Antonio Davi

Americo Santos

Cunha

Davi Antonio

Dos Santos

Americo Cunha

Flight control of a

Keywords: multi-rotor airship, multi-rotor aerial vehicle, flight control system, uncertainty quantification

est destinée au dépôt

Introduction

The traditional airships are lighter-than-air (LTA) aircrafts constituted of a streamlined or elliptical envelop (or hull) filled with a low-density gas (generally, helium), a pair of propellers installed through the gondola on its bottom part and a tail equipped with rudder and elevator.

They mainly rely on the aerostatic lift, resulting from the difference between the air and inner gas densities, for opposing the gravity and keep flying, which provides them with better endurance, payload capacity, and energy consumption characteristics compared to heavier-than-air (HTA) vehicles (such as airplanes and helicopters) [START_REF]Airship Technology[END_REF].

In the last three decades, thanks to advances (and cost reduction) in sensor technology, embedded computers, and materials, the airships have resurged in academia, mainly in non-rigid (blimp), small, and unmanned versions [START_REF] Elfes | A semi-autonomous robotic airship for environmental monitoring missions[END_REF]. Such interest renewal in turn gives rise to many works concerned with the dynamic modeling and identification [START_REF] Gomes | Airship dynamic modeling for autonomous operation[END_REF][START_REF] Li | Airship dynamics modeling: A literature review[END_REF] as well as the flight control [START_REF] Moutinho | Stability and robustness analysis of the aurora airship control system using dynamic inversion[END_REF][START_REF] Liesk | Design and experimental validation of a nonlinear low-level controller for an unmanned fin-less airship[END_REF] of such aerial vehicles.

Despite of the aforementioned advantages of the traditional airships over the HTA vehicles, they suffer from a weak controllability in the vertical and lateral translational degrees of freedom (DOF), besides of being more vulnerable to undesirable aerodynamic effects, such as those due to the drag and the added mass [START_REF]Airship Technology[END_REF]. The interplay between these drawbacks has motivated the design of a number of small and electric hybrid airships [START_REF] Earon | Design and control of a novel hybrid vehicle concept[END_REF][START_REF] Frye | The 6-dof dynamic model and simulation of the triturbofan remote-controlled airship[END_REF][START_REF] Azouz | Computation of the added masses of an unconventional airship[END_REF][START_REF] Lanteigne | Modeling and control of an unmanned airship with sliding ballast[END_REF] provided with an augmented maneuverability by an unconventional combination of actuators. In particular, the reference [START_REF] Earon | Design and control of a novel hybrid vehicle concept[END_REF] presents a fin-less ellipsoidal blimp equipped with four vectoring rotors; the reference [START_REF] Frye | The 6-dof dynamic model and simulation of the triturbofan remote-controlled airship[END_REF] analyzes a small streamlined blimp actuated by one vertical and two horizontal fixed rotors; the reference [START_REF] Azouz | Computation of the added masses of an unconventional airship[END_REF] is concerned with a flying-wing-shaped hull equipped with four vectoring double rotors; and the reference [START_REF] Lanteigne | Modeling and control of an unmanned airship with sliding ballast[END_REF] comes up with a cylindrical blimp with conventional actuators, whose rotor axis can be controlled to slide along a longitudinal keel.

The present paper investigates the dynamic modeling and flight control of a hexa-rotor airship, which is a new hybrid aircraft consisting of an oblate-spheroid balloon filled with helium, attached to a hexa-rotor airframe (see a picture in Figure 1). Their two parts are strapped down by ropes at six contact points. The balloon is made on polyurethane and has 2.5 m of horizontal diameter and 2 1.6 m of height. With a payload of 4.42 kg, the vehicle has a buoyancy ratio of 0.7. In a qualitative pre-analysis, one can say that, in low speed and windless condition, the hexa-rotor airship has potentially a better flight duration and payload capacity than a pure hexa-rotor vehicle, since its aerostatic lift is sized to counteract part of the weight aboard it. On the other hand, compared to other airships, the one investigated here has a full maneuverability in the position and heading DOFs, which is a characteristic inherited from conventional multi-rotor vehicles.

Figure 1: The hexa-rotor airship. It is patented according to reference [START_REF] Araujo | Unmanned ellipsoid multi-rotor airship and respective method of construction[END_REF].

In particular, the present paper is concerned with the dynamic modeling and design of flight control laws for the hexa-rotor airship depicted in Figure 1. A nonlinear six-DOF dynamic model is derived for this vehicle using the Newton-Euler approach, considering its operation in a lowspeed and windless condition. On the basis of this model and considering a time-scale separation between the translational and rotational dynamics, a hierarchical flight control architecture with the attitude control loop put inside the position control loop is adopted here. This architecture is commonly found in the literature of control applied to multi-rotor aerial vehicles (MAV) [START_REF] Bertrand | A hierarchical controller for miniature VTOL UAVs: Design and stability analysis using singular perturbation theory[END_REF][START_REF] Lee | Nonlinear robust tracking control of a quadrotor UAV on SE(3)[END_REF][START_REF] Shi | Almost-global finite-time trajectory tracking control for quadrotors in the exponential coordinates[END_REF].

Here, on the basis of the derived model, the attitude and position control laws are separately designed using feedback linearization and considering the saturation of the control vector within appropriate parallelepipedal sets that ensure the satisfaction of design bounds on the control torque and force, as well as on the vehicle's inclination angle.

The design of position and attitude control laws for the hexa-rotor airship, even if it is intended to operate indoors and in low speed, must face two paramount dynamic aspects not found in the MAV control problem. The first one is the restoring torque, which appears as a consequence of the displacement between the balloon's center of buoyancy (CB) and the vehicle's center of mass (CM) [START_REF] Santos | Dynamic modeling and flight control of a balloon-quadcopter unmanned aerial vehicle[END_REF]. For a fixed CB-CM displacement, the larger the inclination angle of the vehicle with respect to the local vertical, the larger the magnitude of the restoring torque. Therefore, for a given sizing of the rotor set, the attitude controller must respect a maximum bound on the inclination angle for the control system to maintain its effectiveness to counteract the restoring torque (and more). The side effect of such inclination constraint is a low lateral acceleration capability. The second aspect is the added-mass effect [START_REF] Tuveri | Added masses computation for unconventional airships and aerostats through geometric shape evaluation and meshing[END_REF], which consists in the reaction efforts (torque and force) of the surrounding air on the vehicle's body. It turns out that the added-mass effect causes an additional coupling between the translational and rotational dynamics, besides an apparent increasing in the vehicle's mass and inertia. The above two aspects are considered here. Moreover, an uncertainty quantification analysis is carried out to assess the variation in the closed-loop performance as a result of the deviation of the operating temperature and pressure with respect to their nominal values; note that such deviation causes a change in the air and helium densities and, as a consequence, in the aerostatic lift and restoring torque. From the above justification, we can assert that the main contributions of this paper are: 1) the dynamic modeling of the hexa-rotor airship for windless and low-speed conditions; 2) the design of a simple but effective control scheme for this vehicle, considering the aerostatic efforts; and 3) the uncertainty quantification study for assessing its closed-loop performance over a specified range of possible operating temperatures and pressures.

The remaining text is organized in the following manner. Section 2 derives the six-DOF dynamic model. Section 3 is concerned with the design of the nonlinear attitude and position control laws, as well as the stability analysis of the corresponding closed-loop dynamics. Sections 4 and 5 evaluates the overall system numerically. Particularly, Section 4 adopts a nominal scenario, whereas Section 5 carries out a stochastic uncertainty quantification analysis considering a random variation of both the local temperature and pressure conditions over given ranges. Finally, Section 6 concludes the paper.

Dynamic Modeling

This section derives rotational and translational equations of motion, as well as actuator models for the hexa-rotor airship, considering the most relevant efforts in low-speed indoor flight scenarios.

We start with preliminary definitions in Subsection 2. the rotors 1 and 2, the ẑB axis is pointing upward, normal to the airframe, and the ŷB axis completes a right-handed coordinate system. The ground CCS, S G {G; xG , ŷG , ẑG } is fixed to the ground at a known point G, with the ẑG axis pointing upward vertically. For our purposes, S G can be considered as an inertial frame and will be adopted as such.

Denote the set of real numbers by and the set of positive and non-negative real numbers by >0 and ≥0 , respectively. Moreover, denote the special orthogonal group by SO [START_REF] Gomes | Airship dynamic modeling for autonomous operation[END_REF]. Denote arbitrary sets by uppercase calligraphic letters, e.g., A and B. The Minkowski sum of two sets A and B is defined by A ⊕ B {a + b : a ∈ A, b ∈ B}. To clarify the vector notation adopted here, consider two different kinds of vectors: physical (geometric) vectors and algebraic vectors. Physical vectors are denoted by italic letters with a right arrow superscript, e.g., a. The corresponding algebraic vector resulting from the projection of a onto an arbitrary CCS S A is denoted by the same letter, but in a bold format, with the subscript A, i.e., a A ∈ 3 . Matrices are denoted by bold uppercase letters, e.g., A. Let a be an arbitrary physical vector. The relation between its representations a A and a B is a A = D A/B a B , where D A/B ∈ SO(3) is the attitude matrix of S A w.r.t. S B . The inverse of D A/B (which coincides with its transpose) is sometimes denoted by D B/A . The Euclidean norm of a A is denoted by a A . Now, let a A/B represent a physical quantity of S A relative to S B , e.g., r B/G denotes the relative position of S B w.r.t. S G . Let e i denote an n-dimensional (for some n) standard unit vector. The ith component of a is denoted by a i and sometimes by e T i a. Now, consider two physical vectors a and b as well as the respective algebraic representations in S A , a A = [a 1 a 2 a 3 ] T and b A , respectively. Denote the S A representation of the vector product a × b by the matrix multiplication [a A ×]b A , where [a A ×] is the following skew-symmetric matrix:

[a A ×]       0 -a 3 a 2 a 3 0 -a 1 -a 2 a 1 0       . ( 1
)
The text also distinguishes between a variable representing an effective quantity, e.g., a, and the corresponding command (or desired value). The latter is denoted with the same letter as the effective quantity, but including an overbar; e.g., ā.

Rotor Dynamics and Efforts

The set of six rotors equipping the airframe is responsible for generating the control forces and torques as described here. The ith rotor individually produces a thrust force and a reaction torque on the airframe along the ẑB axis with magnitudes denoted by f i and τ i , respectively. We describe these efforts by the following aerodynamic models [START_REF] Mahony | Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor[END_REF]:

f i = k f ω 2 i , (2)
τ i = k τ ω 2 i , (3)
i = 1, ..., 4, where k f is the thrust force coefficient, k τ is the reaction torque coefficient, and ω i is the rotation speed of the ith rotor. The rotor dynamics can be modeled by the following first-order

linear model: ωi = - 1 τ ω ω i + k ω τ ω ωi , (4)
where ωi ∈ [0, ωmax ] is the rotation speed command of the ith rotor, k ω is the speed coefficient, and τ ω is the rotor time constant. The rotation upper bound ωmax is assumed to be known.

Consider that all the six thrusts f i point upward. Moreover, consider that the reaction torque τ 1 is positive, τ 2 is negative, τ 3 is positive, and so on. Therefore, one can show that the magnitude F c of the resulting control force and the S B representation T c B of the resulting control torque are given by

  F c T c B   = Γf, (5)
where

f [f 1 f 2 . . . f 6 ] T and 𝐵 𝑥 B 𝐺 𝑧 G 𝑥 G 𝑦 G 𝑧 B 𝐻 𝐹 𝑏 𝑑 Figure 3: The restoring torque. Γ          1 1 1 1 1 1 l/2 -l/2 -l -l/2 l/2 l -l √ 3/2 -l √ 3/2 0 l √ 3/2 l √ 3/2 0 k -k k -k k -k          ∈ 4×6 , (6)
with l denoting the length of each vehicle's arm, and k k τ /k f .

Aerostatic Lift and Restoring Torque

This subsection models two crucial efforts generated by the balloon. One is an aerostatic lift force F b and the other one is a restoring torque T b . The force F b is explained by the Archimedes' Principle, which says that the buoyancy force always points upwards parallel to the local vertical and its magnitude is equal to the weight of the air volume displaced by the balloon. Therefore,

F b G = V gρ air e 3 , (7)
where V is the volume of the balloon (and of the gas as well), g is the gravitational acceleration, and ρ air is the air density. Denote the magnitude of F b by F b V gρ air .

On the other hand, the restoring torque T b is an effort acting about the vehicle's CM, which appears as a consequence of the displacement d between the balloon's CB and the vehicle's CM.

Figure 3 illustrates the balloon-airframe connection. Assume that this connection is rigid. In this case, the CB, which is placed at point H, is fixed w.r.t. S B . From the illustration, one can immediately write

T b = (dẑ B ) × F b . (8)
By representing equation ( 8) in S B , we finally have

T b B = d[e 3 ×]D B/G e 3 F b , (9)
where D B/G ∈ SO(3) is the attitude matrix of S B w.r.t. S G .

Translational Motion

By invoking the Second Newton's Law considering all the vectors represented in S G , one can immediately write 5

m t + m h rB/G G = F g G + F b G + F c G + F d G , (10)
where m t is the total mass of the vehicle without lifting gas and including the payload, m h ρ helium V is the helium mass, with ρ helium representing the helium density, r

B/G G ∈ 3 is the S G representation of the position of the vehicle's center of mass B w.r.t. G, F g G is the S G representation of the gravitational force, F b G is the S G representation of the aerostatic lift force, F c G is the S G representation of the control force, and F d G is the S G representation of the (unknown) disturbance force. The force F b
G is given by equation ( 7), while F g G and F c G are modeled by

F g G = -m t + m h ge 3 , (11)
F c G = F c D B/G T e 3 . (12)
By replacing equations ( 11)-( 12) into [START_REF] Lanteigne | Modeling and control of an unmanned airship with sliding ballast[END_REF], one can obtain

rB/G G = F c m t + m h n G + V gρ air m t + m h -g e 3 + 1 m t + m h F d G , (13)
where n G ∈ 3 is the transpose of the third line of D B/G , which corresponds to the S G representation of the unit (geometric) vector ẑB .

Rotational Motion

The kinematic equation of the rotational motion of

S B w.r.t. S G is given in SO(3) by ḊB/G = -Ω B/G B × D B/G , (14)
where

Ω B/G B ∈ 3 is the S B representation of the vehicle's angular velocity w.r.t. S G .
Assume that the vehicle has a rigid structure and consider S G as an inertial frame. Therefore, the Euler's Law gives

ḢB + Ω B/G B × H B = T c B + T b B + T d B , (15)
where H B is the S B representation of the total angular momentum of the vehicle, T c B is the S B representation of the control torque (see equation ( 5)), T d B is the S B representation of the (unknown) disturbance torque, and T b B is the S B representation of the balloon restoring torque (see equation ( 9)).

Considering the rotation of both the body and the propellers and noting that the latter rotates much faster, the total angular momentum H B can be written as

H B = JΩ B/G B + J r 6 i=1 (-1) i ω i e 3 , (16)
where J ∈ 3×3 is the inertia matrix of the vehicle and J r ∈ is the moment of inertia of each rotor about ẑB .

Therefore, by replacing equation ( 16) into equation [START_REF] Santos | Dynamic modeling and flight control of a balloon-quadcopter unmanned aerial vehicle[END_REF], one can obtain the dynamic equation of the rotational motion of S B w.r.t. S G :

ΩB/G B = J -1 JΩ B/G B + J r 6 i=1 (-1) i ω i e 3 × Ω B/G B -J -1 J r 6 i=1 (-1) i ωi e 3 + J -1 T b B + J -1 T c B + J -1 T d B . (17)

Added Mass

The added-mass (also called virtual or apparent-mass) forces and torques are aerodynamic reaction efforts from a surrounding fluid on a body which tries to accelerate or decelerate inside it; note that the displaced mass of fluid is accelerated or decelerated together with the body interacting with it (see, e.g., [START_REF] Fossen | Guidance and Control of Ocean Vehicles[END_REF][START_REF] Thomasson | Equations of motion of a vehicle in a moving fluid[END_REF]). This effect is only relevant if the mass of the displaced fluid is significant compared to the body's mass, which is generally the case for conventional or hybrid airships [START_REF] Li | Modeling and simulation of airship dynamics[END_REF][START_REF] Azouz | Computation of the added masses of an unconventional airship[END_REF][START_REF] Tuveri | Added masses computation for unconventional airships and aerostats through geometric shape evaluation and meshing[END_REF]. It is also worth noting that it appears even in low-speed and windless conditions.

From the Kirchhoff equation (for a body moving in an ideal fluid) one can show that the added-mass force and torque are, respectively, given by [START_REF] Fossen | Guidance and Control of Ocean Vehicles[END_REF]  

F a B T a B   = -M a   vB/G B ΩB/G B   -   Ω B/G B × M 11 v B/G B + M 12 Ω B/G B v B/G B × M 11 v B/G B + M 12 Ω B/G B + Ω B/G B × M 21 v B/G B + M 22 Ω B/G B   , (18)
where

M a   M 11 M 12 M 21 M 22   ∈ 6×6 , (19)
is the added mass in S B , M ij ∈ 3×3 , ∀i, j ∈ {1, 2}.

By writing the added-mass force as well as the vehicle's linear velocity in S G , equation [START_REF] Fossen | Guidance and Control of Ocean Vehicles[END_REF] yields

F a G = -D G/B M 11 D B/G vB/G G -D G/B M 12 ΩB/G B + D G/B M 11 Ω B/G B × D B/G v B/G G -D G/B Ω B/G B × M 11 D B/G v B/G G + M 12 Ω B/G B , (20)
T a B = -M 21 D B/G vB/G G -M 22 ΩB/G B + M 21 Ω B/G B × D B/G v B/G G -D B/G v B/G G × M 11 D B/G v B/G G + M 12 Ω B/G B -Ω B/G B × M 21 D B/G v B/G G + M 22 Ω B/G B . (21)
Before computing M a for the oblate-spheroid balloon under consideration, it is convenient to obtain the added mass Ma diag( M11 , M22 ) with respect to a CCS which is parallel to S B and has its origin at CB, using the procedure of [START_REF] Tuckerman | Inertia factors for ellipsoids for use in airship design[END_REF], resulting in

M11 =       m 0 0 0 m 0 0 0 m3       , and M22 =       J 0 0 0 J 0 0 0 0       , (22)
where

m 4π 3 a 2 bρ air α 0 2 -α 0 , (23)
m3 4π 3 a 2 bρ air β 0 2 -β 0 , (24)
J 4π 15 a 2 bρ air a 2 + b 2 β 0 -α 0 2 , ( 25
)
α 0 √ 1 -e 2 e 3 sin -1 e -e √ 1 -e 2 , ( 26
)
β 0 2 √ 1 -e 2 e 3 e √ 1 -e 2 -sin -1 e , (27)
e a 2 -b 2 a 2 . ( 28
)
Finally, to obtain M a (in S B ), one can use the coordinate transformation (see [START_REF] Li | Dynamics modeling and simulation of flexible airships[END_REF], p.27)

M a = U T Ma U (29)
with

U   I 3 -d[e 3 ×]
0 3×3 I 3   . (30)
Therefore, to account for the added mass, the new external inputs represented by F a G and T a B have to be considered in equations ( 13) and [START_REF] Mahony | Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor[END_REF], respectively.

Flight Control System

A hierarchical control strategy is adopted here, as illustrated in B and F c . This architecture is based on the assumption that there is a time-scale separation between the (slower) closed-loop translational and (faster) rotational vehicle dynamics [START_REF] Bertrand | A hierarchical controller for miniature VTOL UAVs: Design and stability analysis using singular perturbation theory[END_REF]. This assumption is ensured in practice by tuning the attitude control loop to converge much faster than the position control loop. Under such assumptions, when designing the position control law, one can assume that the actual attitude D B/G converges to the corresponding command DB/G instantaneously, i.e., D B/G ≡ DB/G . On the other hand, when designing the attitude control law, the attitude command DB/G is assumed to be constant, or equivalently, the angular velocity command ΩB/G B ∈ 3 is assumed to be zero. On the basis of these conditions, the position and attitude control laws can be designed separately.

Position Controller

First, the design model is obtained from equation [START_REF] Lee | Nonlinear robust tracking control of a quadrotor UAV on SE(3)[END_REF], by assuming that: 1) the disturbance force F d G is negligible, 2) the actual control force magnitude F c is identical to the corresponding command F c , and 3) D B/G = DB/G (time-scale separation). The resulting design model is

rB/G G = 1 m t + m h Fc G + V gρ air m t + m h -g e 3 . (31)
Suppose that Fc G is bounded within a parallelepipedal set from

F min [F min 1 F min 2 F min 3 ] T ∈ 3 to F max [F max 1 F max 2 F max 3
] T ∈ 3 . The position controller proposed here is the saturated law

Fc G = σ [F min ,F max ] (γ p ), (32)
where

γ p ≡ [γ p 1 γ p 2 γ p 3 ] T ∈ 3 is given by γ p m t + m h g -V gρ air e 3 + m t + m h K 1 rB/G G -r B/G G -m t + m h K 2 ṙB/G G , (33)
with the matrices K 1 , K 2 ∈ 3×3 representing the controller gains (assumed to be diagonal), and

σ [F min ,F max ] (γ p )       σ [F min 1 ,F max 1 ] (γ p 1 ) σ [F min 2 ,F max 2 ] (γ p 2 ) σ [F min 3 ,F max 3 ] (γ p 3 )       , (34)
σ [F max l ,F max l ] (γ p l )            F min l , γ p l < F min l γ p l , γ p l ∈ [F min l , F max l ] F max l , γ p l > F max l , l = 1, 2, 3. (35)
Note that the proposed controller (32)-( 33) is such that, if no saturation is active, it cancels the second term on the right-hand side of equation ( 33), remaining a double-integrator closedloop dynamics controlled by the proportional-derivative actions appearing in the last two terms of equation (33). In saturation-free conditions, it is straightforward to show asymptotic stability of the proposed translational control loop using linear time-invariant control methods. Subsection

Attitude Controller

Here the design model is obtained from equation ( 17) by: 1) neglecting the disturbance torque

T d B ;
2) replacing the actual control torque T c B by the corresponding command Tc B ; 3) considering that the rotor dynamics are so fast that one can assume ω i = ωi , ∀i; and 4) assuming that ωi ≈ 0, ∀i. The resulting model is

ΩB/G B = J -1 JΩ B/G B + J r 6 i=1 (-1) i ωi e 3 × Ω B/G B + dF b J -1 [e 3 ×]D B/G e 3 + J -1 Tc B . ( 36
)
Suppose that the torque command Tc

B must be bounded from -T max ∈ 3 to T max [T max 1 T max 2 T max 3
] T . The attitude controller proposed here is the saturated law

Tc B = σ [-T max ,T max ] (γ a ), (37)
where One can note that the proposed attitude control law (37)-( 38) is such that, if no saturation is active, it cancels the first three terms on the right-hand side of equation ( 38), remaining a feedback-linearized closed-loop dynamic model controlled by the proportional-derivative actions appearing in the last two terms of (38). Subsection 3.5 presents a stability analysis.

γ a -dF b [e 3 ×]D B/G e 3 + Ω B/G B × JΩ B/G B + J r Ω B/G B × e 3 6 i=1 (-1) i ωi -JK 3 ε -JK 4 Ω B/G B , (38)

Attitude Command Generation

In order to generate the three-DOF attitude command DB/R , note that, by definition, its third line is the transpose of nG . Then, consider the formula to convert from Euler angles (in the 1-2-3 sequence) to the attitude matrix (see [START_REF] Markley | Fundamentals of Spacecraft Attitude Determination and Control[END_REF], p. 52):

DB/R =       * * * * * * s θ -c θ s φ c θ c φ       , (39)
where s and c stands for sin and cos, respectively, and compute φ and θ from φ = -atan e T 2 nG /e T 3 nG ,

Control Allocation

Consider equation ( 5) again; it relates the effective resultant efforts F c and T c B with the six individual rotor forces (stored in f). We can consider that the respective effort commands are interrelated in the same way, i.e.,  

F c Tc B   = Γ f, (43)
where f f1 f2 . . . f6 T .

The control allocation pursued here is to find a solution f to the system of linear equations (43). Since this is an undetermined system (with infinitely many solutions), we choose here the one that minimizes f 2 , considering (43) as a set of equality constraints. The explicit solution to such a problem is well known; by using Lagrange multipliers, it can be found to be

f = Γ T ΓΓ T -1   F c Tc B   . ( 44
)
The above control allocation can be seen as an energy-efficient solution, since it minimizes the squared norm of f. Moreover, considering that the commands F c and Tc B belong to the admissible set of the above optimization problem (meaning that the problem has at least one solution), the resulting control allocation is exact, i.e., it satisfies equation (43).

Stability Analysis

Still considering the time-scale separation assumption, this subsection presents stability proofs for both the position and attitude control loops.

Position control

Denote the admissible-force set by

F F ∈ 3 : F min ≤ F ≤ F max (45)
and define the tracking error r r

B/G G - rB/G G , the viable set L p r, ṙ ∈ 6 : γ p ∈ F , (46)
as well as the maximal 0-centered Euclidean ball inside L p

L * p max λ B λ s.t. B λ ⊆ L p , (47)
where B λ {x ∈ 6 : x ≤ λ}.

The following theorem gives a stability result for the closed-loop system (31)-(33).

B/G G (t), ṙB/G G (t)) ∈ L * p ⊕ (r B/G G (t), ṙB/G G (t)), ∀t ∈ >0 .
Proof. Under the condition that γ p ∈ F or, equivalently, (r, ṙ) ∈ L p , the closed-loop translational dynamics represented by equations ( 31)-( 33) can be re-modeled in terms of r and its time derivatives, resulting in

r + K 2 ṙ + K 1 r = - rB/G G . (48)
Since, by assumption, rB/G G (t) = 0, ∀t ∈ ≥0 and the gain matrices K 3 and K 4 are positive-definite and diagonal, from the linear control theory, one see that the state (r, ṙ) = (0 3×1 , 0 3×1 ) of system (48), without considering the control saturation, is globally exponentially stable. This implies that, for any λ ∈ >0 , if (r(0), ṙ(0)) ∈ B λ , then (r(t), ṙ(t)) ∈ B λ , ∀t ∈ >0 , which means that B λ is an invariant set of system (48).

In particular, consider the ball L * p defined in (47). Since, by construction, L * p ⊆ L p , it holds that γ p ∈ F, from the above argumentation, one can see that L * p is an invariant set of system (31)-( 33), which, in this case, behaves as (48) (with rB/G G (t) = 0, ∀t ∈ ≥0 ). Therefore, if (r(0), ṙ(0)) ∈ L * p , then (r(t), ṙ(t)) ∈ L * p , ∀t ∈ >0 and r converges to 0 3×1 exponentially. From the definition of r, this is equivalent to say that (r

B/G G (t), ṙB/G G (t)) ∈ L * p ⊕ (r B/G G (t), ṙB/G G (t)), ∀t ∈ ≥0 and r B/G G converges to rB/G
G exponentially, thus completing the proof.

Attitude Control

Denote the admissible-torque set

T T ∈ 3 : -T max ≤ T ≤ T max (49)
and define the corresponding viable set

L a ε, Ω B/G B ∈ 6 : γ a ∈ T , (50)
as well as the maximal 0-centered Euclidean ball inside L a

L * a max λ B λ s.t. B λ ⊆ L a . ( 51
)
From the assumption that DB/G (t) is constant (time-scale separation), it follows that ΩB/G B (t) = 0. In this case, the attitude error kinematics can be described by (see [START_REF] Markley | Fundamentals of Spacecraft Attitude Determination and Control[END_REF], p. 73)

ε = A(ε)Ω B/G B , (52)
where

A(ε)       cε 3 /cε 2 -sε 3 /cε 2 0 sε 3 cε 3 0 -cε 3 sε 2 /cε 2 sε 3 sε 2 /cε 2 1       , ( 53
)
with ε i , i = 1, 2, 3, denoting the components of ε.

In this section, we assume that ε has small components. Such small-angle approximation is reasonable, since ε represents an attitude error rather than the total attitude itself and, from the time-scale separation assumption, D B/G is supposed to rapidly converge to DB/G (which means that ε rapidly converges to zero). In this case, equation ( 52) can be approximated by

ε = Ω B/G B . (54)
The following theorem gives a stability result for the closed-loop system (52), ( 37)-(38).

Theorem 2. Consider the closed-loop system (52), ( 37)-(38). Assume that DB/G (t) is constant at any t ∈ ≥0 (time-scale separation) and that ε is so small that its kinematics can be described by (54). In this context, if the initial state (ε(0),

Ω B/G B (0)) ∈ L * a , then D B/G converges exponentially to DB/G and (ε(t), Ω B/G B (t)) ∈ L * a , ∀t ∈ >0 .
Proof. Under the condition that γ a ∈ T or, equivalently, (

B ) ∈ L a , and considering the smallangle kinematics described by (54), the closed-loop rotational dynamics represented by equations ( 54),( 37)-( 38) can be re-modeled in terms of ε and its time derivatives, resulting in

ε + K 4 ε + K 3 ε = 0. ( 55
)
Since the gain matrices K 3 and K 4 are positive-definite and diagonal, from the linear control theory, one see that the state (ε, ε) = (0 3×1 , 0 3×1 ) of system (55), without considering the control saturation, is globally exponentially stable. This implies that, for any λ ∈ >0 , if (ε(0), ε(0)) ∈ B λ , then (ε(t), ε(t)) ∈ B λ , ∀t ∈ >0 , which means that B λ is an invariant set of system (55).

In particular, consider the ball L * a defined in (51). Since, by construction, L * a ⊆ L a , it holds that γ a ∈ T , from the above argumentation and assumptions, one can see that L * a is an invariant set of system (54), (37)-(38), which, in this case, behaves as (55). Therefore, if (ε(0), ε(0)) ∈ L * a , then (ε(t), ε(t)) ∈ L * a , ∀t ∈ >0 and ε converges to 0 3×1 exponentially. From the definition of ε and considering (54), this is equivalent to say that (D B/G ; Ω B/G B ) converges to ( DB/G ; 0 3×1 ) exponentially, thus completing the proof.

Nominal Simulation

As a first step to evaluate the proposed method, this section presents a numerical simulation of the the proposed flight control system in nominal conditions.

It is worth noting at this point that both the aerostatic lift F b and the restoring torque T b depend on the value of ρ air and ρ helium . These densities in turn depend on the local temperature and pressure, as expressed by the Ideal Gas Law:

ρ q = p R q T , (56)
where "q" can be replaced by either "air" or "helium", p is the local pressure in Pa, T is the local temperature in K, and R q is the specific gas constant (R air = 286.9 J/Kg/K and R helium = 2077 J/Kg/K). In particular, in this section, the surrounding air and contained gas are suposed to have the same temperature and pressure; their values are 20 o C and 101325 Pa, respectively. Moreover, the air is assumed to be dry. Under such conditions, the air and helium densities are ρ air ≈ 1.204 kg/m 3 and ρ helium ≈ 0.1664 kg/m 3 . These values are used here to simulate the ground truth as well as to compute the control laws.

Subsection 4.1 describes the simulation and shows the adopted nominal parameters, while Subsection 4.2 presents and analyzes the simulation results.

Plant and Controller Parameters

For simulating the overall closed-loop flight control system represented in Figure 4, we use the models formulated in Section 2 as well as the control laws and control allocation proposed in Section 3. The simulator is implemented in MATLAB, using the forth order Runge-Kutta solver, with an integration step of 0.001 s. Table 1 shows the nominal values of the hexa-rotor airship parameters. On the other hand, Table 2 presents the adopted controller parameters. The controller gains are tuned by trial and error, taking into account their proportional or derivative effect and considering the time-scale separation assumption as well.

In this paper, for obtaining simulation data that are consistent with a typical operation of MAVs, the proposed flight control system is commanded to follow a waypoint-based position trajectory. In this trajectory, the waypoints are connected by straight lines with length of 5 m and a constant desired velocity of v = 0.5 m/s. Moreover, the heading angle command ψ is set to zero.

Nominal Simulation Results

Figures 6-8 are the results of the nominal simulation using the parameters of Tables 12. Figure 6 shows the effective position and the corresponding position command. In the ramp parts of the component trajectories, one can verify a steady-state error of about 2 m. After finishing the ramps, there are no overshoots and the accommodation times (within 5 cm of the final value) are between 10 s and 14 s. In the flat parts of the trajectory, the steady-state errors are zero. The large errors during the ramps and the long accommodation times reflect the vehicle's inherent low acceleration

There are transients at the instants the vehicle is commanded to ascend or to stop ascending, after which, F c converges and stays around 42.1 N. This force value is equivalent to the total weight of the vehicle, payload, and helium, minus the aerostatic lift. The force command F c keeps far from its maximum bound, even during the ascending acceleration. On the other hand, the components of the torque command Tc B deviate from zero at the waypoints, but stay inside their bounds ±T max with a large margin.

Uncertainty Quantification

The present section conducts an uncertainty quantification analysis of the flight control system under consideration using a parametric probabilistic approach [START_REF] Soize | Stochastic modeling of uncertainties in computational structuraldynamics -recent theoretical advances[END_REF][START_REF] Soize | Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering[END_REF]. In this analysis, ambient the temperature and pressure are modeled as independent random variables and, based on such probabilistic models, for each flight realization, one new outcome is generated for both variables.

For simulating the plant, the surrounding air and contained helium are assumed to have the same (varying) temperature and pressure. On the other hand, in the implementation of the control laws, their densities are maintained constant (and equal the respective nominal values; see Section 4).

Probabilistic Model

Let (Θ, ¦, ) be a probability space, where Θ is the sample space, ¦ is a σ-algebra over Θ, and : ¦ → [0, 1] a probability measure. In this stochastic framework, the local temperature T l and pressure P l are modeled by (independent) random variables : ¦ → and : ¦ → , respectively.

We assume that the joint probability distribution of these random variables, P XY (dx dy), admit a probability density function (PDF) (x, y) → p XY (x, y), with respect to dx dy. Similarly, the marginal distributions P X (dx) and P Y (dy) are assumed as having marginal PDFs x → p X (x), with respect to dx, and y → p Y (y), with respect to dy.

The maximum entropy principle [START_REF] Jaynes | Information theory and statistical mechanics[END_REF] is adopted here as a way to specify the aforementioned distributions. This approach provides the least biased distributions in a scenario with little (or no) experimental information on a particular random variable [START_REF] Soize | Stochastic modeling of uncertainties in computational structuraldynamics -recent theoretical advances[END_REF]. In the present investigation, the random local temperature and local pressure are both completely unknown beforehand.

However, one should better specify for them resonable operational ranges, from which it is possible to immediately define the underlying supports. In this way, we assume from now on that and take values in known positive finite intervals [x 1 , x 2 ] ⊂ (0, +∞) and [y 1 , y 2 ] ⊂ (0, +∞), respectively. Therefore, the maximum entropy principle is formulated here as the maximization of the Shannon entropy

(p XY ) = - R 2 ln p XY (x, y) p XY (x, y) dx dy , (57)
subject to the normalization constraint (which brings information about the supports)

y 2 y 1 x 2 x 1 p XY (x, y) dx dy = 1. (58)
By using the Lagrange multiplier method, one can solve the above optimization problem to obtain the following PDF

p XY (x, y) = p X (x) × p Y (y), (59)
with

p X (x) = 1 [x 1 ,x 2 ] (x) 1 x 2 -x 1 and p Y (y) = 1 [y 1 ,y 2 ] (y) 1 y 2 -y 1 , (60)
where 1 X (x) denotes the indicator function of the set X. Note that the above marginal PDFs correspond to uniform distributions on the intervals [x 1 , x 2 ] and [y 1 , y 2 ], respectively. Informally, the above result makes all sense, because since the minimum and maximum values are the only known informaion about the random parameters, the most reasonable choice for their distributions is the one that assigns equal weight to all their possible values.

Due to the randomness of and , the air density ρ air and the helium density ρ helium are also random variables [see equation ( 56)]. Further, since the aerostatic lift [see equation [START_REF] Earon | Design and control of a novel hybrid vehicle concept[END_REF]] and the restoring torque [see equation [START_REF] Azouz | Computation of the added masses of an unconventional airship[END_REF]] depend on ρ air and ρ helium , then the translational [START_REF] Shi | Almost-global finite-time trajectory tracking control for quadrotors in the exponential coordinates[END_REF] and rotational [START_REF] Mahony | Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor[END_REF] dynamic models are rather differential equations with random parameters and, consequently, their variables are stochastic processes. In particular, we choose here the minimum temperature x 1 = 0 o C, the maximum temperature x 2 = 40 o C, the minimum pressure y 1 = 0.7739 atm (at 2000 m and 0 o C), and the maximum pressure y 2 = 1 atm.

Propagation of Uncertainties

To compute the propagation of uncertainties of the random parameters and through the control system dynamics, we employ the Monte Carlo (MC) method [START_REF] Rubinstein | Simulation and the Monte Carlo Method[END_REF]. It consists in a stochastic solver that, based on n s realizations of the random parameters and , generates n s different realizations of the nominal closed-loop dynamics and computes the sample statistics of the respective system responses. These statistics are then used to approximate the solution (or response) of the original stochastic system. In order to guarantee the convergence of the MC solution to the true stochastic behavior of the system, a sufficient number n s of realizations must be considered. The reference [START_REF] Soize | A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics[END_REF] shows that such a convergence occurs in the mean-square sense if the following metrics converge in the point-wise sense:

δ p = 1 n s ns n=1 t f t=0 ||r B/G G (t, n)|| 2 dt 1/2 , (61)
where n denotes an arbitrary MC realization, t denotes the continuous time, t f is the final simulation instant, r B/G G (t, n) is the nth realization of the vehicle's position at instant t, and α B/G (t, n) is the nth realization of the vehicle's attitude (in Euler angles 1-2-3) at instant t. Figure 10 shows a plot of the position (61) and attitude (62) convergence metrics versus n s . One can see that after 50 realizations, both metrics show to converge. Therefore, in the problem under consideration, we consider n s = 100, which is sufficient for the convergence of the MC solution.

Figure 11 shows the position statistics (mean and confidence region). One can see that, as a consequence of the randomness in the temperature and pressure conditions, there is a very small variation in the r 1 and r 2 performance (less the 5 cm), and a considerable variation in the r 3 performance (about 70 cm). Figure 12 shows the statistics (mean and confidence region) of the attitude. The largest variations, which are very small (less than 0.3 degree), occur during the transients. Figures 13 and 14 show the statistics (mean and confidence region) of the force magnitude command and torque command, respectively. One can observe a small random variation in the torque command components. On the other hand, there is a large variation in the force magnitude command; this is a direct consequence of the aerostatic lift variation (see Figure 15), which is proportional to the variation of the air density (see equation [START_REF] Earon | Design and control of a novel hybrid vehicle concept[END_REF]). The extreme values of both commands keep inside their bounds with a good margin.

Concluding Remarks

The paper proposed a flight control system for a new micro aerial vehicle (MAV) resulting from the combination of a hexa-rotor airframe with a balloon filled with helium. This MAV is a straightforward attempt to extend the flight duration and load capacity of conventional multi-rotor vehicles by means of an aerostatic lift. On one hand, such a combination results in a very simple vehicle. On the other hand, even for indoor flight, its control system must be carefully designed to overcome the effects of a restoring torque (which does not occur in conventional multi-rotor vehicles), while being robust to variations in the local temperature and pressure operation conditions. The proposed flight control system is evaluated on the basis of both deterministic and stochastic simulations. In particular, the deterministic simulation shows that in nominal conditions, it is possible to control the vehicle's position to follow a desired waypoint-based trajectory with reference speed of 0.5 m/s. The obtained performance is sufficient accurate and fast for many MAV applications. On the other hand, in the stochastic simulation, the local temperature and pressure are modeled by uniformly-distributed random variables and the resulting performance is rigorously evaluated by Monte Carlo (MC) simulation. The variability verified in the position control performance can be considered small enough to dispense a special design of the control laws in order to suppress it. However, the variability shown in the attitude control error motivates a special robust control method for a better performance of the system in a stochastic sense.

Figure 2 :

 2 Figure 2: Illustration of the hexa-rotor airship and representaiton of the Cartesian coordinate systems.

Figure 4 .Figure 4 :

 44 Figure 4: Architecture of the hexa-rotor airship control system.

3. 5

 5 presents a stability analysis considering the saturation.By constraining the control inputFc G to keep inside a parallelepipedal set, one can deal with

Figure 5 : 1 = F max 1 = 1 = -F max 1 , F min 2 = -F max 2 , and F max 1 = F max 2 = F max 12 = F min 3 tan

 511112212123 Figure 5: Constraint set of the position control input. (a) Perspective view showing the control force command inside the parallelepipedal set. (b) Superior view showing the bounds F max 1

with ε ∈ 3 , K 3 , K 4 ∈

 334 containing the Euler angles (in the 1-2-3 sequence) corresponding to the attitude control error D = D B/G DB/G T 3×3 are controller (diagonal-matrix) gains, and the saturation function σ [-T max ,T max ] (.) is as defined in equations (34)-(35).

  θ s φ + s ψ c φ -c ψ s θ c φ + s ψ s φ -sψ c θ -s ψ s θ s φ + c ψ c φ s ψ s θ c φ + c

Theorem 1 .

 1 Consider the closed-loop system (31)-(33). Assume that the position command rB/G G is such that rB/G G (t) = 0, ∀t ∈ ≥0 . In this context, if the initial state (r

Figure 8 :

 8 Figure 8: Force magnitude command provided by the position controller in the nominal simulation.

Figure 9 :

 9 Figure 9: Torque command provided by the attitude controller in the nominal simulation.

Figure 10 :

 10 Figure 10: Convergence metrics for choosing the number of Monte Carlo realizations.

Figure 11 :

 11 Figure 11: Position statistics (mean and confidence region) estimated by Monte Carlo method.

Figure 12 :

 12 Figure 12: Attitude control error statistics (mean and confidence region) estimated by Monte Carlo method.

Figure 13 :

 13 Figure 13: Torque command statistics (mean and confidence region) estimated by Monte Carlo method.

Figure 14 :

 14 Figure 14: Force magnitude command statistics of the Monte Carlo simulation.

Figure 15 :

 15 Figure 15: Aerostatic lift statistics of the Monte Carlo simulation.

  1, then we model the rotor dynamics and control efforts in Subsection 2.2, the restoring torque and aerostatic lift generated by the balloon in

	Subsection 2.3, the vehicle's translational dynamics in Subsection 2.5, and its rotational dynamics
	in Subsection 2.4. Finally, the added-mass effects are treated in Subsection 2.6.
	2.1. Preliminary Definitions, Notation, and Nomenclature
		Define two Cartesian coordinate systems (CCS) as illustrated in Figure 2. The body CCS,
	S B	{B; xB , ŷB , ẑB } is attached to the vehicle's body with the origin at the vehicle's center of
	mass B, the xB axis is pointing forward, aligned with the bisectrix of the separation angle between

Table 1 :

 1 Physical parameters of the hexa-rotor airship.

	Description	Symbol Value
	Force coefficient	k f	1.2838 × 10 -5 N/(rad/s) 2
	Torque coefficient	k τ	3.0811 × 10 -7 Nm/(rad/s) 2
	Maximum rotor speed	ωmax	906.66 rad/s
	Motor speed coefficient	k ω	1
	Motor time constant	τ ω	0.01 s
	Arm length	l	1 m
	Volume of the balloon	V	5.3 m 3
	CB-CM displacement	d	0.85 m
	Semi-major axis	a	1.25 m
	Semi-minor axis	b	0.8 m
	Total inertia matrix	J	diag(2.0633, 2.0651, 1.9556) kgm 2
	Moment of inertia of the rotors J r	0.001 kgm 2
	Total empty mass	m t	9.392 Kg

Table 2 :

 2 Parameters of the attitude and position control laws.

	Description	Symbol Value
	Proportional gain of the position controller K 1	diag(0.5, 0.2, 0.7)
	Derivative gain of the position controller	K 2	diag(2, 1, 3)
	Proportional gain of the attitude controller K 3	diag(20, 50, 1)
	Derivative gain of the attitude controller	K 4	diag(10, 20, 1)
	Maximum torque command	T max	[ 16.3 14.1 0.58 ] T Nm
	Minimum force command	F min	[ -5.8 -5.8 2.7 ] T N
	Maximum force command	F max	[ 5.8 5.8 54.6 ] T N
	Maximum inclination angle	ϕ max	12 degrees

Acknowledgment

The authors would like to thank all those people who indirectly contributed or motivated us to conduct the present investigation. In special, we are grateful to Mr. Luciano S. Araujo (from Elio Tecnologia), who designed and built the first prototype of the balloon-hexacopter and donated it to the Laboratorio de Robotica Aerea do ITA. The authors are thankful for the financial support provided by São Paulo Research Foundation (FAPESP) grant number XXX, Carlos Chagas Filho Research Foundation of Rio de Janeiro State (FAPERJ) under grants E-26/010.002.178/2015 and E-26/010.000.805/2018.

capability, which can be attributed to both the restricted inclination bound ϕ max and the large inertia parameters. Finally, Figure 8 presents the force magnitude command F c computed by the position controller and Figure 9 shows the components of the torque command Tc B generated by the attitude controller.


Member, IEEE Antonio M Ortiz

Dina Hussein

Member, IEEE Soochang Park

Student Member, IEEE Son N Han

Senior Member, IEEE Noel Crespi

The Cluster Between Internet of Things and Social Networks: Review and Research Challenges

Keywords: Internet of Things, Social Internet of Things, Social Networks

The cluster between Internet of Things (IoT) and Social Networks (SN) enables the connection of people to the ubiquitous computing universe. In this framework, the information coming from the environment is provided by the IoT, and the SN brings the glue to allow human-to-device interactions. This article explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT). Although there have been early-stage studies in social-driven IoT, they merely use one or some properties of SIoT to improve a number of specific performance variables. Therefore, this article first addresses a complete view on SIoT and key perspectives to envision the real ubiquitous computing. Thereafter, a literature review is presented along with the evolutionary history of IoT research from Intranet of Things to SIoT. Finally, this article proposes a generic SIoT architecture, and presents a discussion about enabling technologies, research challenges, and open issues.

I. INTRODUCTION

We are standing on the brink of a new era with real ubiquitous computing and communication where many gadgets, such as sensors, RFID tags, and smart electronic/electromechanical devices, surrounding us will be on the network [START_REF] Union | ITU internet report 2005: The internet of things[END_REF], [START_REF] Gubbi | Internet of Things (IoT): A vision, architectural elements, and future directions[END_REF]. The gadgets would disappear and weave themselves into the fabric of our everyday life to work in concert to support us in carrying out daily life activities, tasks and rituals in an easy, natural way using information and intelligence, hidden in the network connecting the gadgets. This pervasive paradigm known as Internet of Things (IoT) might increase the value of information generated by the number of interconnection between people and gadgets, denoted by things, and transformation of the processed information into knowledge for the benefit of mankind and society [START_REF] Vermesan | Internet of things strategic research roadmap[END_REF].

IoT will usher in a wide range of smart services and applications to cope with many of the challenges that individuals and organizations face in their everyday lives via allowing humans and things to be connected with either anyone or anything, in any place, at any time [START_REF] Union | ITU internet report 2005: The internet of things[END_REF] [START_REF] Zheng | The internet of things [guest editorial[END_REF].

The IoT vision of pervasively connecting billions of things is able to interact with the environment around us and receive information on its status that was previously not available by simply looking at a set of things [START_REF] Zorzi | From today's intranet of things to a future internet of things: a wireless-and mobility-related view[END_REF]. In other words, A. M. Ortiz, D. Hussein (Corresponding Author), S. Park, S. N. Han and N. Crespi are with the Department of Wireless Networks and Multimedia Services, Institut Mines-Telecom, Telecom SudParis, Evry-Cedex, 91011 France (email: {antonio.ortiz torres, dina.hussein, soochang.park, son.han, noel.crespi}@it-sudparis.eu).

Manuscript received October 31, 2013 while previous Intranet of Things [START_REF] Zorzi | From today's intranet of things to a future internet of things: a wireless-and mobility-related view[END_REF] [START_REF] Uckelmann | An architectural approach towards the future internet of things[END_REF], which is a local network of a set of things such as Wireless Sensor Networks (WSNs), Machine-to-Machine (M2M), and smart homes, can only extract regional information containing specific content from the things, IoT can provide large scale, comprehensive, and historical information by collaborating between different intranets of things even if they have heterogeneity regarding devices, local communication technologies, and deployment goals. Furthermore, IoT enables the creation and composition of new services and applications, offering to individual users a new ecosystem where different intranets of things can collaborate.

Humans usually interact with others in a wide variety of relationships during their everyday life. Also, they would utilize many smart services and applications from IoT to improve their life quality. Hence, quality of experience (QoE) of those services and applications depends on how to satisfy the needs driven from the relations among humans. In addition, a high degree of correctness of each need is practically derived by the collaboration with humans in the relationship related to a service. In IoT, as mentioned above, an individual user connects to the other(s) via legacy networks; on the other hand, sets of things collaborate with each other via the Internet for offering information to smart services and applications, while each user uses them. Thus the IoT follows two interaction paradigms: human-to-human and thing-to-thing, and then humans merely utilize data from things as an old-fashioned client-server interaction model [START_REF] Hong | Snail: an ip-based wireless sensor network approach to the internet of things[END_REF] [START_REF] Wu | M2M: From mobile to embedded internet[END_REF]. It means that IoT so far does not adopt a true connection between humans and things, i.e., human-to-thing, for real ubiquitous computing [START_REF] Uckelmann | An architectural approach towards the future internet of things[END_REF].

In order to practically integrate the ubiquitous computing in our future daily life with high QoE, we need to improve the connectivity of all the relationships between users and things, and to enhance the availability of computational power via sets of things surrounding us. Therefore, we take into consideration Social Networks (SN) of all entities (i.e., humans and things) for ubiquitous computing as an evolution beyond the IoT. In other words, things should be socialized for allowing humans to establish relationships with them in an easy way. It does not only mean physical connections between humans and things, but also logical configurations of social communities involving humans as well as things. This logical configuration can be realized through exhibiting features from people's SN and adopts them for the suggested universal SN of all entities. The feature set can include the interactivity scheme, profiling system, recommendation, and mashup of services. This SN assists in the emerging of new communities driven by increasing sociality, inherited from traditional SNs, could give the chance to understand user requirements, and thus they also improve the availability based on trust in each community.

In fact, increasing the availability of processing power would be accompanied by decreasing the visibility [1][9]. Thus, this novel pervasive vision with improved sociality is denoted by Social Internet of Things (SIoT).

Consequently, new ecosystems coming out when clustering social networks and IoT allow the enrichment of/from both paradigms since IoT provides the connection to the physical world by sensing and actuating while social networks contribute to many of the daily aspects of the human world.

This article explores the evolutionary history of what has been called SIoT, analyzes the gaps in social-driven IoT studies in early stage, with an eye on future ubiquitous computing (i.e., SIoT), and comes up with a novel architecture design that integrates different components from previous proposals, and with newly driven requirements and research challenges for SIoT.

The rest of this article is organized as follows. In Section II, we address the role of SNs for IoT evolution as well as discussion of the SIoT values. Then, we go through the history of technical improvements from WSNs to IoT in Section III. Section IV provides literature review of socialdriven IoT studies based on architectures, current research trends, applications, and SIoT in the industry. In Section V, we discuss the new challenges and open issues of SIoT with the architectural elements that will pave the way toward this future-driven SIoT paradigm. Finally, Section VI gives some concluding remarks.

II. SOCIAL INTERNET OF THINGS

In this section, we address three questions to help in understanding the concept of SIoT and its main contributions to the current technology. Next, we discuss each question trying to raise concerns and seek solutions to them.

A. Why are we considering Social Network principles to be integrated with real ubiquitous computing?

The term Community of Interest (COI) generally refers to a set of communicating entities [START_REF] Union | ITU internet report 2005: The internet of things[END_REF] [START_REF] Conti | The internet of things[END_REF]; it may also be further extended to refer to a set of communicating entities engaged in wanted communication, namely "good COI" [START_REF] Gubbi | Internet of Things (IoT): A vision, architectural elements, and future directions[END_REF]. Individuals in such COI share various resources in online and offline settings. They share social relationships, interests, and contextual and multimedia resources. Also, in some scenarios they share physical devices and objects, i.e., office/home appliances. While social relationships and contextual data shared within an online community are considered as productive resources that would facilitate cooperation for mutual benefits [START_REF] Vermesan | Internet of things strategic research roadmap[END_REF], SIoT suggests extending this principle to integrate physical elements and their contextual data from the real world into the social capital to reach a better understanding and modelling of the users real needs and objectives.

In ubiquitous computing environments, realizing a vision of Ambient Intelligence [START_REF] Ducatel | Scenarios for Ambient Intelligence in[END_REF] where many different devices, called things, will gather and process information from many different sources to control both physical processes and interactions with users, we can find diverse interaction models among devices and humans: human-to-human, thing-to-thing, and human-to-thing. They aim at supporting a wide variety of services and applications for individuals and social communities. In fact, when various scenarios of thing-to-thing interaction provide local/global information to services, users trust and utilization of these services are considered as the core value in ubiquitous computing environments. Consequently, close interaction of human-to-thing form the next wave of SIoT. Therefore, it eventually achieves optimization of the best services to be offered to users with improved QoE. Brought by the notion of trust in social communities, this shift from thingto-thing pairing towards human-to-thing implies that users are no longer only consumers of services but they also participate in creating services. This transit brings up new challenges in terms of context and communications as well as benefits. In this article we mainly focus on two benefits: (i) improving the QoE and (ii) enhancing the collaboration within communities involving humans and things.

On the other hand, achieving collaboration is one of the major goals of SNs. With the wealth of social and contextual data shared and stored, online presence of individuals and communities is no longer passive. They collaborate by publishing data and content aiming to meet their needs in everyday life. In SIoT vision, users and mostly devices are the core contributors to create services as well as consumers of services. The wheel starts turning from individuals and communities, it gathers social data and communicates with devices shared on the physical world to eventually offer a set of services and an enhanced collaboration back to communities and individuals.

As the paradigm for Ubiquitous Computing [START_REF] Weiser | The computer for the 21st century[END_REF] implies making the computational power available anytime/anywhere and exploits it toward the benefit of mankind and societies [START_REF] Zheng | The internet of things [guest editorial[END_REF], this paradigm will not be completed without understanding society needs and challenges. Weaving the social/contextual data and relationships available within online social networks with physical thing worlds will ultimately reach this goal.

B. Why SIoT is the next step?

Future ubiquitous computing will usher in a wide range of smart services and applications to cope with many challenges that individuals and organizations face in their everyday lives via allowing humans and things to be connected with either anyone or anything, in any place, at any time. While IoT studies [1][12] have typically mentioned communication to physical world by sensing or actuating through many of different devices to be the biggest novelty, SIoT paradigm however raises important concerns about why and how to utilize these services and applications. For this objective, there are two considerations as shown in Fig. 1: 1) increasing sociality (or connectivity) and 2) improving pervasiveness (or availability).

In order to settle on all the properties of real ubiquitous computing in our future daily life with high QoE, we need to improve the connectivity of all the relationships between humans and things. As mentioned above, in human society a person typically functions both as a consumer and as a producer to communicate with the others; moreover, in either physical or online world, social networks could provide longterm (or proactive) and on-demand (or reactive) information, e.g., needs, interests, locations, demographic properties, relationship characteristics, etc., of individuals or communities through collaboration with each other. Storing, processing, and utilizing these social aspects could eventually help to improve QoE.

In pervasive environments, users tend to access services any time and anywhere using any kind of devices through any kind of communication networks. When overseen through the SIoT vision, pervasiveness will imply the weaving of actual, virtual, and physical things into SIoT.

In SIoT, users themselves can unintentionally participate in the process of improving QoE through things they use and share on a daily basis which represent their interests and needs. Moreover, things will collaborate with other diverse things to satisfy their objectives which are driven from the humans. In this sense, humans and things are no longer seen as separate nodes inside a network, and their objectives and needs will weave together forming what we call SIoT. That is, such close link formation between humans and things would cause increasing availability of both elements (i.e., humans and things) as well as assuring their transparency. The two previously mentioned notions of transparency and availability will eventually drive us to the highly pervasive world, as promoted by future driven ubiquitous computing systems.

C. What are the key perspectives for future-driven SIoT?

In order to achieve an actual implementation of the seamless integration between social and IoT worlds and to gain the benefits promoted by the SIoT vision discussed above, certain perspectives need to be considered. Figure 1 illustrates these perspectives together with the evolution history of ubiquitous computing technology.

• Interactivity Perspective: the pairing between humans and things in IoT can take place in two forms: human-tohuman or thing-to-thing interaction and it can be achieved using the normal physical interaction in case of humans or various computer networks in case of things. In the current state of the art, most of the contributions focus on a single form of communication at a time whereas in this article, we claim that implementing human-to-thing interactions is essential to achieve the completed vision of SIoT. Such kind of communication opens the door to another level of pervasiveness in IoT environments and in fact opens the door to many other networking and communication issues that must be tackled. • Collaboration Perspective: this perspective appears to be the most important one in order to realize a complete convergence of both the social and IoT worlds because it endorses human-to-thing interactions. We explore the roles of humans and things. Considering social values, SIoT ultimately allows humans and things to act as producers or consumers, and this leads to increasing collaboration among all the entities as well as eventually enhancing QoE. • Handled-data Perspective: it is also very important to consider the kind of data acquisition and handling techniques needed to be considered in pervasive environments. We categorize data acquisition techniques into two categories: (i) proactive data acquisition that is commonly uses crawling techniques, learning algorithms, or various data analysis algorithms, and (ii) reactive data acquisition which usually operates in a real time manner using various data mining and query techniques. In SIoT, both kinds of data acquisition can be used depending on the given scenarios. For instance, location information of a user could indicate either the current location for an ondemand query or the historical trajectory by learning and analyzing. Also, this property could cause the same situation for temperature monitoring by things.

III. FROM WSN TO SIOT

Wireless Sensor Networks (WSNs) appeared by the end of the last century, gaining wide attention from the research community and from a number of companies that developed commercial products using this technology. WSNs, composed of resource-constrained (i.e., battery, processing, storage, etc.) devices opened a research topic, where many studies started to propose new protocols, architectures, developments, and applications with the aim of extracting all the potential of this new paradigm [START_REF] Yick | Wireless sensor network survey[END_REF]. Many proposals were oriented to raise new ideas, while others were focused on the adaptation of existing developments to the requirements of this new technology.

WSNs have typically no infrastructure. They consist of a variable number of low-power nodes (from few tens to thousands) that can communicate with each other, covering a region and working together to monitor different variables of the environment that are measured by sensors included in the nodes. Commonly, there exists a base station, central node, gateway, or sink, that gathers the data coming from network nodes to enable further data analysis.

Applications for WSNs include environmental monitoring (e.g., flood detection, precision agriculture, forest fire detection and tracking, etc.), military target tracking and surveillance, health (e.g., telemonitoring of human physiological data, patient tracking, drug administration, etc.), home and building monitoring and automation, security and surveillance, vehicular applications, warehouse management, etc. [START_REF] Akyildiz | Wireless sensor networks: a survey[END_REF]. Moreover, WSNs are an important part of other technologies such as Body Area Networks [START_REF] Latré | A survey on wireless body area networks[END_REF], Vehicular Networks [START_REF] Hartenstein | A tutorial survey on vehicular ad hoc networks[END_REF], Home Automation and Domotic [START_REF] Gomez | Wireless home automation networks: A survey of architectures and technologies[END_REF], and Smart Cities [START_REF] Schaffers | Smart cities and the future internet: towards cooperation frameworks for open innovation[END_REF].

One of the main issues of WSNs is the availability of data (i.e., who and how data can be accessed). Generally, WSNs are proprietary deployments and data is private, and the use of no-standard communication approaches is very common. In that way, WSNs are isolated, and both users and devices cannot take advantage of other deployments, so increasing costs and reducing the functionality. From this point comes the idea of IoT, a network where anything, anywhere, anyone, at anytime is connected, providing communication among different networks through the use of Internet.

IoT has been defined as a world-wide network of interconnected objects uniquely addressable, based on standard communication protocols [START_REF] Bassi | Internet of things in 2020: A roadmap for the future[END_REF]. However, the main challenge of this technology is to find the appropriate approach to integrate generic objects (i.e., devices) into a common framework.

When integrating such as number of devices with the communication capabilities expected from IoT, the application scope automatically grows, including new applications such as aerospace and aviation, automotive, telecommunications, healthcare, independent living, pharmaceutical industry, retail, logistics and supply chain management, smart cities, manufacturing, advanced environment monitoring, agriculture and breeding, media and entertainment industry, insurance, recycling, etc. [START_REF] Bandyopadhyay | Internet of things: Applications and challenges in technology and standardization[END_REF], [START_REF] Miorandi | Internet of things: Vision, applications and research challenges[END_REF].

As the interest of the community grows for the IoT paradigm, so does the need for sharing IoT data, services and applications, and the integration of ubiquitous computing in the everyday life also has to comply with social interactions. At this point is where social networking principles are progressively being integrated in the IoT, so bringing on the paradigm known as Social IoT.

The next section reviews the current technological developments that are oriented to include social skills in the loop, from the pioneer approaches that propose to publish sensor data in micro-blogging networks to the well-established architectures for SIoT, detailing the basics of this new paradigm, new application domains and industrial developments that are based on this technology.

IV. CURRENT TRENDS: IOT IS BECOMING SOCIAL

Although the notion underlying the integration of social aspects with IoT as promoted by SIoT is still new and in an early stage of investigation, some research contributions already paved the way by offering solutions for engaging people, through social networks, along with distributed sensors and embedded devices as a way to enhance services and applications. In [START_REF] Kranz | Things that twitter: social networks and the internet of things[END_REF], for instance, the IoT paradigm is proposed to be enriched with Twitter-communication capabilities to post updates and information about the status of some on-going tasks and activities. Similarly in [START_REF] Baqer | S-sensors: Integrating physical world inputs with social networks using wireless sensor networks[END_REF], an approach based on WSN is presented making also use of Twitter to publish and share sensory data and resources. In [START_REF] Ning | Future internet of things architecture: like mankind neural system or social organization framework?[END_REF], the IoT architecture is considered as a social organization framework to federate ubiquitous IoT architecture. Other approaches propose to extended the IoT through the use of social networks APIs, for instance, [START_REF] Baqer | Enabling collaboration and coordination of wireless sensor networks via social networks[END_REF] suggests a platform that enables people to share their World Wide Web-enabled devices so that others can use them.

A. SIoT Paradigm

The SIoT paradigm represents an ecosystem that allows people and smart devices to interact within a social framework. On top of this framework, applications and services can be offered relying on Web technologies. To realize this framework some major building blocks should be provided. In this section we sum up these key aspects that constitute the basis of SIoT: the social role, intelligence, socialized devices, and everything as a service.

• Social Role: In [START_REF] Atzori | Siot: Giving a social structure to the internet of things[END_REF], [START_REF] Atzori | The social internet of things (siot)-when social networks meet the internet of things: Concept, architecture and network characterization[END_REF], [START_REF] Atzori | Smart things in the social loop: Paradigms, technologies, and potentials[END_REF] the social role initiates from users' social network, where the argument to bring it into IoT world is to guarantee the network navigability, and an efficient service discovery. Similarly in [START_REF] Guinard | A web of things application architecture: integrating the real-world into the web[END_REF] the social role is promoted by the use of popular online social networks and their APIs to maintain a social structure and relationships with smart objects. Also, this suggested social structure allows to share smart objects relying on the trust provided by the community. In [START_REF] Pintus | Paraimpu: a platform for a social web of things[END_REF], users' social networks accounts can help in service operation for SIoT, for instance, to utilize geo-location data or publish devices' status and updates. The social role appears in [START_REF] Zhang | Architecture design for social web of things[END_REF] in terms of utilizing social networks as an interface to control smart objects. • Intelligence: In [START_REF] Atzori | The social internet of things (siot)-when social networks meet the internet of things: Concept, architecture and network characterization[END_REF] the concept of intelligence is mentioned as an essential component of the SIoT paradigm which is responsible for starting, updating and terminating the objects' relationships in SIoT. This is not only the scope of intelligence, in [START_REF] Guinard | A web of things application architecture: integrating the real-world into the web[END_REF] the notion of intelligence is to allow dynamic thing-to-thing service discovery where smart objects can understand each others' services in an automated way. The work presented in [START_REF] Console | Interacting with social networks of intelligent things and people in the work of gastronomy[END_REF] envisions that intelligence is implemented as a middleware combining many technologies such as ontologies, techniques for processing user generated content, and recommendation techniques. In short, intelligence in literature appears to be a sort of decision maker to drive the use of services. • Socialized Devices: The concept of socialized devices as brought in by very early contributions in SIoT such as [START_REF] Atzori | Siot: Giving a social structure to the internet of things[END_REF], [START_REF] Atzori | The social internet of things (siot)-when social networks meet the internet of things: Concept, architecture and network characterization[END_REF], [START_REF] Atzori | Smart things in the social loop: Paradigms, technologies, and potentials[END_REF], [START_REF] Guinard | A web of things application architecture: integrating the real-world into the web[END_REF] may be the most essential architectural component because it implies the mechanism which various smart objects and embedded devices will use to communicate with people through the Internet.

[33] introduced the idea of collaboration between social networks and smart objects, an analysis of the features of social devices is also presented in this article, focusing on the vision of enabling smart devices to "talk" with other objects, to share experience about certain situations and to seek help. In [START_REF] Guinard | A web of things application architecture: integrating the real-world into the web[END_REF], social devices rely on Web protocols to communicate with users through a social network environment. • Everything as a Service: The notion of turning objects and SNs functionalities into services and enable them to be easily discovered and integrated with various other services has been presented in the literature to utilize the convergence between the social and devices roles as promoted by SIoT. Thus, people can share the services offered by smart objects with friends or objects [START_REF] Guinard | Sharing using social networks in a composable web of things[END_REF]. This kind of sharing implies the use of the social role to discover and promote services. However, the concept of turning "everything" to a service is presented in [START_REF] Atzori | The social internet of things (siot)-when social networks meet the internet of things: Concept, architecture and network characterization[END_REF] as a wider vision, by associating smart objects with the services they deliver. The discovery of new services to be utilized or mashed-up with other services can take place endorsed by the social role, where a user can discover trustworthy services within her/his social community.

B. SIoT Architecture

In the literature, the Web of things (WoT) is presented as an evolutionary step following the IoT paradigm [START_REF] Guinard | A web of things application architecture: integrating the real-world into the web[END_REF] where in the former, smart objects and people relies on Web standards and protocols to interact as peers in an integrated ecosystem. The convergence between the social aspects in WoT and IoT paradigms formed the focus of many research papers; however, the two terms themselves are being alternatively used in the literature to refer to almost the same paradigm. In this article we stick with the term SIoT; however, we consider contributions in the field of SWoT as well. Table I summarizes the research papers with their mainly presented architectural components presented in Section IV-A.

C. SIoT Research Trends and Applications Domains

The potentials offered by SIoT make possible the development of a huge number of applications, however due to the relatively novelty of the concept it has not been fully exploited in many applications. We split contributions into two categories: research trends utilizing the social aspects in an IoT environment to offer a higher quality of service, and some partially or fully implemented prototypes that propose the idea of integrating the social networks with IoT to improve aspects our daily life. Various research trends exist in the literature, although they are not originally presented under the SIoT umbrella, they can still fit into it by combining the social role with IoT. We sum up those in Table II. The research trends includes semantic web services environments to enable devices and social service discovery and mash-up. Other trends focus on realizing some social aspects from online social networks or peer-to-peer social networks to offer social aware services. Location based awareness is another research trend where location data are collected and processed to offer customized services. The analysis of the social network graph has been also considered to gain understanding of the social relationships. Finally, trust management is offered to orchestrate the process of service discovery. Table III shows some of prototype examples in the field of SIoT.

D. Industrial Approaches for SIoT

Many companies in different industrial sectors have already approached this new trend by using cutting-edge technologies to develop new products and services that will drive the next wave of innovation in SIoT. These social devices ranging from low-cost sensors to powerful embedded systems can gather [START_REF] Hussein | A framework for social device networking[END_REF] data and communicate these data over the Internet to social networks of people and devices who can respond to a problem, deliver a service, or sell a solution [START_REF] Semmelhack | Social Machines: How to Develop Connected Products That Change Customers' Lives[END_REF]. Table IV summarizes a number of off-the-shelf commercial social devices. These are very appealing products that bring the new experience of SIoT to the customers. Though, except products from Nike+ that provide open APIs for third-party providers to interact with their devices, most of the products available on the market are still in isolated islands, which would be a challenge for the future interoperability of SIoT products.

E. Standardization activities for SIoT

In order to standardize the diverse aspects of SIoT, several standardization bodies accept contributions related to the consideration of Social Network properties into the IoT, and also the inclusion of IoT devices into the Social Networks structure. ITU-T1 for instance, is focused on information and communication technologies, and proposals such as the one presented in [START_REF] Lee | Proposal of a new work item on social and device networking[END_REF], that combines Social Networks and IoT, are part of the new era on standardization activities; oneM2M2 , initiated in 2012, aims at creating a common M2M service layer to ensure that Machine-to-Machine communications can effectively operate in a worldwide manner. Considering the importance of Social Networks in the framework of IoT and thus in M2M, it is expected that a number of contributions to oneM2M are oriented to SIoT.

V. DISCUSSION -CHALLENGES AND OPEN ISSUES

As stated in many research works, Social IoT appears to be the next step in the evolution of ubiquitous computing. However, there are still a number of challenges and open issues that should be faced by the research community in order to mature this technology. After having illustrated the evolution from WSN to SIoT, described the main stages of this transformation, reviewed the literature and the commercial approaches associated to Social IoT, this section exposes the main research directions that will help to create this technology. First, we provide a general purpose architecture for SIoT that integrates the main architectural components proposed in the literature. Later we analyze technological developments, both hardware and software, that will make possible the successful operation of the paradigm. Finally, we focus on non-functional requirements, that constitute an essential part of the SIoT ecosystem.

A. Architecture -General Design

To summarize our vision towards a future driven SIoT, we consider the following elements to be part of the architecture: 1) actors (i.e., smart things and users), 2) an intelligent system to manage and orchestrate actors' interactions, 3) an interface for actors to enable interactions, 4) the Internet to provide open access among all the involved entities. Next we discuss about each element in the architecture in detail (see Fig. 2).

• Actors: the SIoT paradigm suggests a democratic environment, where both humans and things can participate equally by publishing data and recieving control commands for managing data being produced. These data can be represented as profiling data or simply responses to queries sent by users and/or devices. Queries in this sense can be sent to find the closest node, the most reliable node or service, or to simply receive updates about weather or a certain device status. In return, humans and things can receive services, or recommendations for services to consume, that fulfill current situations and long term objectives i.e, power efficiency plan for a smart grid in a smart home. • Intelligent system: is responsible for managing and orchestrating the whole interactions undertaken by the actors, we actually sum up the main sub systems to be part of the intelligent system such as service and applications management, recommendation, service discovery and search, and data and context management.

• Interface: all the interactions with the system take place through an interface that enables the input of data and queries, as well as it provides the requested output (i.e., control commands or services). • Finally, the Internet act as a communication medium to bring smart devices with their services to the users and also to allow them to interact with their devices and services.

B. Enabling Technologies

In order to have a functional platform that successfully implements the requirements and achieves the objectives of SIoT, some advances in the technological field must be reached.

With the objective of making any entity in the network (i.e., users and devices) identifiable and retrievable, it is necessary to map a unique identifier to each component [START_REF] Bandyopadhyay | Internet of things: Applications and challenges in technology and standardization[END_REF]. Apart from the public names or nicks that will be used by the final users, the system needs an addressing scheme that enables different management tasks such as identity administration and authentication to ensure that the heterogeneity of individuals in the network can be identified.

Regarding the hardware developments, there is nowadays a vast variety of devices that can be adapted to be part of the SIoT. Sensor and actuator devices from WSN, M2M, domotic, etc., are good candidates to take part in this new paradigm. However, these devices usually cannot directly connect to the Internet, and need the use of gateways to transfer their readings and receive commands. Since many of the capabilities of SIoT are going to be accessed through web services, the use of web-enabled devices will facilitate the deployment and further use of this paradigm. Moreover, new deployments should address the energy-efficiency as a primary design goal, as well as adaptive and re-configurable interfaces and multi-protocol capabilities.

How this heterogeneous and huge network formed by billions of devices and users will communicate is also an important issue that has to be faced from the beginning. Developments oriented to enable interoperability among devices and users will be a critical pillar in the SIoT design. Again, the energy efficiency plays an essential role; operating systems, communication protocols and algorithms that cooperate in the overall energy saving will be determinant in the establishment of SIoT. Lightweight and open middleware platforms as well as self-adaptive software will also favor the expansion of this technology.

Next, we analyze some of the most relevant research and development challenges that will help to mature this technology and lead the way to the future massive deployment and everyday use of the SIoT ecosystem.

C. Open Research Issues

With the aim of making real the SIoT paradigm, there are still numerous challenges that must be faced prior to the worldwide deployment of this technology.

1) Interoperability, Data Management, and Signal Processing: Due to the heterogeneous nature of the IoT devices, including different information processing and communication capabilities, together with user features and data, relationships and competences coming from the SNs, the system must be able to manage this variety of data types, providing interoperability among all the components. In order to facilitate communication and cooperation, common practices and standards are required [START_REF] Mattern | From the internet of computers to the internet of things[END_REF]. This challenge is aligned with the identification requirement presented in Section V-B, since all components in the SIoT system need to be identified prior to interoperate with each other.

After all components in the network can interoperate, data management arises as another remarkable issue; how all the data coming from devices and users are going to be handled? Here, we can identify at least two issues: data storage and data management. Regarding to storage, it is obvious that it is practically impossible to store all the SIoT data in a unique server, so distributed approaches should be proposed in order to get an efficient storage system. Respecting data management, metadata structures such as those proposed by Metadata Standards3 can be the basis of data structures definition. Afterwards, standards proposed by the W3C4 such as RDF (Resource Description Framework), DAML (Darpa Agent Markup Language, or OWL (Ontology Working Language) will help providing with meaning to the data coming from users and devices.

Of course, advanced data analysis (such as those proposed for Big Data [START_REF] Russom | Big data analytics[END_REF]) and intelligent approaches will provide utility to the SIoT, so serving users and devices with meaningful and valuable information.

2) Discovery and Search Engines: Considering the huge volume of information in the SIoT, available data, services and applications need to be easily reachable. The use of searching and discovery mechanisms is mandatory when working with such a data size. Existing discovery approaches for web services such as UDDI, DPWS or RESTful-based [START_REF] Guinard | Interacting with the soa-based internet of things: Discovery, query, selection, and on-demand provisioning of web services[END_REF], [START_REF] Evdokimov | Comparison of discovery service architectures for the internet of things[END_REF], can be adapted to deal with the requirements of SIoT related to data, services and applications lookup and discovery.

3) Energy Management: Devices taking part in the SIoT are typically moving around, and not connected to an unlimited power supply. So do users, carrying hand-held devices, that usually operate with batteries. Hence, the energy conservation is a conditioning factor in the design and operation of SIoT, and efficient energy management should be implemented at all levels; from M2M device communications to interface design.

All stages in the design of SIoT technologies have to be oriented to low-energy consumption. While energy harvesting technologies do not yet provide enough resources, this is a broad research topic where some approaches proposed for WSNs and other low-power technologies can be adapted to deal with the requirements of the SIoT (e.g., scalability, availability, and heterogeneity).

4) Security, Privacy and Trust: These might be the most sensitive requirements for the success of SIoT; without a secure technology that ensures user privacy, safe communications and trustworthy interactions, the SIoT paradigm will not reach enough popularity to be considered a well-established technology, and all its potential will be lost.

Methods to guarantee data confidentiality and user privacy that are also required for other platforms can likely be reused in the SIoT context, always considering the special requisites of this paradigm. Lightweight mechanisms for data confidentiality and integrity, as well as effective ID management and privacy enhancing technologies represent the basis that will make users trust the SIoT ecosystem. 5) Self-operation, management and organization: As it has been commented before, the SIoT expects to be a worldwide technology composed of billions of devices and people. When imaging how the global management of such a huge platform should be, one easily realizes that automatic operation is needed at most levels.

Mechanisms including self-organization, self-management, self-operation, self-healing, and self-protection capabilities will definitely be a decisive part of the SIoT. But not only automatic network management will be relevant, also autonomic data analysis, and service discovery and composition will contribute to enhance the user experience. Again, the adaptation of approaches proposed for other technologies can be an starting point to automatize the operation of the SIoT ecosystem.

6) Heterogeneity: Sensors, actuators, ID-tags, smartphones, tablets, computers, etc., will be part of the SIoT, different brands and technologies will have to work together to achieve a common goal: provide users with advanced services and applications. For this, SIoT developments have to be able to integrate many types of devices, technologies, and services [START_REF] Zorzi | From today's intranet of things to a future internet of things: a wireless-and mobility-related view[END_REF]. Interoperability at device level ensures that different technologies are able to communicate among them; the system should ideally be open to support a huge variety of different applications, whose characteristics and requirements may be extremely diverse, in terms of bandwidth, latency, reliability, availability, etc.

The necessity of dealing with heterogeneous devices will probably degrade the overall system performance compared to a highly optimized vertical design, but the extended functionality offered by the SIoT ecosystem will compensate this drawback. New designs that efficiently deal with heterogeneous technologies will be of great importance for the successful deployment of SIoT.

7) Interactions and Interfaces:

The SIoT infrastructure will be focused on providing users with an advanced experience able to consume and produce data and services coming from devices and other users. Thus, the human-centric interface should provide a user-friendly medium to interact with devices and users.

How users and devices interact with each other is still an open challenge. Some approaches such as [START_REF] Kranz | Embedded interaction: Interacting with the internet of things[END_REF] and [START_REF] Atzori | Siot: Giving a social structure to the internet of things[END_REF] propose a set of possible interactions among the different elements, but most of them are focused on specific applications. A global set of interactions needs to be defined, as well as methods to manage these interactions, for example, users can get data from their own devices, but how to get data from other users' devices is not completely clear, and it is very aligned with the privacy issues commented before; should I give completely free access to my devices? What about sensitive data such as my current location? Can I provide/get anonymized data? 8) Service Management (discovery and composition): Nowadays there exist a number of approaches to discover and perform service composition in the framework of the IoT [START_REF] Guinard | Interacting with the soa-based internet of things: Discovery, query, selection, and on-demand provisioning of web services[END_REF]. The SIoT demands new capabilities in this area, and intelligent approaches that provide advanced functionality seem to be the best-performance option. SOA-based systems and DPWS [START_REF] Cândido | Soa at device level in the industrial domain: Assessment of opc ua and dpws specifications[END_REF] are a good start for service management in the SIoT, together with semantic-compliant approaches that are able to deal with context and data meaning. 9) Application Development: All the functionality offered by SIoT is meaningless without applications that make use of them. The application development process will vary depending on each situation; what devices and services are involved and which set of users the application is oriented to are determinant in this process. The use of open APIs will be helpful, and the implication of the users will bring new use cases that contribute to make the SIoT more accessible and functional.

10) New Business Models and Stakeholders: How to get benefits from the SIoT technology is a decisive point in the establishment of this new paradigm. When designing an advantageous platform, where both developers, stakeholders and users feel comfortable, and bearing in mind the collaborative nature of the SIoT5 , it is essential to take into account several considerations: (i) offer attractive and useful services and applications that encourage people to use them, (ii) look for non-conflicting business models that boost collaboration, (iii) recognize the customer experience by inviting the customers to participate, and (iv) target the adequate customer segment.

Once users find attractive the use of SIoT-based applications, capitalizing plans can be studied and launched, including: marketing and sales, research and development, advertising, application fees, device commercialization, etc. [START_REF] Heidemann | Online social networks: A survey of a global phenomenon[END_REF].

11) Fault Tolerance: In such a dynamic and mobile world, with context changing rapidly and with the heterogeneity that characterizes the SIoT, the reliable operation of the different components should be guaranteed [START_REF] Mattern | From the internet of computers to the internet of things[END_REF]. Efficient adaptation to challenging situations will make the difference for a trusty platform, as well as a correct architectural organization that supports redundancy at several levels to ensure that it offers reliable information to the final users.

12) Semantics and Context Management: The SIoT is aimed at providing functionality in a number of situations, and a set of devices might be used for several purposes at the same time. Thus, the ability to correctly manage the current context will definitely not only improve the performance of the system, but it will also make it more usable, by providing unambiguously access and data interpretation. Semantic approaches oriented to RDF and OWL [START_REF] Katasonov | Smart semantic middleware for the internet of things[END_REF] can be extended to include descriptors for SIoT users and devices characteristics, so facilitating the interoperability among all the components.

VI. CONCLUSIONS

The Internet of Things paradigm aims at connecting anything, to be accessed at any time from anywhere. It is based on making available a set of services coming from the interoperation of billions of devices, with the ultimate objective of improving users everyday lives.

In this article, we review the juncture of Internet of Things with Social Networks that defines the paradigm, known as Social Internet of Things. This union emerges from inheriting social networking features and values of interactivity, recommendation & filtering and services composition and suggesting a universal framework to combine users, devices and services and the interactions among them. This seamless integration can bring new relationships allowing the creation of novel services and applications that will definitely be of great interest both for final users and stakeholders.

Illustrating the evolution from Wireless Sensor Networks to Social Internet of Things, and providing a detailed description of this new paradigm from several points of view constitutes the first part of the article, to later going through the current situation of the state of the art in this technology, analyzing different proposals and implementations, to finally come up with a general architecture description and a deep review of the challenges and open research issues that must be solved to make real the Social Internet of Things.

Fig. 1 .

 1 Fig. 1. Evolutionary History of Ubiquitous Computing Technology.

Fig. 2 .

 2 Fig. 2. Future SIoT Architecture.

http://www.itu.int/

http://www.onem2m.org/

http://metadata-standards.org/

http://www.w3.org/

http://www.caba.org/resources/Documents/IS-2009-93.pdf

Scientific Advisory Board of FTW, Austria. His current research interests are in service architectures, P2P service overlays, future Internet, and Web-NGN convergence. He is the author/coauthor of more than 250 papers and contributions in standardization.