EFFICIENT ROUTING PATH, CONGESTED PATH, AND ATTACK DETECTION IN VANET BASED VEHICLE TO VEHICLE COMMUNICATION

EFFICIENT ROUTING PATH, CONGESTED PATH, AND ATTACK DETECTION IN VANET BASED VEHICLE TO VEHICLE COMMUNICATION

Abstract

Vehicular Ah-hoc Network (VANET) provides safe and secure transportation for better the vehicle to vehicle communication. The vehicular communication increases the easy accessibility among the source and destination nodes. The RSU and BS also enable well-versed to attain the vehicular field of data communications. When activating the VANET communication, it gives the new energy efficiency in all the vehicular nodes and V2V connection to be improving with lesser time consideration. So, the minimum energy consumption, efficient routing path, lower delay, less execution time, uncongested path, and accuracy of classification will make the model for VANET based efficient communication. In this paper, for dynamic routing path calculation, the proposed Trust based Efficient Clustering for Routing path Algorithm (TECRA) that provides the less delay, less packet loss ratio, high throughput, and reduced residual energy consumption. For congested path identification, the proposed Clustering based Congested Path Detection Algorithm (CCPDA) to be use. The CCPDA provides the average transmission delay, packet delivery ratio with the vehicle generation speed with the Trust based efficient residual energy levels. For malicious node detection in VANET, the proposed Prominence Algorithm (PA) with the malicious node identification to be considers. The improvements on PA give the enhanced packet delivery ratio, reduced end-to-end delay, minimum routing overhead in the network. The overall performance of the VANET communication with the enhanced performance of the malicious node identification.

Key Words: VANET, routing algorithm, TECRA, CCPDA, PA.

1. Introduction

In recent years, the safety and secure communication between the vehicles. The efficient routing path among the vehicles without any traffic, delay of time, efficient routing between the source and destination nodes. The vehicular fields provide the wireless data communications and also capable to communicate among one another. i.e., roadside unit infrastructure with the respective trusted authorities. VANET have split into two concerned methods that are Vehicle-to-Vehicle (V2V), and Roadside-to-Vehicle (R2V) communication. The primitive technology provides the multiple applications like as traffic accidents, vehicle to vehicle communication, vehicle’s traffic levels, drivers, passengers, and pedestrians discussed in (Malhi, Batra, & Pannu, 2020). The high characteristic features such as fast topology changes and high vehicle mobility. The high mobility is the important factor that describing the VANETs from other Ad hoc networks and wireless networks. The vehicle node density in a VANET area is not uniform but exhibits spatiotemporal variation in (Senouci, Harous, & Aliouat, 2020). Historical trajectory data may be a suitable solution to reduce congestion on the road in VANET. It increases fuel wastage, monetary losses, and life-endangering with the prediction of congestion control discussed in (Chaurasia, Manjoro, & Dhakar, 2020). VANET is help to generate every device with the information it necessary to route traffic timely. The routing protocols in VANET based on various internal and external factors like as vehicle node mobility, signals, obstacles and road topology to enable the scalable characteristics in VANET shown in (Bhati & Singh, 2020). The challenges in VANET shown in figure 1.

Figure 1 Challenging factors in VANET

The goal is to maximize the vehicle and the passenger safety and comfort also. In VANET, every vehicle capable to connect to VANET and make a network with a wide range of applications. If any vehicle to be dropped out of the range of transmission, the network also dropped in (Kolandaisamy, Noor, Z’aba, Ahmedy, & Kolandaisamy, 2019). The data packet in the network provides the efficient routing protocols. The routing algorithm gives the less delay, routing overhead, efficient packet delivery ratio, and end-to-end delay in the network. The vehicles in the network working the better routing algorithms to information delivering to the neighboring vehicle position at any instance of time. If the position system fails, this provides high routing cost, utilization of high bandwidth and routing overhead shown in (Shafi & Ratnam (2019)).

The various applications in VANETs that function the various communications such as Vehicle-to-Vehicle communication and Vehicle-to-Infrastructure communication. The information services as the prerequisites to an efficacy and long life multi-hop routing design approach. The VANET’s characteristics gives the dynamic technique, high mobility, road layout limitations, easy access wireless system, and difficult channel conditions are the demerits to design a routing scheme (Chen, Liu, Qiu, Wu & Ren (2019)).

The section II depicts the literature survey of the paper, section III describes the problem statement, section IV describes the proposed methodology, section V shows the results and discussion, and section VI shows the conclusion.

1. Literature survey

(Srivastava, Prakash, & Tripathi, 2020) proposed the VANET based routing protocols define the location of the vehicles, but never rely on route entries of pre destination that solves the challenging factors such as inaccuracy, broadcasting overheads. (Tang, Hu, Hu, & Stettler, 2020) proposed the multi-agent reinforcement learning through the trajectory rejection method to regularize the traffic congestion also minimizes the travel time, and travel distance. The road network based congestion index provides the best traffic efficiency with the adaptive learning method. (Abdelatif, Derdour, Ghoualmi‐Zine, & Marzak, 2020) proposed a VANET‐Cloud layer to enable the network performance and the traffic management. The traffic services perform through the data exchange mechanism help of fuzzy aggregation technique. The VANET cloud layer enables the enhanced traffic safety. (Lin et al., 2020) proposed the Multimodal Nomad Algorithm associative of communication scheme to assist the multiple drones of VAN. The enhanced performance produce the certain space for the global application and its performance like hop count, throughput, and PDR values are better. (Jaballah, Conti, & Lal, 2020) proposed the safety and security based fifth generation network to outperforms the applications and business models along with VANETs and SDR. The new drive of security threats and the vulnerabilities function through the different entities and the various architectural components. The key security services and enhanced Intelligent Transport Systems (ITS) that incorporate the proposed SDVN techniques. (Sun & Samaan, 2020) stated the Vehicular Cloud (VC) computing environments enables through V2V and V2I optimize the traffic intersections along with the signal control strategy. The effect of the traffic patterns functions the probability based traffic signal control parameters.

(Paranjothi, Khan, Patan, Parizi, & Atiquzzaman, 2020) proposed the statistical Network Tomography (NT) based VANETomo for congestion identification. The Connected Vehicle (CV) technology based Dedicated Short Range Communication evolves the vehicular communications. To improve the accurate QoS through the Statistical Network Tomography that concludes the transmission delays over the network nodes. (Zhou, Han, Lu, & Fu, 2020) proposed the framework of distributed collaborative detection to tracking the collection of data. The malicious nodes detection evolves through the dynamic behavior analysis technology. The Stochastic Petri Net used for security based detection of higher detection rate. (Guidoni, Maia, Souza, Villas, & Loureiro, 2020) proposed the vehicular traffic management provides the vehicular traffic congestion paths through the congestion routes and weighted graphs. To identify the congested routes without traffic jams to improve the traffic distance by using Re-RouTE. Re-RouTE used to minimize the traffic jams and enhanced travel time, distance, speed and the traffic flow of the road networks. (Musaddiq et al., 2020) proposed the cloud computing enabled VANET in IoT environments to construct the traffic control and cloud-based vehicular data processing system. The machine learning and data mining techniques how performs the heterogeneous cloud platforms. (de Sousa, Boukerche, & Loureiro, 2020) proposed the DisTraC for traffic congestion control parameters based less communication overhead problems to be satisfied with the minimum travel time and all other external infrastructures.

(Guo et al., 2020) proposed the optimization algorithm for Dynamic Interior Point Method congestion path identification through driver rerouting. DIPM outperforms in knowledgeable real-time deployment of user based optimal approaches. (Pandey & Kushwaha, 2020) proposed the refined congestion control problem may elongated through the traffic-based, resource-based classical techniques. The optimization of classical techniques used for soft computing related approaches. (Obaidat, Khodjaeva, Holst, & Zid, 2020) proposed the Base Station (BS) and Road Side Unit (RSU) over the vehicular medium with the extensive adaptation. To mitigate the security based threats and the privacy to be evolved. (Bwalya, 2020) proposed the Vehicle-to-Vehicle (V2V) communication models provides the efficient access, reliability, and availability through the concept of spatial intelligence. V2V enables the Intelligent Transport Systems (ITS) for exchange of information and intelligent decisions on roads.

III. Problem Statement

VANET have some inability characteristics to attain the efficient routing among the source node of vehicles and the destination node of vehicles. The problem infers that

1. Inefficient routing path, which makes the traffic and delay time among vehicle to vehicle communication.
2. Congested paths infers the inefficient of residual energy
3. Lesser accuracy of classification among the routing paths and also take higher the time while communication.

Here, to overcome all these inabilities and satisfies the accuracy of vehicle to vehicle communication as well a                                                                                                                                                            s enable the safety and secured communication in VANET.

1. Proposed Methodology

4.1 Efficient routing path

For efficient routing path, the algorithm for Trust based Efficient Clustering for Routing path Algorithm (TECRA). The routing path formed through k-means clustering approach. To form a initial cluster and build a minimum spanning tree concept shown in figure 2.

Figure 2 Schematic diagram of Trust based efficient Clustering for routing path algorithm

K-means clustering creation

Input: Set of nodes n and the number of clusters k

Output: k-Clusters

Step 1: Form an un-clustered network consisting of all the nodes of the VANET.

Step 2: Compute the distance of these vehicle nodes from each other.

Step 3: Accept the value of k from the user.

Step 4: Consider the values as k clusters based on the distance values

Step 5: Enable the election CH algorithm and find k number of CH.

Step 6: To check the distances are optimal route path.

Step 7: If it is optimal, to build a minimum routing path using spanning tree method.

Step 8: Repeat the steps On-demand evaluation.

Step 9: End

In Trust based routing path by make the cluster creation, Trust based rule manager never considered for the clustering process and all vehicle nodes imagined to be as trusted nodes. However, trust values are found for each vehicle node before the routing process enabled.

Trust based Score Calculation

From this method, to find the Trust based score for each vehicle nodes considering the two limitations.

1. The vehicle nodes which are genuinely sending their ACK to neighbor vehicle nodes whenever they received the packets.
2. The vehicle nodes that drops more packets.

TSC_i1 = () * 100                                                        (1)

Where, TSC_i = First trust score in percentage for i_th node. ACK = Acknowledgements sent to the neighbours. RP = Total number of packets received.

TSC_i2 = 100 – (() * 100)                                          (2)

Where, DP = Packets Dropped, TDP = Total number of packets dropped.

TSC_i =                                                        (3)

Threshold assignment

To Threshold assignment depends on the mean value of the overall Trust based score and the residual energy optimization for all the vehicular nodes. Initially, to calculate the mean trust based score value for each vehicular node by using the overall trust scores.

TM_i = , j = i                                              (4)

TM_{i =} Trust based Mean value for i_th node.

TS_i = Trust based scores Summation,

TN_j = Neighbor Trust Value,

n = Number of nodes in the network

A threshold value is used to calculate the malicious nodes, mean value Threshold in order to participate in the routing process The Decision Value for selection of vehicular nodes in the routing process through the formula as,

Decision value_i =                      (5)

Where, TM>Threshold

RE is residual energy

D is Distance from the neighbour vehicle nodes

Election of CH

The election of Cluster Head algorithm to calculate the CH with high residual energy optimization and Trust based score but with minimum distance from all the participating vehicular nodes. The threshold manager selects the election of CH.

Input : Number of vehicular nodes in the cluster creation

Output : CH

Step 1: Calculate the neighbors of each vehicular node in an initial cluster.

Step 2: Find the Trust based score and the distance D of the vehicular nodes through TSC_i distance based value.

Step 3: For every vehicular node, find the overall distance and the Trust based score TSi, with all its neighbors of vehicular nodes.

Step 4: Consider the base rules to evaluate the previous vehicular nodes calculation.

Step 5: Select the vehicular node with maximum Trust based score, larger residual energy and min overall distance as the CH.

Step 6: Make the vehicular node selection in step 5 as the CH for the cluster.

Step 7: Repeat steps 1 to 5 for all clusters.

Secure routing through Trust based optimization

The Trust based secure routing algorithm performs effective communication using optimized intelligent agents. It also uses the cluster formation and cluster head election algorithms in order to find the optimal and secure route to perform effective routing. The steps of the proposed Trust based Secure Routing algorithm is as follows:

Input: Node details and Data packets

Output: Best Route and Packets Routed.

Step 1: Initiate a timer and call the route discovery approach from the source vehicular node.

Step 2: Enable all the vehicular nodes to received packets of route discovery.

Step 3: Follow the steps that all the vehicular nodes delivers the route discovery of packets til the destination vehicular node is reached or expired the timer.

Step 4: The destination vehicular node sends the RREP packet to their neighboring vehicular nodes.

Step 5: Repeat the steps until the source vehicular node receives the initial RREP packet.

Step 6: Find the initial Trust based score for all the vehicular nodes through TS_i1 values.

Step 7: Find the next Trust based score for all the vehicular nodes through TS_i2 values.

Step 8: Compute the overall Trust based score for all the vehicular nodes through TSC_i values.

Step 9: Find the Decision value for each vehicular node.

Step 10: Calculate the malicious nodes through some certain conditions

10.1 If Trust based score > threshold, then the vehicular node is normal or non-malicious node.

10.2 If Trust based score< threshold, the vehicular node is malicious or abnormal.

Step 11: Then, to detect all the malicious vehicular nodes from the network.

Step 12: Improve the clusters with normal vehicular nodes and perform the election of CH through Decision values and mobility limitations.

Step 13: Repeat the Route discovery steps and RREP process with the fresh CH.

Step 14: Route the packets through the current CH.

The proposed Trust based efficient Clustering for routing path          algorithm considers Trust based score values, distance and residual energy optimization for unique optimal routing path. The limitation is that the speed of the mobility of a CH must be less than the average speed of the mobility of all the vehicular nodes.

4.2 Congested path identification

The predicted congestion never specifies the actual coordinates also for the congestion time. For                                                                                             congested path identification, Clustering based Congested Path Detection Algorithm (CCPDA) to be used. This algorithm detects the traffic congestion, specifies the location, and congestion time. The schematic diagram of congested path estimation shown in figure 3.

Path clustering

For path clustering, path algorithm enables to cluster the path. This path algorithm performs the Clustering of longitudinal paths and it has 3 steps. Firstly, describes the features. Secondly, the performance of factor analysis to select a subset of data. The third step, cluster analysis performance to find the clusters of the path and assigns every path into one of the clusters. The path clustering method consists of path range, SD, Mean change/time. The path clustering process involves 24 measures for path calculation. The paths clustering have 4 arguments i.e., time, data, verbose, and id. The ‘data’ is a frame of data or matrix level that has the longitudinal values of paths. The ‘time’ has the timestamp values that define the each longitudinal value. The ‘id’ to be set as true if the data frame of ‘IDs’ for every row. Each row represents each path. The verbose is a logical variable that is to set as true, to print the outcome or else to be false. The factor analysis is the important method to evolve the clustering process. This analysis consists of pathMeasures, discard, numberfactors, verbose and so on. The Clustering of paths consists of selection of data from the previous stage of performance. Then, it accepts the original data from the pathfactors. It have the selection of clusters, clustering factors, and trajectory objects.

Estimating the path distance

The path distance calculated through the Harvesine formula such as, For any two points of vehicles, the central angle among the two vehicles as,

hav (d/r) = hav () + cos (𝟇1) + cos

(𝟇2)hav(λ1 – )                                        (6)

Where, hav referred as Harvesine function:

hav () = sin² ( =                                            (7)

Calculate duration of a path

After the distance of each path to be found through the Havesine formula to calculate the time taken for that path to travel that distance. Since there is a timestamp which is additional with each position of the path, the time taken to travel the distance from the path and the time calculation as,

t = t_i – t_i-1                                                                           (8)

Find path and cluster speed

The speed of the path can be found using the formula below;

S = d/t                                                                             (9)

By considering the all paths in each cluster, the average speed per cluster can also be determined by the following;

Cs =                                                                         (10)

Congestion event evaluation

The concept as, Based on the threshold value of bandwidth, Trust and residual energy If the average speed of a cluster of paths is less than the speed of the threshold value, and if it is less than 1 km/h and the duration is greater than a threshold duration, then the event can be classified as a congestion event, else it is not a congestion event. If the stopping congestion event duration is less than the threshold duration then to propose that this is not congestion but could be traffic light stoppages, picking or dropping passengers, etc. If the clustering speed is less than 1 km/h and the event duration is greater than the threshold duration it means the vehicles are stopping for quite a long time and this might mean that the vehicles are locked or parking. If the stoppage event duration is greater than or equal to the threshold duration, then it also implies that this is not a traffic light stoppage or picking passengers but it is actually a congestion event.

Congestion prediction

The performance of results was conducted using day one path and the congested events are recorded in terms of their longitude and latitude positions and the time of occurrence. The new data from days two and three and all the congestion events are also recorded shown in result section.

Figure 3 Schematic diagram of congested path estimation

4.3 Malicious node Detection

For malicious attacks in the vehicular nodes, the proposed Prominence Algorithm (PA). PA used to detect the malicious nodes in the network. The Prominence Algorithm essentially based on the prominence value of the nodes has in the network. The prominence value of the node is depends on the Packet Forwarding Ratio (PFR) of the node, i.e., all the received packets in the network number of packets are forwarded to the nearby nodes. From PA, when any malicious node connects the network, it receives the default prominence value, after that it change depends on the particular node performance. The prominence value of any node is by the perceiver node whenever it is necessary. In Prominence Algorithm, the perceiver node to be selected depends on some parameters. But we never select any node as perceiver node, because to become perceiver node, it should have good prominence value as well as less the computation load.

Assumptions for Prominence Algorithm

There are have the assumption regarding the prominence algorithm.

• Perceiver node “s” is in the range of the node “t”.
• Perceiver node “s” has not reported as a malicious node and has a good prominent value.
• Perceiver node “s” sends the status of node “t” to all the nodes in the network.
• All the malicious nodes receive the node status “t” and update the data knowledge in own routing table.
• The routing table has one extra entry as the status of the neighboring nodes as malicious node or not.
• All the malicious nodes are in indiscriminate mode so that they can also show the neighboring node’s traffic.

Algorithm

The malicious node “s” (perceiver node) needs to find the prominence of node “t” depends on calculation of Packet Forwarding Ratio (PFR) value of node “t”. The attack detection to be clear as well as the Packet delivery ratio, Average end-to-end delay, and Routing overhead to be solve this method.

Packet Forwarding Ratio (PFR) value (P_b) as a input

Status of the malicious node “t” as a output.

Step 1: The node “s” finds the Packet Forwarding Ratio (PFR) value of node “t” described by P_b:

P_b = (u/v)

Where, u as = Number of packets asked to forward by node “s” to node “t”

                v as = Number of packets actually forwarded by node‘t’

Step 2: Depends on the PFR value, it decides the prominence value of node “t” (Direct prominence)

If

1. P_b ≥ T_h and P_b ≤ 1; then prominence value

 = 1; Otherwise

1. = 0;

Step 3: Node “s” asked other neighbour nodes (“n” number) nodes to send the prominence of node “t”:

 =

If

1. R_t ≥ n/2; then reputation value R_t = 1; Otherwise
2. R_t = 0;

step 4: Node “s” computes the final prominence of node “t”:

R _f =  + R_t

If

1. and R_t both are 0; then final prominence value R _f = 0;

Otherwise

1. R _f = 1;

Step 5: For final prominence R_f, value declares prominence of node “t”:

If

1. R _f = 0; then, node “t” is a malicious node;
2. To remove node “t” from the network.

Otherwise

1. R _f = 1; node “t” is trustworthy

Step 6: The message delivers to all the other nodes about the node’s status as‘t’.

The Prominence Algorithm that detect the attacks in V-2-V and V-2-I communication in VANET networks. The attack detection level of the network status is improved and compare to the other existing algorithm.

1. Results and Discussion

For efficient routing path, the work has done through NS2 Simulator. 2500*2500 meters to be utilized and selected the 500 vehicular nodes to perform this simulation and its respective parameters shown in Table 1.

Table 1: Typical parameters in the network

Figure 4 shows the Average delay for multiple vehicle velocity. Figure 5 represents the Throughput Vs multiple Vehicle velocity.

Figure 4 Average delay for multiple vehicle velocity

Figure 5 Throughput Vs multiple Vehicle velocity

Figure 6 shows the average energy consumption Vs Vehicle velocity. Figure 7 describes the packet loss ratio Vs Vehicle velocity. Figure 8 shows the residual energy Vs Mobility levels.

Figure 6 Average energy consumption Vs Vehicle velocity

Figure 7 Packet loss ratio Vs Vehicle velocity

Figure 8 Residual energy Vs Mobility levels

For congested path identification in VANET, the simulation done through NS2 simulator and its respective parameters shown in Table 2.

Table 2: Typical parameters in the network

The performance of average transmission delay, packet delivery ratio values to be computed and compared to the existing methods. Figure 9 represents the average transmission delay Vs Packet generation speed. Figure 10 shows the packet delivery ratio Vs Packet generation speed.

Figure 9 Average transmission delay Vs Packet generation speed

Figure 10 Packet delivery ratio Vs Packet generation speed

For malicious node based attack detection in VANET, the prominence algorithm that represents the attack detection. PA has the Trust based security model with the association of optimized routing algorithm. They used for the direct Trust i.e., prominence value computation equation to find the Trust of any other node in the network. And also the association of optimized routing in the AODV protocol that increases the routing overheads. In our proposed work, we have used the AODV routing algorithm with the combination of trust-based efficient security algorithm.

To calculate the both types of trust values, i.e., direct trust values and indirect trust values. The use of direct and indirect trust values, to calculate the final prominence value. The Packet Delivery Ratio (PDR), End-to-End Delay, and Routing Overhead through NS-2 simulator and compared with the existing results.

The Packet delivery ratio is called as the overall number of packets delivered successfully to the destination mode. The higher value of PDR is < 1, which is necessary to improve the network performance. When malicious nodes perform the packet dropping attacks, it affects the PDR very worst. To observe the packet forwarding ratio of the malicious nodes with the use of the perceiver nodes measurement, to calculate the direct and indirect prominence values of the node. For best network performance, the average End-to-End Delay could be as low. When there are have a highest path breakages due to the malicious nodes, so the routing overhead problem enhances due to the maintenance of frequent paths.

The results of attack detection in VANET shows as follows: Figure 11 represents the packet Delivery Ratio Vs Time (ms). Figure 12 describes the End-to-End delay Vs Time (ms). Figure 13 represents the routing Overhead Vs Nodes

Figure 11 Packet Delivery Ratio Vs Time (ms)

Figure 12 End-to-End delay Vs Time (ms)

Figure 13 Routing Overhead Vs Nodes

Figure 14 represents the Throughput Vs Time (ms). Figure 15 shows the packet drop ratio Vs No of vehicle nodes. Figure 16 illustrates the detection time Vs No of vehicle nodes.

Figure 14 Throughput Vs Time (ms)

Figure 15 Packet drop ratio Vs No of vehicle nodes

Figure 16 Detection time Vs No of vehicle nodes

1. Conclusion

For safe and secure communication in VANET, the enhanced road traffic conditions. The safety to the vehicles such as uncongested traffic jam, without delay time to evolve the efficient communication. From these characteristics, the improvements on efficient routing path, uncongested path identification, and also attack detection also. For less delay, less packet loss ratio, high throughput, and reduced residual energy consumption, the proposed Trust based Efficient Clustering for Routing path Algorithm (TECRA) with the enhanced performance. For average transmission delay, improved packet delivery ratio, and the Trust based efficient residual energy levels, the proposed Clustering based Congested Path Detection Algorithm (CCPDA) to be utilized well. For the attacks from the malicious node detection and identification, the Prominence Algorithm (PA) performed well i.e., the enhanced packet delivery ratio, reduced end-to-end delay, minimum routing overhead in the network. The identification of malicious nodes and the vehicular nodes provided the better communication with BS, RSU to enable the efficient Vehicular Ad-hoc Network.

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