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

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

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

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 tooling for configuring a network of unattended ground wireless sensors

The method for configuring a network of deposited wireless sensors. In certain aspects, these methods include defining performance criteria forming constraints, with associated threshold values, and at least one performance criterion to be optimized, for at least one zone to be equipped with nodes, each performance criterion being defined by a model; defining for said or each zone; the allocation to said or each zone to be equipped, a number of nodes; applying an optimization process per zone on said or each zone; increasing the number of nodes or modifying the performance criteria defined in said or each zone where the performance criteria are not met and reproducing in these zones the optimization process per zone with the new number of nodes or the new performance criteria, and applying the configuration determined at each node in said or each zone