A GPS-less on-demand mobile sink-assisted datacollection in wireless sensor networks

International audienceThe autonomous data collector is a role recentlyintroduced to improve the performance of Wireless SensorNetworks. When a prompt response for data processing andoffloading is necessary, i.e. in the case of event-driven networks,a mobile flying sink could be a good option for that role.In this paper, we introduce FreeFall, a distributed algorithmfor the autonomous navigation of a mobile collector through aWSN for on-demand data offloading that does not rely on anabsolute coordinate system. We show that, under fairly commoncircumstances, it is possible to set the trajectory of the mobilesink and fulfill the offloading requests without the needs ofadditional equipment installed on nodes.We show how our systemis preferable over more classical routing solutions especially in thepresence of localized generation of large amounts of information

Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

[EN] Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants' concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.This work has been partially carried out in the framework of the DIVINA Challenge Team, which is funded under the Labex MS2T program. Labex MS2T is supported by the French Government, through the program "Investments for the Future" managed by the National Agency for Research (Reference: ANR-11-IDEX-0004-02). This work was also supported by the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a Retos de la Sociedad, Proyecto I+D+I TEC2014-52690-R," the "Programa de Becas SENESCYT de la Republica del Ecuador," and the Research Direction of University of Cuenca.Alvear-Alvear, Ó.; Zema, NR.; Natalizio, E.; Tavares De Araujo Cesariny Calafate, CM. (2017). Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility. Journal of Advanced Transportation. 2017:1-14. https://doi.org/10.1155/2017/8204353S1142017Seaton, A., Godden, D., MacNee, W., & Donaldson, K. (1995). Particulate air pollution and acute health effects. The Lancet, 345(8943), 176-178. doi:10.1016/s0140-6736(95)90173-6McFrederick, Q. S., Kathilankal, J. C., & Fuentes, J. D. (2008). Air pollution modifies floral scent trails. Atmospheric Environment, 42(10), 2336-2348. doi:10.1016/j.atmosenv.2007.12.033Mage, D., Ozolins, G., Peterson, P., Webster, A., Orthofer, R., Vandeweerd, V., & Gwynne, M. (1996). Urban air pollution in megacities of the world. Atmospheric Environment, 30(5), 681-686. doi:10.1016/1352-2310(95)00219-7Mayer, H. (1999). Air pollution in cities. Atmospheric Environment, 33(24-25), 4029-4037. doi:10.1016/s1352-2310(99)00144-2Kanaroglou, P. S., Jerrett, M., Morrison, J., Beckerman, B., Arain, M. A., Gilbert, N. L., & Brook, J. R. (2005). Establishing an air pollution monitoring network for intra-urban population exposure assessment: A location-allocation approach. Atmospheric Environment, 39(13), 2399-2409. doi:10.1016/j.atmosenv.2004.06.049Alvear, O., Zamora, W., Calafate, C., Cano, J.-C., & Manzoni, P. (2016). An Architecture Offering Mobile Pollution Sensing with High Spatial Resolution. Journal of Sensors, 2016, 1-13. doi:10.1155/2016/1458147Adam-Poupart, A., Brand, A., Fournier, M., Jerrett, M., & Smargiassi, A. (2014). Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches. Environmental Health Perspectives, 122(9), 970-976. doi:10.1289/ehp.1306566Pujadas, M., Plaza, J., Terés, J., Artı́ñano, B., & Millán, M. (2000). Passive remote sensing of nitrogen dioxide as a tool for tracking air pollution in urban areas: the Madrid urban plume, a case of study. Atmospheric Environment, 34(19), 3041-3056. doi:10.1016/s1352-2310(99)00509-9Eisenman, S. B., Miluzzo, E., Lane, N. D., Peterson, R. A., Ahn, G.-S., & Campbell, A. T. (2009). BikeNet. ACM Transactions on Sensor Networks, 6(1), 1-39. doi:10.1145/1653760.1653766André, M. (2004). The ARTEMIS European driving cycles for measuring car pollutant emissions. Science of The Total Environment, 334-335, 73-84. doi:10.1016/j.scitotenv.2004.04.070Hu, S.-C., Wang, Y.-C., Huang, C.-Y., & Tseng, Y.-C. (2011). Measuring air quality in city areas by vehicular wireless sensor networks. Journal of Systems and Software, 84(11), 2005-2012. doi:10.1016/j.jss.2011.06.043Dunbabin, M., & Marques, L. (2012). Robots for Environmental Monitoring: Significant Advancements and Applications. IEEE Robotics & Automation Magazine, 19(1), 24-39. doi:10.1109/mra.2011.2181683Hugenholtz, C. H., Moorman, B. J., Riddell, K., & Whitehead, K. (2012). Small unmanned aircraft systems for remote sensing and Earth science research. Eos, Transactions American Geophysical Union, 93(25), 236-236. doi:10.1029/2012eo250005Pajares, G. (2015). Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81(4), 281-330. doi:10.14358/pers.81.4.281Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. doi:10.1016/j.isprsjprs.2014.02.013Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138-146. doi:10.1890/120150Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693-712. doi:10.1007/s11119-012-9274-5Bellvert, J., Zarco-Tejada, P. J., Girona, J., & Fereres, E. (2013). Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture, 15(4), 361-376. doi:10.1007/s11119-013-9334-5Erman, A., Hoesel, L., Havinga, P., & Wu, J. (2008). Enabling mobility in heterogeneous wireless sensor networks cooperating with UAVs for mission-critical management. IEEE Wireless Communications, 15(6), 38-46. doi:10.1109/mwc.2008.4749746Khan, A., Schaefer, D., Tao, L., Miller, D. J., Sun, K., Zondlo, M. A., … Lary, D. J. (2012). Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles. Remote Sensing, 4(5), 1355-1368. doi:10.3390/rs4051355Illingworth, S., Allen, G., Percival, C., Hollingsworth, P., Gallagher, M., Ricketts, H., … Roberts, G. (2014). Measurement of boundary layer ozone concentrations on-board a Skywalker unmanned aerial vehicle. Atmospheric Science Letters, n/a-n/a. doi:10.1002/asl2.496Wang, W., Guan, X., Wang, B., & Wang, Y. (2010). A novel mobility model based on semi-random circular movement in mobile ad hoc networks. Information Sciences, 180(3), 399-413. doi:10.1016/j.ins.2009.10.001Wan, Y., Namuduri, K., Zhou, Y., & Fu, S. (2013). A Smooth-Turn Mobility Model for Airborne Networks. IEEE Transactions on Vehicular Technology, 62(7), 3359-3370. doi:10.1109/tvt.2013.2251686Briante, O., Loscri, V., Pace, P., Ruggeri, G., & Zema, N. R. (2015). COMVIVOR: An Evolutionary Communication Framework Based on Survivors’ Devices Reuse. Wireless Personal Communications, 85(4), 2021-2040. doi:10.1007/s11277-015-2888-yMeier, L., Tanskanen, P., Heng, L., Lee, G. H., Fraundorfer, F., & Pollefeys, M. (2012). PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision. Autonomous Robots, 33(1-2), 21-39. doi:10.1007/s10514-012-9281-4Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82-117. doi:10.1016/j.ins.2013.02.041Stein, M. L. (1999). Interpolation of Spatial Data. Springer Series in Statistics. doi:10.1007/978-1-4612-1494-

COMVIVOR: An Evolutionary Communication Framework Based on Survivors’ Devices Reuse

International audienceMobile devices currently available on the market have a plethoraof features and enough computing power to make them, at the same time,information consumers, forwarders and producers. Since they are also providedwith a set of sensors and usually battery operating, they are perfect candidatesto devise a network infrastructure tailored to function during disruptive events.When everything else fails, they could autonomously reorganize and provide tothe civilians and rescue teams valuable services and information. In this paperwe adapt and enhance a previous designed framework, capable to epidemicallydiuse the proper software updates to its nodes, in order to deploy any kind ofservice as a prompt response to the needs raised in emergency situations. Wefurther propose and integrate a new smart positioning strategy, to speed up thediusion of software updates by also keeping under control the overall networkoverhead. The achieved results show the feasibility of our proposal and howthe dynamics of diusion are enhanced by the smart positioning algorithm

Contrôle de formation d'un réseau de drones à base d'apprentissage par renforcement

International audienceNous présentons dans cet article une solution innovante basée sur un algorithme d'apprentissage par renforcement, le Q-learning, pour le contrôle de formation d'un réseau de drones par un unique opérateur. Pour suivre automatiquement le drone maître, le seul téléguidé, tous les autres n'utilisent que les puissances de signal reçues durant les communications ad hoc. Grâce à ces seules valeurs obtenues en temps-réel, nous montrons que la formation peut être parfaitement maintenue en appliquant notre schéma comportemental. La solution proposée a été implantée sous forme protocolaire et testée sous ns-3. Les expérimentations montrent l'efficacité de notre approche

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011

Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC

Measurements are presented of production properties and couplings of the recently discovered Higgs boson using the decays into boson pairs, H →γ γ, H → Z Z∗ →4l and H →W W∗ →lνlν. The results are based on the complete pp collision data sample recorded by the ATLAS experiment at the CERN Large Hadron Collider at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV, corresponding to an integrated luminosity of about 25 fb−1. Evidence for Higgs boson production through vector-boson fusion is reported. Results of combined fits probing Higgs boson couplings to fermions and bosons, as well as anomalous contributions to loop-induced production and decay modes, are presented. All measurements are consistent with expectations for the Standard Model Higgs boson

Hunt for new phenomena using large jet multiplicities and missing transverse momentum with ATLAS in 4.7 fb−1 of √s=7 TeV proton-proton collisions

Results are presented of a search for new particles decaying to large numbers of jets in association with missing transverse momentum, using 4.7 fb−1 of pp collision data at s√=7TeV collected by the ATLAS experiment at the Large Hadron Collider in 2011. The event selection requires missing transverse momentum, no isolated electrons or muons, and from ≥6 to ≥9 jets. No evidence is found for physics beyond the Standard Model. The results are interpreted in the context of a MSUGRA/CMSSM supersymmetric model, where, for large universal scalar mass m 0, gluino masses smaller than 840 GeV are excluded at the 95% confidence level, extending previously published limits. Within a simplified model containing only a gluino octet and a neutralino, gluino masses smaller than 870 GeV are similarly excluded for neutralino masses below 100 GeV

Search for dark matter in events with a hadronically decaying W or Z boson and missing transverse momentum in pp collisions at s√= 8 TeV with the ATLAS detector

A search is presented for dark matter pair production in association with a W or Z boson in pp collisions representing 20.3  fb−1 of integrated luminosity at s√=8  TeV using data recorded with the ATLAS detector at the Large Hadron Collider. Events with a hadronic jet with the jet mass consistent with a W or Z boson, and with large missing transverse momentum are analyzed. The data are consistent with the standard model expectations. Limits are set on the mass scale in effective field theories that describe the interaction of dark matter and standard model particles, and on the cross section of Higgs production and decay to invisible particles. In addition, cross section limits on the anomalous production of W or Z bosons with large missing transverse momentum are set in two fiducial regions.We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC, and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST, and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR, and VSC CR, Czech Republic; DNRF, DNSRC, and Lundbeck Foundation, Denmark; EPLANET, ERC, and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG, and AvH Foundation, Germany; GSRT and NSRF, Greece; ISF, MINERVA, GIF, DIP, and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF, and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society, and Leverhulme Trust, United Kingdom; U.S. DOE and NSF, United States of America. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK), and BNL (U.S.), and in the Tier-2 facilities worldwide

Measurement of the top quark pair production charge asymmetry in proton-proton collisions at √s = 7 TeV using the ATLAS detector

This paper presents a measurement of the top quark pair ( tt¯ ) production charge asymmetry A C using 4.7 fb−1 of proton-proton collisions at a centre-of-mass energy √s = 7 TeV collected by the ATLAS detector at the LHC. A tt¯ -enriched sample of events with a single lepton (electron or muon), missing transverse momentum and at least four high transverse momentum jets, of which at least one is tagged as coming from a b-quark, is selected. A likelihood fit is used to reconstruct the tt¯ event kinematics. A Bayesian unfolding procedure is employed to estimate A C at the parton-level. The measured value of the tt¯ production charge asymmetry is A C = 0.006 ± 0.010, where the uncertainty includes both the statistical and the systematic components. Differential A C measurements as a function of the invariant mass, the rapidity and the transverse momentum of the tt¯ system are also presented. In addition, A C is measured for a subset of events with large tt¯ velocity, where physics beyond the Standard Model could contribute. All measurements are consistent with the Standard Model predictions

Search for direct third-generation squark pair production in final states with missing transverse momentum and two b-jets in √s=8 TeV pp collisions with the ATLAS detector

The results of a search for pair production of supersymmetric partners of the Standard Model third-generation quarks are reported. This search uses 20.1 fb−1 of pp collisions ats√=8TeV collected by the ATLAS experiment at the Large Hadron Collider. The lightest bottom and top squarks (b˜1andt˜1respectively) are searched for in a final state with large missing transverse momentum and two jets identified as originating from b-quarks. No excess of events above the expected level of Standard Model background is found. The results are used to set upper limits on the visible cross section for processes beyond the Standard Model. Exclusion limits at the 95 % confidence level on the masses of the third-generation squarks are derived in phenomenological supersymmetric R-parity-conserving models in which either the bottom or the top squark is the lightest squark. Theb˜1is assumed to decay viab˜1→b∼0χ1and thet˜1viat˜1→b∼±χ1, with undetectable products of the subsequent decay of the∼±χ1due to the small mass splitting between the∼±χ1and the∼0χ1