Mickaël Rimboud

Luc Etcheverry

Mohamed Barakat

Wafa Achouak

Alain Bergel
email: alain.bergel@toulouse-inp.fr

Marie-Line Délia

Hypersaline microbial fuel cell equipped with an oxygen-reducing microbial cathode

Keywords:

MFCs were implemented in hypersaline medium with O 2 -reducing microbial cathodes.

• Stable power density of 209 ± 24 mW m -2 was produced over a week.

• Efficiency of the microbial cathode was linked to a redox system centred at 0.2 V/SCE. • Cathodes were dominated by Gammaproteobacteria (Alteromonadales, Oceanospirillales).

• An unclassified order linked to the genus Thioalobacter may be essential.

Introduction

Microbial fuel cells (MFCs) have shown impressive progress in the last ten years [START_REF] Santoro | Microbial fuel cells: From fundamentals to applications. A review[END_REF] but the power density produced remains much too low to consider large size extrapolation of the technology. A major issue, among others, is the high internal resistance of the cells due to the low ionic conductivity of usual electrolytes (Oliot et al., 2016b). The ionic conductivity (σ) of common electrolytes used in MFCs is often around 1 S m -1 for synthetic media, and it can even be much lower when wastewater is used (Oliot et al., 2016b). It can easily be shown that such low ionic conductivities severely hinder MFC performance. The ohmic loss (ΔU) due to the ionic transport between an anode and a cathode facing each other and separated by a distance l is:

ΔU = l σ J ( 1
)
where J is the current density. This equation indicates, for example, that a current of 50 A m -2 flowing between an anode and a cathode 1 cm apart is opposed by an ohmic drop of 0.5 V in a medium of 1 S m -1 conductivity. This ohmic drop would be of the same order of magnitude as the maximum cell voltage that most MFCs can produce. The low conductivity of common MFC electrolytes precludes the generation of corresponded to 15.6 mM acetate). Nevertheless, the authors suggested that the performance may have been limited by the bioanode because anaerobiosis was not ensured in the anode compartment.

The purpose of the present work is to advance in the development of hypersaline MFC equipped with an OR microbial cathode. Several studies have reported powerful bioanodes formed from salt marsh sediment [START_REF] Rousseau | Microbial bioanodes with high salinity tolerance for microbial fuel cells and microbial electrolysis cells[END_REF]2015). The bioanodes were formed under constant polarization at + 0.1 V/SCE. The highest current densities, up to 85 A m -2 , were obtained with media containing 45 g/L NaCl. These bioanodes have been implemented for weeks in a microbial electrolysis cell for the production of hydrogen [START_REF] Rousseau | Microbial electrolysis cell (MEC): A step ahead towards hydrogen-evolving cathode operated at high current density[END_REF].

Recently, the same salt marsh sediment has shown the capacity to form efficient OR microbial cathodes under polarization at + 0.1 V/ SCE and a constant supply of air [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF]. These biocathodes produced up to -2.2 A m -2 during voltammetric experiments.

In both contexts, bioanodes and biocathodes were designed in the same type of electrolyte, which was derived from a Starkey medium and complemented with 45 g/L NaCl, having a conductivity of 10.4 S m -1 . They were formed under controlled polarization at the same potential value of + 0.1 V/SCE. All these previous investigations were performed in 3-electrode analytical set-ups, and these bioanodes and biocathodes have not yet been coupled together in an MFC.

The purpose of the present work was to design a hypersaline MFC by associating a bioanode and an OR biocathode, both formed from salt marsh sediment using the previously described methodologies. Accordingly, in the first phase, the bioanodes and biocathodes were formed separately under polarization at + 0.1 V/SCE. Bioanodes and OR biocathodes were then associated in double-compartment MFCs. During both polarization and MFC operation, the bioanodes were fed with sodium acetate as electron source and the OR biocathodes were constantly supplied with air.

MFCs were continuously operated with a load of 1200 Ω and they were characterized every three days by power and polarization curves. The bacterial population of the anodic and cathodic biofilms were analysed after pyrosequencing of the DNA coding the 16S rRNA and compared with the microbial compositions available in the literature for similar bioanodes and OR biocathodes formed under controlled polarization in 3-electrode set-ups.

Materials and methods

Medium and inoculum

Salt marsh sediment was collected from salt marshes near the Mediterranean Sea (Salins de Saint Martin, Gruissan, France). Sediment was kept at room temperature in a 50 L closed container and used fresh during the two weeks following sampling to avoid long-term anaerobic evolution that might alter the bacterial composition.

The medium used was derived from the Starkey medium and consisted of 0.5 g L

-1 K 2 HPO 4 , 1 g L -1 NH 4 Cl, 1 g L -1 Na 2 SO 4 , 0.1 g L -1 CaCl 2 ۰2H 2 O, 2 g L -1 MgSO 4 ۰7H 2 O, 45 g L -1
NaCl. Both bioanodes and OR biocathodes were formed using the same mineral medium inoculated at 10% v/v with salt marsh sediment. The pH was fixed at 7.0 in all reactors at the beginning of experiments.

Formation of the microbial electrodes (bioanodes and OR biocathodes)

Four bioanodes and four OR biocathodes were formed separately under polarization at + 0.1 V/SCE using three-electrode set-ups. Each bioanode and OR biocathode was individually formed in a separate cell. The future bioanode or biocathode was the working electrode and was made of 0.5 cm thick carbon felt (Mersen, France). The bioanodes consisted of a rectangular piece of carbon felt of 12 cm 2 (4 cm × 3 cm); the biocathodes consisted of a circular piece of carbon felt 4 cm in diameter (around 12.5 cm 2 ). The bioanodes or biocathodes high current density. Increasing the electrolyte conductivity is a prerequisite if the objective is to increase the power density MFCs can produce and thus to progress towards economically-efficient devices.

Saline (0 to 1% w/v) to hypersaline (greater than3.5% w/v) electrolytes may be seen as a promising solution [START_REF] Nam | Enhanced hydrogen generation using a saline catholyte in a two chamber microbial electrolysis cell[END_REF]Grattieri et al., 2017b). Hypersaline media can reach ionic conductivities above 10 S.m -1 , which results in a proportional decrease in the ohmic drop inside the MFC (equation (1)). A few studies have started to report interesting results with saline and hypersaline MFCs [START_REF] Grattieri | Microbial fuel cells in saline and hypersaline environments: Advancements, challenges and future perspectives[END_REF]. Abrevaya et al. succeeded in producing power density of up to 509 mW m -2 , with a pure culture of the Haloferax volcanii archea, working in a medium of ionic strength around 2.7 M [START_REF] Abrevaya | Archaea-based microbial fuel cell operating at high ionic strength conditions[END_REF]. A power density of 390 mW m -2 was produced with an MFC used to treat seafood wastewater. Actually, no data was provided about the salinity, but it can be assumed to be high given the nature of the inoculum [START_REF] Sun | Electricity generation from seafood wastewater in a single-and dualchamber microbial fuel cell with CoTMPP oxygen-reduction electrocatalyst[END_REF]. More recently, 504 and 572 mW m -2 were reached using MFCs inoculated with rice paddy-field soil working in NaCl up to 1.8 M (105 g L -1 ) [START_REF] Miyahara | Sodium chloride concentration determines exoelectrogens in anode biofilms occurring from mangrove-grown brackish sediment[END_REF]2015). Nevertheless, the power density produced by even these most efficient hypersaline MFCs remains limited and most other hypersaline MFCs have produced much lower power densities, generally of only a few tens of mW m -2 (Grattieri et al., 2017a;2017b;[START_REF] Robertson | Transitioning from batch to flow hypersaline microbial fuel cells[END_REF].

Another rate-limiting step is the cause of the low efficiency of hypersaline MFCs: the slow kinetics of the oxygen-reducing cathodes. This step is known to be rate-limiting for all MFCs [START_REF] Liew | Non-Pt catalyst as oxygen reduction reaction in microbial fuel cells: A review[END_REF][START_REF] Wei | Recent progress in electrodes for microbial fuel cells[END_REF] and it seems to be even more acute for hypersaline MFCs. Several microbial anodes have produced remarkably high current density in hypersaline media [START_REF] Rousseau | Microbial bioanodes with high salinity tolerance for microbial fuel cells and microbial electrolysis cells[END_REF]2015). Such anodes can show their full potential when combined with other cathodes than oxygen-reducing ones. A hypersaline microbial electrolysis cell (MEC) has thus worked with stable current above 50 A.m -2 for weeks [START_REF] Rousseau | Microbial electrolysis cell (MEC): A step ahead towards hydrogen-evolving cathode operated at high current density[END_REF]. In this case, the cathode ensured hydrogen evolution instead of oxygen reduction. It is therefore essential to concentrate efforts on the design of hypersaline oxygen-reducing cathodes.

Almost all hypersaline MFCs reported so far have been equipped with abiotic oxygen-reducing (OR) cathodes. Unfortunately, metallic and organic catalysts for oxygen reduction show poor efficiency at the nearneutral pH values that are required in MFCs. Moreover, to be effective, metallic and organic catalysts require clean electrolyte. In the media used for MFCs, fast fouling and biofouling of the surface of abiotic cathodes severely inhibit their performance (Oliot et al., 2016a;[START_REF] Pasternak | Regeneration of the power performance of cathodes affected by biofouling[END_REF].

Microbial cathodes for oxygen reduction may offer worthwhile alternatives, because of their low cost and their capacity to work at neutral pH [START_REF] Erable | Microbial catalysis of the oxygen reduction reaction for microbial fuel cells: A review[END_REF]. Microbial cathodes can also take advantage of biofilm development on their surface. For instance, it has been observed that Pt-free OR cathodes perform similarly to Pt-loaded cathodes after a few weeks of operation because of the development of an aerobic biofilm on their surface [START_REF] Santoro | Power generation from wastewater using single chamber microbial fuel cells (MFCs) with platinum-free cathodes and precolonized anodes[END_REF]. Notable successes have been reported with MFCs equipped with OR microbial cathodes, with power densities of 554 mW m -2 being produced [START_REF] Xia | Oxygen-reducing biocathodes operating with passive oxygen transfer in microbial fuel cells[END_REF] and up to 620 mW m -2 [START_REF] Milner | Microbial fuel cells with highly active aerobic biocathodes[END_REF][START_REF] Milner | The effect of oxygen mass transfer on aerobic biocathode performance, biofilm growth and distribution in microbial fuel cells[END_REF] or even 679 mW m -2 with a plant-MFC [START_REF] Wetser | Electricity generation by a plant microbial fuel cell with an integrated oxygen reducing biocathode[END_REF]. Finally, one of the greatest advantages of microbial OR cathodes is the room for progress that can be expected [START_REF] Liew | Non-Pt catalyst as oxygen reduction reaction in microbial fuel cells: A review[END_REF]. We are still in the infancy of their engineering development [START_REF] Huang | Electron transfer mechanisms, new applications, and performance of biocathode microbial fuel cells[END_REF] and making headway in deciphering their mechanisms may open avenues for considerable improvement. In particular, OR microbial cathodes often have complex microbial populations and we can hope to improve them by identifying the most active species for oxygen reduction and promoting their development.

To the best of our knowledge, only one study has designed a hypersaline MFC (electrolyte containing 62-65 g/L NaCl) equipped with an OR microbial cathode [START_REF] Sheikhyousefi | Investigation of different configurations of microbial fuel cells for the treatment of oilfield produced water[END_REF]. Power density remained pretty low, with a maximum of 65 mW m -2 when the MFC was fed with acetate (the chemical oxygen demand

The OR biocathodes were designed in reactors derived from Schott bottles containing 600 mL of inoculated medium. The medium was under permanent forced aeration via aquarium pumps during the whole polarization phase. All the polarization phases were performed in an oven at 30 • C.

Cyclic voltammetry was performed at the beginning and the end of the 12-day polarization phase. The potential scan started at the open circuit potential, down to -0.6 V/SCE and up to + 0.2 (bioanodes) or + 0.5 V/SCE (biocathodes). Polarization phases (chronoamperometry) and cyclic voltammetries were performed with a multichannel potentiostat (VMP3, Biologic, France) equipped with the EC-Lab software. were connected using a titanium wire 1 mm in diameter (Alfa Aesar, Germany), where the part in contact with the solution was insulated with a heat-shrinkable sheath. The counter electrodes were 12 cm 2 platinum grids connected with platinum wires and the reference electrodes were saturated calomel electrodes (SCE, E = +0.240 V/SHE).

The bioanodes were directly designed in situ in the sealed container destined to form the anodic compartment of the future MFCs (see MFC design below), which contained 800 mL of inoculated medium. The medium was firstly deoxygenated by 30 min' nitrogen sparging and thereafter fed with sodium acetate (40 mM). The concentration of sodium acetate was monitored throughout the experiment, during both the polarization and the MFC phases. It remained between 15 and 40 mM.

was used to analyse the filtered sequences [START_REF] Caporaso | QIIME allows analysis of highthroughput community sequencing data[END_REF], which were clustered into operational taxonomic units (OTUs) at 97% sequence similarity using uclust [START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF]. RDP classifier was used to perform the taxonomic assignment down to the genus level, with a 0.8 bootstrap cut-off, the most abundant sequence of an OTU being considered representative [START_REF] Cole | The ribosomal database project: Improved alignments and new tools for rRNA analysis[END_REF]. The Greengenes database (release gg_13_8_otus), included with the RDP classifier, was used for the taxonomic affiliations.

Results and discussion

Electrochemical performance

Four bioanodes and four OR biocathodes were formed separately under polarization at + 0.1 V/SCE. The current densities recorded at the end of this 12-day polarization phase are given in Table 1. On this basis, 4 MFCs were designed by associating the bioanode and OR biocathode that produced the highest current densities, in decreasing order from MFC1 to MFC4.

The 4 MFCs produced power continuously through an external resistance of 1200 Ω. The power densities displayed during 13 days are presented in Fig. 2. During days 0 to 6, the four MFCs displayed similar power density, around 40 mW m -2 , except MFC1, which suffered an incidental loss of oxygen supply during days 1 to 3. After day 6, a clear difference was observed between MFCs 1 and 2 on the one hand and MFCs 3 and 4 on the other. MFCs 1 and 2 displayed a rise in power production. MFC1 produced up to 238 mW m -2 on day 8.2. On average, from day 7 to day 13, the power density produced was 209 ± 24 mW m -2 and 143 ± 13 mW m -2 for MFC1 and MFC2, respectively. In contrast, the power generated by MFCs 3 and 4 remained lower, continuously displaying around 40 W m -2 until the end of the experiment. These different behaviours matched the method chosen to design the MFCs, by associating the best performing bioanodes and best performing OR biocathodes in decreasing order from MFC1 to MFC4.

Power curves were recorded on days 0, 3, 6, 9 and 13. The maximum power densities are reported in Table 1. As expected from the record of the power density versus time, two different behaviours were observed, with MFC 1 and 2 on one side, and MFC 3 and 4 on the other. MFC 3 and 4, which were equipped with the less well performing bioanodes and OR biocathodes, produced rather constant power density throughout their operation, with mean values of 57 ± 19 and 43 ± 11 mW m -2 , respectively. In contrast, the power density produced by MFCs 1 and 2 evolved over time. MFC1 produced from 64 mW m -2 on day 0 to 272 mW m -2 on day 9. Power densities recorded with MFC 2 increased along the experimental timescale from 66 to 201 mW m -2 on day 13.

Power curves and polarization curves obtained with MFC1, the best performing MFC, are presented in Fig. 3. The initial (day 0) maximum power density of 64 mW m -2 was obtained with an external resistance R of 3300 Ω, while the maximum of 272 mW m -2 recorded at day 9 corresponded to R = 1000 Ω with a cell voltage of 585 mV. After day 0, the maximum power density fell to lower values (158 mW m -2 at day

Table 1

Maximum current densities recorded with the four bioanodes (J an ) and biocathodes (J cat ) after twelve days of polarization at + 0.1 V/SCE and maximal power densities (P max ) recorded with the four MFCs after 0, 3, 6, 9 and 13 days of operation. * power density lower than normally expected due to an incidental loss of oxygen supply in the catholyte.

MFC J an (A m -2 ) J cat (A m -2 )

Microbial fuel cell design

The microbial fuel cells were made from a sealed PVC container with an adapted cathodic compartment open to the air as described in Fig. 1A. Pictures of the device and its components are presented in Fig. 1B.

The PVC container was a cylinder 11 cm in diameter and 13 cm high, which contained 800 mL of medium. The air-open, cathodic compartment consisted of a PVC tube 4 cm in diameter and 7 cm high. Anodic and cathodic compartments were separated by a cation exchange membrane CMI 7000, 4 cm in diameter. The OR biocathode was placed immediately on the upper side of the membrane and the anode near the bottom of the fuel cell, partly imbedded in the salt marsh sediment used as inoculum. The anode was aligned with the membrane and the OR biocathode. The distance between the two electrodes was in the 6-7 cm range.

At the end of the 12-day polarization phase, the OR biocathodes were taken from their reactors and inserted in the cathodic compartments of the microbial fuel cells, where the bioanodes had already been formed. The best-performing bioanode was associated with the best-performing OR biocathode to design MFC1. The second best-performing electrodes were associated to design MFC2, and so on, in decreasing order of performance, for MFC3 and MFC4. The cathodic compartment was filled with a portion of the medium from the reactors where the biocathodes had been designed.

MFC operation and characterization

The MFCs were connected to a resistance of 1200 Ω and the evolution of the voltage with time was recorded using the VMP3 multichannel potentiostat. During MFC operation, oxygen was supplied to the cathodic compartment using aquarium pumps. Evaporation of the catholyte occurred due to the small volume of the cathodic compartment and the forced aeration. The loss was compensated by addition of distilled water mixed with Starkey medium from the polarization reactors. Concentration of sodium acetate in the anolyte was monitored during the whole MFC operation and maintained between 15 and 40 mM.

MFCs were characterized by their polarization and power curves, which were determined with a range of external resistances from 10 Ω to 150 k Ω (10, 33, 100, 330, 1 000, 3 300, 10 000, 18 000, 33 000, 100 000 and 150 000 Ω). For each resistance, the voltage between the two electrodes and the potential of the OR biocathodes versus the SCE were recorded using two high-input-impedance multimeters (IDM93M, RS Components SAS, France) after 15 min of stabilization. The first power curves were recorded immediately after the implementation of the OR biocathodes. Successive characterizations were then performed every 72 h thereafter. Power curves were recorded at room temperature, whereas MFCs were kept in the oven (30 • C) during operation. The current density values were calculated in relation to the cathode surface area (12.5 cm 2 ).

Population analyses

Biofilms were stripped from the surface of the electrodes and resuspended in 15 mL of PBS by ultrasonication (15 min at 80 W, Fisher Scientific FB 15061). The biofilm suspensions were centrifuged and the solid material was collected. DNA extraction was performed using the DNeasy PowerBiofilm extraction kit from Qiagen (Netherlands). The DNA samples were sent to the Research and Testing Laboratory (RTLab -Lubbok, Texas, USA) for 454 pyrosequencing (Roche) using 28F (5 ′ -GAG TTT GAT YMT GGC TC-3 ′ ) and 519R (5 ′ -GWA TTA CCG CGG CKG CTG-3 ′ ) primers.

Data were screened and trimmed on the basis of quality scores and bases below a quality threshold of 25 were discarded. Sequences with less than 250 nt and chimeric sequences were removed using UCHIME [START_REF] Dowd | Evaluation of the bacterial diversity in the feces of cattle using 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP)[END_REF][START_REF] Edgar | UCHIME improves sensitivity and speed of chimera detection[END_REF]. The open-source software QIIME 13 for R = 1000 Ω).

Polarization curves of the bioanode and the OR biocathode of MFC1 are presented on Fig. 3B. Power and polarization curves observed on day 3 stood apart from the others obtained on days 0, 6, 9 and 13, due to an incidental stop of the air supply to MFC1 between days 1 and 3. However, the performance of the fuel cell was soon restored when air supply to the cathodic compartment restarted, as reflected in curves recorded on days 6, 9 and 13. This shows the robustness of the OR biocathodes and of the microbial catalyst. It also indicates the importance of the air supply to sustain the activity of the OR biocathode and, consequently, the main limitation due to oxygen transfer, already well-identified beyond the scope of hypersaline conditions [START_REF] Milner | The effect of oxygen mass transfer on aerobic biocathode performance, biofilm growth and distribution in microbial fuel cells[END_REF].

The polarization curves revealed a high stability for the bioanode (Fig. 3B, black lines). From the measurements performed on day 3 to the measurements on day 13, the bioanode current-potential curves changed little. In contrast, the biocathode current-potential curves (Fig. 3B, grey lines) evolved significantly. The biocathode produced higher current intensities in increasing order on days 6, 12, and 9, which corresponds to the same increasing order as the power densities produced by the MFC (Fig. 3A). The biocathode clearly controlled the performance of the MFC.

Moreover, the polarization curves also showed that the overpotentials at the biocathode were always considerably higher than those at the bioanode (Fig. 3B). The overpotentials can be evaluated from the polarization curves as the difference between the potential at zero current and the potential obtained when the MFC operated with a given value of the external resistance. The maximum value of the overpotential was measured when the MFC was run with the lowest resistance, here of 10 Ω. For the biocathode, the maximum overpotential was 682, 608 and 613 mV at days 6, 9 and 12, respectively while, for the bioanode, it was only 76, 153 and 102 mV. The consistently higher values of the biocathode overpotential confirm that the biocathode was the rate-limiting element of the MFC.

Interestingly, the polarization curves on days 9 and 13 recalled the shapes of the voltammograms reported with similar OR biocathodes, also formed from salt marsh, in a previous study [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF]. Two distinctive redox systems were reported for oxygen reduction around -0.22 and + 0.20 V/SCE. The previous study investigated the OR biocathodes in analytical conditions only, i.e. in a 3-electrode system. Here, implementing similar OR biocathodes in MFCs, showed that the rise observed in MFC performance from day 9 was directly linked to the apparition of the redox system centred at + 0.20 V/SCE. Similarly, the redox system centred at + 0.20 V/SCE also appeared at day 9 on the MFC2 biocathode, while it was absent for the biocathodes of MFC 3 and 4. Actually, it began to be discernible for MFC3 at the end of the experiment, at day 13.

The evolution of the potential displayed by the MFC1 bioanode and OR biocathode during operation are presented in Fig. 3C. As observed above with the polarization curves, the bioanode remained highly stable throughout the duration of the experiment, displaying a stable potential of -0.42 ± 0.02 V/SCE. An identical stability was observed with the other bioanodes of MFCs 2 to 4, with average potential values of -0.43 ± 0.01, -0.51 ± 0.01 and -0.47 ± 0.02 V/SCE, respectively.

The behaviour of the OR biocathode was clearly different (Fig. 3C). After day 6, the potential of the OR biocathode quickly rose from around -0.20 V/SCE to reach + 0.20 V/SCE on day 7.5. It stabilized thereafter, at around + 0.17 V/SCE, until the end of the operation. This value of potential is in accordance with the voltammetry curves for oxygen reduction previously recorded with similar OR biocathodes [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF].

To sum up, unsurprisingly with constant bioanode behaviour, the evolution of the power generated by MFCs 1 and 2 followed a pattern similar to the evolution of their OR biocathodes. The power increase observed at day 9 was correlated with the appearance of a redox system of oxygen reduction centred at + 0.20 V/SCE on the biocathodes. Interestingly, this redox system also began to appear at day 13 on MFC3, which is encouraging information for the long-term evolution of these OR biocathodes and the related MFCs.

Discussion of the electrochemical performance

The average values of power density, which were sustained here for a week by MFC1 (209 ± 24 mW m -2 ) and MFC2 (143 ± 13 mW m -2 ), were around 3 times the maximum power density reported in the literature for a similar hypersaline MFC equipped with an OR microbial cathode, which was 65 mW m -2 (Roustazadeh [START_REF] Sheikhyousefi | Investigation of different configurations of microbial fuel cells for the treatment of oilfield produced water[END_REF]. Beyond the context of hypersaline electrolytes, maximum power densities of 554 mW m -2 [START_REF] Xia | Oxygen-reducing biocathodes operating with passive oxygen transfer in microbial fuel cells[END_REF], 620 mW m -2 [START_REF] Milner | Microbial fuel cells with highly active aerobic biocathodes[END_REF][START_REF] Milner | The effect of oxygen mass transfer on aerobic biocathode performance, biofilm growth and distribution in microbial fuel cells[END_REF] and 679 mW m -2 [START_REF] Wetser | Electricity generation by a plant microbial fuel cell with an integrated oxygen reducing biocathode[END_REF] have been reported for different MFC systems equipped with OR microbial cathodes.

On the one hand, it is encouraging to note that the performance obtained here was at the level of the highest reported in the literature for MFCs equipped with OR microbial cathodes. Nevertheless, on the other hand, it must be acknowledged that the highest power densities reported in the literature have been obtained with common electrolytes. The benefit of the hypersaline electrolyte used here is consequently not obvious. Actually, this remark is valid for all hypersaline MFCs. To date, the power densities reported for hypersaline MFCs do not exceed those of the MFCs using common electrolytes, while the hypersaline electrolytes should minimize the internal resistance of the system, which is a major sticking point in MFC development.

A simple calculation can help in assessing the impact of the ohmic drop on the MFC performance. From day 7 to day 13, MFC1, which produced the highest power, operated at an average cell voltage of around 0.6 V (Fig. 3C) with an external load of 1 200 Ω. The current was consequently 0.5 mA. According to Ohm's law applied to the electrolytes, the ohmic drop (ΔU) through the electrolyte is:

ΔU = R S I (2)
where R S is the resistance of the solution between the anode and the cathode and I is the current flowing through the electrolyte. R S can be calculated as:

R S = ρ l A ( 3
)
where ρ is the resistivity of the solution (Ω m), l is the distance between the anode and the cathode and A is the cross-sectional area of the solution through which the ionic current flows. Here, l was around 6 cm and A can be taken equal to the electrode area of 12.5 cm 2 . The electrolyte had an ionic conductivity of 10.4 S m -1 , which means a resistivity ρ of 0.1 Ω m. The resistance of the electrolyte R S can thus be estimated at 4.8 Ω (equation ( 3)) and the ohmic drop for a current of Labrenzia, etc.) accounting for the high proportion of Rhodobacterales (Table 2B). Gammaproteobacteria were represented by the orders Alteromonadales (4.1% on MFC 4 to 23.3% on MFC 1) and Oceanospirillales (3.1% on MFC 4 to 7.1% on MFC 1). Genera Marinobacter and Idiomarina were identified at genus level among Alteromonadales, and Marinobacterium genus among Oceanospirillales (Table 2B). Bacteria related to an unclassified order of Gammaproteobacteria already detected in high proportions on efficient OR biocathodes designed under polarization [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF] were detected in proportions from 0.8% (MFC 4) to 12.5% (MFC 1).

Discussion of the microbial populations

With regard to the bioanodes, Desulfuromonas sp. was found in a surprisingly high proportion for the four MFCs. This genus belongs to the same order (Desulfuromonadales) as the well-known anode respiring Geobacter sp. Miyahara et al. demonstrated that the increasing salinity of the medium led to the preferential selection of Desulfuromonas instead of Geobacter in electroactive biofilms [START_REF] Miyahara | Sodium chloride concentration determines exoelectrogens in anode biofilms occurring from mangrove-grown brackish sediment[END_REF]. Desulfuromonas was previously found among the dominant genera inside electroactive biofilms formed from salt marsh sediment (from 5.0 to 45.0%) [START_REF] Rousseau | Correlation of the electrochemical kinetics of high-salinitytolerant bioanodes with the structure and microbial composition of the biofilm[END_REF]2016), but not in such abundance as observed here. In the studies of Rousseau et al., Desulfuromonas sp. was found to be co-dominant in association with Marinobacter sp. in the anodic biofilms of a hydrogen-producing microbial electrolysis cell, which was formed from similar salt marsh sediment. The Marinobacter genus, however, was not detected here. A possible explanation for the lack of Marinobacter sp. in the anodic biofilms may be the use of a well-sealed anodic compartment. As Marinobacter sp. is mostly aerobic, it may have benefitted from a low diffusion of oxygen in the set-up used in the 0.5 mA at 2.4 mV (equation ( 2)). The electrolyte fulfilled its mission perfectly: the small ohmic drop it induced can be neglected. Nevertheless, the same estimation for the type of electrolyte that is commonly used in MFCs, with an ionic conductivity around 1 S m -1 , gives an ohmic drop of only 24 mV. Actually, the current is too low for the ohmic drop due to the electrolyte to be rate-limiting. The hypersaline electrolyte used here divided the ohmic drop by a factor of 10 with respect to usual MFC electrolytes but the current was too small for this benefit to be noticeable. It was observed here that the behaviour of the MFC was controlled by the performance of the OR biocathode, which was ratelimiting. The biocathode limited the MFC performance and, consequently, the current was not high enough for the interest of the high ionic conductivity of the electrolyte to be detected.

For instance, the same estimation made with a current of 15 mA gives an ohmic drop of 72 mV with the hypersaline electrolyte, which is still acceptable, while 720 mV would be reached with a usual electrolyte, which would preclude the production of any power.

Here, a first advance has been achieved by designing an OR microbial cathode that works in a hypersaline MFC and results in power density at the level of the highest values reported in the literature with common MFCs implementing OR microbial cathodes. For the future, the kinetics of the OR microbial cathode needs to be improved in order to take advantage of the hypersaline conditions in terms of minimized ohmic drop. One obvious way to improve the cathode kinetics is to implement an air-breathing configuration [START_REF] Rago | Influences of dissolved oxygen concentration on biocathodic microbial communities in microbial fuel cells[END_REF]2018). Another way should be to promote the development of the redox system centred at + 0.20 V/SCE, which was observed to be linked with the improvement of the OR biocathode during MFC operation here.

Microbial population analyses

Gini-Simpson indexes for the anodic biofilms were in the range of 0.07 (MFC2) to 0.76 (MFC 4) (they were 0.42 and 0.67 for MFC1 and MFC3, respectively). The Gini-Simpson index represents the probability of interspecific encounter. If null, it indicates that only one genus is represented in the sample. An index of 0.07 for the anodic biofilm in MFC2 clearly indicates the high dominance of one genus (Desulfuromonas, see below).

The Gini-Simpson indexes of the cathodic biofilms were slightly higher, in the range of 0.73 (MFC4) to 0.91 (MFC1) (they were 0.86 and 0.88 for MFC2 and MFC3, respectively). As stated in previous observations [START_REF] Rimboud | The current provided by oxygen-reducing microbial cathodes is related to the composition of their bacterial community[END_REF][START_REF] Rimboud | Different methods used to form oxygen reducing biocathodes lead to different biomass quantities, bacterial communities, and electrochemical kinetics[END_REF], microbial populations of cathodic biofilms tend to be more diverse and less dominated (mean Gini-Simpson of 1.77) than those in anodic biofilms (mean Gini-Simpson of 1.06).

The most represented bacterial orders encountered in the anodic and cathodic biofilms can be seen in Fig. 4. The most represented genera (when identifiable) are numbered in Table 2 (2A: anodic biofilms; 2B: cathodic biofilms). The phylum Proteobacteria was the most represented in all biofilms, though differences between anodic and cathodic biofilms appeared at the class level. Anodic biofilms were clearly dominated by bacteria from the class Deltaproteobacteria, with a lower presence of Gammaproteobacteria. Cathodic biofilms were dominated by a variable mix of Alpha-and Gammaproteobacteria, with a characteristic absence of Deltaproteobacteria (mostly anaerobic).

In the anodic biofilms, the order Desulfuromonadales was largely dominant, in proportions from 38.2% for MFC4 up to 96.3% for MFC2. A single genus, Desulfuromonas, accounted for this dominance (Table 2A). Bacteria from order Desulfurobacterales (deltaproteobacteria) were also found at 23.6% on the MFC4 anode. Gammaproteobacteria were represented by bacteria from the Oceanospirillales order, in majority by the Marinobacterium genus (Table 2A).

The Rhodobacterales order (Alphaproteobacteria) was the most represented inside cathodic biofilms (from 35.2% on MFC 1 to 74.1% on MFC 4). At genus level, however, high dispersion was observed, with several genera of the Roseobacter clade (Roseovarius, Lutimaribacter, previous studies, which were not perfectly airtight. On the other hand, with a well-sealed set-up, the development of the strictly anaerobic Desulfuromonas sp. would have been favoured, which can explain the higher proportion of this genus observed here.

To sum up, in the anodic biofilms, the high abundance of Desulfuromonas sp. confirms the key role of these species, as already speculated for similar hypersaline bioanodes formed from salt marsh sediment. However, Desulfuromonas sp. has always been found in association with Marinobacter sp. so far. The absence of the latter species here very strongly reinforces the probability that Desulfuromonas sp. is the key electroactive species responsible for the high current density produced by hypersaline bioanodes.

The biofilms of the biocathodes were dominated by bacteria from the class Alphaproteobacteria, especially by bacteria from the Roseovarius clade of the Rhodobacteraceae family. This was observed in our previous work [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF] and two main and additional reasons may explain this dominance: i) the known ability of these bacteria to form biofilms rapidly (primo-adhesion) [START_REF] Dang | Cross-ocean distribution of Rhodobacterales bacteria as primary surface colonizers in temperate coastal marine waters[END_REF][START_REF] Elifantz | Rhodobacteraceae are the key members of the microbial community of the initial biofilm formed in Eastern Mediterranean coastal seawater[END_REF]; ii) the implication of these bacteria in the bio-geochemical cycle of sulfur, which enables them to thrive in media poor in organic matter [START_REF] Luo | Evolutionary ecology of the marine Roseobacter clade[END_REF]et al., 2016).

In our previous work, similar OR biocathodes were formed from salt marsh under constant polarization in 3-electrode analytical set-ups, without being coupled with bioanodes to operate in MFC configuration [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF]. In this previous study, bacteria from an unclassified order of Gammaproteobacteria, phylogenetically linked to the genus Thioalobacter, were found in great abundance (5 to 52.6%) and the biocathodes produced stable current density in the 0.25 to 0.91 A m -2 range under constant applied potential of 0.1 V/SCE. In the present work, these bacteria were only detected in a significant amount (12.5%) on the OR biocathode of the best-performing MFC (MFC1). The polarization curves (Fig. 3B) show that this biocathode produced the maximum current intensity at day 9, with a current intensity of around -0.75 mA at 0.1 V/SCE, i.e. a current density of around 0.60 A m -2 , which was consistent with the values previously reported. This confirms the previously speculated importance of these bacteria in the design of efficient hypersaline OR biocathodes.

These unclassified Gammaproteobacteria probably need a long time under polarization to develop fully. Their low representation in the less performing bioanodes of the present study, in comparison to the previous study, can be explained by the shorter duration of the polarization phase at + 0.1 V/SCE (12 days) compared to the previous study (21 days) [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF]. Actually, at the end of the 12-day formation phase at + 0.1 V/SCE, when the biocathodes were coupled to the bioanodes, their potential was immediately pulled by the bioanode towards lower values, about -0.4 V/SCE (Fig. 3C). It then took about a week for the potential of the biocathode to recover a value around + 0.1 V/SCE.

Similarly, the lower proportion of the other Gammaproteobacteria (Alteromonadales and Oceanospirillales) compared to the Alphaproteobacteria can be explained by the shorter duration of the polarization phase and the tactical advantages of the Rhodobacteraceae's ability to assemble quickly in biofilms. Several studies have pointed toward the high presence of Gammaproteobacteria in highly active OR biocathodes

Table 2

Percentage of bacterial strains identified at genus level in the (A) anodic and (B) cathodic biofilms. Only genera represented at least at 1% on at least one microbial electrode are presented. * proportions of the unclassified Gammaproteobacteria, though not taxonomically identified to the genus level, are presented for comparison.

Conclusion

For the first time, it has been shown that hypersaline MFCs equipped with OR microbial cathodes can produce power densities at the level of the highest performance reported so far for MFCs working with OR biocathodes. Desulfuromonas sp. were confirmed to be key species of the hypersaline bioanodes. The efficiency of the OR biocathodes was linked to the presence of Gammaproteobacteria (Alteromonadales and Oceanospirillales) and, especially, an unclassified order phylogenetically linked to the genus Thioalobacter. The development of these species may be favoured by maintaining polarization at + 0.1 V/SCE over a long period, typically more than 12 days. [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF][START_REF] Milner | Microbial fuel cells with highly active aerobic biocathodes[END_REF][START_REF] Quemener | Biocathodes reducing oxygen at high potential select biofilms dominated by Ectothiorhodospiraceae populations harboring a specific association of genes[END_REF][START_REF] Rothballer | Monophyletic group of unclassified gamma-Proteobacteria dominates in mixed culture biofilm of highperforming oxygen reducing biocathode[END_REF][START_REF] Strycharz-Glaven | Electrochemical investigation of a microbial solar cell reveals a nonphotosynthetic biocathode catalyst[END_REF]. Their low presence here in the biofilms, compared to the Alphaproteobacteria, may be an explanation of the lower activity of OR biocathodes in MFCs 3 and 4.

To sum up, the efficiency of the OR biocathodes seems to be linked to the presence of an unclassified order of Gammaproteobacteria, phylogenetically linked to the genus Thioalobacter. The OR bioanodes previously reported were formed in similar operating conditions but with a longer period of applied potential. They showed higher proportions of the same unclassified order of Gammaproteobacteria and produced higher current densities. The comparison with the results obtained here supports the hypothesis that increasing the abundance of this species should be the way to improve the efficiency of the OR biocathodes, and that this could be done by extending the time of the biocathode formation phase at + 0.1 V/SCE. Practically, this could be achieved either by extending the time under polarization before coupling anode and cathode, or by adjusting the value of the external resistance of the MFC in order to keep a high cell voltage (i.e. high resistance value) during the early days of MFC operation.

Fig. 1 .

 1 Fig. 1. (A) Scheme of the microbial fuel cell set-up. (B) Pictures of the MFC set-up a: carbon felt anode (rectangular) and cathode (circular); b: Cathodic compartment, with the CMI 7000 membrane at the bottom; c: upper part of the MFC, supporting the three-electrode set-up and the cathodic compartment; d: photo of whole MFC, lateral view; e: whole MFC, top view.

Fig. 2 .

 2 Fig. 2. Evolution of the power density (W m -2 ) displayed by the four MFCs during the thirteen days of operation. Plain black: MFC1; plain grey: MFC2; dashed black: MFC3; dashed grey: MFC4. Brutal changes on the curves observed on days 3, 6, 9 and 13 correspond to the power curve records.

Fig. 3 .

 3 Fig. 3. (A) Power and (B) Polarization curves recorded with MFC1 at 3, 6, 9 and 13 days. Each curve is labelled with the time in days; curves recorded on day 3 are anomalous, due to a loss of the oxygen supply between days 1 and 3; (C) Evolution of the potential of the anode and cathode of MFC1.

Fig. 4 .

 4 Fig. 4. Microbial communities at order level displayed in the electroactive biofilms of the four MFCs. (A) anodic communities; (B) cathodic communities.

Acknowledgments

This work was supported by funding from the French state, managed by the Agence Nationale de la Recherche (ANR), within the framework of the Koropokkuru project (ANR-14-CE05-0004).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Tiago R Gonc

Vineeth S Varma

Salah E Elayoubi
email: salaheddine.elayoubi@centralesupelec.fr

Performance of vehicle platooning under different V2X relaying methods

Keywords: Vehicle platoons, relay, cooperative adaptive cruise control (CACC), wireless communication

We study in this paper the design of the vehicleto-everything (V2X) network for vehicle platooning scenarios. We focus on the impact of packet relaying using Road Side Units (RSU) on the application-level performance, namely the inter-vehicle distance in the platoon. The RSU extends the coverage range of the platoon leader and allows handling the well-known Predicted Cooperative Adaptive Cruise Control (PCACC) scheme. We develop a Markov model for evaluating the performances of the different communication links (inter-vehicle and vehicle-to-RSU), using licensed or unlicensed spectrum. We then develop a cross-layer approach that adapts the application layer (PCACC control parameters) to the observed Medium Access Layer (MAC) performance. The simulations results prove that relaying allows to greatly reduce both the communication link failures and the intervehicular distances while introducing minimal delay in the system. Furthermore, even if using licensed spectrum for the relay link it drastically reduces the Packet Error Rate (PER) compared to the unlicensed spectrum case, the robustness of the application layer scheme makes the latter case viable for the considered scenario.

I. INTRODUCTION

Advanced Driver-Assistance Systems (ADAS) are intelligent systems located inside the vehicle and aiming at increasing driver's safety and comfort, reducing emissions and fuel consumption, and increasing traffic-flow. Our particular interest is in the platooning use case that takes advantage of the particular distribution of a convoy in order to increase road capacity and to decrease fuel consumption, by gathering vehicles close together in order to reduce the air resistance of the platoon's members. However, the feasibility and the deployment of platoons relies on the reliable and fast exchange of information between vehicles as it allows taking control actions based on the most up-to-date information about the road and traffic status. Nevertheless, such an exchange of information occurs over an unreliable wireless communication channel subject to inherent characteristics such as latency and packet loss.

The communication technology adopted and evaluated in this paper is the IEEE 802.11p, based on carrier-sensing multiple access with collision avoidance (CSMA/CA) protocol, and is based on the well known Bianchi model [START_REF] Bianchi | Performance Analysis of the IEEE 802.11 Distributed Coordination Function[END_REF], with extensions that allow considering specificity of platooning systems: The presence of a large number of pointto-point V2V links, coexisting with a broadcast relaying link that conveys the leader's packets to the rest of the platoon. Our model also integrates the impact of radio errors resulting from the physical layer (link performance) within the Medium Access Control (MAC) layer model (packet collision estimation).

We focus in our work on the performance metrics of the end service (inter-vehicular distance of the platoon) when evaluating communication schemes. While the baseline scheme corresponds to the use of V2V links only, we introduce the impact of relaying through Road Side Units (RSU), using licensed or unlicensed spectrum. We first propose a novel model for the channel access schemes with the presence of relaying links through the RSU. In addition to that, we introduced a dynamic control mechanism where some of the parameters of the PCACC controller are adapted based on the expected quality of the radio system. The main contributions of this paper are summarized as follows.

• Proposition of a new analytical model of the network performance, with and without relaying. These models integrate the impacts of link and system levels in both licensed and unlicensed spectrum cases. • Online adaptation of the platoon control parameters based on the observed communication link quality and on the results of the offline optimization. • Assess the performance of the platoon under the different communication solutions and subject to difficult traffic jamming environment on the road. • Compare the communication solutions from an endservice perspective (platoon efficiency), and not only from network-related Key Performance Indicators (KPI) as it is usually the case.

RELATED WORK

As of the literature on V2X communications for platooning, a considerable number of papers have proposed different approaches in order to extend the coverage range of the leader of the platoon to other platoon members. [START_REF] Marzouk | Analysis and Enhancement of Platoon Management in Smart City[END_REF] proposed an enhancement in the relay election procedure by considering that the best relay is the node from which every node in the targeted part of the destination area is able to receive. A recent evaluation on side-link relay for platooning was done by [START_REF] Fu | Performance Analysis of Sidelink Relay in SCMA-based Multicasting for Platooning in V2X[END_REF] where the authors proposed two relay schemes that use geographic location information. Inspired by Bianchi's 2-D Markov chain [START_REF] Bianchi | Performance Analysis of the IEEE 802.11 Distributed Coordination Function[END_REF], the authors in [START_REF] Jia | Improving the Uplink Performance of Drive-Thru Internet via Platoon-Based Cooperative Retransmission[END_REF] proposed a platoon-based cooperative retransmission scheme by formulating a 4-D Markov chain so that one sender can retransmit blocks for its neighbors within the same platoon in case of a previous transmission error. In opposition to these works, we do not aim at proposing new relaying schemes for platooning but use the existing relaying methods through RSU to study the performance from an end-user perspective.

Another important contribution of our paper is related to the joint communication/control approach. Several works evaluate the performance of platooning under different communication approaches. For instance, [START_REF] Vinel | Vehicle-to-vehicle communication in C-ACC/platooning scenarios[END_REF] used the 802.11p technology to evaluate the communication performance under a CACC controller in platoons. Likewise, [START_REF] Vukadinovic | 3GPP C-V2X and IEEE 802.11 p for Vehicleto-Vehicle communications in highway platooning scenarios[END_REF] has adopted both wireless technologies (802.11p and C-V2X) and compared their performances in terms of the intervehicular distance of the platoon. [START_REF] Ploeg | Graceful degradation of cooperative adaptive cruise control[END_REF] proposed a control strategy for graceful degradation based on estimating the preceding vehicle's acceleration in case of packet losses, but it mainly deals with extreme cases like complete link failure or lack of a wireless device on one of the vehicles. [START_REF] Fernandes | Platooning with IVC-enabled autonomous vehicles: Strategies to mitigate communication delays, improve safety and traffic flow[END_REF] suggested different information management algorithms including one with a dynamical control parameter where they simply suggest a lower bound value for it. Our proposed dynamic controller goes far beyond a graceful degradation framework that switches to a completely local controller or a simple performance assessment under errors, as it continuously adapts to the link quality while preserving the robustness. Note that in a previous work [START_REF] Gonc ¸alves | Vehicle platooning schemes considering V2V communications: a joint communication/control approach[END_REF], we proposed a centralized design for the controller under a simple radio model based on V2V only, while in this paper we develop a joint communication/control model that considers sophisticated radio link and system models and in the presence of RSU relaying.

II. SYSTEM MODEL

This paper considers a longitudinal platoon (blue vehicles), as depicted in Fig. 1, of size N arranged as 1 platoon leader and N -1 platoon members. The vehicles in all other lanes are not necessarily in platoons, and a jammer precedes (in red) the platoon leader. In order to stabilize the platoon under the presence of the jammer, while reducing the distance between the platoon members, the platoon leader communicates data about its acceleration and velocity to all the members of the platoon, as does also each vehicle to its following one. If packets are lost or delayed, the platoon performance may be adversely affected. Therefore, the communication links reliability is imperative to the deployment of controlled platoons. We introduce in this section a brief overview of the system with the proposed dynamic control, and the considered communication scenarios specific to platooning.

A. Platoon controller

We here describe briefly the application level control schemes for the platooning scenario. A thorough under-standing of the dynamics of the controller is indeed essential for the design of relevant communication schemes.

1) Predictive Cooperative Adaptive Cruise Control: PCACC, introduced in [START_REF] Sybis | Communication Aspects of a Modified Cooperative Adaptive Cruise Control Algorithm[END_REF], implies that the control effort, the desired acceleration, of the leader (ẍ 0 des ) and of the preceding vehicle (ẍ (i-1) des ) are available to the following vehicle and its control law is given by

a i des = ẍi des = (1 -C)ẍ (i-1) des + C ẍ0 des -(2ξ -C(ξ + ξ 2 -1))ω n ˙ i -(ξ + ξ 2 -1)ω n C( ẋi -ẋ0 ) -ω 2 n i (1)
where

i =x i -x i-1 + L i + D des (2) ˙ i = ẋi -ẋi-1 . ( 3
)
L i is the length of the vehicle and D des is the desired inter-vehicular distance that we want to minimize. The subscript i is the vehicle platoon member index where i ∈ {1, • • • , N -1} and 0 the platoon leader index.

The control parameters to be tuned are C, ξ and ω n .

The parameter C takes on values 0 ≤ C < 1 and is responsible to weigh the contribution of the leader's speed and acceleration. ξ is the controller damping ratio and ω n is the controller bandwidth. Therefore, the most up-to-date values of the (ẍ 0 des , ẋ0 , ẍ(i-1) des ) in ( 1) are subject to wireless inherent characteristics such as packet dropouts or delay.

2) Vehicle dynamics: The vehicle dynamics is modeled as first-order low pass filter due to the actuator lag. So the transfer function is modeled in the frequency-domain as

G(s) = A i (s) A i des (s) = 1 τ s + 1 ( 4
)
where τ is the time constant of the first-order low pass filter.

A i is the output, which can be interpreted as the actual vehicle acceleration, whereas A i des is the vehicle input, which can be seen as the desired acceleration. Note that, •(s) denotes the Laplace transform of the corresponding timedomain variable. The idea is to approximate the dynamics of the throttle body and vehicle inertia in order to avoid instantaneous response. In this paper, we assumed a lag of τ = 0.5 s as in [START_REF] Rajamani | Vehicle dynamics and control[END_REF] in order to be conservative.

3) Proposed adaptive control scheme based on PCACC: In contrast to existing works that assume a fixed control strategy, we adopt an approach that adapts the control parameters based on the communication link quality, as introduced in our previous work [START_REF] Gonc ¸alves | Vehicle platooning schemes considering V2V communications: a joint communication/control approach[END_REF]. In particular, among the control inputs, the parameter D des has a substantial impact on the performance of the system. As mentioned before, D des is the desired inter-vehicular distance that we want to set, but due to actuator lag and delay in the process it does not correspond to the actual average inter-vehicular distance (D avg ). Therefore, in this work we conduct an offline heuristic optimization to determine the best control parameter D des in terms of minimum average inter-vehicular distance without collisions, for any treated communication platoon scenario.

B. Communication mechanisms for platooning

In order for the platoon control to be efficient, there is a need for a reliable exchange of information between neighboring vehicles and from the leader to the rest of the platoon. We introduce in this section the communication solutions, as outlined in Fig. 1, before moving to the performance evaluation model.

1) Baseline scheme with V2V communications only: In this baseline, vehicles use direct communication links based on IEEE 802.11p on the unlicensed spectrum, and thus applying CSMA/CA mechanism. There are three types of links, as follows:

• Vehicle to neighbor links, where each vehicle conveys its acceleration and velocity to its preceding one. For a platoon of N vehicles, there are N -1 such links. We denote by (i, i + 1) the link between vehicle number i and its preceding. Following the CSMA/CA mechanism, a packet on this link is repeatedly transmitted until an ACK is received from vehicle i + 1, or the maximum number of transmissions, say m is reached. • Broadcast link, where the platoon leader communicates its information to the rest of the platoon. As there is no native broadcast link design in CSMA/CA, it cannot be expected that an ACK is received from each vehicle. The simplest scheme is to consider that the leader sends its packets to vehicle 1 (on the link (0, 1) defined above) and expect ACK for vehicle 1 only. Retransmission mechanism is thus subject to the channel quality of link (0, 1). However, this communication is overheard by other vehicles in the platoon that may be able to decode it. We call brodcast link as seen by vehicle i, link (0, i). • Outside links, that correspond to an interfering link that does not belong to the platoon. We consider M such links and model them as interference.

2) RSU relaying of the leader's packets: Looking back at the control mechanism of equation ( 1), the (i, i + 1) links convey local control information that is weighted by parameter (1 -C), while link (0, i) is responsible for carrying information of the leader, weighted by parameter C. Knowing that the distance on link (0, i) is generally large and that it is subject to larger shadowing (because of the existence of cars in-between the leader and the vehicle i), it is of utmost importance to enhance its quality. We adopt in this paper relaying through RSU as a solution for the broadcast link, where the RSU relays the packet received from the leader, in a broadcast manner to all other vehicles. We consider two flavors of relaying:

• Relaying on licensed spectrum, using cellular C-V2X technology. This scheme has the advantage of using a dedicated spectrum in a scheduled mode. There are two additional links in this case to model: The uplink (0, RSU ) on which the leader sends its packet and the downlink (RSU, i) where the RSU broadcasts the information to other cars. Again, we do not consider a feedback mechanisms on the broadcast link. • Relaying on unlicensed spectrum. In this case, the RSU is considered as an additional node using the IEEE 802.11p system; it overhears the packet sent by the leader to vehicle 1 (let (0, RSU ) be this link), and then retransmits it in a broadcast manner, without expecting feedback. Note that since the RSU acts as a broadcast relay, it does not expect or receive any feedback, and therefore, does not attempt re-transmissions even if its packets are lost. The main advantage of the RSU is having a direct line of sight link with both the leader and all the other vehicles in the platoon.

3) Link and system performance metrics: Before moving to the performance model, we identify the main performance metrics on the different links. We differentiate between link and system levels.

At link level, performance is characterized by the average Packet Error Rate (PER), i.e. the probability that a particular transmission of a packet fails, due to fast fading and to interference from other links. This PER takes two different values when the packet is conveyed alone, compared to the collision case when it is transmitted on an occupied channel. Let f 0,(k,l) and f c,(k,l) be the PER for the collision-free and collision case, respectively, for links (k, l) defined above (k and l ∈ {RSU, 0, ..., N -1}.

At system level, the main performance metric is the packet loss, that incorporates the PER on the link level, but also takes into account the m possible retransmissions and the CSMA/CA mechanism. We develop in the next section a performance model on the system level (channel access) that takes into account the link level metrics.

III. PERFORMANCE MODEL

In this section, we propose an analytical model adapted to different platooning scenarios, and integrating both link and channel access levels. We first devise a Markov chain to model the CSMA/CA protocol with retransmissions for point-to-point V2V links. Next, we introduce the broadcast link analysis and derive the probability that a given vehicle, numbered i, receives the packets of the leader. We finally extend the model to consider the relay case for both licensed and unlicensed spectrum.

A. Packet loss probability for V2V transmissions

We model the CSMA/CA channel access procedure using a discrete time Markov chain as illustrated in Fig. 2. However, different from [START_REF] Bianchi | Performance Analysis of the IEEE 802.11 Distributed Coordination Function[END_REF], we consider, in addition to losses due to collisions between packets, losses that are due to imperfections on the radio channel.

1) Vehicle to neighbor channel: At each transmission attempt, and focusing on link (k, l), the packet is correctly decoded with probability

α (k,l) = (1 -p c ) • (1 -f 0,(k,l) ) + p c • (1 -f c,(k,l) ) (5)
p c is the probability that the channel is busy during a slot (collision probability), f 0 is the probability of loss without collision, and f c is the probability of loss with collision, introduced previously and computed in the numerical applications by an SINR (Signal to Interference and Noise Ratio) computation on a vehicular channel. We drop in the following the link identification (k, l) for convenience, except when needed.

In order to capture, the bursty nature of the traffic where small packets are generated by each vehicle following a Poisson process of intensity λ, we have included one inactive state in the Markov chain to model the probability to remains idle on a slot, taken equal to a packet duration T . This is given by 1 -q = e -λT .

To calculate the loss, we have to calculate the stationary probabilities of the states. Define the Backoff Timer (BOT) as a randomly number chosen in the range (0, W 0 -1) where W 0 is the contention window (CS) size 1 . Next, define the Backoff Stage (BOS), as the stage attempt to transmit the packet. Let s(t) be the stochastic process representing the BOS {0, . . . , m -1} and Π(t) representing BOT at time t. Let Π i,j = lim t→∞ P {s(t) = i, Π(t) = j}, i ∈ {0, m -1}, j ∈ {0, W 0 -1} be the stationary distribution of the chain. Therefore, we can recursively calculate the probability of states for the first backoff stage i = 0 and for any timer 2 ≤ j ≤ W 0 -1, given by

Π 0,W0-j = jq (1 -p c )W 0 Π idle . (6)
Now, evaluating the last state (timer expiration j = W 0 ) for the first stage (i = 0), we have the following

Π 0,0 = q W 0 Π idle + (1 -p c )Π 0,1 = qΠ idle . (7)
Therefore, taking into account the probability of success transmission introduced in (5), we can define the following

Π 1,W0-1 = (1 -α) W 0 Π 0,0 +p c Π 1,W0-1 = (1 -α)q (1 -p c )W 0 Π idle . (8
) Recursively, we can calculate the last states probabilities (j = W 0 ) for any backoff stage as

Π i,0 = (1 -α) i qΠ idle , (9)
for 0 < i < m -1. Therefore, the probability of stationary states are given by

Π i,W0-j = jq(1 -α) i (1 -p c )W 0 Π idle , (10)
for 0 ≤ i ≤ m -1 and 1 ≤ j ≤ W 0 -1. Moreover, the steady-state probabilities must satisfy the normalization condition Π Π = 1, that yields The probability that a packet is lost on link (i, i + 1), despite the m possible retransmissions, is then computed by:

Π idle = 1 + q(1 -(1 -α) m ) α • 1 + W 0 -1 2(1 -p c ) -1 . (11)
L i,i+1 = (1 -α (i,i+1) ) m , i ∈ {0, ..., N -1} (12)
The loss depends on the probability of finding the channel occupied during a slot, that we will compute using a fixed point approach.

2) Fixed point analysis: The probability of a slot being busy is computed as the probability that at least one of the competing transmitters is active. We consider N vehicles within the platoon, and M external vehicles that do not belong to the platoon but generate nevertheless packets. We can thus compute the probability of busy channel as

p c = 1 -(1 -τ p ) N -1 (1 -τ e ) M , (13)
where τ p (rep. τ e ) is the probability of trying to transmit for platoon and external vehicles, respectively. Such a probability of transmission can be computed by:

τ = m-1 i=0 Π i,0 = (1 -(1 -α) m ) α qΠ idle . ( 14
)
With the adequate link decoding probability α. For platoon vehicles, α (i,i+1) is used, while for external vehicles, the same Markov model as in section III-A1 can be used, replacing the PER in equation ( 5) by the PER corresponding to a typical distance on a non-platoon link.

The channel occupation probability p c can thus be obtained using a fixed point analysis that solves the set of equations [START_REF] Rajamani | Vehicle dynamics and control[END_REF]13,14).

3) Broadcast links: We make use of the previously developed Markov chain and focus on link (0, 1) that models the medium access between leader-follower. The key point here is that, for other platoon members numbered i ∈ {2, • • • , N -1}, this link is seen as a broadcast link where each packet is repeated n ≤ m times, until the packet is correctly received by vehicle 1. The number of transmissions on the leader-follower link is modeled using the modified Markov chain of Fig. 3, where two absorbing states are added, modeling the loss and success of a packet, starting from the idle state. Here, each stage is contracted within one aggregate state for visibility. The probability of transmitting more than n times is the hitting probability of stage number n starting from the idle state. The probability of transmitting a packet exactly n times is thus computed by: P n = α (0,1) (1 -α (0,1) ) n-1 for n < m (1 -α (0,1) ) m-1 for n = m (15)

Fig. 1 .

 1 Fig. 1. Disposal of a platoon with V2V and V2I communication technologies.

Fig. 2 .

 2 Fig. 2. Proposed Markov chain for baseline scheme.

Fig. 3 .

 3 Fig. 3. Proposed Markov chain for leader-follower link.

The CW may change from one stage to another, but we adopt here, without loss of generality, a constant CW, as advocated for delay-sensitive services using LBT cat3.

The second component is the probability for the leader message to fail at the receiver vehicle i, considering packet collisions and path loss, as in equation [START_REF] Vinel | Vehicle-to-vehicle communication in C-ACC/platooning scenarios[END_REF].

Therefore, considering broadcast link (0, i), its probability of loss is:

(16)

B. Performance with relaying

In this section, we extend the Markov model to the relaying case through the RSU for the packets of the platoon leader. Whenever the leader sends a packet to its platoon, this packet can be also received by the RSU closest to the leader, which then relays the packet as a broadcast. A loss for a broadcast link occurs here only if both the direct (0, i) link and the relaying link fail, increasing the robustness of the system. The relaying link is composed of two links (0, RSU ) and (RSU, i) as shown in Fig. 1. In other words, the final probability of loss between the leader and the vehicle i in the presence of RSU is

where L (0,i) the loss of the direct link, computed in equation (16). L relay,i is the probability of loss of the relay link, corresponding to a loss on either the uplink or downlink, as shown in Fig. 1:

The loss on the relay link depends on the considered relaying technology and the associated spectrum. 1) Licensed spectrum: When using licensed spectrum, the relay link is collision exempt, as transmissions are scheduled. Therefore in (18), L (0,RSU ) = f 0,(0,RSU ) and L (RSU,i) = f 0,(RSU,i) are the probabilities of losses with no collision.

2) Unlicensed spectrum: The RSU in this case is a node like the others in the IEEE 802.11p system. The RSU here overhears the transmission on the leader-follower link, and its probability of loss is computed as in equation ( 16), taking into consideration in α (0,RSU ) the PER on the uplink of the relay.

If the packet is correctly decoded, the RSU is able to broadcast it, only once, to the other platoon members. We model this downlink of the relay by a one-stage Markov chain (like the one in Fig. 2, with m = 1). The loss on the downlink is thus given by

IV. CONTROL AND COMMUNICATION PLATOONING INTERACTION AND PERFORMANCE The objective of this section is to detail the interaction between the proposed dynamic controller and the communication schemes in a realistic platooning simulation.

A. Simulation environment description

An overview of the system diagram is given in Fig. 4. We used the Simulink environment to model the vehicle dynamics and to implement the control law. Therefore, it is responsible to give periodic snapshots of the positions of the platoon vehicles to the communication simulator. As shown in Fig. 4, the communication framework is implemented in Matlab and some main functions are highlighted at the bottom. The first one ("Interference computation") is responsible for computing the SNR (Signal to Noise Radio) and the SINR on the different links (V2V, downlink and uplink relay), provided that the packet is transmitted alone or is subject to collision with another packet. The second function ("Link performance") calculates the average PER for a given link quality (SNR/SINR) using the WLAN Toolbox of MATLAB, for the communication parameters of Table I. Lastly, the channel access to the medium is implemented as modeled using the Markovian model presented above, considering that vehicles broadcast a 500 bytes message each 100 ms in average. Note that for the link (0, i), we assume a shadowing that increases linearly with the number of vehicles in the platoon (1 dB per intermediate vehicle). As for the CSMA parameters, we have adopted W 0 = 30 and m = 1 as the content window size and the retransmission attempts, respectively. Furthermore, for the relays schemes, we have implemented one RSU each one kilometer.

As for the control parameters, the platoon members are equipped with the proposed dynamic controller. The control strategy demands relative position and longitudinal velocity of the preceding vehicle so we assumed that the measurements are sampled each 60 ms with 1 ms delay and done by a long-range radar. Simulations are performed with a platoon size of N = 21, with a jammer profile as the pattern from [START_REF] Vukadinovic | 3GPP C-V2X and IEEE 802.11 p for Vehicleto-Vehicle communications in highway platooning scenarios[END_REF], but repeated 50 times. The maximum road traffic density that generates external interference to the platoon was considered as M = 100 cars/km/lane.

B. Communication system performance

We next describe the performance from a communication perspective. We present a communication radio link com- parison as shown in Fig. 5, where we present the average probability of loss between the leader and the last vehicle (i = 20) over time for each communication approaches considered. We observe from Fig. 5 that V2V scenario without any V2I results in a very high loss rate when compared to the relaying schemes. This loss is expected to be even higher for larger platoons. With relaying, a better quality link is observed when using licensed spectrum, due to the scheduled mode. Having a closer look on the curves of Fig. 5, the baseline performance fluctuates due to the change of the number of interfering vehicles with time. However, relay schemes exhibit a more cyclic behavior due to the evolution of the relative distance of the platoon with respect to the closest RSU from the leader vehicle (recall that a RSU is deployed every 1 Km). Our objective to ensure that the worst packet error rate in the platoon is below 0.25 in the presence of RSUs, which has been observed to result in a smooth control performance and spacing of the platoon. The impact of the RSU density in this scenario is indeed an interesting avenue for future research.

C. Optimization of the average inter-vehicular spacing

We now move to the evaluation of the robustness of our dynamic control scheme under different communication links. To this aim, we have adopted the inter-vehicle distance as the end-service performance metric. We apply the zeroorder hold mechanism as the holding strategy for the control signal during the periods of packet losses. In all simulations, we focus on minimizing the inter-vehicular distance in respect with a fixed value of all the other control parameters while ensuring that zero vehicles collisions occur. Note that we implemented a safety gap distance of 1 m for the emergency braking actuation to avoid collisions in practical settings.

We present the Table II the average inter-vehicular distance for 20 vehicle platoon over all communication approaches considered. Clearly, the V2V only scheme imposes higher inter-vehicular distances in the platoon, as much as 36 m, when compared to relayed schemes where the average distance is around 1.7 m. We also illustrate in Table II the minimum observed distance during the simulation (that must not go below 1 m to avoid emergency breaking) and the maximal observed distance on the platoon.

When looking at the platoon performance with relaying, very similar performance are observed for both relay schemes with values around 6% different, despite the large difference in the loss rates, as recalled in the last rows of Table II. This similarity raises a question related to the necessity or not of licensed spectrum for platooning systems. This fact can be explained by the robustness of the dynamic PCACC control for a sampling rate of 100 ms under moderate packet losses.

V. CONCLUDING REMARKS

In this paper, we have investigated the interplay between vehicular communication approaches and platooning performance. We proposed an analytical model to compute the probability of packet loss in a platoon with and without a relay support through RSU. An offline optimization of the control parameters is then presented based on the computed loss rate. Extensive numerical simulations show that the relaying scheme with licensed spectrum result in the lowest packet loss rate among the considered communication schemes. However, the best compromise between network cost (economic) and platoon performance is achieved when the RSUs are used over an unlicensed spectrum, due to the robustness of the proposed control scheme under sufficiently small communication errors.