Nathalie Lyczko
email: nathalie.lyczko@ifp.fr

Fabienne Espitalier

Olivier Louisnard

Jacques Schwartzentruber

Effect of ultrasound on the induction time and the metastable zone widths of potassium sulphate

Keywords: Induction time, Metastable zone, Ultrasound, Potassium sulphate, Nucleation

A study of cooling crystallisation of a potassium sulphate solution in a batch reactor is described in this paper. The effect of ultrasound on primary nucleation was investigated by measures of induction time and metastable zone width of unseeded solutions. The appearance of crystals is detected by conductivity measurements.

The experimental results show that ultrasound has an effect on the primary nucleation of potassium sulphate. Ultrasound allows induction time and metastable zone width to be significantly reduced.

Introduction

Crystallisation is an important process used in many domains, such as chemical, pharmaceutical and petrochemical industries. Works on the influence of ultrasound in crystallisation/precipitation processes have been published for several years [START_REF] Yaminskii | Effect of ultrasound on nucleation and coagulation in crystallisation from solution[END_REF][START_REF] Martin | Power ultrasound-a new tool for controlling crystallisation[END_REF][START_REF] Srinivasan | Effect of sonication on crystal properties[END_REF].

The aim of our study is to determine if ultrasound has an effect on primary nucleation. Ultrasound is used only as a tool to induce the nucleation. For this, we carried out experiments of cooling crystallisation. Potassium sulphate has been chosen as a model product because a lot of data is available in the literature, such as solubility, nucleation and growth kinetics [START_REF] Mullin | Potassium sulphate crystal growth rate in aqueous solution[END_REF][START_REF] Nyvlt | The Kinetics of Industrial Crystallisation[END_REF][START_REF] Jones | Crystallisation kinetics of potassium sulphate in a draft-tube agitated vessel[END_REF]. This investigation was divided into two parts. First, the induction time has been measured both with and without ultrasound. In the second part, the metastable zone width has been determined at a constant cooling rate for potassium sulphate solutions saturated at 30 • C.

Theory

The primary nucleation occurs in the absence of solid particles of the crystallised substance. For homogeneous primary nucleation, no solid phase is required, while the heterogeneous primary nucleation is catalytically initiated by any foreign surface, such as the reactor wall, the stirring rod or solid impurities.

The primary nucleation rate is defined as the number of nuclei generated per unit volume and per unit time. The homogeneous primary nucleation rate is generally expressed as the following classical relation [START_REF] Dunning | General and theoretical introduction[END_REF][START_REF] Mullin | Crystallisation[END_REF]: 2 (1)

J hom = K hom exp - 4 27
γ 3 s V 2 m β 3 (Tk B υα ln S)
For the heterogeneous primary nucleation we have

J het = K het exp - 4 27
γ 3 s V 2 m β 3 (Tk B υα ln S) 2 f (θ ) (2)
where f(θ) is a function of the angle of contact between the crystalline deposit and substrate

f (θ ) = (2 + cos θ )(1 -cos θ) 2 4 .

Induction period

In order to characterise the primary nucleation, we can measure the induction period which is defined as the time elapsed between the creation of supersaturation and the appearance of crystals. Kashiev et al. proposed a general expression of induction time valid for all nuclei, which appear and grow in a saturated solution [START_REF] Kashchiev | Induction time and metastability limit in new phase formation[END_REF]:

t ind = N c J + α 0 a p JG p-1 (3)
(J mol -1 ) h s thermal coefficient transfer wall-solution (W m -2 • C -1 ) h f thermal coefficient transfer wall-fluid (W m -2 • C -1 ) J n number nucleation rate (number m -3 s -1 ) J m mass nucleation rate (kg m -3 s -1 ) k B Boltzmann constant (=1.38058 × 10 -23 ) (J molecule -1 K -1 ) k n
primary constant nucleation rate (kg (n-1) m (3n- We have tested these different models, the best fit of experimental data was obtained assuming t n ≫ t g . Only the model representing a good correlation of our experimental results is presented here. In our case, we assume that the formation time of a stable nucleus is much higher than the necessary time for the nucleus to grow to a detectable size (t n ≫ t g ). So the induction time can be considered as inversely proportional to the nucleation rate and the induction time is expressed with only the first term of Eq. (3).

T solution temperature ( • C) T b bath temperature ( • C) T 1 solution temperature ( • C) T lim limit temperature ( • C) T sat saturation temperature ( • C) T sol solution temperature ( • C) T w sol inner wall temperature ( • C) T max maximum undercooling ( • C) U global heat transfer (W m -2 • C -1 ) V solution volume (m 3 ) V m molecular volume (m
Combining Eqs. ( 1) and (4) for the mechanism of primary homogeneous nucleation and taking the logarithm of the induction time, we obtained ln(t ind ) = ln N c K hom + 4 27

γ 3 s V 2 m β 3 k 3 B α 2 υ 2 T 3 1 ln 2 S ( 4
)
where S is the supersaturation ratio calculated from the following equation:

S = a ± a * ± = γ ± C γ * ± C * (5)
The logarithm of activity coefficients has been estimated using the Brömley equation [START_REF] Mersmann | Crystallisation Technology Handbook[END_REF]. Eq. ( 4) suggests that for a given temperature, the plot of ln(t ind ) versus (ln 2 S) -1 should be a straight line. In the literature, it has been shown that the plots of ln(t ind ) versus (ln 2 S) -1 consist in two linear segments [START_REF] Söhnel | Interpretation of crystallisation induction periods[END_REF]. For high supersaturations (straightline (1)), the nucleation is predominantly homogeneous, and for low supersaturations (straightline (2)) the heterogeneous nucleation prevails (Fig. 1).

Metastable zone widths

This method consists in the determination of the maximum undercooling T max . A solution of known saturation temperature T sat is cooled to a temperature T lim at which the birth of the first crystals is detectable. The maximum undercooling T max is defined as the difference between the saturation temperature T sat and the temperature T lim . The maximum undercooling can be expressed in terms of the maximum supersaturation, C max by the equation

C max = dC * dT T max (6)
Nyvlt proposed a correlation between the mass of nuclei generated per unit mass of solution and the absolute supersaturation [START_REF] Nyvlt | Kinetics of nucleation in solutions[END_REF] J m = k n C n max [START_REF] Dunning | General and theoretical introduction[END_REF] The nucleation rate can be expressed in terms of the cooling rate ḃ

J m = dC * dT ḃ (8)
Eqs. ( 6)-( 8) yield

ln( ḃ) = d 1 ln dC * dT + ln(k n ) + d 2 ln( T max ) (9)
where ḃ is the cooling rate and d 1 and d 2 are constants. These constants depend on the apparent orders of nucleation or growth. This is a very general relation. However, different assumptions on the physical mechanisms to be taken into account lead to different interpretations of constants d 1 and d 2 . For instance, Nyvlt or Kubota assumed that only nucleation occurs before the crystals are detected: constants d 1 and d 2 are thus, simply related to the apparent order of nucleation, n [START_REF] Nyvlt | Kinetics of nucleation in solutions[END_REF][START_REF] Kubota | On estimation of critical supercooling from waiting times measured at constant supercooling[END_REF]. For Söhnel and Nyvlt, constants d 1 and d 2 depend on the apparent orders of nucleation and growth [START_REF] Söhnel | Evaluation of experimental data on width of metastable region in aqueous solution[END_REF]. If crystal growth is supposed to have a significant contribution, constants d 1 and d 2 are functions of the apparent order of growth, g [START_REF] Janse | On the width of the metastable zone[END_REF].

The determination of the nucleation rate constant k n and the kinetic apparent order of nucleation rate n and the linear growth rate order g are derived from the plot of maximum undercooling T max and cooling rate ḃ on logarithmic coordinates.

Ultrasound

We used power ultrasound with a frequency equal to 20 kHz. When high energy pressure waves are applied to a liquid, cavitation bubbles made of vapour and dissolved gas are created. The cavitation consists in the formation, growth and implosion of these bubbles. The violent implosion of bubbles is called collapse and can induce very high local temperatures and pressures.

The dissipated power of ultrasound reported in the text has been estimated by calorimetry. These dissipated powers represent about 40% of the electrical power consumed by the generator.

Experiments

Aqueous solutions are prepared from potassium sulphate crystals (purity > 99%) and distilled water. The experimental apparatus used for the determination of induction time and metastable zone widths is represented in Fig. 2. It consists of a thermostated reactor (200 ml) stirred with a magnetic rod at a constant rotation speed of about 500 rpm. Ultrasound is applied at the top of the reactor by a stainless ultrasound transducer with a titanium tip. Two ultrasonic power levels have been used: 0.05 and 0.12 W g -1 solution. The conductivity and the solution temperature are respectively measured with instrumental resolutions ±0.1 mS cm -1 and ±0.01 • C. We consider that the appearance of crystals is detected when there is a sudden decrease of the conductivity. Before each experiment, in order to dissolve all crystals, solutions are warmed to a temperature higher by 5 or 10 • C than the saturation temperature T sat for 1 h.

For all experiments, the saturation concentration of potassium sulphate in water solutions is calculated from the following equation [START_REF] Mullin | Potassium sulphate crystal growth rate in aqueous solution[END_REF]: measured as soon as the temperature T 1 is reached or when ultrasound is applied.

C * = 6.7 × 10 -2 + 2.3 × 10 -3 × T -6.0 × 10 -6 × T 2
Measurements of metastable zone widths are carried out for a saturation temperature (T sat ) equal to 30 • C. The cooling rates ḃ of solutions range from 5 to 20

• C h -1 .
The solution is insonified at the beginning of the cooling phase.

Results and discussion

Induction time

The appearance of potassium sulphate crystals is detected by a drop in the conductivity of the solution. The time corresponding to the crystallisation corresponds to the time where Fig. 5. Influence of ultrasound on the induction time of aqueous potassium sulphate solutions: (△) without ultrasound, (᭺) P diss = 0.05 W g -1 solution, (×) P diss = 0.12 W g -1 solution.

the derivative of the conductivity versus the time becomes negative.

Figs. 3 and4 present the variation of the solution temperature and conductivity for an absolute supersaturation of 0.0156 g K 2 SO 4 g -1 water. We can observe that the induction time without ultrasound is about 9000 s whereas with ultrasound it is about 1000 s. Moreover, the conductivity decreases faster with ultrasound than without ultrasound. As the conductivity is proportional to the potassium sulphate concentration, this difference suggests that more crystalline surface is formed with ultrasound.

All results obtained for the variation of the induction time versus absolute supersaturation are presented in Fig. 5 and Table 1. The absolute supersaturation is calculated as the difference between the actual concentration and the saturation concentration. The maximum cooling time for experiments without ultrasound is about 900 s and for experiments with ultrasound it is about 450 s. The mass error for the absolute supersaturation error is about 10 -4 g K 2 SO 4 g -1 water. The maximum error on the determination of the induction time is equal to 15% for experiments without ultrasound, to 11% for experiments at 0.05 W g -1 solution and to 12% for experiments and 0.12 W g -1 solution.

We obtained the same type of curve with and without ultrasound. The induction time decreases when absolute supersaturation increases. This behaviour agrees with classical results on crystallisation. However, we can observe that ultrasound has a significant effect on induction time, especially at low absolute supersaturations (<0.014 g K 2 SO 4 g -1 water). This effect is less significant at high supersaturation when nucleation is very fast anyway. In Fig. 6, the plots of ln(t ind ) versus (ln 2 S) -1 for experiments with and without ultrasound are represented. The values corresponding to an induction time lower than 1400 s

P diss = 0.05 W g -1 solution, ln (t ind ) = (2.35 × 10 -2 ± 0.05 × 10 -2 )(ln 2 S) -1
+(4.42 ± 0.12) ( 12)

P diss = 0.12 W g -1 solution, ln (t ind ) = (0.91 × 10 -2 ± 0.02 × 10 -2 )(ln 2 S) -1
+(6.12 ± 0.06)

The numerical values of the slopes are low, suggesting that the main mechanism is heterogeneous primary nucleation [START_REF] Söhnel | Interpretation of crystallisation induction periods[END_REF]. When ultrasound is applied or when ultrasound power increases, the slope decreases. This change of slope suggests that ultrasound modifies the product [γ 3 s f (θ )] (Eq. ( 1)) [START_REF] Tai-Quin | Nucleation of succrose solution by sound field[END_REF]. Ultrasound seems to change the activation energy to act on either the surface energy crystal-solution or the contact angle. So, ultrasound may decrease the critical radius.

For experiments at 0.12 W g -1 solution, there seems (Fig. 6) to be a break for (1/ln 2 S) contained between 160 and 200. This change corresponds to the low values of the induction time. This break could correspond to a change in the nucleation mechanism.

However, when ultrasound is applied to the solution, a lower wall temperature is required to maintain the same reactor temperature in order to allow the ultrasound power to be removed by the jacket. Therefore, the absolute supersaturation at the wall is higher when ultrasound is applied, and this could result in a shortening of the induction time. In order to distinguish between the specific contributions of ultrasound and of this thermal effect, we have estimated the wall temperature as a function of the solution temperature and ultrasound power (see Appendix A). We can then calculate the absolute supersaturation at the wall C w , as a function of the bulk absolute supersaturation C, for experiments with and without ultrasound (see Table 2). The difference between both supersaturations (bulk and wall) is negligible in experiments without ultrasound, but increases significantly with ultrasound power: one might suspect that the observed effect of ultrasound could be simply due to this artefact.

For experiments with ultrasound, it can be verified that the measured induction time is always much shorter than the induction time, which would have been observed without ultrasound with a supersaturation equal to C w (Fig. 7). For a bulk supersaturation C (point A), the corresponding wall supersaturation is C w (point B). For this supersaturation, the induction time without ultrasound t w ind (about 5800 s, point C) remains much higher than the measured one (1421 s, point B).

As a result, we can conclude that ultrasound has a strong specific effect on nucleation, which can by no means be explained by the wall temperature effect.

Metastable zone widths

The measurements of the metastable zone width with and without ultrasound are presented in Fig. 8 and in Table 3. Correlation equations of the data are We can consider that our results obtained without ultrasound (a) are comparable with those of Nyvlt et al. (c) [START_REF] Janse | On the width of the metastable zone[END_REF][START_REF] Nyvlt | Metastable zone width of some aqueous solutions[END_REF] with respect to the difficult control of the primary nucleation. This behaviour is strongly modified when ultrasound is applied; but we did not observe a significant difference between the two tested ultrasonic powers (0.05 and 0.12 W g -1 solution). The slope of the ln( ḃ) versus ln( T max ) line is strongly reduced with ultrasound, while the intercept point is increased. From Eq. ( 10) we can conclude that ultrasound decreases the apparent order of nucleation n.

As for the determination of the absolute supersaturation at the wall, we can estimate the metastable zone width at the wall. For these measurements, the difference between the solution temperature and the inner wall temperature is not significant. The difference is about 0.16 • C without ultrasound and about 0.55 • C with ultrasound. The maximum undercooling at the wall T w max with ultrasound is always lower than the maximum undercooling at the wall T w max and in the solution T max without ultrasound. Thus, we observe only an effect of the ultrasound on the metastable zone width.

Conclusions

It has been shown that ultrasound can influence the primary nucleation of potassium sulphate.

Ultrasound reduces the induction time especially for low absolute supersaturation. For experiments with ultrasound, the predominant effect has been identified as a specific effect of ultrasound and not an effect of the lower wall temperature. The exploitation of the induction time models shows that the main mechanism is a heterogeneous primary nucleation mechanism. Using the different models of classical primary nucleation theory, ultrasound seems to decrease the activation energy so they can decrease the critical radius. However, these different models do not allow an explanation of the mechanism of the action of ultrasound.

The metastable zone width can also be reduced by the application of ultrasound. The apparent orders of nucleation or growth are decreased by ultrasound. Results indicate that it would be sufficient to apply a low ultrasonic power to decrease the metastable zone width. Hence, ultrasound decreases the apparent order of the primary nucleation rate and increases the rate of appearance of the solid.

It seems that ultrasound modified the mechanism of nucleation itself, since the apparent order of nucleation is strongly reduced in the presence of ultrasound.

Some explanations can be proposed on the action of ultrasound:

• Does ultrasound increase the heterogeneity of the system or does it create new nucleation sites? • The increase of the nucleation rate could be explained by the presence of shock waves in the solution. These waves could increase the probability of collisions of a molecule and a molecular aggregate.

With this study, we have shown that ultrasound permits to set off nucleation at supersaturations lower than without ultrasound. In industrial crystallisation processes, ultrasound can induce nucleation in conditions where spontaneous primary nucleation cannot occur without ultrasound. Moreover, ultrasound could allow seeding to be avoided and thus avoiding the introduction of foreign particles into the solution.

Appendix A

From classical relations of thermal transfer in a reactor we have calculated the difference ᮀT, between the solution temperature (T sol ) and the wall temperature (T w sol ) (Fig. 9). The transfer heat flux is expressed as φ = U S(T sol -T f ) (A.1)

φ = h s S int (T sol -T w sol ) (A.2)
The overall heat transfer coefficient expression is given as 1 where S, S int and S ext are respectively the mean exchange surface, the inner exchange surface and the outer exchange surface.

U S =
Replacing the inner heat transfer, the overall heat transfer can be connected with the stirrer rate N by the following equation:

1

U S = s × N (-a) + 1 h ′ S ′ (A.4) where 1 h ′ S ′ = 1 h f S ext + e λ p S (A.5)
Knowing e, λ p and the different surfaces, the heat transfer coefficient between the wall and the cooling fluid, h f can be calculated from the intercept of Eq. (A.4) (i.e. N c tends to infinity) from experimental overall heat transfer data against N.

Then, with the Eq. (A.3) we can determine the heat transfer coefficient between the wall and the solution, h s and calculate the wall temperature (Eqs. (A.1) and (A.2)).

Fig. 1 .

 1 Fig. 1. Induction time as a function of initial supersaturation.

Fig. 2 .

 2 Fig. 2. Experimental apparatus.

Fig. 3 .

 3 Fig. 3. Solution temperature and conductivity for an absolute supersaturation of 0.0156 g K 2 SO 4 g -1 water.

( 10 )

 10 Fig.4. Solution temperature and conductivity for an absolute supersaturation of 0.0156 g K 2 SO 4 g -1 water and an ultrasound power equal to 0.05 W g -1 solution.

Fig. 6 .

 6 Fig. 6. Induction time as a function of final temperature and ratio supersaturation for potassium sulphate crystallisation: (a) without ultrasound, (b) P diss = 0.043 W g -1 solution and (c), P diss = 0.114 W g -1 solution.

Fig. 7 .

 7 Fig. 7. Induction time as a function of supersaturation with and without for an ultrasound power of 0.114 W g -1 solution.

Fig. 8 .

 8 Fig. 8. Nucleation characteristics of potassium sulphate aqueous solutions which saturation temperature is 30 • C: (a) without ultrasound, (b) with ultrasound and (c) Nyvlt et al.

(a )

 ) Without ultrasound ln( ḃ) = (5.81 ± 0.30) ln( T max ) -(11.69 ± 0.91) (15) (b) With ultrasound ln( ḃ) = (2.74 ± 0.18) ln( T max ) -(3.55 ± 0.52) (16) (c) Nyvlt et al. ln( ḃ) = 13.64 ln( T max ) -31.24 (17)

Fig. 9 .

 9 Fig. 9. Thermal transfer through an exchange wall during a cooling.

Table 1

 1 Values of the induction time measurement

	C (g K 2 SO 4 g -1 water)	t ind (s)	Measure error (±s)
	Without ultrasound
	0.0156	8978	340
	0.0166	4040	200
	0.0174	1830	140
	0.0175	4029	135
	0.0179	769	70
	0.0186	1276	45
	0.0188	2476	175
	0.0198	1520	120
	0.0213	959	65
	0.0215	672	100
	0.0217	591	70
	0.0217	1459	85
	P diss = 0.05 W g -1 solution 0.0119	6493	150
	0.0124	3053	150
	0.0125	3261	60
	0.0125	4080	125
	0.0128	4506	200
	0.0131	2915	150
	0.0131	3235	155
	0.0132	3715	150
	0.0132	2668	120
	0.0136	4275	100
	0.0139	3920	85
	0.0142	1658	120
	0.0148	910	50
	0.0149	1655	90
	0.0154	858	50
	0.0157	869	45
	0.0159	1382	100
	0.0162	975	75
	0.0164	1032	60
	0.0166	689	60
	0.0166	648	65
	0.0174	616	60
	P diss = 0.12 W g -1 solution 0.0093	3464	50
	0.0104	3204	135
	0.0106	3505	85
	0.0108	3837	80
	0.0108	2643	75
	0.0112	3560	150
	0.0124	1453	90
	0.0125	2857	135
	0.0125	1841	65
	0.0127	2308	175
	0.0135	1863	55
	0.0139	1421	25
	0.0140	1635	50
	0.0147	1044	20
	0.0153	1281	45
	0.0156	520	62

Table 2

 2 Calculation of the absolute supersaturation at the wall

	P diss (W g -1 solution) 0.00 0.05 0.12	T = T sol sol ( • C) -T w 0.28 0.89 1.23	C w (g K 2 SO 4 g -1 water) C + 0.0006 C + 0.0019 C + 0.0026
	for experiments without ultrasound and lower than 700 s for
	experiments with ultrasound are not taken into account be-
	cause the cooling time is too high in comparison with the
	induction period. The corresponding equations are
	P diss = 0.00 W g -1 solution, ln(t ind ) = (3.69 × 10 -2 ± 0.06 × 10 -2 )(ln 2 S) -1 +(4.85 ± 0.11)	(11)

Table 3

 3 Average values of the measurement of the metastable zone width for a saturation temperature equal to 30 • C

	ḃ ( • C h -1 )	T max ( • C)	Total error (± • C)	Experiments number
	Without ultrasound
	1.80	8.6	0.80	1
	4.68	10.60	0.20	1
	5.04	10.00	0.70	4
	6.84	10.40	0.50	1
	9.36	11.85	0.85	2
	9.72	10.76	0.96	5
	10.08	11.15	0.85	2
	12.96	10.80	0.40	1
	14.04	11.65	0.45	2
	14.76	11.45	1.05	4
	19.08	12.35	1.05	2
	19.80	11.65	1.35	2
	20.52	12.25	0.75	2
	22.05	13.85	0.85	2
	P diss = 0.05 W g -1 solution 5.04 7.50	0.10	1
	10.44	7.80	0.30	1
	9.72	8.50	0.20	2
	10.08	9.30	0.40	1
	14.76	9.40	1.10	2
	15.48	9.85	0.60	1
	20.16	10.75	0.30	1
	P diss = 0.12 W g -1 solution 7.56 7.70	0.50	1
	9.72	7.85	0.55	2
	13.68	9.90	0.20	1
	15.12	9.70	0.40	1
	17.64	9.50	0.60	1
	18.72	10.50	0.30	1
	20.52	11.40	0.40	1


Cendrine Gatumel

Fabienne Espitalier

Jacques Schwartzentruber

Béatrice Biscans

Anne Marie Wilhelm

Nucleation control in precipitation processes by ultrasound

come


Valérie Issarny

Georgios Bouloukakis

Nikolaos Georgantas

Françoise Sailhan

Géraldine Texier

When Service-oriented Computing Meets the IoT: A Use Case in the Context of Urban Mobile Crowdsensing Invited paper

Keywords: IoT, interoperability, middleware, mobile crowdsensing, urban sensing systems, multiparty calibration

The possibilities of new mobile distributed systems have reached unprecedented levels. Such systems are dynamically composed of networked resources in the environment, which may span from the immediate neighborhood of the users -as advocated by pervasive computingup to the entire globe -as envisioned by the Future Internet and one of its major constituents, the Internet of Things. This paper more specifically concentrates on urban participatory mobile distributed systems where people get involved in producing new knowledge about the urban environment. Service-oriented and cloud computing are evident baseline technologies for the target mobile distributed systems. Service orientation provides the abstraction to deal with the assembly of the relevant heterogeneous component systems. The cloud provides the infrastructure to deal with the gathering and analyses of the observations coming from the sensing infrastructure, including from people. However, cloud-based centralized solutions come at a price, regarding both resource consumption and privacy risk. Further, the high heterogeneity of the participating nodes results in diverse levels of sensing accuracy. This paper provides an overview of our past and ongoing research to overcome the challenges facing urban participatory mobile distributed systems, which leverages mobile collaborative sensing, networking and computing. The experience with the Ambiciti platform and associated mobile app for monitoring the individual and collective exposure to environmental pollution serves as an illustrative use case.

Introduction

The Service Oriented Computing -SOC-(or Service Oriented Architecture -SOA) paradigm has proved particularly appropriate for ubiquitous and mobile computing systems [START_REF] Papazoglou | Service-oriented computing: State of the art and research challenges[END_REF]. Through the abstraction of networked devices and their hosted applications as services delivered and consumed on demand, the SOA approach enables the assembly of systems whose component systems can be retrieved and composed statically as much as dynamically, thanks to service discovery and service interaction protocols. While mobile services incorporate and apply the fundamental principles of SOA, they present a number of specifics that push certain challenges related to service oriented systems to their extreme and additionally introduce new unique research challenges. Such specifics relate to: (i) dynamism -open mobile environments are much more volatile than typical service environments, with services emerging and disappearing in arbitrary ways without prior notification; (ii) heterogeneity -a direct consequence of ad hoc mobile environments is that no safe assumption can be made about the technological and business features of the services encountered; and (iii) the equation among QoS expectations on services, scalability, and required resources is hard to solve, due to the resource constraints of mobile environments. Dealing with the identified specifics gets even more complex if we consider both traditional computing services and services attached to the physical world by means of sensors and actuators, i.e., Things [START_REF] Issarny | Revisiting Service-oriented Architecture for the IoT: A Middleware Perspective[END_REF]. This paper specifically focuses on the design of service-oriented systems supporting the analysis of urban-scale phenomena through the composition of the diverse relevant sensing services, from services supported by urban infrastructure networks to services gathering observations from end-users (Fig. 1). The related research challenges are numerous, including the ones due to the aforementioned specifics of mobile services. Here, we concentrate on the challenges that we have been investigating in relation with the development and deployment of an urban service monitoring the individual and collective exposure of the population to environmental pollution (Section 2). Thanks to the sensors that people may increasingly "hold" and/or "wear" while moving, we are potentially able to monitor urban phenomena at a very fine grain across time and space. However, while the potential is high and examples in the area of traffic monitoring evidences it, the ability to monitor diverse urban phenomena at scale is far from being straightforward. There are indeed many hurdles arising, among which the following ones that we have been and are currently investigating:

-The monitoring of urban phenomena involves analyzing observations across time and space, but also across application domains (e.g., BMS, traffic, health). As a result, the thorough analysis of urban phenomena involves the ability to compose services across horizontal (location dependent) and vertical (application dependent) boundaries. In addition, the urban system architecture must ease such a composition of services at scale. Toward that goal, we investigate system architectures that build upon the pub/sub communication paradigm, which has proven well suited for open, large-scale dynamic systems. Further, we adopt the edge computing paradigm, which includes leveraging the processing power of the mobile nodes. Moreover, the system architecture must ease the integration of highly heterogeneous Things, which goes along with supporting interaction across diverse protocols (Section 3). -While the SOC paradigm together with semantic technologies allow exposing the capabilities of the connected Things despite their high heterogeneity, the large diversity of Things also manifests itself from the standpoint of the accuracy of the delivered observations. It is then essential to assess the quality of the provided observations, which may result in either filtering out or correcting part of the observations. Obviously, the ability to correct observations leads to gathering more knowledge, for which we study automated, collaborative multi-party calibration (Section 4). -The scale of the urban systems significantly challenges the networking and computing infrastructures, especially when the system keeps monitoring phenomena over time. The adoption of the pub-sub communication paradigm together with edge computing in our architecture contributes to overcoming the challenge by fostering communication and computing within the relevant geographical area(s). In addition, the mobility of nodes is worth exploiting to cross geographical regions and/or application domain boundaries. In that direction, we explore the offloading of the urban WSN-based IoT infrastructure networks to "peer" mobile nodes that act as crowdsensors in the same application domain. This allows saving the energy due to communication in the WSN and thereby increasing the WSN lifetime (Section 5).

The above list is only a subset of the challenges that IoT-based systems need to face to enable a thorough analysis of urban phenomena. This paper then concludes with an overview of other challenges that we and/or others are investigating and for which service-oriented and cloud computing are essential baseline technologies but that need to be revisited to handle the specifics of IoT-based systems.

Monitoring the exposure to the urban environmental pollution: A use case

According to the EU 1996 Pollution Directive (www.eea.europa.eu/policy-documents/council-directive-96-61-ec-ippc), "'Pollution' shall mean the direct or in-direct introduction as a result of human activity, of substances, vibrations, heat or noise into the air, water or land which may be harmful to human health or the quality of the environment, result in damage to materiel property, or impair or interfere with amenities and other legitimate uses of the environment." With the majority of the population now living in cities, it is increasingly critical to be able to monitor the exposure to environmental pollution in urban centers. In particular, the research community suggests that the social cost of noise and air pollution in EU -including death and disease-could be nearly EUR 1 trillion, while the social cost of smoking in the EU has been estimated to be EUR 544 billion [START_REF]SEP: Links between noise and air pollution and socioeconomic status -in-depth report 13 produced for the europen commission, dg environment by the science communication unit[END_REF].

The development of the IoT, including the one of the sensing technologies that smartphones now embed, together with advancements in machine learning, allow for the deployment of cloud-based platforms that collect and aggregate the many relevant urban data sources to produce street-level hourly maps about the environmental pollution (e.g., [START_REF] Ventura | Estimation of urban noise with the assimilation of observations crowdsensed by the mobile application Ambiciti[END_REF]). Also, mobile apps that serve collecting observations about the environmental pollution enable to inform users about their individual exposure and related impact on their health [START_REF] Hachem | Sense2Health: A Quantified Self Application for Monitoring Personal Exposure to Environmental Pollution[END_REF]. As part of our research, we have developed a cloud-based platform and related mobile app for monitoring the individual and collective exposure to the environmental pollution [START_REF] Hachem | Monitoring Noise Pollution Using The Urban Civics Middleware[END_REF]. In particular, the app implements noise sensing using the phone's microphone while accounting for the relatively low accuracy of the resulting observations [START_REF] Ventura | Evaluation and calibration of mobile phones for noise monitoring application[END_REF]. In a nutshell, the mobile app implements a crowdsensing application that periodically transfers the collected observations to the cloud server, which filters out the observations that are not deemed accurate enough. The cloud server implements data assimilation techniques to integrate observations from various data sources with mathematical models to simulate the state of a system of an urban phenomenon [START_REF] Tilloy | BLUE-based NO2 data assimilation at urban scale[END_REF].

The research resulted in the Ambiciti solution (ambiciti.io) that enables monitoring the individual exposure to noise and air pollution (Fig. 2-Left), while producing street-level maps about environmental pollution (Fig. 2-Right). In summer 2015, we deployed the first version of Ambiciti, which initially supported noise pollution monitoring only. Since autumn 2016, Ambiciti also monitors the exposure to air quality. The urban-scale deployment of Ambiciti -especially in Paris-has led us to identify a number of recommendations for the IoT-based sensing system and supporting architecture [START_REF] Issarny | Dos and Don'ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment[END_REF][START_REF] Lefèvre | Matching Technological & Societal Innovations: The Social Design of a Mobile Collaborative App for Urban Noise Monitoring[END_REF], which we have been and are still investigating. The major challenge to face is to enable and promote the gathering of observations of sufficient quality at scale so as to enable the thorough analysis of urban phenomena. The following reports on our complementary studies toward that goal. All these studies focus on developing distributed collaborative protocols that contribute to increasing the accuracy of the knowledge produced at the edge, while reducing the overall resource consumption associated with data processing and communication. 3 System architecture for the urban IoT Ubiquitous computing devices featuring sensing capabilities are deployed in a variety of application domains, such as smart cities, smart factories, resource management, intelligent transportation and healthcare to name a few. This enables analyzing observations of different domains across time and space in the so called urban environment. However, enacting IoT urban systems is still raising tremendous challenges for the supporting infrastructure from the networking up to the application layers. Key challenges [START_REF] Al-Fuqaha | Internet of things: A survey on enabling technologies, protocols, and applications[END_REF][START_REF] Teixeira | Service oriented middleware for the internet of things: a perspective[END_REF] relate to deep heterogeneity, high dynamicity, scale, and many others.

To support the deployment of large-scale, heterogeneous and dynamic IoT urban systems we rely on the architecture depicted in Fig. 3. This comprises the following layers:

-Device: This layer serves applications from multiple domains. Each application involves heterogeneous devices and processing capabilities in mobile nodes. -Edge: At this layer, we adopt the Edge computing paradigm to collect multidomain data from end-users being in several regions. We define a region as a bounded geographical area with common features (e.g., a university campus). Additionally, to deal with the heterogeneity of IoT applications (exposed as services), we deploy software interoperability artifacts at this layer. -Cloud: Several domains of IoT applications require powerful processing capabilities (e.g., for executing machine learning algorithms) and thus, at this layer we enable the use of Cloud computing resources.

Supporting wide-scale IoT apps

Mobile IoT applications are typically deployed on resource-constrained devices with intermittent network connectivity. To support the deployment of such applications, the pub/sub interaction paradigm is often employed, as it decouples mobile peers in both time and space. In a pub/sub system, multiple peers interact via an intermediate broker entity -publishers produce events characterized by a specific filter to the broker. Subscribers subscribe their interest for specific filters to the broker, which maintains an up-to-date list of subscriptions.

To support distributed applications spanning a wide-area, the pub/sub system has to be implemented as a set of independent, communicating brokers, forming the broker overlay. Let B = {b : i ∈ [1..|B|]} be the set of pub/sub brokers. As depicted in Fig. 4, in such architectures [START_REF] Bouloukakis | Timeliness evaluation of intermittent mobile connectivity over pub/sub systems[END_REF], peers can access the system through any broker that becomes their home broker. Then, based on the peers' input, the pub/sub system ensures the delivery of the events produced towards the interested subscribers. As still shown in Fig. 4, a message broker can be assigned to support a specific region or one or more application domains inside a region. This depends on the corresponding administration of the related region's vertical markets.

Building an IoT application over a pub/sub infrastructure, requires the selection of an appropriate protocol (e.g., MQTT). Such a protocol enables IoT devices to access the broker overlay and push/receive events. Additionally, to create the pub/sub broker overlay, the corresponding message broker implementation (e.g., VerneMQ, HiveMQ, etc) must be selected. Each message broker technology has different capabilities, such as: compatibility with different protocols, support for clustering (i.e., forming a broker network), provision of performance features, etc. To enable the interconnection between message brokers that employ different protocols for message routing (e.g., MQTT and AMQP in Fig. 4), adapter software components can be utilized. Usually, such components are supported by the corresponding message broker technology (e.g., VerneMQ). An important design-time decision is the provision of resources for message broker deployment. In this work we assume the deployment of brokers at the Edge. To enable the interconnection between a message broker (Edge) and the cloud, we employ an intermediate edge-server which corresponds to a pub/sub subscriber that collects data and forwards them to the cloud.

Supporting Heterogeneous IoT apps

Mobile environments can be very heterogeneous, both locally but also when reaching out to the whole IoT. Service heterogeneity concerns both business semantics and communication middleware. The former issue has led to a wide use of ontologies and related technologies in SOA. As for the latter issue, it is due to the fact that mobile IoT devices may use different communication contexts. This calls for support for heterogeneous interaction styles, namely message-driven, event-driven, and data-driven styles. Different interaction styles apply to different needs; for instance, asynchronous, event-based pub/sub is more appropriate for highly dynamic environments with frequent disconnections of involved entities. This fact makes the various service bindings supported by most service technologies too stringent, since they comply with a single (client/server) message-based interaction style. Service models should be able to abstract and comprehensively specify the various service bindings of mobile IoT devices. This further implies extending the notion of service and introducing adequate service interaction modeling. Moreover, interoperability mechanisms are required between heterogeneous interaction styles based on transformation mappings between their models.

Our work in this context has focused on the problem of interconnecting systems that employ different interaction styles or paradigms [START_REF] Georgantas | Service-oriented distributed applications in the Future Internet: The case for interaction paradigm interoperability[END_REF]. We include in particular the client/server, publish/subscribe, data streaming and tuple space paradigms, which cover the majority of communication middleware protocols enabling system interaction. Our overall approach generalizes the way to design and implement service-oriented distributed applications, where the employed interaction paradigms are explicitly represented and systematically integrated.

As a first step, we identified the semantics of the four principal interaction paradigms and elicited a connector model for each paradigm. Our models represent the essential semantics of interaction paradigms, concerning space coupling, time coupling, concurrency and synchronization coupling. These four categories of semantics are of primary importance, because these are end-to-end semantics: when interconnecting different interaction paradigms, we seek to map and preserve these semantics. Inside each interaction paradigm, we identify further one or more of four interaction types: one-way, two-way synchronous, two-way asynchronous and two-way streaming interaction. These interaction types incorporate the above semantics of end-to-end interactions.

Following the previous step, we introduced a higher-level connector model that comprehensively represents the semantics of various middleware protocols that follow the four base connector models, hence it can represent the majority of the existing and possibly of the future middleware protocols. In particular, this connector models the four interaction types supported by the base connector models in an abstract way. Furthermore, we introduced a generic interface description language for specifying interfaces in an abstract way. This language enables the definition of operations provided or required by an application component that follow the four interaction types. By relying on the above abstractions, we have introduced a solution to seamlessly interconnect IoT devices that employ heterogeneous interaction protocols at the middleware level [START_REF] Bouloukakis | Enabling Emergent Mobile Systems in the IoT: from Middlewarelayer Communication Interoperability to Associated QoS Analysis[END_REF]. Interconnection is performed via one or more message brokers lying at the Edge as presented in the previous section. More specifically, a dedicated software component called a Binding Component (BC) performs the conversion between the middleware protocol of an IoT device and the message broker protocol as depicted in Fig. 4.

Based on our modeling abstractions, we have developed a systematic approach for the automated synthesis of BCs. The development of BCs is a tedious and error-prone process, which can highly benefit from our automated systematic support. This can help application developers integrate heterogeneous IoT devices inside complex applications. Furthermore, the automated BC synthesis is essential for applications relying on the dynamic runtime composition of heterogeneous devices, where there is no human intervention. Our solution was introduced as a core component of the H2020 CHOReVOLUTION project (chorevolution.eu) to enable heterogeneous interactions in services & Things choreographies. Currently, it supports the following middleware protocols: REST, CoAP, MQTT, WebSockets, SemiSpace and DPWS. It is released as open source software (gitlab.inria.fr/zefxis).

Overcoming the low accuracy of mobile crowdsensors

Current crowdsensing approaches are primarily focused on collecting data at a large scale, as in particular supported by the pub/sub system architecture of the previous section. However, without any suitable calibration mechanism, smartphones tend to disclose inaccurate readings. To tackle this issue, we propose to collaboratively and automatically calibrate in the field, the sensors embedded in and/or connected to the smartphones. Thus, when some mobile sensors are in the same spatial vicinity, uncalibrated sensors get automatically calibrated -without involving the end users. Compared to existing solutions that iteratively calibrate one by one a unique mobile sensor at a given time, our approach supports the joint calibration of several uncalibrated sensors, thus reducing the overall calibration delay. This leads to a multi-party, multi-hop calibration for which the history of calibrated sensors is used to assess the best calibration hyperpath, which is the one that minimizes the accumulated calibration error. Our approach towards accurate urban sensing further distinguishes from the traditional WSN setting by enabling mobile, consumer-centric smartphones to interact with both stationary and mobile sensors in their proximity so as to calibrate the smartphones' sensors. In particular, our solution leverages the presence of two types of sensors:

-Reference sensors correspond to high fidelity sensors (e.g., a sound meter in the case of noise sensing) that are regularly calibrated by experts. Reference sensors are typically government-run (see Figure 2 in [START_REF] Maisonneuve | Participatory noise pollution monitoring using mobile phones[END_REF] for an illustration of the few reference sensors measuring noise in the city of Paris) and their measurements are generally made publicly available. They are wirelessly networked [START_REF] Santini | First experiences using wireless sensor networks for noise pollution monitoring[END_REF] as detailed in the next section. -Mobile sensors correspond to low cost and less accurate sensors that are owned by individuals (they typically include the sensors embedded in smartphones) and that need to be remotely calibrated.

Then, the reference sensors that belong to the urban infrastructure support the calibration of any mobile sensor that is within sensing range. And, in turn, the newly calibrated sensors may be used to calibrate the uncalibrated sensors they meet (as they move). The following outlines our solution whose detailed presentation may be found in [START_REF] Sailhan | Opportunistic Multiparty Calibration for Robust Participatory Sensing[END_REF].

Multi-Party Calibration. During a rendezvous, any uncalibrated sensor i attempts to calibrate using the calibrated measurements provided by the m nearby sensors, for which we leverage multivariate linear regression.

As illustrated in Fig. 5, there is a linear dependency between the measurements produced by a non-calibrated sensor and the m surrounding calibrated sensors. Thus, any reading y i (t) of the uncalibrated sensor i at time t can be expressed as the following linear function: where: For this, we rely on the least square method, which minimizes the sum of the squared distance of the data points from the line measured perpendicular to the x axis (see Fig. 5).

y i (t) = β 0 + β 1 x 1 (t) + β 2 x 2 (t) + • • • + β m x m (t) + e i (t) (1)
x j (t) (j ∈ [1, • • • , m])
Multi hops, multi-party calibration. Thanks to the above multivariate linear regression, the calibration coefficients of an uncalibrated smartphone sensor i may be estimated based on the readings provided by the surrounding sensors that are met during a multiparty rendezvous. However, sensor i should not systematically (re-)calibrate: it must do so only if the quality of the regression is sufficient. In particular, a high residual error (i.e., e i in Equation 1) reflects a poor correlation between the readings provided by the surrounding sensors, and in such a case, the conditions are not met for an effective calibration. This may occur when some smartphones are in bags/pockets and others are handheld. Another aspect to consider for assessing the relevance of a given multiparty calibration relates to the history of past calibrations. We must compare the quality of the calibration parameters computed in the current rendezvous against the quality of the previous calibrations (if any). We thus maintain the history of multiparty calibrations using a weighted directed hypergraph: a multiparty rendezvous between sensor i and K sensors is represented by an hyperedge between sensor i and the K sensors. The quality of the regression established by i based on the readings provided by the K sensors is reflected by the weight of the directed hyperedge between i and the K sensors. Ultimately, the preferred calibration is the one that results in the lowest cumulated weight.

Summarizing the proposed multiparty calibration method, any smartphone (embedding a sensor) i participating in the collaborative calibration periodically performs the following process:

1. Smartphone i detects the presence of nearby sensing device(s), i.e., devices in the shared sensing range. 2. If any eligible rendezvous, i exchanges its sensing measurements (i.e., time series) in a synchronized manner so as to establish the linear relationship between the measurements of the nearby sensors and the raw measurements obtained locally. 3. The best calibration path is determined and the calibration function is set.

Overall, the key feature of the calibration system is to enhance the accuracy of the raw data sensing provided by a self-selected and potentially unskillfulness or distrustful crowd. As few anomalies or discordant observations may contaminate the overall knowledge, it is critical to identify, detect and treat such anomalies. To tackle this issue, we attempt to combine two complementary approaches : (i) we harness the power of the crowd to collaboratively sense and calibrate, (ii) we coordinate with city-owned sensors so as to support an initial bootstrapping calibration.

When crowdsensing meets the infrastructured IoT networks

The previous section shows how the participatory sensing benefits from an interaction with smart city sensing infrastructures to bootstrap the calibration of the crowdsensors and deliver more accurate measurements. This cooperation can be deepened to achieve a mutual benefit for crowdsensing and the urban IoT infrastructures. For this, we advocate the possibility of direct interactions between crowd smartphones and the infrastructured sensors that surround them. Firstas illustrated in the previous section-, such interactions can benefit smartphones by allowing them to quickly and directly retrieve (higher quality) measurements taken around them. Second, smartphones can push their participatory engagement further: in addition to making their smartphone's sensors available, they can also offer to share part of their network interconnection to offload the WSN and relay part of its data (as shown in Fig. 7).

In general, urban measurement infrastructures are implemented by Wireless Sensor Networks (WSN), which are more or less extensive and distributed in the city. The sensors perform measurements and send them towards a gateway (the WSN's sink) in charge of publishing to the pub/sub system the collected data through its Binding Component (BC) as shown in Fig. 6. To do this, the WSN implements convergecast communication to allow the sink to collect data generated by each sensor in the network. With the exception of the leafs, each sensor sends its parents both the data it generates and the data transmitted to it by its children. The worst case is that of the nodes near the sink: the emission of the measurements they generate represents only a small fraction of their communications since they spend most of their time relaying their children's data.

But, due to high deployment constraints in urban environments, sensors in the WSNs are often battery powered, which limits the lifetime of the whole urban sensing infrastructure. However, the definition of the WSN lifetime depends on the policy of the network operator. If it does not tolerate any failure, the lifetime of the network is limited by the exhaustion of the energy of at least one sensor. Another strategy is to tolerate a given number of sensors that no longer work, in which case the lifetime of the network is determined by the number of sensors that remain connected to the sink despite the death of a subset of sensors. Whatever lifetime definition is chosen, a smartphone can help the infrastructure survive longer by giving it the ability to relay some of its data. Note that this requires that smartphones can connect to infrastructured sensors, for example via protocols such as Zigbee, 802.15.4 or Bluetooth Low Energy (BLE). The fact is that sensors are increasingly able to communicate and we believe that lack of compatibility is less a technical problem than a market problem. So, although with the exception of BLE, most smartphones do not yet integrate the main interfaces supported by the sensors, we think that this will evolve within a reasonable time. Therefore, we consider that direct communication between the infrastructure and smartphones is not an obstacle for our work and will be available in the future.

Many studies have sought to optimize the lifetime of networks. They can be classified according to the techniques they propose. Some solutions seek to maximize the network lifetime by enhancing RPL energy efficiency [START_REF] Kamgueu | Survey on RPL enhancements: a focus on topology, security and mobility[END_REF]. For example with a RPL metric that integrates the available energy of the WSN nodes [START_REF] Iova | Using multiparent routing in RPL to increase the stability and the lifetime of the network[END_REF] to ensure that the nodes solicited for relaying data are the ones with the highest energy budget. However, using a routing protocol with such a very dynamic metric implies great routing instability. Other solutions are based on Delay Tolerant Networks communication techniques. In particular, the optimization of the data collection by a mobile node, usually through the optimization of the mobile node's trajectory based on variants of the traveling salesman problem [START_REF] Garraffa | Drones path planning for wsn data gathering: A column generation heuristic approach[END_REF].

In [START_REF] Texier | Leveraging the power of the crowd and offloading urban iot networks to extend their lifetime[END_REF], we have presented how the cooperation of the crowd smartphones and the urban WSN can extend the measurement infrastructure's lifetime based on both optimization and traffic engineering techniques. We proposed a model to integrate the crowd smartphones as mobile sinks able to offload a portion of the WSN data transmissions. We optimize the routing to take advantage of the path diversity offered by the WSN topology. With a simple energy model (each sensor has an energy budget given by the capacity of its batteries), we determine the number of communications a sensor is able to perform before exhausting its energy. We can also compute the network lifetime, in our case the time before at least one sensor runs out of energy. Our linear program determines the quantity of data that a sensor should send to each of its neighbors (including mobile nodes) to maximize the network lifetime. Although the efficiency of the solution is strongly linked to the location of mobile nodes, we have shown on a realistic example that when we allow a subset of network sensors to communicate with a mobile node, we can increase the lifetime of the network in significant proportions (in our example, we multiplied the lifetime of the network by 7). Our work opens several research issues that we are exploring as part of our ongoing and future work. One of these issues relates to our load balancing strategy that requires the introduction of proportional routing into the WSN routing mechanism. This modification also makes it possible to support temporary failures of the sensors by offering next alternative jumps if a node having to relay the information is not available at the time of emission.

Conclusion

The IoT holds the promise of blending the physical with the virtual world, while the power of the crowd enables gathering knowledge at unprecedented scales across both time and space. However, the vision raises tremendous challenges for the supporting system architectures, among which keeping pace with the target scale and overcoming the heterogeneity of the IoT nodes. The literature has shown that service-oriented and cloud computing are evident baseline technologies: service orientation brings abstraction to overcome heterogeneity and the cloud brings the computing infrastructure to aggregate the knowledge sensed within the IoT. However, this puts high demand on the networking infrastructure, while collaborative networking and computing at the edge allow for significant resource saving. Our research focuses on such collaborative distributed IoT-based system architectures at the edge, where we build upon our experience on developing and deploying an urban-scale system for the monitoring of the individual and collective exposure to environmental pollution. This paper has outlined our most recent research in the area, spanning the study of: (1) a publish-subscribe system architecture enriched with software interoperability artifacts to support the interconnection of applications implementing heterogeneous communication paradigms; (2) in-network multi-party, multi-hop calibration so as to overcome the low accuracy of low cost sensors, and especially of the sensors embedded in smartphones; and (3) collaboration between the smartphones running crowdsensing apps and the urban WSN-based IoT infrastructures toward increasing the lifetime of the WSN networks.

Crowdsensing-and IoT-based system architectures, from networking up to the middleware and application layers, have been studied extensively over the last ten years, while we have focused our presentation on our specific contributions over the last couple of years. The interested reader is referred to the bibliography provided in the cited references for an overview and analysis of related work.

We have many research challenges ahead of us and some of them have been mentioned along the sections. Among our ongoing research, we would like to stress the study of privacy-aware crowdsensing systems due to the tension that the topic creates: (1) On the one hand, we know that the more accurate and precise knowledge we get about the urban environment, the more we will be able to develop effective solutions to enhance the people's well-being and reduce urban resource consumption; (2) On the other hand, gathering such a precise knowledge also goes along with the ability to infer knowledge about people and even individuals. Part of the solution lies in policy making as for instance illustrated by the recent GDPR data protection policy by the EU. Further, crowdsensing systems must be designed to enforce privacy preservation. Distributed collaborative crowdsensing systems where the individual knowledge remains at the edge and ultimately on the user's device contribute to it. Still, the systems must support the appropriate tradeoffs so that relevant urban-scale knowledge may be computed to inform the city development.

Fig. 1 .

 1 Fig. 1. Monitoring the urban environment using the fixed and mobile IoT.

Fig. 2 .

 2 Fig. 2. Monitoring the exposure to environmental pollution using Ambiciti.

Fig. 3 .

 3 Fig. 3. System architecture for the urban IoT.

Fig. 4 .

 4 Fig. 4. Multi-domain/region pub/sub system.

Fig. 5 .

 5 Fig. 5. An uncalibrated smartphone calibrates using the calibrated measurement of one calibrated smartphone. Regression lies in fitting a straight line to bivariate data using least square regression. The regression minimizes the vertical residual (dashed line on the right figure) by keeping to a minimum the squared vertical distance from each data point to the line.

  denotes the calibrated measurements of the m sensors; β 0 , • • • , β m represent the unknown (and fixed) regression coefficients; and e i (t) is the residual noise, with t ∈ [t 1 , • • • , t p ]. The goal is to find the regression coefficients β 0 , β 1 , • • • + β m that minimizes the residual noise.

Fig. 6 .

 6 Fig. 6. Classical convergecast data collection in the WSN.

Fig. 7 .

 7 Fig. 7. Smartphone offloading data from the WSN.

Acknowledgments:

The authors would like to thank the support of: the Inria@SiliconValley International Lab, CityLab@Inria Project lab, and the EIT Digital innovation activity Env&You. They also gratefully acknowledge the major contribution of their Inria colleagues, Vivien Mallet, Pierre-Guillaume Raverdy and Kinh Nguyen, to the development of the Ambiciti system solution.