The Internet of vehicles and vehicular ad-hoc networks (VANET) offers numerous opportunities for managing the transportation problems effectively. The high mobility and wireless communication in VANET lead to adequate network topology modifications, resulting in network instability and insecure data communication. With an unsteady flow of traffic, vehicles are unevenly distributed in the geographical areas in practice. A new type 2 fuzzy logic-based secure clustering (T2FLSC) with cloud-based data dissemination scheme called the T2FLSC-CDD model for the VANET has been introduced for resolving this issue. The vehicles are dynamically clustered by the use of the T2FLSC technique, which elects the CHs (Cluster Head) by the use of different parameters, namely, travelling speed (TS), link quality (LQ), trust factor (TF), inter-vehicle distance (IVD) and neighboring node count (NCC). The inclusion of the trust factor helps to select the proper CHs for the secure data dissemination process. Once the CHs are selected, a cloud connection can broadcast the Emergency messages to every vehicle. To save the location details of the vehicles in the cloud securely, the Blowfish technique has also been implemented in this work. For validating the effectiveness of the T2FLSC-CDD model, an extensive results analysis has been performed with respect to various measures. The attained simulation outcomes have pointed out that the proposed model has achieved maximum packet delivery ratio (PDR) and throughput with minimum key computation time (KCT), routing control overhead (RCO), time delay (TD), and key recovery time (KRT).
The developing application of the Internet of things (IoT) has been converted to several domains, and one of the existing models is the Vehicular Ad Hoc Networks (VANETs), which has been transformed as a novel method of vehicles and has been named as the Internet of Vehicles (IoV) [
Vehicles rely on a multi-hop technique for forwarding the messages among each other due to the reduced wireless transmission radius [
An ineffective central management system would trigger the dynamic location of the vehicles to cause network instabilities and broadcasting problems, these factors would degrade the functioning of the entire network. A feasible solution is to deploy the RSUs that may be applicable in scheduling and controlling the dynamic systems. But, the access points have been found to include additional embedding and maintenance expenses. The alternate solution is to develop clusters dynamically according to the general metrics for fixing the vehicles on the road. Followed by which the same would be applied for improving the lifespan of a connection by gathering parameters like speed, external position, and travel directions. These clusters can be used on the vehicles for forwarding the messages to the Cluster Head (CH) that would again broadcast the same to the cluster members (CM). Here, there are few limitations, namely, stability, which has to be reported for improving the lifetime of a network and its corresponding channel fading effects.
In the last decades, Cloud Computing (CC) was used for managing the complex processing procedures that were deployed locally. The integration of VANET and CC has improved the ability of a system [
The rest of the paper has been organized as follows. Section 2 offers a brief review of the existing works, and Section 3 discusses the proposed model. In addition, Section 4 provides the experimental validation, and Section 5 concludes the paper.
Several kinds of traditional message disseminating principles have been developed. The protective messages can be transferred to the Internet under the Internet under gateways and can be dispatched to the vehicles present in their respective areas. Along with a comprehensive deployment of the 3G/4G base stations (BS), developers focus on a gateway selection model for linking the VANET to the Internet. In [
The modeling of the CC method emerges in VANET and this effectively produces the model of the vehicular CC [ Networks as a Service (NaaS) Storage as a Service (STaaS) Data as a Service (DaaS)
The major application of the NaaS model is that the cloud can collect the data of vehicles that wants to provide the Internet access; it also gives an appropriate link to the vehicle when there is a requirement. In [
The overall working principle of the proposed T2FLSC-DSS model has been illustrated in Neighbour identification Sensor values CH election Cloud data storage Trust Broadcasting
The network topology has been deployed with n number of vehicles. Later, the sensor connected to the vehicle can be employed for acquiring the position and traffic details. WAVE assisted IEEE 802.11p method has been applied. It helps in minimizing the network delay. A particular CH can be elected for constructing the routes among the source and destination nodes during the message transmission process. Once the CH registration has been completed, a cloud link would be activated for enhancing the trust levels, this can essentially eliminate the unauthenticated data monitoring. The data transfer can be improved with the application of the blowfish concept. As a result, data analysis has been carried out for transmitting a predominant message to the alternate vehicles.
A table has been developed with n number of vehicles i.e., According to the concerned data, the network topology has been developed for accomplishing data transmission procedures. The WAVE protocol has been applied for performing this action that entirely depends upon the IEEE 802.11p standard. The stability has been boosted as well as the delay aspect has been limited in the network using this mechanism. A significant cause of applying WAVE is that it functions according to the multi-channel technique for transferring the mere infotainment data. It assures the delivery of a message with a stable and limited time period. Thus, it appears to be more applicable for the VANET based communication.
Here, the vehicles have been deployed with various sensors for providing the data on temperature, speed, and location. This data can be majorly applied for controlling traffic, road surface detection, and vehicle tracking. The main aim of vehicle monitoring is to minimize the transmission overhead and ensure table interaction. In general, the OBU is considered as a wave device that can be applied by the vehicles for interchanging the series. These units have been constrained with the processing ce for obtaining the required data, and an interface for interacting with the alternate networks relied on the IEEE 802.11p standard. The purpose of employing the OBU is that it is a wireless radio, blocking management, and a wireless radio, blocking management, and secured message. Additionally, it improves the transmission radius by transmitting data to the corresponding OBU. Hence, the message dissemination has been activated under the application of sensors for identifying the distance between the source and the destination nodes.
In VANET, a CH might be assumed as a constant deployment in a roadside according to the transmission radius. Every vehicle would address the present location to the CH. Here, the CH election is activated based on an auto-configuration as it is treated as a location server and records the position details of the individual vehicles. Regarding the details, the route has been built her for the data transfer procedures. Once the CH is elected, the vehicles would be registered in the CH along with a secure and exclusive ID together with a password. Initially, a vehicle would produce an arbitrary secret value that would assist in the computation of the ID and the password. Subsequently, it would forward the registration request to the High Way Authority (HWA) through a protective station. After receiving the request, an arbitrary value would be produced for estimating the ID of the desired vehicle. When a vehicle is linked with a cellular network, the pre-registration process would be carried out, and a random bit identity would be originated for the data transmission purpose.
For selecting the CH, a new model has been employed termed as the Fuzzy Logic (FL). This FL has five input variables such as TS, LQ, TF, IVD and NCC. Followed by which the output features would be comprised with a probability of becoming the CH (PCH). Initially, T would represent the mobile speed of a vehicle, LQ would represent the superiority of the connected vehicles, TF would represent the intensity of its stability, IVD would signify the distance from the vehicles, and NCC would refer to the number of vehicles placed in the nearby location.
T2FL contains 4 levels as depicted in
It is used for converting the actual inputs as fuzzified values. Few input features in-terms of the linguistic attributes have been used for selecting the CH and the cluster size listed in
Variables | Linguistic values |
---|---|
TS | LW, A, H |
LQ | LW, M, H |
TF | LW, M, H |
IVF | N, F, FT |
NCC | LW, A, H |
PCH | VP, P, BA, A, AA, S, VS |
Fuzzy rules/Inference engine
The structure of the T1FL and the T2FL appears to be identical. Here, a collection of 27 rules have been applied. Later, the groups of fuzzy rules for the CHs and the deciding cluster sizes have been portrayed in
Input parameters | Output parameters | ||||
---|---|---|---|---|---|
TS | LQ | TF | IVD | NCC | PCH |
L | LW | LW | N | L | VP |
L | M | M | N | L | P |
L | H | H | N | L | BA |
L | LW | LW | F | L | P |
L | M | M | F | L | BA |
L | H | H | F | L | A |
L | LW | LW | FT | L | BA |
L | M | M | FT | L | A |
L | H | H | FT | L | AA |
A | LW | LW | N | A | P |
A | M | M | N | A | BA |
A | H | H | N | A | A |
A | LW | LW | F | A | BA |
A | M | M | F | A | A |
A | H | H | F | A | AA |
A | LW | LW | FT | A | A |
A | M | M | FT | A | AA |
A | H | H | FT | A | A |
H | LW | LW | N | H | AA |
H | M | M | N | H | S |
H | H | H | N | H | BA |
H | LW | LW | F | H | A |
H | M | M | F | H | AA |
H | H | H | F | H | A |
H | LW | LW | FT | H | AA |
H | M | M | FT | H | S |
H | H | H | FT | H | VS |
T2FL is represented by the supervised membership function (MF) and the inferior MF. These expressions have been shown under the application of the T1FL MF. Next, the space from the two functions signifies a Footprint of Uncertainty (FOU) that describes the T2FL set. Suppose FOU is provided as f. If f є [0, 1], and f → 0, hence MF is termed as T1FL. When f → 0 to 1the T2FL should comprise of different ranges for the FOU that can lie between 0 to 1. However, the development of rules in the T2FL has been found to be similar to that of the T1FL that has been expressed in
Once the PCH has been received, it would broadcast the message to the target vehicles. This message would be enclosed with the vehicle ID and the PCH measures. A vehicle possessing a higher probability would be elected as the CH and would transmit the CH_WON to the corresponding vehicles. Only some vehicles would receive numerous CH_WON from the neighboring vehicles. At this point, it would transmit a CH_JOIN message and would combine it with a closer CH. With the reception of the CH_JOIN message, the nearby CH would verify the given cluster size before including the new members. As the complete CM does not exceed the determined cluster size, it would approve a new CM by means of forwarding the CM_ACCEPT message; otherwise, it would transmit the CH_REJECT message.
When a vehicle receives a CM_REJECT message, it would re-transmit a CM_JOIN message to the future CH with no assumptions of the removed CH, which would be repeated until a new CH is explored. Also, the vehicle cannot be combined with another CH that exists inside a coverage region ‘R’, and then it would select the corresponding CH. Consequently, every vehicle would belong to a cluster in which the separated vehicles would appear in the VANET. By processing the various rounds efficiently the premature death of the CHs can be eliminated, and the rotation function can be carried out by a CH. When the Remaining Energy (RE) of a CH appears to be higher than the threshold value the CH rotation would exist. While the RE of a CH exceeds a threshold value, a new CH would be selected using the PCH. Such functions have been observed to eliminate the primary death of the CH and thereby enhance the lifetime of the network.
Defuzzifier is the process of producing a quantifiable result in the Crisp logic, fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in the fuzzy control systems. This model produces a T1FL result which has been reformed to the mathematical result after the implementation of a defuzzifier has been completed.
In this study, a blowfish method has been applied for eliminating the third-party users’ data monitoring. The primary cause for enabling the cloud-assisted communication in this process has been given below: It removes the broadcast storm issue by controlling the individual nodes from a network effectively. It is sufficient to solve this with a massive number of nodes. Network connectivity is based on the centralized or the decentralized function of a system.
In VANET, the data broadcast has been processed for distributing the emergency data, climatic conditions, and traffic information of the vehicles. Broadcast storm arises due to the sheer amount of simultaneous communications and network terminal issues. It is the constantly varying location and transmitter’s position that influences the multi-dimensional type of broadcasting. Hence, it is has become mandatory for electing the desired vehicle at the combination of message broadcast. Here, the CH selection is carried out and the CC has been activated prior to the telecast of the emergency details to that of the alternate vehicles. It improves the speed of the data transfer process in an effective way. The significant benefits of this study have been listed below: Limited delay Enhanced trust Maximum communication efficiency.
This section briefly explains the experimental results offered by the proposed model over the compared methods under several aspects. The proposed model has been simulated using the NS3 tool. The proposed model has been validated with respect to the following aspects such as, PDR, throughput, key computation time (KCT), routing control overhead (RCO), time delay (TD), and key recovery time (KRT).
Similarly, under the maximum vehicle speed of100 Km/h, it has been ensured that the T2FLSC-CDD model has resulted to a maximum PDR value of 86%. In contrast, the BPAB, 3P3B, and UMBP models have lower PDR values of 38%, 73% and 79%, respectively. These values have ensured that the T2FLSC-CDD model had offered maximum PDR under varying vehicle speeds over the compared methods.
Likewise, under the application of a high vehicle speed of 100 km/hr, it has been assured that the T2FLSC-CDD model tends to produce a maximum throughput value of 89900 kbps and the BPAB, 3P3B, and the UMBP modules has provided minimum throughput values of 84000, 82000 and 86800 kbps respectively. Such values have ensured that the T2FLSC-CDD model has given optimal throughput values under different vehicle speed than that of the other techniques.
This paper has presented a T2FLSC-CDD model for the cloud-based emergence message dissemination in VANET. The proposed model involves a set of different processes namely the neighborhood discovery phase, sensor reading, T2FLSC based CH selection, CH registration, secure cloud connection, data analysis and finally broadcasting. For validating the effectiveness of the T2FLSC-CDD model, an extensive result analyses have been carried out with respect to various measures. The obtained experimental analysis have clearly depicted that the T2FLSC-CDD model outperforms the earlier models in terms of PDR, throughput, KRT, RCO, TD and KCT. The simulation outcome thus shows that the proposed model has achieved the maximum PDR and throughput with minimum KRT, RCO, TD and KCT. In future, the performance of the proposed model can be enhanced by the inclusion of metaheuristic algorithms.