements of classified network flows, we propose a Deep Q-learning Network (DQN) for optimal weight selection in an active queue management system based on Weighted Fair Queuing (WFQ). Our system schedules flows belonging to different priority classes (Gold, Silver, and Bronze) into separate queues, and learns how and when to dequeue from each queue. The neural network implements deep reinforcement learning tools such as target networks and replay buffers to help learn the best weights depending on the network state. We show, via simulations, that our algorithm converges to an efficient model capable of adapting to the flow demands, producing thus lower delays with respect to traditional WFQ

Tee: Traffic-based Energy Estimators for duty cycled Wireless Sensor Networks

International audience—Energy is classically considered as a critical resource in Wireless Sensor Networks (WSNs). These networks are composed of tiny devices that auto-organize around one or few gateways, which may have various roles from simple reference or traffic sinks to full network orchestrator. Such a gateway could influence the network behavior, for instance by decreasing activity when energy becomes scarce. It however needs to be able to estimate the nodes remaining energy. Indeed, this gateway is on the path of all traffic going in or out the WSN. This traffic sample could be used to acquire a coarse estimate of individual nodes energy consumption. The accuracy of this estimation can then be improved by explicit signaling if needed. This paper presents Tee, a set of such Traffic-based energy estimators that operates at the WSN gateway. We evaluate, by simulation, the accuracy of two such estimators in IEEE 802.15.4 networks running RPL and ContikiMAC, a duty cycled MAC layer. Results show that such silent estimators benefit from information already available at the gateway, such as the routing topology. However, they still underestimate the consumption due to the routing control messages, to the packets strobing, or to contention and collisions and can easily be complemented by lightweight explicit calibrations

Demo Abstract: Automating WSN experiments and simulations

International audience—In wireless sensor networks (WSNs), as in every other discipline, people willing to evaluate the performance of an application or a protocol rely on modeling, simulation or experimentation. Simulations and models produce results for large-scale networks in a reasonable time, but trade representation accuracy for speed and hence ignore many physical and system effects, such as interference from the outside world or race conditions inside the nodes. Experimentation provides more representative and precise results, but is limited to small networks. Besides, they require more effort to be deployed and to collect results. These approaches are therefore complementary and should all be involved in the evaluation, which is seldom true, as it requires duplicating the deployment and data collection processes. In this demonstration, we present MakeSense, a framework that simplifies these tasks for both simulation and real experiments environments by creating a whole experimentation chain from a single JSON description file. By using MakeSense, it is possible to organize the compilation, to orchestrate the firmware deployment, to efficiently collect results and to plot statistics. We illustrate the ease of use and efficiency of the complete MakeSense workflow over a simple RPL-UDP deployment scenario evaluated with the Cooja simulator and the FIT IoT-Lab open testbed

MakeSense: Managing Reproducible WSNs Experiments

International audienceWireless Sensor Networks (WSN) users often use simulation campaigns before real deployment to evaluate performance and to fine- tune application and network parameters. This process requires repeating the same experiments under similar conditions and to collect, parse and present data efficiently. This paper introduces MakeSense: a tool that automates this workflow and that allows reproducing simulations easily by defining the whole experiment and post-processing steps in a single JSON configuration file, easy to share and to modify. MakeSense also provides interfaces to interact with a running simulation, allowing to send external stimuli and to collect data in real time. MakeSense currently runs over the COOJA simulator, but has been built to be easily adapted to other architectures, including real testbeds

Adjacency Matrix-Based Transmit Power Allocation Strategies in Wireless Sensor Networks

In this paper, we present an innovative transmit power control scheme, based on optimization theory, for wireless sensor networks (WSNs) which use carrier sense multiple access (CSMA) with collision avoidance (CA) as medium access control (MAC) protocol. In particular, we focus on schemes where several remote nodes send data directly to a common access point (AP). Under the assumption of finite overall network transmit power and low traffic load, we derive the optimal transmit power allocation strategy that minimizes the packet error rate (PER) at the AP. This approach is based on modeling the CSMA/CA MAC protocol through a finite state machine and takes into account the network adjacency matrix, depending on the transmit power distribution and determining the network connectivity. It will be then shown that the transmit power allocation problem reduces to a convex constrained minimization problem. Our results show that, under the assumption of low traffic load, the power allocation strategy, which guarantees minimal delay, requires the maximization of network connectivity, which can be equivalently interpreted as the maximization of the number of non-zero entries of the adjacency matrix. The obtained theoretical results are confirmed by simulations for unslotted Zigbee WSNs

Ottimizzazione di prestazioni in reti wireless di sensori

Negli ultimi anni si è asssisito ad una sempre più larga diffusione di dispositivi senza fili con capacità di rilevare fenomeni fisici, specialmente grazie ad un miglioramento delle tecniche produttive, che hanno portato alla produzione di dispositivi a basso costo e che garantiscano un basso consumo energetico. Per queste ragioni, c'è un crescente interesse, sia in ambito civile che in ambito militare, per applicazioni basate su questi dispositivi wireless. Anche se le prime applicazioni di sorveglianza erano state sviluppate in ambito militare, al giorno d'oggi un gran numero di applicazioni riguardano il monitoraggio ambientale e industriale in ambito civile. Solitamente, le reti wireless di sensori (Wireless Sensor Networks, WSNs) sono dislocate in ambienti ostili, in cui l'intervento di operatori è spesso difficile o, in casi estremi, impossibile. Risulta quindi di fondamentale importanza che una WSN possa funzionare correttamente per un periodo di tempo sufficientemente lungo, e.g., nell'ordine dei mesi. D'altra parte, la massimizzazione del tempo di vita della rete non deve influire sulle sue prestazioni, che devono rimanere sopra un minimo valore di qualità di servizio (Quality of Service, QoS), che dipende dalla specifica applicazione. Per gestire appropriatamente questi trade-off che si generano, una strategia di ottimizzazione deve essere necessariamente usata. In questa tesi saranno investigate alcune strategie di configurazione delle WSN, basate sulla configurazione ottimale dei parametri chiave dei nodi wireless.During recent years, we have witnessed a large diffusion of wireless devices with sensing capabilities, especially due to the improvements in manufacturing techniques, which have lead to the production of cost-effective and energy-efficients components. For these reasons, there is an increasing interest for both civilian and military applications based on wireless sensing devices. Even if the first monitoring applications were related to surveillance in military applications, nowadays a large number of applications concern industrial or environmental monitoring in civilian scenarios. Usually, wireless sensor networks (WSNs) are supposed to be deployed in harsh environments, where the human intervention of external operators is difficult or, in extreme cases, impossible. It is therefore of paramount importance that a WSN can operate properly for a sufficiently long period of time, e.g., on the order of months. On the other side, the maximization of the lifetime should not affect largely the network performance, which should remain above a minimum required Quality of Service (QoS), depending on the specific application at hand. In order to properly deal with this unavoidable trade-off, an optimization strategy must be adopted. In this thesis, we investigate some strategies for the configuration of WSNs, all based on the optimal tuning of key node parameters