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

A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing

[EN] Recently, Unmanned Aerial Vehicles (UAVs) have become a cheap alternative to sense pollution values in a certain area due to their flexibility and ability to carry small sensing units. In a previous work, we proposed a solution, called Pollution-driven UAV Control (PdUC), to allow UAVs to autonomously trace pollutant sources, and monitor air quality in the surrounding area. However, despite operational, we found that the proposed solution consumed excessive time, especially when considering the battery lifetime of current multi-rotor UAVs. In this paper, we have improved our previously proposed solution by adopting a space discretization technique. Discretization is one of the most efficient mathematical approaches to optimize a system by transforming a continuous domain into its discrete counterpart. The improvement proposed in this paper, called PdUC-Discretized (PdUC-D), consists of an optimization whereby UAVs only move between the central tile positions of a discretized space, avoiding monitoring locations separated by small distances, and whose actual differences in terms of air quality are barely noticeable. We also analyze the impact of varying the tile size on the overall process, showing that smaller tile sizes offer high accuracy at the cost of an increased flight time. Taking into account the obtained results, we consider that a tile size of 100 x 100 meters offers an adequate trade-off between flight time and monitoring accuracy. Experimental results show that PdUC-D drastically reduces the convergence time compared to the original PdUC proposal without loss of accuracy, and it also increases the performance gap with standard mobility patterns such as Spiral and Billiard.This work was partially supported by the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a Retos de la Sociedad, Proyecto I+D+I TEC2014-52690-R", 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), the "Programa de becas SENESCYT de la Republica del Ecuador", and the Research Direction of the University of Cuenca.Alvear-Alvear, Ó.; Tavares De Araujo Cesariny Calafate, CM.; Zema, N.; Natalizio, E.; Hernández-Orallo, E.; Cano, J.; Manzoni, P. (2018). A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing. Mobile Networks and Applications. 23(6):1693-1702. https://doi.org/10.1007/s11036-018-1065-4S16931702236Adam-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. Environ Health Perspect 970(2013):1–19. https://doi.org/10.1289/ehp.1306566Agency U.S.E.P. (2015) Air Quality Index Available: http://cfpub.epa.gov/airnow/index.cfm?action=aqibasics.aqiAlvear O, Calafate CT, Hernández-Orallo E, Cano JC, Manzoni P (2015) Mobile Pollution Data Sensing Using UAVs The 13th International Conference on Advances in Mobile Computing and MultimediaAlvear O, Zamora W, Calafate C, Cano JC, Manzoni P (2016) An architecture offering mobile pollution sensing with high spatial resolution. J Sens:2016Alvear O, Zema NR, Natalizio E, Calafate CT (2017) Using uav-based systems to monitor air pollution in areas with poor accessibility. J Adv Transp:2017Alvear OA, Zema NR, Natalizio E, Calafate CT (2017) A chemotactic pollution-homing uav guidance system. In: 2017 13th international Wireless communications and mobile computing conference (IWCMC). IEEE, pp 2115–2120André M (2004) The artemis european driving cycles for measuring car pollutant emissions. Sci Total Environ 334:73–84Basu P, Redi J, Shurbanov V (2004) Coordinated flocking of uavs for improved connectivity of mobile ground nodes. In: 2004 IEEE Military communications conference, MILCOM, vol 3. IEEE, pp 1628–1634Biomo JDMM, Kunz T, St-Hilaire M (2014) An enhanced gauss-markov mobility model for simulations of unmanned aerial ad hoc networks. In: 2014 7th IFIP Wireless and mobile networking conference (WMNC). IEEE, pp 1–8Bouachir O, Abrassart A, Garcia F, Larrieu N (2014) A mobility model for uav ad hoc network. In: 2014 international conference on Unmanned aircraft systems (ICUAS). IEEE, pp 383–388Cox TH, Nagy CJ, Skoog MA, Somers IA, Warner R Civil uav capability assessmentEisenman SB, Miluzzo E, Lane ND, Peterson RA, Ahn GS, Campbell AT (2009) Bikenet: a mobile sensing system for cyclist experience mapping. ACM Transactions on Sensor Networks (TOSN) 6(1):6Erman AT, van Hoesel L, Havinga P, Wu J (2008) Enabling mobility in heterogeneous wireless sensor networks cooperating with uavs for mission-critical management. IEEE Wirel Commun 15(6):38–46Fayyad U, Irani K (1993) Multi-interval discretization of continuous-valued attributes for classification learningHugenholtz CH, Moorman BJ, Riddell K, Whitehead K (2012) Small unmanned aircraft systems for remote sensing and earth science research. Eos, Trans Amer Geophysical Union 93(25):236–236Illingworth S, Allen G, Percival C, Hollingsworth P, Gallagher M, Ricketts H, Hayes H, adosz H, Crawley PD, Roberts G (2014) Measurement of boundary layer ozone concentrations on-board a Skywalker unmanned aerial vehicle. Atmos Sci Lett 15(4):252–258Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, pp 760–766Khan A, Schaefer D, Tao L, Miller DJ, Sun K, Zondlo MA, Harrison WA, Roscoe B, Lary DJ (2012) Low power greenhouse gas sensors for unmanned aerial vehicles. Remote Sens 4(5):1355–1368Kuiper E, Nadjm-Tehrani S (2006) Mobility models for uav group reconnaissance applications. In: 2006 International conference on wireless and mobile communications (ICWMC’06). IEEE, pp 33–33McFrederick Q, Kathilankal J, Fuentes J (2008) Air pollution modifies floral scent trails. Atmos Environ 42(10):2336–2348MQ131 Ozone Sensor (2017) Datasheet: http://www.sensorsportal.com/downloads/mq131.pdfOrfanus D, de Freitas EP (2014) Comparison of uav-based reconnaissance systems performance using realistic mobility models. In: 2014 6Th international congress on ultra modern telecommunications and control systems and workshops (ICUMT). IEEE, pp 248–253Pajares G (2015) Overview and current status of remote sensing applications based on unmanned aerial vehicles (uavs). Photogram Eng Remote Sens 81(4):281–329Pujadas M, Plaza J, Teres Jx, Artıñano B, Millan 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. Atmos Environ 34(19):3041–3056R Core Team: R (2016) A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/Seaton A, Godden D, MacNee W, Donaldson K (1995) Particulate air pollution and acute health effects. The lancet 345(8943):176–178Stein ML (1999) Statistical interpolation of spatial data: some theory for kriging. Springer, New YorkTeh SK, Mejias L, Corke P, Hu W (2008) Experiments in integrating autonomous uninhabited aerial vehicles(uavs) and wireless sensor networks. In: 2008 Australasian Conference on Robotics and Automation (ACRA 08). The Australian Robotics and Automation Association Inc., Canberra. https://eprints.qut.edu.au/15536/Wan Y, Namuduri K, Zhou Y, Fu S (2013) A smooth-turn mobility model for airborne networks. IEEE Trans Veh Technol 62(7):3359–3370Wang W, Guan X, Wang B, Wang Y (2010) A novel mobility model based on semi-random circular movement in mobile ad hoc networks. Inf Sci 180(3):399–413Zhou B, Xu K, Gerla M (2004) Group and swarm mobility models for ad hoc network scenarios using virtual tracks. In: 2004 IEEE Military communications conference, MILCOM 2004, vol 1. IEEE, pp 289–29

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

A Fast Transient Absorption Study of Co(AcAc)3

The study of transition metal coordination complexes has played a key role in establishing quantum chemistry concepts such as that of ligand field theory. Furthermore, the study of the dynamics of their excited states is of primary importance in determining the de-excitation path of electrons to tailor the electronic properties required for important technological applications. This work focuses on femtosecond transient absorption spectroscopy of Cobalt tris(acetylacetonate) (Co(AcAc)3) in solution. The fast transient absorption spectroscopy has been employed to study the excited state dynamics after optical excitation. Density functional theory coupled with the polarizable continuum model has been used to characterize the geometries and the electronic states of the solvated ion. The excited states have been calculated using the time dependent density functional theory formalism. The time resolved dynamics of the ligand to metal charge transfer excitation revealed a biphasic behavior with an ultrafast rise time of 0.07 ± 0.04 ps and a decay time of 1.5 ± 0.3 ps, while the ligand field excitations dynamics is characterized by a rise time of 0.07 ± 0.04 ps and a decay time of 1.8 ± 0.3 ps. Time dependent density functional theory calculations of the spin-orbit coupling suggest that the ultrafast rise time can be related to the intersystem crossing from the originally photoexcited state. The picosecond decay is faster than that of similar cobalt coordination complexes and is mainly assigned to internal conversion within the triplet state manifold. The lack of detectable long living states (>5 ps) suggests that non-radiative decay plays an important role in the dynamics of these molecules

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

Measurement of the top quark-pair production cross section with ATLAS in pp collisions at \sqrt{s}=7\TeV

A measurement of the production cross-section for top quark pairs(\ttbar) in $pp$ collisions at \sqrt{s}=7 \TeV is presented using data recorded with the ATLAS detector at the Large Hadron Collider. Events are selected in two different topologies: single lepton (electron $e$ or muon $\mu$) with large missing transverse energy and at least four jets, and dilepton ($ee$, $\mu\mu$ or $e\mu$) with large missing transverse energy and at least two jets. In a data sample of 2.9 pb-1, 37 candidate events are observed in the single-lepton topology and 9 events in the dilepton topology. The corresponding expected backgrounds from non-\ttbar Standard Model processes are estimated using data-driven methods and determined to be $12.2 \pm 3.9$ events and $2.5 \pm 0.6$ events, respectively. The kinematic properties of the selected events are consistent with SM \ttbar production. The inclusive top quark pair production cross-section is measured to be \sigmattbar=145 \pm 31 ^{+42}_{-27} pb where the first uncertainty is statistical and the second systematic. The measurement agrees with perturbative QCD calculations.Comment: 30 pages plus author list (50 pages total), 9 figures, 11 tables, CERN-PH number and final journal adde

Measurement of the top quark pair cross section with ATLAS in pp collisions at √s=7 TeV using final states with an electron or a muon and a hadronically decaying τ lepton

A measurement of the cross section of top quark pair production in proton-proton collisions recorded with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 7 TeV is reported. The data sample used corresponds to an integrated luminosity of 2.05 fb -1. Events with an isolated electron or muon and a τ lepton decaying hadronically are used. In addition, a large missing transverse momentum and two or more energetic jets are required. At least one of the jets must be identified as originating from a b quark. The measured cross section, σtt-=186±13(stat.)±20(syst.)±7(lumi.) pb, is in good agreement with the Standard Model prediction