Sylvain Rama

Norah Boumedine-Guignon

Marion Sangiardi

Fahamoe Youssouf

Yves Maulet

Christian Lévêque

Maya Belghazi

Michael Seagar

Dominique Debanne

Oussama El Far
email: oussama.el-far@inserm.fr

Chromophore-assisted light inactivation of the V-ATPase V0c subunit inhibits neurotransmitter release downstream of synaptic vesicle acidification

Keywords: V-ATPase, Neurotransmission, Chromophore-assisted light inactivation (CALI), Hippocampal organotypic slice culture, Neurons, GeneGun V-ATPase, Neurotransmission, Chromophore-assisted light inactivation (CALI), Hippocampal organotypic slice culture, Neurons, GeneGun

Synaptic vesicle proton V-ATPase is an essential component in synaptic vesicle function. Active acidification of synaptic vesicles, triggered by the V-ATPase, is necessary for neurotransmitter storage. Independently from its proton transport activity, an additional important function of the membrane-embedded sector of the V-ATPase has been uncovered over recent years. Subunits a and c of the membrane sector of this multi-molecular complex have been shown to interact with SNARE proteins and to be involved in modulating neurotransmitter release. The c-subunit interacts with the v-SNARE VAMP2 and facilitates neurotransmission. In this study, we used chromophore-assisted light inactivation and monitored the consequences on neurotransmission on line in CA3 pyramidal neurons. We show that V-ATPase csubunit V0c is a key element in modulating neurotransmission and that its specific inactivation rapidly inhibited neurotransmission.

Introduction

V-ATPase is an important effector in acidifying intracellular compartments in eukaryotes. This pH sensor [START_REF] Hurtado-Lorenzo | V-ATPase interacts with ARNO and Arf6 in early endosomes and regulates the protein degradative pathway[END_REF] and mechano-chemical energy transducer [START_REF] Yokoyama | Rotation of the proteolipid ring in the V-ATPase[END_REF] is composed of two reversibly attached intertwined multi-subunit domains V1 and V0 [START_REF] Toei | Regulation and Isoform Function of the V-ATPases[END_REF].

The multi-subunit V1 domain carries the ATPase activity and reversibly associates with the membrane-inserted proton transporter V0 domain. Although the structural organization of this enzyme is complex, its molecular integrity guarantees energydependent proton transfer. Regulated dissociation of its constituent domains modulates the coupling of ATP hydrolysis to proton transport and consequent acidification of subcellular compartments. V0 is composed of a hexamer of 16 kDa hydrophobic V0c subunits with 4 transmembrane domains (TM) each, a 100 kDa, 8 TM V0a subunit, a soluble 40 kDa V0d subunit and a 9 kDa single TM V0e subunit, in addition to accessory proteins. Several lines of evidence now point to the involvement of V0 domain subunits; V0a [START_REF] Hiesinger | The v-ATPase V(0) Subunit a1 Is Required for a Late Step in Synaptic Vesicle Exocytosis in Drosophila[END_REF][START_REF] Poea-Guyon | The V-ATPase membrane domain is a sensor of granular pH that controls the exocytotic machinery[END_REF] and V0c [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF]; in regulating SNARE-mediated neurotransmitter release, independently of the ATPase and proton transport activity.

In 2010, we showed that inhibiting V0c interaction with VAMP2 significantly decreases neurotransmission, highlighting the importance of V0c as a modulator of SNAREdependent neurotransmission [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF]. Despite converging data on the implication of V0 subunits in neurotransmission, the mechanism by which the different V-ATPase V0 subunits modulate neurotransmission is still obscure, especially for the extremely hydrophobic hexameric V0 c-subunit. Inactivation of the highly conserved V0 c-subunit [START_REF] El Far | A role for V-ATPase subunits in synaptic vesicle fusion?[END_REF] should allow further dissection of its role in neurotransmission. However the importance of this enzyme in cell viability through the acidification of different cellular compartments, complicates attempts at genetic inactivation [START_REF] Hayami | The murine genome contains one functional gene and two pseudogenes coding for the 16 kDa proteolipid subunit of vacuolar H(+)-ATPase[END_REF][START_REF] Sun-Wada | Acidic endomembrane organelles are required for mouse postimplantation development[END_REF][START_REF] Inoue | Targeted disruption of the gene encoding the proteolipid subunit of mouse vacuolar H(+)-ATPase leads to early embryonic lethality[END_REF] and in order to ascertain the role of c-subunit in neurotransmission, rapid and targeted protein inactivation techniques should represent an exploitable option.

Several membrane-permeant biarsenical dyes have been developed. They specifically bind an exogenous small tetracysteine (tc) motif genetically introduced into proteins of interest [START_REF] Tour | Genetically targeted chromophoreassisted light inactivation[END_REF]. Upon intense illumination, these dyes produce short-lived (approximately 250 ns) reactive oxygen species that locally and rapidly inactivate the tc-tagged protein [START_REF] Tour | Genetically targeted chromophoreassisted light inactivation[END_REF][START_REF] Ochsner | Photophysical and photobiological processes in the photodynamic therapy of tumours[END_REF]. This approach has already been successfully validated to study functional effects of protein inactivation [START_REF] Sano | Chromophore-assisted laser inactivation -towards a spatiotemporal-functional analysis of proteins, and the ablation of chromatin, organelle and cell function[END_REF][START_REF] Tour | Genetically targeted chromophoreassisted light inactivation[END_REF][START_REF] Yan | Fluorophore-assisted light inactivation of calmodulin involves singlet-oxygen mediated cross-linking and methionine oxidation[END_REF][START_REF] Marek | Transgenically encoded protein photoinactivation (FlAsH-FALI): acute inactivation of synaptotagmin I[END_REF][START_REF] Morel | V-ATPase is a pH sensor controlling vesicular membrane fusion[END_REF] including the implication of V0a1 subunit in neurotransmission. In the present study, we designed a light-sensitive V0c subunit and characterized the biochemical nature and consequences of protein damage. We then live-monitored synaptic transmission between connected CA3 pyramidal cells in organotypic slices following chromophore assisted light inactivation (CALI). Our results corroborate the implication of the V-ATPase V0c-subunit (ATP6V0c) in neurotransmitter release downstream of synaptic vesicle acidification.

Material and Methods

Chemicals

Spermidine, water-free ethanol and 2.3-dimercapto-1-propanol also known as British Anti-Lewisite (BAL) were from Sigma Aldrich. Bafilomycin A1, Tris(hydroxypropyl)phosphine (THP) and FiAsH-EDT2 were purchased from Santa Cruz Biotech. Early experimental setups were performed using FiAsH-EDT2 generously provided by Nicolas MOREL [START_REF] Poea-Guyon | The V-ATPase membrane domain is a sensor of granular pH that controls the exocytotic machinery[END_REF]. NiNTA agarose was from QIAGEN.

Protease inhibitors (cOmplete) were from Roche Diagnostics GmbH. Anti-RFP antibodies were from Evrogen and doxycycline from Euromedex. Anti-HSV tag antibodies were from Abcam. HRP substrate TMB (3,3',5,5'-tetramethylbenzidine) was from Interchim. Neon electroporation apparatus (Thermofisher Scientific) was used for PC12 cell transfection. Helios GeneGun apparatus and chemicals for insert preparations were from BioRad. Fos-Choline-12 (n-dodecylphosphocholine) was from ANATRACE.

shRNA design

The Public TRC (The RNAi Consortium) portal of the BROAD institute (https://portals.broadinstitute.org/gpp/public/) was used to predict a siRNA sequence that silences the expression of the RNA of the rat V0c subunit (ATP6V0c; NM_130823.3). siRNAV0c-391-412 5'-tgatcctgatcctcatctttg-3' was synthetized in a shRNA context (shRNAV0c-391-412: TGCTGTTCAGACAGTGAGCGCtgatcctgatcctcatctttgTAGTGAAGCCACAGATGTAc agttaccaccacttgcacTTGCCTACTGCCTCGGA) where: the sequence in lowercase corresponds to the siRNA, underlined sequences to complementarity with subsequently used miR30 primers and the sequence in italics corresponds to the shRNA loop. The shRNA was amplified by PCR using "Phusion® High Fidelity Taq polymerase" with the following primers: the "miR30 context" forward Xho I (in italics) primer 5'-CAGAAGGCTCGAGAAGGTATATTGCTGTTGACAGTGAGCG-3' where the underlined sequence is complementary to the underlined 8 base pairs 5' to the siRNA sequence in the shRNA and the "miR30 context" reverse EcoR I (italics) primers 5'-CTAAAGTAGCCCCTTGAATTCCGAGGCAGTAGGCA-3' where the underlined sequence is complementary to the underlined 14 base pairs 3' to the shRNA.

Cloning

EGFP-ATP6V0c was prepared as described [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF] using Sac I and Sal I restriction sites.

The shRNA was inserted in pINDUCER10 [START_REF] Meerbrey | The pINDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo[END_REF] in the miR30 context using XhoI and EcoR I sites downstream of a doxycycline-inducible promotor (pINDUCER10-sh). In order to design a shRNA-insensitive V0c cDNA sequence, we used the degeneracy of the genetic code and changed the wobble base of degenerate codons throughout the native cDNA sequence (NM_130823.3) (Figure 3a). The wobbled cDNA sequence of ATP6V0c (wATP6V0c), encoding exactly the same amino acid sequence as the native Using directed mutagenesis, his-HSV-tagged tcV0c (HHtc-V0c) was constructed by adding the tetracystein tag between isoleucine 4 and lysine 5 of the previously described His-tagged HSV-V0c [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF].

Bacterial expression and purification of HHtc-V0c and GST-VAMP2

C43 E.coli strain was used to express HHtc-V0c. Cultures were made in TB medium and the expression of the fusion protein was induced by 300 µM IPTG over-night at 18°C. Bacterial pellets were frozen at -20°C until use. All the following steps were performed at 4 °C. Nine grams of bacterial pellets were French pressed in resuspension buffer RP (20 ml Tris-HCl 10 mM pH8, EDTA 1 mM, 5 mM L-methionine containing cOmplete protease inhibitors and 1 mM THP). The supernatant of a 5 min 2500 x g centrifugation was then centrifuged 30 min at 170000 x g and the obtained pellet washed in RP using a Dounce homogenizer. Bacterial membranes were then resuspended at 10mg/ml in Tris 50 mM pH7.4, NaCl 150 mM and frozen at -80°C until use. 15 ml of bacterial membranes were used to solubilize HHtc-V0c by adding FosCholine12 (FC12) at 2% final concentration and protease inhibitors. The supernatant of a 170.000 x g centrifugation was then incubated for two hours with 1 ml packed volume of Ni-NTA agarose in the presence of 20 mM imidazole. After extensive washing with Tris 50 mM pH7.4, NaCl 1 M, 50 mM imidazole, 0.06% FC12, the beads were rinsed with HEPES 50 mM pH 7.4, NaCl 150 mM, 0.06% FC12 and eluted in the same buffer containing 0.5 M imidazole. Aliquots of purified protein were stored at -80°C under nitrogen enriched atmosphere. GST-VAMP2 (2-92) and GST-VAMP2 (2-92; W89,90A) were produced as previously described [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF].

In vitro chromophore assisted light inactivation 0.6 µg (10 µl) of purified HHtc-V0c were deposited in the bottom of an opened Eppendorf tube in the absence or presence of 30 µM FiAsh-EDT2. Tubes were placed on ice and subjected or not to 15 min direct illumination by a 100 watt light bulb positioned 1 cm above an open tube. Immediately after illumination, all tubes received 1 mM BAL and were vortexed. Samples were stored 6 hours at 4°C under nitrogenrich atmosphere before adding trypsin (200 ng) and overnight digestion at 37°C.

Samples were then subjected to mass spectrometry with LTQ-Velos-Orbitrap (Thermo Fisher Scientific, Bremen, Germany) online analysis with a nanoliquid chromatography (LC) Ultimate 3000 system (Dionex, Sunnyvale, CA, USA). Parallel samples were used for ELISA VAMP2 binding tests or western blot analysis using anti HSV antibodies.

Mass Spectrometry

Samples were analyzed by mass spectrometry using a hybrid Q-Orbitrap mass (1) 2.5-25% for 57 min, (2) 25-50% for 6 min, (3) 50-90% for 1 min, and (4) 90% for 10 min. MS spectra were acquired at a resolution of 35,000 within a mass range of 400-1,800 m/z. Ion accumulation was set at a maximum injection time of 100 ms.

Fragmentation spectra of the 10 most abundant peaks (Top10 method) were acquired with high-energy collision dissociation (HCD) at a normalized collision energy of 27%.

All raw data files generated by MS were processed to generate mgf files and searched against either the recombinant V0c sequence or the Uniprot E.coli database using the MASCOT software (www.matrixscience.com). Search parameters were as follow:

variable modifications: oxidation of methionine, histidine, tryptophane, proline, cysteine, lysine and tyrosine residues; mass tolerance: 10 ppm on parent ion and 0.02 Da on fragment ions, and a maximum of two tryptic missed cleavages. Peptide quantification was done using extracted ion chromatograms of peptide ions of interest in the software Xcalibur Qual Browser (Thermo Fisher Scientific, United States). Tris-HCl (pH 7.5), 150 mM NaCl, 0.1% BSA, 0.05% Tween 20 for 3 hrs at 4°C. Bound VAMP2 was then detected using the chromogenic HRP substrate TMB, consequent to the incubation with an anti-VAMP2 (mAb 6F9 [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF]) (1:3000) first antibody and an HRPcoupled anti-mouse secondary antibody (Jackson ImmunoResearch).

ELISA

Organotypic cultures of rat hippocampus

Hippocampal slice cultures were prepared as described previously [START_REF] Debanne | Paired-recordings from synaptically coupled cortical and hippocampal neurons in acute and cultured brain slices[END_REF]. Briefly, postnatal day 5-7 Wistar rats of both sexes were deeply anesthetized by intraperitoneal injection of chloral hydrate, the brain removed and each hippocampus individually dissected. Hippocampal slices (350 µm) were placed on 20 mm latex membranes (Millicell) inserted into 35 mm Petri dishes containing 1 ml of culture medium and maintained for up to 30 days in an incubator at 34°C, 95% O2-5% CO2.

The culture medium contained (in ml) 25 MEM, 12.5 HBSS, 12.5 horse serum, 0.5 penicillin (10000 U/ml), 0.5 streptomycin (10 mg/ml), 0.8 glucose (1 M), 0.1 ascorbic acid (1 mg/ml), 0.4 HEPES (1 M), 0.5 B27, 8.95 sterile H2O. To limit glial proliferation, 5 µM Ara-C was added to the culture medium at 3 DIV for an overnight period.

Transfection and Gene Gun

PC12 cells were electroporated in solution following the manufacturer's instructions using the Neon TM transfection apparatus (1500 Volts, 3 pulses of 10 msec each).

Briefly, 3 µg DNA (mixture of 1 µg pEGFP-ATP6V0c and 2 µg pINDUCER10-sh were used for 750 x10 3 cells per well of a Nunc TM 24 wells plate. Activation of the Tet-on tRFP and shRNA controlling promoter was achieved by adding 600 ng/ml of doxycycline to the culture medium 3 to 4 hours after transfection. Cells were recovered 48h later, lysed and analysed by western blot using anti-GFP and anti-tRFP for ATP6V0c and shRNA expression levels respectively. Anti-GAPDH was used to normalize protein loading on gels. Relative EGFP-ATP6V0c expression after shRNA induction was quantified using Image J. Gene Gun bullets were prepared following manufacturer's instructions using 1 µm gold micro-carriers and a gold (mg)/cDNA (µg) ratio of 6/25. For hippocampal organotypic slice cultures, DIV 7-8 cultures were shot

(1 µg DNA per bullet) at 120 psi. Doxycycline (300 ng/ml) was added one week after transfection and repeatedly supplemented every 36-48 hours until electrophysiological recordings between DIV 21 and 28. Before recordings, cultures were perfused for 10 min with 1 µM FiAsh-EDT2 followed by a 20 min wash with 0.1 mM BAL. In Bafilomycin conditions, after the baseline had stabilized, 50 M Bafilomycin were applied in the bath and neurons recorded for 40 minutes.

Electrophysiological recordings and data analysis

Dual whole-cell recordings were obtained from connected pairs of CA3 pyramidal neurons [START_REF] Debanne | Paired-recordings from synaptically coupled cortical and hippocampal neurons in acute and cultured brain slices[END_REF]. The external solution contained (mM): 125 NaCl, 26 NaHCO3, 3 CaCl2,

Results

Molecular consequences of tcV0c photo-damage. The reactive oxygen species generated by chromophore-assisted light inactivation (CALI) induce oxidization of susceptible residues nearby [START_REF] Lin | Optogenetic inhibition of synaptic release with chromophore-assisted light inactivation (CALI)[END_REF]. However, the biochemical consequences of protein photo-damage are not always addressed in CALI experiments. In order to address the extent of tcV0c damage after light exposure in the presence of the membranepermeant biarsenical dye FiAsh-EDT2 and apprehend the functional and molecular consequences of its potential modifications, we first analysed the oxidative state of the protein. Then, as we have previously shown that V0c interacts with VAMP2 to modulate neurotransmitter release [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF], we addressed the consequences of photo-damage on this interaction. We used bacterially expressed V0c (HHtcV0c) in which a tetra-cysteine tag was inserted in the N-terminal domain (Figure 1a). The protein was purified under conditions that limit oxidization and subjected to an in vitro CALI protocol, in the absence or presence of FiAsh-EDT2 (See Materials and Methods). Samples were analysed by western blot and used in ELISA for VAMP2 binding assays. Their oxidative state was also analysed by mass spectrometry. Western blot analysis using an anti-HSV antibody showed that under our experimental conditions, light exposure of the protein in the presence of FiAsh-EDT2 induced a slight shift in the migration profile of HHtcV0c (Figure 1b). The previously reported V0c dimer [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF] also showed the same slight shift. No shift was observed using V0c devoid of tetra-cysteine tag and SDSresistant oligomeric tcV0c bands were not affected by the CALI protocol (Online Resource 1). Neither light exposure in the absence of FiAsh-EDT2, nor FiAsh-EDT2 addition in the absence of light, induced a similar shift of HHtcV0c (Figure 1). A very similar shift was previously observed upon investigating CALI-damaged calmodulin (CaM) [START_REF] Yan | Fluorophore-assisted light inactivation of calmodulin involves singlet-oxygen mediated cross-linking and methionine oxidation[END_REF]. Methionine oxidizations throughout the CaM protein sequence were found and the binding partner profile was different in pull-down assays using untreated or CALI-treated CaM [START_REF] Yan | Fluorophore-assisted light inactivation of calmodulin involves singlet-oxygen mediated cross-linking and methionine oxidation[END_REF]. We consequently analysed CALI-treated HHtcV0c by mass One of these peptides PepSlyD (Table 1) contained two methionine residues but did not show additional oxidation upon CALI treatment (Online Resource 2b). In order to investigate whether CALI treatment affected binding to VAMP2, we conducted ELISA experiments and tested the binding of GST-VAMP2 (1-92) to immobilized HHtcV0c. As shown in Figure 2, VAMP2 still binds to immobilized CALI-treated HHtcV0c very similar to non-modified or non-CALI treated V0c [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF]. In both cases, this binding was dependent on the integrity of the two membrane proximal tryptophan residues (W89,90) of VAMP2 that were shown to be important in binding to V0c [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF]. These data indicate that CALI Tetra-cysteine tagged ATP6-V0c insensitive to shRNA.

As the proton pump activity of the V-ATPase is crucial for cell survival and in order to study the immediate effect of V0c inactivation on neurotransmission, we devised a means to replace, at least partially, endogeneous V0c by a light-sensitive version. CALI of V0c should allow its rapid inactivation and evaluate its implication in the final steps of neurotransmitter release. We therefore designed a plasmid construct (Figure 3a)

that contained an inducible cassette expressing the fluorescent reporter protein tRFP and a shRNA that partially silences endogeneous V0c. The same plasmid constitutively expresses the reverse tet-transactivator rtTA3 and a tetra-cysteine tagged ATP6-V0c (tcV0c) at exactly the same position as in the bacterially expressed HHtcV0c. In order to limit protein modifications and allow the newly synthesised V0c

to mix with endogenous non-modified V0c, this construct did not contain any additional tag (Figure 3a). The tcV0c DNA coding sequence was modified to render it insensitive to the shRNA (see material and methods). For this purpose, we used the degenerate property of the genetic code and exchanged every codon wobble base, keeping the original amino-acid sequence of V0c (Figure 3b). In the presence of doxycycline, rtTA3

binds and activates a tetracycline-responsive element upstream of the expression cassettes of tRFP and the anti-V0c shRNA in miR30 backbone coding sequences (Figure 3 a,c).

Neuronal transfections and exogenous protein expression. Rat brain hippocampal

organotypic cultures were transfected by GeneGun and the expression of tRFP as well as anti-V0c shRNA was induced by doxycycline. Transfected and induced cells were identified by tRFP fluorescence that homogeneously stained neuronal cell bodies and neurites (Figure 4a). Perfusion with the membrane permeable fluorescein-based arsenical dye FiAsh-EDT2 stained all cells. Excess and non-specific FiAsh-EDT2 binding was then extensively washed out in the presence of BAL, a potent FiAsh-EDT2 antidote that strongly binds to biarsenical dyes. Only tRFP-expressing cells kept fluorescein staining, confirming the specific retention of FiAsh-EDT2 by neurons expressing tcV0c (Figure 4b).

Photo-inactivation of tcV0c inhibits neurotransmission between connected CA3 neurons. Synaptic transmission was continuously monitored in connected CA3

neuronal pairs throughout the photo-inactivation protocol. tRFP-positive neurons were patched and maintained in current clamp (Figure 5a).

Discussion

The V-ATPase is thus thought to play two roles in neurotransmitter release at different stages in the synaptic vesicle cycle, (i) establishing the proton gradient that transporters use to load vesicles with transmitter; (ii) modulate synaptic vesicle fusion at the plasma membrane. In this second role, more than three decades ago, experiments designed to identify proteins involved in acetylcholine release from Torpedo synaptosomes, uncovered a non-elucidated role of V0c subunit in quantal and calcium-dependent acetylcholine release [START_REF] Israel | Purification of a presynaptic membrane protein that mediates a calcium-dependent translocation of acetylcholine[END_REF][START_REF] Falk-Vairant | Cell lines expressing an acetylcholine release mechanism; correction of a releasedeficient cell by mediatophore transfection[END_REF][START_REF] Falk-Vairant | Quantal acetylcholine release induced by mediatophore transfection[END_REF][START_REF] Falk-Vairant | Evoked acetylcholine release expressed in neuroblastoma cells by transfection of mediatophore cDNA[END_REF]. More than a decade ago, studies on yeast vacuolar fusion mechanisms uncovered a crucial role of V0 c and a-subunits in membrane fusion downstream of SNARE complex assembly [START_REF] Peters | Trans-complex formation by proteolipid channels in the terminal phase of membrane fusion[END_REF][START_REF] Bayer | Vacuole membrane fusion: V0 functions after trans-SNARE pairing and is coupled to the Ca2+-releasing channel[END_REF]. Subsequently, an unbiased genetic screen for synaptic malfunction in Drosophila identified the a1subunit of V0 as an essential component in evoked synaptic transmission, also acting downstream of SNAREs [START_REF] Hiesinger | The v-ATPase V0 subunit a1 is required for a late step in synaptic vesicle exocytosis in Drosophila[END_REF]. Interestingly, it was also shown that Ca 2+ /calmodulin interaction with V0a specifically controlled spontaneous neurotransmitter release at Drosophila neuromuscular junction [START_REF] Wang | Ca2+-Calmodulin regulates SNARE assembly and spontaneous neurotransmitter release via v-ATPase subunit V0a1[END_REF]. Moreover, independently from its H + transport function, it has been shown that the a-subunit of the V-ATPase regulates insulin secretion in pancreatic beta-cells [START_REF] Sun-Wada | The a3 isoform of V-ATPase regulates insulin secretion from pancreatic beta-cells[END_REF], lysosome-phagosome fusion in zebrafish [START_REF] Peri | Live imaging of neuronal degradation by microglia reveals a role for v0-ATPase a1 in phagosomal fusion in vivo[END_REF] and plays a crucial role in exosome-mediated apical secretion of Hedgehog-related proteins in C.elegans [START_REF] Liegeois | The V0-ATPase mediates apical secretion of exosomes containing Hedgehog-related proteins in Caenorhabditis elegans[END_REF]. The fact that alternative splicing controls differential sorting of V0 a1-subunit variants to nerve terminals or distal dendrites is also consistent with a specific role for a-subunits in neurosecretion [START_REF] Poea-Guyon | Alternative splicing controls neuronal expression of v-ATPase subunit a1 and sorting to nerve terminals[END_REF]. In mice, inhibiting V0c interaction with VAMP2 significantly decreases neurotransmission, highlighting the importance of V0c as a modulator of higher vertebrate's SNARE-dependent neurotransmission [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF].

Molecularly, the V0 domain subunits of the V-ATPase have been shown to coimmunoprecipitate with SNARE proteins, the calcium sensor synaptototagmin, as well as the synaptic vesicle proteins synaptotaphysin, and SV2 [START_REF] Baldwin | Association of botulinum neurotoxin serotypes a and B with synaptic vesicle protein complexes[END_REF][START_REF] Galli | The V0 sector of the V-ATPase, synaptobrevin, and synaptophysin are associated on synaptic vesicles in a Triton X-100-resistant, freeze-thawing sensitive, complex[END_REF]. Chromophoreassisted light inactivation is a very useful technology that allows live-monitoring of the functional consequences of rapid and targeted protein inactivation [START_REF] Lin | Optogenetic inhibition of synaptic release with chromophore-assisted light inactivation (CALI)[END_REF]. In this report, we describe targeted photo-inactivation of the V-ATPase V0c subunit, analyse its biochemical modifications and monitor the consequences on neurotransmission of its inactivation in living neurons. In synaptic vesicle membranes, V0c is very tightly compacted in the membrane and only loops 1-2 and 3-4 are exposed to the cytosolic side. The N and C-termini as well as loop 2-3 are intraluminal. In order to examine the biochemical modifications that light inactivation can induce in V0c, we used bacteriallyexpressed protein (HHtcV0c) in which a tetra-cysteine tag was inserted in the nonconserved N-terminal part. The choice of the site of tag insertion was driven by the very compact structure of the protein and that modification in the loops might perturb protein structure and complicate functional conclusions. Western blot analysis of in vitro photo-inactivated V0c showed a slight shift in the apparent molecular weight of the protein. This shift resembled the electrophoretic behaviour of oxidized proteins as described for S100A9 [START_REF] Sroussi | Oxidation of methionine 63 and 83 regulates the effect of S100A9 on the migration of neutrophils in vitro[END_REF] and calmodulin after CALI treatment [START_REF] Yan | Fluorophore-assisted light inactivation of calmodulin involves singlet-oxygen mediated cross-linking and methionine oxidation[END_REF]. In order to identify potential oxidization hot spots in CALI-treated V0c, we analysed by mass spectrometry the oxidation profile of amino acids susceptible to oxidization [START_REF] Lin | Optogenetic inhibition of synaptic release with chromophore-assisted light inactivation (CALI)[END_REF] (tryptophan, tyrosine, histidine, cysteine, proline, lysine and methionine). We addressed the specificity of the damage induced by oxygen radicals, by analysing the extent of SlyD oxidation, a bacterial contaminant chaperone that co-purified with HHtcV0c. Mass spectrometry analysis of tryptic peptides generated by CALI in solution indicated that, in all conditions, no significant difference was observed in oxidation levels of SlyD peptide.

Among all amino acid residues analysed, only methionines were oxidized and most of the photo-damage was found in the cytoplasmic V0c loop 1-2. It is noteworthy however that biochemical damage observed in vitro may be unrelated to that which occurs in a living cell. In vivo singlet oxygen should only have access to protein moieties located inside synaptic vesicles: the N-terminus of the protein, loop 2-3 and potentially parts of TM1 that was shown to be accessible to hydrophilic compounds in arthropods [START_REF] Jones | The first putative transmembrane helix of the 16 kDa proteolipid lines a pore in the V<sub>o</sub> sector of the vacuolar H<sup>+</sup>-ATPase[END_REF].

Oxidization might also affect V0c oligomerization status and potentially its interaction with other molecular partners. By introducing a tetracysteine tag in the N-terminal extravesicular side of the V-ATPase V0a subunit, an earlier report [START_REF] Poea-Guyon | The V-ATPase membrane domain is a sensor of granular pH that controls the exocytotic machinery[END_REF] has addressed the consequence of damaging this subunit on neurotransmission. Although careful control recordings were performed, especially to verify that damaging subunits of the V1 domain did not inhibit neurotransmission, extra-vesicular generation of singlet oxygens could very locally damage surrounding cytosolic effectors unrelated to V-ATPase and implicated in SNARE-dependent fusion. In the configuration we used, the synaptic vesicle membrane should serve as a shield and protect vicinal cytosolic proteins, as well as the cytosolic moiety of synaptic vesicle membrane proteins from oxygen radicals.

For experiments in living cells, we combined in the same vector the constitutive expression of a tetracysteine tagged V0c subunit and an inducible cassette coupling the expression of tRFP as a fluorescent transfection marker and an shRNA that specifically downregulates endogenous V0c subunit. Specific chromophore-assisted light inactivation of V0c subunit in a presynaptic neuron rapidly (2-3 min) inhibited neurotransmission between connected CA3 pyramidal pairs. Interestingly, our ELISA experiments showed that the CALI treated V0c still interacts with VAMP2 with the same specificity criterion as wild type V0c. These results suggested that the observed decrease in neurotransmission upon CALI in living neurons is not due to a change in V0c binding to VAMP2. This is corroborated by the fact that no change in PPR was observed after CALI, while perturbing VAMP2 / V0c interaction lead to a significant increase in PPR [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF]. Monitoring neuronal activity in connected non-transfected pairs guaranteed that the stimulation frequency was sufficiently moderate to exclude ready releasable pool depletion. In addition, in reciprocally connected pairs, the CALI protocol only reduced neurotransmission when the presynaptic neuron was transfected (not shown).

Exogenous overexpression of tcV0c might increase the plasma membrane pool of V-ATPase all over the neuronal plasma membrane and hence facilitate endocytosis through alkalinisation of the cytosol [START_REF] Zhang | Vesicular ATPase Inserted into the Plasma Membrane of Motor Terminals by Exocytosis Alkalinizes Cytosolic pH and Facilitates Endocytosis[END_REF]. Our data from reciprocally connected neurons argue against this interpretation since monitoring neurotransmission from a nontransfected presynaptic neuron towards a CALI treated postsynaptic neuron and vice versa do not show any sign of membrane capacitance changes. It could also be argued that singlet oxygens can damage the membranes in which the protein is inserted and therefore lead to changes in membrane permeability, inducing leakage either in neuronal plasma membranes or synaptic vesicles. Our electrophysiological recordings also argue against this interpretation since membrane resistance changes were not statistically significant. In our recording conditions, any impact on vesicular acidification should also be negligible since the stimulation frequency is far too low to deplete reserve synaptic vesicle pools and high concentrations of Bafilomycin, which block the proton pump, showed 5 times slower inhibition kinetics than CALI treated samples.

In conclusion, the present study confirms and strengthens our former findings about the important role of the V-ATPase V0c membrane sector in modulating neurotransmitter release [START_REF] Giovanni | V-ATPase membrane sector associates with synaptobrevin to modulate neurotransmitter release[END_REF]. It suggests that CALI-triggered V0c inactivation induces distinct molecular perturbations that do not affect interaction with VAMP2 and that V0c is positioned strategically close to the site of exocytosis.

G T T A T T A G C G A C T C A T A T A T A T AGCGCCATGGGAGCTGCCTATGGCACAGCCAAGAGTGGCACTGGCATCGCAGCCATGTCGGTCATGAGGCCAGAG T T G C T C T C T A C T G A A G T T A A G A CTGATCATGAAGTCCATCATCCCAGTGGTTATGGCTGGGATCATCGCCATCTACGGCCTGGTGGTTGCAGTACTT A A A T A T G T C C A T A A T T G C T C G T G ATCGCTAACTCCCTGACTGATGGCATCACCCTCTACAGGAGTTTTCTTCAACTGGGTGCTGGCCTGAGTGTGGGG A C T T C G C G T G G T A C C G G C A A T C C A C CTGAGTGGCCTGGCTGCTGGCTTTGCCATTGGCATTGTCGGAGATGCTGGTGTCCGGGGCACTGCCCAGCAGCCT C C G C G A G C G A C A T T C A A T C G A G A A A CGACTGTTCGTGGGCATGATCCTGATCCTCATCTTTGCGGAGGTGCTTGGCCTCTACGGTCTCATCGTGGCCCTA T C T C G A C A G A C C A T A G G T A G A C G T ATCCTCTCCACAAAGTAG

A G G T A A

Table1

Click here to download Table Table 1.pdf

  protein, was synthesized by MWG Biofin and provided in pEX-E vector. The shRNAinsensitive and tetracystein-tagged ATP6V0c (tcV0c) was constructed as follows: the sequence encoding the tetracystein tag (CCPGCC) was inserted, by directed mutagenesis, between isoleucine 4 and lysine 5 of wATP6V0c. The tcV0c was then amplified by PCR using Not I-flanked primers. Not I sites were introduced by mutagenesis in 5' and 3' of the EGFP sequence in pINDUCER11 and the subsequent Not I-EGFP-Not I sequence was excised and replaced by Not I-tcV0c-Not I sequence.

Nunc

  Maxisorp 96-well plates were coated overnight with 100 µl of CALI-treated or control HHtcV0c (2 µg/ml) in 100 mM NaHCO3 (pH 8.9) buffer. After a 1 hr blocking step with 25 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1% casein, 0.05% Tween 20, 300 nM of purified GST-VAMP2-(2-92) or GST-VAMP2-(2-92 W89,90A) was added in 25 mM

2. 5

 5 KCl, 2 MgCl2, 0.8 NaH2PO4 and 10 D-glucose, and was equilibrated with 95% O2-5% CO2. Patch pipettes (5-10 MΩ) were filled with a solution containing (in mM): 120 K-gluconate; 20 KCl; 10 HEPES; 0.5 EGTA; 2 MgCl2; 2 Na2ATP; 0.3 NaGTP (pH 7.4). All recordings were made at 30°C in a temperature-controlled recording chamber (Luigs & Neumann, Ratingen, Germany). Both neurons were recorded with a Multiclamp 700B amplifier (Axon Instruments, Molecular Devices). The pre-synaptic cell was maintained in current clamp configuration and the post-synaptic cell in voltageclamp. Pre-and post-synaptic cells were held at their resting membrane potential (~-70 mV). Presynaptic APs were generated by injecting brief (10 ms) depolarizing pulses of current at a frequency of 0.1 Hz. Paired-pulse ratio was assessed with two presynaptic stimulations delivered at a frequency of 20 Hz. The voltage and current signals were low-pass filtered (3 kHz), and acquisition of 1 s sequences was performed at 10 kHz with pClamp (Axon Instruments) version 10. Data were analyzed with ClampFit (Axon Instruments) and custom made software written in LabView (National Instruments). Synaptic responses were averaged following alignment of the presynaptic action potentials using automatic peak detection. Pooled data are presented as mean ± standard error to mean in all the figures, and statistical analysis was performed using the Mann-Whitney U-test or Wilcoxon rank-signed test with a significance level of p<0.01.

  spectrometry and compared the oxidization state of light-exposed protein in the presence or absence of FiAsh-EDT2, and also FiAsh-EDT2 addition in the absence of light exposure. Tryptic digestion of HHtcV0c reproducibly generated four peptides (PEP1-4), illustrated in Online Resource 2a. PEP1 covered part of the N-terminal tag sequence upstream of the HSV tag, PEP2 covered TM 1 with an additional 10 Nterminal amino acids, PEP3 covered loop 1-2 sequence and PEP4 covered principally TM3 (Online Resource 2a). All these peptides covered 45 % of the V0c sequence. In all conditions, PEP4 did not show any oxidized residue. PEP1 and 2 showed significant oxidization on methionine residues, independently of CALI damage. PEP3 methionine residues M44, M47, M53 showed specific and extensive oxidation under light exposure in the presence of FiAsh-EDT2 (Online Resource 2b and Table1). Analysis of the ratio of oxidized / un-oxidized peak surface areas of the different peptides showed that among all peptides, PEP 3 was the most affected. Triple oxidization of PEP3 dramatically increased (138 fold) after light exposure in the presence of FiAsh, in comparison to approximately 2 and 6 fold in the presence of light or FiAsh respectively.In order to address the specificity of oxidations, we analyzed, in parallel to HHtcV0c, the oxidation extent of the bacterial proteins SlyD that contaminates HHtcV0c preparations and co-purify on Ni-NTA-coupled beads. Tryptic digestion of SlyD generated five different peptides (not shown) covering 46 % of the protein sequence.

  can generate oxidative damage to V0c in solution, without affecting interactions with VAMP2. Furthermore, in vivo, the diffusion of reactive oxygen species would be constrained by the synaptic vesicle membrane to intraluminal protein domains due to the N-terminal position of the tetracysteine tag (see Discussion). Downregulation of V0c subunit by shRNA. In order to prepare and optimize the integration of light-sensitive V0c in functional protein complexes in living neurons, we reasoned that endogenous V0c levels should be partially downregulated. For this purpose we chose to use a shRNA approach. The unavailability of specific antibodies to follow modifications in endogenous V0c expression levels prompted us to evaluate the effectiveness of the shRNA approach in a heterologous system, where we measured the downregulation of exogenously expressed EGFP-V0c in the presence of anti-V0c shRNA. For this purpose, we co-transfected PC12 cells with two different plasmids, one expressing EGFP-V0c and the other anti-V0c shRNA. The latter was cloned in pINDUCER 10 in a miR30 context cassette, the expression of which is inducible by doxycycline. This miR30 backbone should promote long-term stability of the shRNA and therefore more efficient gene silencing[START_REF] Meerbrey | The pINDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo[END_REF]. EGFP-V0c expression levels were monitored using anti-EGFP antibodies and signals were normalized to GAPDH expression levels. Online Resource 3 shows that upon induction of anti-V0c shRNA expression versus the non-induced condition (Control), the EGFP signal was downregulated by 42.87% ± 19.8; n=6 independent experiments p<0.01 (Mann-Whitney U Test).

  Neighbouring synaptically-connected neurons were identified and recorded in voltage-clamp configuration. Presynaptic action potentials were continuously elicited at 0.1 Hz by injecting 10 ms depolarizing current pulses. When the baseline had stabilized (around 10 min), a 100 watt illumination was applied for 5 min. As shown in Figure 5b bottom and 5c right, illumination did not significantly affect baseline recordings in non-transfected neurons (3 % ± 8 %; n=8; p>0.1). In contrast, a significant decrease (33 % ± 4 %; n=8; p< 0.01) was observed in postsynaptic responses of neurons connected to tcV0c-expressing presynaptic ones (Figure 5b Top, 5c left, 5d). The time course of CALI versus Bafilomycin induced inhibition of neurotransmission displayed respectively  2.04 and 10.4 min (Online Resource 4) suggesting that CALI induced inhibition is not related to proton pump inhibition. Paired pulse ratio measurement in CALI-treated neurons showed no significant change (66 ± 4 versus 62 ± 6 % n=8, Wilcoxon p=0.67 before and after light exposure respectively). No change in presynaptic input resistance and capacitance was observed in the CALI-treated neurons (respectively, 96 ± 4% of the control resistance, Wilcoxon test, p>0.1 and 96 ± 4% of the control capacitance). Thisindicates that the free-radicals produced by CALI treatment did not affect the passive properties of the presynaptic neuron. It is noticeable that illuminating tRFP and tcV0c expressing cells in the absence of FiAsh-EDT2 resulted in moderate, but nonsignificant decrease in postsynaptic responses (16 % ± 5 %; n=8; p>0.01) (Figure5d,e), presumably due to tRFP photo-damage. Post-synaptic input resistance was unchanged in all cases (following CALI-treatment: 93 ± 3%, Wilcoxon test p>0.01; in non-transfected cells: 99 ± 3%, Wilcoxon test p>0.1 and in transfected cells without FiAsh: 97 ± 5%, Wilcoxon test p>0.1). No change in post-synaptic membrane capacitance was observed in all cases (from 58 ± 6 pF to 57 ± 5 pF for CALI-treated neurons, 63 ± 12 pF to 59 ± 12 pF for untransfected neurons and from 84 ± 9 pF to 89 ± 11 pF for transfected neurons recorded in the absence of FiAsh.

Fig. 1 Electrophoretic

 1 Fig.1

Fig. 2 VAMP2

 2 Fig.2

Fig. 3 tcV0cFig. 4 FluorescentFig. 5

 345 Fig.3

Figure 1

 1 Figure 1

Figure 2

 2 Figure 2Click here to download Figure Figure2.pdf

Figure 5

 5 Figure 3Click here to download Figure Figure3.pdf

Table 1

 1 Mass spectrometric identification of tryptic peptides

	Notes
	Compliance with ethical standards
	This study does not involve experiments on live animals. All experiments were carried
	out according to the European and Institutional guidelines for the care and use of
	laboratory animals (Council Directive 86/609/EEC and French National Research
	Council) and were approved by the local health authority (# D 13 055 08, Préfecture
	des Bouches-du-Rhône, Marseille).

Table 1 :

 1 Mass Spectrometric identification of V0c tryptic peptides

	Peptide name	Observed m/z	Mr (expected)	Mr (calculated)	Error (ppm )	Charge	Peptide sequence and modification
		512.5554 1534.6443	1534.6439	0.23	3	GSHMASMTGGQQMGR
		776.3265 1550.6384	1550.6388	-0.27	2	GSHMASMTGGQQMGR + Oxidation (M)
	PEP1 V0c	517.8872 1550.6396	1550.6388	0.51	3	GSHMASMTGGQQMGR + Oxidation (M)
		523.2186 1566.6341	1566.6337	0.20	3	GSHMASMTGGQQMGR + 2 Oxidation (M)
		528.5501 1582.6283	1582.6287	-0.21	3	GSHMASMTGGQQMGR + 3 Oxidation (M)
		1050.8084 3149.4032	3149.3933	3.16	3	NNPEYSSFFGVMGASSAMVFSAMGAAYGTAK
	PEP2 V0c	1056.1371 3165.3894	3165.3882	0.39	3	NNPEYSSFFGVMGASSAMVFSAMGAAYGTAK + Oxidation (M)
		1061.4695 3181.3866	3181.3831	1.10	3	NNPEYSSFFGVMGASSAMVFSAMGAAYGTAK + 2 Oxidation (M)
		1066.8012 3197.3816	3197.3780	1.13	3	NNPEYSSFFGVMGASSAMVFSAMGAAYGTAK + 3 Oxidation (M)
		946.4945 1890.9745	1890.9729	0.82	2	SGTGIAAMSVMRPELIMK
		631.3309 1890.9710	1890.9729	-1.03	3	SGTGIAAMSVMRPELIMK
		636.6633 1906.9682	1906.9678	0.17	3	SGTGIAAMSVMRPELIMK + Oxidation (M)
	PEP3 V0c	954.4919 1906.9692 641.9949 1922.9630	1906.9678 1922.9628	0.72 0.11	2 3	SGTGIAAMSVMRPELIMK + Oxidation (M) SGTGIAAMSVMRPELIMK + 2 Oxidation (M)
		962.4891 1922.9637	1922.9628	0.51	2	SGTGIAAMSVMRPELIMK + 2 Oxidation (M)
		970.4858 1938.9570	1938.9577	-0.34	2	SGTGIAAMSVMRPELIMK + 3 Oxidation (M)
		647.3277 1938.9613	1938.9577	1.86	3	SGTGIAAMSVMRPELIMK + 3 Oxidation (M)
		798.3808 1594.7470	1594.7484	-0.83	2	DVFMGVDELQVGMR
	PEP SLYD	806.3791 1610.7437 543.2528 1626.7366	1610.7433 1626.7382	0.29 -0.99	2 3	DVFMGVDELQVGMR + Oxidation (M) DVFMGVDELQVGMR + 2 Oxidation (M)
		814.3766 1626.7386	1626.7382	0.26	2	DVFMGVDELQVGMR + 2 Oxidation (M)

Acknowledgments

We would like to thank S. J. Elledge for the generous gift of pINDUCER 10 & 11 and Nicolas Julien for advice on shRNA construction. The mass spectrometer was obtained using financial support from the "Fédération de Recherche pour le Cerveau" (FRC) through the Rotary operation "Espoir en tête". This work was supported by INSERM, the CNRS and the Agence Nationale pour la Recherche (grant ANR-11-BSV2-020-MOMENT Blanc SVSE2.

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary Material

Click here to access/download Supplementary Material NewSupplementary.pdf


Elio Mansour
email: elio.mansour@univ-pau.fr

Richard Chbeir
email: richard.chbeir@univ-pau.fr

Philippe Arnould
email: philippe.arnould@univ-pau.fr

HSSN: An Ontology for Hybrid Semantic Sensor Networks

Keywords: Semantic Sensor Networks, Ontology, Sensor Mobility

Semantic web techniques (e.g., ontologies) have been recently adopted for sensor network modeling. However, existing works do not fully address these challenges: (i) representing different sensor types (e.g., mobile/static sensors) to enrich the network with different data and ensure better coverage; (ii) representing a variety of platforms (e.g., environments, devices) for sensor deployment, thus, integrating new components (e.g., mobile phones); (iii) representing the diverse data (scalar/multimedia) needed for various applications (e.g., event detection); and (iv) proposing a generic model to allow re-usability in various application domains. In this paper, we propose HSSN, an ontology that extends the Semantic Sensor Network (SSN) ontology which is already re-usable and considers various platforms. We extend the representation of sensors, sensed data, and deployment environments to cope with these challenges. We evaluate the consistency, accuracy, clarity, and performance of HSSN.

INTRODUCTION

Recently, Sensor Networks (SNs) have impacted more and more application domains [START_REF] Sohraby | Wireless sensor networks: technology, protocols, and applications[END_REF] such as environmental sensing, military, and medical fields. Various sensors (e.g., camera, microphone) are nowadays embedded in smart phones, and capable of sensing useful data for various purposes (e.g., pollution monitoring in a city). Therefore, considering such devices, and other equipment capable of sensing, is very beneficial for knowledge extraction in sensor networks. Nonetheless, SNs may produce heterogeneous data, that have to be collected, processed, and analyzed in order to provide various services for network managers. Representing, sharing, and integrating the aforementioned data is a challenging task. In order to address this challenge, semantic web techniques, such as ontologies, have been adopted for their information representation. However, existing approaches on sensor network representation [1-4, 6, 11, 13] are restrictive due to the following issues:

• Lack of platform diversity: existing approaches do not consider equipment with embedded sensors (e.g., smart phones, drones, machines) as platforms, in addition to traditional platforms (e.g., buildings, cities, offices) where sensors are deployed. Extending the platform representation, by both considering and detailing the representation of various types of platforms, allows the addition of new components to the network, nested platforms, and dynamic, collaborative sensing activities (e.g., crowd-sensing).

• Lack of sensor diversity: these works do not represent different sensor types (e.g., mobile/static sensors, simple sensor nodes/multi-sensor devices, sensors capable of sensing scalar/multimedia properties). Providing a more detailed sensor representation that considers various attributes (e.g., mobility) improves network coverage, and allows sensor tracking and dynamic sensing. • Lack of data diversity: most works cover scalar environment properties (i.e., mainly focus on scalar data such as temperature, motion, and neglecting multimedia data such as sounds, images, and videos). Since several devices are capable of sensing both types, and data diversity is required for different application purposes (e.g., event detection), it is important to cover scalar and multimedia data in the representation. • Lack of re-usability: these approaches are heavily linked to a specific application domain. The sensor network modeling should remain generic and re-usable in different contexts.

To answer these challenges, we present here an extension of the widely used Semantic Sensor Network ontology (SOSA/SSN) [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF] called HSSN. It allows the representation of hybrid sensor networks, i.e., networks containing mobile/static sensors, scalar/multimedia properties, and infrastructures/devices as platforms where sensors are deployed. We chose to extend SSN since it is already re-usable in various contexts and allows the representation of different platforms. Nonetheless, sensor and data diversity are not fully developed. Our proposal adds diverse data, sensors, and details the description of various platform types. In addition, HSSN does not contain domain specific knowledge and can be easily aligned with other ontologies (e.g., mobile phone, smart building ontologies [START_REF] Thanos G Stavropoulos | Bonsai: a smart building ontology for ambient intelligence[END_REF]). The rest of the paper is organized as follows. Section 2 illustrates a scenario that motivates our proposal. Section 3 reviews related work regarding mobility, platforms, and sensed data. Section 4 details the HSSN ontology. Section 5 describes the experimental setup and results. Finally, section 6 concludes the paper and discusses future research directions.

MOTIVATING SCENARIO

To highlight the utility of our proposal, we choose the following scenario (we only use this example to concretely illustrate the needs, challenges, and motivations behind our work. We do not consider it to be a generic, all summarizing, sensor network application scenario). Consider a smart mall/shopping center (cf. Fig. 1). In order to optimize client comfort, health, and security, the smart mall relies on a set of sensors (s 1 -s 9 ) to monitor the environment. Video surveillance cameras (s 1 -s 6 ) monitor security related events. Humidity, CO 2 , and temperature sensors (s 7 , s 8 , and s 9 respectively) make observations that help regulate the indoor air quality, and temperature. The sensed data is stored and used for these applications. However, many improvements still need to be integrated: • Need 4-Provide a rich documentation of critical events: in order to increase the understanding of events (e.g., when reporting incidents, providing evidences), rich descriptions should be provided to police with a variety of sensed multimedia and scalar data (e.g., video, audio, image, temperature, humidity). Currently, reports on attack incidents (e.g., gunshot) rely only on video surveillance footage (e.g., no noise levels to confirm the gunshot, no motion data to describe how people ran away). A bigger data variety is needed. • Need 5-Adapt to changing event detection needs: sometimes new/spontaneous events need to be detected, the mall should be able to sense the required data and detect these events. However, the current sensor configuration/deployment and sensed data cannot be easily modified. This doesn't allow the detection of new events. In order to address these issues, the mall managers would need to add more sensors to cover all zones. This ensures full coverage of the mall (Need 3), and allows multiple observations from each zone for aggregation (Need 1). In addition, they could replace the cameras with more advanced ones that enable image processing for tracking purposes (Need 2). However, this increases the equipment, maintenance, and implementation costs without addressing Needs 4 and 5. A more appropriate solution would be to integrate visitors' mobile phones (since they embed sensors) as mobile sensors in the mall's network, while avoiding excessive resource consumption from the devices (e.g., draining a phone's battery). This provides the following benefits: (i) sensor mobility provides observations from different areas of the mall, multiple sensors can therefore collaborate to calculate more reliable air quality/temperature measures (Need 1); (ii) mall visitors can easily be tracked using their connected mobile phones (Need 2), location information can also be used to discover uncovered areas (Need 3); (iii) using various sensors from different devices helps cover a wider array of scalar/multimedia properties (Need 4); and (iv) these devices provide a diversity of hardware (e.g., sensors), software, and services that can be adapted to changing event detection needs (Need 5). However, when adding mobility, diverse data, and devices to the network, the following challenges emerge:

• Challenge 1: How to expressively describe locations in the mall? • Challenge 2: How to consider ad-hoc devices in the network?

How to query them based on their capabilities (e.g., without draining their batteries)? How to represent the services that they provide? • Challenge 3: How to track locations and coverage areas of mobile sensors? • Challenge 4: How to collect scalar/multimedia observations from sensors?

Other challenges also exist when modeling sensor networks. However, we address here the aforementioned four challenges from a data modeling perspective by proposing an extension of the semantic sensor network ontology that includes mobility, platform, and data related concepts.

RELATED WORK

In this section, we study existing sensor network ontologies. We focus our review on sensor mobility, deployment platforms, and semantic representation of multimedia data. We compare these works based on the following criteria:

(1) Sensor diversity: Indicating if different types of sensors exist in the sensor network (e.g., mobile/static sensors, simple nodes/multi-sensor equipment, sensors capable of sensing scalar/multimedia properties). (2) Platform diversity: Stating if the approach allows and details the description of different platforms where sensors are deployed (e.g., in infrastructures, on devices). (3) Data diversity: Denoting the approach's ability to handle various data/properties (e.g., scalar, multimedia). (4) Re-usability: Indicating if the approach is re-usable in various contexts.

Sensor Diversity

In [START_REF] Avancha | Ontology-driven adaptive sensor networks[END_REF], the authors focus mainly on features that describe the sensor nodes, their functionality, and their current CPU, memory, and power supply states (in order to determine the future state of the WSN). However, they do not represent different types of sensors.

In [START_REF] Gomez | An ontology-centric approach to sensormission assignment[END_REF], the authors provide a set of ontologies describing missions, tasks, sensors, and deployment platforms for sensor to task assignment. Unfortunately, different types of sensors were not considered. In [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF], the authors propose the SOSA/SSN 1 ontologies. Together, they describe systems of sensors and actuators, observations, the used procedures, properties, and so forth. SOSA/SSN propose simple sensor node representation, as well as (sensing) systems/devices. However, SOSA/SSN do not propose any mobility-related concepts, nor multimedia data/properties. The authors only consider one aspect of sensor diversity (i.e., simple sensor nodes/sensor systems).

In [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF], the authors propose an extension of SSN, denoted MSSN (Multimedia SSN), where they detail the technical aspects of multimedia data (e.g., video, audio segments, frequencies). In this work, the authors improve the sensor diversity of SOSA/SSN by adding a media sensor (i.e., a sensor type that observes multimedia properties). However, they do not achieve full sensor diversity as they do not consider sensor mobility (i.e., mobile/static sensors).

Platform Diversity

The authors in [START_REF] Maisonneuve | Participatory noise pollution monitoring using mobile phones[END_REF] only consider embedded sensors on mobile phones to monitor noise pollution. In [START_REF] David | Wireless sensors for wildfire monitoring[END_REF], the authors rely on traditional deployment of sensor nodes in the wilderness to detect fire events. The problem is, these works do not provide any platform diversity. In the SSN ontology [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF], sensors are deployed on platforms. SSN also introduces systems, that can integrate various sensors, actuators, and samplers. Therefore, SSN provides a foundation for sensor deployment on various platforms (e.g., traditional deployment on platforms, embedding sensors in systems and devices). However, the differences between theses platforms is not detailed in SSN. The description of physical infrastructures/environments such as smart buildings and cities (where it would be interesting to model maps and locations) is different than of machines, drones, and devices that host sensors (where it would be interesting to model hardware and software). It is better to distinguish and detail the description of different platform types to better understand the environments where sensors are deployed (e.g., for location-based services in infrastructures, task assignment based on hardware/software capabilities for devices). MSSN [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF] suffers from the same limitation since it is based on the SSN ontology and does not add any new concepts related to platforms.

Data Diversity

In [START_REF] Durand | Ontology-based object recognition for remote sensing image interpretation[END_REF], the authors represent images for object recognition purposes. The scope of their work does not extend to other types of multimedia data (e.g., video, audio). In [START_REF] Mezaris | An ontology approach to object-based image retrieval[END_REF], the authors are also limited to image representation, since they propose an approach for object-based image retrieval. In [START_REF] Kumar | Ear-phone: an end-to-end participatory urban noise mapping system[END_REF], the authors monitor noise pollution in urban zones by sensing (audio) noise levels using occupants' mobile phones. The authors only consider noise data, and geo-locations in order to generate a noise level map. Therefore, their proposal does not fully consider data diversity (e.g., video, images, other scalar data). The SSN ontology [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF] does not consider multimedia observations. It details scalar sensed data. This motivated the proposal of MSSN [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF] where the authors represent multimedia data in sensor networks. For each multimedia observation value, the authors associate data descriptors (denoted media descriptors), and data segments (denoted media segments). Their proposed ontology, MSSN, complements the SSN ontology [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF] since the latter does not cover multimedia contents nor multimedia sensors.

Re-usability

In [START_REF] Maisonneuve | Participatory noise pollution monitoring using mobile phones[END_REF], the authors propose a noise pollution monitoring solution in a city using mobile phones to sense noise. The authors enrich the sensed information by allowing users to add contextual information to their sensor observations. However, it lacks the genericity needed for it to be reusable in other contexts. In [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF], the authors propose a multimedia wireless sensor network ontology for event detection purposes (the authors include concepts related to atomic, complex events, and event detection/composition). These added concepts are domain specific and not necessary in other application scenarios. This restricts MSSN's re-usability. Each of these works are taskcentric and heavily linked to an application purpose. The SSN ontology [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF] remains generic and re-usable in various contexts since it is extensible and does not contain any concepts that link it to any specific application.

Discussion

The aforementioned works do not fully integrate sensor diversity in their representation of sensor networks (i.e., static/mobile sensors, simple node/multi-sensor devices, and scalar/multimedia sensors). The SSN ontology [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF] is a culmination of much of the related work on semantic sensor networks and is the most widely used (reusable). In addition, SSN is extensible, facilitates alignments with other standards, and allows the integration of new concepts. The MSSN ontology [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF], integrates multimedia data in SSN. Therefore, we propose to extend SSN since: (i) it partially allows sensor diversity; (ii) it is re-usable and does not contain any domain specific knowledge; and (iii) it allows having various platform types. Moreover, we do not neglect MSSN for its ability to cover multimedia data (data diversity). Therefore, our proposal will extend SSN and use key MSSN concepts in order to achieve full sensor diversity, platform diversity enriched with detailed descriptions of each type (e.g., infrastructures, devices), and finally data diversity through the coverage of scalar/multimedia sensed data.

HSSN ONTOLOGY

In this section, we detail our proposed extension of the SSN ontology, and mainly our additions related to: (i) sensor diversity;

(ii) platform diversity; and (iii) data diversity. The following prefixes sosa:, ssn:, mssn:, time:, and hssn: refer to the SOSA [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF], SSN [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF], MSSN [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF], Time [START_REF] Jerry | Time ontology in OWL[END_REF], and HSSN ontologies respectively. We begin first by describing sensor-related concepts. . This is specifically important for tracking mobile sensors, since static sensors do not change locations (cf. Fig. 3). A hasPastLocation property is added to retrieve the previous positions of a (mobile) Sensor, and also a hasLocation-Time (cf. Fig. 4) property is added to map these positions to time instants or intervals in order to track sensors (temporal entities are extracted from Time ontology [START_REF] Jerry | Time ontology in OWL[END_REF]). The composition of a CoverageArea is explained in Fig. 7. The SensingLocation is equivalent to the sensor's Location, and the angles and range of the CoverageArea are equivalent to the sensor's HorizontalAngle, VerticalAngle, and Range properties that we added in HSSN as part of a system's properties. Since static sensors are immobile, it is easy to know their coverage areas using the sensor's location, and its sensing range and angles. In contrast, knowing the coverage areas of mobile sensors is more challenging, since these areas move when the sensors move. In order to keep track of these changes, the object property currentlyCovers maps each Sensor to its current CoverageArea (cf. Fig. 8). Also, the property hasPastCoverageArea maps mobile sensors to their respective sets of previous coverage areas (cf. Challenge 3 in Section 2). Finally, hasCoverageTime is the property that maps previous coverage areas to temporal entities (i.e., time instant or interval from Time ontology [START_REF] Jerry | Time ontology in OWL[END_REF]) for tracking purposes (cf. Fig. 9). In SSN [START_REF] Haller | The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation. Semantic Web -Interoperability, Usability, Applicability an[END_REF], sensors are deployed on platforms. In Fig. 10, we define the following child concepts of Platform: (i) Infrastructure, a physical environment having locations where sensors could be deployed (cf. Challenge 1 in Section 2); and (ii) Device, an electronic equipment where sensors could be embedded (cf. Challenge 2 in Section 2). This allows different types of deployments such as the traditional deployment in environments (e.g., buildings, malls) or nested deployment of multi-purpose devices that in turn embed sensors (e.g., mobile phones). This provides platform diversity (criterion 2 cf. Section 3). Every Infrastructure describes a specific physical environment where sensors are deployed. Therefore, infrastructures can host platforms such as other infrastructures (e.g., cities host buildings) and devices (e.g., buildings host mobile phones). However, devices can embed systems of sensors, actuators, and samplers but cannot host infrastructures (e.g., buildings). Each Infrastructure is described by a Location Map which contains (isComposedOf property) a set of Locations (cf. Fig. 11). For example, a building is an Infrastructure that has a Location-Map. The latter describes the spatial relations between individual Locations in the building such as floors, offices, etc. HSSN uses topological, distance, and directional relations to describe the spatial ties that exist between individual Locations. We integrate the aforementioned location-related concepts in order to locate sensors, and better understand the spatial constraints/setup of the Infrastructure. A Device has sub-concepts for storage, communication, processing, and power supply, in addition to the ability of embedding sensors (using the de-ployEntity concept cf. Fig. 12). These concepts describe the Hardware of a Device. The Software part is also represented. A Device could be used for various purposes (e.g., representing mobile phones for mobile phone sensing, machines with mounted sensors for fault detection in an Industry 4.0 scenario). The hardware and software representation allows complex queries such as assigning sensing tasks to devices based on their processing capabilities, or battery status (cf. Challenge 2 in Section 2). Finally, each Device can provide a set of services. Fig. 13 illustrates our service modeling, inspired by the Web Service Modeling Ontology (WSMO) [START_REF] Roman | Web service modeling ontology[END_REF]. We created generic concepts that can be aligned with WSMO. We do not aim to detail the service description to allow alignments with any other service ontology. We limit the service modeling to the following concepts: Service Metadata describes the properties of a Service. The Input represents the set of variables and constraints required for correct service execution, while the Output is the set of generated results. The functionality of a service is described by the Capability concept which is mapped to a specific UserGoal or objective (i.e., a user desire satisfied by the service). Users communicate with a service through UserInteractionInterfaces (choreography in WSMO). Finally, services communicate with each other via the ServiceInteractionInterface (service orchestration in WSMO). Finally, the infrastructure and device detailing also improves sensor diversity by allowing the representation of simple sensor nodes in infrastructures, multi-sensor systems, and multi-sensor devices.

Sensor Diversity

Data Diversity

Audio, image, and video data can be sensed by mobile or static sensors (e.g., surveillance cameras, mobile phones). Also, in order to detect complex events (e.g., gunshot) a combination of multimedia and scalar observations is needed. Therefore, we aim to integrate concepts related to multimedia properties (cf. Criterion 3 in Section 3). In MSSN [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF], multimedia data/properties are integrated in SSN. We re-organize MSSN multimedia concepts into scalar (e.g., temperature, motion) and multimedia (e.g., noise, video) properties as illustrated in Fig. 14. Also, we introduce in Fig. 15 the mediaSenses and scalarSenses relationships to map sensors to their corresponding scalar and/or multimedia observable properties (cf. Challenge 4 in Section 2). This highlights the sensor diversity in HSSN since static/mobile sensors can detect scalar and/or multimedia properties. The authors in [START_REF] Angsuchotmetee | MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks[END_REF] also describe technical aspects/metadata of multimedia objects such as annotations, audio (e.g., frequencies), motion (e.g., trajectories), visual (e.g., color histograms). We use these concepts in HSSN to describe sensor observation values. A MediaValue in HSSN is composed of the MultimediaData concept, referring to the audio, video, or image objects/files and the Medi-aDescriptor concepts, describing the metadata of the multimedia objects (e.g., frequencies, colors). ScalarValues are textual (e.g., temperatures, humidity levels). Finally, we map observation values to their related properties using the hasMediaValue and hasScalarValue relationships. Sensors can now be correctly mapped to observable properties and observation values (cf. Challenge 4 in Section 2). In conclusion, new concepts and properties are introduced in HSSN in order to address the challenges presented in Section 2. Our proposal details the representation of infrastructures (a type of platforms) by adding location maps, individual locations, and spatial relations. This allows the expressively describe locations (cf. Challenge 1). In HSSN we describe devices as platforms that host sensors. We detail device hardware, software, and provided services. In addition, we add properties that help locate, track, and query these devices (cf. Challenge 2). HSSN also provides a description of sensor coverage areas and properties that map both locations and coverage areas to mobile/static sensors at any time (cf. Challenge 3). Finally, we address data heterogeneity by detailing multimedia data objects, their metadata, and scalar data. We also map them to their respective sensors (cf. Challenge 4).

IMPLEMENTATION AND EXPERIMENTAL SETUP 5.1 HSSN Implementation

We implemented the HSSN ontology using Protege 5.2.0 2 . The files are available at http://spider.sigappfr.org/research-projects/ hybrid-ssn-ontology/ (External Links -Download ontology files). Also, a complete documentation can be found at http://spider.sigappfr. org/HSSNdoc/index-en.html. In the following, we detail the SPARQL queries used during the experimentation. Then, we describe the experimental setup, before discussing the obtained results from an accuracy, clarity, performance, and consistency standpoint.

Illustration Example

The challenges mentioned in Section 2 can be addressed via the following SPARQL queries: Platform Diversity: In order to expressively describe locations (Challenge 1) in the mall infrastructure, a detailed representation of location maps and locations is needed (Query 1). Also, covered and uncovered areas should be easily found (Query 2). In order to consider ad-hoc devices in the network (Challenge 2), one should be able to query devices, their hardware (e.g., embedded sensors), software, and services. Query 3 shows how to locate a mobile device by querying its embedded sensor. Similarly, one could query a device based on other characteristics (e.g., battery status, processing power). ?sensor isEmbeddedOn ?du. ?du hasExpansionCard ?hd. ?hd isRelatedToDevice ?dev.} Sensor Diversity: To track sensors at all times (Challenge 3), it is important to know current locations/coverage areas for all sensors (Query 4), as well as previous ones (Query 5). Data Diversity: In order to consider data diversity (Challenge 4), on should be able to distinguish scalar/multimedia data and correctly map them to sensors (Queries 6 and 7).

HSSN Experimental Setup

Here, we did not aim to experiment SSN concepts and properties. We evaluated the impact of our newly added concepts (e.g., static/mobile sensors, infrastructures/devices, multimedia/scalar data). Our objectives were the following:

(1) Accuracy Evaluation: Checks if the added concepts/properties answer the aforementioned challenges. This query based evaluation highlights the impact of our extensions in overcoming the challenges mentioned in Section 2. (2) Clarity Evaluation: Checks if the labels used to describe the concepts/properties are clear and unambiguous to domain stakeholders. The aim is to evaluate the compatibility and clarity of our provided description with respect to the application domain. (3) Performance Evaluation: Measures the impact of HSSN additions on performance (i.e., query run time). The aim is to evaluate the feasibility, performance-wise, of integrating HSSN in sensor network applications. (4) Consistency Evaluation: Checks if the added concepts/properties generate inconsistencies (e.g., anti-patterns) within the structure of the ontology. The aim is to evaluate the soundness of the ontology graph.

Accuracy Evaluation.

We created a population of individuals and ran the aforementioned queries. Then, we compared the obtained and expected results. We created two infrastructures, each described by a location map containing 500 locations. Then, 1000 sensors were deployed (500 mobile/static, 500 scalar/media). Each sensor is located in one location, covers one coverage area, observes one property, and produces one observation value. Platform Results:We ran queries 1, 2, and 3. The returned results match perfectly the expected ones. Infrastructures were correctly assigned to their location maps and included locations. This allowed the identification of distinct spaces/areas. Query 2 correctly returned the set of distinct locations included in each coverage area. This allowed the identification of non covered locations. Query 3 allowed the identification of device hardware related to the embedded sensors. Also, the mobile devices were correctly located in the location map.

Mobility Results: We ran queries 4 and 5 on the population of individuals and for each case the returned results matched exactly the expected ones. Sensors were correctly assigned to their current/previous locations and coverage areas.

Data Results: We ran queries 6 and 7 and obtained an exact matching between the actual and expected results. Thus, scalar/multimedia properties were correctly distinguished. Also, sensors were correctly assigned to the scalar or multimedia observations that they produced.

Result Discussion: The test results showed that locating any type of sensor (i.e., simple node/multi-sensor device, static/mobile sensors, and scalar/multimedia sensors), and knowing their coverage areas is possible at any point in time. Hence, allowing tasks such as tracking mobile sensors, and detecting uncovered areas. Also, the results showed that the detailing of infrastructure and device descriptions (platform diversity) allowed a better knowledge of the environment space (also important for locating sensors). Multisensor devices were also detailed by describing their hardware and software which proved useful when querying devices based on their capabilities (e.g., we ran an additional query that returns sensors/devices with good battery status). From a data diversity standpoint, the results showed that sensors that sense multimedia/scalar properties were correctly distinguished and their observations were accurately retrieved. To conclude, the query results confirmed that the added extensions (i.e., regarding sensor, platform, and data diversity) accurately answer the challenges mentioned in Section 2.

Clarity

Evaluation. We created two evaluation forms: the first 3 for evaluating the ambiguity of the labels used to describe the HSSN concepts, and the second 4 for evaluating the ambiguity of the labels used to describe inter-concept relations. We sent the two forms to 50 sensor network and ontology experts (25 networking experts, and 25 computer scientists). Results in Fig. 16 and 17 show that terms considered clear by computer scientists are sometimes found ambiguous by network experts and vice-versa. Result Discussion: The clarity evaluation allowed the identification and correction of ambiguous/unclear labels that we used to describe our added concepts/properties. In the version currently available online, all labels achieve an acceptable level of clarity (based on the stakeholders' feedback). This reinforces the re-usability of HSSN since it is unambiguous and easily understood.

Performance Evaluation.

In order to evaluate the performance of HSSN, we measured the query run-time by running each of the previously mentioned queries 10 times and calculating the average. We varied the size of the population (100 sensors, 1000 sensors, and 10000 sensors) in order to test various scenarios related to mobility, platforms, and data. Mobility impact: In this test, we varied the percentage of mobile sensors in the network (0, 30, 50, 70, and 100 %). Then, we retrieved the current/previous sensor locations (cf. Fig. 18 and19). We measured the run-time for queries 4 and 5. In Fig. 18, we noticed that increasing the number of mobile devices increases the time required to retrieve current sensor locations. This is due to the fact that locating a device (Query 3) was a more complex task than locating a static sensor since we needed to locate the sensor, its deployment unit, hardware, and then the device. We noticed the same pattern for all three cases (100, 1000, 10000 sensors). Finally, the progression from 0% to 100% mobile devices had a quasi-linear impact on query run-time. Similarly, Fig. 19 details the query run-time for retrieving previous different sensor locations. Since mobile sensors have a larger list of previous locations in comparison with static sensors, increasing the mobility percentage (0, 50, 100 %) increases the query run-time. This progression was also quasi-linear for all three cases (100, 1000, 10000 sensors). 20 shows how sensor distribution on locations affected the time needed to map sensors to their current locations. When all sensors were located in one location, the required time to perform this task was minimal. Then, as we began to decrease sensor densities, the query took more time. Finally, the worst case was when every location contained only one sensor. Data impact: Here, we checked the impact of scalar/multimedia data on the run-time of queries 6 and 7 (cf. Fig. 21). For data diversity impact on performance (cf. Fig. 21), we noticed that in all cases (100, 1000, 10000 sensors) the query run-time was similar when considering scalar and multimedia data. This is due to the fact that we were measuring the time required to retrieve the data and not the time needed to capture/sense it. Result Discussion: The performance evaluation showed that the added concepts/properties do not heavily impact the query run time, which remains quasi-linear in most cases. This highlights the feasibility of using of HSSN in sensor applications (from a performance point of view).

Consistency Evaluation.

In [START_REF] Staab | Handbook on ontologies[END_REF], consistency is defined as a criterion that verifies if the ontology allows contradictions. The descriptions in the ontology should be consistent. Consistency Queries: To evaluate consistency, we adopted the following SPARQL queries that search for anti-patterns, a strong indicator of inconsistencies, in the ontology. Query 8 detects concepts with no parent, and query 9 detects abnormally disjointed concepts in the ontology: Results & Discussion: We found no inconsistencies in the HSSN ontology structure. The only concept subsuming nothing is owl:Nothing (Query 8). Query 9 results indicate that there are no concepts that have abnormal disjoint relations with their relatives. This denotes the soundness of the integration of newly added concepts mainly with the SSN core. Finally, to conclude the inconsistency evaluation, we ran Protege's HermiT 1.3.8.413 reasoner, and found no inconsistencies between the asserted class hierarchy and inferred one. This highlights the soundness of the graph structure, which proves critical when considering future alignments between HSSN and other ontologies (e.g., that describe smart buildings, events).

CONCLUSION & FUTURE WORK

Many works adopted ontologies for better semantic representation of sensor networks. These approaches do not fully consider diversity in terms of sensors, data, platforms, and application purposes. In this paper, we propose an extension of the Semantic Sensor Network ontology (SSN), since it is already re-usable in various contexts. Our proposed ontology, denoted HSSN, adds to SSN sensor mobility, and multimedia data related concepts in order to have a representation of hybrid sensor networks. HSSN also extends the platform representation of SSN in order to fully consider platform diversity. We implemented HSSN, evaluated the consistency, accuracy of our additions, and their impact on performance. As future work, we would like to continue the ongoing evaluation of the completeness of the ontology through comparisons with mobility and sensor taxonomies. Finally, we want to represent a sensor network in a smart environment (e.g., smart building, city) for event detection purposes.

Figure 1 :

 1 Figure 1: Smart Mall Example

Figure 2 :Figure 3 :Figure 4 :

 234 Figure 2: HSSN Sensor View

Figure 5 :Figure 6 :

 56 Figure 5: Coverage Area

Figure 7 :

 7 Figure 7: Coverage Area Composition

Figure 8 :

 8 Figure 8: Sensor/Coverage Area Mapping

Figure 10 :

 10 Figure 10: Platform Representation

Figure 11 :

 11 Figure 11: Infrastructures 4.2.2 Device Representation. A Device is another type of Platform where sensors are deployed. It is introduced in HSSN to represent mobile phones and other sensing equipment.A Device has sub-concepts for storage, communication, processing, and power supply, in addition to the ability of embedding sensors (using the de-ployEntity concept cf. Fig.12). These concepts describe the Hardware of a Device. The Software part is also represented. A Device could be used for various purposes (e.g., representing mobile phones for mobile phone sensing, machines with mounted sensors for fault detection in an Industry 4.0 scenario). The hardware and software representation allows complex queries such as assigning sensing tasks to devices based on their processing capabilities, or battery status (cf. Challenge 2 in Section 2). Finally, each Device can provide a set of services. Fig.13illustrates our service modeling, inspired by the Web Service Modeling Ontology (WSMO)[START_REF] Roman | Web service modeling ontology[END_REF]. We created generic concepts that can be aligned with WSMO. We do not aim to detail the service description to allow alignments with any other service ontology. We limit the service modeling to the following concepts: Service Metadata describes the properties of a Service. The Input represents the set of variables and constraints required for correct service execution, while the Output is the set of generated results. The functionality of a service is described by the Capability concept which is mapped to a specific UserGoal or objective (i.e., a user desire satisfied by the service). Users communicate with a service through UserInteractionInterfaces (choreography in WSMO). Finally, services communicate with each other via the ServiceInteractionInterface (service orchestration in WSMO). Finally, the infrastructure and device detailing also improves sensor diversity by allowing the representation of simple sensor nodes in infrastructures, multi-sensor systems, and multi-sensor devices.

Figure 12 :

 12 Figure 12: Device Components

Figure 14 :

 14 Figure 14: Observable Properties

Figure 15 :

 15 Figure 15: Sensors/Properties

Query 1 :

 1 Knowing the spatial description of infrastructures SELECT distinct ?infrastructure ?locationmap ?location WHERE {?infrastructure isDescribedBy ?locationmap. ?locationmap isComposedOf ?location.} Query 2: Knowing covered locations SELECT distinct ?location ?coveragearea WHERE {?location isIncludedIn ?coveragearea.} Query 3: Locating mobile devices, querying device hardware SELECT distinct ?location ?dev WHERE {?location currentlyLocates ?sensor.

  2 https://protege.stanford.edu/ Query 4: Finding current sensor locations/coverage areas SELECT distinct ?location ?sensor ?coveragearea WHERE {?location currentlyLocates ?sensor. ?sensor currentlyCovers ?coveragearea.} Query 5: Finding previous sensor locations SELECT distinct ?location ?sensor WHERE {?location hasPreviouslyLocated ?sensor}

Query 6 :

 6 Mapping sensors to their scalar properties and observations SELECT distinct ?sensor ?property ?observation WHERE {?sensor scalarSenses ?property. ?property isScalarValueOf ?observation.} Query 7: Mapping sensors to their multimedia properties and observations SELECT distinct ?sensor ?property ?observation WHERE {?sensor mediaSenses ?property. ?property isMediaValueOf ?observation.}

Fig. 16 shows

 16 that a few terms do not meet the acceptable ambiguity level (e.g., ComUnit, DeployUnit), while others (e.g., MediaProperty, MediaValue) need some clarification. Therefore, we considered the experts' suggestions in the final version of the ontology by modifying the following: (i) ExpansionCard instead of DeployUnit; (ii) PowerSupply instead of PowerUnit; (iii) NetworkInterface instead of ComUnit; (iv) Memory instead of StorageUnit; (v) Processor instead of ProcessingUnit ; and (vi) Multimedia instead of Media. Finally, Fig.17shows that in most cases, both categories of experts assigned correctly the inter-concept relationships. Networking experts have low success on the first two questions since the latter are outside of their domain of expertise (regarding inheritance between concepts).

Figure 16 :

 16 Figure 16: Concept Evaluation

Figure 18 :

 18 Figure 18: Mobility impact on current location retrieval

Figure 20 :

 20 Figure 20: Platform impact on current location retrieval

Fig.

  Fig.20shows how sensor distribution on locations affected the time needed to map sensors to their current locations. When all sensors were located in one location, the required time to perform this task was minimal. Then, as we began to decrease sensor densities, the query took more time. Finally, the worst case was when every location contained only one sensor. Data impact: Here, we checked the impact of scalar/multimedia data on the run-time of queries 6 and 7 (cf. Fig.21).

Figure 21 :

 21 Figure 21: Data impact on observation retrieval

Query 8 :

 8 Searching for concepts with no parent SELECT ?a WHERE {?a subClassOf owl:Nothing.} Query 9: Searching for abnormally dijointed concepts SELECT distinct ?A ?B1 ?B2 ?C1 WHERE {?B1 subClassOf ?A. ?B2 subClassOf ?A. ?C1 subClassOf ?B1. ?C1 disjointWith ?B2.}

https://www.w3.org/TR/vocab-ssn/

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