Romain Schira

Franck Rabilloud
email: franck.rabilloud@univ-lyon1.fr

Localized Surface Plasmon Resonance in Free Silver Nanoclusters Ag n , n = 20 -147

Absorption spectra of silver nanoclusters, Ag n with n = 20-147, are investigated in the framework of the time-dependent density functional theory (TDDFT) with the use of a range-separated hybrid density functional. Our calculated spectra reproduce well the experimental data. The plasmon-like band energy is situated at about 4 eV for all clusters in gas phase. A description of the plasmonic behavior is given using analyses and tools derived from ab initio quantum calculations. The plasmon band originates from multiple peaks gathered in a relatively small range of energy. High intensive peaks near the center of the band present a strong plasmonic character which has been characterized in terms of transition density, hole-electron excitation, transition contribution map (TCM), and generalized plasmonicity index (GPI).

Introduction

Metal nanoparticles have received much attention in recent years in both science and technology due to their unique optical, electronic and catalytic properties. [START_REF] Schwartzberg | Novel Optical Properties and Emerging Applications of Metal Nanostructures[END_REF] Notably, the collective excitation of conduction electrons and their interaction with a electromagnetic field give raise to the so called localized surface-plasmon resonance (LSPR), and cause a giant absorption band in the UV-visible domain. Plasmonics makes metal nanoparticles very interesting for a range of applications such as biological sensing, 2 cancer therapy, [START_REF] Gobin | Near-Infrared Resonant Nanoshells for Combined Optical Imaging and Photothermal Cancer Therapy[END_REF][START_REF] Bardhan | Theranostic Nanoshells: From Probe Design to Imaging and Treatment of Cancer[END_REF] optoelectronic devices, [START_REF] Aćimović | Plasmon Near-Field Coupling in Metal Dimers as a Step Toward Single-Molecule Sensing[END_REF][START_REF] Chen | Metal-Cluster-Sensitized Solar Cells. A New Class of Thiolated Gold Sensitizers Delivering Efficiency Greater Than 2%[END_REF] photocatalysis, [START_REF] Méndez-Medrano | Surface Modification of TiO 2 with Ag Nanoparticles and CuO Nanoclusters for Application in Photocatalysis[END_REF] photovoltaics, [START_REF] Atwater | Plasmonics for Improved Photovoltaic Devices[END_REF][START_REF] Knight | Aluminium for Plasmonics[END_REF] surface-enhanced Raman spectroscopy (SERS), [START_REF] Seney | Correlation of Size and Surface-Enhanced Raman Scattering Activity of Optical and Spectroscopic Properties for Silver Nanoparticles[END_REF][START_REF] Stamplecoskie | Optimal Size of Silver Nanoparticles for Surface-Enhanced Raman Spectroscopy[END_REF] etc.

The LSPR frequency can be tuned through the shape, size and composition of the nanoparticle [START_REF] Abe | Surface Plasmon Absorption of Various Colloidal Metal Particles[END_REF][START_REF] Haberland | Looking From Both Sides[END_REF][START_REF] Sinha-Roy | Classical and Ab Initio Plasmonics Meet at Sub-Nanometric Noble Metal Rods[END_REF] but is also dependent of the environment. [START_REF] Campos | Plasmonic Quantum Size Effects in Silver Nanoparticles are Dominated by Interfaces and Local Environments[END_REF] The plasmon band originates from multiple peaks gathered in a small range of frequencies. The collective modes are envelopes of coherent superpositions of electron-hole excitations. [START_REF] Brack | The Physics of Simple Metal Clusters: Self-Consistent Jellium Model and Semiclassical Approaches[END_REF][START_REF] Xia | Photoabsorption by Volume Plasmons in Metal Nanoclusters[END_REF][START_REF] Lermé | Size Evolution of the Surface Plasmon Resonance Damping in Silver Nanoparticles: Confinement and Dielectric Effects[END_REF] But at small size, the plasmon band of noble metal generally disappears i.e. the collective effect of the electron excitation is reduced, because of the discreteness of electronic states, the band is fragmented into discrete peaks which are scattered in a large range of frequencies. Then they are likely to be lost in the interbands (transitions associated to d-electrons) [START_REF] Malola | Birth of the Localized Surface Plasmon Resonance in Monolayer-Protected Gold Nanoclusters[END_REF] or in the bands resulting from hybridization with protecting ligands bound to the surface of clusters. [START_REF] Chakraborty | Emergence of Metallicity in Silver Clusters in the 150 Atom Regime: a Study of Differently Sized Silver Clusters[END_REF][START_REF] Bousquet | Absorption Spectra of Aryl Thiol-Coated Silver Nanoclusters: A Time-Dependent Density-Functional Study[END_REF] But silver is a exception since the plasmon-like band is clearly visible down to small size bare clusters of about 18-20 atoms [START_REF] Harb | Optical Absorption of Small Silver Clusters: Ag n , (n=422)[END_REF][START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] thanks to a relatively strong alkali-like character with a large s-d separation. [START_REF] Anak | Time-Dependent Density Functional Study of UV-Visible Absorption Spectra of Small Noble Metal Clusters (Cu n , Ag n , Au n , n = 29[END_REF] Absorption spectra of medium-sized and small-sized silver clusters have been measured in vacuum, [START_REF] Tiggesbäumker | Blue Shift of the Mie Plasma Frequency in Ag Clusters and Particles[END_REF] in solution, [START_REF] Bakr | Silver Nanoparticles with Broad Multiband Linear Optical Absorption[END_REF] in solid rare-gas matrices, [START_REF] Harb | Optical Absorption of Small Silver Clusters: Ag n , (n=422)[END_REF][START_REF] Fedrigo | Collective Dipole Oscillations in Small Silver Clusters Embedded in Rare-Gas Matrices[END_REF][START_REF] Harbich | The Optical Absorption Spectra of Small Silver Clusters (n=8-39) Embedded in Rare Gas Matrices[END_REF][START_REF] Lecoultre | Ultraviolet-Visible Absorption of Small Silver Clusters in Neon: Ag n (n = 19)[END_REF] and in supported samples on glass. [START_REF] Lünskens | Plasmons in Supported Size-Selected Silver Nanoclusters[END_REF] Very recently, spectra of Ag n clusters (n = 5 -120) have been published, [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] they show a plasmon band situated between 3.9 and 4.1 eV in vacuum, with variation with cluster size according to the spherical electronic shell model. Also, the plasmon energy is strongly blue shifted with the decreasing cluster size since it is likely to be close to 3.2 eV for large nanoparticles. [START_REF] Haberland | Looking From Both Sides[END_REF] Theoretically, the description of the optical properties of large noble metal nanoparticles have been intensively and successfully investigated using classical optics by solving the Maxwell's equations for electromagnetic waves interacting with particles characterized by the dielectric function of the bulk, eventually coupled with a hydrodynamic model, [START_REF] Christensen | Nonlocal Response of Metallic Nanospheres Probed by Light, Electrons, and Atoms[END_REF] or with more sophisticated semiclassical methods able to describe the core polarization and its effect on the optical response like the random phase approximation (RPA), [START_REF] Kresin | Collective Resonances in Silver Clusters: Role of d Electrons and the Polarization-Free Surface Layer[END_REF][START_REF] Xuan | Core-Polarization-Corrected Random-Phase Approximation with Exact Exchange for Dipole Surface Plasmons in Silver Clusters[END_REF] or approaches based on time-dependent local density approximation and jellium models. [START_REF] Serra | Core Polarization in the Optical Response of Metal Clusters: Generalized Time-Dependent Density-Functional Theory[END_REF][START_REF] Lermé | Introduction of Quantum Finite-Size Effects in the Mie's Theory for a Multilayered Metal Sphere in the Dipolar Approximation: Application to Free and Matrix-Embedded Noble Metal Clusters[END_REF] In these semiclassical calculations, the plasmon resonance reflects a collective excitation of the s valence electrons, but the effects of the d-electrons are only accounted for by using the bulk dielectric function and the atomic structure is neglected. Investigating small nanoclusters of some tens of atoms requires a fully quantum treatment for all electrons. Such calculations are now possible in the framework of the Time-Dependent Density Functional Theory (TDDFT). [START_REF] Casida | Recent Advances in Density Functional Methods[END_REF] Recently, a few TDDFT studies have been performed on Ag n clusters with n ∼ 8 -300 atoms. [START_REF] Harb | Optical Absorption of Small Silver Clusters: Ag n , (n=422)[END_REF][START_REF] Anak | Time-Dependent Density Functional Study of UV-Visible Absorption Spectra of Small Noble Metal Clusters (Cu n , Ag n , Au n , n = 29[END_REF][START_REF] Jensen | Size-Dependence of the Enhanced Raman Scattering of Pyridine Adsorbed on Ag n (n = 28, 20) Clusters[END_REF][START_REF] Guidez | Quantum Mechanical Origin of the Plasmon: From Molecular Systems to Nanoparticles[END_REF][START_REF] Weissker | Surface Plasmons in Quantum-Sized Noble-Metal Clusters: TDDFT Quantum Calculations and the Classical Picture of Charge Oscillations[END_REF][START_REF] Kuisma | Localized Surface Plasmon Resonance in Silver Nanoparticles: Atomistic First-Principles Time-Dependent Density-Functional Theory Calculations[END_REF][START_REF] Titantah | Ab Initio Calculations of Optical Properties of Silver Clusters: Cross-Over From Molecular to Nanoscale Behavior[END_REF][START_REF] Baseggio | Photoabsorption of Icosahedral Noble Metal Clusters: an Efficient TDDFT Approach to Large-Scale Systems[END_REF][START_REF] Koval | Optical Response of Silver Clusters and Their Hollow Shells From Linear-Response TDDFT[END_REF][START_REF] Zhang | How to Identify Plasmons From the Optical Response of Nanostructures[END_REF][START_REF] Nhat | Elucidation of the Molecular and Electronic Structures of Some Magic Silver Clusters Ag n (n = 8, 18, 20)[END_REF] However, while most studies used a local or semi-local formulation for the exchange and correlation functional, one of us have shown that the use of a hybrid functional is required to correctly describe the excitations of d-type electrons which present charge-transfer and Rydberg characters. [START_REF] Rabilloud | Assessment of the Performance of Long-Range-Corrected Density Functionals for Calculating the Absorption Spectra of Silver Clusters[END_REF][START_REF] Rabilloud | Description of Plasmon-Like Band in Silver Clusters: The Importance of the Long-Range Hartree-Fock Exchange in Time-Dependent Density-Functional Theory Simulations[END_REF] Such excitations cannot be described within TDDFT with a local or semi-local density functional in the adiabatic approximation used in standard calculations. [START_REF] Dreuw | Failure of Time-Dependent Density Functional Theory for Long-Range Charge-Transfer Excited States: The Zincbacteriochlorin-Bacteriochlorin and Bacteriochlorophyll-Spheroidene Complexes[END_REF] The d → sp excitation energies are significantly underestimated at TDDFT/GGA level and consequently the interband transitions lead to a large band close to 3 eV which is often confused, or mixed, with the sp → sp intrabands. For silver clusters, the description of excited states within the adiabatic approximation in standard TDDFT calculations requires a correction of the self-interaction error (SIE) and a correct asymptotic behavior. The best results have been obtained with range separated hybrid functionals (RSHs) which resolve a significant part of the SIE problems and also improve the asymptotic behavior at long range thanks to the inclusion of the Hartree-Fock exchange. [START_REF] Rabilloud | Assessment of the Performance of Long-Range-Corrected Density Functionals for Calculating the Absorption Spectra of Silver Clusters[END_REF][START_REF] Rabilloud | Description of Plasmon-Like Band in Silver Clusters: The Importance of the Long-Range Hartree-Fock Exchange in Time-Dependent Density-Functional Theory Simulations[END_REF] But the cost of calculations becomes heavier.

In a very recent paper [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] , our colleagues have furnished the experimental absorption spectra of Ag n clusters (n = 5-120) embedded in a weakly interacting neon matrix, and we have given some TDDFT/RSH calculations of Ag n @Neon in which both silver and neon atoms were explicitly treated. Our calculated spectra were found in excellent agreement with the experimental ones, suggesting that our simulation methodology is correct. In the present paper, we give the absorption spectra of free Ag n , n = 20 -92, and Ag + 147 nanoclusters calculated within the TDDFT approach with a RSH-type functional, and we furnish a description of the plasmonic behavior using analyses and tools derived from ab initio quantum calculations.

Theoretical Method

Absorption spectra have been calculated in the framework of the Time-Dependent Density Functional Theory (TDDFT) using the range-separated hybrid (RSH) density functional ωB97x [START_REF] Chai | Systematic Optimization of Long-Range Corrected Hybrid Density Functionals[END_REF] which includes an increasing part of exact Hartree-Fock exchange at long range (from 16% at short range to 100% at long range). Calculations treat the 19 valence electrons per silver atom within a relativistic effective core potential (RECP) together with the corresponding Gaussian basis set. [START_REF] Hay | Ab Initio Effective Core Potentials for Molecular Calculations. Potentials for K to Au Including the Outermost Core Orbitals[END_REF] The geometrical structures were taken from a study by Chen et al 51 dedicated to the prediction of structures of silver clusters using a genetic algorithm with an embedded atom method potential, except the structure of Ag 20 which is the ground state structure of C s symmetry optimized at DFT level in Ref. [START_REF] Harb | Optical Absorption of Small Silver Clusters: Ag n , (n=422)[END_REF] Geometrical structures were relaxed with ωB97x before calculating the absorption spectra (Fig. S1 in Supporting Information). Of course we cannot be sure that a more stable cluster than those considered in our calculations does not exist. But the absorption spectra calculated with several isomers of Ag 20 and Ag 42 (Fig. S2 and S3 in Supporting information) show that the spectra characterized by a plasmon-like band are only weakly dependent on the exact geometrical structure as long as the shape is somewhat spherical, as expected for metal clusters with free valence electrons. This is in agreement with a previous work by Durante et al. [START_REF] Durante | Optical Properties of Au Nanoclusters from TD-DFT Calculations[END_REF] which has showed that the position of the band energy in gold Au ∼150 clusters is weakly dependent on the shape of the cluster.

The choice of the exchange and correlation density functional was motivated by previous benchmarking works on small clusters 46,47 that we are extending here to larger clusters.

In Fig. 1, we compare the performance of three commonly used families of exchange and correlation functionals, namely BP86 [START_REF] Perdew | Density-Functional Approximation for the Correlation Energy of the Inhomogeneous Electron Gas[END_REF][START_REF] Becke | Density-Functional Exchange-Energy Approximation With Correct Asymptotic Behavior[END_REF] which is a standard GGA (Generalised Gradient Approximation) density functional, the asymptotically corrected (AC-GGA) LB94 potential model 55 which gives a -1/r behavior at long range, and the range-separated hybrid ωB97x. The calculated absorption spectra of Ag 92 , Ag 58 and Ag + 55 are compared to most recent experimental spectra. [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] As expected, only the range-separated hybrid ωB97x is able to reproduce the experimental data. Indeed, TDDFT calculations with a local exchange and correlation potential in the adiabatic approximation can not correctly describe the interband d → sp transitions since the latter present Rydberg and long-range charge-transfer characters that can only be described with a RSH-type density functional. [START_REF] Rabilloud | Assessment of the Performance of Long-Range-Corrected Density Functionals for Calculating the Absorption Spectra of Silver Clusters[END_REF][START_REF] Rabilloud | Description of Plasmon-Like Band in Silver Clusters: The Importance of the Long-Range Hartree-Fock Exchange in Time-Dependent Density-Functional Theory Simulations[END_REF] For the size n = 55, the spectra are calculated for the cation Ag + 55 (due to the constraints imposed by the ADF Software 56 which can calculate excitation energies only for electronic closed shell systems) while the experimental spectrum is that of the neutral cluster Ag 55 . That is why the calculated spectrum is blue shifted with respect to the experimental one. Experimental data are measured on clusters embedded in a Neon matrix, and are blue-shifted by 0.17 eV to remove the dielectric effects. [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] For the size n = 55, the optical response is calculated for the cation while the experimental curve is measured for the neutral species.

All calculations were performed using the program Gaussian09 57 (ωB97x calculations), and the program ADF 56 (LB94 and BP86 calculations). Pre-and postprocessing operations were performed by using the graphical interface Gabedit. [START_REF] Allouche | Gabedit A Graphical User Interface for Computational Chemistry Softwares[END_REF]

Results and discussion

Absorption spectra

Calculated absorption spectra are given in Fig. 2 together with the experimental ones measured in Neon. The latter have been blue-shifted by 0.17 eV to remove the dielectric matrix shift as supported by our previous study [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF][START_REF] Schira | Effects of Rare-Gas Matrices on the Optical Response of Silver Nanoclusters[END_REF] where TDDFT calculations performed in gas phase and in neon matrix have shown that the unique effect of the rare gas matrix is a redshift of the whole spectrum ; this allows to compare the spectra to calculated gas phase values. Let us note that the shift value of 0.17 eV is predicted to have no significant size dependency for n = 20 -92 atoms. The experimental spectrum of Ag 42 was measured by surface second harmonic generation spectroscopy on size-selected clusters supported on BK7 glass substrate. [START_REF] Lünskens | Plasmons in Supported Size-Selected Silver Nanoclusters[END_REF] The calculated absorption spectra showed in Fig. 2 For Ag 20 , the main band is centered at 4 eV and composed of four strong peaks in the 3.97-4.04 eV range. A shoulder at 3.72 eV is also visible. For Ag 35 , the spectrum shows a relative wide band, composed of two peaks at 3.95 and 4.12 eV, respectively. The first peak is mainly due to two excitations at 3.91 and 3.96 eV respectively, while the second one is due to several less intense transitions in the 4.11-4.18 eV range. The calculated spectrum fits well the experimental spectrum that also shows two peaks. For Ag 42 , the spectrum consists of a large band due to a range of peaks spanning the 3.7-4.5 eV energy range. The experimental spectrum in Neon is not available, but Lünskens et al. [START_REF] Lünskens | Plasmons in Supported Size-Selected Silver Nanoclusters[END_REF] have measured the band plasmon by surface second harmonic generation spectroscopy on clusters supported on BK7 glass substrate. Their spectrum is reproduced in Fig. 2 with a band centered at 3.78 eV. The red-shifted by about 0.15 eV compared to our calculated data in gas phase is likely due to dielectric effects of the oxide substrate. Fig. S2, in ESI, gives the spectra calculated with two different isomers, predicted to be the ground state using energetic models based on atomistic potentials. [START_REF] Chen | Prediction of Structures and Atomization Energies of Small Silver Clusters[END_REF][START_REF] Yang | Structural Variation of Silver Clusters From Ag 13 to Ag 160[END_REF] Both spectra are found to be very similar, which confirms that the optical response of metal clusters is only weakly dependent on the exact geometrical structure as long as the shape is somewhat spherical. For Ag 55 , the plasmon band is centered at 4.11 eV, slightly blueshifted with respect to the experimental band at 4.07 eV. A secondary band centered at 4.56 eV is also calculated, but not see in experiments. [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] Interestingly both bands were calculated at 4.23 and 4.66 eV respectively for the cation Ag + 55 (Fig. 1). The calculated spectrum of Ag 58 shows a main transition at 4.08 eV and a less intense one at 3.77 eV, in excellent agreement with the experimental data showing these features at 4.11 and 3.81 eV [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] . The spectrum of Ag 84 shows a wide band due to a range of peaks spanning the 3.7-4.5 eV energy range, with a maximum at 4.01 eV, and two shoulders at 3.91 eV and 4.32 eV respectively. The C 3v symmetry of Ag 92 leads to a relatively narrow absorption band, it is centered at 4.05 eV, slightly blue-shifted compared to the experimental value of 3.97 eV [START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] .

Finally, we give the absorption spectrum of the cation Ag + 147 with a I h geometrical structure (calculation is less cumbersome for the cation than it is for the neutral cluster, thanks to an even number of electrons). The giant band with a narrow width of around 0.2 eV at full width at half maximum (FWHM),is centered at 4.22 eV, and composed of three main transitions at 4.22 and 4.25 eV. Following the shift of 0.12 eV between spectra of Ag + 55 and Ag 55 (see above), the absorption band of neutral Ag 147 is likely to be redshifted to about 4 or 4.1 eV.

Overall, our calculated spectra obtained with cumbersome calculations (TDDFT/RSH with more 3200 primitive gaussians is time-consuming) reproduce well the experimental ones.

Analysis of excitations

To characterize the plasmonic excitation, we first furnish some visual interpretations. In the adiabatic linear-response formalism of the TDDFT, the transition density associated to a single excited state is typically expanded in the product of occupied and virtual Kohn-Sham molecular orbitals:

T ( r) = i→s F i,s φ i ( r)φ * s ( r) + i←s F s,i φ * i ( r)φ s ( r), (1)
where i and s label the occupied and virtual orbitals respectively, and F i,s is the component of the TDDFT eigenvector, which is composed of occupied-unoccupied i → s excitations and i ← s desexcitations. As a matter of fact, in the current formalism the transition density is equivalent to the induced density defined by the Fourier transform of the difference between the charge density evolving after a perturbation and the ground state density F T (ρ( r, t)ρ 0 ( r)). Isosurface plots of the transition density for some intense peaks in the absorption spectra of Ag 20 , Ag 92 and Ag + 147 are showed in Fig. 3. As explained above, the plasmon band originates from multiple peaks gathered in a small range of energy, and can be seen as an envelope of coherent excitations represented by the lorentzian curve in Fig. 2. The excitations considered in Fig. 3 are those with highest oscillator strengths and are located at the center of the band (near 4 eV). The transition densities display a typical dipolar shape, which represent the oscillation of the electron density along one direction, which could be expected to be, for instance, that of the electric field of a laser. The density change affects many two opposing sides of the cluster, with strong contribution from s electrons, while less intense contributions from d electrons polarize in the opposite direction and thus create a countering screening field. The collective behavior is visible even for the small cluster Ag 20 .

All plots correspond to the classical picture of LSPRs as a charge cloud oscillating between the opposite sides of the cluster.

We can also furnish a representation in terms of electron-hole pair where an electron leaves the hole domain and goes to electron domain during the excitation. The "hole" and the "electron" can be defined in different ways. In previous studies, [START_REF] Rabilloud | Description of Plasmon-Like Band in Silver Clusters: The Importance of the Long-Range Hartree-Fock Exchange in Time-Dependent Density-Functional Theory Simulations[END_REF][START_REF] Harb | Optical Absorption of Silver Clusters: A Study of the Effective Potential Core Size[END_REF] we gave the electron density difference between the excited and ground states for some of the main peaks of Ag 20

clusters. Here we adopt the definition of hole and electron proposed by T. Lu and C. Zhong in Multiwfn program. [START_REF] Lu | Multiwfn: a Multifunctional Wavefunction Analyser[END_REF] The density distribution of hole and electron is defined as:

ρ hole ( r) = ρ hole local ( r) + ρ hole cross ( r), (2)
= i→s (F i,s ) 2 φ i ( r)φ i ( r) - i←s (F i,s ) 2 φ i ( r)φ i ( r) + i→s j =i→s F i,s F j,s φ i ( r)φ j ( r) - i←s j =i←s F i,s F j,s φ i ( r)φ j ( r), ρ elec ( r) = ρ elec local ( r) + ρ elec cross ( r), (3)
= i→s (F i,s ) 2 φ s ( r)φ s ( r) - i←s (F i,s ) 2 φ s ( r)φ s ( r) + i→s i→u =s F i,s F i,u φ s ( r)φ u ( r) - i←s i←u =s F i,s F i,u φ s ( r)φ u ( r).
The terms "local" and "cross" stand for the contribution of local term and cross term to the hole and electron distribution, respectively. Both ρ hole and ρ elec integrate to one over all space. The charge density difference between excited state and ground state is then evaluated as

∆ρ( r) = ρ elec ( r) -ρ hole ( r). (4)
Isovalue plots of ∆ρ( r) for some intense peaks are given in Fig. 4. Red colored regions correspond to depletion of the electron density during the transition, while blue regions correspond to an increase of the electron density. In the case of Ag 92 and Ag + 147 , plots show clearly that during the transition the electron is transferred from the core to the surface of the cluster. Actually, the hole density is distributed over both the core and the surface of the cluster while the electron density is mainly located on the surface of the cluster. For Ag 20 , there is only one atom in the core, and then the hole is partially localized on the surface.

Consequently, the transfer from the core to the surface is less pronounced.

Change densities are mainly associated to s and p orbitals, with very few d contributions.

In a previous works, [START_REF] Rabilloud | Description of Plasmon-Like Band in Silver Clusters: The Importance of the Long-Range Hartree-Fock Exchange in Time-Dependent Density-Functional Theory Simulations[END_REF] the d character in the plasmonic excitation was evaluated to about 10%, which seems to be in agreement with the classical picture of the plasmon likely to be associated to excitations of s electrons, with a few contribution of d electrons.

Following previous works, [START_REF] Malola | Birth of the Localized Surface Plasmon Resonance in Monolayer-Protected Gold Nanoclusters[END_REF][START_REF] Baseggio | Photoabsorption of Icosahedral Noble Metal Clusters: an Efficient TDDFT Approach to Large-Scale Systems[END_REF][START_REF] He | First-Principles Study and Model of Dielectric Functions of Silver Nanoparticles[END_REF] we also furnish an analysis of the electron transitions based on transition contribution map (TCM) plots. In the TDDFT approach, each excited states can be described by a sum of monoexcitations and desexcitations with a relative contribution F i,s (See equation 1). In the TCM plot associated to a given excited state, each monoexcitation is represented by one point in a map where the axis are the eigenvalue of the initial state ξ i and the eigenvalue of the final state ξ s , while the magnitude of each transition is broadened by Gaussians (with a smoothing parameter σ):

M T CM (ξ o , ξ v ) = i,s |F i,s | σ 2 2π e (ξo-ξ i ) 2 +(ξv -ξs) 2 2σ 2 . ( 5
)
TCM plots reveal the energies of the initial and final orbitals of the main transitions involved in an excited state. Fig. 5 shows the TCM maps in the case of Ag 92 together with the density of state (DOS). The sp band covers all occupied and virtual orbitals closed to the Fermi level, while the d orbitals are confined in a band with an upper limit at about 4.7 eV below the Fermi level. The HOMO-LUMO gap is calculated to be 3 eV because of the use of an hybrid functional which shifts upward the energies of unoccupied orbitals. This leads to get much better excitation energies. All intense spots in the TCM plots for Ag 92 correspond to intraband sp → sp configurations, as expected for plasmonic excitations. In Fig. 5, we show TCM plots for two representative transitions. One is a strong transition (label (a)) at 3.98 eV, near the center of the plasmon, while the other (b), much less intense, is located at 4.35 eV. The maps reflect that the excited state (a) has a stronger plasmonic character than the transition (b). Indeed, the excited state (b) is due to monoexcitations located in a narrowed diagonal band with a few intense spots (in red) placed on a line corresponding to ξ i -ξ s = ∆, where ∆ is a constant. In contrast, the excitation (a) is made of many intense spots (in red) distributed on a large domain of ξ i -ξ s values. The collective nature of the plasmon can be inferred by the presence of many spots with strong contributions in the TCM. Some desexcitations implying sp orbitals are also visible for the transition (a). The TCM maps for three transitions in Ag 20 is plotted in Fig. 6. Two transitions, labelled (b) and (c), located near the center of the plasmon-like band present a strong plasmon character with several intense spots distributed in a large domain of energy. In contrast, the state situated at 3.69 eV is mainly due to a few intense peaks. The interband d → sp transitions are more visible than in the case of Ag 92 .

In conclusion, TCM maps show that the excited states with intense oscillator strengths and placed near the center of the band originate from many monoexcitations distributed on a large domain of energy, while the states placed further away from the center of the plasmon band present a less collective behaviour. However, it is worth noting that all peaks belong to the plasmon. The difference of nature that we noted is the consequence of the fragmentation of the plasmon band that occurs when considering small-sized clusters.

Several studies have tried to classify the electronic excitations in nanostructures, particularly with the aim of characterizing the emergence of plasmons in opposition to single-electron transitions. Plasmon resonances can be distinguished from single-electron transitions since they manifest a strong dependency in electron-electron interaction. [START_REF] Guidez | Quantum Mechanical Origin of the Plasmon: From Molecular Systems to Nanoparticles[END_REF][START_REF] Zhang | How to Identify Plasmons From the Optical Response of Nanostructures[END_REF][START_REF] Bernadotte | Plasmons in Molecules[END_REF] Recently a generalized plasmonicity index (GPI) has been proposed to quantify the plasmonic character of optical excitations. [START_REF] Malola | Birth of the Localized Surface Plasmon Resonance in Monolayer-Protected Gold Nanoclusters[END_REF][START_REF] Zhang | How to Identify Plasmons From the Optical Response of Nanostructures[END_REF][START_REF] Bursi | Quantifying the Plasmonic Character of Optical Excitations in Nanostructures[END_REF] The electron density change can be evaluated from the Kohn-Sham response function χ KS as

δρ( r, t) = χ KS ( r, r , t, t ) [v ext ( r , t ) + v ind ( r , t )] d r dt , (6)
where v ext is the external potential applied to the system of interest, and v ind is the induced potential responsible for the external field enhancement, which reads as follows v ind = v H + v xc , the sum of the Hartree and exchange-correlation potentials. GPI is then defined as the

quantity 44 η = | δρ( r, ω)v * ind ( r, ω)d r| | δρ( r, ω)v * ext ( r, ω)d r| , (7)
where δρ( r, ω) is the Fourier transform of δρ( r, t), and v * ext and v * ind denote the complex conjugate of the corresponding quantities. The index η is a quantitative measure of the interaction between electrons following an external potential, it will be relatively large for plasmonic excitations which produce a large v ind , but much smaller in case of single-electron transitions for which the induced potential remains very low.

For a more practicable use, Zhang et al. [START_REF] Zhang | How to Identify Plasmons From the Optical Response of Nanostructures[END_REF] have showed that the GPI can be expressed as

η = E plasmon Γ = T ( r)T ( r ) | r-r | d rd r Γ , (8)
where T ( r) is the transition density for the considered excitation. Γ is a broadening parameter coming from the expression of χ KS in the Lehmann representation. As we have no means to express rigorously the damping energy Γ in our TDDFT calculations, and being expected that this term does not contribute to the plasmonic character, we have calculated the E plasmon value as a descriptor for the plasmonic character of a given transition. Transitions with relative large values for E plasmon will present a more pronounced plasmonic character.

The term E plasmon is a dimensionless number, and is not normalized. For Ag 92 , the value of E plasmon is calculated at 0.019 and 0.003 for the transitions (a) and (b) considered in Fig. 5, respectively. The factor of 6 between the two values confirms that the excitation (a) is more plasmonic than the exciation (b). For Ag 20 , the value of E plasmon is 0.013, 0.036, and 0.066 for transitions (a), (b), and (c), respectively, showed in Fig. 6. Therefore the plasmonic character is stronger in the last two transitions than the former. The excitation near the center of the band present a stronger plasmonic character than those located at lower or higher energies.

Conclusions

The optical response of silver clusters have been calculated at TDDFT/RSH level. Overall, the present calculated spectra reproduce well the recently measured experimental spectra.

Plasmon band energies are calculated at about 4 eV for all clusters in gas phase. Plasmon excitations have been characterized in terms of transition density, electron-hole excitation, transition contribution map (TCM), and generalized plasmonicity index (GPI). The plasmon band originates from multiple peaks gathered in a relatively small range of frequencies, and can be seen as an envelope of coherent excitations represented by the lorentzian-type curve for large clusters. High intensive peaks close to the center of the band present a strong plasmonic character described by (i) a transition density which displays a typical dipolar shape that represents the oscillation of the electron density along one direction, (ii) an electron transfer from the core to the surface during the excitation in a electron-hole scheme, (iii) a collective nature which can be inferred by the presence of many spots with strong contributions and distributed on a large domain of energy in the TCM plot, (iv) a relative high value of the GPI.

The plasmon band originates from several peaks distributed over a relatively small energy range, all mainly due to intraband sp → sp excitations, in agreement with the classical description of the plasmon phenomena as a collective excitation of valence electrons. The present work is one of the first to correctly reproduce the experimental absorption spectra of silver nanoclusters and to describe them with the help of analyses and tools derived from ab initio quantum calculations.

Figure 1 :

 1 Figure1: Absorption spectra of silver clusters Ag n for sizes n = 92,58, and 55 calculated with three different families of density-functionals. Experimental data are measured on clusters embedded in a Neon matrix, and are blue-shifted by 0.17 eV to remove the dielectric effects.[START_REF] Yu | Optical Properties of Size Selected Neutral Ag Clusters: Electronic Shell Structures and the Surface Plasmon Resonance[END_REF] For the size n = 55, the optical response is calculated for the cation while the experimental curve is measured for the neutral species.

Figure 2 :

 2 Figure 2: Calculated absorption spectra of silver cluster Ag n (red lines) together with experimental data (black curves) taken from Ref 23 except the spectrum of Ag42 which was measured by surface second harmonic generation spectroscopy on cluters supported on BK7 glass substrate.[START_REF] Lünskens | Plasmons in Supported Size-Selected Silver Nanoclusters[END_REF]

Figure 3 :

 3 Figure 3: Isosurface plots of the transition density for three main transitions in Ag 20 (3.98, 4.00, 4.04 eV), Ag 92 (3.98, 4.03, 4.10 eV) and Ag + 147 (4.21, 4.22, 4.25 eV). Red and blue surfaces indicate positive and negative isovalues. Isovalue = 0.0006 and 0.0003 for Ag 20 , 0.0002 and 0.0001 for both Ag 92 and Ag + 147 .

Figure 4 :

 4 Figure 4: Isosurface plots of the charge density difference between particle and hole (depletion in red, accumulation in blue) for a main transition in Ag 20 (3.98 eV), Ag 92 (3.98 eV) and Ag + 147 (4.21 eV). Isovalue = 0.0001 and 0.00006 for Ag 20 , 0.00005 and 0.00002 for both Ag 92 and Ag + 147 .

Figure 5 :

 5 Figure 5: Transition contribution map (TCM) for some excited states of Ag 92 , together with the Density of states (DOS) where the d and s bands are showed in grey and yellow respectively. Are included transitions with a component |F i,s | ≥ 0.01. Vertical and horizontal lines represent the energy of the HOMO and LUMO respectively. a. TCM for the excited state at 3.98 eV. b. TCM for the excited state at 4.35 eV. c. Spectrum of Ag 92 .

Figure 6 :

 6 Figure 6: Transition contribution map (TCM) for some excited states of Ag 20 , together with the Density of states (DOS) where the d and s bands are showed in grey and yellow respectively. Vertical and horizontal lines represent the energies of the HOMO and LUMO respectively. a. TCM for the excited state at 3.69 eV. b. TCM for the excited state at 3.97 eV. c. TCM for the excited state at 4.01 eV. d. Spectrum of Ag 20 .

  give the oscillator strength as a function of the excitation energy together with a curve obtained by a Lorentz

broadening with a full width at half-maximum (fwhm) of 0.08 eV. For all clusters, the peaks with strong oscillator strengths are in the 3.8-4.2 eV range, resulting in an intense band centered at around 4 eV. Both position and shape of experimental spectra are well reproduced.

The narrower absorption widths for Ag 38 (D 2h ), Ag 55 (I h ), Ag 92 (C 3v ), Ag + 147 (I h ) are due to the high symmetry of the geometrical structure which tends to gather the peaks on a narrow band of energy, while the other clusters are less symmetrical.

Acknowledgement

This work was performed using HPC resources from GENCI-IDRIS (Grant A0030807662) and the Pôle Scientifique de Modélisation Numérique (PSMN). It has received a financial support from the French National Research Agency (Agence Nationale de la Recherche, ANR) in the frame of the project FIT SPRINGS, ANR-14-CE08-0009. The authors gratefully acknowledge J. Lermé for helpful discussions and A.R. Allouche for support for the use of Gabedit.

Supporting Information Available

The supporting information includes the geometrical structures (Figure S1), and the absorption spectra calculated with several isomers of Ag 20 and Ag 42 (Figures S2 andS3). This information is available free of charge via the Internet at http://pubs.acs.org.

Table of Contents Graphic (TOC)


Hantanirina Felixie Rafalimanana

Jean Luc Razafindramintsa
email: razafindramintsa.jeanluc@yahoo.fr

Syl- Vain Cherrier²
email: sylvain.cherrier@u-pem.fr

Thomas Mahatody
email: tsmahatody@gmail.com

Laurent George
email: laurent.george@u-pem.fr

Victor Manantsoa
email: vmanantsoa@moov.mg

Jason-RS, a Collaboration between Agents and an IoT Platform

Keywords: BDI Agent, BeC 3, Internet of Thing, Jason

In this article we start from the observation that REST services are the most used as tools of interoperability and orchestration in the Internet of Things (IoT). But REST does not make it possible to inject artificial intelligence into connected objects, ie it cannot allow autonomy and decision-making by the objects themselves. To define an intelligence to a connected object, one can use a Beleive Desire Intention agent (BDI an intelligent agent that adopts human behavior) such as Jason Agentspeak. But Jason AgentSpeak does not guarantee orchestration or choreography between connected objects. There are platforms for service orchestration and choreography in IoT, still the interconnection with artificial intelligence needs to be built. In this article, we propose a new approach called Jason-RS. It is a result of pairing Jason BDI agent with the web service technologies to exploit the agent capacity as a service, Jason-RS turn in Java SE and it does not need any middleware. The architecture that we propose allows to create the link between Artificial Intelligence and Services choreography to reduce human intervention in the service choreography. In order to validate the proposed approach, we have interconnected the Iot BeC 3 platform and the REST agent (Jason-RS). The decision-making faculty offered by Jason-RS is derived from the information sent by the objects according to the different methods of REST (GET, POST, PUT, and DELETE) that Jason-RS offers. As a result, the objects feed the inter-agent collaborations and decision-making inside the agent. Finally, we show that Jason-RS allows the Web of Objects to power complex systems such as an artificial intelligence responsible for processing data. This performance is promising.

Introduction

The tendency towards the Web of Object (WoO) does not cease to increase from time to time. Given the number of objects to facilitate the daily life, the number of possible interactions and the applications that one can imagine become more powerful and rich.

But what are the ways to make these application in a generic way? The object web orientation is considered as a great resource not only for managing objects [START_REF] Laine | Restful Web Services for the internet of things[END_REF] but also for questions of flexibility of exchanges. In the Web of Object (WoO) one is rather directed towards the use of Web Service [START_REF] Laine | Restful Web Services for the internet of things[END_REF].

We note that the most used Web Service type is REST1 [START_REF] Gupta | Event-Driven SOA-Based IoT Architecture[END_REF] [1] [START_REF] Wilde | Putting things to REST[END_REF]. REST (Representational State Transfer) or RESTful is an architecture style for building applications (Web, Intranet, Web Service). It is a set of conventions, rules and good practices to respect and not a technology in its own right. Not only REST is stateless [START_REF] Guinard | A resource oriented architecture for the Web of Objects[END_REF] (meaning that the service consumer does not have to store information about the way he is using the service); it is design to reduce the coupling between software pieces. In the Iot, REST may allow reconfiguration of the object, it reuse in a different application for a multiplatform application, REST service structure is easy to describe. REST allows machine to machine communication and is simplier to do than SOAP. The communication is done through the HTTP protocol. Nevertheless the possibility of using the resource via the web given by the REST, it still lacks intelligent data processing. Our contribution aim to add a complex data processing of the object web. We then opt for the reuse of our search fruit in [START_REF] Rafalimanana | Publish a Jason Agent BDI Capacity as Web Service REST and SOAP[END_REF] which we call Jason-RS or Jason-Rest for more precision.

Jason-RS gives the REST Web Service ability to the BDI agent Service [START_REF] Rafalimanana | Publish a Jason Agent BDI Capacity as Web Service REST and SOAP[END_REF]. Jason is a multi-agent platform based on the Agentspeak-L language [START_REF] Rao | AgentSpeak(L): BDI agents speak out in a logical computable language[END_REF] [START_REF] Ricci | AgentSpeak(ER): An Extension of AgentSpeak(L) improving Encapsulation and Reasoning about Goals[END_REF]. It is a platform for BDI agent, that is to say goal-oriented software agent, it is a smart agent. We have to look at the choice of the BDI agent because it is close to human behavior. [START_REF] Norling | Capturing the quake player: using a bdi agent to model human behaviour[END_REF] [START_REF] Kamphorst | Incorporating BDI agents into human-agent decision making research[END_REF]. So the connexion of the latter with the REST Web Service allows him to publish his capacity as a Web Service. The processing of a complex action can be done inside Jason (prediction, composition of services, constraint logic programming) and could be published as a Web Service. Jason-RS can serve as an intelligent treatment center.

We focus in the collaboration between agent and Iot platform cause connected object had no intelligence, designed differently, so with this collaboration we can open in the intelligence of Iot with a sensor less, little human intervention. In our approach, we collect the information from the connected object and by Jason-RS we delegate the intelligent agent works. All change in the object behavior is sent to the agent BDI (as a perception) in order to provide a decision or to change the agent comportment. Through this perception that the agent can decide and delegate a task to agent or service choreography to object.

The rest of this article is organized as follows: Section 2 will discuss the related work of our work. Section 3 describes in general the architecture of our contribution. In section 4 we will evaluate our work through an architecture. In section 5 we discuss a discussion and open challenge. We will conclude this article with a conclusion (section 6)

2

Related work

In this article we propose a new form of Web Service that the WoO can use. The use of REST in WoO is not an innovative work as researchers have already done research for the implementation of the latter to allow connected objects to trade [10] [11]. The Internet of Things is a system of physical objects that can be discovered, monitored, controlled or interact with electronic devices that communicate on various network interfaces and can potentially be connected to the wider Internet.

The webs of object is a concept say recent in computing that offers a future with which object of the daily life can integrate into the web [START_REF] Guinard | A Web of Objects application architecture: Integrating the real-world into the web[END_REF]. The Web of Object is considered as a subset of the Internet of Things. WoO applies to standards and software infrastructures such as REST, HTTP and URIs to create applications and services that combine and interact with a variety of network devices. The key point of the WoO is that its implementation does not involve the reinvention of the means of communication given that they use the existing standards. It also gives an easy access to a wide population of programmer that used to handle such an architecture. It facilitates the integration of the IoT in the internet. It is also a solution that can open the connection of the Industry 4.0 [START_REF] Kim | A review of cyber-physical system research relevant to the emerging IT trends: industry 4.0, IoT, big data, and cloud computing[END_REF].

View the exponential increase of the connected object, to reduce the cost, the required performance and to facilitate communication in the object internet, the W3C recently launched the working group on the Web of Objects. They aim to fight the fragmentation of IoT. They developed the initial standards for the Web of Objects [START_REF]W3C Web of Objects at W3C[END_REF]. To facilitate service choreography for Iot Cherrier describes in his thesis a new approach based on a platform to standardize the interoperability of heterogeneous objects [START_REF] Cherrier | BeC 3: behaviour crowd centric composition for IoT applications[END_REF].

Castellani and Al focused on the implementation of both CoAP and EXI technology for communication between objects by adopting the REST approach [START_REF] Castellani | Web Services for the Internet of Things through CoAP and EXI[END_REF]. They implemented the CoAP protocol to enable communication between distributed connected objects. Then they also implemented the EXI library for compressing XML data into binary.

In the paper [START_REF] Gupta | Event-Driven SOA-Based IoT Architecture[END_REF], they discuss how to create an event SOA. They found a new approach for coordinating IoT's services with the resource service. They named EDSOA the result of their research. It is an asynchronous SOA and responsive it can be considered as an event based service. We have inspired by this approach for the technical aspect of Jason-RS.

The Web of Object is used in almost any IoT field even in the field of health. On what [START_REF] Pescosolido | An IoTinspired cloud-based Web Service architecture for e-Health applications[END_REF] proposed a new instance of cloud server and set up data collection services on the heterogeneous devices they use. This aims to improve online health service by properly implementing the architectural web object principle for remote control.

Based on WoO, [START_REF] Guinard | A resource oriented architecture for the Web of Objects[END_REF] describes the object-based web architecture that adopts the principle of Resource Oriented Architecture (ROA), hence the basis of RESTful. They implemented a smart gateway for smart meters. This allows the connected object management servers to keep track of the status and movements of the objects. [START_REF] Gupta | Event-Driven SOA-Based IoT Architecture[END_REF] the HTTP protocol is used to facilitate access to the service. The answer emitted is in JSON format. The architecture proposed by [START_REF] Guinard | A resource oriented architecture for the Web of Objects[END_REF] allows developers to use web programming languages such as (HTML, Python,...).

All of the research cited above are interesting from the point of view of exposure and communication capacity in the Internet of Things. Each with its strong point in communication and interconnection in the world of IoT. They all almost used the REST Web Service. But for us, a simple service is not enough not only to bathe the connected objects in the artificial intelligence but also to treat in intelligent way the data captured at the level of the objects. This treatment helps automatic decision-making thanks to the intelligent BDI agent that has the same reasoning mode as humans. We focus our research on the intelligent Web Service, that is, a Web Service associated with artificial intelligence in order to reduce human intervention to the orchestration of services in connected objects and decision-making. We have exploited here our previous research work, Jason-RS. An intelligent Web Service with BDI agents that perform the resolution and automation of certain tasks thanks to their ability to cooperate and exchange using the AgenSpeak (L) agent language. These agents may then exhibit functionality as a Web Rest Service. Jason-RS has the methods used in REST (GET, PUT, POST, and DELETE) to ensure the dialog with heterogeneous clients.

3

Proposed Architecture

General architecture

In this section we discuss the general architecture of our approach. As we are in the world of the IoT there is a mean of communication: the network. There are many types of networks today for the connectivity of objects such as wifi, Bluetooth for common devices, LoraWAN for IoT devices using long distance communication, and ZigBee, 6LowPan, EnOcean, Zwave for short range communication devices, etc. As connected objects do not have enough memory space and very limited energy to feed themselves permanently so it is important to outsource most of its function. Although the design of its objects is different (ie the objects are heterogeneous), a standardization platform is important to allow inter-object choreography. Taking into account all these parameters we have our control center server constituted by the Jason-RS server and the IoT server illustrated by Fig. 1. Generally the IoT server is a centralized platform of (Cloud, big data) whether it is an IoT server that administers the objects. Jason plays a complex problem solver role as well as the complex system such as optimization ... The autonomous and goal-oriented capability through the BDI agent that it implements allows it to perform a task reasonably. This capability is now exploitable through a distributed component as a service [START_REF] Rafalimanana | Publish a Jason Agent BDI Capacity as Web Service REST and SOAP[END_REF] through Jason-RS. Then the objects can use it not only to send the sensor data but also to initiate a decision-making process at the agent level.

Communication between the IoT and the web is done through the HTTP protocol. With Jason-RS we can use all the existing methods of the REST and most importantly there is an interaction with the agent. As a result we can send data to the central control servers via the POST method. We can also retrieve the data or decision made by the GET method. JASON-RS recognizes directly the task that will send back to the agent.

At the agent level as soon as the service is called by the objects or other platform, the agent reacts and changes state in relation to the perception it receives from it environment.

Fig. 1. General architecture of the JASON-RS implementation in the Web of Object

We have already studied in our article [START_REF] Rafalimanana | Publish a Jason Agent BDI Capacity as Web Service REST and SOAP[END_REF] the publication of Jason's BDI agent capability as a Web Service. We have proposed an approach allows deploying and running Web Services (REST and SOAP) pairs with a Jason BDI agent in java SE environment. We didn't use a modern Web-App server or application server, and we didn't developed a different middleware cause it requires a lot of time. Deploying an agent inside a server is a tedious task. Then in our strategy, we reused the existing Java framworks called Non-Blocking Input Output. A small illustration for brightening the architecture is shown in Fig. 2.

Fig. 2. General architecture of the JASON-RS micro-service [5]

Jason-RS offers an opportunity to exploit the world-class logic, which is embedded in Jason. It can associate them with other elements or heterogeneous application. When we can manipulate first-order logic, we can imagine a kind of possibility of complex problem solving as well as the common problem in artificial intelligence. What is interesting here is that the reasoning mode of Jason agent is near the human reasoning mode to achieve a goal. Jason-RS aims to outsource the capacity present in Jason as a service.

This initial Jason-RS that we have exploited with the collaboration with the Iot platform to intelligently process the data received from the connected object. By exploiting the horn clause and the first-order logic included in Jason we will use this ability to decide the action of objects. Then we take into account the behavior of the objects as perception for the agents.

To validate our approach we tested the interoperability of Jason-RS and heterogeneous objects choreographed with BeC 3 .

Application of the architecture with a python object and BeC3

Scenario. To illustrate and motivate our approach, we introduce a concrete application using the following scenario. We take the example of the disposal of household waste in our paper [START_REF] Rafalimanana | Publish a Jason Agent BDI Capacity as Web Service REST and SOAP[END_REF]. Waste disposal agents have waste disposal costs that vary according to time constraints, people, distances traveled and evacuation loads. A decision-maker who gives these opinions to optimize the evacuation of waste thanks to the information that the evacuator agents gives him. Information of each of its agents could be changed thanks to the perception emitted by different connected objects. The Fig. 3 illustrates the application of this scenario. We used BeC 3 for the service Mashup. BeC 3 is a composer who allows a user to choreograph heterogeneous objects. In our experimentation we have created sensor objects choreographed in BeC 3 with other objects. This sensor sends the data captured via Jason-RS in POST for the agents to change the perception of the Jason agent (receiver agent). A decision-maker analyzes the present data and the cost of performing the tasks for each of his agents in order to decide which one is durable, and which one can perform a task at lower cost.

The sensor is controlled by the Python version of service compatible with BeC 3 . When the service is launched, it can send a POST request to BeC 3 to identify itself and then another POST request to set up its feature handled by the device. Any mashup designed by a user can then be assured by a communication the service on each object. The decision made by the agent could be consumed via a GET request to the Jason-RS.

Fig. 3. Application interaction between object choreography and Jason-RS

Result and discussion

We tested this approach in two different objects: a python object, a mobile phone. We used Jason-RS to communicate the BeC 3 platform with choreographed objects and BDI agents who take the place of a decision maker instead of a human being (such as it is originally designed in BeC 3 .

There is a demo script (in Python) APIDemo.py in Pylite project (resources dir). We have used xmpp2 server for login, its specificity is the presence. So as we describe in the section 3 we must log in BeC 3 in order to create a virtual programmable object:

POST /login with content { "username": {xmpp login}, "password": {xmpp password}, "service": {xmpp service (for example im.bec3.com)} } After login, create a BeC 3 object with the following JSON format:

POST /feature with content { "name": "tot2", "path" : "truc/bidul21", "type": "switch", "details": "ooo", "widget": "none", "mqtt": false } Available types are accelerometer, button, buzzer, gauge, gps, These object can be delete, update. DELETE /feature/{id} PUT /feature/{id} { "data": {data (dependent of object type} } One object is associate with one BDI agent. To connect BeC 3 an URL is available from Jason-RS. To change the agent BDI perception a POST request is send by this url format:

POST jason_rs_based_url/object_agent/ { "data":value_from_python_object } To receive a decision from BDI agent, Jason-RS provide an URL that we can consume:

GET jason_rs_based_url/agent_decider/decision

The following sequence diagram presented in Fig. 4 shows the exchange between these two platforms

Fig. 4. Sequence diagram for communication between object a Jason agent by Jason-RS

We analyzed the performance of our approach via a browser. We tested our approach on both devices. During our experiment the return is almost <= 1000 ms. Our test area are done through the WIFI network so the discussion field is open for other types of networks available for the Internet of Things.

Agent-based internet of things is an interested field of research now [START_REF] Savaglio | Agent-based Internet of Things: State-of-the-art and research challenges[END_REF], in the face of these works, we focus in the technical side of things, and we compare in table 2 what differentiates our approach compared to the state of the art. So in our approach we taking in count the heterogeneity of IoT by using a platform BeC 3. To permit an automatic decision making we exploit the horn logic provided in Jason. This collaboration permit a flexibility in interacting between agent and heterogeneous connected object.

Conclusion

This article describes our contribution on service improvement in the world of Web of Object. We have integrated our previous Jason-RS research work into the sensor device connected to the web. Object of the IoT do not support the execution of artificial intelligence within themselve because there resources are very limited. Our approach is towards the decentralization of this AI treatment with Jason-RS. Not only is Jason-RS a Web Service but it has an artificial intelligence BDI engine running. So the complex data processing in the connected web object is provided by the Jason. The communication is done through HTTP protocol as it is more flexible and commonly adopted in the world of web. We tested our approach with a Mashup service platform for heterogeneous connected objects called BeC 3 .Our approach therefore allows the object connected to the web to open up to data processing with artificial intelligence as well as complex systems itself. As perspective we are considering the automatic injection of service inside the connected object to reduce the human task of defining the service choreography.

Table 1 .

 1 The calculation time of the exchanges between the objects and Jason-RS

	Device	Method REST	Task	Duration (millisecond)
	Python Object	GET	Consumation WS	200-450
	Smart Phone	POST, GET	Sending data	428-1000

Table 2 .

 2 Comparison with a similar research domain work

	Work in field Ja-	Agent flexibility in	Horn Logic	Taking in count
	son agent and	internet		Heterogeneity of IoT
	IoT
	[19]	?	yes	?
	[20]	?	?	yes

https://www.supinfo.com/articles/single/5642-qu-est-ce-qu-une-api-rest-restful

https://xmpp.org/