A M Guénault

A Guthrie

R P Haley

S Kafanov

Yu A Pashkin

G R Pickett

M Poole

R Schanen

V Tsepelin

D E Zmeev

E Collin

O Maillet

R Gazizulin

Probing superfluid 4 He with high-frequency nanomechanical resonators down to mK temperatures

Probing superfluid 4He with high-frequency nanomechanical resonators down to mK temperatures

Mechanical resonators are frequently used as tools in experiments involving quantum fluids such as: investigation of quantum turbulence [START_REF] Blažková | [END_REF][2][3][4] , cavitation 5,6 , acoustics in superfluids [7][8][9] , for thermometry at low-temperatures 10 , and quasiparticle detection in superfluid 3 He 11 . Nanoelectromechanical systems (NEMS) are increasingly attracting interest from the scientific community due to their small size, high intrinsic quality factors and exceptional force sensitivity 12,13 . Such unique characteristics make NEMS the perfect candidates for sensing applications in modern low temperatures experiments, such as probing the complex properties of the quantum fluids 3 He and 4 He. NEMS provide a unique opportunity to probe superfluids at lengths comparable to the de-Broglie wavelength of thermal excitations in superfluid 4 He, and the quantum coherence length in superfluid 3 He. Additionally, NEMS have demonstrated unparalleled mass sensitivity on the order of several yoctograms 14,15 , which could be utilized for the detection of quantum vortices in superfluids 16 .

We present operation of two magnetomotively driven, doubly-clamped, composite nanobeams consisting of an aluminum film deposited on pre-stressed silicon nitride from 4.2 K down to 7 mK in liquid 4 He. Superfluid 4 He is an ideal starting place for studying the interaction of nanoscale mechanical objects with quantum fluids, since the viscosity of the 4 He is lower than that of 3 He and the superfluid transition temperature is higher ∼ 2.17 K. Using the well understood range of thermal excitations 17 and topological defects [START_REF] Tsubota | Quantum Turbulence[END_REF] of superfluid 4 He 19 we directly contrast established theoretical framework with the experimental observations. In our earlier work, we have already demonstrated that the nanobeams are extremely sensitive to the normal-fluid component in the hydrodynamic regime [START_REF] Bradley | [END_REF] , above 1 K. Here we extend this research to the ballistic regime, in the temperature range down to 7 mK, detecting the presence of the thermal excitations: phonons and rotons. Furthermore, we argue that nanobeam motion in superfluid generates acoustic waves (first sound), a mechanism that dominates damping at the lowest temperatures.

The two devices discussed in this work have width w = 300 nm, thickness t = 130 nm, and length l = 30 µm and 150 µm, with fundamental mode frequencies of 11.6 MHz and 1.6 MHz, respectively. We fabricated devices on commercially available, undoped silicon wafers covered with a 100 nm thick layer of silicon nitride, pre-stressed tot becomes constant as we approach the internal damping of the beams in vacuum along with acoustic emission in the liquid. Inset: Frequency dependence for acoustic damping in superfluid 4 He. The dashed line shows the f 2 0 dependence calculated by Eq. ( 4) for dipole acoustic emission with no fitting parameters [START_REF] Landau | Fluid Mechanics[END_REF] . (b) Temperature dependence of damping for a 150 µm-long sample immersed in a liquid 4 He measured in a range of magnetic fields. Notations on the top indicate dominant damping mechanisms for each temperature range, with T λ = 2.17 K indicating the superfluid transition temperature. The damping of the nanobeam below 1 K is explained by ballistic scattering of the thermal excitations in superfluid 4 He on the nanobeam surface. The combined damping due to phonons Eq. ( 2) and rotons Eq. ( 3) is shown by the blue dashed line. The red solid line shows total damping calculated as a sum of phonon, roton and acoustic emission term Eq. (4). For magnetic fields exceeding 0.1 T the magnetomotive damping becomes dominant at low temperatures.

to 0.9 GPa. The deposited 30 nm thick aluminum layer was patterned using electron-beam lithography and dryetched in chlorine-based plasma. Suspension of the doubly clamped beams was performed by creating an undercut through selectively etching Si in XeF 2 . Figure 1 shows an SEM image of the longest 150 µm nanobeam and principle measurement scheme. For the experiment the nanobeams were driven using an AC current that passed through the devices in a constant, perpendicular magnetic field, B. The resulting Lorentz force induced oscillatory motion of the samples, creating a back-emf which was measured as a drop in the transmitted signal S 21 of a vector network analyzer.

The measurements presented here were taken in a brass cell inside a cryogen-free dilution refrigerator with a base temperature of 7 mK. The cell was attached to the mixing chamber plate and filled with 4 He using two capillary lines, which were thermally anchored to each temperature stage of the refrigerator using silver-sintering. The cell temperature was inferred from a calibrated RuO 2 thermometer thermally anchored to the mixing chamber.

Initially, we characterized the nanobeams in vacuum to determine the internal losses for the two resonators, both in the normal and superconducting states of the aluminum. The inset in Fig. 1 shows the resonance response in vacuum for the 150 µm beam in the superconducting state at base temperature in a 10 mT magnetic field. The measurements show that under these conditions the internal losses of the nanobeam, defined as the inverse quality factor, are very low, Q -1 int ≈ 2 × 10 -7 . These losses are presumably caused by an ensemble of two-level fluctuators, as has been previously demonstrated in experiments involving superconducting resonators [START_REF] Lulla | [END_REF] , since the clamping losses are expected to be extremely small due to the high aspect ratio of the resonators 24 .

Figure 2(a) shows the dependence of damping of the nanobeams on the external magnetic field from 60 mT up to 5 T. In these magnetic fields aluminum is in the normal state, and internal damping of the 150 µm beam is ∼ 10 -6 . This damping is magnetic field independent up to 150 mT, above which the magnetomotive loading (dissipation arising from moving the conductor in the magnetic field) starts to dominate, with the expected B 2 dependence. The behavior of the 30 µm-long beam in the magnetic fields is similar but the internal losses are larger and hence remain field-independent up to 1 T.

After characterization in vacuum, we operated the nanobeams in liquid 4 He over the temperature range from 7 mK to ∼ 3 K. This is not trivial, since over this interval the damping arising from the surrounding liquid helium varies over four orders of magnitude. We therefore had to adjust the magnetomotive force according to the level of damping, meaning that measurements had to be made over a range of magnetic fields up to 5 T.

Figure 2(a) shows that both the 150 µm and 30 µmlong beams demonstrated an order of magnitude larger damping in superfluid 4 He at 7 mK than in vacuum, even in low magnetic fields where magnetomotive losses are negligible. This is rather counter-intuitive: the Stokes' drag from interaction with the superfluid normal component should be negligible at these temperatures, since the density of thermally excited phonons and rotons is insignificant 25 . Later in this paper we will show that these losses can be attributed to acoustic emission in superfluid 4 He. Since the behavior of the 30 µm-long beam in the fluid is almost independent of the magnetic field, we have mainly focused our studies on the 150 µm-long beam.

In order to clarify the damping mechanisms in the liquid helium, the temperature dependence of the quality factor of the 150 µm-long beam was measured, as shown in Fig. 2(b). The measurements map out rather consistently the damping arising from the liquid over the whole temperature range. As can be seen, in addition to the beam's internal, Q -1 int , and magnetomotive, Q -1 mm , losses, there are three distinct temperature regimes distinguished by the dominant damping mechanisms originating in the liquid. These are: (with increasing temperature) acoustic emission Q -1 ac , ballistic scattering of thermal excitations (phonons, Q -1 ph , and rotons, Q -1 rot ) and finally, above 1 K, hydrodynamic losses Q -1 hy . These mechanisms together constitute the total damping:

Q -1 tot = Q -1 int + Q -1 mm + Q -1 ac + Q -1 ph + Q -1 rot + Q -1 hy .
(1) It is remarkable that all these distinct behaviors can be extracted with a single device. The hydrodynamic Stokes' drag by the normal fluid component is substantial at temperatures above 1 K and can be described in the framework of the phenomenological two-fluid model of superfluid 4 He 20 .

To model the damping at lower temperatures we can take the dissipation as arising from the scattering of individual phonons and rotons at the beam surface. This is justified by the fact that below 1 K the mean free paths of thermal excitations in the superfluid (rotons and phonons) are longer than the effective nanobeam diameter, while the de Broglie wavelengths are much shorter 25 . The phonon drag force, F ph = -Aρ ph c ph v w d/2, exerted per unit length of the beam is obtained by summing the momentum of phonons colliding against the beam, with phonon density, ρ ph , effective beam diameter, d ≈ 150 nm, phonon velocity, c ph = 229 m s -1 , and wire velocity, v w . This gives the damping contribution 25 :

Q -1 ph = A k 4 B 45 3 df 0 (ρ + ρ s )c 4 ph T 4 , (2)
where f 0 is the resonance frequency of the beam in liquid, ρ and ρ s are the densities of the beam material and superfluid respectively, and A = 2.67 is a constant taking into account the "cylindrical" beam geometry 25 . Following similar arguments, however now using the number density of rotons, n rot [START_REF] Landau | Collected Papers of L.D. Landau[END_REF] , the roton drag force is F rot = -An rot p 0 v w d/2, and the damping arising from roton scattering becomes:

Q -1 rot = Ap 0 π 2 df 0 ρ w m * k B T 2π 2 3/2 exp - ∆ k B T , (3)
where p 0 , m * and ∆ are the Landau roton parameters 25 . The combined damping from the phonon and roton scattering,

Q -1 ph + Q -1
rot , is shown by the blue dashed line in Fig. 2(b), and describes well the envelope of the experimental data in the temperature range 0.4 K -0.7 K with no fitting parameters. At lower temperatures, the deviation from the experimental results and theoretical predictions is clear, and measured damping saturates at mK temperatures.

This implies the presence of another temperature independent damping mechanism, which is significantly larger than that of the intrinsic losses of the beams measured in vacuum. Figure 2(a) shows that damping in the liquid reaches Q -1 ac ≈ 5 × 10 -6 for the 150 µm-long, and Q -1 ac ≈ 2 × 10 -4 for the 30 µm-long nanobeams, respectively. We attribute this mechanism to acoustic losses which is described in the framework of dipole emission 22 :

Q -1 ac = π 3 2 ρ s ρ df 0 c ph 2 , (4)
predicting a quadratic dependence on the frequency of the nanobeam oscillating in fluid. The inset in Fig. 2(a) shows the measured losses of the nanobeams in superfluid helium as a function of their resonance frequencies, and contrasts them with the expected damping from acoustic emission obtained from Eq. ( 4) without fitting parameters. Remarkably, the measured values of losses for both beams are in excellent agreement with this dependence. The solid line in Fig. 2(b) shows the resulting sum of the damping mechanisms from the thermal excitations in the helium and acoustic emission from the nanobeam. This dependence describes well the data for the lowest magnetic field where the magnetomotive damping is negligible. Existence of considerable acoustic emission at high frequencies, should be taken into account for the development of the phononic experiments in quantum fluids [START_REF] Schwab | [END_REF]28 . According to Eq. 4 acoustic losses are expected to be negligible at lower frequencies, where the condensate should behave essentially as a mechanical vacuum.

The acoustic losses could be reduced by utilizing lower frequency detectors, or beams with a smaller diameter, for example, carbon nanotubes. Alternatively, the mechanical resonators can be enclosed in a cavity, with a size smaller than half of the acoustic wavelength of the emitted sound along the axis of the dipole emission. This will restrict the number of acoustic modes available for emission. For the frequency of ∼ 1.6 MHz, the characteristic size of the acoustic cavity should be ∼ 50 µm. Nanobeams could also be utilized for studies in thinfilm rather than bulk superfluid, as has been previously demonstrated using nanotubes 29 .

In summary, nanobeams have been measured in 4 He down to mK temperatures, characterizing the various dissipation mechanisms. We observe that ballistic quasiparticles dominate damping below 1 K, with acoustic emission taking over at the lowest temperatures. We demonstrate excellent agreement with a model incorporating acoustic emission, and interaction with ballistic phonons and rotons in the low-temperature limit. Versatility of the devices could be further improved by suppressing acoustic losses through mounting the nanobeams in some confined enclosure. Nanobeams provide a convenient, tunable and precise tool to characterize the properties of an environment with high accuracy. This experiment paves the way towards using nanobeams as ultrasensitive detectors for probing the properties of 3 He -B at µK temperatures, where the size of the probe becomes comparable to the superfluid coherence length.

All data used in this paper are available at http://dx.doi.org/10.17635/lancaster/researchdata/xxxx, including descriptions of the data sets. We would like to acknowledge the excellent technical support

FIG. 1 .

 1 FIG. 1. (Color Online) False color scanning electron microscope image of a 150 µm long composite aluminum on silicon nitride nanobeam. The device is driven by a network analyzer through 80 dB of attenuation distributed over several temperature stages of the cryostat. At the output, two 40 dB low-noise amplifiers are used at room temperature to improve the signal-to-noise ratio. Inset shows an example of a frequency response taken in vacuum at 7 mK with a magnetic field of 10 mT demonstrating a quality factor of 5 × 10 6 .

  of A. Stokes, M. G. Ward, M. Poole and R. Schanen and very useful scientific discussions with S. Autti, E. Laird, A. Jennings, M. T. Noble and T. Wilcox. This research was supported by the UK EPSRC Grants No. EP/L000016/1, EP/P024203/1 and No. EP/I028285/1, and the European Microkelvin Platform, ERC 824109. EC acknowledges the support from the ANR grant MajoranaPRO No. ANR-13-BS04-0009-01 and the ERC CoG grant ULT-NEMS No. 647917.


Member, IEEE Omar Sami Oubbati
email: s.oubbati@lagh-univ.dz.

Member, IEEE Abderrahmane Lakas
email: alakas@uaeu.ac.ae.

Pascal Lorenz
email: pascal.lorenz@uha.fr.

Mohammed Atiquzzaman

Fellow, IEEE Abbas Jamalipour
email: a.jamalipour@ieee.org.

Leveraging Communicating UAVs for Emergency Vehicle Guidance in Urban Areas

Keywords: Emergency vehicle, VANET, UAV, Routing, Backbone, Search and Rescue. !

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INTRODUCTION

E VERYBODY knows the context: you see an incident on the road and what you need to do next. It is frequently too late to locate the incident, to call the emergency, and what would be the optimal path to reach the area of interest (AoI). Indeed, people often react wrongly by firstly evaluating the collateral and material damage and then taking decisions, which can waste more time, forming a traffic jam on the way the AoI, and thus cluttered all the roads in front of rescue teams putting the lives of victims in danger.

The proliferation of Unmanned Aerial Vehicles (UAVs) in urban environments and their assistance to existing Vehicular Ad hoc Networks (VANETs) on the ground have provided a plethora of applications [START_REF] Oubbati | Intelligent UAV-Assisted Routing Protocol for Urban VANETs[END_REF]. In fact, UAVs are extensively used in traffic monitoring [START_REF] Ke | Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow[END_REF], search and rescue missions [START_REF] Liang | A Scalable Model-Based Learning Algorithm with Application to UAVs[END_REF], connectivity enhancement in VANETs [START_REF] Oubbati | A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs)[END_REF], and more recently in urban surveillance [START_REF] Sharma | Secure and Efficient Context-Aware Localization of Drones in Urban Scenarios[END_REF]. The latter kind of applications is accomplished based only on multiple UAVs forming an aerial sub-network, which they can cover a wide urban area and detect any events occurred on the ground. However, without being aware of the situation on the ground, UAVs cannot achieve the planned application in an optimal way. As a solution, UAVs can communicate with ground nodes to be aware of the situation on the ground. In [START_REF] Li | Vehicle-assist resilient information and network system for disaster management[END_REF], UAVs are used to detect the isolated victim's smartphones located in a disaster area and connect them with central servers. Nevertheless, UAVs are not fully exploited neither during the search of the victims (i.e., victims without smartphones) nor during the road navigation. To address these two problems, the exploitation of the UAVs' processing of captured images and their knowledge of the covered area. The work in [START_REF] Verykokou | UAV-Based 3D modelling of disaster scenes for urban search and rescue[END_REF] provides a 3D modeling system based on UAVs to help rescue teams to detect the victims. However, this system can be easily affected by the weather or other factors distorting the captured images. This can be addressed using sensors placed on the affected area and communicate directly with UAVs [START_REF] Goodrich | Supporting wilderness search and rescue using a camera-equipped mini UAV[END_REF]. Moreover, in [START_REF] Menouar | UAV-enabled intelligent transportation systems for the smart city: Applications and challenges[END_REF], it is supposed that UAVs have unlimited energy capacity during their deployment. Since the majority of the proposed systems and applications neglect the constraints of the restricted energy capacity of UAVs and more particularly during the exchange of messages, it is a mandatory condition to consider energy-efficient techniques.

To clearly define the use cases of our system, we consider Fig. 1 as our motivating scenario. A set of UAVs is deployed over an urban area monitoring the fluidity of the traffic and detecting any incidents on the roads. All this information is shared among UAVs in order to both have a global vision of the traffic density and what are the appropriate road segments to be traversed by the relevant services in case of incidents. Moreover, UAVs reliably inform the relevant services of any detected incidents using an energy-efficient routing protocol. This allows to facilitate the intervention of the relevant services (e.g., the rescue teams if it was a traffic accident) by reaching quickly the AoI. In this paper, we have divided our system into four main parts as follows:

Ambulance

The near-optimal path towards the accident area Alert message UAV H Fig. 1: Motivating scenario.

• The weighting of the road segments based on their fluidity of traffic. • Network organization is performed to set up a permanent and robust backbone among UAVs following an energy-saving technique and connectivity measurements.

• A reactive routing is deployed only on the created backbone to establish a communication between the AoI and the relevant service. • The calculation of the near-optimal path in terms of traveling time towards the AoI. The rest of the paper is organized as follows. We review the most relevant and recent related work in Section 2. In Section 3, we present the system model and the method of traffic calculation. In Section 4, we investigate the organization of UAVs and how the exchange of data is structured. The performance evaluation of the system is provided in Section 5, and Section 6 draws conclusions for this work.

RELATED WORK

UAV assistance for urban surveillance and rescue missions still remains a topical issue. Indeed, a team of UAVs can provide assistance to the rescue teams in real-time and perform stable communications in order to complete the mission faster. In [START_REF] Lodeiro-Santiago | Secure system based on UAV and BLE for improving SAR missions[END_REF], UAVs are deployed in a specified area and tried to scan and detect any beacons emitted from the missing persons' smartphones. This allows to accurately locate any missing person based on the GPS position. However, not all victims have a smartphone or can keep their smartphones during an incident, which calls this technique into question. To overcome this limitation, there are multiagent cooperative searching, acquisition, and tracking techniques that can be adopted [START_REF] Rizk | Decision making in multiagent systems: A survey[END_REF]. In [START_REF] Ragi | UAV path planning in a dynamic environment via partially observable Markov decision process[END_REF], a path-planning algorithm is proposed to guide UAVs and tracking ground targets in rescue missions. Nevertheless, this approach does not consider the limited energy capacity of UAVs. The work in [START_REF] Sharma | LoRaWAN-Based Energy-Efficient Surveillance by Drones for Intelligent Transportation Systems[END_REF] adopted UAVs as LoRaWAN gateways for urban monitoring. To communicate, three factors are considered, such as the area of stress (i.e., UAVs are deployed in highly dense areas), the resilience factor, and the energy consumption factor. As a drawback, this work uses different core components with additional features, thus making the network more complex. In [START_REF] De Albuquerque | Evaluating data communications in natural disaster scenarios using opportunistic networks with Unmanned Aerial Vehicles[END_REF], different studies about the deployment of delay tolerant network (DTN) routing protocols in disaster areas during the communications between rescue teams and command center of search and rescue missions. Nevertheless, this kind of protocols uses the technique of store-carry and forward (SCF) that is not suitable for urgent cases, and most particularly when it comes to human life to rescue. In [START_REF] Song | Unmanned Aerial Vehicles Rapid Delivery Routing of the Emergency Rescue in the Complex Mountain Region[END_REF], a rapid data delivery mechanism is adopted, where the graph modeling, the dynamic programming, and the use of a tabu list are all considered to calculate the optimal routing of UAVs. But, no technique of energy conservation is proposed, and especially when the optimal routing path comprises UAVs having a low residual energy. In [START_REF] Kuiper | Geographical routing with location service in intermittently connected MANETs[END_REF], a DTN routing protocol is proposed adopting two routing strategies according to the situation of the network. However, this protocol does not take into account the battery level of UAVs and it can fail when the next hop has a low residual energy.

UAVs can also form a substitutional connectivity solution in the sky instead of damaged infrastructures on the ground. In [START_REF] Naqvi | Drone-aided communication as a key enabler for 5G and resilient public safety networks[END_REF], an efficient technique is used to allow an important number of users and devices to communicate, where the data rate requirements and interference are used as key metrics to the self-adaptive power control of UAVs. Moreover, a routing protocol based on the greedy forwarding with the same behavior as in [START_REF] Lin | A geographic mobility prediction routing protocol for Ad Hoc UAV Network[END_REF] is adopted, which suffers from a local optimum problem. A similar technique is used in [START_REF] Merwaday | Improved throughput coverage in natural disasters: Unmanned aerial base stations for public-safety communications[END_REF], where the localization of UAVs is optimized to enhance the throughput over the covered area, while neglecting the energy constraint of UAVs. The authors in [START_REF] Erdelj | Wireless sensor networks and multi-UAV systems for natural disaster management[END_REF] combine Wireless Sensor Networks (WSNs) with UAVs in order to carry out the real-time assessment of the disaster area. But, there is any common measure adopted against the limited energy of sensors and UAVs. In the same way, the work in [START_REF] Xiao | UAV assisted USV visual navigation for marine mass casualty incident response[END_REF] proposes to coordinate UAVs with Unmanned TABLE 1: Features comparison of the related applications for surveillance and rescue management.

Features

Ref. [START_REF] Naqvi | Drone-aided communication as a key enabler for 5G and resilient public safety networks[END_REF] Ref. [START_REF] Merwaday | Improved throughput coverage in natural disasters: Unmanned aerial base stations for public-safety communications[END_REF] Ref. [START_REF] Lodeiro-Santiago | Secure system based on UAV and BLE for improving SAR missions[END_REF] Ref. [START_REF] Erdelj | Wireless sensor networks and multi-UAV systems for natural disaster management[END_REF] Ref. [START_REF] Sharma | LoRaWAN-Based Energy-Efficient Surveillance by Drones for Intelligent Transportation Systems[END_REF] Ref. [START_REF] De Albuquerque | Evaluating data communications in natural disaster scenarios using opportunistic networks with Unmanned Aerial Vehicles[END_REF] Ref. [START_REF] Song | Unmanned Aerial Vehicles Rapid Delivery Routing of the Emergency Rescue in the Complex Mountain Region[END_REF] Ref. [START_REF] Xiao | UAV assisted USV visual navigation for marine mass casualty incident response[END_REF] Our application Surface Vehicles (USVs) to enhance the rescue missions of drowning victims. As a disadvantage, a human operator needs to be permanently present.

Our system is designated as part of search and rescue applications, which can address several features at once. Indeed, it is based on UAV-to-Ground (U2G) communication to collect beacons exchanged between vehicles forming a VANET to estimate their densities. UAV-to-UAV (U2U) communication is established relying on an energy-efficient protocol. Using its embedded digital map and GPS, our system is aware of all the zones' positions in urban roads. UAVs are permanently deployed and prepared to detect any incidents on the roads using its handling capacities of the captured images. Also, this system has the ability to plot the fastest path for rescue teams to intervene in the AoI.

TABLE 1 provides a summary of features comparison among the most relevant applications previously described, with those considered by our proposed application.

SYSTEM DESCRIPTION

In a classical monitoring application exploiting the UAVassisted vehicular network, UAVs can be considered as the efficient support to cover the area, to collect, to analyze, and to transmit crucial information about the events occurred on the ground. Our system is designed to allow UAVs to sense the surrounding road segments and monitor the variation of the traffic status. Also, UAVs coordinate between each other in terms of exchanging messages, organization, and monitoring, in order to detect any incident on the roads, reliably inform the relevant services, and facilitate their intervention. In this section, we first describe the assumptions and then present the weight calculation method for road segments by combining the traffic density and the speed of vehicles.

Assumptions

Consider a UAV system consisting of a set of n UAVs fairly dispersed in a 3 dimensional (3D) area moving randomly above the different road segments. Each UAV is initially equipped with a fully-charged battery and it is aware of both its own movement information (i.e., position, speed, and velocity) and all details of the neighboring UAVs. Besides, a UAV has a state that can either be a Normal UAV or a Backbone UAV. To communicate, both UAVs and existing vehicles on the ground adopt the IEEE 802.11p wireless interfaces since they can provide a wide transmission coverage [START_REF] Machardy | V2X Access Technologies: Regulation, Research, and Remaining Challenges[END_REF]. Each road segment is assumed to be divided into identified fixed zones. The size of each zone is defined based on the communication range of vehicles (≈300m). According to several simulation experiments that showed their good performances, we suppose that there is a sufficient number of UAVs where each road segment is covered by at least one UAV to increase the probability to detect any incidents on the roads.

Since, as widely known, UAVs have a limited energy capacity [START_REF] Wang | Taking drones to the next level: Cooperative distributed unmanned-aerial-vehicular networks for small and mini drones[END_REF], therefore, we have defined three ratio (%) intervals of residual energy levels: (i) High energy level [66,100], (ii) Medium energy level [33,66[, and (iii) Low energy level [0,33[. It is worthy to note that UAVs have ≈300m of line of sight (LoS) range and they are hovering at a low altitude which does not exceed ≈300m. All UAVs hovering in clear weather can detect any incident on the road using its processing capabilities of the captured images, which is out of the scope of this work.

Weight calculation

To determine the weight of a road segment, the hovering UAV gathers Hello packets that are periodically exchanged between vehicles. An intercepted Hello packet comprises the movement information of the vehicle (i.e., position and speed). Regardless of its energy level and state, the UAV fills and maintains a monitoring table of the traffic density as and when intercepting the Hello packets from all vehicles traveling on a given road segment. As shown in Fig. 2, we observe four UAVs u 1 , u 2 , u 3 , and u 4 trying to collect the exchange of Hello packets from vehicles located on four different road segments divided into three fixed zones.

As an illustration, we take the monitoring table of u 3 (see TABLE 2) to calculate some crucial parameters which are required for the W eight calculation of the segment between the two intersections I X and I Z . From TABLE 2 and as a generalization, we can easily deduct the well-regulation of the traffic density by calculating the standard deviation which shows how vehicles are fairly distributed in a given road segment:

σ = 1 |S I i , I j | × |S I i , I j | i=1 (T(Zone i ) -µ) 2 (1)
where,

T(S I i , I j ) = |S I i , I j | i=1 T(Zone i ) µ = 1 |S I i , I j | × |S I i , I j | i=1 T(Zone i ) T(S I i , I j )
is the total number of vehicles in the road segment S I i , I j delimited by intersections I i and I j . µ is the average number of vehicles per zone, T(Zone i ) is the number of vehicles in the zone Zone i , and |S I i , I j | is the number of fixed zones within a specific road segment S I i , I j . If for example σ ≈ 0, it means that vehicles a fairly dispersed or, in a general case, they are moving, or this segment is almost empty of vehicles, which are the suitable scenarios. Otherwise (i.e., σ > 0), it means that vehicles constitute nearly isolated clusters (e.g., at red lights), which is the inappropriate scenario.

Based on the aforementioned metrics, a multi-criteria W eight can be calculated for S I i ,I j as follows:

W eight = T(S I i ,I j ) σ + 1 × d(I i , I j ) (SP av × 1(s)) + 1 (2)
where, d(I i , I j ) is the length of the road segment S I i ,I j . SP av is the average of vehicles' speeds in the road segment. In order to get Weight with no dimension, SP av is multiplied by the factor "1(s)". W eight has a proportional relationship with T(S I i , I j ) and d(I i , I j ). As we can observe, formula ( 2) is an empirical equation to compute Weight, which is a scalar that grows only in the positive side (i.e., W eight ≥ 0) and taking into account those four parameters impacting the fluidity degree of a given road segment. The less W eight, the better is the road segment and vice versa. All the calculated weights are included into the Hello packets and they are shared with the adjacent backbone UAVs.

To exemplify the W eight calculation, we consider the scenario of Fig. 2. The different calculated metrics combining the W eight are described in TABLE 3.

ORGANIZATION AND DATA ROUTING

It is a challenging task to establish stable and reliable data delivery paths relaying alert messages while incorporating a maintenance strategy in the case of disconnections. To achieve this goal, a stable backbone is built by considering both the connectivity degree between UAVs and their residual energy. Similar works, such as [START_REF] Kies | Routing Protocol Based on CDSE Virtual Topology in Ad Hoc Network[END_REF], [START_REF] Sharmila | Strategic location-based connected dominating set for mobile ad hoc networks[END_REF] have been proposed across the literature with the aim to form a stable backbone. However, they are mostly dedicated to low dynamic networks and cannot be adapted to a network comprising nodes with high mobility, such as UAVs.

Modeling a network as a graph provides facilities to use a set of well-known algorithms in graph-theories to build a robust backbone. In this work, we assume that UAVs, as well as the different target services (i.e., destinations), are modeled as an undirected graph G(V, E). V is a set of vertices (i.e., UAVs and services) and E is a set of undirected edges (i.e., bidirectional links between vertices existing at time t). In the subsequent discussion, we use terms vertex, UAV, and node interchangeably.

Connected dominating set formation

A connected dominating set or CDS is a subset D ⊆ V such that every node not in D is joined to at least one node of D by some edge. Furthermore, there exists at least a path P = {e i , e a , e b , . . . , e n , e j } between any pair of nodes i, j ∈ D, where a, b, . . . , n ∈ D. To illustrate the creation of the subset D, we refer to Fig. 3. First, each UAV periodically exchanges Hello packets containing the additional fields shown in Fig. 4.

The I D field represents the UAV identifier, RE is its remaining energy, Movement information is its mobility details (i.e., position, speed, and velocity). Each receiving node can use the information included in the Movement information field together with its own mobility details to calculate the To designate the backbone of UAVs or CDS, a marking process is used to mark every UAV in the connected network. m(u i ) is a marker for vertex u i ∈ V, where m(u i ) = T or m(u i ) = F signifying marked or unmarked, respectively. Initially, we suppose that all UAVs are unmarked, except for the T arget service (T S) which is permanently marked (or framed in Fig. 3(a)) and it belongs to the CDS. N(u i ) = {u j |{u i , u j } ∈ E } represents the neighbors of vertex u i , i.e., u i N(u i ). Three steps are required for the marking process:

1) A marker F is assigned to every u i ∈ V.

2) Every u i exchanges its N(u i ) with all its neighbors. 3) A marker T is assigned to every u i having two unconnected neighbors.

In the example of Fig. 3

(b), N(u 1 ) = {u 2 , u 3 }, N(u 2 ) = {u 1 , u 3 , u 4 , u 5 }, N(u 3 ) = {u 1 , u 2 , u 5 }, N(u 4 ) = {u 2 , u 5 , u 6 }, N(u 5 ) = {u 2 , u 3 , u 4 , u 6 , u 7 }, N(u 6 ) = {u 4 , u 5 , u 7 ,
T S}, and N(u 7 ) = {u 5 , u 6 , T S}. At the second step of the marking process, u 1 has N(u 2 ) and N(u 3 ), u 2 has N(u 1 ), N(u 3 ), N(u 4 ), and N(u 5 ), u 3 has N(u 1 ), N(u 2 ), and N(u 5 ), u 4 has N(u 2 ), N(u 5 ), and N(u 6 ), u 5 has N(u 2 ), N(u 3 ), N(u 4 ), N(u 6 ), and N(u 7 ), u 6 has N(u 4 ), N(u 5 ), and N(u 7 ), u 7 has N(u 5 ) and N(u 6 ). By applying the last step of the process, u 2 , u 3 , u 4 , u 5 , u 6 , and u 7 are marked T.

As a result of the marking process, UAVs that are marked T form a subset D, where From Fig. 3(b), we distinguish that the majority of UAVs are forming the CDS. Consequently, the established CDS is non-minimum and it has to be reduced, since the problem of minimizing a CDS is NP-complete. To reduce |D|, we propose two rules based on the residual energy of each UAV and the connectivity degree between UAVs.

D = {u i |u i ∈ V, m(u i ) = T }. This form a subgraph M of G, where M = G[D].

Rule 1. If N[u

i ] ⊆ N[u j ] and RE u i < RE u j , m(u i ) = F. Where u i , u j ∈ M. N[u i ] = N(u i ) u i which is called the closed neighbors of u i . RE u i is the residual energy of u i .
When the closed neighbors of u i are covered by those of u j ,

u i can be removed from M if RE u i < RE u j . It is not difficult to prove that M -{u i } is still a CDS of G. The condition N[u i ] ⊆ N[u j ]
means that u i , u j ∈ D are connected. Therefore, Properties 1 and 2 are still preserved after applying Rule 1.

In Fig. 3

(c), since N[u 7 ] ⊆ N[u 6 ] and RE u 7 < RE u 6 , node u 7 is removed from M. Node u 3 is also removed from M, since it is observed that N[u 3 ] ⊆ N[u 2 ] and RE u 3 < RE u 2 . Rule 2. If N[u i ] ⊆ N[u j ] and ACL u i < ACL u j , m(u i ) = F. ACL u i
represents the average connectivitylifetime between u i and N(u i ). Let CL u i , N (u i ) = {CL u i ,u 1 , CL u i ,u 2 , . . . , CL u i ,u j } be the set of the estimated connectivity-lifetimes between u i and its neighbors N(u i ).

The average of the connectivity-lifetimes can be expressed as follows:

ACL u i = | N (u i )| j=1 CL u i ,u j |N(u i )| (3)
CL u i ,u j is the remaining time of u i and u j to stay connected, which can be calculated based on the method proposed in [START_REF] Uddin | Link expiration time-aware routing protocol for UWSNs[END_REF]. As shown in Fig. 6, let u i and u j be two UAVs with a LoS range of R, non zero speeds V i and V j , their initial locations be (X i , Y i , Z i ) and (X j , Y j , Z j ), and their respective velocity angles θ i , φ i and θ j , φ j .

i j ϴ j, Φ j V j V i ϴ i, Φ i (X' i, Y' i, Z' i ) (X' j, Y' j, Z' j )
Fig. 6: Connectivity-lifetime between u i and u j .

The connectivity lifetime between u i and u j can be calculated based on the following equation:

CL u i ,u j = -y ± y 2 -4xz 2x (4)
where,

x = w 2 + x 2 + y 2 y = 2iw + 2 j x + 2oy z = m 2 + n 2 + o 2 -R 2 m = (X i -X j ) n = (Y i -Y j ) o = (Z i -Z j ) w = (V i sin θ i cos φ i -V j sin θ j cos φ j ) x = (V i sin θ i cos φ i -V j sin θ j cos φ j ) y = (V i cos θ i -V j cos θ j )
In Fig. 3(d), since N[u 4 ] ⊆ N[u 5 ] and ACL u 4 < ACL u 5 , node u 4 is removed from M. As a result, every vertex u i ∈ M has a high probability of staying connected to the CDS (i.e., the backbone) while ensuring a long lifetime to the backbone. It is worthy to note that the CDS formation is based only on the periodical exchange of Hello packets and it is automatically and permanently carried out to ensure a robust connectivity until the target services.

Routing

Once the CDS is determined, a novel routing strategy is deployed in any data communications between any UAVs and the relevant services. A reactive strategy is adopted while considering two factors: (i) excluding UAVs with a low residual energy level and to spare them from any data transmissions and (ii) considering the robustness of each link composing the discovered paths. To clearly highlight the novelties of our routing strategy, TABLE 4 depicts the limitations of [START_REF] Kuiper | Geographical routing with location service in intermittently connected MANETs[END_REF] and [START_REF] Lin | A geographic mobility prediction routing protocol for Ad Hoc UAV Network[END_REF] that are dedicated to UAV ad hoc networks compared with our routing strategy. TABLE 4: Our routing strategy vs. Ref. [START_REF] Kuiper | Geographical routing with location service in intermittently connected MANETs[END_REF] and Ref. [START_REF] Lin | A geographic mobility prediction routing protocol for Ad Hoc UAV Network[END_REF] Our strategy

Ref. [START_REF] Kuiper | Geographical routing with location service in intermittently connected MANETs[END_REF] Ref. Movement in f ormation is the same field included into the Hello packet format depicted in Fig. 4. It allows to calculate the connectivity-lifetime between two successive nodes based on equation ( 4). CL P is the connectivity lifetime of the full path, which can be defined as the lowest connectivity lifetime between any two successive nodes belonging to the path. RE P is the residual energy ratio of the full path, which can be defined as the lowest energy level ratio in a given node belonging to the path. It is worthy to note that CL P and RE P are calculated progressively to the target destination.

Distance is the number of transited UAVs.

As depicted in Fig. 5(b), the RREP packet includes the same fields as those included in the RREQ packet. These fields allow the source node to have a global knowledge of the selected path, such as its connectivity-lifetime and its residual energy. Certain values of these fields is cached in the routing tables of all the nodes constituting the path. Once the RREP packet reaches the source node, the routing path is established and the alert message comprising useful fields is ready to be sent (c.f., Fig. 5(c)). The Alert I D corresponds to a unique identification of an incident. Alert t ype defines the kind of the incident occurred on the ground, which is used to determine the appropriate target service to inform. AoI includes the coordinate of the exact location of the incident. As the alert message crossing the selected path to the target service, the Segments field accumulates the covered segments along with their corresponding weights by each transited UAV. This field is used by T S to calculate the near-optimal path on the ground towards the incident.

Routing process

To illustrate the discovery process, let us consider the concrete example depicted in Fig. 7. When an accident is detected on a road segment, the UAV sends immediately an alert message to the adjacent backbone UAV u 1 . This message comprises useful information about the accident, such as the Alert I D, the AoI, and the Alert t ype. Initially, the Segments field contains the weighted road segments covered only by the UAV detecting the accident. When u 1 intercepts the alert message, it has to engage a data communication with the target service T S (Hospital) in order to bring back of the ambulance into the accident area. u 1 generates and broadcasts a route request (RREQ) packet to find the appropriate sequence of backbone UAVs towards T S which also belongs to the backbone. While transiting the CDS, the RREQ packet records a set of information which defines both the connectivity lifetimes and the residual energy levels of the discovered paths. To reduce the broadcast storm problem, the UAV drops the RREQ if it has already received an RREQ with the same Transmission I D.

Once the first RREQ is intercepted by T S, a short timer is started to collect all possible RREQs corresponding to existing routing paths. After the expiration of the timer, all RREQs are dropped and the flooding is considered to be achieved. In this case, T S has to make a routing decision to select the most connected path having a sufficient energy level. By taking into account all the calculated parameters included in the intercepted RREQs, we can define a multicriteria score for each sequence of UAVs using the following equation:

Score = RE P × N(S) Distance × CL P Delay P (5)
From equation ( 5), we have the following observations:

• The floor of C L P Delay P is a scalar representing whether the routing path still remains connected or not during the data delivery. Therefore, it grows only on the positive side and can be equal to zero only when CL P < Delay P or CL P = 0, which means that the path can be disconnected at any time during the data delivery. However, if CE P Delay P > 0, it means that there is a high probability that this routing path remains connected during the data transmission.

• The calculated Score has a proportional relationship with RE P and N(S) which play a key role to determine the energy-efficiency of a path and its amount of information about the road segments, respectively. • A path with a high score is suitable because it can ensure reliable data delivery while providing important global knowledge about the traffic on the road segments.

From TABLE 5, we can easily select the appropriate sequence of UAVs for alert delivery. Based on the calculated parameters included in the two intercepted RREQs, a score is calculated for Path 1 and Path 2 using the equation ( 5). The target T S selects Path 1 (i.e., the sequence u 1 → u 2 → u 4 → u 5 → T S) since it has obtained the highest score. An RREP is generated including the calculated parameters of the selected path and it is sent back to the source u 1 through Path 1 . During the transition of the RREP packet, a set of updates is carried out in each routing table of the transited UAVs.

N (S)=7 N (S)=6 UAV N (S) UAV N (S) u 1 2 u 1 2 u 2 1 u 2 1 u 4 2 u 3 1 u 5 2 u 6 1 T S u 7 1 T S Scor e = 2.

625

Scor e = 0.3

Once u 1 receives the RREP packet, it adds a new entry in its routing table (c.f., Fig. 8). This is crucial to start the alert transmission and maybe to send other information (e.g., video recordings of the incident) in the future if the selected path is not expired. It should be stressed that all routing tables are purged after 10 (s) of inactivity, and the discovery process is a mandatory condition to make other alert transmissions. u 1 , in turn, adds the road segments covered by itself along with their weights and it sends the alert message to the next hop. The same maneuver is executed by the forwarder until that the alert message will be delivered to the target T S.

Routing When T S receives the alert message, it has accurate details about the incident occurred in the segment between the intersections I F and I K . T S has also an idea about the traffic density in the majority of the road segments in the area. TABLE 6 shows the different covered road segments along with their respective weights.

Near-optimal path towards the AoI

To reach the AoI (e.g., accident area) in a timely manner, T S has to calculate the near-optimal path between the starting point Ambulance and goal Accident area based on the different calculated weights for road segments (see TABLE 6). In graph theory, the Dijkstra algorithm is highly used to find the shortest path on a road network modeled as a graph. When applying such algorithm, the cost function of a path needs to be calculated. Overall, each link (or edge) connecting two vertices on a path is weighted. Therefore, TABLE 6: Covered road segments with their weights. the cost function is the sum of the weights of all the edges constituting the path. Here, an edge is a road segment and its weight is the same weight calculated in Section 3.2. For instance, the cost between two nodes a and b is defined as Cost a,b = c W eight c , where c is the number of least weighted segments.

To illustrate the calculation of the near-optimal path, let us consider Fig. 7 which depicts a scenario of a near-optimal path calculation executed by T S. First, the road network is modeled as an undirected graph H = (K, L), where K is a set of vertices (Intersections, T S, and Accident area) K = {I A , I B , I C , I H , I F , I K , T S, Accident area}, and L is a set of undirected edges (road segments) connecting the vertices. By applying the Dijkstra algorithm, we obtain the following TABLE 7 which summarizes the near-optimal paths from T S towards each vertex in the road network.

T S → IA T S → IA 0.1 T S → IH T S → IH 3 T S → IB T S → IA → IB 0.1 T S → IC T S → IA → IB → IC 0.3 T S → IF T S → IA → IB → IC → IF 0.3 T S → IK T S → IA → IB → IC → IF → IK 2.4 T S → Accident ar ea T S → IA → IB → IC → IF → Accident ar ea 0.3
Based on TABLE 7, the Ambulance can quickly reach its target destination by following the shortest path obtained as T S → I A → I B → I C → I F → Accident area. The cost of this path is 0.3, which can be updated on a real-time according to the traffic variation.

PERFORMANCE EVALUATION

A set of experiments is conducted to evaluate the performance of our application. We considered NS-2 that is complemented by SUMO [START_REF] Behrisch | SUMOsimulation of urban mobility: an overview[END_REF] and MobiSim [START_REF] Mousavi | Mobisim: A framework for simulation of mobility models in mobile ad-hoc networks[END_REF] as two mobility generators producing the movements of vehicles and UAVs, respectively. A test urban area stretched over 3 × 3 km 2 is imported from OpenStreetMap [START_REF] Haklay | Openstreetmap: User-generated street maps[END_REF], which knows a flexible and perpetual movement of vehicles (c.f. Fig. 9(a)). In the selected area, the relevant road segments and intersections are marked as blue lines and red circles (c.f. Fig. 9(b)). The rest of the simulation parameters are summarized in TABLE 8.

Despite its unsuitability for UAVs, a Random Way Point (RWP) mobility model is deployed for up to 100 UAVs in order to study the impact of random motions on routing protocols. Both the routing tables and the list of neighbors are purged after 10 (s) of inactivity. All UAVs are fairly distributed over the network and operate at their high energy levels. Three different experiments are performed: (i) the performance of our routing protocol is evaluated and compared with relevant routing protocols, (ii) the energy consumption is studied for each routing protocol, and (iii) different outputs of our applications are analyzed.

Routing performance

Three evaluation metrics are calculated during the experiment, such as Packet Delivery Ratio (PDR), End-to-End Delay (EED), and Overhead (OH) (c.f., Fig. 10). LAROD [START_REF] Kuiper | Geographical routing with location service in intermittently connected MANETs[END_REF] and MPGR [START_REF] Lin | A geographic mobility prediction routing protocol for Ad Hoc UAV Network[END_REF] adopting different routing strategies are selected to be compared with the performance outputs of our routing protocol. Twenty communications between different accident locations and T S are established. It should be stressed that each point of the obtained results represents the mean of 30 simulation runs with 95% confidence interval. In terms of PDR (c.f., Figs. routing paths remaining valid for multiple data transmission, thus gaining more time. Figs. 10(c) and 10(f) show that the control overhead required for our protocol is reduced as the density of UAVs is increasing. This is because many control packets are generated during the discovery process, and especially when the network of UAVs is poorly dense. However, the reason behind the decrease of the overhead is caused by the reduced number of route discoveries due to the long lifetime and the energy-efficient organization of the discovered paths.

Energy consumption performance

To examine the energy consumption of UAVs for all the evaluated protocols, we study the contour of the remaining energy levels of 50 UAVs at the end of each run. As a result, three graphs represented in Fig. 11, which have been smoothed based on simple interpolation.

Fig. 11(a) reveals that our protocol conducts a wellregulated energy consumption among UAVs. In essence, compared to MPGR and LAROD, our protocol relies only on backbone UAVs to transmit data packets to their corresponding destinations, where the forwarder UAVs are at their high energy levels. In addition, the backbone is permanently updated as the energy levels are under a constant variation, which will equitably distribute the transmission load among UAVs. However, we observe an important and the unbalanced energy consumption across all UAVs in MPGR and LAROD. As shown in Fig. 11 energy consumption among UAVs that can reach up to 60%. This is due to the fact that UAVs in LAROD continue to broadcast data packets over the network if they do not overhear the broadcast of the next hops, thus consuming more energy. As for Fig. 11(c), unbalanced energy consumption is remarked, which is explained by the selection of the same chain of UAVs for the data transmissions, thus some UAVs consume more energy while others much less.

Application performance

To test the performance of our application, different experiments are performed on the road segment coverage, the number of backbone UAVs, and the travel time of the ambulance to reach the AoI. As shown in Fig. 12(a), we deduct that the integral coverage of all road segments is reached on average at 70 UAVs. This is due to the fact that UAVs has a transmission range of 300m and the length of the road segments is a random variable, where certain segments require more than one UAV to be covered. Fig. 12(b) shows the average number of backbone UAVs according to the total density of UAVs. Indeed, we observe a uniform increase of backbone UAVs since all UAVs initially operates at their high energy levels and they are uniformly distributed over the network. As for Fig. 12(c), it is clearly shown that the average travel time taken by the ambulance through the paths provided by our application is significantly less than that achieved by the shortest path. This is explained by the fact that in the worst case, as the number of UAVs increases, our application always provides the least crowded paths, where the ambulance will cross them at its highest speed. However, the shortest paths in terms of distance can be bottled by vehicles and the ambulance has to follow them until the road empties, thus wasting more time.

CONCLUSION

As shown in the motivation scenario, the response time to emergency situations is crucial to saving human lives. Using UAVs to permanently monitor the roads, detect incidents, and inform the relevant services can help to make the rescue mission efficient and faster. With this work, we want to directly take part in the design of such systems. In fact, a weighting method is carried out in real time for all road segments hovered by the existing UAVs to measure their densities and their fluidity. This allows to have a global vision of the covered road segments and the near-optimal path to take in case of incidents. To extend the UAV network lifetime, a virtual backbone is created based on the connectedness of UAVs with a high energy level. This backbone keeps its properties by permanently updating itself according to the topology variation and the energy consumption of UAVs. In the case of an incident occurred in a given road segment, it will be detected by the UAV in charge of monitoring this segment. An alert message containing all details about the incident and the state of the roads is generated and sent through the backbone to the relevant service using an efficient routing protocol. To overcome the high mobility and the restricted energy capacity of UAVs, the deployed routing protocol exploits the discovery process to predict any link failure prior to its occurrence while achieving a regulated energy consumption. Once the relevant service gets the alert message, it calculates the near-optimal path towards the AoI. The proposed application is evaluated through a series of simulations that demonstrate its effectiveness and feasibility in real scenarios. However, we are aware of the very specific use case of our system, but we believe it can be adopted in many urban surveillance applications. Therefore, we are currently extending it to support tracking mobile objects (e.g., suspect vehicles), managing traffic by controlling traffic lights, adjusting the mobility of UAVs, and taking over UAV failures. In addition, our system attempts to be a first step in this field by performing more experiments on much larger data sets, which are still needed to enhance the results achieved to date.

Fig. 2 :

 2 Fig. 2: Weight calculation for each road segment.

  Initial network.

  Energy-based CDS.

  Connectivity-based CDS.

Fig. 3 :

 3 Fig. 3: Connected dominating set (CDS) formation.

Fig. 4 :

 4 Fig. 4: Hello packet format.

Property 1 .

 1 Two desirable properties are distinguished from the induced graph M. The subset D forms a dominating set of G. Property 2. M is a connected subgraph.

1

 1 Packet formatAs shown in Fig.5(a), the route request (RREQ) packet format comprises several fields. The Transmission I D field identifies the discovery process to which the packet belongs. N(S) is the cumulative number of segments covered by each transited UAV. Delay P is the required time for the RREQ to transit the path between the source and T S. Source and T S represent the identifiers of the communicating nodes.

Fig. 5 :

 5 Fig. 5: Format of routing packets.

Path 1 2 Delay P = 2 25 Dist ance = 4

 122254 Path (s) Delay P = 3 (s) C L P = 5 (s) C L P = 3.5 (s) RE P = 0.75 RE P = 0.Dist ance = 5

Fig. 7 :

 7 Fig. 7: Principle of our alert model functioning.

  T S , I A ) 0.1 S(I T S, IH ) 3 S(I A , I B ) 0 S(I B , I K ) 7 S(I B , I C ) 0.2 S(I C , I H ) 5 S(I C , I F ) 0 S(I K , I Ac c i d e nt ar e a ) 2 S(I F , I Ac c i d e nt ar e a ) 0.1

Fig. 9 :

 9 Abbott Street

Fig. 10 :

 10 Fig. 10: Simulation results of our routing protocol vs. Density of UAVs.

Fig. 11 :

 11 Fig.11(a) reveals that our protocol conducts a wellregulated energy consumption among UAVs. In essence, compared to MPGR and LAROD, our protocol relies only on backbone UAVs to transmit data packets to their corresponding destinations, where the forwarder UAVs are at their high energy levels. In addition, the backbone is permanently updated as the energy levels are under a constant variation, which will equitably distribute the transmission load among UAVs. However, we observe an important and the unbalanced energy consumption across all UAVs in MPGR and LAROD. As shown in Fig.11(b), a very high

Fig. 12 :

 12 Fig. 12: Simulation results of our application.

Omar

  Sami Oubbati is an Associate Professor at the Electronics departement, University of Laghouat, Algeria and a Research Assistant in the Computer Science and Mathematics Lab (LIM) at the same university. He received his degree of Engineer (2010), M.Sc. in Computer Engineering (2011), M.Sc. degree (2014), and a PhD in Computer Science (2018). From Oct. 2016 to Oct. 2017, he was a Visiting Student with the Laboratory of Computer Science, University of Avignon, France. His main research interests are in Flying and Vehicular ad hoc networks, Visible light communications, Energy efficiency and Internet of Things (IoT). He is a reviewer in many international journals and a TPC member in many international conferences. He is a member of the IEEE and IEEE Communications Society. Abderrahmane Lakas received his MS (1990) and PhD (1996) in Computer Systems from the University of Paris VI, Paris, France. He joined the College of Information Technology, UAE University in 2003. He is teaching various courses on computer networks and network security. Dr. Lakas had many years of industrial experience holding various technical positions in telecommunication companies such as Netrake (Plano, Texas, 2002), Nortel (Ottawa, 2000) and Newbridge (Ottawa, 1998). He spent two years (94-96) as a Research Associate at the University of Lancaster, UK. Dr. Lakas has been conducting research in the areas of network design and performance, voice over IP, quality of service and wireless networks. He is a member of the IEEE and IEEE Communications Society. Dr. Lakas is in the editorial board of Journal of Communications (Actapress), and Journal of Computer Systems, Networks, and Communications (Hindawi). He is serving on the technical program committees of many international conferences GLOBCOM, ICC, VTC, etc.

TABLE 2 :

 2 Monitoring table of u 3 .

Zone

Vehicle (position (x,y)) Speed (m/s) Zone 1 v 1 (100.00,5.00) 10 Zone 2 v 2 (90.00,305.00) 8 v 3 (90.00,405.00) 8 Zone 3 v 4 (90.00,505.00) 8 v 5 (100.00,610.00) 14 Total number of vehicles T (S IX , IZ ) = 5 Average speed S P av = 9.6 m/s

TABLE 3 :

 3 Weight calculation scenario.

	Segments	d(I i , I j )	T (S Ii, Ij )	S P av	σ	W eight
	S IX , IZ	1500m	5	10 m/s	0.47	463.82
	S IZ , IY	1500m	0	0 m/s	0	0.00
	S IY , IW	1500m	2	14 m/s	0.47	136.05
	S IW , IX	1500m	12	0 m/s	1.41	7468.87

S I Z ,I Y obtains the best W eight and could be selected as a road segment to be traversed by the emergency vehicle.

TABLE 5 :

 5 Discovered paths.

Table u1

 u1

	Next hop Destination Alert ID CL P (s) RE P
	u2	TS (Hospital)		5(s) 0.75
	…	…	…	…
		Fig. 8: Routing table of u 1 .

TABLE 7 :

 7 Shortest path calculation and costs.

	Path	Shortest path	Cost

TABLE 8 :

 8 Simulation parameters

		Parameter	Value
	PHY & MAC	Frequency Band Transmit power Sensitivity Path loss model MAC layer Data rate	5.9 GHz 21.5 dBm -81.5 dBm Free-space IEEE 802.11p 1 Mbit/s
		Area size	3 × 3 km 2
	Scenario	Simulation time Number of UAVs Number of vehicles Ambulance speed v ma x UAV speed v ma x	300 s [10, 100] 100 17m/s 20m/s
		UAV altitude	300m
		vehicle speed v ma x	14m/s
	Routing	Communication range of UAVs Hello interval Data size Number of accidents (senders)	≈ 300 0.1 (s) 1 KB 20
		Initial energy of UAVs	2000 J