Mathieu Grandemange
email: mathieu.grandemange@ensta-paristech.fr

Axel Mary

Marc Gohlke

Olivier Cadot

Effect on drag

Keywords: Flow control, Drag reduction, Induced drag, Ahmed geometry

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Introduction

Many industrial flows are related to the motion of bluff bodies in a fluid; the bluffness associated with the functional shape provokes flow separations on the afterbody and consequently complex wake dynamics. Over the past three decades, growing issues on energy have motivated research activities to improve the understanding of the wake past basic geometries. The objective is then often to limit unwanted aerodynamic effects such as drag or vibrations through optimization of the geometry or flow control. In the case of the automotive aerodynamics, the work of Ahmed et al. [START_REF] Ahmed | Some salient features of the time-averaged ground vehicle wake[END_REF] describes the effect of the afterbody shape of a vehicle on the flow topology and on the drag. Their work proves the critical influence of the slant angle of the rear window on a simplified road vehicle. The drag plotted versus the slant angle up to 30 • presents parabolic shape with a minimum drag reduction of 8% for a slant inclination of 12.5 • . Over this optimal value the drag increases continuously; the worst case (slant angle close to 30 • ) is associated with a 50% increase in drag in comparison to the 0 • case. Over a critical angle close to 30 • , the drag decreases abruptly to recover values close to the 0 • configuration. In terms of flow topologies, the blunt after-body is responsible for a massive separation on the base. For slant angles less than 30 • , the flow remains attached on the rear window (at least partially) and then a pair of counterrotating vortices develops from the sides of the slanted face [START_REF] Beaudoin | Cavitation as a complementary tool for automotive aerodynamics[END_REF]: the intensity of these streamwise vortices gradually grows in parallel with the drag increase. Over 30 • , the flow separates upstream of the rear window, a massive recirculation region without energetic streamwise vortices, similar to the 0 • case, is recovered.

The geometry used in the experiments of Ahmed et al. [START_REF] Ahmed | Some salient features of the time-averaged ground vehicle wake[END_REF] is massively used to explore drag reduction strategies needed by industry. Thus, literature reports the use of various different control devices. Much work has been devoted to the 25 • slant angle configuration; significant drag reductions are achieved through passive or active control strategies such as splitter plates [START_REF] Gilliéron | Aerodynamic drag reduction by vertical splitter plates[END_REF], flaps [START_REF] Beaudoin | Drag and lift reduction of a 3D bluff body using flaps[END_REF][START_REF] Fourrié | Bluffbody drag reduction using a deflector[END_REF], boundary layer streaks [START_REF] Pujals | Drag reduction of a 3d bluff body using coherent streamwise streaks[END_REF] or even pulsed jets [START_REF] Bruneau | Active procedures to control the flow past the ahmed body with a 25 rear window[END_REF]. Diverse strategies equally provide interesting base pressure recovery in the square-back case: splitter plates [START_REF] Khalighi | Experimental and computational study of unsteady wake flow behind a bluff body with a drag reduction device[END_REF], porous devices [START_REF] Bruneau | Coupling active and passive techniques to control the flow past the square back ahmed body[END_REF] or active control [START_REF] Littlewood | Aerodynamic drag reduction of a simplified squareback vehicle using steady blowing[END_REF].

The recent experiments of Littlewood & Passmore [START_REF] Littlewood | The Optimization of Roof Trailing Edge Geometry of a Simple Square-Back[END_REF] depict the effect of a small chamfer at the upper trailing edge of a square-back geometry. The geometry of the afterbody is similar to the 0 • Ahmed configuration. Despite its small size in the streamwise direction (only 15% of the base height), an optimal drag reduction of 4.4% is obtained for a chamfer angle of 12 • . The parabolic-like dependence between the chamfer angle and the drag is reminiscent of the results of Ahmed et al. [START_REF] Ahmed | Some salient features of the time-averaged ground vehicle wake[END_REF] for moderate slant angles where an optimal 8% drag reduction is obtained for a 12.5 • . Thus, the significant drag reduction obtained by Littlewood & Passmore [START_REF] Littlewood | The Optimization of Roof Trailing Edge Geometry of a Simple Square-Back[END_REF] with a small chamfer highlights the high sensitivity of the flow to its orientation at the trailing edge. This approach using chamfered shapes is particularly interesting since it corresponds to the method applied by the car manufacturers on real vehicles to limit the drag. Indeed, the angles of the spoiler and of the diffuser (when one is present) are set empirically to reach the optimum drag point given the vehicle parameters (global shape, ground clearance...), the characteristic length of these devices remaining small compared to the vehicle size.

The present work clarifies the link between the angles of the trailing edge, the flow topologies and the aerodynamic forces. The objective is to provide a comprehensive analysis to the method commonly used during the development of real cars that has not been provided in literature yet. In particular, the coupling effect between the angles of the separations from the top and bottom faces is evidenced and discussed. The article is organized as follows. In Sec. 2, the experimental setup and measurements are presented. Then, Sec. 3 is devoted to the results which are discussed in Sec. 4. Eventually, the concluding remarks are presented in Sec. 5.

Experimental set-up

The studied bluff body is the square-back model used in the experiments of Ahmed et al. [START_REF] Ahmed | Some salient features of the time-averaged ground vehicle wake[END_REF]. The flow is con-trolled by two flaps mounted downstream of the top and bottom faces of the model as depicted in Fig. 1(a). The streamwise length of the flaps is 50 mm, their crossflow length matches the width of the geometry and their thickness is 2 mm. The slant angles of the flaps can be controlled in the range (ϕ ]; these ranges remain reasonably small to keep the flow attached on the flaps in all the configurations. The slant inclination of the flaps is set with a precision better than 1 • . The different angle couples studied are presented in Fig. 1(b). The coordinate system is defined as x in the streamwise direction, z normal to the ground and y forming a direct trihedral. The origin of the coordinate system is in the reflectional plane of symmetry on the ground at the position of the base in the x direction. The experiments are performed in the PSA in-house wind tunnel used in the experiments of Beaudoin and Aider [START_REF] Beaudoin | Drag and lift reduction of a 3D bluff body using flaps[END_REF] or Pujals et al. [START_REF] Pujals | Drag reduction of a 3d bluff body using coherent streamwise streaks[END_REF]. This facility is an open wind tunnel with a 6 m long test section and a rectangular cross-section 2.1 m high and 5.2 m wide. The turbulence intensity is 1.3%. The main flow velocity is U 0 = 40 m s -1 and the Reynolds number based on the height of the base of the geometry (Re = U 0 H/ν) is 7.4 10 5 . The velocities are defined as u = u x .e x + u y .e y + u z .e z ; u ij is the amplitude of velocity at the considered point in the plane (e i ,e j ). A = ⟨a⟩ is the time-averaged value of any quantity a. The height of the base H, density ρ and inlet velocity U 0 are used to obtain non-dimensional values marked with an asterisk. For example, the dimensionless mean vorticity is denoted

T , ϕ B ) ∈ [-12 • , 17 • ] × [-
Ω * = rot U H/U 0 .
Drag and lift, denoted respectively F x and F z , are obtained using a strain balance. The dimensionless coefficient C i of the aerodynamic force in the i direction is defined according to Eq. ( 1) using S = 0.118 m 2 the projected area of the geometry in a cross-flow plane.

C i = F i 1 2 ρSU 0 2 , ( 1
)
with i ∈ {x, z}. Each measurement consists in five samples of 60 s at the frequency of 50 Hz. The precision in the measurement of C x and C z are 0.001 and 0.002 respectively.

The pressure on the body is measured at 47 locations around the entire geometry in the reflectional plane of symmetry. The levels of pressure are analyzed through the dimensionless pressure coefficient defined as:

C p = P -P 0 1 2 ρU 0 2 . ( 2
)
The pressure measurement is based on the time-averaged value obtained with one sample of 60 s; the precision is better than 4 Pa. To locate the taps in the plane y * = 0, the curvilinear abscissa s is used; its origin is taken in the middle of the base and goes positive on the top face.

In addition, a wake analysis is made from Particle Image Velocimetry (PIV) measurements. The system is comprised of a Quantal Big Sky Laser (dual pulse Nd:YAG) and a DANTEC CCD cameras (FlowSense MkII, 4 Mpx). The set-up acquires image pairs at a rate of 10 Hz; each acquisition records 400 image pairs. The interrogation window size is 32×32 pixels with an overlap of 75%. The mean velocities are taken into account only when more than 80% of the 400 measurements are valid vectors. Note that the flaps are transparent which enable the PIV measurement close to the base.

Results

In this section, the force measurements, pressure levels on the geometry and flow topologies are depicted varying the top and bottom slant angles. First, the effect of the top flap orientation ϕ T in the case ϕ B = 0 • is considered in Sec. 3.1. Then, the analyses are extended to the different bottom slant angles in Sec. 3.2. Finally, the drag and lift are presented in the bi-dimensional domain (ϕ T , ϕ B ) in Sec. 3.3. First, the inclination of the bottom flap is fixed at ϕ B = 0 • . The results of the drag and lift coefficients associated with the different top flap angles are pre-sented in Fig. 2(a). As reported by Littlewood & Passmore [START_REF] Littlewood | The Optimization of Roof Trailing Edge Geometry of a Simple Square-Back[END_REF], the lift is an affine function of the top flap inclination. It can be fitted by:

Study of the ϕ

B = 0 • cases
C z = 0.600 ϕ T -0.143, (3)
with ϕ T in radians.

On the other hand, the drag presents a minimum for ϕ T = 6 • with a 1.8% reduction in comparison to the case (ϕ T , ϕ B ) = (0 • , 0 • ). The experimental data follows a 2 nd order polynomial fit given by:

C x = 0.243 + 0.457 (ϕ T -0.110) 2 , ( 4
)
with ϕ T in radians. Similar parabolic-like dependences toward ϕ T for the drag are presented in the experiments of Ahmed et al. [START_REF] Ahmed | Some salient features of the time-averaged ground vehicle wake[END_REF] and Littlewood & Passmore [START_REF] Littlewood | The Optimization of Roof Trailing Edge Geometry of a Simple Square-Back[END_REF]. From these results, a clear quadratic dependence between the drag and the lift is reported. The relationship plotted in Fig. 2(b) is given by:

C x = 0.244 + 1.242 (C z + 0.083) 2 . ( 5
)
Fig. 3 Pressure distribution in the plane y * = 0 around the entire geometry (a) and focused on the afterbody (b) for

ϕ B = 0 • with different ϕ T : -, ϕ T = 6 • ; ---, ϕ T = 0 • ; • • •, ϕ T = -6 • .
These force evolutions are associated with changes of the wall pressure distribution on the model which are presented in Fig. 3. The effect of the flaps on the pressure is limited to the after-body (see Fig. 3a). Therefore, the affine dependence between the lift and ϕ T (Eq. 3) as well as the quadratic relationship between the drag and the lift (Eq. 5) remain valid when the rear lift is considered instead of the global lift; depending on the measurement facilities, it may give more accurate results. Focusing on the afterbody, the effect of the inclination of the top flap is displayed in Fig. 3(b). The angle of the top flap significantly affects the pressure on the top face before separation as well as the base pressure distribution (especially the base pressure gradient in the z direction). Slight variations in the pressure levels on the bottom face are also observed. These levels of pressure are consistent with the classification of the configuration in terms of drag and lift. These evolutions of pressure levels correspond to modifications of the flow topologies around the afterbody. The velocity measurements in the plane y * = 0 are presented in Fig. 4. The configurations correspond to the cases studied in Fig. 3 through the pressure distributions (ϕ T = 6 • , 0 • and -6 • with ϕ B = 0 • ). The change of the top slant angle then induces differences in the flows organization around the recirculation bubble. First, the angle of the top flap affects the streamline orientation in the flow over the body. For ϕ T = 6 • (see Fig. 4a), the flow separates at the end of the flap following the slant inclination. Moreover, the orientation of the flap induces changes in the streamline direction even upstream of the separation and up to z * = 1.5. The results are similar for ϕ T = 0 • and ϕ T = -6 • (see Figs. 4b-c); despite its small length, the flap is able to orient the potential flow before the separation on the top face. On the contrary, the direction of the streamlines from the under-body flow remains unchanged. These observations are consistent with the differences in the pressure levels on the afterbody. In particular, the classification of the streamline curvature before separation on the top face is directly linked to the classification of pressure levels visible in Fig. 3(b). As the inclination of the flow before separation is affected by the top flap angle ϕ T , the height of the wake after the end of the recirculation is strongly modified and the length of the recirculation region is slightly affected. The direction of the backward flow in the middle of the recirculation region (x * ≈ 0.8, z * ≈ 0.6) is equally changed which may be confronted to the base pressure gradient in the z direction visible in Fig. 3(b). The lowest pressure on the base is measured at the opposite side of the mean backward flow: for example, the flow presented in Fig. 4(a) presents a backward flow oriented toward the ground and then the pressure gradient in the z direction on the base is negative (see Fig. 3b). Such an association between an asymmetry in the recirculation bubble and a base pressure gradient is equally observed in Grandemange et al. [START_REF] Grandemange | On experimental sensitivity analysis of the turbulent wake from an axisymmetric blunt trailing edge[END_REF]. On the contrary, the centers of the averaged recirculation structures remain at first order at the same location; what changes is the relative position of the saddle point at the end of the recirculation: it is centered for ϕ T = 0 • whereas it goes close to the upper recirculation structure for ϕ T = -6 • and to the lower one for ϕ T = 6 • . The optimal drag configuration for ϕ B = 0 • is measured at ϕ T = 6 • , the velocities and pressure measurements show that this case do not correspond to the best top / bottom symmetry of the recirculation region: the saddle point at the end of the recirculation closure is off-centered (see Fig. 4a) and the base pressure gradient in the z direction is clearly negative (see Fig. 3b) in comparison to the ϕ T = 0 • case. As a result, the flow is highly sensitive to the top flap angle in the case ϕ B = 0 • because it controls the top / bottom equilibrium of the recirculation region as well as the wake height. The influence of the bottom flap angle on the previous results is now considered in Sec. 3.2.

T : +, ϕ B = 6 • ; ⃝, ϕ B = 0 • ; ⋄, ϕ B = -6 • ; △, ϕ B = -12 • ; ▽, ϕ B = -17 • ; ---, 2 nd order polynomial fits.
The dependences between the lift and the drag for different values of the bottom flap angle ϕ B varying the top flap angle ϕ T are plotted in Fig. 5. For each value of ϕ B , a quadratic relation is found between C x and C z ; the results are similar to the ϕ B = 0 • case presented in Sec. 3.1. Thus, as presented in Eq. ( 5), the data varying ϕ B at constant ϕ T follows:

C x = C x0 + α (C -C z0 ) 2 ,
with C x0 , C z0 and α the three parameters defining the 2 nd order polynomial fit. Figure 6 presents the data on their respective value of C x0 and C z0 . For all the values of ϕ B , the data points superimpose well on the master curve:

C x -C x0 = α (C z -C z0 ) 2 , ( 7
)
with α = 1.25. Therefore, the curves shown in Fig. 5 are identical and α is independent of the angle of the bottom flap ϕ B . The relationship between the lift and the drag are then perfectly defined from the two parameters C x0 and C z0 . Besides, it is observed that the optimal top flap angle ϕ T is a function of the bottom flap angle ϕ B indicating that the optimal drag configuration results from a coupling between the two angles and not from independent optimization regarding ϕ T and ϕ B .

C z0 : +, ϕ B = 6 • ; ⃝, ϕ B = 0 • ; ⋄, ϕ B = -6 • ; △, ϕ B = -12 • ; ▽, ϕ B = -17 • ; ---,
2 nd order polynomial fit of all the centered data.

The relationship between the forces of the optimal points C x0 and C z0 associated with the different values of ϕ B is now studied. Figure 7(a) displays the dependence between ϕ B and C z0 . The optimal lift linearly depends on the bottom slant angle according to:

C z0 = 0.580 ϕ B -0.079, (8)
with ϕ B in radians.

On the other hand, the optimal drag presents C x0 a quadratic evolution referring to C z0 given by: 1b). The corresponding velocity field is presented in Fig. 8. The flow respects a certain top / bottom symmetry of the recirculation structures since the mean backward flow is mostly oriented along x and the saddle point is centered in the z direction at the end of the recirculation. The wake height is strongly reduced in comparison with the one associated with the case ϕ T = ϕ B = 0 • presented in Fig. 4(b); the recirculation length in the streamwise direction is also decreased. However, it is worth noting that neither the wake height nor the recirculation region length are the only parameters of order of the drag. Indeed, the case ϕ T = 12 • and ϕ B = -17 • (optimal top angle for ϕ B = -17 • ) presents a thinner wake and a shortened recirculation region (see Fig. 9) but it still presents a larger drag coefficient (C x = 0.248). Therefore, past a certain point, when the slant angles are oriented further toward the recirculation region, the wake height keeps decreasing but the drag increases because of three-dimensional effects.

C x0 -C x opt = β (C z0 -C z opt ) 2 , ( 9
The inclination of the flaps leads to pressure differences between the top (or bottom) face and the side faces upstream of the base (see Fig. 3b). As a result, streamwise vortices develop from the lateral edges of the flaps. Such structures, already suggested in the results of Littlewood & Passmore [START_REF] Littlewood | The Optimization of Roof Trailing Edge Geometry of a Simple Square-Back[END_REF] must then be responsible for part of the drag [START_REF] Onorato | Drag measurement through wake analysis[END_REF].

To evidence this point, contours of streamwise vorticity in the plane x * = 0.6 are presented in Fig. 10 for different configurations: 10(a), there are no signs of vortices; however, as soon as the flaps are inclined (see Figs. 10b-c), strong vortices are measured in the near wake downstream of the side edges of the flaps; the sign of the vorticity depends on the flap orientation. When the signs of ϕ T and ϕ B are identical, the vortices from the one side of the model (y * > 0 or y * < 0) are co-rotating (see Fig. 10b) whereas, when the signs are different, the vortices are counter-rotating (see Fig. 10c).

(ϕ T = 0 • , ϕ B = 0 • ), (ϕ T = 12 • , ϕ B = 6 • ) and (ϕ T = 12 • , ϕ B = -12 • ). In the case (ϕ T = 0 • , ϕ B = 0 • ) presented in Fig.
As the height of the body is smaller than its width, the vortices from one side of the geometry are sufclose to further downstream in the wake. Contours of streamwise vorticity in the plane x * = 2.0 are presented in Fig. 11 for same three configurations:

(ϕ T = 0 • , ϕ B = 0 • ), (ϕ T = 12 • , ϕ B = 6 • ) and (ϕ T = 12 • , ϕ B = -12 • ).
Like in the near wake, there is no significant vorticity in the case (ϕ T = 0 • , ϕ B = 0 • ) (see Fig. 11a) but very different structures are reported depending on whether the vortices from one side of the geometry co-rotate or counter-rotate. In the configuration (ϕ T = 12 • , ϕ B = 6 • ) (see Fig. 11b), the pairs of co-rotating vortices from the sides of the model (y * > 0 and y * < 0) merge which lead to two strong counterrotating vortices. In the case (ϕ T = 12 • , ϕ B = -12 • ) presented in Fig. 11c), the pairs counter-rotating vortices have a destructive contribution to the streamwise vorticity; as a result, moving downstream, the vortices gradually lose their intensity.

Study in the domain (ϕ T ,ϕ B )

To go further in the analyses, the measurements of lift and drag are considered in the bi-dimensional domain (ϕ T , ϕ B ). The experimental lift as a function of the angles ϕ T and ϕ B is displayed in Fig. 12(a). The affine dependence toward the top slant angle depicted in Fig. 2 for the case ϕ B = 0 • is confirmed for all the values of ϕ B . Besides, Fig. 12(a) proves that the lift is equally an

* = 2.0 for (ϕ T = 0 • , ϕ B = 0 • ) (a), (ϕ T = 12 • , ϕ B = 6 • ) (b) and (ϕ T = 12 • , ϕ B = -12 • ) (c).

Continuous and dashed

are respectively positive and negative values, contour intervals are 2, contour 0 is not plotted. affine function of ϕ B for all the values of ϕ T . Thus, it can be fitted by:

C z = -0.135 + 0.566 ϕ T + 0.858 ϕ B , (10)
with ϕ T and ϕ B in radians. The affine fit given in Eq. ( 10) is shown in Fig. 12(b). Like the lift, the drag can be plotted in the domain (ϕ T ,ϕ B ). The results displayed in Fig. 13(a) confirms the presence of the minimal drag configuration for (ϕ T ≈ 10 • ,ϕ B ≈ -10 Eqs. ( 6) and ( 9), enable the drag to be expressed as:

C x = C x opt + α (C z -C z0 ) 2 + β (C z0 -C z opt ) 2 . ( 11
)
Since the dependence of C z and C z0 toward ϕ B and ϕ T are given in Eqs. ( 8) and [START_REF] Littlewood | Aerodynamic drag reduction of a simplified squareback vehicle using steady blowing[END_REF], the drag coefficient is then obtained as a function of the top and bottom slant angles. The result is displayed in Fig. 13(b); the drag evaluated from the successive fits is defined as:

C x = 0.248 -0.079 ϕ T + 0.062 ϕ B + 0.400 ϕ T 2 + 0.480 ϕ B 2 + 0.393 ϕ T ϕ B . ( 12
)
with ϕ B and ϕ T in radians. Equation ( 12) depicts all of the drag trends observed in the experiments. Besides, it is worth noting that the minimum of drag is not particularly sensitive to the angles ϕ T and ϕ B : a low value of drag, close to C x opt , can be obtained in a reasonably wide range of parameters around (ϕ T opt , ϕ B opt ). Finally, the term proportional to ϕ T ϕ B in Eq. ( 12) corresponds to the coupling effect of the top and bottom slant angles. This might be related to the fact that the inclination of the top flap leads to slight variations of pressure on the bottom face as evidenced in Fig. 3(b) and then alters the effect of the bottom flap; reciprocally, the orientation of the bottom flap affects the influnece of the top one. The mechanisms responsible for the selection of the drag and of the lift are then discussed in Sec. 4.

Discussion on the drag sources

The quadratic relationship between the drag and the lift is reminiscent of the notions of induced drag commonly used in aeronautics [START_REF] Batchelor | An introduction to fluid dynamics[END_REF]. However, the lift is not the only parameter responsible for the selection of the drag because of the presence of a massive flow separation on the base. Then, as mentioned in Sec. 3.2, the height of the wake seems to have a critical influence, however three-dimensional effects may become dominant over a certain point. The expression of drag given in Eq. ( 12) can be rewrit-ten as:

C x = 0.
with C z following the affine function defined in Eq. ( 10) and ϕ B and ϕ T in radians. The different terms ( 13), ( 14) and ( 15) may respectively be interpreted as the drag contribution of the wake size, the drag induced by the lift and a local drag induced by the slant angles. First, the term (13) corresponds to the drag associated with the recirculation region using a bi-dimensional vision. The low pressure in the recirculation region is related to the curvature of the streamlines around the separatrix [START_REF] Riabouchinsky | On steady fluid motions with free surfaces[END_REF]. This curvature can be evaluated as (πϕ T + ϕ B )/L with L the recirculation length. In the theoretical limit case (ϕ T = π/2, ϕ B = -π/2) with a flow attached on the flaps, this drag contribution would be nil. One may then expect an expression following:

C x 2D L 0 L ( 1 - ϕ T -ϕ B π ) , (17)
with L 0 the recirculation length of the case ϕ T = ϕ B = 0 • . The values of L for the different slant orientation is not known; yet, assuming L 0 /L ∼ 1, the evaluation of the linear term [START_REF] Onorato | Drag measurement through wake analysis[END_REF] gives: 0.248

( 1 - ϕ T -a ϕ B b ) , ( 18
)
with C x 2D = 0.248, a = 0.778 ∼ 1 and b = 3.14 ∼ π. a ̸ = 1 is likely to be an effect of the ground: the bottom flap is less efficient than the top one in orienting the potential flow upstream of the separation. The positive effect of an increase in ϕ T -a ϕ B on drag may then be seen as a transfer of pressure between the wake and the top or bottom face of the geometry. In the case ϕ T = ϕ B = 0 • , the curvature of the streamlines related to the low pressure region is concentrated near the separatrix of the massive recirculation region. If one of the two flaps is oriented toward the recirculation region, then the flow separates following the flap orientation; it induces some streamline curvature upstream of the base which leads to a decrease in pressure upstream of the oriented flap. Therefore, this phenomenon is directly measured on the lift and explains the affine dependence between C z and the slant angles. It is important to mention that, in literature, drag reductions are often related to increased mean recirculation lengths past cylindrical shapes [START_REF] Roshko | Perspectives on bluff body aerodynamics[END_REF][START_REF] Parezanović | Experimental sensitivity analysis of the global properties of a 2D turbulent wake[END_REF] because the flow orientation at the base separation remains the same whatever the recirculation length is; thus it remains consistent with Eq. ( 17).

The second term [START_REF] Batchelor | An introduction to fluid dynamics[END_REF] refers to the drag induced by the lift like in the formalism introduced in aeronautics; in particular, the coefficient 0.405 is expected to be a parameter only dependent on the geometry. This induced drag is associated with the possible presence of strong counter-rotating in the far wake like past the 25 • Ahmed geometry. Eventually, the third term (15) may be interpreted as a local drag induced by the slant angles. As seen in Sec. 3.2, the sides of the flaps are sources of vortices which are responsible for part of the drag. Since these vortices can cancel out after merging, they are not necessarily associated with lift and then may not be taken into account in the term [START_REF] Batchelor | An introduction to fluid dynamics[END_REF].

In Eq. 16, the variations of the drag contributors are of similar magnitude in the studied ranges of angles ϕ T and ϕ B . Depending on the configuration, one or two of these sources are negligible; in order to have a global view of these different drag sources, sketches interpreting the flow topology of three configurations are presented in Fig. 14. Figure 14(a) depicts the case ϕ T = ϕ B = 0 • . There are no vortices developing from the side edges of the flaps because there is almost no pressure differences between the top (or bottom) and side faces: the term ( 15) is then close to 0. The is no intense streamwise vortices downstream of the recirculation region either, the lift force is reasonably small: the term ( 14) is limited. Thus, the main drag source is the massive separation on the base contained in the term [START_REF] Onorato | Drag measurement through wake analysis[END_REF]. The case ϕ T = ϕ B ∼ 10 • is displayed in Fig. 14(b). Since ϕ T -a ϕ B ≈ 0 • , the term ( 13) is very close to the previous configuration. In addition, the slant angles are responsible for the development of vortices at the side edges of the flaps, the term ( 15) is no more negligible. Besides the flaps have constructive effect on the lift so the terms ( 14) is significant. The drag is then increased in comparison to the previous case ϕ T = ϕ B = 0 • . Figure 14(c) presents the case ϕ T ∼ 10 • and ϕ B ∼ -10 • . The comparison of Figs. 4(b) and8 shows that the height of the wake is strongly reduced compared to ϕ T = ϕ B = 0 • . The term ( 13) is then less contributor to the drag. As they have a destructive effects on the lift, the term (14) remains limited. However, each flap is associated with the development of a pair of vortices from their sides which are drag sources and taken into account in the term [START_REF] Riabouchinsky | On steady fluid motions with free surfaces[END_REF]. The total drag is reduced: for moderate slant angles (less than 10 • to 15 • ), the decrease of the term [START_REF] Onorato | Drag measurement through wake analysis[END_REF] is larger than the local drag generated by the vortices from the flaps. However, when the slant angles are more important (typically over 15 • ), the drag is increased since the reduction of the wake size do not compensate any more the drag linked to the vortices from the corner of the base. Therefore, the competition of these two effect explains the optimal drag configuration for ϕ T ∼ 10 • and ϕ B ∼ -10 • .

Concluding remarks

The inclination of two small flaps downstream of the top and bottom face of the square-back Ahmed geometry [START_REF] Ahmed | Some salient features of the time-averaged ground vehicle wake[END_REF] is proved to have a critical influence on both drag and lift as well as on the flow topology. When the bottom flap is fixed, the lift and the drag are 1 st and 2 nd order polynomial functions of the top flap angle respectively. This leads to a quadratic dependence between the drag and the lift. This drag versus lift relationship is self similar for five different bottom flap angles. In the bi-dimensional domain (ϕ T , ϕ B ), the lift is in good agreement with an affine function of ϕ T and ϕ B . The drag can be interpolated with a second order polynomial of ϕ T and ϕ B adding a term proportional to ϕ T ϕ B . This coupling term shows that the optimal top slant angle relies on the bottom flap orientation and the minimum of drag cannot be achieved from independent optimization of the two slant angles. From these results, a classification of three drag sources is suggested. First, the linear term proportional to ϕ Ta ϕ B quantifies the interest of having a thin wake to limit the base drag. The associated mechanisms may be viewed as a transfer of streamline curvature from the near wake to the top or bottom faces which leads to pressure recovery in the recirculation region. A second term corresponds to a notion of induced drag directly linked to the lift force. However, these two drag contributors are not sufficient to explain the drag results presented in Fig. 13 and a drag contribution associated with the local development of streamwise vortices from the side edges of the flaps is identified. The presence of terms linked to induced drag points out the interest of having a certain axisymmetry in the recirculation closure. Therefore, the use of additional flaps on the sides is very likely to provide greater drag reductions. Besides, smooth junctions between the flaps would limit the formation of local vortices and then lead to additional drag reduction.

Fig. 1

 1 Fig. 1 (a) Experimental set-up of the model; O sets the origin of the coordinate system; dihedral angles ϕ T and ϕ B are displayed positive and negative respectively. (b) Couples of slant angles (ϕ T , ϕ B ) studied.

Fig. 2

 2 Fig. 2 (a) Drag (+) and lift (⃝) as a function of the top slant angle ϕ T for ϕ B = 0 • ; ---, 1 st and 2 nd order polynomial fit for drag and lift respectively. (b) Drag versus lift as a parametric function of the top slant angle ϕ T for ϕ B = 0 • : +, experimental data; ---, 2 nd order polynomial fit.

Fig. 4

 4 Fig. 4 Velocity measurements in the plane y * = 0 for ϕ T = 6 • (a), ϕ T = 0 • (b) and ϕ T = -6 • (c) with ϕ B = 0 • ; crosses are saddle points.

3. 2 Fig. 5

 25 Fig. 5 Drag versus lift as a parametric function of the top slant angle ϕT : +, ϕ B = 6 • ; ⃝, ϕ B = 0 • ; ⋄, ϕ B = -6 • ; △, ϕ B = -12 • ; ▽, ϕ B = -17• ; ---, 2 nd order polynomial fits.

Fig. 6

 6 Fig. 6 Drag versus lift for different values of ϕ B centered on their respective value of C x0 andC z0 : +, ϕ B = 6 • ; ⃝, ϕ B = 0 • ; ⋄, ϕ B = -6 • ; △, ϕ B = -12 • ; ▽, ϕ B = -17 • ; ---,2 nd order polynomial fit of all the centered data.

) with β = 1 . 14 ,

 114 C x opt = 0.238 and C z opt = -0.155. This relationship can be seen in Fig. 7(b). C x opt is then the optimal drag point considering both parameters ϕ T and ϕ B ; C z opt is the associated lift. This optimal concorresponds to the flap angles ϕ T opt = 9.2 • and ϕ B opt = -7.4 • . Experimentally, the best configuration in terms of drag is obtained for the case (ϕ T = 12 • , ϕ B = -6 • ) with C x = 0.240 and C z = -0.133. The associated drag and the corresponding flap angles are relatively close to C x opt and (ϕ T opt , ϕ B opt ) given the precision of the force measurement and the limited resolution of the parameters ϕ T and ϕ B in the experiments (see Fig.

Fig. 7

 7 Fig. 7 (a) C z0 as a function of the bottom slant angle ϕ B = 0 • : +, experimental data; ---, 1 st order polynomial fit. (b) C x0 versus C z0 as a parametric function of the bottom slant angle ϕ B = 0 • : +, experimental data; ---, 2 nd order polynomial fit.

Fig. 8

 8 Fig. 8 Velocity measurements in the plane y * = 0 for ϕ T = 12 • and ϕ B = -6 • ; cross is saddle point.

Fig. 9

 9 Fig. 9 Velocity measurements in the plane y * = 0 for ϕ T = 12 • and ϕ B = -17 • ; cross is saddle point.

Fig. 10

 10 Fig. 10 Contours of streamwise vorticity Ω x * in the plane x * = 0.6 for (ϕ T = 0 • , ϕ B = 0 • ) (a), (ϕ T = 12 • , ϕ B = 6 • ) (b) and (ϕ T = 12 • , ϕ B = -12 • ) (c). Continuous and dashed lines are respectively positive and negative values, contour intervals are 2, contour 0 is not plotted.

Fig. 11

 11 Fig. 11 Contours of streamwise vorticity Ω x * in the plane x* = 2.0 for (ϕ T = 0 • , ϕ B = 0 • ) (a), (ϕ T = 12 • , ϕ B = 6 • ) (b) and (ϕ T = 12 • , ϕ B = -12 • ) (c). Continuous and dashedare respectively positive and negative values, contour intervals are 2, contour 0 is not plotted.

Fig. 12

 12 Fig. 12 Lift depending on ϕ T and ϕ B : experimental data (a) and affine fit given in Eq. (10) (b). Contour intervals are 0.02.

Fig. 13

 13 Fig. 13 Drag depending on ϕ T and ϕ B : experimental data (a) and law given in Eq. (11) (b). Contour intervals are 0.002.

Fig. 14

 14 Fig. 14 Sketch of the flow topologies for different configuration: (a), ϕ T= ϕ B = 0 • ; (b), ϕ T = ϕ B ≈ 10 • ; (c), ϕ T = -ϕ B ≈ 10 • .

  248 -0.079 (ϕ T -0.778 ϕ B )

				(13)
	+ 0.405 C z	2		(14)
	+ 0.271 ϕ T	2 + 0.182 ϕ B	2 .	(15)


Riccardo Petrolo

Roberto Morabito
email: roberto.morabito@ericsson.com

Valeria Loscrì

Design of a Gateway for the Cloud of Things

In the last decades, Sensor Networks (SNs) have played a primary role for the research community. However, the progress focused more on communication and networking aspects between such devices than on the interpretation of the produced data.

, there is an effort to understand data, which comes out, and goes into sensor networks.

As stated by Diaz et al. in [START_REF] Diaz | Semantic as an interoperability Enabler in Internet of Things[END_REF], semantic technologies, such as ontologies and linked data, can be used to solve the problem of scalability -many different devices can be added to the system at a fast pace, by formatting the information -. Interoperability is made easier as well: the data format provided by ontologies makes services equally available; regardless of the device they are being provided.

Those technologies, together with the Cloud Computing are at the base of the Cloud of Things (CoT) that we propose in [START_REF] Petrolo | Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms[END_REF]. The CoT aims to better use distributed resources, putting them together and enabling therefore a horizontal integration of various Internet of Things (IoT) platforms.

Similar concepts to CoT, such as Capillary Networks [START_REF] Sachs | Capillary networks -a smart way to get things connected[END_REF], and Fog Networks [START_REF] Bonomi | Fog computing and its role in the Internet of Things[END_REF] include in their topology, the presence of intelligent nodes that, although often characterized by limited computation resources, can easily manage all the applications running on top of them.

Adopting this approach, in this paper we design the architecture of a Gateway for the Cloud of Things. The Gateway is able to manage semantic--like things and to act as an end--point for the dynamic presentation of real world data to consumer applications and users.

In order to semantically annotate the properties of the sensors -e.g. measurement capability, position etc. -we embed a light ontology into sensors' firmware. This will improve the efficiency in the discovery process of sensor properties.

Prediction algorithms on data production are used between the Gateway and sensors in order to reduce the number of communications between them, and therefore to lower the battery consumption and interferences. Specifically, in our proposal, we design and develop an efficient gateway through the use of virtualized software and, in particular, by exploiting all the benefits introduced by emerging lightweight virtualization technologies.

As exhaustively explained in [START_REF] Morabito | Hypervisors vs. Lightweight Virtualization: A Performance Comparison[END_REF], these technologies introduce an almost negligible overhead and they are modeled in such a way to guarantee a lightweight and dense deployment of services.

Those platforms are indeed, not designed to be exclusively used in large data--center or in specific cloud environments, they are equally efficient, even when operating on gateway and/or embedded systems.

Container--based solutions implement processes isolation by avoiding the emulation of virtual hardware and therefore reducing the overhead that characterize other virtualization technologies such as hypervisors .

Therefore, the advantage of achieving higher density of virtualized processes -which run within each container -is a direct consequence.

With specific relation to our scenario, the versatility of container technologies allows a dynamic and optimized usage of the gateway, so that services can be allocated only when needed and according to the functionality of the SN.

In conclusion, we show that the combination of all these features can represent a valuable way to convey several advantages -both sensors and gateway side -and noticeable improvements in terms of resource allocation, service management, and energy efficiency and discuss different ways of future investigations.


Riccardo Petrolo

Valeria Loscrì

Nathalie Mitton

Towards a Smart City based on Cloud of Things, a survey on the smart city vision and paradigms

published or not. The documents may come

Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms

INTRODUCTION

Cities are growing steadily and urban living poses major challenges in our daily lives. As of 2007, 50% of the world population was living in cities rather than rural areas. The United Nations Population Fund forecasts that by 2030 approximately 60% of the world population will live in an urban environment [START_REF] Naphade | Smarter cities and their innovation challenges[END_REF]. In this context, Information and Communication Technologies (ICT) together with local governments and private companies, play a key role for implementing innovative solutions, services and applications to make smart cities a reality. In this context the Internet of Things (IoT) paradigm is playing a primary role as an enabler of a broad range of applications, both for industries and the general population. The increasing popularity of the IoT concept is also due to the constantly growing number of very powerful devices like smartphones, tablets, laptops and lower powerful devices like sensors that are able to join the Internet. In the context of Smart Cities, it makes sense to consider the scenario of the various different and heterogeneous devices, the Wireless Sensor Networks interconnected to each other and to exploit these "interconnections" to activate new type of services. The ICT trends suggest that the sensing and actuation resources can be involved in the Cloud and solutions for the convergence and evolution of IoT and cloud computing infrastructures arise. Nevertheless, there are some challenges that need to be faced such as: 1) the interoperability among different ICT systems; 2) a huge amount of data to be processed provided in real-time by the IoT devices deployed in the smart systems; 3) the significant fragmentation deriving from the multiple IoT architectures and associated middleware; 4) heterogeneous resources mashup, namely how to orchestrate resources of the various Clouds. Concerning the last item, the concept of IoT, with underlying physical objects abstracted according to thing-like semantics, seems a valid starting point for the orchestration of the various resources. In this context, the Cloud concept could play the role to connect the IoT with the Internet of People through the Internet of Services, by the means of an horizontal integration of various silos. We will refer to this horizontal integration and to the Cloud computing associated to the IoT as the Cloud of Things. As we will see, this concept goes beyond to the interconnection and hyperlink of things. In this paper, we will explain how abstraction, virtualization and management of things have to be properly designed and implemented in order to develop solutions for the convergence of diverse IoT platforms and Clouds. A precise design of these mechanisms will permit 1 the development of a technological-agnostic architecture, where the integration and deployment of diverse devices and objects can be considered by neglecting their underlying architecture. We will present the VITAL project as a Cloud of Things-based architecture, able to meet many critical requirements of a smart city, and we will show how this platform can be considered to bridge different and heterogeneous IoT silos. A preliminary description of the VITAL platform has been presented in [START_REF] Petrolo | Towards a Smart City based on Cloud of Things[END_REF] and [START_REF] Petrolo | Towards a cloud of things smart city[END_REF], where the authors just introduce the platform and the Cloud of Things paradigm. In this work, we will give more details about the services that VITAl implements and we will describe specific use cases where the VITAL platform plays a very key role. The paper is structured as follows: in Section II we revise the literature regarding the IoT platforms. Section III introduces the concept of Cloud of Things (CoT), starting from the traditional Cloud computing concept. Section IV is devoted to present the main requirements of a city to be considered smart. In Section V we consider both paradigms, the IoT and the CoT, as potential effective solutions to make the cities smarter and more sustainable, based on the specific requirements individuated in Section IV. In Section VI we describe the VITAL platform as a CoT-based platform and effective solution to be applied for the realization of a smart city. In the Section VII, we give some practical examples of VITAL application, by defining some use cases. In the Section VIII, we describe the main ICT actors that the deployment of a Smart City implies. Finally, Section IX concludes the paper by investigation open research directions.

IoT PLATFORMS

"The Internet of Things has the potential to change the world, just as the Internet did. Maybe even more so" [START_REF] Ashton | That 'Internet of Things' Thing[END_REF]. With this sentence, in 1998, Kevin Ashton introduced for the first time the term "Internet of Things" (IoT). Some years later, in 2005, the International Telecommunication Union (ITU) formally introduced IoT, according to which: "from anytime, anyplace connectivity for anyone, we will now have connectivity for anything" [START_REF]The Internet of Things[END_REF].

Since then, IoT starts to be a hot topic in academic and industry fields. Several EU projects have been launched with the goal to provide solutions for the realization of the IoT and its integration in different application domains. The main reasons behind this interest are the capabilities that the IoT will offer. It promises to create a world where all the objects around us are connected to the Internet and communicate with each other with minimum human intervention [START_REF] Perera | Context Aware Computing for The Internet of Things: A Survey[END_REF]. These potentialities make possible the development of a huge number of applications in different domains as shown in Figure 1 according to [START_REF] Asin | DAVID. 50 Sensor Applications for a Smarter World[END_REF].

At the same time and together with the several IoT platforms, the middleware started to gain more and more importance. Middleware gained in the last years a lot of importance due to their major role in simplifying the development of new services and the integration of legacy technologies into new ones [START_REF] Atzori | The Internet of Things: A survey[END_REF]. In the following, we will present some of the most representative IoT platforms without pretending to be exhaustive: GSN [19] is a platform developed in Java, aiming at providing flexible middleware to address the challenges of sensor data integration and distributed query processing. It lists all the available sensors in a combo-box which users need to select the ones they need. GSN's purpose is to make the GSN applications hardware-independent and the changes and variations invisible to the application. The main limitation of GSN is that it lacks semantics to model the metadata.

IoT

LSM [START_REF] Lsm | [END_REF] (Linked Sensor Middleware) is a platform that brings together the live real world sensed data and the Semantic Web. It provides many functionalities such as, wrappers for real time data collection and publishing; a web interface for data annotation and visualization; and a SPARQL endpoint for querying unified Linked Stream Data and Liked Data.

Sensor-Cloud [START_REF] Yuriyama | Sensor-Cloud Infrastructure -Physical Sensor Management with Virtualized Sensors on Cloud Computing[END_REF] is an infrastructure that aims at managing physical sensors by connecting them to the cloud. This infrastructure provides the service instances (virtual sensors) to the users in an automatic way at the same fashion as these virtual sensors are effectively part of the IT resources. The generation of the services instances implies that the sensor devices and service templates (used to create the virtual sensors) should be firstly prepared.

It uses SensorML to describe the metadata of physical sensors. It just focuses on managing sensors via cloud.

OpenIoT [31] represents a joint effort of several contributors to IoT-based applications according to a cloud computing delivery model. It concentrates on providing a cloud-based middleware infrastructure in order to deliver on-demand access to IoT services, which could be formulated over multiple platforms. Its middleware infrastructure permits to collect and filter data information from the Internet-connected objects, by the means of opportunistic algorithms. It is strictly related to the FP7 VITAL project [START_REF] Schiele | Moving Towards Interoperable Internet-of-Things Deployments in Smart Cities[END_REF].

Xively [START_REF]XIVELY[END_REF] (formerly known as Cosm and Pachube) offers a public cloud for the IoT that simplifies and accelerates the creation, deployment and management of sensor in scalable manner.

As we will better explain, these approaches strengthen the vision towards the Cloud of Things, but a lot of new enhancements are still needed to realize a Cloud of Things (CoT) platform for Smart City.

TOWARDS CLOUD OF THINGS

Cloud Computing attracts the attention from both academy and industry across the world, because it is capable of transforming service provision models over the entirely current IT (Information Technology) industry. It provides criterion for service provisions with reduced upfront investment, expected performance, high availability, tremendous fault-tolerance capability, infinity scalability, and so on [START_REF] Zhou | Services in the Cloud Computing era: A survey[END_REF].

According to [START_REF] Patidar | A Survey Paper on Cloud Computing[END_REF] the services can be divided in three layers (Figure 2):

• Infrastructure as a Service (IaaS) which offers computing resources such as processing or storage; • Platform as a Service (PaaS) designed for software developers, in order to allow them to write their applications according to the specifications of a particular platform without needing to worry about the underlying hardware infrastructure; • Software as a Service (SaaS) is the most visible layer of Cloud Computing for end-users, because it is about the actual software applications that are accessed and used.

In addition to the above main layers, some others are also introduced and discussed in literature such as Data as a Service (DaaS), Network as a Service (NaaS), Identity and Policy Management as a Service (IPMaaS), and so on. In [START_REF] Banerjee | Everything as a Service: Powering the New Information Economy[END_REF] authors introduce XaaS (everything as a service model) that promotes the "pay as you go" method, allowing the consumers to consume a service from a service provider by paying only for the amount of resources they actually use. Within the Internet of Things context, this approach gave the input to the so-called Cloud of Things (CoT) [START_REF] Distefano | Enabling the Cloud of Things[END_REF], which deals to implement indexation and querying services of things, and provide them to

Software as a Service

Platform as a Service

Infrastructure as a Service Figure 2. Cloud Computing service models [START_REF] Patidar | A Survey Paper on Cloud Computing[END_REF].

final users, developers, provides, as a service (Figure 5). One interesting model to enable CoT is comprehensively discussed by authors in [START_REF] Perera | Sensing as a service model for smart cities supported by internet of things[END_REF]. They focus indeed, on the Sensing as a Service model based on Internet of Things (IoT) infrastructure, which introduces four conceptual layers:

• Sensor and Sensor Owners Layer that consists of sensors and how the owner manages them, allowing or not, the publication in the cloud. • Sensor Publishers that detect available sensors, communicate with the sensor owners, and get permission to publish sensors in the cloud. • Extended Service Providers which communicates with multiple Sensor Publishers in order to select sensors based on costumer's requirements. • Sensor Data Consumers that need to register themselves in order to consume sensors data.

The advantages and benefits promised by the Sensing as a Service model are numerous, and just to name the majors we have: sharing and reusing of sensor data (if someone has already deployed the sensors, others can have access to them by paying a fee to the sensor owner), reduction of data acquisition cost due to the shared nature, collect data previously unavailable (thanks to the business model, companies are stimulated to "sell" them sensors data).

In the last few years, researchers have mainly focused on representing the observation and measurement data from sensor networks, according to the Sensor Web Enablement (SWE) proposed in [START_REF] Botts | OGC Sensor Web Enablement: Overview And High Level Architecture[END_REF] by the Open Geospatial Consortium (OGC). However, these standards do not provide facilities for abstraction, categorization, and reasoning rather offered by [START_REF] Compton | The SSN ontology of the W3C semantic sensor network incubator group[END_REF], within the W3C Semantic Sensor Network Incubator group (SSN-XG) defined an OWL2 [START_REF] Hitzler | OWL 2 Web Ontology Language Primer[END_REF] ontology, answering the need for a domain-independent and end-to-end model for sensing applications by merging sensor-focused (e.g. SensoML [START_REF] Botts | OGC Sensor Web Enablement: Overview And High Level Architecture[END_REF]), observation-focused and system-focused views. It has received consensus from the community and has been adopted in several projects like Spitfire EU Project * .

Regarding the heterogeneity in communications technologies, interesting solutions for resource-constrained devices (e.g. sensors) are provided by 6LoWPAN [START_REF] Montenegro | Transmission of IPv6 Packets over IEEE[END_REF] and CoAP [START_REF] Costantino | Performance analysis of an LTE gateway for the IoT[END_REF].

6LoWPAN enables the integration of sensors to the Internet thanks to mechanisms which allow transmission of IPv6 packets. In order to convert IPv6 packets to 6LoWPAN and vice versa, a gateway (i.e. border router) is required. It handles the necessary tasks such as header compression and enables the seamless usage of IPv6 across the heterogeneous network architectures. 6LoWPAN has been developed in an early stage for the TinyOS, Contiki, and FreeRTOS operating systems for WSNs [START_REF] Mazzer | Comparisons of 6LoWPAN Implementations on Wireless Sensor Networks[END_REF].

CoAP is an application layer protocol designed for energy constrained devices. It deals with Constrained RESTful Environments (CoRE) [START_REF] Taherkordi | Restful service development for resourceconstrained environments[END_REF], providing a lightweight alternative to HTTP. Devices supporting CoAP provide flexible services over any IP network using UDP. Any HTTP client or server can interoperate with CoAPready endpoints by simply installing a translation proxy between the two devices [START_REF] Castellani | Web Services for the Internet of Things through CoAP and EXI[END_REF].

Summing up, IoT devices can be connected to the Internet, their data can be annotated using a sensor ontology (i.e., SSN ontology), encoded in standard Web formats (i.e., RDF), and made available on the Cloud, establishing therefore the Cloud of Things.

SMART CITY REQUIREMENTS

Before to relate the IoT and CoT paradigms to what is considered as a very emerging and important application domain, namely Smart City, it is useful to identify the main requirements, in terms of ICT-based services and solutions, that a city presents. To this purpose, by following the same approach introduced in [2], we make reference to two different types of requirements, that are: 1) service/application, considered from the point of view of the citizens and 2) operational, seen from the city authorities and administrators of the networks viewpoint. Concerning the service/application aspects, the end-users devices equipped with multiple radio technologies and several sensors and actuators deployed all over the cities, make possible the individuation of novel services and applications for the citizens. These services will have specific features, like: a) user-centric: based on the specific context and the preferences of the users, b) ubiquitous: reachable everywhere and from any devices, and c) highly-integrated: based on the integration of services and data from several and different applications or on the social cooperation of multiple users. Of course, beyond the citizens, also the stakeholders of a city, like educational institutions, healthcare and public safety providers, governmental organizations, etc. will be in conditions to exploit the key features of these new services that make the city more sustainable. On the other hand, the Smart City concept considered from the point of view of the administrations and the networks providers are translated into a network infrastructure that is: a) highlyinterconnected: by overcoming the heterogeneity of the devices and the IoT platforms, it is possible to provide ubiquitous connectivity, b) cost-efficient: the deployment and organization of the network should be as much automatic as possible and independent from the human intervention, c) energy-efficient, able to realize an efficient resource utilization, in order to meet the main requirements of green applications d) reliable: that connectivity, the ubiquity of the network should be guaranteed above all in the case of exceptional and adverse conditions. The real scenario we can observe at the moment, is characterized with a high level of fragmentation of technologies, lack of ubiquity in terms of both connectivity and coverage, due to the plethora of technologies and devices present in a city. This fragmentation is mainly due to the presence of many access networks usually managed by different operators (i.e. Universal Mobile Telecommunications System -UMTS, Worldwide Interoperability for Microwave Access -WiMAX, WiFi, etc.). Even if some steps ahead have been moved thanks to some projects, most of these initiatives are related to specific cities and do not consider general architectures. By considering the main IoT platforms and the CoT concept, we will try to explain how the main requirements of a city to become a smart city can be fulfilled and at the end we will show how the VITAL platform can play the role of "interconnecting" heterogeneous ICT silos and devices.

IoT AND CoT FOR A SMART CITY

Among all the domains that can take advantages from the IoT platforms, the Smart City concept received in the last few years a significant research effort and technological development. The most notable reason for these attentions, is attributable to the population growth and the urbanization trend. According to the United Nations, the urban populations will grow to an estimated 2.3 billion over the next 40 years, while as much as 70 % of the world's population will live in cities by 2050. The rapid growth of cities became the main driver of global environmental changes, as cities, occupying only 2 % of the earth landmass consume about 75 % of the world's energy and produce 80 % of its greenhouse gas emission. Such a dramatic expansion of the cities has brought to focus the need to develop cities in a sustainable manner, while also making the quality of life in the cities better [START_REF] Theodoridis | Developing an iot smart city framework[END_REF].

Although there is not a formal and universally accepted definition of "Smart City" yet, in [START_REF] Hancke | The role of advanced sensing in smart cities[END_REF], authors try to delineate the concept, defining a Smart City as a city which functions in a sustainable and intelligent way, by integrating all its infrastructure and services into a cohesive whole and using intelligent devices for monitoring and control, to ensure sustainability and efficiency. This interpretation makes evident, therefore, that Smart City concept (Figure 3) needs interoperability between the different IoT deployments that are, today, mainly closed and vertically integrated to specific application domains [START_REF] Sanchez | SmartSantander: Experimentation and service provision in the smart city[END_REF]. These solutions are, indeed, based on multiple architectures, standards and platforms, which have led to a highly fragmented IoT landscape and make challenging the realization of the Smart City concept. According to [START_REF] Khan | Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges[END_REF], we can recognize the IoT structure divided into five layers (Figure 4):

Smart City

• Device Layer that deals with the identification and collection of objects specific information by the sensor device; • Network Layer used to send data collected by the Device Layer to the information processing system; • Middleware Layer that performs information processing and ubiquitous computation and takes automatic decision based on the results; • Application Layer which provides global management of the application based on the information processed through the Middleware; • Business Layer that is responsible for the management of overall IoT system.

Within the context of Smart City [START_REF] Caragliu | Smart cities in Europe[END_REF], the Cloud of Things is expected to play a significant role. CoT indeed, can make better use of distributed resources, puts them together in order to achieve higher throughput and be able to tackle large scale computation problems [START_REF] Rimal | A Taxonomy and Survey of Cloud Computing Systems[END_REF], enabling therefore, the horizontal integration of various (vertical) Internet of Things platforms and so the Smart City vision. Moreover, it allows users to express the service they want providing the relevant data back to them quickly without asking the users to manually select the sensors which are relevant to their requirement. Certainly, CoT needs to deal with several challenges, but the major in terms of research regards the heterogeneity in sensor types (e.g., temperature, NFC, RFID), and in protocols and communications technologies (e.g. Wi-Fi, ZigBee); interoperability among different sensors hardware and cloud solutions. It is therefore important to define an abstraction level, in order to bridge the gap between the disparate technologies. Regarding the various sensor type, the use of the technologies developed in the semantic web [START_REF] Berners-Lee | The semantic web[END_REF] such as ontologies, semantic annotation, linked data [START_REF] Berners-Lee | Linked data[END_REF] and semantic web services has recently gained momentum in this field. These technologies promote interoperability among IoT resources, information models, data providers and consumers and simplifies effective data access and integration, resource discovery, semantic reasoning, and knowledge extraction [START_REF] Barnaghi | Semantics for the Internet of Things[END_REF].

VITAL as A CoT-based SMART CITY PLATFORM

One of the most important objectives of VITAL is about the integration of Inter-Connected Objects (ICOs) among multiple IoT platforms and ecosystems. The project explores the convergence and federation of multiple IoT platforms by taking account of cost efficiency of the deployments. In the context of VITAL, a very key factor is represented by the virtualization of interfaces that in combination with cross-context tools that enable the access and management of heterogeneous objects supported by different platforms and managed by different administrative stakeholders let us to define the VITAL platform as a Cloud of Things architecture. In Figure 6 we show an overview of the VITAL platform.

As we can observe in Figure 6, the data and services access of the heterogeneous objects involved in VITAL, is based on the implementation of the VUAIs (Virtualized Universal Access Interfaces), that makes possible to consider a single virtual access by making the architecture platform-agnostic. These key features of VITAL make this platform able to embrace the CoT philosophy. The VUAI layer is built upon a so-called meta-architecture and migration layer and includes several to communicate and interconnect different IoT platforms and clouds. In practice, this module deals with issues related to the management of the overall VITAL infrastructure built on top of existing IoT architectures and cloud platforms and enables heterogeneous mashup. The VUAIs allow the implementation of a kind of abstraction, where "objects" handler that point to physical items, can be discovered, selected and filtered and also allocated by following a "Things as a Service" (TaaS) paradigm. In this sense, the VITAL as CoT platform is something that goes beyond the interconnection and hyperlinking "things" of the IoT paradigm. VITAL also includes a datastore for data like geographical information and smart city stakeholders. Of course, it is expected that the management of this kind of information giving location awareness and other context related information can be effectively exploited in the optimization of computing and sensing of the management of the various clouds. The CoT paradigm implies the implementation of querying services and indexing of things, the aggregation of heterogeneous resources based on a given thing-like semantics and provided to the final stakeholder (final user, developer, etc.). Moreover, the CoT concept explicitly has to consider mechanisms to abstract, virtualize and manage things as performed in VITAL. It is worth outlining that VITAL is based on W3C SSN ontology, that is considered ideal as a basis for unifying the semantics of different IoT platforms, since it is domain independent and extensible. Several additional concepts have to be considered to enhance the ontology starting from information about city-wide, stake-holders, IoT system, etc. The ontology update with additional functionalities will allow the migration of smart city application across different urban environments.

VITAL ontologies

The main challenge of VITAL is to integrate in a platform agnostic way a multitude of heterogeneous data and functionalities produced and offered by different sensors and IoT systems managed along by disparate (independent) organisations and entities. This can be achieved thanks to Linked Data functionalities, which aim to describe and integrate data provided by different organisations in an interoperable way [START_REF] Heitmann | Implementing Semantic Web applications: reference architecture and challenges[END_REF]. VITAL indeed relies on Linked Data standards (i.e., RDF, JSON-LD, and ontologies) for modeling and accessing data; in particular, ontologies are used to specify data formally. As shown in Table I, we reuse as many ontologies as possible, while new ontologies need to be exploited to model relevant Smart Cities entities and for describing the VITAL system itself.

The VITAL ontologies can be grouped into four main areas -sensors and sensor measurements, Smart Cities, IoT systems and services, VITAL systems and services that we describe in the following.

Sensor and sensor measurements represent a crucial part of the VITAL data model. Many ontologies have been already developed in the field, and are based on multiple layers of abstraction [START_REF] Group | W3C SSN Incubator Group Review of Sensor and Observation Ontologies[END_REF]. It is important to clarify that since there are many application-specific cases, it is impossible to specify a single ontology that defines the semantics of all possible data items. VITAL combines several ontologies. It uses Semantic Sensor Network (SSN) [START_REF] Compton | The SSN ontology of the W3C semantic sensor network incubator group[END_REF] as a generic sensor ontology since it permits to describe sensors, including their accuracy and capabilities, observations, methods for sensing, concepts for operating and survival ranges, and deployments. Beyond this, in order to describe other basic concepts like time, location and unit of measurement, VITAL uses wellknown ontologies [START_REF] Hobbs | Time Ontology in OWL[END_REF][START_REF] Hodgson | QUDT -Quantities, Units, Dimensions and Data Types Ontologies[END_REF][START_REF] Polo | Measurement units ontology[END_REF].

Smart Cities have many capabilities that must be modeled as part of a smart city ontology. According to the VITAL use case as defined in Section 7, the most relevant producers of data come under the following headings:

• transport -e.g. dynamic route calculation informed of accidents and congestion; • energy -e.g. reporting of faults;

• emergency services -e.g. detection of accident and crimes; • waste management -e.g. detection of full bins;

• air and water -e.g. reporting of air and water pollution; • recreation -e.g. produce data on large events such as concerts to inform public transport. The majority of the VITAL semantic information on cities are obtained via the classic DBpedia † dataset. However, modeling Smart Transport is a non-trivial task since it covers a wide range of domains (e.g. tracking pedestrian congestion, smart traffic light systems, etc.). Therefore, VITAL uses, again, a combination of ontologies, and the core of them is the Ontology for Transportation Networks (OTN) [START_REF] Semantics | Ontology of transportation networks[END_REF] that allows an easy modeling of a transport network graph with connections between infrastructures (e.g., bus) as well as dynamic events such as accidents and blocked passages. It is important to emphasize that VITAL is not restricted to transport scenarios; a user who would like to adopt VITAL for other smart city aspects can do so by specifying additional ontology elements. Thanks to the nature of Linked Data indeed, additional elements can be added at any time without the need to redesign the whole system.

The integration of heterogeneous IoT systems and services represents another challenging point for the VITAL consortium. In order to model them, and in particular the four platforms so far chosen -GSN [19], Reply H1, FIT IoT-Lab [17], and Xively [START_REF]XIVELY[END_REF] -VITAL uses † http://dbpedia.org/ the SSN ontology. In this way, an IoT system is modeled as a subclass of ssn : System with a number of additional properties (e.g. operator, status, etc.). In addition, an IoT system may specify a set of IoT services offered.

Above the IoT systems aforementioned, we need also to model the VITAL system itself and its services. From the semantic point of view, a VITAL system is similar to an IoT system, therefore we decided to model it as a subclass of IoT System.

VITAL'S USE CASES

In this section, we describe a potential VITAL use case, where the concept of the different services that characterize VITAL (i.e. Discovery, Filtering , Orchestration and Complex Event Processing (CEP)) play a key role. Note that more use cases are available on the project website ‡ and in the project deliverables.

Use Case 1: VITAL for Smart Street Management

An effective management of the roads would make easier the citizens' life and would also have an economic impact. Just as an example, we could consider Public Lightening Management, which represents a significant source of energy wasting. In this context, the VITAL platform "reinterprets" every light source (each equipped with sensors) as an Internet-Connected Object (ICO). The VITAL services (e.g. Discovery, Filtering, Orchestration and CEP) allow an efficient management of the information, through the interconnection of data originated from different clouds. This intelligent manipulation of the data will be exploited in several ways such as: 1) it will be translated in "actuation" for an automatic regulation of the luminous intensity; 2) it will be used to infer useful information such as the actual schedule of public transportation (e.g. if a bus is incoming and the exact number of the bus); 3) it will be used for the intelligent management of the trash containers (e.g. if a trash container has to be emptied), etc. Concerning the intelligent management of the trash container, the VITAL platform could be very effective. In fact, the discovery of the areas where the trash containers are deployed and the filtering of the zones where an immediate intervention is necessary (e.g. a trash container that needs to be emptied), represent just an example of intelligent and dynamic manipulation and actuation deriving from the VITAL architecture. From this very simple use case scenario, it is also possible to understand that data in the cloud could help the stakeholders, by redefining the deployment of the trash containers dynamically. In practice, the concept of services as defined in the VITAL platform is to allow the manipulation and management of data coming also from zones that are geographically far to each other also through the definition and implementation of complex algorithms.

Use Case 2: VITAL for Smart Traffic Management

In the last years, the technological advancements allowed a very deep diffusion of complex GPS navigators. Several drivers make use and exploit these tools, that give very valuable information on the traffic conditions also in real time. They are also very valuable in the case of accidents alerts. Anyway, also in this context, the VITAL platform, through the definition and implementation of its Cloud of Thing paradigm, is able to play a key role. Let us refer to a scenario where an accident is broadcast/"alerted" (e.g. through the use of a GPS navigator). Normally, after such an alert occurs, the "minor" roads are taken as viable alternative solutions, with the side effect that they will be soon overcrowded. Moreover, this type of navigators do not take special events into consideration, such as concerts or festivals. The elaboration of data coming from different ICOs allows a smarter elaboration of different kinds of information. In this sense, the VITAL platform does not limit its "intervention" to a simple alert and alternative roads, but the VITAL services will output alternatives that consider many different "aspects" and the final user will play an active and aware role by choosing based on different type of info.

TECHNOLOGIES

Deploying a Smart City is a multi-disciplinary task, that involves various stakeholders from different thematic areas, like politics, finance, city management and organization, and information and communication technologies (ICT). From the ICT perspective, the number of devices, technologies, and standards involved is huge, therefore, it is necessary to have an overall view. In the next subsections we summarize the main technology's actors.

Devices

Sensors represent an essential component of any intelligent control system. Thanks to technology advances today a multitude of different sensors is available, enabling applications that were unimaginable in the past. From the perspective of the requirements for Smart Cities, wide availability of these technologies translates to a large number of opportunities in terms of sensing, i.e., meters to determine gas, electricity, water etc. Sensor nodes are usually scattered in a sensor field; each of these scattered sensor nodes has the capabilities to collect data and route data back to a special node called sink by a multi-hop infrastructureless architecture [START_REF] Akyildiz | A survey on sensor networks[END_REF]. Currently, most of commercial Wireless Sensor Network (WSN) are based on the IEEE 802.15.4 standard, which defines the physical and MAC layers for low-power, low bit-rate communications wireless personal area networks (WPAN). However, IEEE 802.15.4 does not include specifications on the higher layers of the protocol stack, which is necessary for the seamless integration of sensor nodes into the Internet [START_REF] Atzori | The Internet of Things: A survey[END_REF]. The role of SN in the IoT is crucial and well examined in [START_REF] Perera | Context Aware Computing for The Internet of Things: A Survey[END_REF], wherein authors point out on the relation between IoT and Sensor Networks: SN can exist without the IoT, however, the IoT cannot exist without SN.

Smartphones represent another interesting sensing scenario. Indeed, the ever increasing number and the presence of a mixture of sensors such as GPS, gyroscopes, accelerometers and compasses, enabling a variety of crowd sourcing applications, which will eventually be augmented by the IoT. For instance, as users regularly update their location status on social networks like Twitter and Facebook, based on this location information, it is possible to aggregate this data, enabling tasks to be dispatched to people in specific locations [START_REF] Hancke | The role of advanced sensing in smart cities[END_REF].

Regarding to short range communications, two interesting technologies in the smart cities context are Radio Frequency Identification (RFID) and Near Field Communication (NFC).

RFID is a method of identifying unique items using radio waves. Typical RFID systems are made up of 2 major components: readers and tags. The reader sends and receives RF data to and from the tag via antennas. The tag is made up of the microchip that stores the data, an antenna, and a carrier to which the chip and antenna are mounted [START_REF] Sharma | RFID based mobiles: Next generation applications[END_REF]. RFID can be used to develop a large number of Smart City applications, to name a few: smart parking [START_REF] Pala | Smart Parking Applications Using RFID Technology[END_REF], traffic monitoring [START_REF] Paul | RFID based vehicular networks for smart cities[END_REF], library management [START_REF] Renold | An Internet based RFID library management system[END_REF], transportation tickets, etc.

NFC is a contactless wireless communication technology based on RFID and Internet technologies [START_REF] Wang | Cloud architecture based on Near Field Communication in the smart city[END_REF]. It supports the communication within 20 cm, and it represents a prominent technology in the Smart City scenario, enabling a range of applications in order to make people's life more convenient and fast, i.e. the digital wallet introduced in [START_REF] Al | mFerio: the design and evaluation of a peer-to-peer mobile payment system[END_REF] or building access control.

Networking

Integrating resource-constrained devices into the Internet is difficult since ubiquitously deployed Internet protocols such as HTTP, TCP, or even IP are too complex and resource-demanding [START_REF] Pfisterer | SPITFIRE: toward a semantic web of things[END_REF].

6LoWPAN [START_REF] Montenegro | Transmission of IPv6 Packets over IEEE[END_REF] defines mechanism which allows the transmission of IPv6 packets. CoAP [START_REF] Costantino | Performance analysis of an LTE gateway for the IoT[END_REF] is an application layer protocol designed for energy constrained devices. It deals with Constrained RESTful Environments, providing a lightweight alternative to HTTP. CoAP and 6LoWPAN allow sensor nodes to be integrated into the web, through the use of proxies for HTTP to CoAP conversion.

However the short range transmission problem still exists. Regarding the Link Layer, Smart City system requires a set of link technologies that can easily cover wide geographical area and support, at the same time, a possibly large mount of traffic.

CONCLUSION AND FUTURE CHALLENGES

In this paper, we have considered the IoT platforms as a viable solution to make cities smarter. We have shown how the proliferation of ICT represents new opportunities for the development of novel services, contributing to make the cities more sustainable. The different IoT platforms, the various IoT clouds and the several IoT applications and services have resulted in different and heterogeneous IoT ecosystems, that introduce a significant degree of fragmentation. We presented several IoT platforms that can be efficiently considered in the context of Smart City, but in order to bridge the gap between the different IoT platforms it is necessary to consider a convergence of these platforms and ecosystems. In this paper we envisaged in the Cloud computing a valid bridge of the IoT, Internet of people through the Internet of Services. This novel perspective allows the realization of an horizontal integration of various vertical platforms. Through the implementation of a specific virtualization level, the VITAL platform in the context of the correspondent FP7 EU project, ensures the semantic interoperability of various and different IoT platforms. The Virtualized Unified Access Interfaces (VUAIs) implement a meta-architecture and migration layer, with different connectors and drivers to permit the communication among the various platform. In this way, VITAL CoT-based platform can be considered as a very promising solution for the fragmentation issues in the context of Smart Cities.

There are different challenges related to the Cloud of Things in smart cities, from both technical and privacy points of view.

Big Data. The overall IoT data produced by things is growing up fast, becoming really big data. Within this context, the challenges can be divided in two main categories [START_REF] Bizer | The meaningful use of big data[END_REF], engineering in order to perform data management activities such as query, and storage efficiently; and semantic, in order to extract the meaning of the information from massive volumes of data.

Privacy & Security. Privacy and Security issues exist for a long time in the computing literature, and many law acts have been published to protect users [START_REF] Zhou | Services in the Cloud Computing era: A survey[END_REF]. Certainly the problematic within the CoT context is to define mechanisms in order to let the decision to publish or not data to "sensor owners". Other issues may come from the cyber-crime, indeed the system can be prone to cyberterrorism and cyber-vandalism.

Figure 1 .

 1 Figure 1. IoT Application Domains [4].

Figure 3 .

 3 Figure 3. Smart City concept.

Figure 4 .

 4 Figure 4. The IoT Architecture [27].

Figure 5 .

 5 Figure 5. Cloud of Things.

Figure 6 .

 6 Figure 6. VITAL Platform

Table I .

 I VITAL ontologies

	Prefix	Ontology -Language	Namespace
	dcn otn	Ontology of Transportation Networks	http://www.pms.ifi.lmu.de/rewerse-wga1/otn/OTN.owl

‡ http://vital-iot.eu

ACKNOWLEDGMENT

This work is partially supported by CPER Nord-Pasde-Calais/FEDER CIA, the European Community in the framework of the VITAL FP7 project (Virtualized programmable InTerfAces for smart, secure and costeffective IoT depLoyments in smart cities under contract number FP7-SMARTCITIES-608662 and the Inria Project Lab SmartCities@Inria. The authors acknowledge help and contributions from all partners of the project.

Delivery Context ontology http://www.w3.org/2007uwa/context/deliveryContext.owl# dul DOLCE+DnS Ultralite ontology http://www.ontologydesignpatterns.org/ont/dul/DUL.owl# geo Basic Geo (WGS84) ontology http://www.w3.org/2003/01/geo/wgs84_pos# hrest hRESTS ontology http://www.wsmo.org/ns/hrests# msm Minimal Service Model ontology owl Web Ontology Language http://www.w3.org/2002/07/owl# rdfs RDF Schema ontology http://www.w3.org/2000/01/rdf-schema# sawsdl Semantic Annotations for WSDL and XML Schema ontology http://www.w3.org/ns/sawsdl# ssn Semantic Sensor Network ontology http://purl.oclc.org/NET/ssnx/ssn# time OWL Time ontology http://www.w3.org/2006/time# vital VITAL ontology http://vital-iot.com/ontology# wsl WSMO-Lite ontology http://www.wsmo.org/ns/wsmo-lite# xsd XML Schema Definition http://www.w3.org/2001/XMLSchema# qudt Quantities, Units, Dimensions and Data Types Ontologies http://qudt.org/schema/qudt# foaf Friend of a Friend http://xmlns.com/foaf/ s4ac Social Semantic SPARQL Security for Access Control http://ns.inria.fr/s4ac/v2#