Celia Yasmine Tazibt
email: tazibt.celia@univ-paris13.fr

Nadjib Achir
email: nadjib.achir@univ-paris13.fr

Paul Muhlethaler
email: paul.muhlethaler@inria.fr

Tounsia Djamah

UAV-based Data Gathering

I. INTRODUCTION

The latest technological advances in wireless communications have allowed the development of wireless sensors with low energy consumption. These small devices are interconnected to form wireless sensor networks and are then deployed in an area of interest to handle specific tasks. These networks are useful in many fields such as: military, environmental, industrial and medical applications [START_REF] Anastasi | Energy conservation in wireless sensor networks: A survey[END_REF] [START_REF] Bujari | Flying ad-hoc network application scenarios and mobility models[END_REF]. Recent progress has led to the development of increasingly efficient sensors that can now handle increasingly complex data. The expected contribution of these elements makes it possible to envisage autonomy and a direct interaction with the environment thus offering the users a set of increasingly complex actions / reactions. The richer this interaction is, the more relevant the services to the users are. This type of sensor, however, brings their share of constraints, mainly due to hardware limitations as well as the types of information processed.

One of the consequences of the proliferation of wireless sensors is the development of flying equipment (also called drones or UAVs) at low cost and which can be used for a multitude of civilian or military applications. This new type of wireless sensor can be used in a variety of applications such as vehicle tracking, traffic management, fire detection, and assistance of crisis response teams [START_REF] Waharte | Supporting search and rescue operations with uavs[END_REF] [15] [START_REF] Adams | A Survey of Unmanned Aerial Vehicle (UAV) Usage for Imagery Collection in Disaster Research and Management[END_REF]. These mobile wireless sensors are able to fly autonomously at different altitudes and are generally equipped with units to monitor the environment and communicate to exchange data with other drones, ground sensors or even central stations. Among the possible applications are the collection and dissemination of data in environments that are difficult to access or hostile. Indeed, one or more drones can be used as a mobile data collection sink to navigate an area where wireless sensors are already deployed. In this case, the objective of the drone(s) is to collect information in an efficient and coordinated manner [7] [8].

UAV path Planning has been used (combined) with many other methods such as Genetic Algorithms [START_REF] Liu | A hybrid artificial potential field: genetic algorithm approach to mobile robot path planning in dynamic environments[END_REF], A* Algorithms [START_REF] Khuswendi | Uav path planning using potential field and modified receding horizon a* 3d algorithm[END_REF] and the Artificial Potential Field (APF) [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF]. The APF method is commonly used in path planning because of its concise mathematical description and the suitability for the real time control [START_REF] Liu | A virtual-waypoint based artificial potential field method for uav path planning[END_REF] [START_REF] Chen | Uav path planning using artificial potential field method updated by optimal control theory[END_REF].

In this paper, we are interested in the use of a potential field method for data collection in wireless sensor networks. We follow on from the idea proposed by [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF]. Indeed, in this approach they use a robot that is in charge of collecting WSN data. Each sensor is a source of an attractive force that attracts the robot. The robot will then have to move towards the node that emits the strongest signal (i.e., the sensor node containing the most data). The aim of our work is to collect the maximum amount of data while limiting the energy consumption of the drone (its flight time).

The remainder of this paper is organized as follows. Section II shows a state of the art on the different studies already proposed for the use of the potential field method for UAV guidance. Sections III and IV describe the system and the platform proposed for UAV data gathering. Tests and results are reported in Section V. Finally, Section VI concludes this paper.

II. THE STATE OF THE ART

The artificial potential field (APF) for robot obstacle avoidance was first used by Khatib [START_REF] Khatib | Real-time obstacle avoidance for manipulators and mobile robots[END_REF]. Briefly, this method consists in providing the workspace with an artificial potential field, in which the obstacles are represented by repulsive forces that push the robot back and prevent it from colliding with these obstacles. While the destination point is modeled by an attractive force that helps the UAV to reach the goal. The state of the art for UAV path planning using APF presents different uses of this method [START_REF] Baker | A combined mechanism for UAV explorative path planning, task allocation and predictive placement[END_REF].

Target tracking is one of the application uses of the APF method. These approaches are designed to find a known number of targets that are in motion in a workspace. In [START_REF] Chang | Moving target tracking of a uav formation[END_REF] the authors present a system to track a moving target using a swarm of UAVs under the influence of an artificial potential field. In their approach, they choose one UAV as the leader of the group, this leader being endowed with virtual attractive force to keep the swarm together. Each member of the group has repulsive forces to prevent from colliding together. The swarm of UAVs is under the influence of the moving target witch is endowed with an attractive force to help the leader to reach the target position. [START_REF] Alexander | Dynamic target tracking and obstacle avoidance using a drone[END_REF] presented a dynamic target tracking and obstacle avoidance system using a drone. This system is based on Potential Fields and it is extended to take into account not only the relative position of the target but also to modify the velocity of the UAV so that it will be able to pursue it.

The second use cases of potential fields are to help the robots (or UAVs) to reach a set of goal locations. In [START_REF] Liu | A virtual-waypoint based artificial potential field method for uav path planning[END_REF] the authors use the method of potential fields for the path planning of their drone. They update the method by an additional control force that helps the UAV get out from a local minimum. At first, the authors bring together in one block obstacles that are geographically close to one another to avoid the drone getting blocked between them. In a second step, they define virtual points equipped with attractive forces that help the UAV to avoid local minima points. In [START_REF] Khuswendi | Uav path planning using potential field and modified receding horizon a* 3d algorithm[END_REF] the authors present an autonomous navigation system for UAVs. Their method is based on a combination of virtual potential fields and the A* algorithm. Virtual forces are used to set up the environment (define obstacles and goals) while the A* algorithm is used to optimize the path. Thus, they study the performances of their different algorithms ( the hierarchical A* 3D Algorithm, the A* 3D and the receding Horizon A* 3D) in terms of processing time and distance traveled by the UAV. Another study [START_REF] Chen | Uav path planning using artificial potential field method updated by optimal control theory[END_REF] presents a UAV path planning method using an artificial potential field updated by optimal control theory. They use an improved APF method enhanced by introducing additional control forces, based on performance constraints (the speed and acceleration), the space constraint (the UAV needs to avoid the obstacles) the dynamic constraints (the relation between the force, the acceleration, the speed and the position) and the boundary condition (the path is represented by a starting point and a target point). Therefore, the constrained optimization problem is translated into an unconstrained optimization problem.

Finally, the APF method can be used to explore an area in order to map the environment. Another contribution [START_REF] Ji | Cooperative search by multiple unmanned aerial vehicles in a nonconvex environment[END_REF] presents a cooperative research algorithm where drones explore the environment to search for several unknown targets while avoiding the obstacles. They aim to minimize the searching time. The authors in [START_REF] Waharte | Supporting search and rescue operations with uavs[END_REF] present the different parameters to be taken into account when using potential fields for search and rescue operations. Their study is mainly based on time, i.e., they aim to reduce the time of discovery of a target (of a victim in a natural disaster for example). In a separate study [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF] propose a potential field approach for collecting data from sensor networks using mobile robots. They consider a capture field consisting of m sensor nodes and a robot R whose function is to circulate in the workspace in order to collect the captured data. The authors consider that each sensor node is a source of an attractive potential function, which leads to choosing the radial basis function (RBFs) centered on each sensor node, this choice is motivated by the possibility to determine the region of influence of each of the forces. The cost function that the authors use is based on the amount of data harvested; specifically, the robot goes to the nodes with the least available space. When the robot is in the communication space of a node, the available space is equal to the capacity of the node from which the space occupied by the captured data is deduced, adding to this the amount of data transferred to the robot. On the other hand, when the robot is far from a sensor node, the remaining space is equal to the initial space minus the space occupied by the sensed data. Once the forces have been calculated, the direction vector to be followed by the robot is obtained by adding all the vectors to the same vector of the strongest attractive force

III. PROBLEM DEFINITION

In the following, we consider a drone D, and a set of m sensors N = (s 1 , s 2 , ..., s m ) deployed within a sensing area A. In addition, we assume that there is no obstacles in the considered area. The drone is represented by its coordinates q d = (x d , y d , z d ). We do consider that the sensor nodes are on the ground, each node is represented by its coordinates on a 3D plane s i = (x i , y i , z i ), in our case, the coordinate of the 3rd dimension of all the nodes is equal to "0".

Each sensor node has several sensors (temperature, humidity, etc.) and is able to communicate with the other sensor nodes as well as with the drone through a wireless communication device with a communication range r i . The sensor nodes have a limited storage capacity denoted as C i : ∀i ∈ m. We consider that each sensor is able to capture the environmental data with a capture frequency denoted as g i : ∀i ∈ m (different frequencies for each node of the network). Thus, the amount of data collected by a sensor node at a time t is represented as d i (t) = g i * t.

As introduced earlier, in this paper we extend the work proposed by Pereira and al. Thus, as in [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF], in this work we aim to collect as much data as possible while minimizing both the time needed to collect this data and the drone's traveling time. To achieve this goal, our UAV will have to go towards the region presenting the maximum data to harvest or the areas that contain the nodes having the least available storage space. The remaining space c i for each sensor node is estimated with respect to the data transfer frequency h i as well as the distance between the sensor nodes and the drone as depicted in the following:

c i (t) = C i -d i (t) + h i * t f or||q d -q i || < r d C i -d i (t) otherwise (1)
Thus, the amount of data stored by each sensor node is then equal to the capacity of this node from which we subtract the remaining storage space. The following formula summarizes this property:

D i = [C i -c i ], 1 ≤ i ≤ m (2)
As in [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF], we use potential fields to direct the drone. We considered that we have only attractive forces, and the drone will be attracted by the point that emits the greatest force of attraction. However, unlike [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF], in this case, we do not consider that the sensor nodes are the sources of attractive forces but all possible points within the sensing area A. For seek of simplicity, we discretized this space into small hexagonal cells, and the center of each cell is considered as being a possible position of the drone. In this case, the center of the cell is considered as a possible the source of an attractive force.

According to the last considerations, each point of the area A is than a possible destination of the drone. If the drone is placed on this point, we have to compute the amount of data that could be collected by the drone. In this paper, we assume that the amount of data available at each cell is equal to the sum of data available at each sensor located at a maximum range of r d from that cell. Formally, the amount of potentially harvested data in each cell is computed by the following equation:

Q data (k) = j∈V k D j (3)
where V k is the set of neighboring cells of cell k and D j is the amount of data present in each cell.

IV. POTENTIAL FUNCTIONS

As defined previously, the Virtual Potential Fields (VPF) method consists of an artificial potential field that models the workspace. The obstacles are modeled by repulsive forces while the goal(s) is represented by attractive force. In this paper, we consider that our workspace is represented by a potential function where the drone is attracted by the cells containing the maximum amount of data. We make use of the Radial Basis Function [START_REF] Michael | Radial basis function for multivariable interpolation: a review[END_REF] defined for each cell of the capture field. With this feature, we can manage the intensity of the strength of each cell and its radius of influence. The function used is of the form:

φ k (q d ) = α k e -1 2P 2 k q d -q k 2 (4)
where, k denote the k th hexagonal cell. α k is the maximum amplitude of the function. P k is the basis radius. q d refer to the drone's position. q k are the coordinates of the center point of the hexagons.

In this case, we can define the gradient of the function as following:

φ k (q d ) = -α k q d -q k P 2 k e -1 2P 2 k q d -q k 2 (5)
The basis radius of this function must be defined so that the area with the most amount of data has the largest basis radius and areas with less data have a smaller basis radius; the computation of the basis radius of each zone P k is shown by the following equation:

P k = ε[Q data (k)] (6)
In addition, the amplitude of the function must also be proportional to the amount of data present in the cell, and therefore proportional to the base radius. For this, as in [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF], we have used a parameter b that links these two parameters (α k and P k ) ad following:

α i = p b i (7)
As we can see, as the amount of data increases, the basis radius and the magnitude of the function increase, which increases the force of attraction on the drone.

After computing the strength of each cell, the drone must then move to the area that has the largest basis radius (the greatest force of attraction). It will have to follow a vector v:

v = max( φ i (q d )) (8)
Finally, by following the vector with the maximal force, the drone harvests the data that is on its way to the destination point. The quantity of data collected at a time t is equal to the sum of the data found on the path of the drone.

V. TEST AND RESULTS

In this section, we evaluate the performance of our approach. Our aim is to collect the maximum amount of data while minimizing both the time needed to collect this data and the drone's traveling time. The first step is to divide the network into hexagonal cells, as shown in Figure 1. The second step is to define and compute the potential functions of each cell and, finally, that the drone follows the greatest force. We have conducted some tests, considering an area of 250m x 250m which we divided into a set of hexagonal cells of 34x29 cells. We deployed sensors using the Random Poisson Process over the sensing area. The drone was initially positioned at coordinate (0.0, 0.0). Finally, we consider 50 sensor nodes randomly deployed in A. We consider that all the sensor nodes have the same communication range, the same initial energy (battery capacity) and the same initial storage capacities. Regarding the energy consumption model, we used the energy values of the CC2420 family [START_REF][END_REF]. Table I illustrate the characteristics of the considered sensors.

In order to evaluate the performances of our approach, we compare our results with the performances achieved using the approach proposed by Pereira et al. in [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF]. We computed the amount of data collected by the drone when using each of the two methods. Figure 2 shows the amount of data collected over time. We note that our method offers better results than Pereira's method in terms of data collection time. Over a time interval of 20 seconds, for example, we find that when we use Pereira's method, the amount of data collected is equal to 3500 Bytes while in the same using our approach we were able to harvests 5900 Bytes. This is due to the fact that the drone is moving towards areas where we can collect the data of more than one sensor node, whereas with the Pereira method, the drone is moving towards a sensor node and collects only that node's data.

In figure 3, we plot the amount of data collected versus the distance traveled by the drone for both our approach and Pereira's approach. We can clearly see that when the drone travels small distances, the performances of both algorithms are quite similar. Indeed, by traversing up to 1000 m, both methods allow the drone to harvest about 2000 Bytes. However, when the drone travels a longer distance, the amount of data collected using our approach is much greater. For example, for a flight of 5000 m, the drone harvests 10000 Bytes using our method while with the other method it harvests only 5900 Bytes. In Figure 4 we plot the distances traveled by the drone to collect different amounts of data. As we can see, using Pereira's method the drone need to cover a greater distance to collect the same amount of data as our algorithm. More precisely, for a small amount of data 1000 Byte, the drone travels almost the same distance for both methods. However, when we increase the amount of data to collect, we find that using Pereira's method, the drone travels more than twice the distance traveled using our method.

Finally, we carried out a second series of simulations when we varied the number of sensor nodes within the network (from 10 sensors to 100 sensors), and we computed the distances required to collect the data. The obtained results are plotted in Figure 5. As we can see, increasing the number of sensors leads to a decrease in the traveled distance. For example, using Pereira's method and for 10 sensor nodes, the drone has to travel nearly 16 km to collect 8000 bytes while for 50 sensor nodes the distance is reduced to 9 km. On the other hand, using our method and for 10 sensors, the drone need to travel 10 km to harvest this amount of data. While using 50 sensor nodes, the distance is reduced by more than a half. Comparing the two methods, we find that our method gives better results in terms of the distance necessary to collect different amounts of data using different numbers of nodes To conclude, according to our simulations, we found that our method provides an improvement in performance compared to the method proposed by [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF], in terms of distance traveled and amount of data collected. In this paper, we presented a simple potential field-based model for collecting data using a drone. We use as our starting point the idea used by Pereira et al. [START_REF] Guilherme | A potential field approach for collecting data from sensor networks using mobile robots[END_REF]. However, we extend this work by considering that each cell in the area apply an attractive force on the drone, not only the deployed sensors. We compared our results with those obtained with Pereira's method and we obtained better performance in terms of data collection time. In other words, for the same period of time our method collect more data. The second advantage of our approach is that it leads to a significant reduction in the distance that the drone must travel.

In future work, we intend to extend this approach by deploying several drones to collect the data. This will introduce a new challenge to this problem since we also need to consider repulsive forces in order to avoid collisions between drones.

Fig. 1 :

 1 Fig. 1: Representation of the division of the sensing area into hexagonal cells

Fig. 2 :

 2 Fig. 2: Time Versus Amount of Data

Fig. 3 :

 3 Fig. 3: Distance Versus Amount of Data

Fig. 4 :Fig. 5 :

 45 Fig. 4: Histogram of comparison between the distance traveled by the drone to collect different quantities of data

TABLE I :

 I Characteristics of CC2420

	parameter	Value
	Communication Range 20 m
	Battery Capacity	1000 mAh
	Voltage	3.3V
	Tx Current	17.4mA
	Rx Current	18.4mA
	Data Packet Length	26 Bytes
	Buffer size	1000kbyte


Sadia Ingrachen

Nadjib Achir
email: nadjib.achir@univ-paris13.fr

Paul Muhlethaler
email: paul.muhlethaler@inria.fr

Tounsia Djamah

Amine Berqia

A Collaborative Environment Perception Approach for Vehicular Ad hoc Networks

In this paper, we focus on vehicular safety applications based on the Dedicated Short Range Communication (DSRC) standard. We propose a new mechanism to alleviate channel congestion by reducing the beacons load while maintaining an accurate awareness level. Our scheme is based on the collective perception concept which consists in sharing perceived status information collected by vehicles equipped with different types of sensors (radars, lidars, cameras, etc.). To achieve our goal, we propose two main schemes. The first one consists in implementing the collective perception capability on vehicles and adding a new category of status messages to share locally collected sensor data in order to reduce channels load and enhance vehicles' awareness. The second scheme concerns the accuracy level of the received information from the collective perception enabled vehicles by fixing a prior error threshold on the position. The method proposed is validated by simulations and the results obtained are compared to those of an application based on the traditional beaconing scheme of the IEEE802.11p standard. The simulations show that the proposed scheme is able to significantly reduce the load on the control channel incurred by the beacons and the packet error ratio for different network densities and built-in sensors characteristics.

I. INTRODUCTION

Nowadays, we are witnessing a tremendous growth in the number of vehicles on roads all over the world. A major consequence of this has been an increase in the number of accidents and inevitably the loss of human life. This is why governments and research communities are investigating the development of passive and active safety solutions for road users. The recent advances in software and hardware technologies and the evolution of wireless communications have assisted researchers and allowed the emergence of a very promising application area of networking. This application concerns Vehicular Ad hoc Networks (VANET). Wireless communications in vehicular environments allow a variety of wide-ranging applications [START_REF] Palazzi | An overview of opportunistic ad hoc communication in urban scenarios[END_REF] [START_REF] Bujari | Would current ad-hoc routing protocols be adequate for the internet of vehicles? a comparative study[END_REF]. Before extending their utilization to the infotainment applications, the prime objective of VANETs was to enhance road safety by reducing traffic congestion and accidents. In such VANET applications, vehicles periodically exchange their status information like position, speed, heading, etc. using the IEEE 802.11p [START_REF]Draft standard for wireless access in vehicular environments (wave)-multi-channel operation[END_REF] [3] standard which is based on on one-hop broadcasts and the 1609 protocol stack. This mechanism allows vehicles to acquire information about traffic conditions in their vicinity. Beaconing is a corner stone component of the V2V communications. However, these periodic beacons lead to the saturation of the channel and packet losses as the IEEE 802.11p MAC layer is based on the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel access method, which cannot cope with all possible collisions. On the other hand, the vehicular environment is characterized by a rapidly changing the very fluctuating topology and limited radio resources.These characteristics have a negative impact on Quality of Service (QoS), particularly when the network density is high, since a large number of vehicles are simultaneously competing to access the radio channel. In addition, vehicular safety applications have stringent requirements in terms of QoS parameters such as delay and reception rate. Therefore, the most challenging task for cooperative safety communications is how to maintain a satisfactory awareness level while reducing congestion on the radio shared channel. To resolve this issue, different approaches have been explored. The main ones are based on an adaptation of the transmission rate, the transmission power or both. While power adaptation schemes limit the vehicle's neighborhood, and thus the number of vehicles which receive the beacons, rate adaptation schemes try to distribute the bandwidth fairly between the vehicles. In the literature, these tracks have been widely explored in varying network conditions [START_REF] Muhammad | A cooperative safety zone approach to enhance the performance of vanet applications[END_REF] [18] [14] [START_REF] Kim | Bi-beacon: Twolevel power control for enhancing beacon performance in vehicular ad hoc networks[END_REF] [10] [11] [5] [START_REF] Sepulcre | Application-based congestion control policy for the communication channel in vanets[END_REF]. However, these approaches present drawbacks related to network density and the limited wireless resources.

In this paper we introduce a new beaconing scheme in order to alleviate channel saturation effects on the performance of cooperative safety applications and optimize the network resource utilization. To reduce the exchange of periodic Car Awareness Messages (CAMs), we use the collective perception Concept [START_REF] Gunther | The potential of collective perception in vehicular ad-hoc networks[END_REF] [START_REF] Günther | Realizing collective perception in a vehicle[END_REF]. Recent technological advances have made possible to design low costs and high performance components for autonomous driving systems where vehicles are equipped with different types of sensors such as Radars, Lidars (Light Detection and Ranging), acoustic sensors and cameras. The built-in sensors allow these vehicles to obtain an extended view of their surroundings and share these views via a wireless interface with other vehicles. This asset may be exploited in the network to reduce the number of communications between vehicles as we did in this work.

The rest of the paper is organized as follows. Section II discusses the state of the art of the different approaches proposed to resolve the congestion problem in vehicular environments. Section III deals with our mechanism to resolve the channel load problem. In Section IV, simulations are conducted to evaluate the performance of the mechanism proposed and its efficiency. In section V the simulation results are discussed. Finally, Section VI concludes the paper.

II. RELATED WORKS

The beaconing adaptation issue has been widely studied and several approaches have been proposed to cope with the overload and saturation of CCH in V2V communications. These approaches usually rely on the aforementioned schemes.

The authors in [START_REF] Danda B Rawat | Enhancing vanet performance by joint adaptation of transmission power and contention window size[END_REF] present a new mechanism based on the adaptation of transmission power and contention window size to improve the efficiency of information dissemination. Transmission power adaptation relies on estimating the local density of vehicles, whereas the contention window size is adapted to the collision rate.

In [START_REF] Kim | Bi-beacon: Twolevel power control for enhancing beacon performance in vehicular ad hoc networks[END_REF] the authors propose a Bi-beacon level scheme where two types of beacons are emitted, while keeping the same transmission rate. The first type of beacons with a long transmission range to guarantee large awareness in the network and the second one with a short transmission range to give drivers time to react efficiently.

In [START_REF] Zemouri | Smart adaptation of beacons transmission rate and power for enhanced vehicular awareness in vanets[END_REF], the authors propose a combined rate/power adaptation scheme to deal with the channel load in order to improve channel utilization and reduce collisions. The method uses the current collision rate to adjust the transmission parameters. By using a binary search algorithm, the optimal channel load is located and then used by vehicles. This approach results in a reduced the beacon rate while maintaining an acceptable level of awareness for closer neighbors.

A Cooperative Safety Zone is proposed in [START_REF] Muhammad | A cooperative safety zone approach to enhance the performance of vanet applications[END_REF]. The basic idea of the protocol consists in selecting the minimum transmission power for CAMs to keep vehicles connected within a safety zone . The transmission power is selected based on the distance between a source vehicle and its critical neighbors. Cooperative piggybacking is used to extend awareness beyond the safety zone.

Another approach, where the vehicle's driving context is considered, is studied in [START_REF] Sepulcre | Application-based congestion control policy for the communication channel in vanets[END_REF]. To reduce channel loads and give better network performance, the transmission parameters are adjusted according to the application's security requirements and driving conditions.

In [START_REF] Lei | Power adjustment based congestion control in vehicular ad-hoc networks[END_REF], power adjustment is made dynamically according to channel status, the history of transmission delays and the message priority. The method is able to achieve better performance than the DSRC standard where the transmission power is fixed in advance.

In order to provide high reliability to disseminated beacons, [11] proposes a solution where the beacon adaptation rate is based on the channel load evaluated by two parameters which are the channel busy ratio (CBR) and the local density observed by the vehicles.

In [START_REF] Günther | Collective perception and decentralized congestion control in vehicular ad-hoc networks[END_REF], the authors use the concept of collective perception to achieve a decentralized congestion control where different sensing capacities of the vehicles are exploited to enhance traffic awareness and alleviate channel congestion. This approach is efficient when used to reduce channel congestion.

In this paper, we address the channel load control from a different angle. Instead of making use of power/rate adaptation mechanisms like in the previously presented approaches, which are closely related to network dynamics, we will take advantage of different sensing technologies integrated in recent vehicles, which is a promising avenue for the automotive industry and autonomous vehicles.

III. PROPOSED MECHANISM

In this paper, we propose a new mechanism to enhance QoS of cooperative safety communications in vehicular environments and mitigate the channel load issue introduced above. To meet this requirement, our idea consists in using collective perception. The collective perception concept in intelligent transportation systems involves sharing gathered and preprocessed sensor data about the local view of traffic dynamics between connected vehicles. The cooperation between vehicles is carried out via cooperative wireless communication technologies such as V2X (vehicle to everything) communication [START_REF] Rauch | Analysis of v2x communication parameters for the development of a fusion architecture for cooperative perception systems[END_REF]. In this way, all equipped vehicles participate in extending the awareness information to others beyond their Field of View (FoV). The sensor data fusion models are beyond the scope of this paper.

In the solution given in this paper, we assume that all vehicles are equipped with GPS that provide them with information about their positions and driving status and DSRC transceivers to exchange their own information via wireless messages. In addition to this and unlike existing studies, a second category of vehicles is assumed to be equipped with different types of sensors (cameras, acoustic sensors, radars, lidars ..etc) allowing them to have a vision of the surrounding vehicles. The latter category has an important role in the network as it contributes greatly to reducing channel congestion as will be demonstrated by simulations later. Additionally, we suppose that no obstacles exist on the road and that the perceived dynamic objects consist only of vehicles. A vehicle is described by a unique identifier and its dynamic measurements such as position, speed, heading, etc.

Figure 1 illustrates how our method works. Vehicles A and B are collective perception enabled. Collected and fused information generated by the different sensors of vehicle A and B is included in an Extended CAM (ECAM) where each perceived vehicle is described by its unique identifier, position, heading and velocity. The ECAM contains the description of Before the descriptor of a vehicle is added to the ECAM, the distance between the origin vehicle and the perceived vehicle is calculated in order to ensure that only vehicles in the communication range of the vehicle are included in its neighborhood table. Since sensors perceived information is error-prone, its data precision may be altered as distance increases or due to different intern or environmental factors. So, in order to make our model more realistic, the estimation errors are previewed. In this paper, we consider an error rate in the longitudinal position while estimating neighbors' positions.

In our method, the transmission will be triggered by crossing an error threshold on the vehicles' positions. The decision of a vehicle to disseminate or not its own information at any time depends on the information received from the equipped vehicles. The dynamic information included in the objects descriptors represent what all the vehicles in the communication range of the origin vehicle consider it to be. If the estimation made by the equipped vehicles is not exact, the receiving vehicle broadcasts its status information. Otherwise, there is no need to send the beacons since vehicles in the vicinity are aware of its positions thanks to the ECAM sent by the equipped vehicles.

The vehicles' movement is taken into consideration while choosing the error threshold. For instance, when an equipped vehicle V i sends, at instant t, an ECAM containing the estimation of the position of V j Pij (t), the position of the receiving vehicle V j is P j (t), where P j (t+1) = Pij (t)+ε long with ε long being the error induced by the vehicle movement on the longitudinal axis. When a vehicle is moving fast, the error threshold may be frequently crossed. In such a situation, messages will be sent frequently. On the other hand, when the vehicle's acceleration is lower, the position estimation is more accurate, the error threshold is respected and hence messages will be broadcast less often.

IV. PERFORMANCE EVALUATION

In this section, we evaluate our mechanism with different parameters to investigate the impact of the vehicle density, sensors sensitivity and percentage of equipped vehicles on the performance of the safety applications in terms of packet error rate, channel busy ratio, latency represented by the number of backoffs performed by a vehicle before sending it packet in addition to the beacons load. We conducted several simulations using the simulation framework for vehicular networks Veins [START_REF] Sommer | Bidirectionally coupled network and road traffic simulation for improved ivc analysis[END_REF] and the microscopic traffic simulator Simulation of Urban Mobility [START_REF] Krajzewicz | Recent development and applications of sumo-simulation of urban mobility[END_REF]. The road scenario we used is a bidirectional 5 km three-lane section. The simulation parameters are summarized in Table 1. The results are compared with a simple application based on the basic IEEE 802.11p standard. The metrics we used to evaluate our mechanism are the beacons load which consists of the number of packets sent by each vehicle during the simulation time. The second metric is the packet error rate which represents the packet loss rate due to bit error (SINR not achieved). The channel busy ratio (CBR) is also considered, which represents the ratio of the total time that a vehicle occupies the channel while transmitting divided by the simulation time:

CBR i = total busy time i /sim time
Where CBR i is the average CBR that a given vehicle i observes throughout all the simulation time. The last one is the total number of backoff intervals a vehicle waits before sending it packets. In order to show the effectiveness of our scheme, extensive simulation was conducted with different market penetration rates.

Simulation results presented in this section evaluate our method for different parameters along with an application based on the standard periodic beaconing scheme proposed in DSRC. According to the following simulation results, we can attest that the impact of our mechanism based on the dissemination of sensors collected information is noteworthy. Figure 2 depicts the average packet load for different percentages of vehicles with collective perception capabilities with different network densities. We can see that the packet load decreases as the market penetration rate increases. For instance, for 100% and 20% penetration rates of the collective perception, the packet load is reduced by 98% and 46% respectively without degrading the vehicles' awareness with a 40 m sensors detection range and an error threshold on the vehicle's longitudinal position of 1 m. With high densities, we see that the proposed mechanism achieves better performance than periodic beaconing.

In Figure 3 the packet error ratio due to a bit error is represented and compared to an application where the standard beaconing scheme of IEEE 802.11p is used. It shows the advantage of using a collective perception-based beaconing scheme compared to the standard periodic beaconing. We can note that our solution alleviates packet losses in the network compared to standard solutions. For 20% and 50% we attest that the loss rate is significantly reduced since the packet load is very low. For example, with 100%, the PER is almost zero because the packet load is very low with an improvement of 97% compared to the IEEE 802.11p fixed rate beacons. CBR represents the amount of time that a transmission at the level of the MAC layer of a vehicle is not feasible because the channel is busy. A high value of the CBR reflects a high congestion level of the wireless channel. This is why CBR is considered as the best indicator of the performance in the IEEE 802.11p MAC layer [START_REF] Rostami | Stability challenges and enhancements for vehicular channel congestion control approaches[END_REF]. Figure 4 shows the CBR for different rates of equipped vehicles with different network densities. We can see that in case of 0%, the standard beaconing may cause high channel congestion since all vehicles compete to send their CAMs periodically. Channel congestion becomes worse when traffic density increases. For 20% and 50%, the CBR is significantly reduced, as is the packet loss rate, as depicted in Figure 3.

As collective perception extends the awareness range of vehicles by sharing sensor data in additional cooperative messages, it significantly reduces the packets overhead, and hence channel congestion while reducing packet losses and delays. Figure 5 depicts the important improvements of the packets' latency, expressed by the number of backoff slots performed by a node, achieved by our method in comparison to the standard periodic beaconing scheme. We can notice that the collective perception mechanism allows a significant reduction in the packets access delays. This is mainly due to the decrease in the packet load, hence the decrease of contention to access the channel. In this case, a node wishing to disseminate its beacons will not perform many backoff intervals before sensing the channel free. Even with of high densities, an important gain can be seen. For example, in 200 veh/km traffic density, a gain of 28% and 72% is achieved by a 5% and 50% of equipped vehicles respectively compared to 0%.

V. CONCLUSION

In this paper, we have presented a new collective perception mechanism to reduce the channel congestion issue due to CAMs in vehicular environments and enhance the QoS of cooperative safety applications in terms of the packet load, PER and average channel busy time. The vehicles with collective perception capacity share the collected information from the built-in sensors with other vehicles. We introduce a new type of message to share sensor data that we call ECAM. After the reception, vehicles verify the accuracy of the information included in the message. According to the result, a transmission could be triggered or not as required by the mechanism. The simulations conducted show that our scheme performs well under different network conditions such as variable traffic densities and percentage of vehicles equipped with our mechanism compared to the periodic beaconing scheme proposed by the DSRC standard.

Figure 1 :

 1 Figure 1: Detection of vehicles in the FoV and sharing their information with vehicles in the communication range

Figure 2 :Figure 3 :

 23 Figure 2: Average packet load versus vehicle density, T hr= 1m, det r = 40m

Figure 4 :

 4 Figure 4: Channel busy ratio versus vehicle density, T hr= 1m, det r = 40m

Figure 5 :

 5 Figure 5: Number of backoffs for different rates of equipped vehicles at different densities, T hr= 1 m, det r = 40 m

Table I :

 I Simulation parameters

	Simulation parameter	Value
	Simulation time	400s
	Packet size	32 bytes
	ECAM size	202 bytes
	bit rate	3Mbps
	beacon interval	20ms
	max vehicles speed	70km/h
	Number of Nodes	(50,100, ..., 200)veh/km
	Number of Equipped Nodes	(5,10,20,50,100)%
	Highway length	5 km (3 lanes)
	Number of runs	30
	Error Threshold	1.0m
	Detection range	40m
	Confidence interval	95%