N Falstad
email: niklas.falstad@chalmers.se

F Hallqvist

S Aalto

S König

S Muller

R Aladro

F Combes

A S Evans

G A Fuller

J S Gallagher

S García-Burillo

E González-Alfonso

T R Greve

C Henkel

M Imanishi

T Izumi

J G Mangum

S Martín

G C Privon

K Sakamoto

S Veilleux

P P Van Der Werf

Hidden or missing outflows in highly obscured galaxy nuclei? ⋆

Keywords: galaxies, evolution -galaxies, nuclei -galaxies, ISM -ISM, molecules -ISM, jets and outflows

Context. Understanding the nuclear growth and feedback processes in galaxies requires investigating their often obscured central regions. One way to do this is to use (sub)millimeter line emission from vibrationally excited HCN (HCN-vib), which is thought to trace warm and highly enshrouded galaxy nuclei. It has been suggested that the most intense HCN-vib emission from a galaxy is connected to a phase of nuclear growth that occurs before the nuclear feedback processes have been fully developed. Aims. We aim to investigate if there is a connection between the presence of strong HCN-vib emission and the development of feedback in (ultra) luminous infrared galaxies ((U)LIRGs). Methods. We collected literature and archival data in order to compare the luminosities of rotational lines of HCN-vib, normalized to the total infrared luminosity, to the median velocities of 119 µm OH absorption lines, potentially indicating outflows, in a total of 17 (U)LIRGs.

Results. The most HCN-vib luminous systems all lack signatures of significant molecular outflows in the far-infrared OH absorption lines. However, at least some of the systems with bright HCN-vib emission do have fast and collimated outflows that can be seen in spectral lines at longer wavelengths, including in millimeter emission lines of CO and HCN (in its vibrational ground state) as well as in radio absorption lines of OH. Conclusions. We conclude that the galaxy nuclei with the highest L HCN-vib /L IR do not drive wide-angle outflows detectable using the median velocities of far-infrared OH absorption lines. It is possible that this is due to an orientation effect where sources which are oriented in such a way that their outflows are not along our line of sight also radiate a smaller proportion of their infrared luminosity in our direction. It could also be that massive wide-angle outflows destroy the deeply embedded regions responsible for bright HCN-vib emission, so that the two phenomena cannot coexist. This would strengthen the idea that vibrationally excited HCN traces a heavily obscured stage of evolution before nuclear feedback mechanisms are fully developed.

Introduction

Luminous and ultraluminous infrared galaxies ((U)LIRGs) are gas-rich systems that radiate intensely in the infrared portion of the electromagnetic spectrum, with infrared luminosities (L IR = A&A proofs: manuscript no. hcn_vs_oh_outflow_v5 L(8-1000 µm)) in excess of 10 11 L ⊙ and 10 12 L ⊙ , respectively (e.g., Sanders & Mirabel 1996). These large luminosities are due to dust-reprocessed radiation from intense star formation, an active galactic nucleus (AGN), or both. In the local Universe, (U)LIRGs are relatively rare compared to less luminous objects, but surveys at millimeter and submillimeter wavelengths have shown that they are much more numerous at high redshifts (e.g., Smail et al. 1997;Hughes et al. 1998), indicating that they play an important role in the evolution of galaxies. Two important questions in this context are: how did the supermassive black holes (SMBHs) found in the nuclei of most galaxies grow, and how are they related to the evolution of their host galaxies? From observations, we know that tight relations between the mass of the central SMBH and various properties of the host galaxy exist in large ellipticals (e.g., Magorrian et al. 1998;Ferrarese & Merritt 2000;Gebhardt et al. 2000;Kormendy & Ho 2013). It has been suggested that these relations were partly established through a process, mostly taking place in the early Universe, in which galaxies of similar masses collide and merge (see, e.g., Kormendy & Ho 2013, for a review). In such mergers, large amounts of gas are funneled into the new nucleus, giving rise to intense starbursts, AGN activity, or both. The exact physics of how the black hole-host relations would be established are still obscure, but several suggested galaxy merger scenarios include a starburst dominated phase followed by a phase of obscured AGN activity. Eventually, the AGN becomes strong enough to drive outflows that clear the surrounding material and reveal the central activity (e.g., Sanders et al. 1988a;Hopkins et al. 2006;Urrutia et al. 2008;Simpson et al. 2012). Thus, studies of the most dust-embedded phase of evolution and the early stages of feedback are essential in order to understand how SMBHs grow together with their host galaxies.

However, observations of the most enshrouded galaxy nuclei are hampered by the large amounts of obscuring material that surround them. One solution to this problem is to observe at millimeter and radio wavelengths, where the dust is less optically thick (e.g., Barcos-Muñoz et al. 2015). Some common tracers of obscured galaxy nuclei at these wavelengths are the low-J rotational lines of the HCN and HCO + molecules. Recently, however, it has been shown that, in the most obscured systems, even these dense-gas tracers are heavily affected by continuum-and/or self-absorption by cooler foreground gas (e.g., Sakamoto et al. 2009;Aalto et al. 2015b;Martín et al. 2016). A better suited tracer of the most obscured systems is offered by HCN in its first vibrationally excited state, v 2 = 1 (hereafter HCN-vib). Strong emission lines from rotational transitions within the v 2 = 1 state have been detected in external galaxies at (sub)millimeter wavelengths, first by Sakamoto et al. (2010) and then in many subsequent observations. With energy levels that lie more than 1000 K above the ground state, HCN-vib is mainly excited by absorption of mid-infrared radiation. However, to be efficiently populated it requires brightness temperatures in excess of 100 K at 14 µm, the wavelength of the transition between the ground and first vibrationally excited states of HCN, translating into a H 2 column density larger than 2 × 10 23 cm -2 for a dust temperature of 100 K (Aalto et al. 2015b). This makes bright HCN-vib emission an excellent unabsorbed (Martín et al. 2016) tracer of high column density gas with high mid-infrared surface brightness. While the direct mid-infrared radiation from such regions is often hidden from us by the large quantities of obscuring material, the rotational transitions inside the vibrationally excited states occur at millimeter and submillimeter wavelengths where the dust opacity is lower. The low-J rotational lines of HCN-vib are thus useful probes of the nuclear mid-infrared source in heavily obscured objects. Since the first extragalactic (sub)millimeter detection of HCN-vib rotational line emission by Sakamoto et al. (2010), the field has developed rapidly (e.g., Imanishi & Nakanishi 2013;Aalto et al. 2015a,b;Imanishi et al. 2016aImanishi et al. ,b, 2018;;Martín et al. 2016), notably thanks to the Atacama Large Millimeter/submillimeter Array (ALMA).

In their study of HCN-vib in obscured galaxies, Aalto et al. (2015b) found that all galaxies with detected HCN-vib emission show evidence of inflows, outflows, or both. In addition, they found a tentative trend that galaxies with fast molecular outflows have fainter HCN-vib emission relative to their total infrared luminosity. The trend is especially striking when comparing the strength of the HCN-vib emission to the velocity of the wide-angle OH outflows studied by, for example, Veilleux et al. (2013). Based on a relatively small sample of nine galaxies, Aalto et al. (2015b) tentatively suggest that strong HCN-vib emission is connected to a rapid phase of nuclear growth that occurs right before the onset of strong feedback.

In this paper, we present the results of a study employing a sample of 19 galaxies including data from the literature as well as previously unpublished data, in order to further investigate the connection, or lack thereof, between strong HCN-vib emission and molecular outflows. The sample used in this study is described in Sect. 2 and in Sect. 3 we present the results which are then discussed in Sect. 4. Finally, the conclusions that we draw from the analysis are summarized in Sect. 5. Throughout the paper, a H 0 = 73 km s -1 Mpc -1 , Ω m = 0.27, and Ω Λ = 0.73 cosmology is adopted.

Sample

We have searched for (U)LIRGs with existing ALMA observations of HCN-vib that are either already published or that have the data publicly available in the the ALMA science archive 1 . In addition, we have included published HCN-vib observations taken with the Plateau de Bure Interferometer (PdBI), the Northern Extended Millimeter Array (NOEMA), or the Submillimeter Array (SMA). We have not included non-detections, unless they have existing observations of the OH doublet at 119 µm, which we use to determine the outflow velocity. Non-detections have also been excluded when a meaningful upper limit to the HCN-vib flux could not be determined, either due to low sensitivity spectra or due to significant flux from the HCO + line which is separated from HCN-vib by ∼400 km s -1 . This is a possible source of bias, as HCN-vib emission may be hidden by the HCO + line in sources with outflowing gas. In practice, most sources that have been excluded either do not have any outflow signatures in the far-infrared OH lines that we use to trace outflows (see below), or have not been observed in these lines. The two exceptions are IRAS 19254-7245 (Imanishi et al. 2016a) and NGC 6240 (ALMA project 2015.1.01448.S, PI: R. Tunnard), in which the median velocities of the OH 119 µm lines are ∼ -250 and ∼ -200 km s -1 , respectively. In total, 10 sources have been excluded, most of them due to lack of OH measurements.

For consistency of the sample, we ensure that outflow velocities were measured using the same common tracers and following the same procedures. All sources with HCN-vib observations have therefore been checked for observations of the far-infrared OH doublet at 119 µm with the Herschel Space Observatory (Pilbratt et al. 2010) as this is a known tracer of molecular outflows on subkiloparsec scales, which has been observed in a large number of (U)LIRGs (e.g., Fischer et al. 2010;Veilleux et al. 2013;González-Alfonso et al. 2017b). The OH molecule has high abundances in photodissociation regions as well as in X-ray dominated regions (e.g., Maloney et al. 1996;[START_REF] Goicoechea | [END_REF]Meijerink et al. 2011). Unlike other common outflow tracers like the millimeter rotational transitions of CO and HCN, the ground state OH 119 µm doublet is often detected in absorption towards the far-infrared background (e.g., Veilleux et al. 2013), providing an unambiguous tracer of the gas motion. The transitions between excited states (for example the doublets at 65 and 84 µm) are generally weaker and the line wings are not always detected (González-Alfonso et al. 2017b). Recently, the 119 µm doublet was also used to detect a fast outflow ( 800 km s -1 ) in a galaxy at a redshift of 5.3 (Spilker et al. 2018). Systematic searches of outflow signatures in the far-infrared OH lines have revealed evidence of molecular outflows in approximately two thirds of the observed (U)LIRG samples (Veilleux et al. 2013;Spoon et al. 2013). From this detection rate, Veilleux et al. (2013) inferred a wide average opening angle of ∼145 • , assuming that all objects in their sample have outflows. In fact, based on radiative transfer models as well as spatially resolved observations of two outflows, González-Alfonso et al. (2017b) argue that the 119 µm OH absorptions are primarily sensitive to wide-angle outflows. On the other hand, unambiguous signatures of inflows are only found in one tenth of the sources in the sample of Veilleux et al. (2013), suggesting that OH inflows are either rare or have, on average, smaller opening angles.

In particular, the 43 sources in the far-infrared OH sample of Veilleux et al. (2013) have been checked for observations of the (sub)millimeter HCN-vib J = 3-2 or J = 4-3 lines. Fourteen of these had HCN-vib detections or data that allowed for meaningful upper limits to be estimated. One of them, IRAS 05189-2524 (see Appendix B), has previously unpublished data in the ALMA science archive while data for the remaining thirteen were taken from Sakamoto et al. ( 2010 ). In addition, five sources not included in the sample of Veilleux et al. (2013) were found to have usable observations of HCN-vib. One, IRAS 12224-0624 (see Appendix B), has previously unpublished data in the ALMA science archive while the other four were taken from Aalto et al. (2015b) and Imanishi et al. (2016a). Three of these five sources, Zw 049.057, IRAS 20414-1651, and NGC 7469, also have existing observations of the OH 119 µm doublet (see Appendix C).

In total, 19 (U)LIRGs with existing HCN-vib observations were found. Of these, 17 also have observations of the OH 119 µm doublet. The final sample, with the adopted redshift and distance of each source, is presented in Table 1.

Results

Vibrationally excited HCN luminosities, L HCN-vib /L IR ratios, and OH median velocities are presented in Table 2. We adopt the L HCN-vib /L IR ratio as a parameter to describe the strength of HCN-vib in a normalized fashion. However, we are aware that some part of the total L IR may be unrelated to the dusty nucleus, and instead come from, for example, an extended starburst. In particular, in systems with multiple nuclei, for example Arp 220 and IRAS 12112+0305, L IR will have contributions from both nuclei. In our sample, we are not aware of any sources were these effects would affect the L HCN-vib /L IR ratio enough to significantly change our results. We use the median velocity of the OH lines instead of the terminal outflow velocity as the former provides more robust values (Veilleux et al. 2013). Velocities are only given for those sources with absorption in the OH 119 µm doublet, as this means that the gas is in the foreground and that a positive or negative velocity shift can be interpreted as evidence of gas moving towards or away from the background nucleus, respectively. We note that this does not necessarily indicate in-or outflowing gas in all cases as, for example, interactions between two nuclei can also affect the OH kinematics. In addition, it is important to take the uncertainties in the velocity determination into account. For example, Veilleux et al. (2013) define an outflow as having an OH absorption feature with a median velocity more negative than -50 km s -1 , which is the typical uncertainty on the velocities. The requirement to detect the OH doublet in absorption is a possible source of bias as this requires a high enough column density of OH in front of a strong enough farinfrared background source. However, we note that all galaxies with an HCN-vib detection, if observed, have also been detected in absorption in the OH 119 µm doublet. Therefore, no sources with a combination of high HCN-vib luminosity and a fast outflow have been missed due to this effect.

The line luminosities presented in Table 2 have been calculated following Eq. ( 1) in Solomon & Vanden Bout (2005), applied to HCN-vib:

L HCN-vib = 1.04 × 10 -3 S HCN-vib ∆v ν rest (1 + z) -1 D 2 L , (1)
where L HCN-vib is the HCN-vib luminosity measured in L ⊙ , S HCN-vib ∆v is the velocity integrated flux in Jy km s -1 , ν rest is the rest frequency in GHz, and D L is the luminosity distance in Mpc. Due to differences in the adopted redshifts and distances, these values differ slightly from those given in the original reference for some of the sources. In case of non-detections, we have included the 3σ upper limit to the line luminosity. In the case of IRAS 15250+3609, the HCN-vib line is likely blended with a potential outflow signature in the nearby HCO + line. Here, we have attributed the entire emission feature to the HCN-vib line, and the value given should be considered an upper limit to the HCN-vib luminosity. A short discussion of this source as well as descriptions of the analysis of previously unpublished HCN-vib observations and new OH outflow measurements can be found in Appendices A, B, and C. In Fig. 1, the OH absorption median velocities are plotted against the luminosities of the HCN-vib line, normalized to the total infrared luminosity of each galaxy. When available, the J = 3-2 line has been chosen. In the four sources where both transitions have been observed, the luminosity ratio between the J = 4-3 and J = 3-2 lines lies in the range 2-3, with a mean of 2.5. For sources where only the J = 4-3 line has been observed, we have therefore divided its luminosity by 2.5 and included it in the plot with an extra 20% uncertainty added in quadrature. The two galaxies with only emission in the 119 µm doublet, I Zw 1 and NGC 7469, are not included in the plot, but we note that neither of them have an HCN-vib detection.

In the plot in Fig. 1 we see that, in our sample, the sources with high L HCN-vib /L IR ratios, in excess of 10 -8 , all lack fast outflows as traced by the median velocity of the OH 119 µm doublet. However, galaxies without fast outflows do not necessarily have high L HCN-vib /L IR ratios. The lack of sources in the upper left portion of the diagram is likely due to selection effects; that area should be populated by less obscured sources of lower luminosity that are not able to drive outflows or provide the necessary conditions for efficient HCN-vib excitation. (1992), except for IRAS 05189-2524 where the redshift from Sanders et al. (1991) was used. (c) Luminosity distances were calculated from the redshifts following the same procedure as Sanders et al. (2003). (d) Infrared luminosities were calculated using the prescription in Table 1 of Sanders & Mirabel (1996) (originally from Pérault (1987)) using IRAS fluxes taken from Sanders et al. (2003), except for IRAS 20414-1651 where fluxes were taken from [START_REF] Moshir | IRAS Faint Source Survey[END_REF], and I Zw 1 where they were taken from Sanders et al. (1989).

Discussion

Our comparison of HCN-vib line luminosities and OH 119 µm absorption line velocities in nearby (U)LIRGs shows a trend of positive, or low negative, velocities, indicating inflows or slow outflows, in sources with bright HCN-vib emission relative to the total infrared luminosity. This might indicate that regions with the high mid-infrared brightness temperatures required for efficient population of HCN-vib are not affected by inflowing gas, but are destroyed by, or cannot form in the presence of, strong outflows. In this section, we discuss possible reasons for the lack of outflow signatures in the OH 119 µm absorptions in HCNvib luminous sources, and how the HCN-vib lines relate to other tracers of heavily obscured regions.

Why do the HCN-vib luminous galaxies lack outflow signatures in the far-infrared OH lines?

There are several scenarios that could explain the lack of outflow signatures in the galaxies that have bright HCN-vib emission. One is that there are no outflows, another that the outflows are hidden from detection using the median velocity of the OH 119 µm absorption lines, and a third is that the distribution in Fig. 1 is due to orientation effects. These scenarios are discussed in the following sections. We also briefly discuss a possible evolutionary scenario in which the HCN-vib luminous galaxies are in a pre-feedback phase. Admittedly, our sample is quite heterogeneous in that it contains galaxies of different merger types, in different merger stages, of different optical classifications, and with infrared luminosities spanning more than an order of magnitude. It is therefore possible that there are several independent reasons for the lack of far infrared outflow signatures in HCNvib luminous galaxies.

No outflows

If there are no outflows, it might be due to the HCN-vib luminous galaxies being intrinsically different from the galaxies with weaker HCN-vib emission. For example, Veilleux et al. (2013) find a tendency for systems with dominant AGN to have faster OH outflows; it might then be that the HCN-vib luminous systems, which do not show fast OH outflows, are instead starburst dominated. Indeed, in the two sources with the highest absolute HCN-vib luminosities, IRAS 17208-0014 and Arp 220, less than 10% of the bolometric luminosities are estimated to come from AGN activity, based on the 15 to 30 µm continuum ratio (Veilleux et al. 2013). However, this diagnostic can be misleading in galaxies where the nuclei are optically thick into the far infrared. Furthermore, at least two other sources with strong HCN-vib emission, NGC 4418 and UGC 5101, have AGN fractions 50%. While the uncertainty on the AGN fraction determined in this way is estimated to be 20% on average, it is likely much higher in the strongly buried sources discussed here. Another possible explanation for a real lack of outflows in some sources is that we are witnessing a pre-feedback phase in young systems that will later evolve into objects with weak HCN-vib emission and fast outflows. This possibility is discussed further in Sect. 4.1.4. As discussed by González-Alfonso et al. (2017b), the lack of outflows could also be due to the fact that, in the most extremely buried sources, it is difficult to find paths with more moderate columns that can be efficiently accelerated to high velocities. Notes. (a) Median OH outflow velocities taken from Veilleux et al. (2013) or, for sources not in their sample, from archival data following the same procedure. Sources that have not been observed or where the doublet was detected in emission are indicated with not obs. or emi., respectively. The typical uncertainty on the velocities is 50 km s -1 . (b) Imanishi et al. (2016a) interpret an emission feature at the position of the HCN-vib line as a sub-peak of the nearby HCO + line. Here, we use this value as an upper limit to the HCN-vib luminosity. (c) The L HCN-vib /L IR is calculated using the total L IR of the system, possibly introducing some bias in systems with multiple nuclei. (d) The median velocity of the lines in IRAS 05189-2524 has been adjusted to correct for the different redshift adopted compared to the one used by Veilleux et al. (2013). Interestingly, as Aalto et al. (2015b) notes, fast molecular outflows have been found, with other tracers and at longer wavelengths, in some of the HCN-vib luminous systems. For example, a compact v > 800 km s -1 CO J = 2-1 outflow has been found in the ULIRG IRAS 17208-0014 (García-Burillo et al. 2015) and an equally fast, compact, and collimated, HCN J = 1-0 outflow has been detected in the ULIRG Arp 220 (Barcos-Muñoz et al. 2018). Possible outflow signatures are also found in Zw 049.057 (Falstad et al. 2018) as well as in NGC 4418 and IRAS 22491-1808 (Fluetsch et al. 2018), although at lower velocities. If we also consider the fact that UGC 5101 has faint high-velocity wings in its OH 119 µm absorption lines (Veilleux et al. 2013), it turns out that all of the six most HCN-vib luminous sources have signatures of molecular outflows.

Obscured, episodic, or collimated outflows

For the HCN-vib bright systems that do have outflows, there must be a reason why we do not see them in the far-infrared OH absorption lines. One possibility is that these objects have extreme optical depths in the far-infrared dust continuum, so that much of the outflowing gas cannot be seen due to obscuration by the dust. In at least some of the sources, high optical depths have indeed been inferred from radiative transfer modeling of Herschel observations (e.g., González-Alfonso et al. 2012;Falstad et al. 2015). If this is combined with young outflows that are still very compact, any signatures of them may be completely hidden in the far-infrared. In such a situation, inflows on larger scales, for example as the one seen in Arp 299A (Falstad et al. 2017), may still be detectable. In fact, at least three of the five most obscured sources (Arp 220, IRAS 17208-0014, and Zw 049.057; Baan et al. 1989;Falstad et al. 2018) have outflows that are seen in the OH lines at centimeter wavelengths but not in the far-infrared. In Zw 049.057, infrared and optical images reveal multiple clouds in a polar dust structure, which might suggest that the outflow in this galaxy is not steady in time (Gallagher et al. in prep.). It is thus possible that, in some galaxies, we are witnessing an episodic process where the nuclear region is alternating between feedback and accretion dominated states.

Looking at the spectra presented by Veilleux et al. (2013), UGC 5101 and IRAS 17208-0014 even exhibit weak blueshifted line wings in the OH 119 µm absorption lines. This indicates the presence of gas moving towards us, but as the absorptions at negative velocities are shallow, they do not have a strong effect on the median velocities of the lines. As discussed by González-Alfonso et al. (2017b), this may suggest that we are witnessing collimated outflows in these sources, as opposed to the wide-angle outflows that the OH 119 µm lines seem to be primarily sensitive to. With all this in mind, it seems that the regions responsible for the bright HCN-vib emission are not destroyed by (fast) outflows in general, but rather by the kind of wide-angle outflows from the nuclear infrared-emitting region that are able to shift the median velocity of the OH 119 µm ground state lines by several hundreds of km s -1 .

Turning to the two sources that show pure emission in the OH 119 µm doublet, I Zw 1 and NGC 7469, we note that they lack HCN-vib detections and that the upper limits to their L HCN-vib /L IR ratios are among the lowest in our sample. Furthermore, NGC 7469 has also been observed in the OH 65 µm doublet, another tracer of obscured nuclei, but no absorption was detected (González-Alfonso et al. 2015). This is consistent with the results of Veilleux et al. (2013) that the sources with pure OH emission are the ones where an AGN dominates the luminosity and that these objects represent a phase where the feedback has subsided after clearing a path through the dusty surroundings.

Orientation effects

An alternative explanation to the distribution of galaxies in Fig. 1 is that it is produced by a simple orientation effect. For example, using the OH 119 µm doublet, Veilleux et al. (2013) found unambiguous evidence of outflows in 70% of their sources, a detection rate which is consistent with all sources having molecular outflows, with an average opening angle of ∼145 • . The sources with no outflow signatures would then be those that are oriented approximately edge-on, with outflows perpendicular to our line of sight. It is harder to explain why these would also be the sources with bright HCN-vib emission, as the obscuration in a dusty edge-on disk would rather have the effect of lowering the visible HCN-vib flux, unless the line is masing as seen towards some circumstellar envelopes (e.g., Lucas & Cernicharo 1989;Bieging 2001;Menten et al. 2018).

However, the comparison so far has been based on the HCNvib luminosity divided by the total infrared luminosity which, due to the higher dust opacity in the infrared, is more affected by orientation effects. The infrared luminosity that we see will thus depend on the orientation, with higher values for face-on galaxies. This effect may indeed be significant; for example, Efstathiou et al. (2014) estimated that the true infrared luminosity of IRAS 08572+3915 may be as high as 1.1 × 10 13 L ⊙ , more than five times higher than the value inferred assuming the luminosity is isotropic. Another possible example is Arp 220 where the total infrared luminosity estimated from the IRAS fluxes is ∼1.7 × 10 12 L ⊙ while Sakamoto et al. (2017) estimate the bolometric luminosity from the western nucleus alone to be ∼3 × 10 12 L ⊙ .

If we do not divide L HCN-vib by the total infrared luminosity, we see that Mrk 231 has an HCN-vib luminosity comparable to those of the HCN-vib bright galaxies. However, it is still less luminous than in the ULIRGs Arp 220 W and IRAS 17208-0014, and only a factor of two or three brighter than in the LIRGs Zw 049.057, IC 860, and NGC 4418. Furthermore, none of the other sources with v 50 -200 km s -1 have strong HCN-vib emission. Clearly, a larger sample will be required to investigate this issue further. If the distribution of sources in Fig. 1 is indeed due to an orientation effect, more sources with both strong HCN-vib emission and outflow signatures in the far-IR OH lines should show up in an extended sample.

Evolution

Following Aalto et al. (2015b), we present a possible evolutionary scenario in which bright HCN-vib emission is tracing extremely obscured nuclei in a phase that occurs before the nuclear feedback is able to drive wide-angle outflows, or at least before such outflows can be observed in the far-infrared OH lines. In this scenario, the deeply embedded regions which provide the necessary conditions for HCN-vib excitation can coexist with fast collimated outflows, but are disrupted once nuclear wideangle outflows have developed. A schematic view of one possible version of the scenario is presented in Fig. 2. Here, the most obscured sources consist of a mid-infrared core, responsible for the HCN-vib emission, surrounded by an obscuring layer of cooler dust that accounts for the far-infrared emission. Outflows in these objects are collimated and often so compact that they are still embedded in the outer layer of dust, and thus obscured from view in the far-infrared. In the next phase, the outflows have broken through the cooler dust layer and widened considerably, making them clearly detectable in most galaxies. As gas and dust is transported from the regions around the nucleus, the conditions in the mid-infrared core change, becoming less favorable for the excitation of HCN-vib. Finally, when a path has been cleared through the surrounding dust, OH is no longer detectable in absorption and the conditions for HCN-vib excitation in the core are no longer met.

A similar evolutionary sequence has been suggested by González-Alfonso et al. (2017a), and we note that, apart from UGC 5101, the HCN-vib luminous sources from our sample coincide with the sources in their extremely buried pre-feedback phase. It should be noted that, even if an evolutionary scenario like this applies in some cases, all sources in our sample may not be on the same evolutionary track. For example, a lot of nuclear power is required in order to push large columns of gas to high velocities, and LIRGs like NGC 4418 and Zw 049.057 may therefore never develop powerful wide-angle outflows detectable in the far-IR OH lines. Instead, they might evolve along the horizontal axis in Fig. 1.

Finally, an interesting comparison can be made with the highly collimated molecular outflows that are found around lowmass protostars in the earliest (class 0) stages of their formation (e.g., Konigl & Pudritz 2000;Codella et al. 2014). With time, gas at larger and larger angles from the outflow axis may be en-~100 pc ~10 pc trained, eventually sweeping away most of the envelope around the star (e.g., Arce & Sargent 2006). Furthermore, vibrationally excited HCN has been detected toward molecular hot cores around massive protostars using the direct l-type transitions, that is transitions between the l-type levels inside a rotational state, at cm-wavelengths (e.g., Rolffs et al. 2011).

HCN-vib compared to other tracers of obscured nuclear regions.

As 2013) also find that the fastest outflows are found in those sources that are still deeply embedded as indicated by strong mid-infrared silicate absorptions. However, as already noted by Aalto et al. (2015b), González-Alfonso et al. (2015) find that silicate absorptions are biased toward relatively unobscured mid-infrared emitting regions, again indicating that the HCN-vib is actually tracing a more extreme form of obscuration.

Conclusions

We explore a possible correlation between the strength of molecular outflows in the far-infrared OH 119 µm lines and the luminosity of rotational lines of vibrationally excited HCN. A simple comparison of HCN-vib line luminosities, normalized to the total infrared luminosity of the host galaxy, and the median velocities of OH 119 µm absorption lines shows that galaxies with unusually bright HCN-vib emission tend to lack fast molecular outflows, but that galaxies without fast outflows do not necessarily have bright HCN-vib emission. This may be an orientation effect or something that reflects a true difference between the sources, for example that the most obscured sources cannot drive wide-angle outflows or that their outflows are young and have not yet dispersed the nuclear gas and dust concentrations. Following Aalto et al. (2015b), we present an evolutionary sequence, that may apply to some of the sources, in which bright HCN-vib emission is tracing extremely obscured nuclei in a phase that occurs before the onset of wide-angle outflows. Once these massive outflows have been launched, they quickly disrupt the deeply embedded regions responsible for the bright HCNvib emission. We note, however, that more studies are needed in order to extend the sample and explore the relation further. As the Herschel Space Observatory, which was used for the OH outflow measurements, is no longer operational, such studies should concentrate on observations of HCN-vib in galaxies which have already been observed in the OH 119 µm doublet. Another possibility is to search for hidden outflows using the radio lines of OH, either in its ground state (Baan et al. 1989) or in rotationally excited states (Falstad et al. 2018).

A&A proofs: manuscript no. hcn_vs_oh_outflow_v5 ment (HIPE; Ott 2010) version 14.0.1. Before analyzing the absorption lines, polynomials of order two were fitted to the continuum and then subtracted from the spectra. For consistency, the profiles of the 119 µm OH doublets were modeled using the same procedure as in Veilleux et al. (2013). Each line was fitted with two Gaussian components characterized by their amplitude, position, and width. The separation between the two lines of the doublet was fixed at 0.208 µm in the rest frame and the amplitude and width of the two lines were the same for each component. The median velocities of the absorptions (v 50 (abs)) were then determined from these fits. The fitting procedure was carried out using the spectroscopic analysis toolkit PySpecKit [START_REF] Ginsburg | PySpecKit: Python Spectroscopic Toolkit, Astrophysics Source Code Library[END_REF] and the continuum subtracted spectra with the fits overplotted are presented in

  ); Aalto et al. (2015a,b); Imanishi et al. (2016a,b); Martín et al. (2016); Privon et al. (2017); Aladro et al. (2018); and König et al. (in prep.

References. ( 1 )

 1 Sakamoto et al. (2010); (2) Aalto et al. (2015b); (3) Imanishi et al. (2016a); (4) König et al. (in prep.); (5) Imanishi et al. (2016b); (6) Aladro et al. (2018); (7) Martín et al. (2016); (8) Aalto et al. (2015a); (9) This work; (10) Imanishi & Nakanishi (2013); (11) Privon et al.(2017)

Fig. 1 .

 1 Fig. 1. Median OH absorption velocity as a function of the HCN-vib luminosity relative to the the total infrared luminosity. For sources with observations of the J = 3-2 transition, only this line is plotted. For sources with detections only in the J = 4-3 line, the HCN-vib luminosity has been scaled down by a factor of 2.5, with an extra uncertainty of 20% included in the error bars. Squares, circles, diamonds, and triangles represent LINER, H ii, Seyfert 1, and Seyfert 2 optical spectral types, respectively. Colors indicate the total infrared luminosity of each system. Upper limits (3σ) are indicated with arrows.

Fig. 2 .

 2 Fig.2. Schematic view of a possible evolutionary scenario. The upper left panel shows the most obscured phase where both the mid-infrared core and the outflow are still completely embedded in a layer of cooler dust. The upper right panel shows the conditions in the mid-infrared core in this phase. A central energy source is heating the dusty core through radiative trapping. Mid-infrared photons, which cannot escape the dusty environment, vibrationally excite the HCN molecules which may then undergo rotational transitions and emit (sub)millimeter photons that are able to penetrate the obscuring dust. In the lower left panel, the outflow has broken through the cooler layer of dust and widened, making it visible in the far-infrared. The lower right panel shows a third stage where the outflow has cleared a path through the surrounding dust layer. In the two final stages, conditions in the core are no longer sufficient for luminous HCN-vib emission to occur.

Fig

  Fig. C.1. Spectral fits to the OH 119 µm absorption lines in the galaxies Zw 049.057, IRAS 20414-1651, and NGC 7469. The solid black histograms represent the data, the solid magenta lines are the best multicomponent fits to the data, and the dashed blue lines are the individual components. The velocity scale is set relative to the frequency of the blue component of the doublet. Dashed vertical lines indicate the expected positions of the two absorption components given the adopted redshifts.

Table 1 .

 1 Sample galaxies.

	Name	Type a	z b	D L	c	L IR	d
				(Mpc)	(10 11 L ⊙ )
	NGC 4418	Sy 2	0.0071	33.6 ± 2.5	1.32 ± 0.19
	IC 860	H ii	0.0130	62.1 ± 4.3	1.60 ± 0.22
	IRAS 12224-0624	L	0.0264 119.5 ± 8.2	1.98 ± 0.27
	Zw 049.057	H ii	0.0130	62.4 ± 4.3	2.04 ± 0.28
	NGC 7469	Sy 1	0.0164	68.2 ± 4.7	4.13 ± 0.57
	I Zw 1	Sy 1	0.0610 258.2 ± 17.7 8.22 ± 1.13
	UGC 5101	L	0.0394 169.7 ± 11.6 9.50 ± 1.30
	IRAS 20551-4250	H ii	0.0430 185.2 ± 12.7 10.65 ± 1.46
	IRAS 15250+3609	L	0.0552 243.7 ± 16.7 11.08 ± 1.53
	IRAS 08572+3915	L	0.0584 253.1 ± 17.3 13.38 ± 1.84
	IRAS 05189-2524	Sy 2	0.0428 180.2 ± 12.4 13.50 ± 1.85
	IRAS 22491-1808	H ii	0.0778 337.0 ± 23.1 13.88 ± 1.92
	Mrk 273	Sy 2	0.0378 165.6 ± 11.3 14.81 ± 2.03
	IRAS 20414-1651	H ii	0.0871 383.4 ± 26.3 15.12 ± 2.18
	Arp 220	L	0.0181	84.1 ± 5.8	17.25 ± 2.36
	IRAS 13120-5453	Sy 2	0.0308 137.8 ± 9.5 19.20 ± 2.65
	IRAS 12112+0305	L	0.0733 326.3 ± 22.4 20.35 ± 2.79
	IRAS 17208-0014	H ii	0.0428 189.3 ± 13.0 26.21 ± 3.59
	Mrk 231	Sy 1	0.0422 184.3 ± 12.6 34.30 ± 4.70

Notes.

(a)

Optical spectral types, H ii, L (for LINER), Sy 1 and Sy 2 (for Seyfert 1 and 2, respectively). When available, we have used the classifications from

Veilleux et al. (1995)

and

Kim et al. (1998)

, otherwise, we have used classifications from

Sanders et al. (1988b)

,

Baan et al. (1998), andVéron-Cetty &Véron (2001)

.

(b)

In priority order, we have used redshifts from

Veilleux et al. (2013)

,

Sanders et al. (2003)

, or

Strauss et al.

Table 2 .

 2 L HCN-vib and outflow properties.

	Name	L HCN-vib	L HCN-vib /L IR v 50 (abs) a Observatory References
		(10 3 L ⊙ )	(10 -8 )	(km s -1 )
	J = 3-2
	NGC 4418	5.0 ± 1.2	3.77 ± 0.71	111	SMA	1
	IC 860	4.2 ± 0.8	2.65 ± 0.37	not obs.	PdBI	2
	Zw 049.057	7.3 ± 2.0	3.57 ± 0.76	37	PdBI	2
	NGC 7469	< 0.1	< 0.03	emi.	ALMA	3
	I Zw 1	< 1.7	< 0.21	emi.	ALMA	3
	UGC 5101	20.5 ± 5.5	2.15 ± 0.50	-9	NOEMA	4
	IRAS 20551-4250	2.3 ± 0.7	0.21 ± 0.06	-381	ALMA	5
	IRAS 15250+3609	< 11.3 b	< 1.02	189	ALMA	3
	IRAS 08572+3915 NW	< 6.4	< 0.48	-489	ALMA	3
	IRAS 22491-1808 E	13.2 ± 3.9	0.95 ± 0.25	99	ALMA	3
	Mrk 273	< 16.9	< 1.14	-201	NOEMA	6
	IRAS 20414-1651	< 7.5	< 0.50	-32	ALMA	3
	Arp 220 W	40.9 ± 7.0	2.37 ± 0.25 c	21	ALMA	7
	Arp 220 E	8.9 ± 1.5	0.51 ± 0.05 c	21	ALMA	7
	IRAS 12112+0305 NE	14.3 ± 4.0	0.70 ± 0.17 c	-117	ALMA	3
	Mrk 231	14.5 ± 3.1	0.42 ± 0.07	-237	PdBI	8
	J = 4-3
	NGC 4418	12.4 ± 2.5	9.41 ± 1.25	111	SMA	1
	IRAS 12224-0624	16.9 ± 4.5	8.52 ± 1.95	not obs.	ALMA	9
	IRAS 20551-4250	4.8 ± 1.2	0.45 ± 0.09	-381	ALMA	10
	IRAS 05189-2524	8.0 ± 2.7	0.59 ± 0.18	-387 d	ALMA	9
	Arp 220 W	108.1 ± 19.9 6.27 ± 0.77	21	ALMA	7
	Arp 220 E	25.8 ± 4.4	1.49 ± 0.15	21	ALMA	7
	IRAS 13120-5453	< 5.5	< 0.29	-195	ALMA	11
	IRAS 17208-0014	101.9 ± 17.5 3.89 ± 0.40	51	ALMA	2

  discussed inAalto et al. (2015b), a prerequisite for the excitation of HCN-vib is the presence of warm and dusty regions, making it a good tracer of deeply buried nuclei. Following this logic, our results indicate that the most obscured nuclei lack evolved, wide-angle outflows. Interestingly, the results ofGonzález-Alfonso et al. (2017b), who use the equivalent width of the OH 65 µm doublet as a measure of the obscuration, suggest that the fastest outflows arise in some of the most obscured nuclei. They do however point out that sources with high equivalent width in OH 65 µm do not necessarily have fast outflows. By comparing our Fig.1with their Fig.4, we see that five of the six most HCN-vib luminous sources that are included in both samples also belong to the group of sources with high equivalent width but low outflow velocity, indicating that they are obscured A&A proofs: manuscript no. hcn_vs_oh_outflow_v5 also when using the OH 65 µm equivalent width as a measure. On the other hand, five of the sources with low HCN-vib luminosity are also found among the sources with high OH 65 µm equivalent width, indicating that they also contain obscured nuclei. It appears that the two measures trace different parts or physics of the galaxy nuclei. Indeed,González-Alfonso et al. (2017b) state that high equivalent widths require dust temperatures in excess of 60 K, while dust temperatures above 100 K are required for bright HCN-vib emission(Aalto et al. 2015b). It is thus possible that bright HCN-vib emission exclusively traces more obscured parts of the nuclei, where the dust is hotter.In a similar manner, Spoon et al. (

Acknowledgements. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2015.1.00708.S, ADS/JAO.ALMA#2016.1.00140.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

⋆ Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA.

Appendix A: Vibrationally excited HCN in IRAS 15250+3609

The ULIRG IRAS 15250+3609 was observed in the HCO + J = 3-2 line by Imanishi et al. (2016a). They interpreted a peak close to the expected position of the HCN-vib line as an outflow signature in the HCO + line. Their interpretation is supported by the fact that similar features are seen in the HCN J = 3-2 and J = 4-3 lines (Imanishi et al. 2016a(Imanishi et al. , 2018)). However, relative to the main lines, the sub-peak close to the HCO + line is stronger than the one close to the HCN line. This could be due to differing abundances or excitation in the outflowing gas but it could also be a contribution from HCN-vib. Furthermore, IRAS 15250+3609 has a high equivalent width in the OH 65 µm doublet (González-Alfonso et al. 2015), indicating that the galaxy is highly obscured, as well as an HCN absorption at 14 µm (Lahuis et al. 2007). Here, we have attributed the feature as completely due to HCN-vib emission, but it is likely that it is indeed blended with an HCO + outflow signature and the value given here should be considered an upper limit to the HCN-vib luminosity.

Appendix B: Previously unpublished HCN-vib detections

We found two sources with previously unpublished detections of HCN-vib in the ALMA science archive, IRAS 12224-0624 (project 2015.1.00708.S, PI: L. Armus), and IRAS 05189-2524 (project 2016.1.00140.S, PI: D. Iono). The observations of IRAS 05189-2524 were conducted during two runs on 2016 November 12 and 15 with a total of 42 antennas with baseline lengths ranging between 15.1 m and 1.0 km. A spectral window centered at 341.8 GHz covered a bandwidth of 1.875 GHz (∼1650 km s -1 at the frequency of HCN-vib J = 4-3), with a frequency resolution of 3.9 MHz. During both runs J0522-3627 was used as bandpass and flux calibrator, and J0457-2324 was used as phase calibrator. The total on-source time of the observations was 3340 s and the final sensitivity achieved was 0.5 mJy beam -1 per 20 km s -1 (∼24 MHz) channel. For the imaging we used Briggs weighting with a robustness factor of 0.5. The resulting beam size is 0.29 ′′ × 0.22 ′′ (PA ∼63 • ).

The observations of IRAS 12224-0624 were conducted during a single run on 2016 April 27 with a total of 38 antennas with baseline lengths ranging between 15.1 m and 452.8 m. A spectral window centered at 347.4 GHz covered a bandwidth of 1.875 GHz (∼1600 km s -1 at the frequency of HCN-vib J = 4-3), with a frequency resolution of 3.9 MHz. The calibrators used were J1256-0547, which was used for bandpass and flux calibration, and J1246-0730, which was used for phase calibration. The total on-source time of the observations was 395 s and the final sensitivity achieved was 1.5 mJy beam -1 per 20 km s -1 (∼24 MHz) channel. For the imaging we used Briggs weighting with a robustness factor of 0.5. The resulting beam size is 0.51 ′′ × 0.44 ′′ (PA ∼82 • ).

For both sources, the Common Astronomy Software Applications (CASA; [START_REF] Mcmullin | Astronomical Data Analysis Software and Systems XVI[END_REF]) package was used to reapply the calibration and perform imaging. The line fluxes used to calculate the HCN-vib luminosities were then found by fitting Gaussians to the HCO + and HCN-vib lines in spectra that were spatially integrated over the sources. These spectra are presented in Fig. B.1. We note that the wavelengths of the HCO + and HCN-vib lines in IRAS 05189-2524 are offset from those expected when using the redshift of 0.0426 adopted by Veilleux et al. (2013). They are however consistent with the redshift of ∼0.0428 found from the CO J = 1-0 observations reported by Sanders et al. (1991). Using this redshift instead of the one adopted by Veilleux et al. (2013) Spectral fits to the HCO + and HCN-vib J = 4-3 lines in the two galaxies IRAS 05189-2524 and IRAS 12224-0624. The solid black histograms represent the data and the solid magenta lines are the best fits to the data. The velocity scale is set relative to the frequency of the HCO + J = 4-3 line. Dashed vertical lines indicate the expected positions of the HCO + and HCN-vib J = 4-3 lines given the adopted redshifts. The dotted lines in the plot for IRAS 05189-2524 indicate the expected positions given the redshift adopted by Veilleux et al. (2013).

Appendix C: New OH outflow measurements

Three of the sources with existing HCN-vib observations also had observations of the OH doublet at 119 µm taken with the Photodetector Array Camera and Spectrometer (PACS; Poglitsch et al. 2010) on Herschel but were not included in the sample of Veilleux et al. (2013). The observations of Zw 049.057 (OBSID: 1342248368, PI: E. González-Alfonso) were conducted on 2012 July 20 for a duration of 1478 s, the observations of IRAS 20414-1651 (OBSID: 1342217908, PI: D. Farrah) were conducted on 2011 April 5 for a duration of 8879 s, and the observations of NGC 7469 (OBSID: 1342235840, PI: E. González-Alfonso) were conducted on 2011 December 31 for a duration of 1086 s. All observations were performed in high spectral sampling, range spectroscopy mode. The observations had been processed with version 14.2 of the standard pipeline and were not in need of reprocessing. In all sources, the nuclear far-IR emission is spatially unresolved in the central 9.4 ′′ (∼2 kpc) spaxel of the PACS 5 x 5 spaxel array. As the central PACS spaxel is smaller than the point spread function of the spectrometer, the spectrum was extracted using the point source correction task in the Herschel interactive processing environ-


Lucas Rivoirard

Dr Martine Wahl

Dr Patrick Sondi

Multipoint Relaying vs Chain

Keywords: Branch-Leaf Clustering Performance Clustering, Routing protocols, Cooperative vehicles, V2V, VANET, Performance evaluation

HAL is

I. INTRODUCTION

Vehicular Ad Hoc Network (VANET) makes it possible to imagine a wide range of Intelligent Transport System (ITS) applications in terms of comfort, road traffic optimization and safety. The complexity of these systems is inherent to the VANET architectures.

A first VANET architecture is based on vehicle-toinfrastructure (V2I) communications. Road side units (RSU) are deployed along the roads (i.e. the infrastructure) at regular intervals. This architecture allows a centralized management of the dissemination of vehicle messages, thus offering efficient scheduling and optimization. However, the deployment of RSUs is expensive not only at the time of their installation, but also during their lifetime due to maintenance costs. Both the management of the equipment obsolescence and the compatibility of the various applications which are embedded in RSUs and deployed in different regions are two challenges. In addition, V2I architecture raises a critical issue: how will the driver assistance applications still be operational when the infrastructure is down, and what about the areas that are not equipped?

In order to address this latter issue, vehicle-to-vehicle (V2V) communication that works without any preexisting infrastructure has been introduced as a second architecture. The vehicles collaborate in a distributed manner to form an ad hoc network [START_REF] Rivoirard | CBL: A Clustering Scheme for VANETs[END_REF]. Especially, V2V communication can help to bypass a failure in the infrastructure by providing a complementary and redundant communication structure in order to guarantee an adequate level of quality of service for safety applications. However, V2V communication relies on distributed routing algorithms which performance is more unpredictable than that of V2I protocols.

The V2V routing protocols can lead to either a flat topology or a hierarchical topology [START_REF] Cunha | Data Communication in VANETs: A Survey, Challenges and Applications[END_REF]. The organization of the network according to a hierarchy consists in differentiating certain nodes of the network by giving them a particular role or specific functions. This hierarchical organization is one key factor that can be used by ad hoc routing protocols to optimize network management and to improve scalability. Several approaches have been proposed in order to achieve this hierarchical organization such as multipoint relaying (MPR), clustering schemes and backbone-based approaches.

In order to optimize the flooding of broadcast traffic, the Optimized Link State Routing protocol (OLSR) [START_REF] Clausen | Optimized link state routing protocol (OLSR)[END_REF] resorts to the concept of MPR nodes. These latter are the only nodes allowed to generate and to broadcast, through the entire network, the link state information used when building and updating the routing tables. Also, only MPR nodes are able to relay the messages from a source node to the destination. During MPR selection, each node in the network selects the smallest subset of its symmetric one-hop neighbors that allows it to reach every node in its two-hop neighborhood. A broadcast message transmission is illustrated Fig. 1. At the beginning, a source node (drawn in red) sends a broadcast message to its one-hop neighbors. When the blind flooding strategy is used (Fig. 1 (a)), each node retransmits the message that it receives, which creates a significant number of redundant retransmissions of the same message. When the MPR technique is applied (Fig. 1 (b)), only the MPR nodes (drawn in green and blue) retransmit the message, which limits the number of redundant retransmissions and therefore the load Clustering is another way to achieve a virtual division of the network into groups. Resorting to clusters optimizes the range of packet flooding by limiting the packet retransmission to one or several clusters according to a predefined strategy. A cluster includes different types of nodes: one group leader that is a "cluster-head" connected to a set of nodes called "ordinary members". "Gateway nodes" are members of several clusters, thus making a link between them (Fig. 2). The clustering schemes can generate separated clusters (without gateway nodes) or not (with gateway nodes). Clustering methods are active, passive, or hybrid. In active clustering, dedicated control messages are sent for cluster management. In passive clustering, clusters are created on demand when data need to be transmitted. In hybrid clustering, the information needed for cluster management is added to the packets. The cluster size can be also characterized by the number of hops. For instance, in one-hop clusters, each ordinary member node is directly connected to its cluster-head. Over the last two decades, many clustering schemes have been proposed in order to enhance the performance of ad hoc routing protocols according to various link or node metrics [START_REF] Cooper | A Comparative Survey of VANET Clustering Techniques[END_REF]. Many of them were first studied for Mobile Ad hoc Networks (MANETs) [START_REF] Basu | A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks[END_REF]- [START_REF] Dhurandher | Weight based adaptive clustering in wireless ad hoc networks[END_REF]. In the case of VANET, the road traffic environment and the velocity due to vehicle mobility are important factors in the design of a clustering scheme. The previous approaches have been completed recently with many proposals targeting specifically VANETs by building time stable clusters [START_REF] Mohammad | Using traffic flow for cluster formation in vehicular ad-hoc networks[END_REF]- [START_REF] Arkian | A Stable Clustering Scheme Based on Adaptive Multiple Metric in Vehicular Adhoc Networks[END_REF]. In order to achieve the connection between the clusters in road traffic, several backbone approaches have been also proposed in the literature [START_REF] Di Felice | Dynamic backbone for fast information delivery invehicular ad hoc networks: an evaluation study[END_REF]- [START_REF] Togou | A stable minimum velocity cdsbased virtual backbone for vanet in city environment[END_REF].

In order to evolve from the plethora of existing approaches towards standardized solutions, the European Telecommunications Standards Institute (ETSI) has published the Geonetworking requirements [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications; GeoNetworking; Part 4: Geographical addressing and forwarding for point-to-point and point-to-multipoint communications; Sub-part 1: Media-Independent Functionality[END_REF] recently that fix the design guidelines of VANET architectures. Geonetworking is a geographic-based routing protocol that supposes the existence of a location service that collects the location information of the vehicles and provides them upon request.

In a recent work [START_REF] Rivoirard | Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2v communications[END_REF], we presented in detail and discussed all these references. We also introduced a clustering scheme, chain-branch-leaf (CBL), that builds a virtual backbone in the VANET, similar to that obtained with RSUs, while relying only on V2V communications. CBL functioning requires only the position and velocity information of the vehicles in the closest neighborhood (one hop). Therefore, unlike most of the geographic-based routing protocols that would require an infrastructure at least for the location service, CBL can perform without any preexisting infrastructure. Moreover, CBL supports unicast communications and oriented broadcast (upstream or downstream) in the road traffic, which can be useful for some applications such as cooperative perception and cooperative localization [START_REF] Rivoirard | A cooperative vehicle ego-localization application using v2v communications with cbl clustering[END_REF]. Through simulation, we already showed that CBL reduces routing traffic load [START_REF] Rivoirard | Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2v communications[END_REF] in OLSR in comparison with the MPR, without degrading network performance for the applications, notably on end-toend delay and packet delivery ratio [START_REF] Rivoirard | From multipoint relaying to chain-branch-leaf: Improving the clustering in olsr for vehicular ad hoc networks[END_REF].

However, our previous studies did not investigate the structures created by both MPR and CBL in order to explain why CBL performs better while selecting 75% less relays than MPR. Therefore, the purpose of this work is to present a comparative analysis of the structures created in an OLSRbased VANET by both the MPR technique and the CBL clustering scheme, and analyze their impact on network stability and performance. The rest of the paper is organized as follows. Section II describes the functioning of the native OLSR protocol using multipoint relaying. Section III recalls briefly the functioning of CBL, and provides all the details about its implementation in OLSR in replacement of the MPR technique. Section IV presents the performance evaluation carried out through simulation over MPR-OLSR and CBL-OLSR and a comparative analysis of the related results.

II. OLSR ROUTING PROTOCOL

The Optimized Link State Routing protocol (OLSR) [START_REF] Clausen | Optimized link state routing protocol (OLSR)[END_REF] is a hierarchical protocol that uses proactive route search. The objective is to list all the intermediate nodes (relays) that will relay application packets from a sender node to one or more destination nodes. To that end, each node maintains several tables, including the routing table, the one-hop neighbor table, the two-hop neighbor table, the table of MPR nodes elected by the node, and the MPR Selector table (i.e. the table of the neighbor nodes that elected this node as a MPR). This routine uses two types of routing messages: HELLO messages for discovering the neighborhood and TC messages for sharing routing tables.

Each node periodically broadcasts routing packets through the network, independently from any packet transmission request. These routing packets contain the list of the routes which the node is aware of. The routing table contains at least the address of a potential destination node and the address of the first designated relay (next hop) in the onehop neighbor table that announces a route to that destination node. According to OLSR protocol specification, a metric may be associated with each route. By default, this cost is the number of relay nodes needed to reach the destination (number of hops). When transmitting in unicast or multicast mode, a node sends its packets to the address of the next hop node to the destination node(s). Upon receipt of each packet of the message, the relay node proceeds the same way by sending it to the next relay node on the route to the destination node(s).

The first step of the OLSR protocol is to discover the neighboring nodes. Each node periodically broadcasts a HELLO message (parameter HELLO INTERVAL set by default to 2 s) that contains information about its neighborhood. This information is the address and the connection type of the neighbors (symmetric, asymmetric, MPR, lost, or not specified). If the link is symmetric, this means that the link between the two nodes is valid and bidirectional. If the link is asymmetric, the link is unidirectional. If the connection has a MPR type, it means that this neighbor node is a MPR. By receiving these messages, neighboring nodes process the information (by updating their routing tables), but they do not relay this message. In fact OLSR protocol uses a TTL (Time To Live) counter system of which the value is included in each packet. The source node determines the TTL counter value. When a message is relayed by a node, the TTL counter is decreased by 1. A node that receives a message of which the TTL counter reaches the value of 1 does not retransmit this message. In the case of the HELLO messages, the TTL counter is always set to 1 by its source node. In addition, when a neighborhood change is detected, the routing table is recalculated to update the information. If a node has not sent any HELLO message for a time longer than a specific threshold Vtime (parameter Neighbor hold time), it is deleted from the routing tables. This timer system is used to ensure that the information is not obsolete. The OLSR protocol transmits the values needed to calculate this delay through the Vtime field in the header of the messages transmitted within OLSR packets.

Nodes selected as MPR broadcast TC (Topology Control) packets at a frequency set through the TC interval parameter. These messages are only relayed by MPR nodes. A TC packet from a node contains information about the neighboring nodes that selected it as a MPR node. By receiving TC packets, each node in the network updates its routing tables and calculates the paths to other nodes in the network using Dijkstra's shortest path algorithm with the number of hops as metric. The operation is as follows. The N i node receives a TC message initially sent by the N k node. The TC message contains the list of N j nodes that have selected the N k node as their MPR node. There is a route through the N k node to join the N j nodes. If the N j nodes are present in the routing tables of the N i node, the node calculates the current number of relay nodes that allow it to join the N j nodes. If the number of relay nodes on the route passing through the N k node is lower, then the N i node updates the routing table.

In all these operations, the MPR nodes play a key role. The structure built in the VANET by OLSR protocol in order to reduce routing traffic load and save radio ressources for the application relies on MPR nodes. During the MPR selection, each node N i in the network selects the smallest set of its onehop neighbors, allowing it to reach every node in its two-hop neighborhood. Only MPR nodes selected by N i are allowed to forward broadcast traffic originating from this latter. The selection procedure for node N i can be described as follows:

• Node N i puts in a set U (U for Uncovered) all its two-hop neighbors.

• Each neighboring node N j having its Willingness parameter value set to WILL-ALWAYS is automatically added to the MPR set of N i . Every two-hop neighbor connected to the nodes selected at this step is removed from U. • The node N i calculates for each one-hop neighbor N j , the number D1 (degree) of the nodes in the two-hop neighborhood of N i that can be reached through node N j . If a node N j is the only one-hop node with a communication link to a two-hop neighbor in U, then node N j is added to the MPR set of N i . The node N i removes any node in U connected to the chosen node N j . • The following procedure is repeated until U is empty (which means each two-hop neighbor of the node N i has a communication link to one of the selected MPRs). For each one-hop neighbor node N j , the node N i calculates the number D2 (reachability) of nodes in U that have a communication link with node N j . Node N i adds to its MPR set, the node N j with the highest N willingness parameter value and a non-zero value for D2. In the case of multiple candidate nodes, the node with the highest value for D1 is added to the MPR set of N i . The nodes from U connected to N j are removed from U.

III. CBL CLUSTERING PROPOSAL

A. Definitions

CBL is a completely distributed algorithm described in detail in [START_REF] Rivoirard | Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2v communications[END_REF]: each communication node initiates its own process. CBL creates a hierarchy between the nodes in order to build one-hop clusters so that each node of a cluster can directly communicate to the cluster-head without going through another relaying node. It defines two kinds of nodes (Fig. 3): branch nodes and leaf nodes. Both kinds of nodes emit periodic HELLO messages in order to build a structure, called a chain, that connects the nodes in each traffic direction.

In order to build stable chains, CBL uses a metric called connection time or contact time (CT). This metric evaluates the duration of the connection between the network nodes and allows the nodes managing the election of their branch node. Precisely, branch nodes, leaf nodes, chain, and connection time are defined as follows:

• A branch node (Fig. 3) is a cluster-head node that is elected by the other nodes (branch or leaf) in its one-hop neighborhood. It emits HELLO messages like every node, but it is the only one allowed to emit topology control messages (TC), to forward application messages, and to participate in the construction of a chain. When relaying a message, according to the application request specified in the header fields, a branch node can forward it to: its own leaf nodes; its upstream branch nodes; its downstream branch nodes; its branch nodes (even in other traffic direction).

These destination options are coded into the message type of the original format of the packets defined in OLSR protocol [START_REF] Clausen | Optimized link state routing protocol (OLSR)[END_REF] (see Table I).

• A leaf node is an ordinary node which has to connect itself to the closest branch node. If no branch node is detected, the leaf node elects the neighbor moving with the lowest speed and in the same traffic direction as a branch. A leaf node generates and transmits only HELLO messages and applications data traffic. • A chain is a virtual backbone made up of connected branch nodes. Ideally, one chain should be created per traffic direction. On longitudinal road context such as highways, the chains behave as a virtual backbone similar to the one that would be obtained with an infrastructure RSUs deployed along the road. It offers to its branch nodes a path to forward application messages over long distance.

• BranchChoice, the address of the branch node chosen by a leaf node. The BranchChoice field is empty if the node is a branch node; • ChainUP, the address of the branch node chosen for relaying upstream traffic. The ChainUP field is empty if the node is a leaf node; • ChainDO, the address of the branch node chosen for relaying downstream traffic. The ChainDO field is empty if the node is a leaf node; • The Connection Time (CT) is the duration expected for the communication between two nodes N i and N j when they keep the same speed. This metric, also called "contact time", has been used in [START_REF] Su | Mobility prediction and routing in ad hoc wireless networks[END_REF]- [START_REF] Jemaa | Multicast Communications for Cooperative Vehicular Systems[END_REF]. CT is approximated using (1). This equation takes into account the position of the nodes ([X i , Y i ] for the node N i and [X j , Y j ] for the node N j ), their speed (V i and V j ), their steering angle (σ i and σ j ), and the radio range (R max )):

CT = -(ab + cd) + (a 2 + c 2 ) * R 2 max -(ab -bc) 2 a 2 + c 2 (1)
a = V i cos(σ i ) -V j cos(σ j ) b = X i -X j c = V i sin(σ i ) -V j sin(σ j ) d = Y i -Y j

B. CBL scheme

The algorithms involved in CBL behavior are described in details in [START_REF] Rivoirard | Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2v communications[END_REF]. In this section, we only summarize the CBL process. At the time the initialization of the network starts, no group is formed, all the nodes are ordinary (leaf) nodes. The HELLO messages exchanged by the nodes activate the topology creation process. In addition to the information usually transmitted in OLSR HELLO messages, a CBL HELLO message contains additional information such as the position and type (branch or leaf) of its originator. A node N i performs several tasks each time it receives a HELLO message from a neighbor N j . Firstly, it updates the table containing the list of one-hop neighbors as in OLSR. Secondly, according to the type (branch or leaf) of the receiver node N i , it respectively changes the branch status of N i into the leaf status if N i no longer received HELLO messages from any neighboring node that had elected it for some remaining time, or it turns the leaf status of N i into the branch status when the HELLO message from node N j announces that N j has elected N i as a branch node. Lastly, if N i is a branch node, it elects or updates the upstream (ChainUP) and downstream (ChainDO) branch nodes. Otherwise, if N i is a leaf node, it elects or updates its own branch node (BranchChoice).

C. Implementation of CBL in the OLSR protocol: CBL-OLSR

CBL is actually a clustering scheme that can be integrated in any routing protocol, provided that this latter maintains a table of the one-hop neighbors by the means of periodic messages. In this work, in order to perform an accurate comparison with the multipoint relaying technique, we have chosen to integrate CBL in OLSR protocol (CBL-OLSR). In this version, the CBL branch nodes inherit the functionalities of the MPR nodes in native OLSR: generating and forwarding TC messages through the network, and relaying efficiently broadcast traffic from their leaf nodes beyond its one-hop neighborhood (through ChainUP and ChainDO).

D. Packet format in CBL-OLSR

The implementation of CBL within the OLSR protocol requires a first modification to include the message direction information. This information is added to the OLSR message header at the message type level, initially coded on 8 bits.

However, in native OLSR, the message type can only take integer values between 1 and 4. The proposed implementation uses the first four bits to code the direction information (see Table I and Fig. 3). When a message is received by a branch node, it is forwarded as follows:

1) If the value of the first bit is 1, the message is forwarded to every branch nodes within the one-hop neighborhood (including branch nodes of another traffic direction). This retransmission mode is the classical broadcast. 2) If the value of the second bit is 1, the message is forwarded to the downstream branch node. 3) If the third bit value is 1, the message is forwarded to the upstream branch node. 4) If the value of the fourth bit is 1, the message is forwarded to the electing leaf nodes. The message direction information allows CBL-OLSR to support the four communication scenarios [START_REF]Intelligent Transport Systems (ITS); vehicular communications; geonetworking; part 2:scenarios[END_REF] for automotive applications specified by the ETSI: point-to-point, point-tomultipoint, GeoAnyCast and GeoBroadcast. In CBL-OLSR, the point-to-point and point-to-multipoint communication scenarios are performed respectively by the unicast and multicast mechanisms of the OLSR protocol. The GeoAnyCast and GeoBroadcast scenarios can be achieved using the position information contained in the one-hop neighbor table and the message direction information (Table I shows the message type encoding). Let us consider a geographic zone of relevance for a message of a given source node. This latter identifies the relative position upstream or downstream of this area of interest. It sends an application message (containing a field identifying the position and size of the geographic area) to the branch nodes within its own chain in the right direction. At each relaying branch node that receives a message with GeoAnyCast or GeoBroadcast scenario, the node checks if the position of the next branch node is in the target zone. In the case of a GeoAnyCast scenario, when this zone is reached, the relaying node overwrites the TTL field of the message and sets its value to 1, thus allowing a last retransmission. In the case of a GeoBroadcast scenario, when this zone is reached, the relaying node overwrites the "Message Type" field of the message and sets its value to "1001xxxx", thus allowing local broadcast to all branch nodes and leaf nodes. Each branch node that is located in the target area and that receives this message will broadcast it. The others ignore it.

E. Header of the HELLO message in CBL-OLSR

Implementing CBL in OLSR implies some modifications to the HELLO packet format in order to include the information about the node type (branch or leaf), its position, its speed, and its steering angle. Only 32 additional bits have been added to the HELLO packet header of the native OLSR (Fig. 5) in order to limit the impact on communication resources, since HELLO messages are sent periodically with a high frequency. The different are coded as follows :

• Speed, on 8 bits, from 0 to 256 km/h with a resolution of 1 km/h. • Steering angle, on 8 bits, from 0 to 360 degree according to the North, with a resolution of 1.5. • Longitude:

-(Second Longitude), on 10 bits, coding the minutes and seconds of longitudinal positioning angle. -(dsec Lon), on 4 bits, coding the tenths of seconds of longitudinal positioning angle.

• Latitude:

-(Latitude Second), on 10 bits, coding the minutes and seconds of latitude positioning angle. -(dsec Lat), on 4 bits, coding the tenths of seconds of latitude positioning angle.

• Vehicle type (T), on 1 bit (0: leaf node, 1: branch node).

The destination nodes of a HELLO message are in the onehop neighborhood of the source node, thus within its direct communication range. The network technology used in this work is IEEE 802.11p, which has a maximum radio range of 1 km. Therefore, the nodes do not have to transmit the position values related to the angle and the tenths of minutes of the angle in the HELLO message, since these values are almost the same for the nodes within the same communication range.

F. Content of the HELLO message for CBL-OLSR

A modification at the link code level has also been made in order to include the node choices regarding the chain structure (@BranchChoice, @ChainUP, and @ChainDO). In native OLSR, the link code is encoded on 8 bits, the last four bits coding the link type and the node type of the neighbor as illustrated in Fig. 4. The values taken by the "Link Type" and "N.Type" fields are as follows:

• Link Type=1, link is not specified (UNSPEC); • Link Type=2, link is lost (LOST); • Link Type=3, unidirectional link (asymmetric, ASYM); • Link Type=4, bidirectional link (symmetric, SYM); • N.Type=1, there is at least one symmetric link with this neighboring node (SYM NEIGH); • N.Type=2, there is at least one symmetric link with this neighboring node that has been selected as MPR (MPR NEIGH); • N.Type=3, there is no symmetric link with this neighboring node (NOT NEIGH).

In CBL-OLSR, when Link Type=SYM LINK and N.Type=MPR NEIGH, the information about @BranchChoice, @ChainUP, and @ChainDO are included in the HELLO message. To that purpose, three bits of the link code are then used, as illustrated in Table II, to code the storage order of these neighboring node addresses in the fields of the message following that link code. Link code 1 2 3 4 5 6 7 8 @ChainUP in 1st position ---1 @ChainUP not elected ---0 @ChainDO in 1st position --1 0 @ChainDO in 2nd position --1 1 Compliant @ChainDO not elected --0 -@BranchChoice in 1st position -1 0 0 @BranchChoice in 2nd position -1 1 0 @BranchChoice in 2nd position -1 0 1 with OLSR @BranchChoice in 3rd position -1 1 1 @BranchChoice not elected -0 --TABLE II. Proposed modifications of the link code for CBL-OLSR in order to include the order of appearance of the choices for relaying nodes (@BranchChoice, @ChainUP, and @ChainDO) Fig. 5 shows an example of a HELLO message sent by a branch node that has selected a branch node upstream (@ChainUP) and another downstream (@ChainDO). A. Road configuration and simulation parameters SUMO (Simulation of Urban MObility) [START_REF] Santana | How to Simulate Traffic with SUMO[END_REF] is used in order to generate the mobility traces of the vehicles over three road networks, R1, R2, and R3:

• R1 is a 5 km-long three-lane one-way highway; • R2 is a 5 km-long three-lane two-way highway; • R3 is a 5 km-long three-lane two-way highway, with an entrance and a highway exit. The exit is located at 1.8 km and the entrance at 3 km from the beginning of the road section.

Also, low, medium and high traffic density are modeled in using a ratio of 1/6 trucks and 5/6 cars (Table III); therefore defining three scenarios per road network. In the network R3, 25% of the vehicles arrive via the highway entrance, 25% of the vehicles take the exit and 50% of the vehicles just cross the whole road section. The same parameters are used for SUMO simulation than those taken in our previous study [START_REF] Rivoirard | Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2v communications[END_REF]. Where S1 to S9 are the scenarios.

Simulation time for each of the nine scenarios is 500 s. Nodes send a HELLO message every 1 s. The OLSR threshold Vtime is set at 3 s. The free space propagation model is used, with a maximal transmission range set at 500 meters.

B. Comparison of CBL-OLSR and native OLSR structures

As mentioned in section I, previous simulation-based evaluation through OPNET showed that CBL reduces the global number of TC generated and retransmitted in the VANET in comparison with the MPR technique [START_REF] Rivoirard | Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2v communications[END_REF]. Moreover, it was shown that CBL actually offers better performance to application traffic regarding the delay and packet delivery metrics [START_REF] Rivoirard | From multipoint relaying to chain-branch-leaf: Improving the clustering in olsr for vehicular ad hoc networks[END_REF]. In these evaluations, we will compare the structures created in a OLSR-based VANET, respectively when using CBL and MPR. Therefore, fifteen structural metrics (Ms) are introduced, and they can be described as follows:

• Ms1 (Leaf/Vanet): number of leaf nodes in the network Multipoint relaying technique design assumes an open area, and it leads to a mesh structure where each node can reach the others through several relays. Once every nodes have performed their MPR selection, the network contains a large majority of MPR nodes (Fig. 6a). When assuming a physically constrained area such as road traffic, it seems reasonable to choose only one relay for upstream traffic, and another one for downstream. CBL design follows that idea, and it leads to a structure similar to a chain along the road in each traffic direction (Fig. 6b). The nodes, which speed is close to that of the major part of the traffic, evolve in the chain as branch or leaf nodes, while those moving with higher speed only evolve as leaf from branch to branch.

Only the results of S3 scenario, related to R1 road configuration, will serve for the comparative analysis. However, the results for all the other scenarios are summarized in Table IV for CBL-OLSR metrics, and Table V for MPR-OLSR metrics. About 50% of the time (Fig. 7), CBL builds a single connected chain in the network. Sometimes the chain is broken into two or three smaller chains, mostly due to the changes in the order of the branch nodes (overtaking) inside the chain, but it is quickly reconstructed. Fig. 9 and 7 together show that there are about 30 branch nodes when there is only one chain, and about 10 branch nodes per chain in the presence of several chains. We noticed that the chain breaks increase with the density and the road configuration, R3 scenarios reaching the highest scores due to a lot of vehicles entering or leaving the road section (see Ms2 in Table IV).

When the traffic becomes stable (Fig. 8), after the first 150 s of simulation, there are about 200 vehicles on the road section. Using CBL-OLSR, 85% of the nodes are of leaf type and only 15% actually act as relays (branch nodes). MPR-OLSR leads to 90% of the nodes acting as relays (MPR status) and only 10% are non-MPR nodes. Since not all the MPR nodes are involved in the retransmission of a specific broadcast traffic, these results only explain why CBL-OLSR generates less routing traffic than MPR-OLSR. Indeed, though the traffic related to HELLO messages is the same for both protocols, only 15% of the nodes generate TC messages using CBL-OLSR, while 90% when using MPR-OLSR.

These results are confirmed in every scenario, except in S1, S4 and S7, where the traffic density is low, and therefore the clustering is less efficient (more than 50% of branch nodes). Intuitively, when the network is sparse, the vehicles are more spaced and there are more isolated nodes that become branch nodes. However, even in this cases, the results obtained using CBL-OLSR are still better than those provided by MPR-OLSR. This is confirmed in every scenario (see Ms10 in Table V), where the proportion of non-MPR nodes never exceeds 25%.

One explanation to these results is the fact that in MPR-OLSR each node may select several MPRs, as much as necessary in order to cover every two-hop neighbors. Thus, at any time, the union of the different sets of MPRs selected by each node may have almost the same size than the entire VANET. Contrary to that, CBL-OLSR forces each node to select only one or two branch nodes, depending on whether the node is a leaf or a branch. Almost all the nodes crossing the area covered by a branch node will attach to this latter, and adopt themselves a leaf status. As a result, CBL selects a number of branch nodes approximatively equal to the length of the road section divided by the double of the communication range. The denser the VANET is, the lower the percentage of branch nodes will be.

Each node has an average of 38 one-hop neighbors (Fig. 10) using both protocols, which is normal since the network discovery process through HELLO messages is exactly the same. However, an unexpected result shows that both protocols achieve almost the same number of nodes attached to each relay (Fig. 11): the number of leaf nodes per branch (Ms5) is almost equal to the number of selectors per MPR (Ms11). This observation is limited to scenario 3. In the other scenarios, there are more nodes in the MPR selector sets than leaf nodes per branch. The first reason is that each MPR selector set includes both non-MPR nodes and other nodes that are themselves MPR nodes, while the leaf nodes that attach to a branch in CBL cannot be branch nodes themselves. The second reason is that a leaf node cannot attach to several different branch nodes in CBL-OLSR, while in MPR-OLSR a node may select several different MPR nodes. Each selected node remains a branch for about 70 s (Fig. 12). Even for a vehicle moving at the lowest speed of 80 km/h allowed on French highways in normal traffic conditions, it remains a branch over 1.5 km (three times the maximum range), which is a significant distance. Indeed, in the same conditions, a RSU would serve the nodes that attach to it only over a distance two times the maximum range. On the other hand, the time that a node remains a MPR is approximately 35 s, which is the half of the branch duration. The difference between the branch duration and the MPR duration tends to decrease in the two-way highway (R2) and in particular road traffic configurations, such as those with an entrance and an exit (R3). Despite that, the duration that a node remains a branch node is longer than the one a node remains a MPR node in every scenario (see Ms6 in in Table IV, and Ms12 in Table V). Each leaf node remains attached to the same branch for about 21 s (Fig. 13). For most V2V safety applications that are known to have a message transmission periodicity ranging from 50 ms to 500 ms, these 21 s represent enough time to send at least up to 40 alerts from a leaf attached to the relaying branch node to the entire network. These values are almost the same for every scenario (see Ms7 in Table IV). Except in the scenarios S1 and S7, that have the lowest vehicle density profiles, the duration that a node remains attached to the same MPR is lower than the duration a leaf remains attached to its branch node (see Ms13 in Ms12 in Table V). In scenario S3, this duration using MPR-OLSR is approximately 9.4 s, half of the duration observed when using CBL-OLSR. Both the duration a node remains a relay (branch node or MPR node) and the duration that a node (leaf or MPR selector) remains attached to the same relay (branch or MPR) suggest that the structure created using CBL-OLSR is more prone to stability than that obtained with MPR-OLSR. Fig. 14 shows the number of relay nodes selected by each node, otherwise the size of the MPR Set of each selecting node in MPR-OLSR, and the number of branch nodes selected by each node in CBL-OLSR (a single one for leaf nodes, and up to two for branch nodes, which implies an average close to one due to the high proportion of leaf nodes). In scenario S3, after the stabilization of the network, this number reaches approximately 4 when using MPR-OLSR. The results in every scenarios present the same proportion between CBL-OLSR and MPR-OLSR, except in S1 (lowest VANET size, lowest traffic density). In other words, each broadcast traffic generated in the network is retransmitted about four times more when using MPR-OLSR than when using CBL-OLSR.

These comparisons may seem a bit unfair for the MPR technique. Indeed, the MPRs are expected to guarantee that every two-hop neighbors will be reached, which is not strictly guaranteed with branch nodes. When using CBL, each leaf node elects a single branch node and a branch node elects one or two branch nodes (upstream and downstream), while MPR-OLSR imposes the selection of as much as necessary MPR nodes in order to reach the two-hop neighborhood. It would be difficult to establish which of them achieving an efficient one-hop based clustering (CBL) or two-hop based clustering (MPR) is philosophically better. However, it is possible to establish which approach actually performs well regarding broadcast traffic flooding (routing and application ones), and the performance for unicast traffic. We noticed that, for this latter category, previous work [START_REF] Rivoirard | Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2v communications[END_REF] concludes that CBL is better. Also, it has been showed that by reducing the number of relays (branch nodes), CBL-OLSR generates lower TC routing traffic than OLSR. Finally, Fig. 15 shows that the number of MPR nodes involved in the retransmission of a broadcast message from a source to the entire network (through its MPRs, then the MPRs of its MPRs, and so on) is almost three to four times higher than the total number of branch nodes involved in the same process, in every scenario except S1. This ensures that CBL also reduces the traffic related to TC message retransmissions. V. CONCLUSION AND FUTURE WORK When using CBL clustering scheme, the vehicles that move at lower speed in the same traffic direction are good candidates (branch nodes) for building a stable backbone that we call a chain. The greater the number of vehicles, the longer the chain. Each vehicle moving faster is a leaf that attaches itself to a branch node covering its current location in order to communicate with the entire VANET. The evaluations show that CBL leads to a structure that may improve VANET performance regarding several metrics. First, the branch nodes represent only 15% to 25%, contrary to MPR nodes that never decrease under 75% of the VANET nodes. In this way, CBL allows a better optimization of the flooding of broadcast traffic, including TC message generation and retransmissions. Indeed, we showed that four times more MPRs are involved in the flooding of a given broadcast traffic, when compared to the number of the branch nodes involved in the same situation. Then, among all the one-hop neighbors of a given branch, only those having the better link quality with this latter (25% to 55%) actually choose it as their branch (cluster-head). The others select other branch nodes, which will result in a global structure with better quality link in the VANET. Finally, this study shows that CBL leads to significant stability since a node elected as a branch remains a branch for 70 s, and it can serve each of its leaf nodes for 20 s, while MPR nodes only remain in this role for a duration about 35 s and is attached to its selectors only 9 s. Future work will consist in finding optimal values of CBL parameters for different road configurations and traffic conditions, including when integrated in other routing protocols for vehicular ad hoc networks than OLSR.

Fig. 1 .

 1 Fig. 1. Blind flooding and MPR strategies

Fig. 3 .

 3 Fig. 3. Example of a CBL structure on a three-lane one-way highway

Fig. 4 .

 4 Fig. 4. Link code defined by OLSR protocol

Fig. 5 .

 5 Fig. 5. Example of a HELLO message modified for CBL-OLSR

Fig. 6 .Fig. 7 .

 67 Fig.6. State of the structures built in scenario S5 at time T=500s

Fig. 8 .

 8 Fig. 8. Node type repartition (Ms1 and Ms10) in scenario S3

Fig. 9 .Fig. 10 .

 910 Fig. 9. Number of branch nodes per chain (Ms3) in scenario 3

Fig. 11 .

 11 Fig. 11. Number of nodes attached to a relay node (Ms5 and Ms11) in scenario 3

Fig. 12 .

 12 Fig. 12. Duration that a node remains a relay node (Ms6 and Ms12) in scenario 3

Fig. 13 .Fig. 14 .

 1314 Fig. 13. Duration that a node remains attached to the same relay node (Ms7 and Ms13) of scenario 3

Fig. 15 .

 15 Fig. 15. The number of relay nodes involved in a broadcast transmission (Ms9 and Ms15) in scenario 3

TABLE I .

 I Modification of the "Message Type" of OLSR packet in order to take into account the retransmission modes available in CBL-OLSR.

TABLE III .

 III Scenarios and values of road traffic demand.

	Density	Car traffic	Truck traffic	R1	R2	R3
		(veh/h/direction)	(veh/h/direction)
	Low	500	100	S1	S4	S7
	Medium 2000	400	S2	S5	S8
	High	4000	800	S3	S6	S9

  Ms12 (MPR time): duration that a node remains a MPR • Ms13 (NToMPR Time): duration that a node remains attached to the same MPR • Ms14 (MPRs/Node): number of MPRs selected by a node (size of the MPR set of the related node) • Ms15 (MPR/Broadcast): number of MPR nodes involved in a single broadcast transmission

	• Ms2 (NB Chains): number of chains in the network
	• Ms3 (Branch/Chain): number of branch nodes per chain
	• Ms4 (one-hop/Branch): number of one-hop neighbors (in
	the same traffic direction)
	• Ms5 (Leaf/Branch): number of leaf nodes that attach to
	the same branch node
	• Ms6 (Branch time): duration that a node remains as a
	branch node
	• Ms7 (Leaf time): duration that a leaf node remains
	attached to the same branch node
	• Ms8 (Branch/Node): the number of branch nodes selected
	by a node (branch or leaf) in the network

• Ms9 (Branch/Broadcast): the number of branch nodes involved in a single broadcast transmission • Ms10 (Non MPRs/Vanet): number of nodes that do not have a MPR status • Ms11 (Nodes/MPR): number of nodes that have selected a node as a MPR (size of its MPR selector set) •

TABLE IV .

 IV Mean result values in all scenarios for CBL-OLSR. .96 2.04 3.29 2.68 3.63 3.36 6.36 9.29 Ms3 13.7 15.9 18.9 12.1 20.4 17.8 9.4 10.4 11.3 Ms4 3.9 18.8 38.6 4.8 21.2 38.2 4.1 17.7 37.8 Ms5 1.94 4.75 7.03 1.94 4.89 6.84 2.31 3.43 4.42 Ms6 (s) 64.4 71.9 70.0 65.1 73 73.5 59.4 53.6 57.7 Ms7 (s) 18.8 20.6 21.3 15.6 22.5 22.6 8.6 23.8 16.5 Ms8 1.60 1.20 1.13 1.62 1.20 1.14 1.41 1.26 1.20 Ms9 17.6 24.1 29.5 36.9 47.8 56.6 25.3 61.5 94.1 See section IV-B for the definition of performance metrics.

	Network		R1			R2			R3
	Scenario	S1	S2	S3	S4	S5	S6	S7	S8	S9
	Density	-	+	++	-	+	++	-	+	++
	Node	25	102 198 50	205 369 46	189 390
	Ms1 (%)	28 76	85	25	77	84	46	67	76
	Ms2	1.65 1

TABLE V .

 V Mean result values for all scenarios with MPR-OLSR.

	Network		R1			R2			R3
	Scenario	S1	S2	S3	S4	S5	S6	S7	S8	S9
	Density	-	+	++	-	+	++	-	+	++
	Node	25	102 198 50	205 369 46	189 390
	Ms10 (%) 22.1 7.4 10.2 6.9 6.5 7.5 11.5 7.8 8.0
	Ms11	3.0 6.8 9.0 5.6 11.2 13.9 5.1 10.8 14.3
	Ms12 (s)	39.1 41.6 34.6 53.4 53.8 53.7 43.9 48.3 50.7
	Ms13 (s)	29.0 13.0 9.4 10.9 8.0 6.6 10.1 8.4 6.0
	Ms14	1.8 3.30 3.3 3.0 5.2 6.2 2.8 5.2 6.7
	Ms15	13.9 66.9 118 37.2 176 317 31.4 156 308

Univ Lille Nord de France, IFSTTAR, COSYS, LEOST, F-59650 Villeneuve d'Ascq first-name.name@ifsttar.fr

[START_REF] Cunha | Data Communication in VANETs: A Survey, Challenges and Applications[END_REF] Univ. Littoral Côte d'Opale, LISIC -EA 4491, F-62228 Calais, France The authors acknowledge the support of the CPER ELSAT2020 project which is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts-de-France Region Council.

Lucas Rivoirard received the Engineer degree in Transport and Mobility Engineering from the