Aude Jary
email: aude.jary@aphp.fr

Valentin Leducq

Romain Palich

Adélie Gothland

Diane Descamps

Véronique Joly

Sidonie Lambert-Niclot

Corinne Amiel

Ana Canestri

Audrey Mirand

Elyanne Gault

Astrid Vabret

Marc-Antoine Valantin

Christine Katlama

Vincent Calvez

Nicolas Dupin

Jean-Philippe Spano

Anne-Geneviève Marcelin
email: anne-genevieve.marcelin@aphp.fr

Usefulness of Kaposi's Sarcoma-Associated Herpesvirus (KSHV) DNA Viral Load in Whole Blood for Diagnosis and Monitoring of KSHV-Associated Diseases

Keywords: Kaposi sarcoma-associated herpesvirus, Kaposi sarcoma-associated diseases, DNA, whole blood, biomarker

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Kaposi Sarcoma-Associated Herpesvirus (KSHV) is the etiologic agent of Kaposi's sarcoma (KS) but also in multicentric variant of Castleman disease (MCD) and primary effusion lymphoma (PEL), diseases occurring primarily in HIV-infected patients [START_REF] Gonçalves | HIV-associated Kaposi sarcoma and related diseases[END_REF]. Several studies have demonstrated that KSHV-DNA levels and virus state in the target cells differ according to the pathology and its severity [START_REF] Asahi-Ozaki | Quantitative analysis of Kaposi sarcoma-associated herpesvirus (KSHV) in KSHV-associated diseases[END_REF]. In peripheral blood from patients with active disease, KSHV-DNA levels were higher from patients with MCD, followed by patients with PEL and then from patients with KS (3). Moreover, variations of KSHV-DNA rates in blood were associated with progression or regression under successful treatment of KS (4, 5) and MCD [START_REF] Marcelin | Rituximab therapy for HIV-associated Castleman disease[END_REF]. However recently, Haq and colleagues demonstrated that KSHV plasma levels has a very limited value, the only potential role was the suggestion that an undetectable plasma KSHV exclude a diagnosis of MCD [START_REF] Haq | The clinical application of plasma Kaposi sarcoma herpesvirus viral load as a tumour biomarker: results from 704 patients[END_REF].

This retrospective transversal study included 149 patients with KS (111), MCD (32) and PEL [START_REF] Marcelin | Rituximab therapy for HIV-associated Castleman disease[END_REF]. One whole blood sample per patient and 4 total effusion fluids from PEL patients were analyzed and obtained at the time of diagnosis. Extracted DNA were amplified by real-time PCR which focus on both ORF73 and albumin genes [START_REF] Lallemand | Quantitative analysis of human herpesvirus 8 viral load using a real-time PCR assay[END_REF]. Quantification was expressed in copies/million cells. GraphPad software was used to perform non-parametric tests: the Mann-Whitney U, Spearman rank and Kruskal-Wallis tests.

Patients' characteristics and results are shown in table 1. KSHV-DNA viral load was undetectable in 22% of studied cases or detectable with low levels in KS patients while it was always detectable in MCD and PEL patients. The three KSHV-associated diseases were associated with significantly different levels of KSHV-DNA in whole blood (p<0.0001)(Fig. 1). KSHV-DNA rates from MCD's patients were the highest (median, 3.94 log10 copies/10 6 cells [range, 1.00-7.00]), followed by PEL patients (3.46 log10 copies/10 6 cells [2.23-4.83]) and finally KS patients (1.92 log10 copies/10 6 cells [1.00-5.60]). In patients with KS and MCD, KSHV-DNA levels and CD4 count cells were negatively correlated (respectively rs=-0.25; p=0.02 and rs=-0.43; p=0.02) confirming the role of immunosuppression in KSHV-diseases development [START_REF] Tedeschi | Viral load of human herpesvirus 8 in peripheral blood of human immunodeficiency virus-infected patients with Kaposi's sarcoma[END_REF]. Among PEL patients, KSHV-DNA levels in total effusion fluids were approximately 100 to 1000 times higher than in blood which is in agreement with the physiopathology of the disease but also linked to the high number of KSHV copies per lymphoma cell [START_REF] Asahi-Ozaki | Quantitative analysis of Kaposi sarcoma-associated herpesvirus (KSHV) in KSHV-associated diseases[END_REF]. KSHV-DNA levels in whole blood of patients with MCD were significantly higher than those of KS patients (p<0.0001) in contrast to Haq and al results in plasma. We know that KSHV virus remain mostly latent in KS and PEL whereas in MCD, about 15% of them are in lytic cycle [START_REF] Asahi-Ozaki | Quantitative analysis of Kaposi sarcoma-associated herpesvirus (KSHV) in KSHV-associated diseases[END_REF]. Moreover, latent and replicating viral KSHV-DNA are presents in cells [START_REF] Purushothaman | KSHV Genome Replication and Maintenance[END_REF]. Thus, plasma samples might underestimate KSHV-DNA levels in peripheral blood compartment in comparison to whole blood, especially in MCD's patients.

Our study reinforce that KSHV-DNA biomarker would be helpful to guide diagnosis of and manage KSHV-associated diseases. Even if for KS diagnosis, KSHV-DNA quantification should be interpreted with cautious (about a quarter undetectable), increase levels in blood should trigger KS disease progression or other underlying KSHV-malignancies.

Acknowledgments

We thank the CANCERVIH network and especially Marianne Veyri for her administrative support.

Potential conflicts of interest: No conflicts of interest


B Pérot

C Carasco

C Deyglun

G Sannié

J Gameiro

G Corre

K Boudergui

V Konzdrasovs

Detection Of Special Nuclear Materials with Tagged Neutrons

Keywords: Tagged neutron inspection system, associated particle technique, Special Nuclear Materials, MCNP PoliMi

The Associated Particle Technique can be used to detect Special Nuclear Materials in cargo containers. A DT neutron generator, based on the 3 H( 2 H,n)α fusion reaction, produces 14 MeV neutrons which are tagged both in direction and time with an embedded alpha detector. Prompt neutrons and gamma rays emitted during neutron-induced fissions are detected by plastic scintillators, in coincidence with the alpha particle. Nuclear materials are differentiated from non-nuclear materials and crosstalk events using high multiplicity coincidences. The data acquisition electronics is made of compact FPGA boards. Experiments performed with a mock-up of the measurement system are reported and compared to calculations to validate numerical simulation and post-processing tools. Measurements have been performed with different targets made of iron, lead, or uranium, placed in metallic or wood cargo matrixes. The performances of a full scale cargo container inspection system, with a larger number of detectors optimized for container inspection, are also studied by numerical simulation. Random background due to neutrons which are not correlated with an alpha particle, counting statistics, time and energy resolutions of the data acquisition system, are all taken into account to model the measurements as realistically as possible. A wide range of inspections, with the suspicious item in different positions in different cargo matrixes, have been studied.

I. INTRODUCTION

Among existing nondestructive interrogation technologies for detecting Special Nuclear Materials (SNM), only fast neutrons or high-energy X rays produced by an electron LINAC are sufficiently penetrating to perform in-depth inspection of cargo containers and to induce a clear fission signal [START_REF] Belichenko | Tagged Neutrons from Portable Neutron Generator for Detection of High Explosives and Fissile Materials in Cargo Containers[END_REF]- [START_REF] Stevenson | Linac based photofission inspection system employing novel detection concepts[END_REF]. The possibility to detect SNM hidden in cargo containers with fission prompt neutrons and gamma rays induced by 14 MeV tagged neutrons is investigated in this paper. A primary X-ray scan is used to identify a suspect region of interest in the cargo container, namely with a high density in case of SNM, before neutron interrogation is performed as second-level inspection. A DT fusion neutron generator with an embedded position sensitive alpha detector is used to perform the so-called "Associated Particle Technique" (APT). The alpha particle produced in the 2 H+ 3 H→α+n fusion reaction allows G. Sannié, J. Gameiro, G. Corre, K. Boudergui and V. Konzdrasovs are with CEA, DRT, LIST, Saclay, F-91191 Gif-Sur-Yvette, France.

determining the direction of the produced 14 MeV neutron, which is emitted almost in the opposite side. Time-of-flight (TOF) is measured between the detection of the alpha particle and any fission particle induced by a 14 MeV neutron. The APT has already been implemented to detect explosives, illicit drugs, contraband materials, or chemical warfare in cargo containers, or unattended luggage, thanks to spectroscopic analysis of inelastic scattering gamma rays induced by 14 MeV tagged neutrons [START_REF] Carasco | In-field test of the EURITRACK tagged neutron inspection system[END_REF], [START_REF] Pérot | Acquisition of neutron-induced gamma signatures of chemical agents[END_REF]. For SNM detection, instead of using spectroscopic analysis, coincidences between the alpha particle and prompt fission particles induced by 14 MeV tagged neutrons are detected in plastic scintillators. TOF is also used to differentiate fission gamma rays from fission neutrons, as shown in further Fig. 6 and7. A schematic drawing of a cargo container inspection system employing tagged neutrons to detect SNM is presented in Fig. 1. The coincidences between fission particles are detected in plastic scintillation detectors surrounding the container. A shield, made of polyethylene and lead, limits the count rate created by non-tagged neutrons in the scintillation detectors near the neutron generator.

II. SIMULATION TOOLS VALIDATION

The detection of explosives and other illicit materials with the APT has been simulated with MCNP dedicated data processing tools [START_REF] Carasco | MCNP output data analysis with ROOT (MODAR)[END_REF], which allowed to model realistic measurements taking into account the counting statistics, random background, time and energy resolutions of the data acquisition system. Since the application of APT for the detection of SNM involves multiple coincidence analysis, MCNPX PoliMi [START_REF] Padovani | MCNPX-PoliMi User's Manual[END_REF] has been used here to track the events taking place in the plastic scintillators for each tagged neutron history, and specific post-processing tools have been developed with ROOT framework [START_REF] Brun | ROOT -An object oriented data analysis framework[END_REF] to take into account the random background and to implement cross-talk rejection algorithms [START_REF] Deyglun | Monte Carlo parametric studies of neutron interrogation with the Associated Particle Technique for cargo container inspections[END_REF]. To test the validity of the simulation tools, the experimental setup shown in Fig. 2 has been built at CEA DEN Cadarache. The setup reproduces, at a smaller scale, the inspection of a cargo container with a SODERN associated particle neutron generator and an acquisition electronic specially developed for this application by CEA LIST Saclay. Uranium, lead or iron targets can be placed at different depths in 0.2 g.cm -3 matrices (e.g. iron, wood, fabrics…) simulating the load of a cargo container, this density being an average value of commonly transported freights [11] [12]. Fig. 3 to 6 shows TOF spectra measured in 10 min with a total neutron generator emission of 2 . 10 7 n/s, first without a target, or a matrix in the container, just to record the active background of the inspection system and of DANAIDES irradiation room (Fig. 3), and then with iron (Fig. 4), lead (Fig. 5) and uranium (Fig. 6) targets in the empty container. The comparison between experiment and calculation shows that simulation correctly describes the measurement, both quantitatively (pulse-height and counting statistics) and qualitatively (position and width of peaks and bumps in the time-of-flight spectrum). Further tests will be conducted with the same targets hidden in iron or wood cargo matrices. However, as the demonstration system only includes 8 detectors, counting statistics is very limited for high multiplicity coincidences. Therefore, simulation was used in next paragraph to estimate the performances of a full-scale inspection system with much more detectors.

III. SIMULATED PERFORMANCES FOR REAL CONTAINERS

The system proposed in Fig. 1 includes three large panels of plastic scintillators (300 cm length, 240 cm sides, and 10 cm thickness), each panel being made of 48 individual 37.5 cm × 40 cm × 10 cm detectors [START_REF] Deyglun | Monte Carlo parametric studies of neutron interrogation with the Associated Particle Technique for cargo container inspections[END_REF]. As indicated in this reference, a hole in the panel facing the neutron generator allows limiting the detection of tagged neutrons which did not interact in the container. Fig. 4 shows TOF spectra obtained in 10 min inspections with a total neutron emission of 10 8 n/s, which is the nominal emission of a new generation APT neutron generator recently developed by SODERN. A cube filled with a small sample (a few kg) of metallic Highly Enriched Uranium (HEU), and/or shielding materials (lead, cadmium), have been interrogated in the center of a cargo container loaded with a 0.2 g.cm -2 iron matrix. The TOF spectrum corresponding to the detection of three gamma rays or neutrons, in coincidence with the alpha particle, indicates that HEU can be detected and clearly discriminated from non-nuclear materials, the fission neutron signal being quite independent on metallic shields surrounding HEU.

Figure 7: Simulation of a 10 min inspection of a container loaded with a 0.2 g.cm -3 iron cargo, in which a small sample (a few kg) of HEU has been modeled in the middle of the container (unshielded: pink spectrum; shielded by lead and cadmium: red spectrum), or with the shield alone in the middle of the container (blue spectrum), or just with the iron matrix (green spectrum).

The number of three-fold coincidences, i.e. one alpha particle in coincidence with three neutrons or gamma rays in the fission neutron time window (between ~70 ns and 150 ns), can be used to detect the presence of uranium. SNM presence can be assessed by the ratio of three-fold coincidences in the voxel of interest and in another region of the matrix which is free of suspicious item, previously determined with the X-ray scan (like the voxel of interest). The denominator of the ratio (matrix signal) thus constitutes the reference signal of the matrix, without nuclear material. A large number of simulations have been performed to calculate this ratio for any (y,z) position of the shielded HEU sample inside the cargo container (Fig. 8), and for same-size iron (Fig. 9) or lead (Fig. 10) samples. The x coordinate corresponds to the lengthwise direction of the cargo container (and truck); y corresponds to the transversal depth inside the cargo, i.e. along the tagged neutron beam, and z is height. The borders of the fission neutron time window have been adapted to each (y,z) position [START_REF] Deyglun | Monte Carlo parametric studies of neutron interrogation with the Associated Particle Technique for cargo container inspections[END_REF], according to neutron TOF. The evolution of the three-fold coincidence ratio between the item (HEU, iron, or lead) and the bare iron matrix can be explained by a combination of different effects. For instance, when HEU is close to the detection panels, i.e. when y tends to -110 cm or +110 cm, fission neutrons can be detected inside the same plastic scintillator, or in neighboring detectors. In such instances, only one particle is counted by cross-talk rejection algorithms [START_REF] Deyglun | Monte Carlo parametric studies of neutron interrogation with the Associated Particle Technique for cargo container inspections[END_REF], and the coincidence multiplicity is therefore underestimated. Therefore, the coincidence ratio decreases, but one can note that it remains larger than 1.5 regardless of (y,z) position. On the other hand, when an iron or lead block is close to the neutron generator, i.e. when y tends to -115 cm, most of the counts are due to backscattered neutrons and inelastic scattering gamma rays produced in these dense items. As such pulses have a shorter TOF than the beginning of the inspection time window, which is optimized for induced fission neutrons, triple coincidences are fewer than in the case of the bare iron matrix, and the ratio drops below unity. When moving away from the generator (i.e. when y increases), the ratio is growing but it does not exceed 1.5, whether for the iron or lead blocks. In conclusion, Fig. 8 to 10 show that a threshold near 1.5 on the three-fold coincidence ratio could be used to trigger an alarm for SNM presence, without triggering false alarms due to benign materials. Further calculations are underway to test this approach in wood and ceramic cargo matrices.

IV. CONCLUSION AND PROSPECTS Experiment vs. calculation comparisons have shown that numerical simulation with MCNPX PoliMi, and data postprocessing tools developed with ROOT, can be used to reproduce coincidences due to fissions or other reactions induced by tagged neutrons. In addition, calculations with iron, lead, or uranium blocks, located in any position inside a cargo container filled with a metallic matrix, have shown that three-fold coincidences, i.e. at least three induced particles in the time window triggered by the detection of the alpha particle associated to a tagged neutron, can be used to evidence the presence of uranium unambiguously. Practically, the ratio of three-fold coincidences in the time window of fission neutrons (useful signal) and those corresponding to a region of the container free of suspicious item (matrix signal) can be used to trigger an alarm. Further experiments and calculations are underway with other cargo matrices, like wood and ceramics.

Figure 1 :

 1 Figure 1: Concept design of a cargo container tagged neutron inspection system, in which the tagged beam is aiming at a suspicious item previously identified by an X-ray scanner.

Figure 2 :

 2 Figure 2: Experimental setup for the inspection of a container loaded with a 0.2 g.cm -3 wood matrix. The plastic scintillators are shielded from the generator by 45 cm polyethylene and 5 cm lead shields.

Figure 3 :

 3 Figure 3: Time-of-flight background spectra without target and matrix in the container, when a alpha particle is detected in coincidence with one, two or three gamma rays or neutrons.

Figure 4 :

 4 Figure 4: Time-of-flight spectra with an iron target (~2.5 kg) inside the empty container, when an alpha particle is detected in coincidence with one, two or three gamma rays or neutrons.

Figure 5 :

 5 Figure 5: Time-of-flight spectra with a lead target (~5 kg) inside the empty container, when an alpha particle is detected in coincidence with one, two or three gamma rays or neutrons.

Figure 6 :

 6 Figure 6: Time-of-flight spectra with a natural uranium target (a few kg of metallic uranium with 8 mm of lead shield around) inside the empty container, when an alpha particle is detected in coincidence with one, two or three gamma rays or neutrons.

Figure 8 :

 8 Figure 8: Ratio between three-fold coincidences in the fission neutron time window, for the shielded HEU item and for the bare 0.2 g/cm 3 metallic matrix, respectively, as a function of (y,z) coordinates of the suspicious item in the cargo container. The depth y = -110 cm corresponds to the first wall of the container (close to the neutron generator, red point on the 2D projection), and y = + 110 cm corresponds to the second wall.

Figure 9 :

 9 Figure 9: Ratio between three-fold coincidences of an iron block and of the bare 0.2 g/cm 3 metallic matrix, as a function of the (y,z) coordinates of the suspicious item in the cargo container.

Figure 10 :

 10 Figure 10: Ratio between three-fold coincidences of a lead block and of the bare 0.2 g/cm 3 metallic matrix, as a function of the (y,z) coordinates of the suspicious item in the cargo container.


Timothé Godou

Clément Chauvier

Pierre Thuéry

Thibault Cantat
email: author.thibault.cantat@cea.fr

Iron-Catalyzed Silylation of Alcohols by Transfer Hydrosilylation with Silyl Formates

Keywords: silylation, iron catalysis, silanes, alcohols, silyl formates, hydrosilanes, CO2

An iron catalyst is shown for the first time to promote transfer hydrosilylation with silyl formates and utilized for the silylation of alcohols. Attractive features of this protocol include the use of an earth abundant transition metal catalyst, mild reaction conditions and the release of gases as the only by-products (H2 and CO2).

Transfer hydrosilylation consists in the formal transfer of a hydrosilane moiety (R3SiH) from a silicon reagent (that bears no Si-H bond) to a substrate. 1 It is reminiscent of transfer hydrogenation where a hydrogen donor (e.g. isopropanol or formic acid) is used to deliver an equivalent of H2 to reduce a C=C or C=O unsaturation. 2 Although the two methodologies are conceptually related, examples of transfer hydrosilylation reactions have only been disclosed recently with the use of silicon-substituted cyclohexa-1,4-dienes (1,4-CHDN) (1) able to deliver a hydrosilane equivalent with the concomitant release of a benzene derivative (Scheme 1). While Studer et al. explored the radical activation of these hydrosilane surrogates in the transfer hydrosilylation of alkenes, [3][4] Oestreich et al. exploited their ionic activation for the transfer hydrosilylation of carbonyls, alcohols and alkenes. [5][6][7] In 2016, Cantat et al. introduced a new type of hydrosilane surrogates with the use of silyl formates (2, R3SiOCHO). 8 While silyl formates are usually observed as products or intermediates in the hydrosilylation of CO2, [9][10][11][12] the reversible decarboxylation reaction was used to promote the transfer hydrosilylation of aldehydes (Scheme 1). 8 An advantage of this methodology is the release of a gaseous by-product, CO2. Earlier this year, the same methodology was employed to convert alcohols to silyl ethers by dehydrogenative and decarboxylative silylation of O-H bonds with silyl formates. 13 Importantly, the use of silyl formates in transfer hydrosilylation reactions requires a catalyst able to promote the decarboxylation of a formate ligand and the silylation of alcohols and aldehydes could only be achieved using molecular ruthenium(II) complexes. 8,13 Further utilization of silyl formates would strongly benefit from the use of noble metal free catalysts and, herein, we show for the first time that iron complexes, supported by a chelating tetraphosphine ligand, enable the silylation of alcohols by transfer hydrosilylation with silyl formates. An improved synthesis of silyl formates is also reported. Although silyl formates can be obtained by catalytic hydrosilylation of CO2, [9][10][11][12] a more convenient access to these reagents relies on the silylation of formic acid with chlorosilanes, in the presence of an organic base, such as pyridine or triethylamine. 8 Nevertheless, using this synthesis, free formic acid can be found as an impurity in samples of silyl formates and the presence of such protic species influences the catalytic activity in transfer hydrosilylation. The bulky triisopropylsilyl (TIPS) formate (2d) could not be obtained in satisfactorily yields and its synthesis was performed with formic acid, in the presence of triethylamine. 8 Diethyldichlorosilane was efficiently converted to the bisformate derivative 2g (94 % yield) upon reaction with an excess sodium formate (Scheme 2). Silyl formates 2b and to a lesser extent 2a were found to be sensitive towards moisture as they are readily hydrolyzed to formic acid and silanols and siloxanes.

Scheme 2 Synthesis of silyl formates by salt metathesis from sodium formates and chlorosilanes. [a] The reaction was performed at 60 °C Having in hand a collection of silyl formates bearing different alkyl and aryl substituents on the silicon center, their reactivity was evaluated in the dehydrogenative and decarboxylative silylation of alcohols. Silyl ethers are useful chemicals, utilized for the temporary protection of alcohols but also for the synthesis of hybrid materials. [START_REF] Wuts | Greene's Protective Groups in Organic Synthesis[END_REF][START_REF] Huang | [END_REF][16] Two main routes have been developed to access the O-Si linkage. They rely either on chlorosilanes or hydrosilanes. While the reaction between an alcohol and a chlorosilane, in the presence of a base, generates a stoichiometric amount of a salt, [17][18][19] the catalytic dehydrogenative silylation with hydrosilanes has the advantage of releasing H2 gas as the only byproduct. [20][21][22][23][24][25][26] This year, our group showed that replacing the hydrosilane reagent with a silyl formate enabled the silylation of a variety of alcohols when a catalytic amount of (triphos)Ru(OAc)2 (triphos = 1,1,1tris(diphenylphosphinomethyl)ethane) was added. 13 The role of the Ru catalyst is to promote the decarboxylation of a formate ligand to yield a reactive hydride complex. Based on this assumption, we turned our attention to iron catalysts that are able to engage in similar reaction paths. In particular, Laurenczy, Ludwig, Beller et al. demonstrated the potential of iron(II) salts supported by the tetraphosphine ligand P(C2H4PPh2)3 (4, also labelled PP3) in the catalytic decomposition of formic acid to H2 and CO2. 27 Capitalizing on this work, the silylation of benzylalcohol (3) was attempted with Et3SiOCHO (2a), in the presence of 2 mol% Fe(OAc)2 and 2 mol% 4. After 5 h at 70 °C in CH2Cl2, the reagents were fully consumed and 5a was formed in quantitative yield (Scheme 3) [only 5 % 5a were observed in the absence of catalyst]. 1 H and 13 C NMR spectra of the crude mixture also confirmed the production of H2 and CO2 as by-products of the reaction. The formation of silyl ether 5a from 2a represents the second example of a dehydrogenative silylation of alcohols using silyl formates as surrogates of hydrosilanes. 13 Although the reaction proceeded well in THF, acetonitrile or benzene, the best conversions were noticed in CH2Cl2 (see SI). Decreasing the reaction temperature to 25 °C also enabled formation of 5a in 98 % yield, after a prolonged reaction time of 21 h. In comparison, (triphos)Ru(OAc)2 was found inactive at room temperature (RT). 13 Scheme 3 Iron-catalyzed silylation of benzylalcohol (3) to 5a with triethylsilyl formate (2a).

The influence of the silyl group was then investigated on the influence of the transfer hydrosilylation reaction. All the silyl formates 2a-2g successfully reacted with 3 to yield silyl ethers 5a-5g, respectively (Scheme 4). Full conversion of 3 was reached within 3 h at 70 °C for the trimethylsilyl derivative 2b while the production of the more congested congeners 5c and 5d, bearing TBDMS and TIPS groups, were limited to 56 and 34 % yield, respectively, after 5 h. Interestingly, the aryl-substituted silyl formates 2e and 2f and the bis-formate 2g were fully converted to the corresponding silyl ethers 5e-5g within only 1.5 h, presumably because of the enhanced acidic character at the silicon center. The potential of the Fe(OAc)2/4 catalytic system was further evaluated in the dehydrogenative and decarboxylative silylation of alcohols having various functional groups (Scheme 5). Aliphatic as well as aromatic alcohols were successfully silylated with Et3SiOCHO (2a), as exemplified with the formation of 6a-15a (from primary and secondary alcohols) and 17a-18a (from phenols). Primary and secondary alcohols exhibit similar reactivities as attested with the formation of silyl ethers 8a and 13a in >96 % yields from 2-phenyl-1-propanol and cycloheptanol, respectively. As such, the bis-silylation of the polyfunctional 1-phenyl-1,2-ethanediol to 20b is readily achieved with 2 equiv. 2b. Importantly, the method is also tolerant to oxidizing functional groups such as iodo, nitro, keto and vinyl groups (see 6a, 7a, 12a, and 16b). For example, despite the reductive conditions imposed by the combination of silyl formate 2b and the Fe(OAc)2/4 catalyst, the silylation of testosterone to 16b is carried out in 92 % yield within 2 h without reduction of the enone functionality. Overall, the Fe(OAc)2/4 presents a catalytic activity and a reaction scope very similar to (triphos)Ru(OAc)2, with the benefit of utilizing a first row transition metal complex. This is further confirmed with the isolation of silyl ester 21a that can be obtained from the silylation of benzoic acid with 2a and either the iron or ruthenium catalyst. 13 Scheme 5 Scope of the transfer dehydrogenative silylation of alcohols with silyl formates. Reaction conditions: alcohol (0.50 mmol), silyl formate (0.60 mmol), Fe(OAc)2 (2 mol%), 4 (2 mol%), CH2Cl2 (2.0 mL; 0.25 M); 70 °C. Isolated yields are provided for reaction times indicated in brackets. [a] The stoichiometry was adapted with the use of 1.2 mmol 2a.

Preliminary mechanistic investigations with (triphos)Ru(OAc)2 have shown that the silylation of alcohols with silyl formates results from a two-step process where the metal complex first catalyzes a trans-silylation equilibrium between the reagents to yield the corresponding silyl ether and free formic acid. 13 The ruthenium plays a second role by catalyzing the irreversible decomposition of formic acid to H2 and CO2, thereby driving the production of silyl ether to completion. The transient formation of free formic acid was observed by 1 H NMR spectroscopy upon silylation of 3 with silyl formate 2a and Fe(OAc)2/4 (Scheme 3), suggesting that the iron catalyst follows a similar reaction path.

The hypothesis was further confirmed as Fe(OAc)2/4 readily dehydrogenates formic acid to H2 and CO2, at 90 °C under basefree conditions in CH2Cl2 (see SI). To gain deeper insights into the nature of the active iron(II) species, the stoichiometric reaction between Fe(OAc)2, 4 and 2 equiv. Et3SiOCHO (2a) was carried out in CH2Cl2 at 70 °C. Rapid gas evolution indicated the transformation of the iron acetate complex. Isolation of the resulting iron complex by recrystallization from CH2Cl2/pentane enabled the structure determination of 22 by X-ray diffraction (Figure 1). 22 is an 18-electron iron(II) hydrido chloro complex supported by the tetraphosphine ligand 4, coordinated with a κ-4 mode. [28][29] The formation of 22 demonstrates the ability of Fe(OAc)2/4 to yield iron-hydride species in the presence of silyl formates and this chemical behaviour is consistent with previous findings from Beller et al. on the reactivity of iron(II) complexes supported by 4 in the presence of formic acid. 30 In conclusion, we have described an efficient synthetic route for the synthesis of silyl formates from chlorosilanes and sodium formates and the first catalysts able to utilize silyl formates as surrogates of hydrosilanes. [START_REF]General procedure for the synthesis of silylformates (2) In a glovebox, a flame-dried round bottom flask equipped with a Solv-seal connection and a J-Young valve was charged with sodium formate (42 mmol, 1.4 equiv.)[END_REF] Fe(OAc)2, supported by P(C2H4PPh2)3 (4), is an efficient catalyst for the dehydrogenative and decarboxylative silylation of alcohols to silyl ethers. The iron catalyst exhibits a catalytic behaviour similar to (triphos)Ru(OAc)2 in this reaction and was shown to produce iron-hydride species in dichloromethane, in the presence of silyl formates. Further efforts are underway in our laboratories to exploit the reducing ability of such intermediates in new transfer hydrosilylation reactions.

Funding Information

For In a glovebox, a flame-dried flask equipped with a J-Young valve was charged with Fe(OAc)2 (1.7 mg , 0.01 mmol, 2 mol%) and P(C2H4PPh2)3 (4) (6.7 mg , 0.01 mmol, 2 mol%) followed by CH2Cl2 (2 mL, C = 0.25 M). To the resulting white suspension were added the alcohol (0.5 mmol, 1 equiv.) and the silyl formate (0.6 mmol, 1.2 equiv. per hydroxyl group) reagents. The flask was then sealed, brought out of the glovebox and immersed in a pre-heated oil bath at 90 °C (oil temperature). A purple coloration appeared when heated. At this temperature, all the reactions were generally complete within 1.5 h with silyl formates 2. The coloration observed turned from purple to bright orange. Yields of silyl ethers were determined after isolation and purification by column chromatography on silica gel. Analytical data for 16b is provided below, as a representative example:

1 H NMR (200 MHz, d8-THF) δ 5.59 (s, 1H), 3.58 (m, 2H), 1.89 (m, 14H), 1.20 (s, 3H), 1.12 -0.78 (m, 4H), 0.75 (s, 3H), 0.06 (s, 9H). 13

Scheme 4

 4 Scheme 4 Iron-catalyzed silylation of benzylalcohol (3) to 5 with silyl formates (2b-2g).

Figure 1 of 5 Scheme 5

 155 Figure 1 View of complex 22 ([Fe(4)HCl]). Displacement ellipsoids are drawn at the 50% probability level and carbon-bound hydrogen atoms are omitted.The catalytic implication of 22 was tested in the silylation of 3. As expected, 22 possesses a catalytic activity similar to Fe(OAc)2/4 and the complex promotes the formation of 5a in the reaction described in Scheme 3. Based on these data, a mechanistic proposal is drawn in Scheme 6: an active iron(II) hydride species (likely 22) is formed from Fe(OAc)2 in the presence of 4 and the silyl formate reductant. The complex is then able to decompose HCO2H by successive dehydrogenation and decarboxylation steps, in order to displace the equilibrium involving the alcohol, the silyl ether, and free formic acid to the formation of the silyl ether product.

  Scheme 1 Surrogates of hydrosilanes used in transfer hydrosilylation.

  financial support of this work, we acknowledge CEA, CNRS, the CHARMMMAT Laboratory of Excellence and the European Research Council (ERC Starting Grant Agreement n.336467). T.C. thanks the Foundation Louis D. -Institut de France for its support.

	Analytical data for 2e is provided below, as a representative
	example:
	1 H NMR (200 MHz, d8-THF) δ 8.18 (s, 1H), 7.71 -7.56 (m, 4H), 7.38
	(m, 6H), 0.85 (s, 3H). 13 C NMR (50 MHz, d8-THF) δ 160.88, 135.09,
	134.47, 130.99, 128.53, -2.64. Elemental Analysis: calcd (%) for
	C14H14O2Si (243.3 g.mol-1): C 69.39, H 5.82, found: C 69.03, H 5.67.
	General procedure for the transfer hydrosilylation of alcohols
	with silylformates (5-21)

Template for SYNLETT © Thieme Stuttgart • New York 2019-01-30 page 5 of 5

  C NMR (50 MHz, d8-THF) δ 195.30, 167.91, 122.75, 80.52, 53.42, 49.26, 41.92, 37.52, 35.86, 35.00, 34.79, 32.75, 31.49, 30.85, 29.81, 22.38, 19.69, 15.74, 9.80, -1.61. HRMS (ESI) m/z [M + H]+ calcd. for C14H25O2Si + 361.2557; found : 361.2557.

Acknowledgment

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Nancy El Rachkidy

Alexandre Guitton
email: guitton@sancy.univ-bpclermont.fr

Bassem Bakhache
email: bakhache@hotmail.com

Michel Misson
email: misson@sancy.univ-bpclermont.fr

PiRAT: Pivot Routing for Alarm Transmission in Wireless Sensor Networks

Wireless sensor networks are increasingly used for remote monitoring, fire detection, emergency response. Such networks are equipped with small devices powered by batteries and designed to be operated for years. They are often based on the ZigBee standard which defines low power and low data rate protocols. As network size and data rates increase, congestion arises as a problem in these networks, especially when an emergency situation generates alarm messages in a specific area in the network. Indeed, congestion occurs as the alarms converge to a specific destination, which results into packet losses and higher delays. In this paper, we propose a solution for congested links, called the PiRAT (Pivot Routing for Alarm Transmission) protocol. It is based on multi-path routing in order to add some diversity in routing the alarms. PiRAT uses intermediate nodes as pivots to reach the destination. Simulation results show that PiRAT has better performance than previous protocols in terms of packet loss, end-to-end delay, congestion and node overload.

I. INTRODUCTION

Sensor devices are powered by batteries and communicate with each other in order to form an ad hoc wireless network. Wireless sensor networks (WSN) are composed of multiple devices capable of sensing the environment and detecting critical situations such as fire or intrusion.

When an emergency occurs in an area, the sensors of this area are requested to transmit urgent data at a high data rate. These urgent data, called alarms, must be treated with a higher priority than normal traffic. It is important to receive as many alarms as possible in order to be immediately informed of the emergency triggered in the network. This can be achieved by having efficient routing protocols that reduce the end-to-end delay and the packet loss rate.

Reactive routing protocols such as AODV (Ad hoc On-Demand Distance Vector) [START_REF] Perkins | Ad hoc on-demand distance vector (AODV) routing[END_REF] are not suitable for alarm routing. Indeed, reactive routing protocols require to establish a path from an alarm source to the alarm sink before alarm messages can be forwarded. The time needed to establish this path is significant as it depends on the distance between the source and the destination and on the network size.

Proactive deterministic protocols are able to route alarms as soon as they are produced, but lead to congestion on most of the paths to the destination. Indeed, all the sensors located in the area of the event send alarm messages to a remote sink using paths that converge quickly. The nodes on the common part of these paths have to route alarms for all the sources. This network status causes high contention for the medium acces, and therefore the zone around these nodes is congested.

Congestion has two major impacts. First, alarm messages can be lost if the alarm traffic is too important. Second, congestion yields to an increase in the energy consumption as it causes many packet collisions. Then, those packets need to be retransmitted. A limited number of nodes are solicited for routing these packets and then, their energy decreases, which increases the network capacity and the robustness of the network.

In this paper, we propose the PiRAT (Pivot Routing for Alarm Transmission) protocol. It aims to reduce the congestion of the transmitted alarms. It is a proactive probabilistic protocol based on multi-path routing. It consists on routing packets via a pivot node randomly selected for each source. This approach increases the number of nodes that participate in packet routing as it brings diversity in the routing process.

The remainder of this paper is organized as follows. In Sect. II, we discuss the related work. In Sect. III, we describe the congestion problem and we propose the PiRAT protocol as a solution. Simulation details and results are presented in Sect. IV and conclusions to this work are summarized in Sect. V.

II. STATE OF THE ART

In this section, we briefly introduce the IEEE 802.15.4 and the ZigBee standards. Then, we describe routing protocols that are relevant to our study.

A. IEEE 802.15.4 standard

The IEEE 802.15.4 standard [START_REF]Part 15.4: Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs)[END_REF] defines the physical (PHY) and the medium access control (MAC) layers of a wireless personal area network (WPAN). It can be used to interconnect ultra low-cost sensors, actuators, and processing devices, which constitute the infrastructure to sense the physical environment [START_REF] Zheng | Will IEEE 802.15.4 make ubiquitous network a reality?: A discussion on a potential low power, low bit rate standard[END_REF], [START_REF] Callaway | Home networking with IEEE 802.15.4: A developing standard for low-rate wireless personal area network[END_REF]. The PHY layer is designed to operates at several frequencies, including the 2.4 GHz ISM band. The MAC layer uses a Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism to access the channel. Two operating modes can be used: the beaconenabled mode which uses slotted CSMA/CA, and the nonbeacon-enabled mode which uses unslotted CSMA/CA.

An IEEE 802.15.4 device is either a full-function device (FFD) or a reduced-function device (RFD), depending on its capabilities or available resources. An FFD can communicate with both FFDs and RFDs, while an RFD can communicate only with a single FFD. IEEE 802.15.4 only defines the star topology, where the center of the star is an FFD and all the other devices are RFDs. The communications between the RFDs are always performed through the central FFD.

B. ZigBee standard

The ZigBee standard [START_REF] Zigbee | ZigBee Specification[END_REF] defines the upper layers of a IEEE 802.15.4 WPAN. Its characteristics cover dynamic network formation, addressing and routing.

A ZigBee device is either the PAN coordinator, a router or an end-device. A PAN coordinator is a FFD which is in charge of the network construction. Apart from that, the PAN coordinator is considered as a router. A router is an FFD and is in charge of routing messages to other FFDs or to its enddevices. An end-device is a RFD.

The ZigBee standard defines three topologies: the star, the cluster-tree and the mesh. The star topology is similar to the IEEE 802.15.4 star topology. In the cluster-tree and mesh topologies, a tree is initiated by the PAN coordinator. As routers and end-devices join the network, they are associated with routers that are already associated, forming parent-child relationships. In the cluster-tree topology, packets are routed according to the tree structure (see Subsect. II-B2). In the mesh topology, packets are routed according to the AODV protocol (see Subsect. II-C).

1) Distributed address allocation scheme: The distributed address allocation scheme is used to allocate an unique address to any device in the network. To compute the addresses of the devices, each node has to know the values of three parameters: C m , R m and L m . C m determines the maximum number of children a router is allowed to have. R m determines the maximum number of children (among the C m children) that can be routers. L m determines the maximum depth of the cluster-tree.

Each router is allocated an address space. The first address of the address space is used as the address of the router itself, and the remaining addresses are distributed to its children. The length of the address space given to a router at depth d + 1, called Cskip(d), is computed as follows (see paragraph 3.6.1.6 of [START_REF] Zigbee | ZigBee Specification[END_REF]):

Cskip(d) = 1 + C m • (L m -d -1) if R m = 1, 1+Cm-Rm-Cm•R Lm-d-1 m 1-Rm otherwise.
The PAN coordinator address is always 0. A parent device that has a Cskip(d) greater than zero can accept devices as children and can assign addresses. If Cskip(d) is zero, it can not have children devices and then it must be treated as an end-device. If a parent node of depth d is assigned an address A parent , the address A k of its k-th router and the address A n of its n-th end-device are calculated according to the following equations: In this example, we have Cskip(0

A k = A parent + Cskip(d).(k -1) + 1, A n = A parent + Cskip(d).R m + n.
) = 1 + C m •(L m -d-1) = 1 + 2 • 2 = 5
. Thus, the router child of the PAN coordinator is assigned the address 1 and the end-device child is assigned the address 1 + Cskip(0) = 6.

2) Hierarchical tree routing protocol: The hierarchical tree routing protocol (see paragraph 3.6.1.6 of [START_REF] Zigbee | ZigBee Specification[END_REF]) is based on the distributed address allocation scheme. End-devices forward messages to their parent routers. When a router receives a message for a destination, it checks whether the destination is within its own address space or not. If it is the case, the destination is a descendent of the router. The router determines which child router the destination belongs to, and sends the message to the concerned child. If it is not the case, that is if the destination is not within the address space of the router, the router sends the message to its parent.

Let us consider the topology shown on Fig. 1, and let us suppose that a message is sent from the end-device 6 to the end-device 4. End-device 6 sends the message to its parent, which has address 0. Router 0 (which is the PAN coordinator) has to determine if the destination 4 is within its address space [0; 6] or not. As it is the case, the destination 4 is a descendant of the router 0. Then, the router has to determine if the destination is one of its children, or if the message has to be sent to an intermediate child router. In this case, the router 0 detects that the destination 4 is within the address space of its child 1, so it forwards the message to the router 1. Router 1 has to determine if the destination 4 is within its address space [1; 5] or not. As it is the case, the destination 4 is a descendant of the router 1. The same process is repeated and router 1 sends the message to its child 2. Then, router 2 detects that the destination 4 is within its address space [2; 4] and that it is its own child. Thus, it sends the message to the destination 4.

In [START_REF] Cuomo | Routing in ZigBee: Benefits from exploiting the IEEE 802.15.4 association tree[END_REF], authors have proposed a routing protocol that takes advantage of the MAC layer associations to perform the routing functionality. This protocol is a simple proactive routing protocol based on the distributed address allocation scheme described in the previous paragraph. Routes towards a destination are established before any sensor node needs to perform packet routing. The main advantages of this routing protocol are its simplicity and its limited use of resources. Indeed, it does not require additional control messages or complex routing tables.

The main drawback of protocols based on the hierarchical tree routing is the fact that the routing paths are not optimized. Indeed, the messages always follow the tree topology, even if shortest paths exist.

3) Neighbor routing protocol: The neighbor routing protocol (see paragraph 3.6.3.3 of [START_REF] Zigbee | ZigBee Specification[END_REF]) improves the hierarchical tree routing by using one-hop neighbor tables. When a router has to forward a message to a destination, it first checks if the destination is physically within communication range, i.e., if the destination is in its neighbor table. If it is the case, the router sends the message directly to the destination. Otherwise, it follows the hierarchical tree routing protocol.

C. AODV routing protocol

AODV [START_REF] Perkins | Ad hoc on-demand distance vector (AODV) routing[END_REF] is a reactive routing protocol: routes to destinations are acquired in an on-demand manner. When a node has to route packets to a destination, it broadcasts a Route REQuest (RREQ) message. As this message is spread through the network, each node that receives it sets up a reverse route, i.e., a route towards the requesting node. As soon as a RREQ message reaches a node with an established route to the destination (including the destination itself), a Route REPly (RREP) unicast message is sent back to the requesting node. Intermediate nodes use the reverse routes to forward RREP messages.

However, reactive routing protocols, such as AODV, contribute to a significant end-to-end delay due to the time required to establish the route.

D. Shortcut tree routing protocol

The shortcut tree routing protocol [START_REF] Kim | Shortcut tree routing in ZigBee networks[END_REF] improves the hierarchical tree routing by using one-hop neighbor tables and shortest paths. When a router has to forward a message to a destination, it computes the expected distance from each neighbor (including its parent and its children) to the destination according to the hierarchical tree routing. Then, it sends the message to the nearest neighbor to the destination. Figure 2 illustrates an example of the shortcut routing protocol. Router D has to send a message to router F . For each neighbor, D computes the expected distance to the destination F . For neighbor B, the expected path is (B, A, C, F ) and the distance is 3. For neighbor E, the expected path is (E, B, A, C, F ) and the distance is 4. Therefore, router D sends the message to its neighbor B. For each neighbor, router B computes the expected distance to the destination F . For neighbor D, the expected path is (D, B, A, C, F ) and the distance is 4. For neighbor A, the expected path is (A, C, F ) and the distance is 2. For neighbor E, the expected path is (E, B, A, C, F ) and the distance is 4. For neighbor C, the expected path is (C, F ) and the distance is 1. Therefore, the router B sends the message to its neighbor C. Router C detects that the destination F is one of its neighbors, and sends the message directly to it.

III. PIRAT PROTOCOL

In this section, we show that reactive routing protocols and proactive deterministic routing protocols produce congested areas in the network. Then, we propose the PiRAT (Pivot Routing for Alarm Transmission) protocol in order to deal with this problem. Finally, we describe the topology and our modeling of the alarm traffic.

A. Problem of congestion due to alarms

Congestion is defined as the state when a node receives more packets than it can transmit. As nodes use a shared radio medium, congestion occurs in areas where congested nodes are located [START_REF] Jardosh | Understanding congestion in IEEE 802.11b wireless networks[END_REF].

Congestion happens when a limited area of the network generates most of the data towards the same destination. Moreover, congestion increases as the data generation rate of the alarm traffic increases. The radio range also affects the congestion of nodes: when the radio range increases, the number of nodes in range of each other increases, and the access to the medium becomes more difficult.

Congestion negatively impacts the performance of the network. First, as the congestion increases, the contention for the medium also increases, which results into more collisions. Collisions result into packet losses and retransmissions. Retransmissions require energy and lead to an increase in the end-to-end delay. Second, the congested nodes have to buffer the packets in their queues, which increases the delay.

Reactive protocols, such as AODV, are not suitable for alarm traffic transmission due to the time needed to establish the routes. Moreover, such protocols cause high overhead in order to establish the routes [START_REF] Kumar | Mitigating performance degradation in congested sensor networks[END_REF] [START_REF] Cuomo | Routing in ZigBee: Benefits from exploiting the IEEE 802.15.4 association tree[END_REF]. For example, in AODV, the routers broadcast RREQ messages in the whole network, for each source-destination pair.

Deterministic proactive protocols, such as the hierarchical tree routing and the shortcut protocols, produce important congestion. Indeed, those protocols are deterministic, which means that the packets always follow the same routes. As packets approach the destination, the routes converge and the amount of traffic to be routed dramatically increases, which overloads all the nodes near the destination, and leads to a bottleneck.

B. PiRAT protocol description

The PiRAT protocol provides a simple solution that aims to reduce the congestion induced by alarm transmission in a wireless sensor network. It uses two steps. The first step consists in selecting pivot nodes for each source-destination pair (see Subsect. III-C). The second step consists in forwarding alarm packets via a randomly chosen pivot node to the destination. Thus, PiRAT is a probabilistic proactive protocol.

The PiRAT protocol is based on diversity routing due to its probabilistic nature, and also on the shortcut routing protocol. Indeed, the paths from the source to the pivot and from the pivot to the destination are computed according to the shortcut tree routing. PiRAT possesses another probabilistic feature. When several neighbors lead to a destination with the same minimum distance, PiRAT chooses one of these nodes randomly.

The main advantage of PiRAT is that it provides multi-path routing to the destination. Indeed, it allows a larger number of nodes to participate in the routing activity. Thus, the energy usage is more balanced among the nodes of the network than with deterministic proactive protocols. This feature has the benefit of extending the lifetime of the network.

C. Selection of the pivot nodes

Selecting pivots is the key aspect of the PiRAT protocol. Selecting pivots close to the shortest path from the source to the destination reduces the hop count but also increases the congestion. Selecting pivots far from the shortest path from the source to the destination increases the hop count and the delay. The distance between pivot nodes is also an important issue in order to avoid interferences between paths.

We propose to select the pivots in a large area in order to avoid the convergence of paths and thus balance the load of alarm traffic in the network. A sensor node is considered as a pivot if it satisfies the following conditions:

1) its distance from the source is strictly greater than its distance to the sink, 2) it is not located on the shortest path from the source to the destination, 3) it is not located on the boundaries of the network. The first condition ensures that the pivot is closer to the destination than to the source. This is required to push the path convergence as far away as possible from the source. The second condition ensures that the path via the pivot does not follow the shortest path from the source to the destination, which is the most congested area. The third condition is needed as nodes on the boundaries of the network have less routing options than the other nodes, which reduces the available routing diversity and increases congestion on the boundaries.

The distance used in the previous conditions is ideally the physical distance between nodes. However, as nodes have no knowledge of their geographical positions, we propose to consider the number of hops according to the shortcut tree routing as our distance. This metric is better than the number of hops computed according to the hierarchical tree routing, as it is based on the environment (through the neighbor tables) rather than on the tree topology only.

Once a source has determined a set of possible pivot nodes, it randomly chooses one of them. Then, packets are routed through this pivot.

The pivot selection protocol is the following. First, we assume that the nodes that are located in the potential alarm areas know by advance that they are potential sources. Thus, they initiate a pivot discovering phase by broadcasting a Pivot Discovery Message (PDM). This message contains the distance between the source s and the destination d (computed according to the shortcut tree routing). When a node n receives a PDM, it verifies that all the three following conditions hold:

• d(s, n) > d(n, d), • d(s, n) + d(n, d) > d(s, d) + ε 1 ,
where ε 1 is a threshold chosen according to the network size,

• the number of neighbors of n is greater than a threshold ε 2 . All the nodes that satisfy the three conditions are candidates for the pivot selection. Each candidate sends a Pivot Notification Message (PNM) to the source to inform it that it is a potential pivot. Once the source has received a given number of PNM, or has waited for a given time duration, it chooses randomly one of them to be its dedicated pivot.

D. Topology description and alarm traffic production

In the following simulations, we considered for simplicity reasons a set of 100 sensor nodes uniformly distributed on a 100 m×100 m grid. All sensors are FFD and operate on the non-beacon-enabled mode. The PAN coordinator is located at the center of the area. The network parameters are defined as follows: C m = 5, R m = 5 and L m = 5. We varied the radio range from 30 m to 40 m. Figure 3 shows an example of the associations between nodes using the parentchild relationships with a fixed association range of 20 m. We define the association range as the maximum distance between a parent and a child. It is smaller than the radio range in order to ensure that nodes are associated through high quality links. As it can be seen on the figure, nodes are randomly associated with each other. For example, if the sensor node 20 has to transmit a packet to the sensor 50 according to the hierarchical tree routing protocol, the following path is used: (20, 22, 23, 34, 45, 54, 52, 50).

The alarm traffic is produced in the following way. First, we assume that an emergency event occurs at the bottom lefthand corner of the network. This event is detected by all the sensors located within a given radius. In our simulations, we set the detection radius to 25 m. Thus, eight nodes are sources. Notice that the event occurs in the network after all the associations are performed and the pivot selection algorithm is accomplished. There is no background traffic as we focus on the alarm traffic only. We assume that the sink is located in the top right-hand corner. All the nodes of the network participate to the multi-hop routing process. Finally, we assume that the alarm notifications last for 30 seconds and we vary the alarm data rate from 1 packet per second to 30 packets per second. Alarm notifications are produced periodically to inform the sink of the evolution of the event in a real-time manner.

E. Performance metrics

PiRAT is evaluated and compared to the existing protocols according to several performance metrics:

• End-to-end delay: the end-to-end delay is the time interval between the transmission of a packet by the source and the reception of the same packet by the sink. Thus, the end-to-end delay only takes into account the packets that are correctly received.

• Packet loss: the packet loss is defined as the ratio of the number of packets successfully received by the sink over the number of packets generated by the source nodes. Thus, the packet loss ratio takes into account the losses due to collisions or queue overflows.

• Number of hops: the number of hops is defined as the number of intermediate nodes required to forward a packet from the source to the sink. Only the packets received by the sink are considered.

• Node usage: the node usage indicates how many sensor nodes are used in the routing process, and how many times they have to forward packets.

IV. SIMULATION RESULTS In this section, we describe the extensive simulations that we conducted in order to evaluate PiRAT. Simulations are carried out using the network simulator NS2 [START_REF]Network simulator 2[END_REF], version 2.31. We used the existing implementation of the IEEE 802.15.4 PHY and MAC layers. We used the two ray ground propagation model (with default parameters). The transmission power is set to a realistic value of 3.16 µW, which is -25 dBm, and the reception threshold is set to 0.347412 pW (for a radio range of 30 m) or 0.195419 pW (for a radio range of 40 m). NS2 also requires the user to specify a carrier sense threshold. The carrier sense threshold is equal to the reception threshold as the IEEE 802.15.4 throughput is limited to only 250 kbps. We decided to limit the size of the nodes queue to 5 packets of 34 bytes (at the PHY layer), due to their limited storage capabilities.

We compared the PiRAT protocol with the hierarchical tree routing protocol, referred to as tree, and the shortcut tree routing protocol, referred to as shortcut. We studied AODV as an example of the reactive routing protocols.

Each simulation is performed over one hundred repetitions. We displayed the 95% confidence interval on the following figures.

A. Route establishment time with AODV

We used the existing implementation of AODV in NS2. In order to show the inefficiency of AODV for alarm transmissions in wireless sensor networks, we considered a simple topology of 100 nodes, uniformly distributed in a 100 m×100 m area. We selected the node 0 as source and the node 45 (the center of the network) as the destination, and we measured the delay for each packet when the source transmits one packet per second. AODV tries to establish several routes from the source to the destination, in case the optimal route becomes congested. This takes a significant time.

Figure 4 plots the average end-to-end delay for each packet correctly received. We can see on the figure that the packets suffer from a large delay. This is due to the fact that the source broadcasts RREQ messages to its neighbors in order to establish an optimal route to the destination. Thus, the route discovery penalizes the transmission delay of the first generated packets. As we can see in Fig. 4, the transmission delay of the first received packet is about 9 seconds and about 8 seconds for the second packet. For each packet, the average delay decreases by one second because packets are transmitted with a rate of one packet per second. After the ninth packet (ID 29), the time of route establishment has no effect on the end-to-end delay. However, we observe a significant delay for the packets having the IDs 30 and 31. These delays are due to the reestablishment of the route between the source node and the sink. Note that the packets having an ID from 1 to 19 have been dropped because AODV was unable to establish a route.

These simulation results show that AODV is not suitable as an alarm routing protocol because of the large end-to-end delay it causes. Because of this drawback, we concentrate the remaining of our study on the proactive routing protocols only.

B. End-to-end delay

Figure 5 and Fig. 6 show the mean end-to-end delay between the generation and the reception of a packet, as a function of the frequency of the alarm transmission rate with a radio range of 30 m and 40 m respectively.

For the hierarchical tree routing, the delay increases quickly and becomes stable after 15 packets per second. This is due to the fact that routes are long and the number of retransmissions is high (as the packet loss is high, see Subsect. IV-C). As the data transmission rate increases, the packet loss becomes higher and several packets are dropped. The packets most likely to be dropped are those that correspond to long routes. Only packets that follow short routes are received by the destination, which reduces the end-to-end delay.

For the shortcut tree routing and PiRAT, the delay increases with the data transmission rate. This is mainly due to the increasing congestion and the necessary retransmissions. However, these two protocols outperform the hierarchical tree routing. When the alarm transmission rate is high, PiRAT has the best behavior in terms of delay. The end-to-end delay reduction of PiRAT over shortcut reaches 28% when the alarm transmission rate reaches 30 packets per second, for a radio range of 30 m, and 40% for a radio range of 40 m. When the radio range increases, shortcut and PiRAT have better performances as nodes have more neighbors to route packets to.

C. Packet loss

Figure 7 and Fig. 8 show the mean packet loss as a function of the frequency of the alarm transmission rate with a radio range of 30 m and 40 m respectively. We notice that the packet loss for all the protocols increases consistently with the data transmission rate. As the hierarchical routing protocol uses long paths, the probability of loosing packets is high. It can reach up to 80% for 30 packets per second and a radio range of 30 m, and up to 85% for 30 packets per second and a radio range of 40 m. The shortcut and PiRAT protocols are able to achieve lower packet loss rates by shortening the paths from the sources to the destination. They achieve only 60% of packet loss rates for 30 packets per second and both radio range.

D. Number of hops

Figure 9 and Fig. 10 represent the average number of hops as a function of the alarm transmission rate. As expected, the number of hops does not depend on the network load. With the hierarchical tree routing, packets suffer from paths of 9 hops independently from the radio range. Only the association range is taken into account in the hierarchical tree routing protocol. In our simulations, we set this range to 20 m. The shortcut routing algorithm decreases the number of hops since it routes packets via a short path. We notice an average path length of 5.5 hops for a radio range of 30 m and an average path length of 4.4 hops for a radio range of 40 m. This is due to the fact that as the radio range increases, more neighbors can be used to shortcut the tree. With PiRAT, the routes are longer than those used by the shortcut tree routing, but our pivot selection algorithm ensured that the number of hops does not become too large. The average path length for PiRAT consists of 6.5 hops for a radio range of 30 m and of 4.8 hops for a radio range of 40 m.

E. Node usage

The node usage metric is the most important metric for PiRAT. PiRAT improves the shortcut routing algorithm by using diversity in routing and decreasing the congested areas in the network. With PiRAT, only the area around the destination is congested (see Fig. 11 and Fig. 12).

Figure 11 shows the participation of all the nodes in the routing process for the shortcut tree routing protocol. Sources are nodes 0, 1, 2, 10, 11, 12, 20 and 21. A thick line indicates that the link is used multiple times while a thin line indicates that a link is rarely used. As we can see, the routes used by the shortcut tree routing protocol share a significant number of nodes. The large number of thick lines proves that the shortcut tree routing protocol causes congestion along the common path. Moreover, as soon as one of the nodes of the path drains its energy because it has transmitted too many packets, the route becomes inactive and the network lifetime decreases.

Figure 12 shows the participation of all the nodes in the routing process for the PiRAT protocol. The pivot nodes selected by the sources are represented using dashed circles. The routes from the sources to the destination avoid the central area in order to reduce congestion. The probabilistic nature of PiRAT can be seen as each node uses several paths to reach a given destination (either the pivot node or the sink). While the shortcut tree routing protocol used 21 nodes, PiRAT uses a total of 42 nodes. Thus, PiRAT doubles the number of nodes used, and then, it reduces the amount of the transmitted packets per node. This leads to reduce the overloaded paths and balance the energy consumption of the nodes. This phenomenon greatly improves the network lifetime.

As we can see on Fig. 11, the path from the PAN (node 45) to the sink (node 99) via the intermediate nodes 66, 86, and 97 is overloaded. This is due to the fact that all the packets follow the same path. However, as it can be seen on Fig. 12, the nodes used by the shortcut protocol are less solicited to route packets when the PiRAT protocol is used. With PiRAT, new nodes participate in the routing process; the traffic load is reduced for each node. Indeed, because of the diversity in routing, node 45 sends packets to nodes 47 and 75 and does not use the wireless link (45-66). Then, node 86 receives less packets from more nodes (56, 68, 83, 84) than on Fig. 11. The same phenomenon occurs at node 97, which reduces the congested areas. However, since the destination is always the same (node 99), the sink is always overloaded with both protocols. Thus, the area around it is congested.

V. CONCLUSION

In this paper, we showed that congestion is a major issue when dealing with alarms in a wireless sensor network. We proposed a new protocol called PiRAT which uses pivot nodes in order to add diversity to the routing process. It also adds diversity to the shortest paths it uses in routing packets. Simulation results showed that PiRAT outperforms the reactive protocols as well as the proactive deterministic routing tree protocols in terms of end-to-end delay (reduction up to 60% and 28% for a communication range of 30 m, and up to 80% and 40% for a communication range of 40 m compared to the hierarchical tree routing protocol and the shortcut tree routing protocol respectively), without negatively impacting the packet loss. Moreover, PiRAT performs better than the existing routing protocols in terms of congestion as it uses a larger number of nodes while routing packets from source to destination. This leads to a good balancing of the energy usage, which in turn extends the network lifetime. The perspectives of this work include the optimization of the pivot selection algorithm, the reduction of the number of messages required to select the pivot candidates, and the packet loss reduction.

VI. ACKNOWLEDGMENT

This work has been partially supported by a research grant from the Lebanese National Council for Scientific Research (LNCSR).

1 Figure 1 .

 11 Figure 1. Example of the distributed address allocation scheme with Cm = 2, Rm = 1 and Lm = 3.

Figure 1

 1 Figure 1 shows an example of the distributed address allocation scheme, where the PAN coordinator is represented by a double circle, the routers are represented by circles and the end-devices are represented by squares. The network parameters are assigned the following values: C m = 2, R m = 1 and L m = 3. The PAN coordinator has 0 as address.In this example, we have Cskip(0) = 1 + C m •(L md-1) = 1 + 2 • 2 = 5. Thus, the router child of the PAN coordinator is assigned the address 1 and the end-device child is assigned the address 1 + Cskip(0) = 6.2) Hierarchical tree routing protocol: The hierarchical tree routing protocol (see paragraph 3.6.1.6 of[START_REF] Zigbee | ZigBee Specification[END_REF]) is based on the distributed address allocation scheme. End-devices forward messages to their parent routers. When a router receives a message for a destination, it checks whether the destination is within its own address space or not. If it is the case, the destination is a descendent of the router. The router determines which child router the destination belongs to, and sends the message to the concerned child. If it is not the case, that is if the destination is not within the address space of the router, the router sends the message to its parent.Let us consider the topology shown on Fig.1, and let us suppose that a message is sent from the end-device 6 to the end-device 4. End-device 6 sends the message to its parent, which has address 0. Router 0 (which is the PAN coordinator) has to determine if the destination 4 is within its address space [0; 6] or not. As it is the case, the destination 4 is a descendant of the router 0. Then, the router has to determine if the destination is one of its children, or if the message has to be sent to an intermediate child router. In this case, the router 0 detects that the destination 4 is within the address space of its child 1, so it forwards the message to the router 1. Router 1 has to determine if the destination 4 is within its address space[1; 5] or not. As it is the case, the destination 4 is a descendant of the router 1. The same process is repeated and router 1 sends the message to its child 2. Then, router 2 detects that the destination 4 is within its address space [2; 4] and that it is its own child. Thus, it sends the message to the destination 4.In[START_REF] Cuomo | Routing in ZigBee: Benefits from exploiting the IEEE 802.15.4 association tree[END_REF], authors have proposed a routing protocol that takes

Figure 2 .

 2 Figure 2. An example of the shortcut tree routing protocol.

Figure 4 .

 4 Figure 4. Average end-to-end packet delay with AODV: on this example, it takes 9 seconds to establish the route.

Figure 5 .

 5 Figure 5. Average end-to-end delay for a radio range of 30 m.

Figure 6 .

 6 Figure 6. Average end-to-end delay for a radio range of 40 m.

Figure 7 .

 7 Figure 7. Average packet loss for a radio range of 30 m.

Figure 8 .

 8 Figure 8. Average packet loss for a radio range of 40 m.

Figure 9 .

 9 Figure 9. Average number of hops for a radio range of 30 m.

Figure 10 .

 10 Figure 10. Average number of hops for a radio range of 40 m.

Figure 12 .

 12 Figure 12. Node usage with the PiRAT protocol.

  Node usage with the shortcut tree routing protocol.

	90	91	92	93	94	95	96	97	98	99
	80	81	82	83	84	85	86	87	88	89
	70	71	72	73	74	75	76	77	78	79
	60	61	62	63	64	65	66	67	68	69
	50	51	52	53	54	55	56	57	58	59
	40	41	42	43	44	45	46	47	48	49
	30	31	32	33	34	35	36	37	38	39
	20	21	22	23	24	25	26	27	28	29
	10	11	12	13	14	15	16	17	18	19
	0	1	2	3	4	5	6	7	8	9
	30 40 50 60 70 80 90	000 000 111 111 111 111 000 000 000 000 111 111 111 111 38 48 58 68 78 88 Figure 11. 000 000 000 000 000 000 111 111 111 111 000 000 000 000 111 111 111 111 000 000 000 000 111 111 111 111 000 000 000 000 111 111 111 111 31 32 33 34 35 36 37 41 42 43 44 45 46 47 51 52 53 54 55 56 57 61 62 63 64 65 66 67 71 72 73 74 75 76 77 81 82 83 84 85 86 87 91 92 93 94 95 96 97 98	39 49 59 69 79 89 99
	20	21	22	23	24	25	26	27	28	29
	10	11	12	13	14	15	16	17	18	19
	0	1	2	3	4	5	6	7	8	9