Blockchain with Quantum Mayﬂy Optimization-Based Clustering Scheme for Secure and Smart Transport Systems

: Blockchain (BC) with a clustering scheme can be used to build secure and smart Vehicular Ad-Hoc Networks (VANETs), which provide improved data integrity, enhanced security, efﬁcient resource allocation, and streamlined processes. This technology has revolutionized the transport industry by enabling safer, more efﬁcient, and transparent transportation networks. Therefore, this paper concentrates on the design of a new Blockchain with a Quantum Mayﬂy Optimization-based Clustering Scheme for Secure and Smart Transport Systems (BQMFO-CSSTS) technique. The objective of the presented BQMFO-CSSTS technique is to build a secure VANET via a BC-based technology and clustering process. Moreover, the BQMFO-CSSTS technique initially uses a Quantum Mayﬂy Optimization (QMFO) system with a ﬁtness function for the selection of cluster heads (CHs) and the cluster construction process. In addition, BC technology is used as trust infrastructure to provide trustworthy services to the user and protect the privacy of the CHs and cluster members (CMs). The proposed scheme leverages the decentralized and immutable nature of BC to establish trust and ensure the integrity of cluster formation in VANETs. Finally, the BQMFO-CSSTS technique uses trajectory similarity metrics to protect the integrity of the CMs against attacks. The simulation results of the BQMFO-CSSTS technique are validated using a series of measures. The comprehensive results reported the superior outcomes of the BQMFO-CSSTS method over other recent approaches, with the maximum throughput being 1644.52 kbps. Therefore, integration of BC technology provides a transparent and secure framework through which to manage cluster membership, data sharing, and trust establishment among vehicles.


Introduction
Smart public transport systems (such as community scooters, buses, subways, and taxis) are a vital part of the growth of the dependable smart cities initiative, since they contribute to enhanced mobility and diminished carbon footprints [1]. For instance, the Internet of Vehicles (IoV) refers to a dispersed network of nodes in automobiles that allow automobiles to exchange data and link and interact with one another over the Internet [2,3]. The IoV aids in the understanding of smart transportation systems, since it enables automobiles fortified with computing nodes, sensors, and software to communicate Das et al. [18] devised an identity generation and management approach that utilizes BC to solve present difficulties in ITS applications related to the security of identity management and validation of vehicles. In [19], the authors presented the BC-related decentralized trust score structure that uses participating nodes to find and blacklist internal aggressors in VANETs. The authors devised a two-level recognition mechanism, in which at the primary level, neighbouring nodes separately computed trust. At the secondary level, a consortium BC-based system included certified Road Side Units (RSU) as validators, which created total trust scores for vehicular nodes. Next, depending on trust scores created by the neighbouring nodes, the blacklist node table was dynamically modified. Wang et al. [20] introduced a BC-related special vehicle priority access guarantee method (BSV-PAGS). The authors utilized a Machine Learning (ML) approach to train and design special vehicle detectors to enhance the accuracy of detection methods used to identify special vehicles.
In [21], the authors devised a new method that integrated BC technology and a Deep Learning (DL) approach to better protect smart public transport mechanisms. As a hybrid DL-related approach that Integrated Multilayer Perceptron (MLP) and Auto Encoder (AE), this method could identify Distributed Denial Of Service (DDoS) attacks that try to block or halt the urgent and serious exchange in transport maintenance data between stakeholders. Akhter et al. [22] introduced a multilevel BC-related privacy-preserving validation system. The study explained the process of forming of key generation tasks, validation centres, and vehicle registration. Though several models are available in the literature, there is still a need to improve their performance.
Chaudhary and Singh [23] examined a decentralized VANET design that contained BC technology. The presented BC-based method for VANET mechanisms had 4 steps: vehicle registration, BC maintenance, BC network initialization, and pseudonym upload. It could effectively resolve the issues that developed in centralized structures and solved trust problems that affected the entities. Gazdar et al. [24] solved these problems by presenting a decentralized BC-based Trust Management Framework (BC-TMF) that calculated trust metrics for vehicles. These trust metrics depended on the messages' authenticity. Alharthi et al. [25] examined a Biometrics BC (BBC) structure used to secure data distribution between vehicles in VANETs and maintain statuary data using conventional and trusted methods. Using the presented system, the author presented benefits of using biometric data to keep a record of the genuine identities of the message senders, thus maintaining secrecy. Thus, the presented BBC technique ensured trust and security between vehicles connected via VANETs and had the capacity to trace identities when needed.

The Proposed Model
In this paper, a BQMFO-CSSTS technique is developed via the use of clustering and BC in VANETs. The presented BQMFO-CSSTS technique aims to enable secure VANETs using BC-based technology and clustering processes. It involves a series of processes, namely clustering, BC-based privacy-preserving, and security mechanisms. Figure 1 establishes the overall flow of the BQMFO-CSSTS approach.

Design of QMFO-Based Clustering Process
In this study, the BQMFO-CSSTS technique initially uses the QMFO model and a fitness function for the selection of CHs and cluster construction processes. The MFO can be referred to as a bionics system, as it inspired by the social behaviours of the Mayfly (MF) [26]. The optimum and suboptimum individuals in all the populations, as well as the movement mode and reproduction processes of female and male individuals, are carefully chosen. In the meantime, via mating between the optimum female and male individuals, the optimum offspring generation and suboptimum offspring generation can be attained. The movement direction of all mayflies was impacted by the collective optimum position and dynamics of individual female and male MF targets when moving towards the location.
The flight mode of male MF is the same as the movement mode of the birds in a Particle Swarm Optimization (PSO) algorithm, and the distances and directions travelled by male MFs were changed based on their own flight experience using Equation (1): where x n i and v n i denotes the present location and speed, respectively, of male MF i on the n th search, as given in Equation (2): Since male MFs dance on the water surface to attract females, the locations of the male MFs continuously vary. v n ij is the speed of n-th search of MF i-th at j-th dimension, and x n ij denotes the location at that time. α 1 and α 2 are assessed based on the positive attraction coefficient of social interaction, and β denotes the visibility coefficients of the MF. In the meantime, the optimum locations of the individual and collective MFs are denoted as p best j i and g best j i , respectively. Furthermore, the distances from an existing location to p best i i and g best i i are represented as l p and l g , respectively, and evaluated via Equation (3): A fixed dance pattern should be used to better represents MFs within the population. In the meantime, a random component was presented to ensure that the speed changed continuously, as defined in Equation (4): In Equation (4), d denotes the dance coefficient, and r indicates the random number. The female MF movement relies on the attraction of male MFs, and the location renewal relies on the rise of speed that is formulated via Equation (5): Speed updating is a specific procedure that guarantees the offspring quality; thus, the superior female should be attracted to the superior male. It is represented in Equation (6): where y n ij denotes the location of the female MF, g indicates the random walking coefficient of the female MF, and l f shows the distance between male and female MFs.
During mating, the optimum and suboptimum individuals in the female and male groups must be chosen for reproduction based on the fitness function. The outcomes of interbreeding that generate the optimum and suboptimum offspring are evaluated using Equation (7): In Equation (7), m and f m represent the male and female in the parent group, respectively, and L denotes the random integer within a specific range.
The conventional MFO algorithm could precisely search for the optimum value in a single-peak function using the features used in MF reproduction. However, due to the complicated process of assessing a large population, the convergence is not fine, and the search speed is slower, as it is easier to become trapped in local optima while handling multi-peak functions. Thus, the quantum concept was proposed using the classical MFO algorithm, thus forming the QMFO algorithm. Meanwhile, the location and velocity of MF could not be defined in quantum space; thus, wave function was utilized to characterize the MF location, and the Monte Carlo algorithm was employed to resolve the problems using Equation (8).
In Equation (8), r and a are uniformly distributed random values in the range 0-1, and c shows the last random motion parameter. N and n denote the numbers of individuals and iterations, respectively. m besi n denotes the average past optimum location of the male MF, and P n i denotes the modified location of the i th male MF at n-th iterations. The implementation steps of the QMFO algorithm are shown below: Step 1: initialize the position of female and male MFs in the space.
Step 2: compute the average optimum position m best of male MFs based on Equation (8).
Step 3: Compute the fitness value and compare it to the prior iteration value. When the present function value is lesser than the prior iteration, the existing MF location is modified based on the individual optimum location; otherwise, it retains the prior iteration. Thus, the optimum male individual position p besi and collective position g besi are attained.
Step 4: estimate the new locations of both MFs based on Equations (5) and (8), respectively, and mate in sequence.
Step 5: evaluate the fitness function and update p besi and g besi .
Step 6: repeat Steps 2 to 5 until the stopping criteria are satisfied. The QMFO algorithm derives a fitness function for the optimum cluster creation procedure [27]. The fitness function used in the BQMFO-CSSTS is introduced as a multiobjective fitness function, as given in Equation (9).
In Equation (9), f n (1) and f n (2) functions characterize the sum of distances between all of the CMs and CHs of each cluster in the network and the differences between clusters in terms of route length. Based on Equation (10), the function f n (1) is computed.
where EU Dist CM q,c , CH q.c shows the Euclidean distance evaluated for the total number of clusters. The distances between every CM q,c vehicular network and the CHs CH q.c are related to all of the clusters based on the overall number of clusters. At the same time, function f n (2) represents the absolute degree, as equated in Equation (11).
where CM q,c signifies the overall number of CM nodes based on route length, with the degree Deg C emphasizing the constant value of cluster density. We note that lesser density can be recognized as the lowest value.

BC Technology for Privacy Preserving
The BQMFO-CSSTS architecture uses BC as a trust infrastructure to provide trustworthy services to the user and protect their privacy [4]. Two kinds of BC nodes are available based on the locations and capabilities of the entity in the ITS framework, vehicle gateways, servers, and RSUs that are set to these types and integrated with other functions. One example is a full BC node. The node works as a miner, as it synchronizes and maintains a full copy of the blocks. Meanwhile, this node could see every transaction that takes place in the BC; only the authorized node with high ability could be used as one of this type of node, which includes servers, gateways, and RSUs in the core network. Another example is a lightweight BC node. These types of nodes are analogous to the Simplified Payment-Verified (SPV) nodes used in Ethereum. The set of nodes could not authenticate transactions via mining; on the other hand, they could identify a transaction based on the network. It should be noted that based on the needs and positions of the ITS operator, the network entities, including RSUs, gateways, servers, routers, etc., are set as lightweight and full BC nodes. In particular, vehicles were set to lightweight and full BC nodes, allowing them to be deeply included in the ITS (i.e., included in the BC network to secure additional benefits) or utilize the secured services with data protection.

Security Mechanism
A new security mechanism can be developed to find malicious nodes to further improve the security and availability of VANETs [28]. In cluster networks, the security and availability of CHs are very important. CHs help the server to transmit to and collect data via CMs. If the attacker wants to access the private information of other vehicles, they must act as the CHs. The vehicles controlled by a malicious attacker provide various identities (i.e.,vehicles) during the Sybil attack, and each vehicle has an analogous position, direction, maximal acceleration, and speed. Henceforth, this vehicle should have a high probability of being chosen as the CH and high relative mobility metrics.
A S t trajectory similarity metric is defined to protect network attacks for the CMs' privacy. The similarity metric is formulated in Equation (12): In Equation (12), li f etime i shows the lifetime of the i node, and ∆t i denotes the duration of c and node i that belongs to a similar cluster. The server evaluates the trajectory similarity metric used whenever a CH is selected. If a single CH has high trajectory similarity metrics, the server checks every node in the cluster to identify malicious attackers.

Performance Validation
In this section, the experimental validation of the BQMFO-CSSTS technique takes place, with consideration given to various aspects, such as the varying number of nodes n, transmission range r, and speed limit v. The proposed model was simulated using the NS3 tool. The existing models [29]  In Table 1 and Figure 2, the average cluster head lifetime (ACHL) results of the BQMFO-CSSTS technique are compared to those of recent models with varying r and v values [29].   In Table 2 and Figure   In Table 2    In Table 3 and Figure 4, the ACDL results of the BQMFO-CSSTS method are compared to recent approaches with varying r and v values.   Furthermore, with the values n and r values of 120 and 300, the BQMFO-CSSTS method gains an increased ACDL of 599.75 s, while the ATCM IOT-ESTS, WOACNET, DL-SCHS, and ST methods gain decreased ACDLs of 589.14 s, 555.52 s, 564.37 s, and 566.14 s, respectively.
In Table 3 and Figure 4,      In Table 5 and Figure    In Table 5 and Figure

Conclusions
In this paper, a privacy-preserving cluster scheme known as the BQMFO-CSSTS tech- Figure 7. Comparative ECON outcome of BQMFO-CSSTS approach using varying numbers of nodes.

Conclusions
In this paper, a privacy-preserving cluster scheme known as the BQMFO-CSSTS technique on VANET is developed. The presented BQMFO-CSSTS technique aims to accomplish secure VANET via BC technology and clustering process. It involves a series of processes, namely clustering, BC-based privacy-preserving, and security mechanisms. Moreover, the BQMFO-CSSTS technique initially uses the QMFO approach with a fitness function for the selection of CHs and the cluster construction process. In addition, BC technology is used as trust infrastructure to provide trustworthy services to the user and protect the privacy of the CHs and CMs. Finally, the BQMFO-CSSTS technique uses trajectory similarity metrics to protect the privacy of the CMs against attacks. The simulation results of the BQMFO-CSSTS technique are validated utilizing a series of measures. The comprehensive outcomes stated the superior performance of the BQMFO-CSSTS method over those of other recent algorithms. While the integration of BC technology provides the groundwork for enabling secure clustering in VANETs, future research can concentrate on improving the security aspects using privacy-preserving techniques, robust consensus mechanisms, and secure communication protocols to protect sensitive information and prevent malicious attacks from occurring within the clustering technique. In addition, the proposed clustering scheme should be implemented and tested in real-world VANET environments to validate its effectiveness and evaluate its performance under various realistic scenarios.  Data Availability Statement: Data sharing concerns do not apply to this article, as no datasets were generated during the current study.