domain: spi info

text:
Hichem Sedjelmaci

Sidi Mohammed Senouci

Mosa Ali Abu-Rgheff

An Efficient and Lightweight Intrusion Detection Mechanism for Service-Oriented Vehicular Networks

Keywords: Intrusion detection, service attacks, serviceoriented vehicular networks

Vehicular ad hoc networks (VANETs) are wireless networks that provide high-rate data communication among moving vehicles and between the vehicles and the road-side units. VANETs are considered as the main wireless communication platforms for the intelligent transportation systems (ITS). Serviceoriented vehicular networks are special categories for VANETs that support diverse infrastructure-based commercial infotainment services including, for instance, Internet access, real-time traffic monitoring and management, video streaming. Security is a fundamental issue for these service networks due to the relevant business information handled in these networks. In this paper, we design and implement an efficient and light-weight intrusion detection mechanism, called efficient and light-weight intrusion detection mechanism for vehicular network (ELIDV) that aims to protect the network against three kinds of attacks: denial of service (DoS), integrity target, and false alert's generation. ELIDV is based on a set of rules that detects malicious vehicles promptly and with high accuracy. We present the performance analysis of our detection mechanism using NS-3 simulator. Our simulation results show that ELIDV exhibits a high-level security in terms of highly accurate detection rate (detection rate more than 97%), low false positive rate (close to 1%), and exhibits a lower overhead compared to contemporary frameworks.

I. INTRODUCTION

W ITH THE development and advancement of wireless communication technology, researchers conceptualized the idea of vehicular communication networks, also known as vehicular ad hoc networks (VANETs). These networks aim to turn cars into intelligent machines that communicate with each other (V2V) or with an infrastructure (V2I) in order to improve traffic safety and comfort of driving [START_REF] Mershad | A framework for secure and efficient data acquisition in vehicular ad hoc networks[END_REF]. VANETs applications can be categorized into two classes: road-traffic safety and service-oriented applications. With service-oriented applications, road-side units (RSUs) are deployed along the roads for H. Sedjelmaci and S. M. Senouci are with the DRIVE Laboratory, University of Burgandy, Dijon 58000, France (e-mail: sid-ahmed-hichem.sedjelmaci@ u-bourgogne.fr). M. A. Abu-Rgheff is with the Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, U.K. users to request any location-based service (finding restaurants, downloading a map, locating a gas station or a parking space, etc.), internet-based services (multimedia, instant messenger), or real-time traffic concerns. It is expected that service-oriented vehicular networks attract a great deal of investment in largescale deployment of wireless infrastructures [START_REF] Mershad | A framework for secure and efficient data acquisition in vehicular ad hoc networks[END_REF], [START_REF] Zhu | Security in service-oriented vehicular networks[END_REF].

The success of such service-oriented vehicular networks depends mainly on the underlying communication system, and particularly, the information security since these networks are exposed to attacks generated and handled by these networks [START_REF] Zhang | Defending connected vehicles against malware: Challenges and a solution framework[END_REF]. The Intrusion Detection System's (IDSs) techniques show that they are very effective in protecting the network against both internal and external attacks [START_REF] Sedjelmaci | An efficient intrusion detection framework in cluster-based wireless sensor networks[END_REF]- [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF]. Therefore, in this paper, we design and develop an efficient and lightweight intrusion detection mechanism for vehicular networks (ELIDVs) that aim to protect the network against malicious vehicles. In this research work, we focus to detect three kinds of attacks: 1) denial of service (DoS) that aims to disturb the network operation; 2) integrity target that alters the message that is exchanged between legitimate vehicles or provides false information (FI) such as false locations; and 3) false alert's generation that broadcasts a false alert message. ELIDV relies on a set of detection rules related to each attack to model a normal (and anomaly) behavior of a vehicle. Furthermore, with the help of the proposed detection mechanism, we developed a vehicle's behavior evaluation (VBE) protocol that evaluates the trustworthiness level of a vehicle according to its behavior and the available information it provides. We have designed a light-weight detection framework with no need to any additional hardware resource (e.g., firewall) to achieve a high level of security. In addition, ELIDV is extensible for new functionalities that would allow detecting more complex attacks.

We note that, to the best of our knowledge, we are the first dealing with the intrusion issue on service-oriented VANETs, and the detection of the most dangerous attacks that could occur in such networks. In fact, most of the works such as [START_REF] Mershad | A framework for secure and efficient data acquisition in vehicular ad hoc networks[END_REF], [START_REF] Zhu | Security in service-oriented vehicular networks[END_REF], [START_REF] Coronado | Service discovery, and service access in wireless vehicular networks[END_REF] apply cryptography techniques to prevent external attackers penetrating the network.

This paper is organized as follows. In Section II, we describe the categories of attacks that can take place on service-oriented vehicular networks and highlight some intrusion detection schemas for VANETs proposed in the current literature. In addition, we introduce the network model that we attempt to secure. Section III presents details about our intrusion detection mechanism ELIDV followed by the VBE protocol. The latter is capable of evaluating the trustworthiness level of each vehicle in the network. Section IV provides NS3 simulation results. Finally, in Section V, we conclude the paper and give some perspectives that we envisage to carry out in the near future.

II. BACKGROUND AND RELATED WORK

In this section, we describe main attacks that target serviceoriented networks and attempt to detect these attacks using our proposed framework, namely, Integrity target, DoS, and False alert's generation attacks. Then, we summarize some relevant intrusion detection schemes presented in the literature and discuss their main shortcomings. Finally, we describe the network architecture that we attempt to secure.

A. Common Attacks on Services-Oriented Vehicular Networks

We present three common categories of attacks that may target services-oriented vehicular networks and explain their characteristics.

1) Integrity Target Attack: Such an attack aims to alter the message that is exchanged between legitimate vehicles or provide FI such as location. We can cite Sybil attack that we described as follows.

Sybil attack: In vehicular networks, the vehicles usually discover their neighbors by periodically broadcasting cooperative awareness messages (CAMs), in which they claim their identities and positions [START_REF] Yu | Detecting Sybil attacks in VANETs[END_REF]; thereby, a Sybil node aims to create multiple identities with providing false locations. Furthermore, such threat could also launch a variety of attacks such as DoS or simulate a fake crash, congestion, etc. 2) DoS Attack: The malicious vehicle that launches such an attack aims to disturb the network operation or the used routing protocol. Among the main dangerous DoS attacks, we cite black hole attack, which is briefly described below.

Black hole attack: The vehicle that carries out such an attack aims to drop all the received packets from legitimate vehicles. 3) False Alert's Generation Attack: In this case, the malicious vehicle sends an alert message to its k-hop neighbors (RSUs, vehicles) to urge them to take some evasive actions. The idea of the attacker is to send a false alert message in order, for instance, to clear the road for itself or create a traffic jam in the road. We summarize, in the following table, some alert messages.

B. Related Work

IDSs have proved their efficiency to detect intruders with high accuracy compared to cryptography mechanisms [START_REF] Sedjelmaci | An efficient intrusion detection framework in cluster-based wireless sensor networks[END_REF]- [START_REF] Raya | Eviction of misbehaving and faulty nodes in vehicular networks[END_REF]. Recently, several security schemes have used such a system to address security issues in vehicular networks. In [START_REF] Raya | Eviction of misbehaving and faulty nodes in vehicular networks[END_REF], the authors propose a security mechanism to detect and then evict the malicious vehicles from the network. To address the intrusion detection issue, they have developed a malicious node-detection system that relies on anomaly-based detection.

The latter uses an entropy approach to model a normal behavior (NB) of a monitored vehicle and any deviation from this model is detected as an intrusion. This mechanism aims to detect two attacks: DoS and an attack that collects and disseminates FI (i.e., integrity target attack). According to their simulation results, these attacks were detected with a high accuracy. However, the performance of their scheme decreases when the number of attacks is high. In addition, the collisions rate increases when the density increases. In [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF], the authors propose a new detection framework called Trust-aware Collaborative Learning Automata-based Intrusion Detection System (T-CLAIDS) that uses a learning automat technique for anomaly detection. This technique has the ability to model a normal and abnormal pattern with a high accuracy. In this work, the authors aim to detect intruders that collect packets from a legitimate vehicle and disseminate false copies of such information (i.e., integrity target attack). In their simulations, the authors prove that their scheme is able to detect up to around 90%-95% of malicious packets. Nevertheless, embedding such learning algorithm within a vehicular network is computationally expensive and could generate a significant delay, which makes it inappropriate in such networks specifically for safety applications.

In [START_REF] Ruj | On data-centric misbehavior detection in VANETs[END_REF], the authors introduce a data-centric intrusion detection scheme to detect the malicious vehicle that generates a false alert, e.g., a false crash. In their approach, a set of predefined rules is used to model the behavior of the vehicle after it sends an alert. Then, the expected behavior and the action that performs by the vehicle after sending alert are compared. When there is no correspondence, this vehicle is identified as malicious that generates a false alert. In their simulation, the authors prove that their approach generates a low overhead compared to other security scheme proposed in the literature. However, the authors did not evaluate the detection accuracy of their scheme when such attacks occur.

C. Network Architecture of Service-Oriented VANETS

The architecture of the service-oriented VANET (as illustrated in [START_REF] Zhu | Security in service-oriented vehicular networks[END_REF] and [START_REF] Zhang | Efficient identitybased batch verification scheme for vehicular sensor networks[END_REF]) is composed of two layers. The first layer comprises the onboard vehicles and the RSUs wireless communication devices, for communications either between vehicle and road-side infrastructure (V2I) or between vehicles themselves (V2V) using a dedicated short-range communications (DSRC) standard [START_REF]ETSI-World Class Standard Dedicated Short Range Communication (DSRC)[END_REF]. The second layer is composed of service provider (SP) server and a central authority (CA). Examples for the SP server are traffic control analysis centre [START_REF] Zhang | Efficient identitybased batch verification scheme for vehicular sensor networks[END_REF], multimedia content service, and location-based service [START_REF] Zhu | Security in service-oriented vehicular networks[END_REF]. RSUs are connected to the SP server and CA through a wired communication and uses transport layer security (TLS) protocol [START_REF] Fadlullah | DTRAB: combating against attacks on encrypted protocols through traffic-feature analysis[END_REF].

Communication devices, onboard vehicles, and the RSUs exchange data using multihop routing protocols. In this paper, we use a greedy forwarding scheme based on unicast approach, where the forwarder node is selected according to its capability to provide a higher progress toward the final destination (i.e., RSU) [START_REF] Rondinone | Contention-based forwarding with multi-hop connectivity awareness in vehicular ad-hoc networks[END_REF]. This scheme also uses a store-and-forward (SNF) mechanism, which has the ability to store the information and forwards it periodically until it finds the next neighbor or the final destination. The choice of the forwarding period is crucial as it impacts the bandwidth resource and the end-to-end delay. Therefore, the SNF period should take into account the application' requirements (i.e., low bandwidth consumption and low end-to-end delay).

III. ELIDV

IDSs are the most reliable mechanisms to detect internal attacks that are aware of the network's cryptography key. In this paper, we propose an efficient and light-weight intrusion detection mechanism, where IDS agents require a low overhead to detect and prevent, with a high accuracy, three attack categories that are intended to disrupt the networks communication. We embed ELIDV in flat topology that uses a unicast greedy and forwarding protocol.

We first estimate the number of intrusion detection agents that are located within the link's radio range. We then present our detection policies based on rules related to each attacks cited above. Finally, we describe our VBE protocol that has the ability to assign a malicious level (ML) to malicious vehicles. Based on the ML, the vehicle is categorized into one of the following classes: trustworthy, uncertain, or untrustworthy node.

A. Estimation of the Number of Intrusion Detection Agents

In our proposed detection scheme, each vehicle has the ability to activate an intrusion detection agent in order to monitor its neighbors on a promiscuous mode and apply detection rules related to each attack. The promiscuous mode means that since vehicle v 1 is within the radio range of vehicle v 2 , it can overhear communications to and from v 2 as illustrated in Fig. 1.

In this section, we analyze the minimum number of vehicles that have the ability to play the role of an intrusion detection agent, i.e., could monitor a link (v 1v 2 ). We call this agent a guard vehicle.

Using the greedy forwarding protocol, the farthest vehicle from a communicating vehicle (v 1 ) forwards the transmitted packets. Therefore, if vehicle v 2 is a forwarder node, it should be located mostly on vehicle v 1 's radio-range boundary as illustrated in Fig. 1.

Let x be the distance between v 1 and v 2 , and we assume that all the vehicles have the same radio range (r). For any distance x, the radio coverage area where guard vehicles are located is the area of the rhombus ABCD subtracted from the area of the sectors ABC and ADC which is calculated as follows [START_REF] Khalil | LITEWORP: A lightweight countermeasure for the wormhole attack in multi hop wireless networks[END_REF]:

A(x)=2r 2 cos -1 x 2r -2x r 2 - x 2 4 . (1)
The average number of guard vehicles for each link is equal to E[A(x)] * d, where d is the network vehicles density and E[A(x)] is the expectation of a function A(x), which is computed as follows:

E [A(x)] = r 0 A(x)dG(x).
(2)

G(x) is the probability distribution of x, which is given by

G(x)= ⎧ ⎨ ⎩ 0,x ≤ 0 x 2 r 2 ,x > 0 E [A(x)] = r 0 2x r 2 2r 2 cos -1 x 2r -2x r 2 - x 2 4 dx ⇔ E [A(x)] ≈ 0.29πr 2 .
The average number of guard vehicles, which depends on the network vehicles density d is given by 0.29πr 2 d.

(

As a result, we conclude that there is at least one guard vehicle at each link to monitor the vehicles that are within its radio range, and the number of these monitoring vehicles depends on the network vehicles density.

B. Rules-Based Intrusion Detection Technique 1) Detection of DoS Attacks

Black hole attack's detection: Black hole attack drops all received packets from legitimate vehicles. Therefore, to detect such attacks, each guard vehicle (G) monitors the behaviors of its neighbors (e.g., G and G ′ monitor the sender v 1 and a relay node v 2 ). Furthermore, the greedy forwarding selects a capable forwarder to provide a higher progress toward the destination [START_REF] Rondinone | Contention-based forwarding with multi-hop connectivity awareness in vehicular ad-hoc networks[END_REF]. Thereby, the sender v 1 knows the position of the relay vehicle v 2 and can use promiscuous mode to monitor whether the relay forwards the packets it received or not. In case v 1 could not determine whether v 2 forwards the packets or not, due to the incertitude caused by a high mobility and collisions, it broadcasts a monitoring alert to guard vehicles to monitor the link between v 1 and v 2 . The guard vehicle (G) captures and stores packets going out of vehicle v 1 and monitors whether v 2 forwards these packets. Therefore, when G finds that v 2 does not forward any packets from v 1 , it concludes the following possibilities may have occurred: 1) the forwarded packets could not be observed due to packets collision; 2) vehicle v 2 does not find any neighbors to forward packets and hence a SNF mechanism is launched; or 3) vehicle v 2 carried out a black hole attack. To determine whether collision or a malicious behavior (MB) is taking place, a cooperative detection is launched. In this detection, the guard vehicle cooperates with its guard neighbors (located within radio range of link v 1v 2 ) to determine whether v 2 forwards the packets sent by v 1 . When SNF period is elapsed and all the guard vehicles claim that a vehicle v 2 does not forward any packets from v 1 , v 2 will be labeled as a black hole attacker. The SNF period depends on the greedy forwarding protocol that is used (see Table II).

2) Detection of Integrity Target Attacks Sybil attack's detection: According to the works in [START_REF] Yu | Detecting Sybil attacks in VANETs[END_REF], [START_REF] Golle | Detecting and correcting malicious data in VANETs[END_REF], and [START_REF] Newsome | The Sybil attack in sensor networks: Analysis & defenses[END_REF], detection of Sybil attacks usually relies on one of these three detection approaches: radio resource testing, identity registration, and position verification. Our detection mechanism applies a position verification approach to prevent the occurrence of such attack. To estimate the position of a suspected vehicle and hence detect such attack, we propose two detection algorithms that rely on signal strength intensity (SSI) and packet's round trip time (RTT) to determine accurately the distance (or location) of a vehicle.

The guard vehicle monitors the SSI generated by its neighbors (SSIn) and computes the distributed SSI model (SSId) using a shadowing radio propagation model [START_REF] Yu | Detecting Sybil attacks in VANETs[END_REF] as follows:

P r (L) P r (L 0 ) = -10β log L L 0 + X dB . (4)
X dB is a Gaussian random variable with zero mean and a standard deviation σ dB , and β is called the path-loss exponent [START_REF] Yu | Detecting Sybil attacks in VANETs[END_REF]. L 0 is a reference position.

L is the distance from the guard vehicle G with a position (x vG , y vG ) to suspected vehicle v 1 with a position (x v1 , y v1 ), computed as follows:

L = (x vG-x v1 ) 2 +(y vG-y v1 ) 2 . (5)
The guard vehicle G obtains the suspected node's coordinates (x v1 , x v1 ) through the periodic exchanges of CAM messages. The distributed model SSId at a distance L from the suspected vehicle is computed as follows:

SSId(L)=E P r (L) P r (L 0 ) : = -10β log L L 0 ( 6
)
where E is the statistical expectation function.

To check whether the suspected vehicle-claimed position is correct, the result of (7) should follow a Gaussian distribution, i.e., it should lie within Mean(S) -3 * σ(S i ) and Mean(S)+ 3 * σ(S i ) [START_REF] Yu | Detecting Sybil attacks in VANETs[END_REF], [START_REF] Sedjelmaci | Efficient and lightweight intrusion detection based on nodes' behaviors in wireless sensor networks[END_REF], where σ is the standard deviation Both SSId and SSIn are computed at each time period ti, and n is the number of observations. When S i does not follow a Gaussian distribution, the vehicle v 1 is suspected to provide a false location, hence could carry out a Sybil attack. In order to confirm such attack, the packet RTT is computed as in [START_REF] Sastry | Secure verification of location claims[END_REF], according to the following steps.

S i = |SSId ti -SSIn ti | ,i = {1,...,n} (7) Mean(S)= n i=1 S i n σ(S i )=S i -Mean(S).
The guard vehicle G with a position (x ′ vG , y ′ vG ) sends to a monitored vehicle v 1 a request packet. This vehicle should immediately reply with its position (x ′ v1 , y ′ v1 ), as it receives this packet. Then, G computes the RTT between G and v 1 , RTT G-v1 . G checks whether RTT G-v1 satisfies ( 8)

RTT G-v1 = 2 * L C +∆t ( 8
)
where L is the distance from G to v 1 given by ( 5) and C is the speed of light. ∆t is the time delay incurred by vehicle v 1 due to collusions, and processing the incoming packet (∆p) [START_REF] Sastry | Secure verification of location claims[END_REF]. The guard vehicle estimates the probability of collision (PC), which depends on the vehicle density and speed. A model to estimate this probability is proposed in [START_REF] An | Analysis of collision probability in vehicular ad hoc networks[END_REF]. When PC = 0 and ∆t = ∆p, otherwise ∆t =∆p +back-off time.I nt h ereply packet sent by vehicle v 1 , it adds, with its position, the ∆p and back-off time values. If [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF] does not hold, v 1 is detected as a node that provides a false location. As a result, it will be identified as a Sybil attacker.

3) False Alert's Generation Attacks Detection: The detection of such attacks is fundamental for road safety. Here, we focus on alerts that are generated for safety application, which are described in Table I. In the following, we describe our detection policy to identify the malicious node. The vehicle v 1 sends CAM messages periodically to a vehicle v 2 that includes its coordinate (x camv 1 , y camv 1 ) and the time when a particular message is generated (t cam ).

However, when an event occurs, such as crash or road congestion, vehicle v 1 sends alert messages to v 2 that include the alert type and the same information as in a CAM message [i.e., time (t alarm ) and v ′ 1 s coordinates (x alarm v1 , y alarm v1 )]. Node v 2 computes the following parameters t 1 , d 1 , s 1 before an alert is issued and then computes these parameters again as t 2 , d 2 , s 2 after the alert generation in order to monitor the behavior of v 1 and detect whether the generated alert is valid. These parameters are defined as follows.

a) t 1 is the period that v 1 spends during the generation of a CAM and subsequent alert message, d 1 is a distance traveled during this period, and s 1 is the average speed. These parameters are computed as follows:

t 1 = t alarm -t cam d 1 = (x alarmv 1 -x camv 1 ) 2 +(y alarmv 1 -y camv 1 ) 2 s 1 = d 1 t 1 .
b) t 2 is the period that v 1 spends during a generation of an alert message and subsequent CAM, d 2 is a distance traveled during this period, and s 2 is the average speed. These features are computed as follows:

t 2 = t ′ cam -t alarm d 2 = (x ′ camv1 -x alarmv1 ) 2 +(y ′ camv1 -y alarmv1 ) 2 s 2 = d 2 t 2 .
Vehicle v 2 applies the values of the computed parameters to compare the behavior of vehicle v 1 after an alert is issued with the expected action provided by the authors in [START_REF] Ruj | On data-centric misbehavior detection in VANETs[END_REF] and illustrated in Table I. For instance, when PCN, SVA, or RHCN alerts are raised, v 2 checks v 1 's speed: s 1 (before crash detection) and s 2 (after crash detection), and whether v 1 changes a lane (or route). In case the speed s 2 is not decreased and/or it traverses the same lane (or route), v 2 ignores such alert and v 1 will be identified as a malicious vehicle. The expected behaviors when the remaining alerts are raised are given in Table I.

It's important to note that v 2 could provide wrong coordinates information (i.e., false position), such behavior may lead to a Sybil attack. The detection of the Sybil is explained above.

C. Vehicle's Behavior Evaluation Protocol

During the network lifetime, a malicious vehicle could switch to and operate as a legitimate [START_REF] Ruj | On data-centric misbehavior detection in VANETs[END_REF]; hence, the behavior of the node oscillates between a legitimate and malicious patterns. Thereby, it is not necessary to disconnect a malicious vehicle from VANET immediately when it exhibits a MB. Furthermore, we assume that a malicious vehicle could carry out several attacks discussed above.

The VBE protocol assigns a ML to a node that carries out malicious activities through processes explained in the following.

1) Vote Process: Malicious vehicles could cooperate between each other and claim that a legitimate vehicle exhibits an attack or vice versa. Therefore, in order to overcome this issue, a vote mechanism is applied. Thereby, when a guard vehicle v i suspects a node as malicious, it forwards a ballot vote to nearby RSU encrypted with a session key KS RSU-vi . This ballot vote contains the identity of v i , the identity of the suspected vehicle v j , type of detected attack, and the time when the attack was detected. When the RSU is not within guard vehicle's radio range, a store and forward mechanism is launched as explained above. The RSU carries out a vote mechanism to check the detection' reliability claimed by a guard vehicle since this latter could provide a false detection, i.e., claim a legitimate vehicle as malicious and hence leading to increase the false positive rate.

Furthermore, during the passage through RSU's radio range, the monitored node can oscillate between a legitimate and malicious modes, so the RSU collects the feedbacks from guard vehicles related to this vehicle and computes the attack probability of each detection period ∆time as follows:

P attack (∆time)= detection rate nb_total (9)
where detection rate is the number of guard vehicles that detect vehicle v j as malicious during a period ∆time, and nb_total is the number of v j 's guard vehicles neighbors during this period. When P attack (∆time) > 0.5, the suspected vehicle v j is declared as malicious, otherwise guard vehicle v i is designated as a node that provides a false detection. In the following, we explain how to compute a reputation related to each vehicle and their related ML.

2) Reputation and Monitored Vehicles' Categorization: Due to the high mobility of vehicles and the large scale of VANETs, traditional reputation mechanisms applied in MANET cannot be used. Therefore, we propose a reputation mechanism adapted to vehicular networks. In this mechanism, each RSU computes the reputations of the vehicles that are located within its radio range, and the CA aggregates the reputations of all vehicles within the network and assigns a ML to each node that exhibits intrusion attack.

When the RSU confirms the attack that a suspected vehicle exhibits (i.e., P attack (∆time) > 0.5), both guard's good reputation and suspected vehicle's bad reputation are increased. Otherwise, the guard's bad reputation is increased. Furthermore, when a vehicle exhibits a legitimate behavior during its passage through the RSU's range, its good reputation is increased.

According to [START_REF] Ruj | On data-centric misbehavior detection in VANETs[END_REF], it is more important to detect FI (e.g., Sybil attacks, false alerts, and false detections) than to detect a MB (e.g., black hole attacks) since FI can cause chaos in the network, i.e., increase the false positives and create a fake congestion. As a result, the vehicle that provides FI, its bad reputation should be increased rapidly compared to the one who exhibits a MB. Furthermore, the guard vehicle that provides a correct detection (CD), its good reputation should be increased rapidly compared to the one who exhibits a legitimate behavior. Based on these arguments, exponential and linear functions are used to represent a vehicle that provides FI (or CD) and exhibits a MB (or NB), respectively. Consequently, the reputation of vehicle v j at RSU level is computed using [START_REF] Yu | Detecting Sybil attacks in VANETs[END_REF], where the The RSU stores the reputation Rep of each vehicle located within its radio range in the Reputation_database as (vehicle v j , Rep vj ) and periodically forwards this list to the CA. This latter aggregates the reputation of each vehicle in the network and computes the ML using ( 11) and ( 12), respectively. Afterward, the ML is compared with a trust formula in [START_REF] Fadlullah | DTRAB: combating against attacks on encrypted protocols through traffic-feature analysis[END_REF], proposed in [START_REF] Naseri | Evaluating workflow trust using hidden Markov modeling and provenance data[END_REF]. We note that ML ∈ [0, 1]

Rep_total vj = n k=1 Rep vj k n ( 11
)
where n is the number of RSUs that compute the reputation of vehicle v j if Rep_total vj < 0,v j is a trustworthy node Otherwise,

ML vj = E[Rep_total vj ]. (12)
Here, E is the statistical expectation of the reputation function Rep_total

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ML vj ∈ [0.71]
The vehicle v j is categorized as untrustworthy node ML vj ∈ [0.30.7] The vehicle v j is categorized as uncertain node ML vj ∈ [0, 0.3] The vehicle v j is categorized as trustworthy node.

(13)

IV. PERFORMANCE EVA L UAT I O N

We evaluated the performance of ELIDV via simulations performed using NS-3.17 [START_REF]Network Simulator (NS-3)[END_REF]. In this section, we discuss the experiment's methodology, highlight our simulation environment setup, and present our main results.

A. Experiment's Methodology and Metrics

ELIDV is compared with the intrusion detection frameworks proposed in [START_REF] Raya | Eviction of misbehaving and faulty nodes in vehicular networks[END_REF]- [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF]. Specifically, we computed the detection rate, false positive rate, and the overhead. These metrics together with the simulation results from NS-3.17 are used to evaluate the performance of ELIDV. We varied the number of malicious vehicles from 5% to 50% of overall vehicles.

The metrics that we simulated are as follows. 1) Detection rate measures the percentage of correctly identified malicious vehicles and their categorization in appropriate lists. 2) False positive rate measures the ratio of the number of legitimate vehicles that are incorrectly classified as malicious over the total number of legitimate vehicles 3) Overhead computes the cost for securing the communications and detecting intruders. This metric measures the amount of information generated by the vehicle, i.e., communication overhead. Main simulation parameters are summarized in Table II and were chosen to be as realistic as possible. Our results are based on averaging the simulation readings obtained from 15 simulation runs.

B. Results Analysis

In this section, we compared ELIDV's performances with local revocation protocol by voting evaluators (LEAVE) [START_REF] Raya | Eviction of misbehaving and faulty nodes in vehicular networks[END_REF], data-centric misbehavior detection (DCMD) [START_REF] Ruj | On data-centric misbehavior detection in VANETs[END_REF], and T-CLAIDS [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF]. In our simulations, we injected separately the attacks cited above and investigated the effects of each attack in isolation by varying the number of intruders from 5% to 50% of overall nodes. We summarized, hereafter, the most important results.

1) Detection Rate: Fig. 2 shows the proposed intrusion detection mechanism ELIDV exhibits a high detection rate when the attacks considered in our paper occurred. Furthermore, ELIDV outperforms other detection frameworks [START_REF] Raya | Eviction of misbehaving and faulty nodes in vehicular networks[END_REF]- [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF] in terms of attacks detection. In the worst case (i.e., the number of attackers is equal to 50% of overall nodes), the detection rate of ELIDV is equal to 98.4%, 98.66%, and 97.33% when DoS, integrity target, and false alert's generation attacks occurred, respectively. These results are achieved thanks to the cooperative detection between the guard vehicles on one hand and the detection rules related to each attack for modeling the legitimate behavior of the nodes.

2) False Positive Rate: Fig. 3 shows when the number of intruders increases, the false positive rate increases. Furthermore, the VBE influences the ELIDV performances. This is evident when considering the performance of framework that did not use VBE protocol (see Fig. 3) where the false positives increase rapidly, specifically when the number of intruders approaching 50% of overall nodes. As a result, we can claim that by using VBE protocol, the number of false positives is increasing slowly even when the number of intruders increased. According to Fig. 3, we can see that ELIDV outperforms the detection frameworks [START_REF] Raya | Eviction of misbehaving and faulty nodes in vehicular networks[END_REF]- [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF] in terms of low false positive. When the number of intruders is 50% of overall nodes, the false positive rate of ELIDV is 1.33%, 0.6%, and 1.33% for DoS, integrity target, and false alert attacks, respectively. This result is achieved thanks to VBE protocol, which distinguishes with a high accuracy, the malicious vehicle 3) Overhead Analysis: Our intrusion detection mechanism has the ability to detect an important number of attacks compared to the detection frameworks mentioned above. On one hand, we can see in Fig. 4(a) that ELIDV and DCMD generate approximately the same overhead. However, DCMD framework detects only one type of attack, which is a false alert attack. On the other hand, as illustrated in Fig. 4(b), LEAVE and T-CLAIDS frameworks require a high overhead to identify the attackers since they use an anomaly-based detection technique to monitor the behavior of the target vehicle, which is greedy in terms of computing and communication process. As a result, we can conclude that ELIDV requires a low overhead (between 4.1 and to 4.5 kB) compared to LEAVE and T-CLAIDS frameworks. This result is achieved thanks to the following reason: guard vehicles rely on a policy that minimizes the amount of exchanged information between each other and transferred to the RSU. In fact, only the suspected vehicle's id, guard vehicle' id, and the type of detected attack are exchanged.

V. CONCLUSION

The security in service-oriented VANETs is a challenging issue. In this paper, we proposed and implemented ELIDV, a new intrusion detection mechanism, that has the ability to detect both internal and external attacks and differ from the contemporary detection schemes and that only use cryptography algorithms to enhance the privacy and protection for the networks from external attacks. ELIDV detects three kinds of family attacks: DoS, Integrity target, and false alert's generation. Our detection mechanism relies on a light-weight detection technique that uses a set of rules, compared to contemporary detection mechanisms [START_REF] Raya | Eviction of misbehaving and faulty nodes in vehicular networks[END_REF], [START_REF] Kumar | Collaborative trust aware intelligent intrusion detection in VANETs[END_REF] that use a heavy algorithm such as anomaly-detection to model a legitimate vehicle's behavior. Furthermore, using a newly proposed VBE reduces the false positive rate that could incur in the network. This protocol has the ability to evaluate the behavior of the monitored vehicles during their passage through the network and assign a ML to each vehicle according to the attacks they generate. According to our simulation results, the ELIDV mechanism exhibits a high accuracy of attack detection rate (more than 97%) and low false positive rate (close to 1%). In addition, ELIDV generates a low overhead. These results are achieved when the number of vehicles and intruders are equal to 300 and 50% of overall nodes, respectively. Now, our goal in the CarCoDe project [START_REF]European Project ITEA 2 CarCoDe Project[END_REF] is to embed ELIDV in real-time vehicular network test bed and compare the simulation and experimental results.

Fig. 1 .

 1 Fig. 1. Guard vehicle neighbors of a link v 1 -v 2 .

  good and bad reputations are defined as GRep vj and BRep vj , respectively, BRep vj 1=α 1 exp FI + β 1 BRep vj 2=α 2 MB + β 2 BRep vj = BRep vj 1+BRep vj 2 GRep vj 1=α 1 exp CD + β GRep vj 2=α 2 NB + β 2 GRep vj = GRep vj 1+GRep vj 2 Rep vj =(BRep vj -GRep vj ) (10) here α 1 ,β 1 , α 2 ,β 2 ∈ [0, 1].

Fig. 2 .

 2 Fig. 2. ELIDV's detection rate under (a) DoS; (b) integrity target; and (c) false alert's generation attacks.

Fig. 3 .

 3 Fig. 3. ELIDV's false positive rate under (a) DoS; (b) integrity target; and (c) false alert's generation attacks.

Fig. 4 .

 4 Fig. 4. Comparison of overhead generated by the detection frameworks.

TABLE I ALERTS

 I AND EXPECTED BEHAVIOR[START_REF] Ruj | On data-centric misbehavior detection in VANETs[END_REF]


domain: info math

text:
Meysam Mayahi

Anna Maria Vegni
email: anna-maria.vegni@uniroma3.it

INVISIBLE2.0: an Enhanced Interference-based

Handover Technique for Visible Light Communications in Vehicular Networks Meysam Mayahi, Valeria Loscrí, and Anna Maria Vegni

Abstract-During the last decade, the exponential growth of mobile devices and their applications, especially in the framework of the Internet of Things (IoT), induced an enormous demand for radio frequency (RF) technologies. However, the lack of sufficient resources led to the spectrum crunch phenomenon, pushing researchers to explore alternative technologies outside the RF band. Among many strategies that have been adopted to maximize the exploitation of these precious resources, Visible Light Communication (VLC) demonstrates a huge capacity to complement conventional RF technologies in plenty of applications. In this work, we investigate the coexistence of VLC and the long-term evolution (LTE) cellular network in a vehicular environment. The coordination of data transmission among different technologies is managed through a novel vertical handover mechanism, namely INVISIBLE2.0. This technique is triggered by the error probability to meet the 3GPP requirements on packet reception ratio while endorsing VLC as the preferred technology bearer. We demonstrate that INVISIBLE2.0 achieves a more stable latency, and energy efficiency based on a detailed power model represented here for VLC and LTE. Index Terms-VLC, C-V2X, Handover, Energy Efficiency, Power Consumption Model.

I. INTRODUCTION

T He total number of global mobile subscribers is expected to grow from 5.1 billion (i.e., the 66% of the world's population) in 2018 to 5.7 billion (i.e., 71% of the world's population) by the end of 2023 [START_REF]Cisco Annual Internet Report[END_REF]. During the same period, connected home applications will have nearly half of Machineto-Machine (M2M) communications in the framework of the Internet of Things (IoT), and connected car applications will grow the fastest at a Compound Annual Growth Rate (CAGR) of ≈ 30%. The higher cost and limited free RF spectrum able to host further mobile data traffic, ask for an alternative communication medium to offload traffic from the RF spectrum and provide more capacity.

In recent years many technologies have been presented to solve the "spectrum crunch" problem. Visible Light Communications (i.e., VLC) take place as an RF assistant technology that employs optical wireless signals in the range from 430 THz to 790 THz as a signal carrier, with the twofold aim of both illuminating and communicating simultaneously. VLC demonstrates a great capacity to complement RF systems including the huge unregulated bandwidth, a reasonable level of immunity to interference with RF technologies, free-ofcharge operations, and can be applied to many communication scenarios. For instance, VLC can be used in electromagnetic waves restricted zones such as hospitals and airplanes, or it can be applicable in underwater communications where the RF propagation is strongly affected by water [START_REF] Chi | Visible Light Communication in 6G: Advances, Challenges, and Prospects[END_REF].

Affordable and widespread applications of Light Emitting Diodes (i.e., LEDs) in the automotive industry, traffic lights, as well as broad employment of control cameras and sensors in Intelligent Transportation systems (i.e., ITS) grab the attention of VLC standardization efforts and R&D studies in this market. In ITS, vehicular communications include vehicles and other communication entities around it, such as roadside infrastructures, wireless networks, and the devices carried by individuals and pedestrians, setting up reliable and scalable communication links among Vehicles (V2V), Vehicleto-Infrastructure (V2I), and Vehicle-to-Pedestrian (V2P) [START_REF] Cȃilean | Current Challenges for Visible Light Communications Usage in Vehicle Applications: A Survey[END_REF].

Vehicular Visible Light Communication (V ehVLC) represents one of the most attractive paradigms for vehicular communications, relying on a fully wireless optical system. The aim of V ehVLC is to exchange information among close vehicles and their surroundings via direct Line-of-Sight (LoS) VLC links, in order to improve the safety and driving experience on the roads. There are excellent opportunities for V ehVLC in automobile applications, including adaptive cruise control, cooperative driving, collision avoidance, and even autonomous driving [START_REF] Singh | A tutorial survey on vehicular communication state of the art, and future research directions[END_REF]. Furthermore, different from RFbased solutions that have congested licensed-band and rely on complex structures and high deployment costs, V ehVLC is a less complex, unlicensed, low cost, reliable, low energy, efficient, secured, and interference-free solution. LoS VLC links occur among vehicles and from vehicles to fixed nodes like Road Side Units (RSUs), via V2V and V2I connectivity modes, respectively. For instance, car platooning is a popular application in vehicular communications where autonomous vehicles are accessing each other's information and are grouped within close proximity. V ehVLC can assist the safety message dissemination in multi-platoon when the performance of the dominant radio access technology degrades due to congestion [START_REF] Ucar | Visible light communication assisted safety message dissemination in multiplatoon[END_REF].

Due to the dynamic nature of the vehicular environment, V ehVLC connectivity links are not always available and it becomes difficult to maintain active LoS links. Also, the presence of multiple interference signals, such as sunlight and artificial lights like the traffic jam, causes a degradation of the link performance, resulting in the need of considering an alternative communication link, if available. In the vehicular environment, cellular V2X technology (C-V2X) exploits the RF spectrum, providing a larger coverage and more stable connectivity links, as compared to VLC. The integration of C-V2X to V ehVLC is then expected to fully support connectivity needs in vehicular environments. How to manage the two technologies is an open challenge that needs to define first Quality of Service (QoS) requirements and then a policy for connectivity switching, also known as handover technique.

Following the above motivations, in this paper, we introduce a handover mechanism that guarantees seamless vehicular connectivity in a heterogeneous C-V2X and V ehVLC network environment. Our proposed approach has been initially presented in [START_REF] Mayahi | INVISIBLE: Enhanced Handover technique for Vehicular Visible Light Networks[END_REF], where a preliminary handover approach is described, working for pure vehicular VLC networks only. However, due to the limitation of VLC in dynamic scenarios, in this paper, we aim to extend the handover mechanism in [START_REF] Mayahi | INVISIBLE: Enhanced Handover technique for Vehicular Visible Light Networks[END_REF] to consider a heterogeneous network environment with both C-V2X and VLC links. We define the proposed approach as INVISIBLE 2.0, which recalls the initial approach INVISIBLE as introduced in [START_REF] Mayahi | INVISIBLE: Enhanced Handover technique for Vehicular Visible Light Networks[END_REF] and limited to VLC networks only, now extended to heterogeneous overlapping C-V2X and VLC networks.

Main contributions

In this paper, we present the INVISIBLE 2.0 technique, an extended handover approach that considers both horizontal and vertical connectivity switching from C-V2X to VLC, and vice versa. Differently from the initial version described in [START_REF] Mayahi | INVISIBLE: Enhanced Handover technique for Vehicular Visible Light Networks[END_REF], INVISIBLE 2.0 aims to prefer VLC connectivity over C-V2X, whenever available, due to its higher QoS performance, expressed as huge data rate and low latency, as well as reduced energy consumption. In case VLC links are not supported, C-V2X allows guaranteeing a ubiquitous service, thanks to a larger coverage. Horizontal handovers are supported among VLC links on the basis of interference metric, while vertical handovers occur in case of a lack of reliable VLC links, towards C-V2X.

This paper presents the following contributions: 1) A hybrid horizontal/vertical handover mechanism, namely INVISIBLE2.0, involving overlapping C-V2X and V ehVLC networks is proposed, in order to maintain the 3GPP requirements; 2) A detailed analysis of the power consumption models for VLC and V2X networks. Energy consumption is then investigated as a performance metric for the IN-VISIBLE2.0 approach, compared w.r.t the scenario of no handover execution; 3) A performance comparison of V ehVLC and C-V2X is carried out, expressed in terms of energy efficiency, packet latency, and Packet Reception Ratio. The rest of this paper is organized as follows. Section II introduces the main features of C-V2X and presents recent advances on how to manage heterogeneous networks for vehicular communications. Particular emphasis is given to VLC technology. Section III presents the INVISIBLE2.0 handover mechanism, which involves connectivity switching among VLC links in case of high interference level. Vertical handovers are also considered whenever VLC links are not available, relying on the C-V2X network cell. INVISIBLE2.0 is prospecting a robust Packet Reception Ratio (PRR) with higher power efficiency and lower latency, as compared to ad hoc C-V2X and V ehVLC technologies. In Section IV, we present two different energy efficiency models for C-V2X and V ehVLC networks, both at the transmitter and the receiver. The performance of the two technologies is then compared in Section V, for safety traffic data dissemination, called Cooperative Awareness Messages (CAMs). The performance analysis considers energy efficiency, packet latency, and PRR, carried out in different vehicular traffic scenarios and propagation modes. Based on this analysis, we evince the effectiveness of the INVISIBLE2.0 approach, providing acceptable energy consumption. Finally, conclusions are drawn at the end of this paper.

II. BACKGROUND

In an Intelligent Transportation system (ITS), Vehicular Communications (VC) include vehicles and other communication entities around it such as roadside infrastructures, wireless networks, and the devices carried by pedestrians, setting up a reliable and scalable Vehicle to Everything (V2X) communication links, in order to send and collect basic awareness information, aiming to increase safety on the road, and the driving experience [START_REF] Cȃilean | Current Challenges for Visible Light Communications Usage in Vehicle Applications: A Survey[END_REF]. In Europe, the basic awareness traffic is encapsulated in Cooperative Awareness Message (CAM), including information about speed, driving direction, vehicle characteristics, etc, and is broadcasted once:

• The vehicle has moved more than 4 m; • The absolute velocity has changed by 0.5 m/s; • The vehicle heading has turned at least 4 ○ ; • The elapsed time is more than 1 s; since previous CAM [START_REF] Molina-Masegosa | Empirical Models for the Realistic Generation of Cooperative Awareness Messages in Vehicular Networks[END_REF].

The CAM generation rules are applicable regardless of the access technology. Several Radio Access Technologies (i.e., RATs) have been proposed for V2X communications, such as Dedicated Short Range Communications (DSRC), Long Term Evolution (LTE) [START_REF] Chen | Topology Control in Hybrid VLC/RF Vehicular Ad-Hoc Network[END_REF], and recently fifth Generation-New Radio (5G-NR) [START_REF] Marabissi | Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks[END_REF]. DSRC was regulated for Wireless Access in Vehicular Environment (WAVE) based on IEEE 802.11p standard. DSRC promised higher robustness and adaptation to rapid movement in vehicular applications with a 3 Mbps data rate at the 5.9 GHz frequency band. For collision avoidance mechanism, DSRC uses CDMA/CA protocol which has rather doubtful reliability in high traffic densities [START_REF] Cailean | A survey on the usage of DSRC and VLC in communication-based vehicle safety applications[END_REF]. After launching the early version of LTE/4G in 2008, the first standard for Cellular V2X (C-V2X) was introduced in the 3GPP release 14 and finalised in September 2016. It uses an upgraded Deviceto-Device (D2D) wireless interface called ITS-G5 to enable Vehicle-to-Infrastructure (V2I) communications, while PC5sidelink is enabling Vehicle-to-Vehicle (V2V) to share CAMs among cars [START_REF] Santa | Experimental evaluation of CAM and DENM messaging services in vehicular communications[END_REF]. The resources in LTE C-V2X are allocated by the base station (eNB) for V2I according to Mode 3, on the other hand, vehicles in Mode 4 autonomously select their sub-channels based on common sets of parameters such This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d as the number of sub-channels and the number of Resource Blocks (RBs) per sub-channels [START_REF] Molina-Masegosa | Comparison of IEEE 802.11p and LTE-V2X: An Evaluation With Periodic and Aperiodic Messages of Constant and Variable Size[END_REF]. To this aim, vehicles use the sensing-based Semi-Persistent Scheduling (SPS) defined in [START_REF]LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedure[END_REF] to reserve sub-channels for their next transmission. LTE C-V2X demonstrates superior reliability over DSRC thanks to more efficient Physical layer [START_REF] Ali | 3GPP NR V2X Mode 2: Overview, Models and System-Level Evaluation[END_REF], however, its performance is degrading due to significant Doppler shift in high-speed V2X scenarios [START_REF] Flore | Initial Cellular V2X standard completed[END_REF]. Nowadays, the evolutionary trend in V2X is moving toward complementary solutions such as 5G-NR when the Quality of Service (QoS) becomes more stringent. 3GPP release 16 has included a study item to support new applications with more stringent requirements. As a main design objective, 5G-NR V2X is neither backward compatible with LTE C-V2X, nor to replace it, but rather to supplement C-V2X in supporting those use cases that cannot be supported by LTE C-V2X [START_REF] Flore | Initial Cellular V2X standard completed[END_REF]. Resource reservation in 5G-NR V2X is defined in centralized (i.e., Mode 1) and distributed (i.e., Mode 2) manners, which are similar to Mode 3 and Mode 4 of LTE C-V2X. Mode 1 uses the same sensing-based scheduling approach which has severe impacts on 5G-NR V2X performance. The sensing window increased, and obliged the receiver to detect signaling for a longer time, which in consequence increases the power consumption for signaling messages. Mode 2 random resource selection is addressed in an approved 3GPP NR Release 17 to reduce the complexity of user equipment (i.e., UE) and consequently the power consumption [START_REF] Flore | Initial Cellular V2X standard completed[END_REF]. Random resource allocation mechanism causes excessive resource collision, accordingly increasing the packet loss and the packet latency, notably in dense vehicular environment [START_REF] Ali | 3GPP NR V2X Mode 2: Overview, Models and System-Level Evaluation[END_REF]. Finally, the lack of orthogonal resources in covered areas, as well as synchronized resources (infrastructures) in remote scenarios calls for an alternative paradigm to enhance the V2X performance [START_REF] Singh | A tutorial survey on vehicular communication state of the art, and future research directions[END_REF].

A. Vehicular VLC

VLC represents one of the most attractive paradigms of V2X. Contradicting to RF-based VC (e.g., C-V2X) Vehicular VLC (i.e., V ehVLC) is a less complex, unlicensed, low cost, reliable, low energy, efficient, secured, and interference-free solution [START_REF] Singh | A tutorial survey on vehicular communication state of the art, and future research directions[END_REF]. The widespread employment of LEDs in car lamps, traffic lights as well as control cameras and sensors in ITS, lends additional motivation for researchers and industries to invest in vehicular applications using VLC. V2I communication in VLC is oriented between high-power traffic/street lights and vehicles which can achieve stable communication within a range of 50 meters [START_REF] Cailean | A survey on the usage of DSRC and VLC in communication-based vehicle safety applications[END_REF]. Moreover, integrated VLC systems in headlights and tail lights are enabling reliable V2V links [START_REF] Cailean | A survey on the usage of DSRC and VLC in communication-based vehicle safety applications[END_REF]. Alongside many favorable features in VC, the VLC link has a comparatively short communication range and can be blocked by obstacles like cars, therefore, reliability might be degraded in high-mobility scenarios [START_REF] Chen | Topology Control in Hybrid VLC/RF Vehicular Ad-Hoc Network[END_REF]. Guaranteed coverage in VLC networks requires many Access points (APs) installation, which in dynamic scenarios (e.g., VC) can result in frequent disconnectivity between mobile nodes and associated AP. In [START_REF] Mayahi | INVISIBLE: Enhanced Handover technique for Vehicular Visible Light Networks[END_REF] new horizontal handover mechanism namely "INVISIBLE" proposes a backup VLC link based on dominant disturbance in an outdoor vehicular environment. When no AP is available in the current network, the vehicle may maintain the connection via different networks throughout vertical handover mechanisms establishing a hybrid network [START_REF] Elnaz | Li-Wi: An upper layer hybrid VLC-WiFi network handover solution[END_REF].

B. Hybrid VLC-RF vehicular network

The hybrid VLC-RF structure is beneficial to an efficient and reliable VC [START_REF] Chen | Topology Control in Hybrid VLC/RF Vehicular Ad-Hoc Network[END_REF]. A hybrid handover solution called "Li-Wi" has been proposed to address mobility and handover challenges in both Data Link and Transport layers using Multipath TCP (i.e., MPTCP) for industrial indoor environment [START_REF] Elnaz | Li-Wi: An upper layer hybrid VLC-WiFi network handover solution[END_REF]. In [START_REF] Shao | Optimizing handover parameters by Q-Learning for heterogeneous radio-optical networks[END_REF], the optimal time to trigger (i.e., TTT) has been found according to historical Signal to Noise Ratio (i.e., SNR) by means of Q-learning algorithm in order to carry out vertical handover from LTE eNB to VLC AP. One of the pioneering field tests on the joint use of 5G and VLC was presented in [START_REF] Marabissi | Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks[END_REF]. Data from road sensors have been collected by a 5G network and then sent to the VLC network for diffusion to cars through traffic lights. This experiment measured an end-to-end latency equal to 9.5 ms for 5G and for VLC technology equal to 2.5 ms. Hybrid Vehicular networks assisted by VLC technology remain potential in the presence of some critical issues such as an effective handover mechanism for efficient mobility management, QoS stability through VLC integration to the vehicular radio access technologies (e.g., C-V2X), as well as adaptive resource allocation and medium access process. As a matter of fact, to make VLC more operational in a real vehicular environment, an efficient design of Medium Access Control (i.e., MAC) protocol is required to ensure a seamless exchange of information in a challenging V2X nature coming from high dynamicity, environmental disturbances, and cochannel interference.

However, any further enhancement in the performance of a communications protocol is ignored if it does not comply with the reliability requirements. Packet reception ratio (PRR) is defined as the number of vehicles with acceptable error probability residing within the transmitter range, to all vehicles at the baseline distant d from the transmitter, and calculated as X/Y , where X and Y are the numbers of vehicles with successful packet delivery, and the total vehicles in the range respectively [START_REF] Eckermann | Performance Analysis of C-V2X Mode 4 Communication Introducing an Open-Source C-V2X Simulator[END_REF]. The appropriate error probability is following the LTE service requirements for V2X services [18], while baseline distance is derived as a break-point from the WINNER+B1 channel model [START_REF]FRANCE: 3rd Generation Partnership Project[END_REF]. On the other hand, VLC has been selected as a primary transmission method, assisted by C-V2X. Accordingly, INVISIBLE 2.0 is developed in this paper to follow the PRR baseline of 3GPP in a hybrid VLC-RF vehicular regime.

III. INVISIBLE2.0 HANDOVER

In this section, we introduce a novel vertical handover mechanism, called INVISIBLE2.0, aiming to disseminate CAMs via VLC, acting as a preferred technology over LTE radio technology, in a vehicular heterogeneous network. Owing to the multiple advantages that have been discussed in the earlier section, INVISIBLE2.0 prefers exploiting VLC technology as the primary transmission option as compared to LTE radio technology, which is the second option for data transmission.

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d The main trigger for vertical handover between VLC and C-V2X, and vice versa, is the link reliability, such that when the performance of the VLC link degrades below a given threshold, INVISIBLE2.0 switches to LTE to maintain an acceptable PRR level. Moreover, this handover mechanism gives a good compromise between VLC and LTE ad-hoc networks in terms of energy consumption and delay performance.

In order to provide high performance, we pose a high threshold for the Packet Error Rate (PER) i.e., 95%. Furthermore, the energy consumption and the delay performance of the INVISIBLE2.0 handover mechanism represent a good compromise as compared to V ehVLC and C-V2X. Algorithm 1 describes the main steps of the hybrid handover algorithm. CAM is transmitted in a multi-hop manner via V ehVLC, as a preferred technology if available, as well as if the link performance is good enough as compared to the PER threshold. In case of low performance or lack of VLC connectivity link, C-V2X is used as an alternative technology, assumed as always available. Fig. 1 depicts the heterogeneous network scenario for vehicular communications. Notice the presence of RSU (e.g., street lamps and base stations) allows V2I/I2V communication links.

A heterogeneous LTE-VLC network has been simulated at vehicular applications for V2V and V2I communications in urban and highway scenarios. It is assumed that the mobile nodes are already registered in the LTE network according to the Radio Resource Control (i.e.,RRC) procedure. Subsequently, CAMs are generated in the mobile nodes according to the European Telecommunications Standards Institute (i.e.,ETSI) ITS model and delivered to the flow switch to be disseminated through the VLC link if optical intensity (e.g., SN R o ) is above the threshold, otherwise, the CAM message is sent via LTE network. When a mobile node does not have any active link, the CAM generation is prevented and new messages will be transmitted as soon as a connectivity link will be available, following the store-carry-and-forward paradigm as typical of vehicular networks.

The proposed hybrid handover mechanism considers both horizontal (HHO) and vertical (VHO) handover occurrences. In the first case, a horizontal handover is executed from a serving lighting cell to a candidate one, that overlaps with each other, selected based on the interference criteria. Specifically, the HHO technique evaluates the VLC link performance based on the prominent component of the disturbances that may

if SN R i > SN R o ▷ A V2LC link is available then if P ER i < T h then R HHO/V HO = R i,V LC ▷ Access to the i-th VLC LED Compute IN R T ot,i ▷ Eq. (2) if IN R T ot,i > 10 then Compute IIR ▷ Eq. (3) if IIR ≥ β AND SN R ℓ ≥ SN R min then R HHO = R ℓ,V LC ▷ HHO to the ℓ-th VLC LED else R V HO = R C-V 2X ▷ VHO to C-V2X else R V HO = R C-V 2X ▷ VHO to C-V2X else R HHO/V HO = R C-V 2X ▷ C-V2X connectivity link else R HHO/V HO = R C-V 2X ▷ C-V2X connectivity link occur.
To this end, it computes the Interference to Noise Ratio (i.e., INR) of the i-th LED as follows

IN R i,ℓ = (rP r,i ) 2 σ 2 n + I ℓ , ( 1
)
where r is the PD responsivity, P r,i is the received power from the i-th LED (with i = [1, 2, . . . , N ]), I ℓ is the interference level generated by the ℓ-th VLC signal (i.e., ℓ ≠ i and ℓ = [1, 2, . . . , L] with L ≤ N ), and σ 2 n is the Additive White Gaussian Noise (AWGN), comprised of the thermal σ 2 t and the shot noise σ 2 s components i.e., σ 2 n = σ 2 t + σ 2 s . The total interference-to-noise ratio is then computed as the sum of multiple contributions of the INR at the i-th LED i.e.,

IN R T ot,i = L ∑ ℓ IN R i,ℓ , (2)
that is, we consider all the L inferferent signals (with L ≤ N ).

The i-th LED can be considered interference-dominant if the interference power is higher than the noise power. Specifically, we assume a factor 10 as a comparison, so that if IN R T ot,i < 10, the noise and the interference power are comparable, and

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d

we treat them equally as noise. Otherwise, the i-th LED is assumed as interference-dominated, and it is possible to search for the dominant interferent by means of the IIR (Interferer to Interference Ratio) expression i.e.

IIR = max ℓ ( P r,ℓ ∑ L ℓ P r,ℓ
) ,

where P r,ℓ is the power level of the ℓ-th interferer. Notice that lower values of IIR fit well with the Gaussian distribution assumption, while higher values of IIR prove less normal distribution and the Gaussian assumption leads to a heavier tail in the distribution. In the case that IIR → 1, the only noticeable interference comes from a single dominant interferer.

At the interference dominant system, there could be single or multiple dominant interferers. The IIR is able to identify the dominant interferer if the following expression holds i.e.,

IIR ≥ β, ( 4
)
where β is the minimum amount of IIR the system can detect and it is experimentally selected according to the node density. The interferers exhibiting the relation IIR ≤ β are not dominant and are considered as noise. On the other hand, the ℓ-th dominant interferer exhibiting the relationship in Eq. ( 4) will be nominated as a candidate for handover initiation, if it satisfies the QoS requirements of safety applications, expressed in terms of reliability and stability. Specifically, by fixing the maximum error probability for vehicular safety applications equal to 10 -3 , the ℓ-th dominant interferer that verifies Eq. (4) will be nominated as a candidate lighting cell for handover if the minimum required SNR ratio is satisfied i.e., SN R ℓ ≥ SN R min ,

where SN R ℓ is the SNR value of the ℓ-th dominant interferent. Fig. 1 represents the scenario where the mobile nodes rely on VLC technology to transmit their CAMs. The INRbased handover mechanism guarantees seamless horizontal handover inside VLC coverage. When the performance of the VLC link degrades, CAMs are sent through LTE radio access technology.

CAMs are generated on the mobile nodes based on the ETSI ITS model, then they are disseminated through the VLC link if optical SNR is above the threshold (i.e., SN R o ), otherwise the CAM is sent via LTE network. It assumed that the mobile node is already registered in the LTE network according to the RRC procedure. When the mobile node does not have any active link, the CAM generation is prevented.

IV. ENERGY EFFICIENCY MODEL

The energy efficiency of communication networks is progressively attracting the research communities due to the exploding wireless technologies. The energy efficiency is usually evaluated in an extensive measurements regime using smallscale or actual devices, which increase the total cost of projects and require further expertise for implementation. In addition, new energy-saving methods and optimization techniques proposed in academia require a comprehensive power model considering the major energy-consuming components and their performance during different phases such as transmission, reception, and ideal states.

Nokia proposed a power model which considers only radio resource configuration mode for 3GPP but it does not take into account different data rate power control at the transmitter [START_REF] Jensen | LTE UE Power Consumption Model: For System Level Energy and Performance Optimization[END_REF]. The chip manufacturers have detailed models based on their development platforms, however, they are confidential. Launching LTE opened new horizons to empirical models. A precise model based on the LTE USB dongle has been proposed in [START_REF] Jensen | LTE UE Power Consumption Model: For System Level Energy and Performance Optimization[END_REF], which is robust and transferable. This model describes an LTE user equipment (UE) radio modem, which is depending on power levels and data rates.

The current theoretical power model is based on the major power components of LTE and VLC systems.

A. Energy Consumption

The energy consumption occurring during a packet transmission time interval (i.e., T T I [s]), is the summation of the energy consumed at the transmitter (E T x ) and the receiver (E Rx ) sides,as follows:

E T T I = E T x + E Rx , (6)
whose expressions respectively are:

E T x = P (s) T x D (s) + P (i) T x D (i) + P (t) T x D t , (7)
and

E Rx = P (s) Rx D (s) + P (i) Rx D (i) + P (r) Rx D (r) , (8)
with D (x) [s] and P

T x/Rx [W] as the time spent and the power consumed in state x occurring at the transmitter/receiver side, respectively. Specifically, four different states are defined, named as s (sensing), i (idle), t (transmission), and r (reception). For instance, the term P (i) Rx stands for the receiver consumption power during ideal state.

Finally, from the expressions of energy consumption, we can define the energy efficiency (i.e., E ef f [bit/Joule]) as the ratio of data rate R b [bps] over the energy consumption E T T I [Joule/s] i.e. [START_REF] Björnson | Massive MIMO networks: Spectral, energy, and hardware efficiency[END_REF],

E ef f = R b E T T I . (9)
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d

B. LTE Power Consumption

Fig. 3 (a) shows the LTE physical layer components and the user equipment (UE) model parameters. The envisioned UE model depends on the received (Rx) and the transmitter (Tx) power levels, the uplink (UL) and downlink (DL) data rate, and RRC mode.

We can express the power consumption of an LTE UE device as:

P T otal = P T x + P Rx , (10)
where P T otal [Watt] is the total power consumption, while P T x [Watt] and P Rx [Watt] are the transmitter and receiver power consumption, respectively, whose expressions are

P T x = P DSP,T x + P ASP,T x + P DAC , (11)
and

P Rx = P DSP,Rx + P ASP,Rx + P ADC , (12)
where the indexes ADC and DAC refer to the Analogto-Digital and Digital-to-Analog converter, respectively. The power consumption in ADC/DAC is represented by the offset b and the slope k of the ADC/DAC circuit design, which is further scaled with the operating bandwidth B, i.e.

P DAC/ADC = B ⋅ k DAC/ADC + b DAC/ADC . (13)
Furthermore, in Eq. ( 11) and ( 12) the digital signal processing unit power consumption at the transmitter/receiver i.e., P DSP,T x/Rx , is assumed identical for the transmitter and the receiver [START_REF] Desset | Flexible power modeling of LTE base stations[END_REF], and can be simply defined as

P DSP = P leak ⋅ p dyn , (14)
where p dyn represents the power dissipation at the dynamic charging and discharging of the CMOS inherent capacitors, and P leak denotes the leakage power dissipated due to reverse leakage of the employed CMOS switches i.e.,

P leak = η leak ⋅ P dyn , (15)
where η leak stands for the leakage power ratio, which is measured in Giga Operations per Second per Watt [GOPS/W], and P dyn [Watt] refers to the dynamic power at the reference bandwidth B ref [Hz] i.e.,

p dyn = O C ⋅ B η CM OS ⋅ B ref . ( 16
)
In Eq. ( 16), the term O C represents the digital computational complexity function at the reference bandwidth B ref , which is scaled linearly with the operating bandwidth value B. Furthermore, η CM OS is the intrinsic CMOS power-efficiency factor.

The analog unit includes different elements of a transmitter and the receiver such as carrier generation, modulator, mixer, low noise amplifier (LNA), variable gain amplifier (VGA), filter, buffer, and power amplifier (PA).

The power consumption of the transmitter ASP can be modeled considering only the aggressive power consumer components of the direct conversion transmitter:

P ASP,T x = P IQ modulator + P clock + P V CO , (17)
where P V CO is the nominal power consumption at the voltage-controlled oscillator, P IQ modulator is the in-phase and quadrature modulators power consumption, and P clock is the power needed for clock generation, and buffering in [mW]. On the other hand, the power consumption of the receiver ASP is derived as P ASP,Rx = P LN A + P attenuator + P mixer + P clock ,

where P LN A stands for low-noise amplifier power consumption, P attenuator is the power lost in the main variable attenuator, and P mixer is the dual mixer power.

C. VLC Power Consumption

A similar power consumption model is presented based on the VLC system block diagram of Fig. 3 (b), where the frontend blocks are divided into digital and analog components according to the place of DAC and ADC converters in the transmitter and the receiver sides, respectively [START_REF] Kong | Power Consumption Evaluation in High Speed Visible Light Communication Systems[END_REF]. This model accounts for the most significant power consumer VLC components, specifically:

• At the transmitter side the signals are processed digitally using Digital Signal Processing and then converted to an analog waveform by digital to analog converter DAC. • Transmitter Amplifier amplifies and biases the analog signal to fit the linear region of LED transfer characteristic.

• LED transforms the electrical signal to visible light and then passed it to the optical wireless channel. • At the receiver side, the PD is used as a passive element to convert the optical signal back to a current signal. When a bias is applied to the photodiode, the current output can

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d be controlled to provide thresholding, linear response, or nonlinear response. • The trans-impedance amplifier TIA alters the current to a voltage signal. • The analog voltage waveform is digitized by the analog to digital converter ADC and eventually delivered to DSP for further processing. The analog power consumption at the transmitter can be decomposed to the power consumption at the transmitter amplifier and the power consumption of the LEDs i.e., P ASP,T x = P T x Amp. + N ⋅ P LED ,

where the LED's power consumption P LED is determined by the DC bias level and the total number of the deployed LEDs (i.e., N ). In the same way, the analog power consumption at the receiver part is decomposed to the PD power consumption and the trans-impedance amplifier power consumption i.e.,

P ASP,Rx = m ⋅ P P D + P T IA , ( 20
)
where m is the number of photodiodes and bounded transimpedance amplifiers.

V. PERFORMANCE ANALYSIS

A vehicular environment with heterogeneous overlapping LTE-VLC networks has been simulated for the assessment of the proposed handover approach. Specifically, both V2V and V2I communications are provided, in urban and highway scenarios. The performance of INVISIBLE2.0 is evaluated in terms of PRR, latency, transmitter, and receiver energy efficiency.

Assuming the vehicular environment as depicted in Fig. 1, vehicles are generating CAMs based on the empirical model proposed in [START_REF] Molina-Masegosa | Empirical Models for the Realistic Generation of Cooperative Awareness Messages in Vehicular Networks[END_REF], which is derived from ETSI ITS communication architecture. In LTE-V2I, CAMs are generated and broadcast by the vehicle through a given wireless interface (i.e., ITS-G5), while the V2V communication employs PC5 or sidelink to share CAMs among cars [START_REF] Santa | Experimental evaluation of CAM and DENM messaging services in vehicular communications[END_REF]. The on-board C-V2X units are small-scale base stations (Femto) used in cars. They use more power-efficient dedicated components compared to large base stations (i.e., macro and micro), and the downscaling factor defined in [START_REF] Desset | Flexible power modeling of LTE base stations[END_REF] is used to reduce the power on smaller base stations due to less constraining space and different hardware implementation. This approach is due mainly because of (i) the number of blockages that can affect the transmission of a small base station is limited, leading to more relaxed linearity specs, and hence less power is needed, and (ii) the smaller base stations can work from a lower supply voltage, further reducing their consumption.

The main parameters for C-V2X used in our simulations are collected in Table I, which represents the power consumption of the analog components in the LTE on-board unit. In order to simulate the MAC performance of the LTE-V2X, we assume n eNBs are uniformly distributed such that the Tx-Rx distance will meet the LTE rel.14 requirements in terms of PRR, defined as the ratio of the number of vehicles with successful packet delivery over the number of total vehicles in the range, equal to 95%. In fact, in order to concentrate on the MAC performance we cancel the performance degradation of the physical layer (i.e., propagation loss and shadowing effect) [START_REF] Molina-Masegosa | Configuration of the C-V2X Mode 4 Sidelink PC5 Interface for Vehicular Communication[END_REF] following the WINNER+B1 channel model by the 3GPP [START_REF] Gpp | Study on LTE-based V2X services[END_REF]. The path loss [dB] for the 5.9 GHz band can be calculated in the case of LoS and NLoS, respectively as in Eq. ( 21) and Eq. ( 22), where the effective breakpoint distance

d ′ BP [m] is computed as d ′ BP = 4h ′ BS h ′ M S f c c , (23)
with c = 3 ⋅ 10 8 [m/s] as the light speed and h ′ BS/M S as the effective antenna/mobile station height i.e., h ′ BS/M S = h BS/M S -1 [m], [START_REF] Eckermann | Performance Analysis of C-V2X Mode 4 Communication Introducing an Open-Source C-V2X Simulator[END_REF]. Based on [START_REF] Gpp | Study on LTE-based V2X services[END_REF], the antenna height is set to h BS = h M S = 1.5 [m] for V2V communications, while h BS = 5 [m] and h M S = 1.5 [m] are assumed for V2I/I2V communications.

Similarly, Table II collects the power consumption of the VLC components used in the proposed energy model. The street light poles have been used as VLC infrastructure based on global lighting design guidance [START_REF]Lighting Design Guidance[END_REF]. In this design, the pole height varies from 8 to 10 m and the spacing between two light poles should be roughly from 2.5, to 3 times the height of the pole. This regulation allows for uniform distribution of the light poles with 25-meter spacing in highway scenarios, and 23 meters in urban scenarios.

Regarding vehicle velocity, it is defined according to the federal highway administration of the US government since the urban scenario follows the Manhattan mobility scenarios. Following this regulation, the speed in the urban areas varies from 45 to 72 [km/h], and on highways is set from 90 to 105 [km/h], [START_REF]Speed Limits[END_REF]. Single-carrier OOK modulation has been used for its low complexity and robustness. The computational complexity factor O C for OOK modulation is set to 505.15 GOPS at B ref = 20 MHz reference bandwidth. According to [START_REF] Kong | Power Consumption Evaluation in High Speed Visible Light Communication Systems[END_REF], CMOS power efficiency is 3.6e + 4 GOPS/W, while the leakage ratio was 24.3 in 2020. Additionally, under good coverage of the serving access point, the adaptive CAMs disseminates are based on ETSI ITS regulation [START_REF]Intelligent Transport Systems (ITS) Vehicular Communications Basic Set of Applications (Specification of Cooperative Awareness Basic Service)[END_REF].

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d The energy efficiency model defined in Section IV-A has been implemented in the ns3 module for visible light communication in vehicular networks, and the Multi-Stack VANET framework for ns-3 (i.e., ms-van3t) for LTE-V2X compliant application [START_REF] Malinverno | A Multi-stack Simulation Framework for Vehicular Applications Testing[END_REF]. The receiver energy efficiency is expressed in [Kbits/Joule] and digitizes the amount of data received per unit of energy consumed at the receiver and it is calculated according to Eq. ( 9). This metric is compared versus the traffic density in Fig. 4, in case of V ehVLC, C-V2X, and INVISIBLE2.0 approach, for different traffic scenarios (i.e., urban and highway) and communication links (i.e., V2I and V2V). Traffic density [veh/km] describes the average number of vehicles per unit of road length per day [30]. From Fig. 4 (a), we observe the traffic density growth does not increase the infrastructure density in V2I, and so energy efficiency is independent of traffic density in the V2I scenario, which shows a flat behavior. However, the number of in-range VLC nodes increases in the dense traffic scenario, which forces the receiver to stay in the "active" mode most of the time, which in return decreases the energy efficiency. As expected, for V2V mode we can observe a reduced trend for increasing the traffic density, meaning that in crowded scenarios vehicular communications are more common, thus reducing the receiver energy efficiency.

P L (LoS) = ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ 22
In Fig. 4 (b) the C-V2X receiver energy efficiency versus the traffic density is presented. Compared to the V ehVLC technology, the C-V2X approach allows for collecting more information per unit of energy. In V2V scenarios, the performance is enhanced in dense scenarios because the receiver is receiving more and more bits per unit of energy, whereas in the best case, it reaches up to 15 Kbits per Joule in highdensity V2V. For V2I scenarios, the receiver energy efficiency is sustained with increasing the number of vehicles due to the central dissemination of CAM messages, which are fixed per car.

Finally, the energy efficiency at the receiver achieved with INVISIBLE2.0 is depicted in Fig. 4 (c). We observe an improved trend, as compared to the ad-hoc V ehVLC, especially in high traffic density. We notice that when the traffic density is growing, it has a positive effect on the V2V receiver EE, while it is less effective in V2I scenarios. Moreover, the receiver in urban V2V has the highest performance, and the lowest performance is happening in the urban V2V environment. The receiver energy efficiency in highway V2I is equivalent to the C-V2I curve at the same condition. It means that INVISIBLE2.0 is mostly using LTE in highway V2I. At the transmitter side of V ehVLC, the energy consumption efficiency is constant in V2I communication as Fig. 5 (a) shows. The transmitter energy efficiency in the V2V scenario is worsening due to the worthless transmitter functioning in high traffic densities. The energy efficiency is less sensitive to the traffic density in the C-V2X transmitter. The C-V2X transmitters are very harsh in energy consumption such that they cannot transmit more than a few kilobytes per Joule of energy. Fig. 5 (b) shows the LTE transmitter energy efficiency in different scenarios, and we can observe the best performance of transmitter energy efficiency in the C-V2X is achieved at highway V2I scenario.

Finally, Fig. 5 (c) shows the energy efficiency at the transmitter side achieved with INVISIBLE2.0 versus the traffic density. Compared to the C-V2X, the transmitter energy efficiency is improved in urban V2I, urban V2V, and highway V2V, and it is identical to C-V2I in highways. In the sparse traffic density, INVISIBLE2.0 uses the energy more efficiently at the transmitter to send CAMs to other mobile nodes, and as the traffic density increases, the transmitter's energy efficiency decreases. In V2I communication, the energy efficiency at the transmitter is steady but it is higher than ad-hoc C-V2X and INVISIBLE2.0.

For the PRR computation, packet accounts as delivered if the number of erroneous bits (symbol) is less than the coding threshold. Since in VLC, we do not use any channel coding This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d scheme (e.g., FEC, ARQ, etc), a packet is correctly received when all the packet's bits (symbols) are correctly detected.

Assuming OOK modulation scheme [START_REF] Aldalbahi | Visible Light Communication Module: An Open Source Extension to the ns3 Network Simulator With Real System Validation[END_REF], the Packet Error Ratio can be defined as:

P ER = 1 -(1 -BER) M , (24)
where M [bit] is the packet size and BER is the bit error rate, which for the OOK modulation scheme becomes, [START_REF] Rahaim | Optical interference analysis in Visible Light Communication networks[END_REF] BER

OOK = 0.5 Q(SN R o ), (25)
with Q(⋅) as the tail probability of the standard normal distribution. In Eq. ( 25) the term SN R o is the optical signalto-noise ratio related to the link from the i-th LED transmitter to the j-th PD receiver, and is expressed as

SN R o = (rP t h LoS i,j ) 2 σ 2 n , (26)
where h LoS i,j is the DC channel gain in LoS expressed as

h LoS i,j = r(m + 1)A 2πd 2 i,j cos m (θ i,j ) cos(ϕ i,j )G(ϕ i,j )c(ϕ i,j ), (27)
with m = -ln(2)/ ln(cos(θ 1/2 )) as the Lambertian order of the LED, r [A/W] is the receiver responsivity, A [m 2 ] is the aperture area of the receiver, d i,j [m] is the Euclidean distance between the i-th LED to the j-th PD, and θ 1/2 is the half power angle of the LED. Furthermore, θ j,i is the irradiance angle of the (i, j)-th LED/PD link, ϕ i,j is the incident angle of the (i, j)-th LED/PD link, G(ϕ i,j ) is the gain of the optical filter at the receiver, and c(ϕ i,j ) is the optical non-imaging concentration gain expressed as

c(ϕ i,j ) = ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ ρ 2 sin 2 (Φ F oV ) , 0 ≤ ϕ i,j ≤ Φ F oV , 0, ϕ i,j > Φ F oV , (28)
where ρ is the refractive index of the concentration and Φ F oV is the field-of-View (FoV) of the PD. Finally, we assume G(ϕ i,j ) = 1. Fig. 6 (a) demonstrates the packet reception ratio at the mobile nodes that are transmitting CAM packets using cellular long-term evolution Mode 3 and Mode 4. We observe that LTE-V2I has the best PRR performance in urban and highway scenarios regardless of the traffic density, owing to the wide coverage and reliable resource allocation strategy of LTE. In the next world, the packet reception ratio of V2V is smaller, due to the lower coverage range of onboard modules in the vehicles compared to the eNB station at V2I scenarios. PRR is decreased by increasing the number of vehicles in V2V urban and highway scenarios. In cellular V2V, each vehicle has to manage its own communication session in a distributed way, it gives a higher chance to an individual mobile node to find a free channel for transmission at lower density, while in the higher traffic density, the completion to acquire RF resource for transmission is stronger and decrease the chance of finding a free channel, so the PRR decreases.

Fig. 6 (b) depicts the PRR of V ehVLC scheme. The best performance belongs to V2V in the highway scenario while the lowest PRR is in highway V2V because the distance between neighboring AP is higher than urban scenario and that increases the risk of packet loss on the cell edge.

Finally, in Fig. 6 (c) we show the PRR obtained with INVISIBLE2.0. The 95% 3GPP baseline is always respected because the reliability (represented in PER) is the triggering metric for the INVISIBLE 2.0 protocol. When the PER degrades below the given threshold (i.e., 10 -3 ), the mobile node uses the INR-based approach to select an alternative VLC link and then sends CAM. In case of failure, the LTE network is the final option. Furthermore, we observe that the PRR is slightly increasing in V2V communication when the traffic increases, while PRR in V2I is almost constant. The INVISIBLE2.0 performance in V2I is higher than V2V and Highway V2I has the best PRR. Due to the fact that VLC does not fulfill the 3GPP requirement in terms of PRR, in the highway V2I, INVISIBLE2.0 totally relies on C-V2X for CAM transmission.

Vehicular applications have some requirements in terms of latency. In [START_REF] Cen | Enabling vehicular visible light communication (V2LC) networks[END_REF] latency has been defined as the maximum time span during which vehicular traffic may be successfully delivered to the target vehicle. In our application, latency is the time delay from the CAM generated, until the successful delivery to the destination.

In Fig. 7 the latency of C-V2X, V ehVLC, and INVIS-IBLE2.0 is compared in multiple scenarios. Generally, the latency in LTE-V2X is much longer than the end-to-end latency in VLC. In LTE-V2V scenarios, increasing the traffic This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d density decreases the distance among communicating vehicles which reduces the latency by its part. However, the latency in V2I scenarios does not change significantly in dense traffic in both technologies, since the average distance between the cars and the infrastructure is constant. This distance is much lower in VLC technology i.e., the coverage range of VLC technology is lower than LTE-V2X, which makes communication faster. Finally, the latency achieved with INVISIBLE2.0 is depicted in Fig. 7 (c) still in case of different communication modes and vehicular scenarios. We observe that with INVISIBLE2.0 the latency trend is improved by one order of magnitude as compared to ad-hoc C-V2X in the case of V2V communication mode. On the other side, in the urban V2I scenario, the latency is decreased significantly, while in highway V2I, INVISIBLE2.0 and C-V2X are identical.

VI. CONCLUSIONS This study described the detailed power models for LTE user equipment and VLC front-end and focused on how power is scaled in vehicular scenarios. The given analysis in terms of energy efficiency, latency, and PRR motivates the design of a vertical handover mechanism where the VLC has been chosen as a primary technology to transmit cooperative awareness messages and LTE was supporting CAM dissemination when VLC does not accomplish with 3GPP packet delivery baseline. The INVISIBLE 2.0 performance has been compared to the standalone C-V2X and V ehVLC for V2V and V2I communications in urban and highway environments. INVISIBLE2.0 complies with the 95% PRR baseline and represents a steady energy efficiency and low packet delivery latency. Although the model was designed for simplicity, flexibility, and good accuracy, the absolute power numbers can not be guaranteed. This would require extensive physical measurements and further validation before using it to explore non-measured scenarios.

Fig. 1 .

 1 Fig. 1. Vehicular environment with heterogeneous C-V2X and V ehVLC technologies. Both V2V and V2I communication modes are allowed.

Fig. 2 .

 2 Fig. 2. Point-to-point transmission model.

Fig. 3 .

 3 Fig. 3. Schematic of (a) LTE, and (b) VLC power consumption model, respectively.

Fig. 4 .

 4 Fig. 4. Receiver energy efficiency in case of (a) V ehVLC, (b) C-V2X, and (c) INVISIBLE2.0 technique, vs. the traffic density for different communication modes and environments.

Fig. 5 .

 5 Fig. 5. Transmitter energy efficiency in case of (a) V ehVLC, (b) C-V2X, and (c) INVISIBLE 2.0 technique, vs. the traffic density for different communication modes and environments.

Fig. 6 .

 6 Fig. 6. PRR comparison vs. the traffic density in case of (a) C-V2X and (b) V ehVLC technology, and (c) INVISIBLE2.0, for different communication modes and environments.

Fig. 7 .

 7 Fig. 7. Latency comparison vs. the traffic density, in case of (a) C-V2X and (b) V ehVLC technology, and (c) INVISIBLE2.0, for different communication modes and environments.

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4604994 P r e p r i n t n o t p e e r r e v i e w e d