Bias reduction in traceroute sampling: towards a more accurate map of the Internet

Traceroute sampling is an important technique in exploring the internet router graph and the autonomous system graph. Although it is one of the primary techniques used in calculating statistics about the internet, it can introduce bias that corrupts these estimates. This paper reports on a theoretical and experimental investigation of a new technique to reduce the bias of traceroute sampling when estimating the degree distribution. We develop a new estimator for the degree of a node in a traceroute-sampled graph; validate the estimator theoretically in Erdos-Renyi graphs and, through computer experiments, for a wider range of graphs; and apply it to produce a new picture of the degree distribution of the autonomous system graph.Comment: 12 pages, 3 figure

Demonstration of the synchrotron-type spectrum of laser-produced Betatron radiation

Betatron X-ray radiation in laser-plasma accelerators is produced when electrons are accelerated and wiggled in the laser-wakefield cavity. This femtosecond source, producing intense X-ray beams in the multi kiloelectronvolt range has been observed at different interaction regime using high power laser from 10 to 100 TW. However, none of the spectral measurement performed were at sufficient resolution, bandwidth and signal to noise ratio to precisely determine the shape of spectra with a single laser shot in order to avoid shot to shot fluctuations. In this letter, the Betatron radiation produced using a 80 TW laser is characterized by using a single photon counting method. We measure in single shot spectra from 8 to 21 keV with a resolution better than 350 eV. The results obtained are in excellent agreement with theoretical predictions and demonstrate the synchrotron type nature of this radiation mechanism. The critical energy is found to be Ec = 5.6 \pm 1 keV for our experimental conditions. In addition, the features of the source at this energy range open novel perspectives for applications in time-resolved X-ray science.Comment: 5 pages, 4 figure

Analytical modeling of the detection capability in ultra-low power wireless sensor networks

This paper addresses the problem of target detection with unattended Wireless Sensor Networks (WSNs) used for monitoring areas of interest. As battery energy depletion is very critical, especially in long-lasting surveillance scenarios, an attractive approach consists in making the wireless nodes switch on and off their sensing module according to given duty cycles. This operation has an impact on the network lifetime and the probability of target missed detection (Pmd), which mainly depends on the number of deployed nodes and the sensing duty cycle. In order to optimize the system parameters according to performance objectives, we derive an analytical model which allows to evaluate the Pmd, under the assumption of random node deployment. Then, we show different performance indicators to assess the detection capability of the system, not only for single-target detection but also in the case of a multiple-target detection scenario

Energy-efficient mobile target detection in wireless sensor networks with random node deployment and partial coverage

This paper addresses the problem of engineering energy-efficient target detection applications, using unattended Wireless Sensor Networks (WSNs) with random node deployment and partial coverage, for long-lasting surveillance of areas of interest. As battery energy depletion is a crucial issue, an effective approach consists in switching on and off, according to proper duty cycles, sensing and communication modules of wireless sensor nodes. Making these modules work in an intermittent fashion has an impact on (i) the latency of notification transmission (depending on the communication duty cycle), (ii) the probability of missed target detection (depending on the number of deployed nodes, the sensing duty cycle, and the number of incoming targets), and (iii) the delay in detecting an incoming target. In order to optimize the system parameters to reach given performance objectives, we first derive an analytical framework which allows us to evaluate the probability of missed target detection (in the presence of either single or multiple incoming targets), the notification transmission latency, the detection delay, and the network lifetime. Then, we show how this ‘‘toolbox’’ can be used to optimally configure system parameters under realistic performance constraints

Engineering energy-efficient target detection applications in wireless sensor networks

This paper addresses the problem of engineering energy-efficient target detection applications using unattended Wireless Sensor Networks (WSNs) for long-lasting surveillance of areas of interest. As battery energy depletion is an issue in this context, an approach consists of switching on and off sensing and communication modules of wireless sensors according to duty cycles. Making these modules work in an intermittent fashion impacts (i) the latency of notification transmission (depending on the communication duty cycle) and (ii) the probability of missed target detection (depending on the number of deployed nodes and the sensing duty cycle). In order to optimize the system parameters according to performance objectives, we first derive an analytical engineering toolkit which evaluates the probability of missed detection (Pmd), the notification transmission latency (D), and the network lifetime (¿) under the assumption of random node deployment. Then, we show how this toolbox can be used to optimally configure system parameters under realistic performance constraints

Method and device for configuring a network of removed wireless sensors

Abstract (en) The method involves defining performance criteria, and defining characteristics of a geographical area and characteristics of unattended ground sensors in the area. A number of nodes is allocated to the area, and an area optimization process is applied in the area. The number of nodes is increased or the performance criteria defined in the area, where the performance criteria that are not satisfied are modified. The area optimization process is reproduced in the area with new number of nodes or new performance criteria, and a determined configuration at each node in the area is applied. Independent claims are also included for the following: (1) a device for configuring a network of removed wireless sensors (2) a computer program comprising a set of instructions for implementing a method for configuring a network of removed wireless sensors. Abstract (fr) Le procédé de configuration d'un réseau de capteurs sans fils déposés comporte les étapes suivantes : 1 Définir (étape 12) des critères de performance constituant des contraintes (CPC), avec des valeurs seuil associées (CPC*), et au moins un critère de performance à optimiser (CPO), pour au moins une zone (Z i ) à équiper de noeuds, chaque critère de performance étant défini par un modèle 2 Définir (étape 10) pour la ou chaque zone (Z i ) a. Des caractéristiques (d) de la zone b. Des caractéristiques (E j ) des capteurs dans la zone 3 Allouer (étape 10) à la ou chaque zone (Z i ) à équiper, un nombre de noeuds 4 Appliquer (étape 22) un processus d'optimisation par zone sur la ou chaque zone (Z i ), 5 Augmenter (16) le nombre de noeuds ou modifier (14) les critères de performance (CPC* et CPO) définis dans la ou chaque zone où les critères de performance ne sont pas satisfaits et reproduire dans ces zones le processus d'optimisation par zone avec le nouveau nombre de noeuds ou les nouveaux critères de performance (CPC* et CPO), et 6 Appliquer la configuration déterminée à chaque noeud dans la ou chaque zone