Biased Random Walk Model to estimate Routing Performance in Wireless Sensor Networks

International audienceLes réseaux de capteurs sans fils sont constitués d'un grand nombre de noeuds assujettis `à de sévères contraintes en terme d'énergie, de capacité de traitement et de communication. Dans ce contexte, afin de réduire la complexité, un des défis majeurs rencontrés dans ce type de réseau est le calcul des routes et la mise en oeuvre de schémas de routage efficaces tout en minimisant la quantité d'information utilisée sur l' état du système. De nombreux travaux ont ́etudié ce compromis de façon qualitative ou grâce à des simulations. Nous proposons un modèle basé sur la théorie de la marche aléatoire pour estimer analytiquement ce compromis en considérant plus particulièrement l'influence du degré de connaissance de l'état du système que posséde un noeud sur le temps moyen de collecte dans un réseau de capteurs sans fils

Random Walk Based Routing Protocol for Wireless Sensor Networks

International audienceIn recent years, design of wireless sensor networks using methodologies and mechanisms from other disciplines has gained popularity for addressing many networking aspects and providing more flexible and robust algorithms. We address in this paper the problem of random walk to model routing for data gathering in wireless sensor networks. While at first glance, this approach may seem to be overly simplistic and highly inefficient, many encouraging results that prove its comparability with other approaches have been obtained over the years. In this approach, a packet generated from a given sensor node performs a random motion until reaching a sink node where it is collected. The objective of this paper is to give an analytical model to evaluate the performance of the envisioned routing scheme with special attention to two metrics: the mean system data gathering delay and the induced spatial distribution of energy consumption. The main result shows that this approach achieves acceptable performance for applications without too stringent QoS requirements provided that the ratio of sink nodes over the total number of sensor nodes is carefully tuned

On the data delivery delay taken by random walks in wireless sensor networks

International audienceIn recent years, the use of random walk techniques in wireless sensor networks has attracted considerable interest among numerous research efforts. The popularity of this approach is attributed to the natural properties of random walks such as locality, simplicity, low-overhead and inherent robustness. However, throughout the variety of research works that assess the effectiveness of random walk techniques, most results are derived from a qualitative view or by means of simulations. Furthermore, when analytical tools are used, the obtained results often provide bounds on various performance metrics of interest, which may have little consequences for practical applications. Instead, our goal in this paper is to quantify the effectiveness of such techniques based on the derivation of closed-form expressions. In particular, we focus on the data delivery delay taken for the random walk to deliver messages from sensor to sink nodes and study its statistics through closed-form derivations

Slowdown of BCM plasticity with many synapses

During neural development sensory stimulation induces long-term changes in the receptive field of the neurons that encode the stimuli. The Bienenstock-Cooper-Munro (BCM) model was introduced to model and analyze this process computationally, and it remains one of the major models of unsupervised plasticity to this day. Here we show that for some stimulus types, the convergence of the synaptic weights under the BCM rule slows down exponentially as the number of synapses per neuron increases. We present a mathematical analysis of the slowdown that shows also how the slowdown can be avoided

Weight dependence in BCM leads to adjustable synaptic competition

Models of synaptic plasticity have been used to better understand neural development as well as learning and memory. One prominent classic model is the Bienenstock-Cooper-Munro (BCM) model that has been particularly successful in explaining plasticity of the visual cortex. Here, in an effort to include more biophysical detail in the BCM model, we incorporate 1) feedforward inhibition, and 2) the experimental observation that large synapses are relatively harder to potentiate than weak ones, while synaptic depression is proportional to the synaptic strength. These modifications change the outcome of unsupervised plasticity under the BCM model. The amount of feed-forward inhibition adds a parameter to BCM that turns out to determine the strength of competition. In the limit of strong inhibition the learning outcome is identical to standard BCM and the neuron becomes selective to one stimulus only (winner-take-all). For smaller values of inhibition, competition is weaker and the receptive fields are less selective. However, both BCM variants can yield realistic receptive fields

Transcranial modulation of brain oscillatory responses: A concurrent tDCS–MEG investigation

The physiological mechanisms underlying the effects of transcranial direct current stimulation (tDCS) are still largely unknown. To provide novel insight into the neurobiology of tDCS, stimulation was applied concurrently with Magnetoencephalography (MEG). This occurred while participants completed a visuomotor task before, during and after stimulation. Motor beta band (15–30 Hz) and visual gamma band (30–80 Hz) responses were localised using Synthetic Aperture Magnetometry (SAM). The resulting evoked and induced brain oscillatory responses were analysed. A significant reduction of average power was observed in the visual gamma band for anodal compared to sham stimulation. The magnitude of motor evoked responses was also demonstrated to be modulated by anodal tDCS. These results highlight that MEG can be used to draw inferences on the cortical mechanisms of DC stimulation

Transcranial Magnetic Stimulation for the treatment of tinnitus: Effects on cortical excitability

<p>Abstract</p> <p>Background</p> <p>Low frequency repetitive transcranial magnetic stimulation (rTMS) has been proposed as an innovative treatment for chronic tinnitus. The aim of the present study was to elucidate the underlying mechanism and to evaluate the relationship between clinical outcome and changes in cortical excitability. We investigated ten patients with chronic tinnitus who participated in a sham-controlled crossover treatment trial. Magnetic-resonance-imaging and positron-emission-tomography guided 1 Hz rTMS were performed over the auditory cortex on 5 consecutive days. Active and sham treatments were separated by one week. Parameters of cortical excitability (motor thresholds, intracortical inhibition, intracortical facilitation, cortical silent period) were measured serially before and after rTMS treatment by using single- and paired-pulse transcranial magnetic stimulation. Clinical improvement was assessed with a standardized tinnitus-questionnaire.</p> <p>Results</p> <p>We noted a significant interaction between treatment response and changes in motor cortex excitability during active rTMS. Specifically, clinical improvement was associated with an increase in intracortical inhibition, intracortical facilitation and a prolongation of the cortical silent period. These results indicate that intraindividual changes in cortical excitability may serve as a correlate of response to rTMS treatment.</p> <p>Conclusion</p> <p>The observed alterations of cortical excitability suggest that low frequency rTMS may evoke long-term-depression like effects resulting in an improvement of subcortical inhibitory function.</p

A family of high speed digital GaAs gate arrays with an application for 432 MBit/S synchronous transmission

A GaAs gate array family is fabricated with Thomson Composants Microondes Self Aligned Gallium Arsenide process with 0.8 um Leff E/D MESFETs, 3 metal layers on 4" wafers. The complexity ranges from 3.000 to 30.000 DCFL 2 inputs NOR gates. The unloaded gate delay is 70 ps with a power of 0.3 mW. Two personnalizations of the 3K array have been designed by the Laboratoire d'Elecyronique de Rennes for an HDTV system. They consist in 11:1 serializer and 1:11 deserializer with a throughput of 432 MBit/s