DAHP–TOPSIS-Based Channel Decision Model for Co-Operative CR-Enabled Internet on Vehicle (CR-IoV)

: The Internet of Vehicles (IoV) is an important idea in developing intelligent transportation systems and self-driving cars. Vehicles with various wireless networking options can communicate both inside and outside the vehicles. IoVs with cognitive radio (CR) enable communication between vehicles in a variety of communication scenarios, increasing the rate of data transfer and bandwidth. The use of CR can meet the future need for quicker data transport between vehicles and infrastructure (V2I). Vehicles with CR capabilities on VANET have a different appearance than regular VANET vehicles. This paper aims to develop effective spectrum management for CR-equipped automobiles. An improved channel decision model has been proposed with proven outcomes to boost the pace of transmission, eliminate end-to-end delays, and minimize the number of handoffs. Many high-bandwidth channels will be used in the near future to communicate large-sized multimedia content between vehicles and roadside units (RSU) for both entertainment and safety purposes. Co-operative sensing promotes energy-constrained CR vehicles for sensing a wide spectrum, resulting in high-quality communication channels for requesting vehicles. Our research on the CR-VANET focuses on channel decision instead of spectrum sensing and it differs from previous studies. We used the DAHP–TOPSIS model under multi-criteria decision analysis (MCDA), a sub-domain of operations research, to boost proﬁts, i.e., transmission rate with less computing time. We constructed a test-bed in MATLAB and carried out several analyses to demonstrate that the suggested model performs better than other parallel MCDA models because there has been a limited amount of research work conducted with CR-VANET


Introduction
One of the key study topics in intelligent transportation systems (ITS) is transportation safety. Most people prefer to commute in their own vehicle in today's fast-paced society. Vehicle users are expanding on a daily basis in populous places, particularly in Asian countries, such as India and China. On the one hand, vehicle manufacturers are working hard to create technology-based vehicles that will appeal to consumers by adhering to safety criteria that include crash tests, vehicle maintenance, and emission testing. On the other hand, the majority of people drive recklessly and threaten other people's lives. This results in a higher number of deaths and injuries due to organ loss. When we are living a sophisticated life with vehicles, we occasionally reflect on the past as we drive through traffic. Most auto insurance companies are having difficulty determining the specific cause of accidents. The Department of Telecommunications in the United States provides an Sustainability 2021, 13, x FOR PEER REVIEW 3 of 25

Related Works
Xu et al. [8] offered a comparison study between the DSRC and LTE standards, which was evaluated with various communication scenarios, and the authors concluded that DSRC is superior to LTE in collision avoidance and safety applications. The integrated DSRC and LTE systems, according to the author, can help provide safe communication over a greater distance. Kumar et al. [9] proposed an efficient integrated DSRC WiMAX system based on common physical layer parameters to create communication for the intelligent transportation system. Kumar et al. [10] established an uplink communication simulation model based on several vehicular communication scenarios, such as sending data from WiMAX to DSRC using FFT subcarrier matching. Kandar et al. [11] presented a physical layer OFDM parameter, which was simulated by mixing DSRC and WiMAX and tested with image samples to demonstrate that successful communication between multiple standards can be established. Kumar et al. [12] investigated the performance of several vehicular communication situations by considering payload calculation and resource allocation. It can be evaluated in terms of how the resource is assigned to the vehicle's users via sub-channelization. Kumar et al. [1] presented a novel approach to converge DSRC and WiMAX by considering common physical layer parameters and testing them using picture transmission between DSRC and WiMAX. Thun et al. [13] developed an OpenFlow SDN controller-controlled vehicle ad hoc network augmentation technique. Khadim et al. [14] developed an optimal energy-efficient system for vehicle networks, which they evaluated using a multicast routing protocol controlled by SDN and fog computing. In a vehicle network, Kalokhe et al. [15] suggested a durable and efficient routing technique and a secure channel using software-defined networks.
Chen et al. [16] presented a DCF mechanism in the DSRC standard to increase the performance of a vehicular network utilizing SDN, which they tested with a vertical handoff method between the car and the roadside unit. By addressing load balancing and multipath routing, Toufga et al. [17] offered a comparative analysis of how the softwaredefined network helps to govern vehicular networks and how it may be used for intelligent transportation system applications. Based on SDN technology, Kalinin et al. [2] suggested an architecture to establish a secure traffic vehicular network to prevent unwanted user access. Huo et al. [18] proposed an efficient model for traffic analysis using a software-defined network to describe the vehicular communication network. Huang et al. [4] suggested an offloading technique based on handover between different networks, such as DSRC and Wi-Fi, as well as an analysis of network quality and signal strength for the vehicle to a fixed infrastructure scenario. The SDN aids in the control and monitoring of a stable IEEE 802.11P and Wi-Fi connection. C. Lai et al. [19] proposed a unique technique based on IP and software vehicular networks to offer safe and lossless communication.

Related Works
Xu et al. [8] offered a comparison study between the DSRC and LTE standards, which was evaluated with various communication scenarios, and the authors concluded that DSRC is superior to LTE in collision avoidance and safety applications. The integrated DSRC and LTE systems, according to the author, can help provide safe communication over a greater distance. Kumar et al. [9] proposed an efficient integrated DSRC WiMAX system based on common physical layer parameters to create communication for the intelligent transportation system. Kumar et al. [10] established an uplink communication simulation model based on several vehicular communication scenarios, such as sending data from WiMAX to DSRC using FFT subcarrier matching. Kandar et al. [11] presented a physical layer OFDM parameter, which was simulated by mixing DSRC and WiMAX and tested with image samples to demonstrate that successful communication between multiple standards can be established. Kumar et al. [12] investigated the performance of several vehicular communication situations by considering payload calculation and resource allocation. It can be evaluated in terms of how the resource is assigned to the vehicle's users via sub-channelization. Kumar et al. [1] presented a novel approach to converge DSRC and WiMAX by considering common physical layer parameters and testing them using picture transmission between DSRC and WiMAX. Thun et al. [13] developed an OpenFlow SDN controller-controlled vehicle ad hoc network augmentation technique. Khadim et al. [14] developed an optimal energy-efficient system for vehicle networks, which they evaluated using a multicast routing protocol controlled by SDN and fog computing. In a vehicle network, Kalokhe et al. [15] suggested a durable and efficient routing technique and a secure channel using software-defined networks.
Chen et al. [16] presented a DCF mechanism in the DSRC standard to increase the performance of a vehicular network utilizing SDN, which they tested with a vertical handoff method between the car and the roadside unit. By addressing load balancing and multipath routing, Toufga et al. [17] offered a comparative analysis of how the software-defined network helps to govern vehicular networks and how it may be used for intelligent transportation system applications. Based on SDN technology, Kalinin et al. [2] suggested an architecture to establish a secure traffic vehicular network to prevent unwanted user access. Huo et al. [18] proposed an efficient model for traffic analysis using a software-defined network to describe the vehicular communication network. Huang et al. [4] suggested an offloading technique based on handover between different networks, such as DSRC and Wi-Fi, as well as an analysis of network quality and signal strength for the vehicle to a fixed infrastructure scenario. The SDN aids in the control and monitoring of a stable IEEE 802.11P and Wi-Fi connection. C. Lai et al. [19] proposed a unique technique based on IP and software vehicular networks to offer safe and lossless communication.
Zhu ming et al. [5] suggested an SDN-based routing technique for vehicular networks that may be used for geographical position sharing by routing messages over connected, accessible mobile networks. A survey of SDN-based security threats and services for automotive networks was proposed by Shafiq et al. [20]. Based on a background analysis of several SDN-based VANET systems, the authors concluded that deploying a single distant SDN controller will improve overall network efficiency. By utilizing effective fog computing, Arif Muhammad et al. [21] suggested virtualized security for vehicle networks. This methodology aids in the secure and rapid communication of vehicles operating in highdynamic environments. Balta et al. [22] suggested a fuzzy-based SDN controller improves traffic signals in large cities by taking into account various environmental scenarios, such as emergencies, typical traffic, and congestion. The authors claimed that a three-stage fuzzy SDN controller aids in decision-making based on priority and demand vehicle routing data. Kalokhe et al. [23] proposed employing an SDN controller to provide efficient and robust routing information for vehicular traffic monitoring and secure communication between vehicles. SDN aids in separating network control from the transmission and reception of data packets using various standard network protocols. SDN is a central control unit that can manage numerous network topologies on a shared distributed network platform.  [24,25]. Figure 2 depicts the architecture of the OpenFlow SDN controller to select the networks based on different vehicular communication scenarios. There are two modes of communication mentioned in the architecture, i.e., V-to-V and V-to-I. Mostly in V2V, communication takes place only within a limited 1000-m range. Hop-to-hop communication is supported with the help of DSRC communication, when all the vehicles should be in the range of 1KM. In the above architecture, vehicle V2 can communicate with vehicle V4 with the help of V3. The range between V2 and V4 is 2 km. In this scenario, DSRC cannot provide direct communication. Therefore, hop-to-hop communication occurs between V2 to V3 and V3 to V4 [26,27].
The application layer, control layer, and infrastructure layer are all controlled by the SDN controller. Each layer performs its own set of tasks. The top application layer aids load balancing by filtering unauthorized car users through the firewall. The control layer is responsible for general control of the OpenFlow SDN, whereas the infrastructure layer contains switches and devices that connect vehicles and base stations. The flow table is used to keep track of many communication data, such as the distance between neighboring vehicles and the base station, as well as the direction and speed. Vehicle V1 can use the data in the flow table to seek a nearby network by examining the scenario. Vehicles can support two forms of communication: DSRC to DSRC with the use of a base station (BS) and DSRC to mobile networks hybrid communication (3G, LTE, WiMAX). Mobile networks, on the other hand, offer a wide choice of communication options but do not support significant mobility [28,29].
When the vehicle delivers communication over a distance of more than 1 km, the design shown in Figure 2 is used. As a result, Hybrid DSRC/WiMAX allows users to communicate across a distance of up to 10 km using RSU. Without an SDN controller, combining IEEE 802.11P and IEEE 802.16D is a difficult operation. However, the IEEE standards are not the same. Uplink and downlink communication use the same physical layer settings. The transmission of OFDM signals faces several hurdles, including a 10 MHz bandwidth, a common 5.9 GHz RF, and subcarrier matching. The DSRC 64 FFT  The application layer, control layer, and infrastructure layer are all controlled by the SDN controller. Each layer performs its own set of tasks. The top application layer aids load balancing by filtering unauthorized car users through the firewall. The control layer is responsible for general control of the OpenFlow SDN, whereas the infrastructure layer contains switches and devices that connect vehicles and base stations. The flow table is used to keep track of many communication data, such as the distance between neighboring vehicles and the base station, as well as the direction and speed. Vehicle V1 can use the data in the flow table to seek a nearby network by examining the scenario. Vehicles can support two forms of communication: DSRC to DSRC with the use of a base station (BS) and DSRC to mobile networks hybrid communication (3G, LTE, WiMAX). Mobile networks, on the other hand, offer a wide choice of communication options but do not support significant mobility [28,29].
When the vehicle delivers communication over a distance of more than 1 km, the design shown in Figure 2 is used. As a result, Hybrid DSRC/WiMAX allows users to communicate across a distance of up to 10 km using RSU. Without an SDN controller, combining IEEE 802.11P and IEEE 802.16D is a difficult operation. However, the IEEE standards are not the same. Uplink and downlink communication use the same physical layer settings. The transmission of OFDM signals faces several hurdles, including a 10MHz bandwidth, a common 5.9 GHz RF, and subcarrier matching. The DSRC 64 FFT subcarriers may be equal to the WiMAX 256 FFT subcarriers in our proposed system if other modulation methods and padding bits are used [ [32][33][34]. Each OFDM symbol associated with I/Q signaling in this mathematical model will be: For DSRC 64 FFT:   [32][33][34]. Each OFDM symbol associated with I/Q signaling in this mathematical model will be: For DSRC 64 FFT: For 256 FFT: For example, I stands for in-phase and Q stands for quadrature phase. The employment of two sinusoids with the same frequency and a 90 • relative phase shift is referred to as I/Q signaling. When combining two separate wireless networks, such as IEEE 802.11P vehicular communication and IEEE 802. 16 WiMAX, common physical layer orthogonal frequency division multiplexing (OFDM) parameters are needed to establish communication in order to enhance the range of CR-based vehicular networks. All observed RF signals are orthogonal in nature, and that can be modulated in amplitude, phase, and frequency by summing amplitude-modulated I/Q signals. Quadrature modulation is a type of modulation that uses both I and Q signals. Quadrature phase-shift keying is achieved by adding I and Q carriers that have each been multiplied by +1 or −1 in accordance with the incoming digital data [31]. For downlink transmission, i.e., mobile network to DSRC, the 16 QAM modulation type estimates the FFT subcarrier value. The OFDM 256 FFT physical layer is modulated with 16 QAM modulation. As a result, each 256 FFT OFDM signal is modulated with 14 bits per symbol, making the DSRC 64 FFT subcarrier comparable. The 64 FFT DSRC OFDM signal = 256 × 1/4 bits (16 QAM). The 64 FFT subcarriers are separated into 52 used data carriers, 4 pilot subcarriers, and 8 unused subcarriers for DSRC. In IQ signaling, the 52 data carrier is modulated into +26 and −26 FFT. The 256 FFT carrier is divided into 200 usable data carriers, and the remaining 56 subcarriers are utilized for pilot and unused subcarriers in IEEE 802. 16. In CR-based mobile networks, OFDM is employed as a digital modulation technique. Here, ∆f denotes the frequency separation between subcarriers, and the carrier signal 2πk ∆f (t) at f c , where f c is the determining factor for which technology is employed. The sent data are represented by C k , where k is the frequency offset index of the subcarriers for one sample. For DSRC vehicular communication, f c is the common RF frequency of 5.9GHz. In each time domain, f 1 (t) represents the 64FFT DSRC carrier frequency and f 2 (t) represents the 256 FFT WiMAX carrier frequency.
After applying I and Q function values from Equations (1)-(4), C k is derived as follows in Equation (5): Each carrier frequency C k can be represented as: C k is the input data signal, which is modulated to I k and Q k signaling. The "j" operator is utilized to convert a complex number to √ 1 (90 • phase shift), so C k = cos(k) + j·sin(k) signaling. Overall, these studies show the limitations of the existing V2V model for certain futuristic scenarios. Since the future applications in VANET will include multimedia and other real-time streaming between vehicles, we need optimized and enlarged bandwidth communications. Either vehicle industries can utilize new bands by adopting radio frequency regulation bodies or effectively utilizing existing bands. We have proposed a novel scheme DAHP-TOPSIS for a channel decision model for CR-enabled VANET, which falls under the second category to match future needs in V2V communications.

Spectrum Management for CR-VANET Using MCDM
CR-VANET is a type of vehicular communication infrastructure that may be used to solve a variety of decision issues. It can also be employed in a variety of communication scenarios. AHP, TOPSIS, ELECTRAE, PROMETHEE, VIKOR, and SAW are only a few of the CR-VANET decision-making models. We used the combination of AHP and TOPIS in this paper. AHP has been improved to become DAHP, which can be used for a variety of situations and services between sender and receiver vehicles. Centralized decisioncentralized sensing and centralized decision-co-operative sensing are two network models for implementing the DAHP-TOPSIS channel decision model [34,35].

Co-Operative Spectrum Management in CR-VANET
There are two different models, as shown in Figure 3a,b, that can be used to perform spectrum sending and decision tasks. In the proposed model, 3.b-based infrastructure needs to be established to take channel decision, since the available RSU enabled, with consistent and uninterrupted power supply and enough computational ability, this model chosen to take channel decision.

Channel Decision Based on Channel Ranking
Rank-based decision-making is used to decide the channel to communicate with other networks based on weights assigned to the channel. Hybrid combined TOPSIS and DAHP techniques are used for decision-making and initiating communication to suitable networks. TOPSIS Algorithm is depicted in Algorithm 1.

Co-Operative Spectrum Management in CR-VANET
There are two different models, as shown in Figure 3a,b, that can be used to perform spectrum sending and decision tasks. In the proposed model, 3.b-based infrastructure needs to be established to take channel decision, since the available RSU enabled, with consistent and uninterrupted power supply and enough computational ability, this model chosen to take channel decision.

DAHP Data Services Implementation Procedure
The channel decision factor deals with some quality metrics to analyze the weight of the channel for decision-making in terms of signal-to-noise ratio, bit error interference ratio, and bandwidth. Optimizing the channel strength needs to give priority to major channel parameters. Initially, AHP generated a rating from 0 to 1 to improve the better channel selection. Scores need to be compared to produce a better decision-making strategy. The AHP rating parameters are as follows.
v. Calculating weight of alternatives where, S * -rowSum of Positive idle matrix as in Equation (11) S − -rowSum of Negative idle matrix as in Equation (12 Let, BestComp(n) → n channels for Best Computation time WFRatio(n) → n channels for Worst fault ratio BestRelativeSD(n) → n samples for Best relative standard deviation BAT(n) → n channels for best throughput If (BestComp(n) > comp_time(t)) BestComp(n) = comp_time(t);  Table 2 will be compared with one another. Normalizing is an operation which will convert the table values between 0 to 1.
where, Z[i][j]-comparative matrix for taken deciding parameters for channel.

Calculation of Normalized Row Sum
Square matrix for Z[i][j] has to be calculated then each row sum should be calculated as in Equation (17), where i is from 1, 2, . . . , m: number of rows The above Equations (16) and (17) has been applied to Tables 3 and 4, and sum of row calculated as in Table 5.  The sum of row SumR(i) is normalized to calculate the priority of ordering mentioned in Equation (18). Therefore: Finally, by considering the various factors, the performance is analyzed by calculating the weight. The consistency ratio is calculated by dividing an index of consistency (CI) and random (RI) values, and it is mentioned in Equation (20). In addition, n is the number of DAHP factors. Calculated weighs for performance factors are presented in Table 6.  (20) and (21) various data services, such as data, audio, and video, can be provided by calculating CRAT values, and they are less than 0.1 in value, as mentioned in Table 7. Computation of the normalized sum of row is calculated in Equation (22).
The sum of normalized row SumR(i) helps to reduce the weight of each channel by DAHP qualitative analysis metrics by calculating the parameters of text, audio, and video, and it is mentioned in Table 8. By comparing all qualitative parameters, we can see in Table 8 that packet loss ratio is comparatively less in text data when compared with audio and video data. Therefore, channel quality is compared by transmitting all kinds of data formats, which are mentioned in Figure 4. The signal-to-noise ratio is the maximum weight of CAV. Therefore, CAV helps to decide the channel quality for all data services.  Figure 4. The signal-to-noise ratio is the maximum weight of CAV. Therefore, CAV helps to decide the channel quality for all data services.

TOPSIS Channel Ranking
TOPSIS channel ranking is used to eliminate the choice of choosing poor weights in terms of providing good qualitative data service to the channels. Enhanced TOPSIS helps to provide the best decision-making strategy for channel selection.

TOPSIS with DAHP for Providing Qualitative Data Service
The ranking method can be followed in TOPSIS to enhance the weight of the decision matrix to choose a better channel for communicating. Combining DAHP with TOPSIS provides a better qualitative decision matrix for all data service applications. There are three steps to follow to provide better qualitative service in TOPSIS: channel properties collection, normalizing the decision matrix, and qualitative channel ranking. Table 9 depicts the simulation parameter of the proposed model by considering 20 to 200 channels in different weights and normalized values as shown in Equations (6) and (23), the below X(i,j) represented as matrix in Table 10:

TOPSIS Channel Ranking
TOPSIS channel ranking is used to eliminate the choice of choosing poor weights in terms of providing good qualitative data service to the channels. Enhanced TOPSIS helps to provide the best decision-making strategy for channel selection.

TOPSIS with DAHP for Providing Qualitative Data Service
The ranking method can be followed in TOPSIS to enhance the weight of the decision matrix to choose a better channel for communicating. Combining DAHP with TOPSIS provides a better qualitative decision matrix for all data service applications. There are three steps to follow to provide better qualitative service in TOPSIS: channel properties collection, normalizing the decision matrix, and qualitative channel ranking. Table 9 depicts the simulation parameter of the proposed model by considering 20 to 200 channels in different weights and normalized values as shown in Equations (6) and (23), the below X(i,j) represented as matrix in Table 10: Therefore, positive-and negative-ideal solutions as in Equations (9) and (10) help to calculate the weight mentioned in Table 10. Tables 11 and 12 analyze the channel by using text services. As per Equations (11) and (12), Tables 11 and 12 has been calculated. Therefore, the same methodology as with text services, positive-and negative-ideal solutions, helps to calculate the weight mentioned in Table 13. Tables 14 and 15 analyze the channel by using audio services. Therefore, the same methodology used for text and audio services, positive-and negative-ideal solutions, helps to calculate the weight mentioned in Table 16. Tables 17 and 18 analyze the channel by using video services. By implementing a rank-based channel selection process using DAHP-TOPSIS, all kinds of data services, such as text, audio, and video, are illustrated by comparing them with 20 channel ranks. In all CR-VANET scenarios, it is possible to communicate with various kinds of services, and channels 5 and 20 provide the best text, audio, and video data transmission by utilizing the optimized channel weight, and the same is represented as a table to display each channel and its rank in Table 19 as per Equations (13)- (15).

Performance Analysis by Calculating Computation Time
The proposed DAHP-TOPSIS methodology provides a better result by comparing other channel selection techniques, such as DAHP-SAW and random DAHP-MOORA. SAW and MOORA also can be integrated with DAHP to take channel decisions. SAW and MOORA are lightweight techniques to handle a smaller number of alternatives. Computation time and delay are much lower in the proposed techniques by comparing them with techniques and the comparative qualitative metrics mentioned in Table 20. The results of Table 20 are visualized in plots in terms of best, worst, and average cases, as shown in Figures 5-8.

Handoff Analysis in the Proposed DAHP-TOPSIS
The operational cost for handoffs is much costlier compared with other operations, especially in CR-VANET. By considering 20, 100, and 200 channels used to analyze the various spectra utilized in CR_VANET handoffs, comparative analysis of DAHP-TOPSIS, DAHP-MOORA, DAHP-SAW, and random helps to identify the best quality of service provided by CR-VANET in terms of average end-to-end delay, packet delivery ratio, packet loss ratio, and control overhead. Figures 9 and 10 depict the comparison of various channel selection respective handoff schemes for PU low traffic = 0.2 and PU high traffic = 0.6. Table 21 depicts the various handoff strategies by varying types of data, channel size, and traffic in PU, i.e., low = 0.2, high = 0.6.

Conclusions
OpenFlow-controlled CR-IoV enables efficient vehicular communication in high-mobility situations based on the scenario of vehicle direction, speed, and range in various handoff scenarios. Due to the high mobility of cars, identifying the available network and routing of vehicles is a critical task that can be performed using vertical handoff and the OpenFlow SDN controller. DAHP-TOPSIS is used to analyze the packet delivery ratio and channel selection for CR-VANET by comparing it to other techniques, such as DAHP-TOPSIS, DAHP-MOORA, DAHP-SAW, random packet loss, and rank-based channel selection. Proposed DHAP-TOPSIS has been validated along with parallel competing techniques in the MCDA domain. Due to the speed and route failure of the vehicle in terms of low traffic of 0.2 and high traffic of 0.6, the decision of rank-based channel selection and packet delivery ratio fluctuates from time to time. CR-VANET delivers various data services, such as audio, video, and text, on roughly 20 to 200 channels in various scenarios. Text services perform better in the CR-VANET than audio and video services when employing DAHP-TOPSIS, and they cost less than audio and video services. Tables 20 and  21 provide the findings on the number of handoff conditions and computation time in the CR-VANET. The proposed DAHP-TOPSIS system employs CR-VANET to provide communication to vehicle users. It may be used to address a variety of societal and environmental issues, including traffic congestion control, safety applications, such as driver assistance and automatic crash area notification, and transportation safety in handoff circumstances using an SDN-controlled vehicular network.

Conclusions
OpenFlow-controlled CR-IoV enables efficient vehicular communication in highmobility situations based on the scenario of vehicle direction, speed, and range in various handoff scenarios. Due to the high mobility of cars, identifying the available network and routing of vehicles is a critical task that can be performed using vertical handoff and the OpenFlow SDN controller. DAHP-TOPSIS is used to analyze the packet delivery ratio and channel selection for CR-VANET by comparing it to other techniques, such as DAHP-TOPSIS, DAHP-MOORA, DAHP-SAW, random packet loss, and rank-based channel selection. Proposed DHAP-TOPSIS has been validated along with parallel competing techniques in the MCDA domain. Due to the speed and route failure of the vehicle in terms of low traffic of 0.2 and high traffic of 0.6, the decision of rank-based channel selection and packet delivery ratio fluctuates from time to time. CR-VANET delivers various data services, such as audio, video, and text, on roughly 20 to 200 channels in various scenarios. Text services perform better in the CR-VANET than audio and video services when employing DAHP-TOPSIS, and they cost less than audio and video services. Tables 20 and 21 provide the findings on the number of handoff conditions and computation time in the CR-VANET. The proposed DAHP-TOPSIS system employs CR-VANET to provide communication to vehicle users. It may be used to address a variety of societal and environmental issues, including traffic congestion control, safety applications, such as driver assistance and automatic crash area notification, and transportation safety in handoff circumstances using an SDN-controlled vehicular network.
Author Contributions: V.D.K. and M.A., writing, original draft preparation, designing, analysis, editing. L.J., I.U., D.I. and O.G., review, editing, investigation. All the authors also contributed to writing, reviewing, and structuring the work. All authors have read and agreed to the published version of the manuscript.