Simulation Module and Tools for XDense Sensor Network

Workshop on NS-3 (WNS3'17). 13 to 14, Jun, 2017. Porto, Portugal.We present a NS-3 module developed for wired 2D mesh grid sensor network systems, that resemble Network-on-Chip architectures. It has been designed to enable complex feature extraction from sensed data in realtime with distributed processing. We provide the design specifications, communication and processing delay models and a high level system model for XDense using NS-3. We validate our module by comparing its performance with a hardware implementation.info:eu-repo/semantics/publishedVersio

Extensive Analysis of a Real-Time Dense Wired Sensor Network Based on Traffic Shaping

XDense is a novel wired 2D mesh grid sensor network system for application scenarios that benefit from densely deployed sensing (e.g., thousands of sensors per square meter). It was conceived for cyber-physical systems that require real-time sensing and actuation, like active flow control on aircraft wing surfaces. XDense communication and distributed processing capabilities are designed to enable complex feature extraction within bounded time and in a responsive manner. In this article, we tackle the issue of deterministic behavior of XDense. We present a methodology that uses traffic-shaping heuristics to guarantee bounded communication delays and the fulfillment of memory requirements. We evaluate the model for varied network configurations and workload, and present a comparative performance analysis in terms of link utilization, queue size, and execution time. With the proposed traffic-shaping heuristics, we endow XDense with the capabilities required for real-time applications

A module for the XDense architecture in ns-3

Demo in Workshop on ns-3 (WNS3 2015). 13 to 14, May, 2015. Castelldefels, Spain.The acquisition of data regarding some dynamic phenomena can require extremely dense deployments of sensors and high sampling rates. We propose XDense [1], a wired mesh grid sensor network architecture (see Figure 1a) tailored for scenarios that benefit from thousands of sensors per square meter. XDense has scalable network topology and it enables complex feature extraction in real-time from the observed phenomena, by exploiting distributed processing capabilities and inter-node communication, the latter being represented in Figure 1b

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

Searches for Nonresonant Higgs Boson Pair Production and Long-Lived Particles at the LHC and Machine-Learning Solutions for the High-Luminosity LHC Era

This thesis presents two physics analyses using 137 fb−1 proton-proton collision data collected by the CMS experiment at √s = 13 TeV, along with a series of machine-learning solutions to extend the physics program at the LHC and to address the computational challenges in the High-Luminosity LHC era. The first analysis searches for nonresonant Higgs boson pair production in final states with two photons and two bottom quarks, with no significant deviation from the background-only hypothesis observed. The observed (expected) upper limit on the product of the Higgs boson pair production cross section and branching fraction into bb&#773;γγ is 0.67 (0.45) fb, corresponding to 7.7 (5.2) times the Standard Model prediction. The modifier of the Higgs trilinear self-coupling is constrained within the range -3.3 &lt; κλ &lt; 8.5. The modifier for coupling between a pair of Higgs bosons and a pair of vector bosons, along with the 2-dimensional constraint of the modifiers of Higgs self-coupling and Yukawa coupling, are also reported. A graph-based algorithm to identify boosted H → bb&#773; jets to improve future Higgs search is presented. The second analysis searches for long-lived supersymmetry particles decaying to photons and gravitinos in the context of gauge-mediated supersymmetry breaking model. Results are presented in terms of 95% confidence level expected exclusion limits on the masses and proper decay lengths of the neutralino, which exceed the limits from the previous searches by up to 100 GeV for the neutralino mass and by five times for the neutralino proper decay length. A strategy for model-independent new physics searches is presented with an anomaly trigger based on unsupervised learning algorithms that can be deployed in both the high-level trigger and the Level-1 trigger in CMS. Three other machine-learning solutions are presented to address the computational challenges in the HL-LHC era: a layer based on multi-modal deep neural networks that can reduce the false-positive events selected by the trigger by over one order of magnitude while retaining 99% of signal events, a full-event simulation algorithm based on recurrent generative adversarial networks that has potential to replace traditional simulation method while being five orders of magnitude faster, and a fast simulation algorithm for specific analyses based on encoder-decoder architecture that would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow.</p