Enhancing Data Dissemination Security and Quality Through the Authenticated Relay Selection and Scheduling Framework (ARSSF) in VANET

A VANET was created in accordance with the principles of a Mobile Ad Hoc Network (MANET) [1]. It automatically generates mobile devices within the vehicle's domain, where network communication is established, thus generating renewed interest in mobile communication applications. A VANET for vehicle-to-vehicle communication was introduced in 2001. Later, it was demonstrated for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), and vehicle to vehicle-to-the roadside unit (V2R) to ensure secured roadside services [2]. Subsequently, VANET has assumed a pivotal role in the development of the intelligent transport system (ITS) owing to its ability to facilitate a clear understanding of the boundaries of the Internet of Vehicle (IoV) and support the deployment of autonomous vehicles [3]. VANET communication leverages a variety of wireless networking technologies, including short-range radio technologies like ZigBee and Wi-Fi (WLAN), as well as cellular technologies such as LTE and visible light communication (VLC) [4]. Owing to its efficient communication technologies, VANET serves a wide range of purposes, encompassing applications such as electronic brake lights, traffic information systems, platooning, on-road services, and emergency services in the domain of road transportation [5]. Concerning this matter, data distribution plays a critical role in delivering security and safety services to vehicles. Every vehicle situated within a designated Area of Interest (AoI) receives diverse information pertaining to congestion status [6], Information like local weather updates, traffic conditions, business advertisements, and more is received from the road side unit (RSU) [7]. The RSU's main purpose is to oversee the environment of the intelligent transportation system (ITS), traffic conditions, and weather status, thus transmitting the collision notices to the vehicles for safety. In short, such effective dissemination of traffic data enables to avoid accidents [8].

Modern transportation systems depend on VANETs for vehicle-to-vehicle and vehicle-to-infrastructure interaction. However, the full potential of VANETs in modern transportation systems hinges on addressing the fundamental challenges that accompany their implementation. Among these challenges, data dissemination stands as a prominent concern. Ensuring that information reaches its intended recipients in a timely and accurate manner, particularly in dynamically changing environments, poses a significant hurdle. Reliability is also paramount, especially in the context of safety-related applications, as it necessitates robust communication even in the presence of network disruptions and interference. The integrity of safety-critical messages and the privacy of sensitive data must be protected, and the network must be secure to prevent attacks. Furthermore, technical factors like voltage and frequency regulation are important in VANETs. Effective data transfer and network performance need constant and precise monitoring and management of voltage and frequency levels within devices connected to a VANET. For reliable communication within VANETs robust and dependable, these parameters must always remain within prescribed ranges. The main issue addressed in this section is the challenge of data dissemination within Vehicular Ad Hoc Networks (VANETs). It is stressed how difficult it is to guarantee the timely and accurate dissemination of information, especially in contexts that are always evolving. In addition, the difficulties of ensuring data integrity in safety-critical applications are discussed, as are the requirements for precise control of voltage and frequency levels within VANETs to ensure efficient data transfer and network performance Issues with maintaining network stability, random interactions that enhance collision likelihood, and high-time-variant mobile channels are additionally covered. Because of these issues, an Authenticate Relay Selection and Scheduling Framework (ARSSF) is required to fix the data distribution and network performance issues. The primary objective of VANET is to swiftly convey information with minimal delay and achieve the highest data transmission rate possible [9]. Typically, the propagation delay is calculated based on the duration it. Despite the assurance of minimum latency and uncertainty in the data dissemination process, the V2V channel modeling related to high-time variant mobile channels remains challenging [10]. Moreover, VANET faces constraints when it comes to initiating transmission requests during random interactions, consequently elevating the likelihood of collisions [11]. Hence, when a collision impacts the network, there is a risk of data loss, and this could potentially expose sensitive information to intermediate users, leading to security and privacy concerns [12]. Recent years have seen a surge in in-depth research aimed at analyzing diverse features and network structures within VANET, enhancing comprehension of data transmission strategies that can address the highly time-varying challenges involves developing random-channel protocols. However, these protocols have proven inadequate in sustaining network stability over extended periods, thus giving rise to security issues [13].

To overcome the shortcomings of VANET, an authentication framework is established in tandem with a central server to mitigate collision issues in the process of data dissemination [14]. In this study, the Authenticate Relay Selection and Scheduling Framework (ARSSF) is introduced, which utilizes relay node selection and dynamic scheduling to address issues related to data dissemination in VANETs. ARSSF is a novel VANET communication method that prioritizes trustworthy relay nodes and dynamic scheduling to increase network performance and safety as data dissemination needs rise. The study aims to investigate its effects on VANET security and performance, including voltage and frequency variations, end-to-end delay, and network throughput. In real-world VANET scenarios, the research attempts to show its practical consequences. In this framework, the scheduling process used the feedback stage, relay nodes selection, and transmission that allowed maintaining the network coverage and latency. Furthermore, every message transmitted by the vehicles was subject to verification through the generation of a secret key and trust authority, ensuring the proper management of vehicle authorization [15]. The AoI region underwent thorough examination, with the vehicle's position and velocity being leveraged to monitor changes in the network. Consequently, the network gained the capability to anticipate potential relay nodes, thus reducing packet loss in VANET. This research yielded the following significant contributions:

• The issue of collisions in VANET was circumvented through the selection and transmission of relay nodes based on a central server, ensuring a more reliable network.
• The introduction of the novel Authenticate Relay Selection and Scheduling Framework (ARSSF) effectively minimized delays and reduced data transmission failures by incorporating relay nodes.
• Enhanced system reliability and quality were achieved by implementing authentication procedures for vehicles and transmitting packet details through a secret key generated by a trusted authority.
• The ARSSF played a crucial role in efficiently managing data security and vehicle authorization, as evidenced by the simulation results.

The organization of this paper is as follows: In Section 2, we provide an overview of related works, encompassing essential concepts, frameworks, and theoretical analyses of data dissemination within VANET. Section 3 delves into the proposed ARSSF, elaborating on its details. In Section 4, we evaluate the performance of the ARSSF. The paper conclusion in the final section.

As aforementioned, the information in the VANET environment is transmitted from V2V, V2I, and V2R. Particularly, vehicle-based communication in VANET requires extreme security and safety. Hence, ITS is required to deliver message transmissions of the highest quality, ensuring both complete security and trust in the information dissemination process. In this context, the primary goal of the recently introduced ARSSF is to enhance the security and safety of the transmitted vehicle data, as illustrated in Figure 1. Furthermore, the system's design must uphold the resilience and reliability of the messages being conveyed.

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Figure 1. Overview of ARSSF architecture

The ARSSF is an infrastructure overhaul for VANETs that prioritizes data safety and quality. It implements registration and request methods and employs authentication and trust authorities to authenticate vehicle identities and decode data using secret keys. Collisions and delays are managed using a combination of signal-to-noise ratio (SNR) utility, relay node utility, and a relay selection technique. Reduced wait times and better adaptation to shifting vehicle locations result from constant feedback monitoring. The ARSSF emphasizes authentication of vehicles and selection of relay nodes, guaranteeing that only authorized vehicles can participate and considering the network’s capacity and dependability.

Figure 1 provides an overview of the ARSSF architecture in VANET, emphasizing security, authentication, and message robustness in both V2V and V2I communication facilitated by the RSU. The RSU plays a pivotal role in delivering information to end-users in the Area of Interest (AoI). Internet connectivity is established with the RSU, allowing the distribution of information to vehicles within its coverage (V2I) and to nearby vehicles requesting messages (V2V). Within the AoI, there are p RSUs denoted as R1, R2, … Rp, and an ITS system with k vehicles denoted as U1, U2, … Uk. Each vehicle (Vk) within the RSU is equipped with a GPS system, enabling synchronized position data acquisition during data transmission. Notably, control channels and service channels are integrated into the data dissemination process to ensure system reliability.

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Figure 2. RSU-guided relay node selection and collision resolution process

The process of data dissemination necessitated the involvement of control nodes in selecting relay nodes, as this allowed for the effective management of node capacity and coverage. In this network, each vehicle registered its information with the RSU. This approach offers a holistic view of the vehicle topology and scheduling process, facilitating a thorough understanding. A central node or server was consistently maintained to ensure collision-free operation during the communication process. However, in scenarios where two vehicles were adjacent but selected by different RSUs (e.g., RSU1 and RSU2), this situation could lead to contention and unintended scheduling conflicts among neighboring vehicles. Consequently, it was imperative to prevent collisions during data dissemination. This was achieved through a relay node selection process that also addressed collision avoidance, managed by the RSU as illustrated in Figure 2.

In Figure 3, we observe the collision-free selection of RSU nodes. Prior to the selection of RSU's relay nodes, a central server (CS) was established and linked to the RSU to oversee the scheduling process, as previously depicted. As mentioned earlier, the RSU had access to vehicle details, encompassing position, velocity, and vehicle decoding information, which it shared with the CS  to assist in making scheduling decisions within the Area of Interest (AoI). The scheduling process involved three distinct phases: relay node selection, transmission, and feedback. These three phases were strategically utilized to establish a robust data dissemination approach within VANET Additionally, the CS played a pivotal role in scheduling the vehicles, and the time interval between the two scheduling events represented the data dissemination cycle. This cycle included all three phases of the scheduling process.

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Figure 3. Relay node selection process by RSU using a central server

3.1 Relay selection stage

During the initial phase, known as the relay selection stage, the decision-making process was overseen by the CS, and the selected nodes were subsequently passed on to the relay transmission stage. Inside the RSU, the number of transmission frames was denoted as (R=t₁, t₂, …, t_n). For each transaction, the CS determined the relay selected node (Ω_r) with the goal of maximizing dissemination efficiency. Once the RSU had chosen the relay node for each transaction (r=1, 2, 3, …, R), the vehicle's status transitioned from failure to success. Throughout the relay selection process, specific vehicle position and velocity information was required after the completion of the (t_{he tr-1}) frame. Let R_x represent the relay node involved in the ITS-RSU $\left\{R_x \in R S U \cup U\right\}$. In each transaction (r=1, 2, 3, …, R), R_x had to be chosen. The Signal-to-Noise Ratio (SNR) of the node value was initially calculated to estimate the relay node. SNR played a crucial role in determining the node utility measure, as it considered both network coverage and energy levels. The relay value of R_x was determined from the vehicle U_k. The decoded packet was then calculated using the following process:

$\gamma_{k n}^{t_r, R_x}=\sum_{i=1}^{r-1} \frac{P_k^i L_k^i\left\|h_k^i\right\|^2}{N_0 \epsilon_n} \hat{\beta}_{i k n}^{t_{i-1}}+\frac{P_{R_x} L_{R_x}^{t_r}\left\|h_{R x k}^t\right\|^2}{N_0 \epsilon_n} \hat{\beta}_{x n}^{t_{r-1}}$      (1)

The calculation of the node utility value $\left(\gamma_{k n}^{t_r, R_x}\right)$ was based on factors including the power transmission by the RSU $\left(R_x\right)$ denoted as $\left(P_{R_x}\right)$ and the path loss $\left(L_{R_x k}^{t_r}\right)$ in the transmission frame $\left(t_r\right)$ between vehicle $U_k$ and $R_x$. The path loss $\left(L_{R_x k}^{t_r}\right)$ was influenced by the specific attributes of $U_k$ and $R_x$, and can be represented as $L_{R_x k}^{t_r}=f_L\left(d_{R_x k}^{t_r}\right)$. The value of $R_x k$ was estimated using the distance between the vehicles $U_k$ and $R_x$ in the transaction frame $\left(t_r\right)$.

Therefore, $\quad R_x k=\sqrt{\left(X_{R_x}^{t_r}-X_k^{t_r}\right)^2+\left(Y_{R_x}^{t_r}-Y_k^{t_r}\right)^2} \quad$ was calculated from the position of $R_x\left(X_{R_x}^{t_r}, Y_{R_x}^{t_r}\right)$ and vehicle $U_k$ $\left(X_k^{t_r}, Y_k^{t_r}\right)$ in $t_r$. The nodes $(U)$ positions were determined based on the $t_f$ node feedback positions $\left(X_U^{t_f}, Y_U^{t_f}\right)$ of $U$. Thus, the obtained node position was $X_U^{t_r}=X_U^{t_f}+v_U^X . T_{r f}$ and $Y_U^{t_r}=Y_U^{t_f}+v_U^Y$. $T_{r f}$. The $U$ velocity $\left(v_U^X, v_U^Y\right)$ and frame as well as feedback time $t_r$ and $t_f$ were used to obtain the time distance and then for calculating the SNR value of the node in $R_x$. The average Signal-to-Noise Ratio (SNR) value was computed using the following equation to assess the node's average utility within the Area of Interest (AoI):

$\hat{\beta}_{k n}^{t_r}= \begin{cases}1, & \gamma_{k n}^{t_r, R_x} \geq \gamma_{t h} \\ 0, & \gamma_{k n}^{t_r, R_x}<\gamma_{t h}\end{cases}$      (2)

The decoding status of packets by vehicles was assessed using Eq. (2) with the decoding threshold value (γ_th). Similarly, the utility of R_x was computed based on the relay node's transmission frame (t_r) as follows:

$\Phi^{t_r, R_x}=\sum_{k=1, U_k \in N^r R_x}^K \sum_{n=1}^N \varphi_{k n}^{t_r R_x}$          (3)

Subsequently, the utility $\left(\Phi^{t_r, R_x}\right)$ of the relay node was computed based on the utility of the vehicle $\left(U_k\right)$ in the context of packet $X_n$ being decoded in time slot $t_r\left(\varphi_{k n}^{t_r R_x}\right)$. These values, $\left(\varphi_{k n}^{t_r R_x}\right)$, were calculated as the difference between $\hat{\beta}_{i k n}^{t_{i-1}}-$ $\hat{\beta}_{x n}^{t_{r-1}}$. The set of neighboring nodes in $R_x$ at time slot $t_r$ was denoted as $N^r{ }_{R_x}$. Following the calculation of the Signal-toNoise Ratio (SNR) value $\left(\gamma_{k n}^{t_r, R_x}\right)$, the utility of the vehicle node for decoding packet $X_n\left(\hat{\beta}_{k n}^{t_r}\right)$ and the utility of the relay node in $R_x$ for vehicle $U_k\left(\Phi^{t_r, R_x}\right)$ were determined. A relay selection scheme was then formulated to facilitate data dissemination. The relay node selection was determined based on the utility factor of nodes and $R_x$ during time slot $t_r$. The chosen optimal nodes were denoted as $\Omega_r=$ $\left\{R_1^*, R_2^*, R_3^*, \ldots R_{L_r}^*\right\}$. The relay node selection approach was derived from:

$\max _{L_r . \Omega_r} \sum_{i=1}^{L_r} \Phi^{t_r, R_i^*}$    (4)

$N^r{ }_{R_i^*} \cap N^r{ }_{R_j^*}=\emptyset \quad i \neq j \quad \forall i, j=1,2,3, \ldots L_r$     (5)

$\Phi^{t_r, R_i^*}>0, \quad \forall i=1,2,3, \ldots L_r$     (6)

As data distribution commenced, the RSU at R_x initially possessed the utility value for the dissemination cycle. Leveraging this utility value, the system selected the first relay node, denoted as Ω₁. However, with the increase in the number of vehicles and transmissions, the system encountered challenges associated with computational complexity. To address this issue, a utility rate was calculated for the R_x packet decoding status, and a suboptimal relay selection scheme was implemented. As a result, the initial choice of the relay node was made in accordance with the maximum utility rate from $R_{L_r}^*$. When the relay node $R_{L_r}^*$ shared common neighboring nodes with other nodes, the remaining nodes were removed. The candidate relay node included a set of $R_x$ and $U_k$, represented as $\left\{\operatorname{RSU}\left(R_x\right) \cup U_k\right\}$. Initially, the chosen relay node $\left(L_r\right)$ was set to 0 , and the set of chosen relay nodes was not empty, denoted as $\Omega_r \neq \emptyset$. Consequently, for the subsequent relay node selection, $L_r$ was incremented $\left(L_r=L_r+1\right)$, and the node was chosen from set A based on the maximum utility of $R_{L_r}^*$ (as described in Eq. (4)). The selected node was then added to the $\Omega_r$ set, which can be defined as $\Omega_r \leftarrow \Omega_r \cup$ $\left\{R_{L_r}^*\right\}$. Collisions were prevented by removing common neighboring nodes from set A with respect to $R_{L_r}^*$.

Following that, the set A was modified as $\left.\mathcal{A} \leftarrow \mathcal{A}-\left\{\hat{R} \mid N_{\hat{R}}^r \cap N_{R_{L_r}^*}^r \neq \emptyset\right\}\left(R_{L_r}^*\right) \neq \emptyset\right\}$. Finally, the selected relay nodes were updated within Ω_r. Once the relay node selection was completed, the packet decoding status of vehicle U_k was refreshed with each transmission t_r using the following equation:

$\hat{\beta}_{k n}^{t_r}=\left\{\begin{array}{cc}\hat{\beta}_{k n}^{t_r, R_k^r}, \text { if } & U_k \in N^r{ }_{R_k^r} \in \Omega_r \\ \hat{\beta}_{k n}^{t_{r-1},}, & \text { otherwise }\end{array}\right.$      (7)

The subsequent relay node selection for transmission (t_r+1) was determined based on $\hat{\beta}_{k n}^{t_r}$. This approach effectively reduced collisions and effectively managed network capacity.

3.2 Relay transmission stage

The output of the relay selection phase was employed to facilitate the transmission of both vehicle and RSU data according to a predetermined schedule. This transmission process was characterized by time slots t₁, t₂, t₃, …, t_r t, which denoted the selection of relay nodes from Ω_r, where Ω₁ represented the first relay node, and Ω_r was the final relay in the data transmission chain. The duration of each time slot t_r was denoted as t_r. A relay node was selected for each time slot t_r and subsequently decoded using the space-time network coding technique. This packet decoding process significantly enhanced the efficiency of data dissemination. Prior approaches entailed sending packets from the roadside unit to vehicles without utilizing acknowledgment procedures. Nevertheless, the systems security and reliability were ensured through the application of an authentication procedure. Consider a packet X={X_n|n=1, 2, …, N} disseminated within the Area of Interest (AoI) using a relay node-based scheduling process. This packet contained several pieces of confidential information and symbols that needed protection from external interference. As a result, each node involved in the transmission phase underwent authentication by the Trust Authorities (TA) by possessing a unique node ID and generating a private key (θ_TA) using the hash function h(.), as indicated by the following equation:

$\theta_{\mathrm{TA}}=\mathrm{h}\left(\mathrm{ID}_{\mathrm{TA}} \| \Re_{\mathrm{TA}}\right)$     (8)

The private key θ_TA was derived from the TAs ID and the random number, $\mathfrak{R}_{T A}$, generated by the TA. It necessitated either an input packet $X_n \in X$ or a message length, and the resultant output was converted into 128 bits. Security measures were implemented by utilizing the MD5 hash function, which divided the data block into 16 sub-blocks, each containing 32 bits. The result was obtained by concatenating the 32-bit segments from four groups. This methodology was applied within the framework of VANET communication, users were required to register their vehicles U_k with their ID $\left(I D_{U_k}\right)$ and security key $\left(S_{U_k}\right)$. This registration process was designed to reduce computational overhead when the respective vehicles entered the communication environment. Vehicle registration with specific parameters was accomplished as follows:

$\mathrm{U}_{\mathrm{k}}=\mathrm{h}\left(\mathrm{ID}_{\mathrm{U}_{\mathrm{k}}} \| \mathrm{S}_{\mathrm{U}_{\mathrm{k}}}\right)$     (9)

Vehicle-related attributes, including position and velocity, were computed, and the vehicle information was consolidated as $\mu_{U_k}=A_{U_K}+B_{U_k}$, which was then relayed to the Trust Authority (TA). Upon receiving $\mu_{U_k}$, the TA generated a random number for executing the hash function. This hash function played a critical role in authenticating the vehicles during data dissemination in VANET. Subsequently, the TA computed the trust factor using.

$\rho_{T A}=h\left(\mu_{U_k} \| U_k\right) \oplus \theta_{T A}$    (10)

The resulting hash value of the trust authority, h_TA, and θ_TA were communicated to the vehicle. This procedure played a crucial role in authenticating the user vehicle and enhancing security during data transmission within the VANET environment. The computed Trust Authority (TA) factors were verified by comparing the transmitted information with the registered details. When a match was confirmed, the user gained authorization to share data. Additionally, the data transmission process for the connection was validated through a request and reply message verification process, thereby ensuring the security of data dissemination.

A. Request message

When vehicle U₁ intended to transmit a message to U₂, it initiated the process by sending a request to U₂ while monitoring the request time. Meanwhile, U₁ generated the trust authority factors $\mu_{U_k}$ and the hash value associated with U₂. Subsequently, with the assistance of the Roadside Unit (RSU), the vehicle segregated these factors and computed the secret key for the trust authority (TA) as follows:

$\sigma_{T A}=h\left(\mu_{U_k} \| \omega_{U_k}\right) \oplus \theta_{T A} ; \omega_{U_k}=I\left(D_{U_k} \| h_{T A}\right)$     (11)

The definition of the transmission request was as follows:

$T_{U_k}=h\left(\theta_{T A} \| S_{t x}\right) \oplus \mu_{U_k}$      (12)

$S_{U_k}=T_{U_k} \oplus \mu_{U_k} \oplus \theta_{T A}$      (13)

Ultimately, the message transmission request was created, incorporating a timestamp (S_tx) determined by the following relationship.

$\eta_{U_k}=\operatorname{Req} \oplus T_{U_k} \oplus \theta_{T A} \oplus S_{t x}$        (14)

The derived θ_TA and hash h_TA value were transmitted to vehicle U_k. Upon receiving the request message, the generated factors ($\mu_{U_k}, \omega_{U_k} h_{T A}$, and $\sigma_{T A}$), h_TA, and σ_TA, were retained in the database for subsequent verification.

B. Relay transmission stage

Vehicle $U_2$ received the request message $\eta_{U_k}$. In the preceding step, the factors $\left(T_{U_k}, \eta_{U_k} \wedge S_{t x}\right)$ were designated as $S_{r x}$. The request timestamps were compared with the system factor $\alpha \mathrm{S} 1$, and if there was a delay in transmission, it was determined by $S_{r x}-S_{t x} \geq \alpha S 1$. When this condition was met, the received factors ( $T_{U_k}, \eta_{U_k} \wedge S_{t x}$ ) were considered expired, thereby ensuring the security of data transmission. Subsequently, $U_2$ recalculated the secret key as $S_{U_k}$ and the hash factor $h_{U_2=} T_{U_k} \oplus h$ via:

$\widehat{S_{U_k}}=T_{U_k} \oplus h_{U_2} \oplus \theta_{T A}$      (15)

The request message was scrutinized as Req $=\eta_{U_k} \oplus T_{U_k} \oplus \theta_{T A} \oplus S_{t x}$, and it was validated by vehicle U₁ through the computation of two factors using the provided parameters.

$F_{U_2}=h\left(h_{U_1}\|\widehat{\alpha S} 1\| \oplus \theta_{T A}\right)$     (16)

$L_{U_2}=F_{U_2} \oplus \theta_{T A} \oplus \widehat{\alpha S 1 \oplus} h_{U_2}$     (17)

Upon the computation of these parameters, the confirmation was established, and the response was provided in a decrypted format. EN_reply was defined as $E C D_{F_{U_2}}$. Subsequently, the response was forwarded to the original request as $\left\{E_{\text {reply }}, L_{U_2}\right\}$. The successive generation of these factors played a crucial role in maintaining data confidentiality and consequently reducing computational time.

C. Communications units

After receiving a response from U₂, vehicle U₁ accepted and stored the trust authority's incoming factors. Following this, S_tx examined the reply message $\left\{E N_{\text {reply }}, L_{U_2}\right\}$ to determine the response delay. Subsequently, the delay was calculated as S_rx-S_tx≥αS2. If this condition was met, the vehicle engaged with another vehicle; otherwise, the communication was terminated. Finally, the reply messages were decrypted to retrieve the original dissemination message as per:

Reply $=D C P_{F_{U_2}}\left(E N_{\text {reply }}\right)$    (18)

$\widehat{F_{U_2}}=L_{U_2} \oplus \theta_{T A} \oplus S_{U_k} \oplus h_{U_1}$   (19)

In summary, in a VANET environment, the exchange of information among vehicles was carried out with a primary emphasis on ensuring system authentication, security, and reliability, all of which hinged on the decryption process.

3.3 Feedback stage

$T A S_{U_k}$ in the communication process between vehicles U_k, collisions were prevented by verifying that there were no neighboring vehicles with which they were already acquainted. Moreover, the issue of collisions was resolved by coordinating vehicle allocation through the control channel during the feedback phase. With each transaction t_r, the feedback system maintained the currency of vehicle information, encompassing $T A, S_{U_k}$, and hash factors. All information, block transformations, and control channel details within the feedback phase were consistently renewed and applied in subsequent transactions. As vehicles transitioned from one Central Server (CS) range to another within a single dissemination cycle, a new CS was established based on the feedback section. Consequently, the old CS was removed from the list of registered vehicles, which effectively prevented collisions during communication. These three phases were carried out periodically, ensuring the execution of data dissemination. In summary, the system was engineered to provide dependability, security, authentication, and efficient data distribution while minimizing computational complexity and time constraints.

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Figure 4. Flowchart of ARSSF algorithm

The flowchart in Figure 4 illustrates the logic behind the ARSSF algorithm, which aims to improve data sharing on VANETs without compromising security. It verifies that only approved vehicles are participating, encodes the data for security, optimizes the communication line, and constantly monitors and changes the process to ensure dependable data transmission. Improving VANET safety and efficiency relies heavily on this algorithm.