Dynamic Changes In Network Topology Research Articles

A resilient routing strategy based on deep reinforcement learning for urban emergency communication networks

In the context of urban informatization, meeting the stringent requirements of emergency communication presents a significant challenge for Urban Emergency Communication Networks (UECNs). Mobile ad hoc networks deployed in these environments often experience node degradation and link disruptions due to the complex urban landscape, leading to frequent communication failures. This paper introduces a novel resilient routing strategy, termed Deep Reinforcement Learning-based Resilient Routing (DRLRR). The proposed routing strategy first utilizes node and link state information to accurately characterize dynamic changes in network topology. The routing decision-making process is then formalized as a Markov decision process, integrating multiple performance metrics into a reward function tailored for the specific demands of urban emergency communications. By leveraging deep reinforcement learning, DRLRR effectively adapts to the complexities of urban environment, enabling intelligent and optimal route selection during network topology fluctuations to ensure seamless data transmission during emergencies. Comparative simulations conducted using NS3(Network simulator 3) demonstrate that DRLRR significantly outperforms three other routing protocols, achieving notable improvements in packet delivery rate, average end-to-end delay, and throughput, thus fulfilling the requirements for reliable and consistent communication in urban emergency scenarios.

Adaptive and low-cost resource synchronization based on data distribution service in high dynamic networks

In disaster-stricken areas monitoring, management, search, and rescue operations, unmanned aerial vehicles (UAVs) play a crucial role in disaster management and emergency communication due to their high mobility. To efficiently coordinate and plan UAVs and their carried sensor and base station resources, synchronization is essential to establish consistency, laying the foundation for high-level demands. In such scenarios, synchronization relies on request–response (RR) or publish–subscribe (PS) forms of information exchange. Existing research in the field typically focuses on higher-level applications and selects either RR or PS synchronization, thereby overlooking the potential advantages that could be gained from combining both methods to meet synchronization requirements. We propose a resource synchronization method based on the Data Distribution Service (DDS) and a linear time complexity subscription mechanism tailored to specific query demands, which considers the pros and cons of the above two information exchange forms and the bottom-layer network topology. Experimental results using open-source simulation tools demonstrate that the proposed method adapts to scene requirements and decreases bandwidth by at least 21.2% and packet rate by at least 3.7% compared to different baseline methods across three topologies, while satisfying delay and query success rate requirements. Furthermore, the method maintains robust performance in the face of dynamic changes in network topology, showcasing its robustness.

TSFIS-GWO: Metaheuristic-driven takagi-sugeno fuzzy system for adaptive real-time routing in WBANs

Wireless body area network (WBAN) is an internet-of-things technology that facilitates remote patient monitoring and enables medical staff to administer timely treatments. One of the main challenges in designing WBANs is the routing problem, which is complicated due to dynamic changes in network topology and the limited resources of nodes. Several heuristic and metaheuristic methods have been presented to solve the routing problem in WBANs. Although metaheuristics outperform heuristics by producing higher-quality solutions, they cannot respond to real-time requests. This paper introduces a reactive routing protocol for WBANs that combines a fuzzy heuristic with a metaheuristic learning model. It utilizes a Takagi-Sugeno Fuzzy Inference System in conjunction with the Grey Wolf Optimizer (named TSFIS-GWO). The objective is to simultaneously benefit from the advantages of both approaches, namely, the effectiveness of metaheuristics for offline hyperparameter tuning and the quickness of fuzzy heuristics for real-time routing. At every round, the tuned fuzzy system takes multiple parameters of the current state of the nodes and links to construct the multi-hop routing tree under IEEE 802.15.6. To optimize the performance of the protocol for each WBAN, the fuzzy rules of the TSFIS model are automatically adjusted through a learning method based on GWO. This is done in accordance with the specific requirements of the application, and the tuning process takes place once before the protocol is applied. Simulation results in three applications demonstrate that the proposed TSFIS-GWO model is capable of providing real-time solutions while outperforming the existing methods in terms of application-specific performance measures.

Routing in Mobile ad Hoc Networks Using Machine Learning Techniques

The significance of Mobile Ad Hoc Networks (MANETs) has increased in the current period due to the proliferation of mobile devices and the emergence of Internet of Things (IoT) applications. The function of routing in decentralized wireless networks is of paramount importance in facilitating efficient data transfer. Conventional routing techniques need help in effectively adjusting to the dynamic changes in network topology and the constraints imposed by restricted network resources. To tackle these concerns, a Machine Learning-Based Optimized Routing Algorithm (ML-ORA) is proposed in this research. ML-ORA has been developed to offer adaptable and intelligent routing solutions, especially in MANET. This is achieved via the integration of parameter settings, the use of a Hybrid Particle Swarm Optimization (HPSO) algorithm for Cluster Head (CH) selection, the inclusion of a clustering stage, and the incorporation of a k-Nearest Neighbors (k-NN)-based intrusion detection system. The performance of ML-ORA is assessed by conducting simulations using Network Simulator 3 (NS-3), demonstrating its efficacy in dynamic network settings. The findings exhibit a latency of 15 milliseconds, a packet delivery ratio of 95%, a network throughput of 4Mbps, and an energy efficiency of 85%. ML-ORA presents a potentially advantageous resolution for tackling the obstacles encountered in MANET routing, thereby creating opportunities for enhanced efficacy and security in the transmission of data inside dynamic wireless networks.

Dragon fly algorithm based approach for escalating the security among the nodes in wireless sensor network based system

A new technology that is gaining popularity today is the Wireless Sensor Network. Smart sensors are being used in a variety of wireless network applications, including intruder detection, transportation, the Internet of Things, smart cities, the military, industrial, agricultural, and health monitoring, as a result of their rapid expansion. Sensor network technologies improve social advancement and life quality while having little to no negative impact on the environment or natural resources of the planet are examined in sensor networks for sustainable development. Real-world applications face challenges ensuring Quality of Service (QoS) due to dynamic network topology changes, resource constraints, and heterogeneous traffic flow. By enhancing its properties, such as maintainability, packet error ratio, reliability, scalability, availability, latency, jitter, throughput, priority, periodicity, deadline, security, and packet loss ratio, the optimized QoS may be attained. Real-world high performance is difficult to attain since sensors are spread out in a hostile environment. The performance parameters are divided into four categories: network-specific, deployment phase, layered WSN architecture, and measurability. Integrity, secrecy, safety, and security are among the privacy and security levels. This article leads emphasis on the trustworthiness of the routes as well as the nodes involved in those routes from where the data has to pass from source to destination. First of all, the nodes are deployed and cluster head selection is done by considering the total number of nodes and the distance from the base station. The proposed work uses AODV architecture for computing QoS parameters that are throughput, PDR and delay. K-means clustering algorithm is used to divide the aggregated data into three possible segments viz. good, moderate and bad as this process does not involve the labelling of aggregated data due to its supervised behavior. The proposed trust model works in two phases. In first phase, data is divided into 3 segments and labelling is done. In second phase, uses generated class objects are to be applied viz. the route records to publicize the rank of the routes followed by the rank of nodes. The proposed technique employed the statistical machine learning and swarm intelligence strategy with dragon fly algorithm in order to address the issues related effective rank generation of nodes and improving the network lifetime. Deep learning concepts can be combined with fuzzy logics approach for resolving issues like secure data transmission, trustworthiness of ranking nodes and efficient route discovery.

Coevolutionary dynamics of strategy and network structure with publicity mechanism

Understanding the emergence of cooperation on complex dynamic networks, especially how the dynamic changes of network topology interact with individual decision-makings, is an important interdisciplinary topic. This paper studies the coevolution of individual strategies and network structures by proposing a linking dynamics and introducing a publicity mechanism during the rewiring process of networks. Individuals can expand their popularity by spending some cost to publicize themselves, thereby increasing the likelihood of being connected in dynamic networks. It is found that the comparison between the publicity intensity of cooperation and defection determines which strategy is dominant in the evolutionary process. However, the existence of publicity costs will suppress the expansion of hype strategies such as defection. Excessive publicizing often fails to earn returns equivalent to huge costs. It is further revealed that in the process of dynamic adjustment of the network, the preference of network links, i.e. the combination of link breaking probabilities between different types of nodes, is an important factor that directly affects the evolution of cooperation.

FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs

Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score.

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).

An adaptive message return strategy for same direction multilane in vehicular networks

Due to the roadside unit can not fully achieve seamless coverage and the dynamic change of network topology in vehicular networks, problems such as the interruption of vehicle service, poor stability of communication link, and high network delay will be caused. To solve these problems, this paper proposes a codirectional multilane adaptive message return (CMAMR) strategy based on edge computing. Firstly, we establish a CMAMR communication model. Then, according to the movement trajectory of the receiving node, we propose a single-hop and a multi-hop message return link strategy. The former combines the single-hop link quality and node location information to construct a node selection function to assist the head node in selecting an optimal forwarding node to form a single-hop message return link. The latter assists the RSU to select the optimal multi-hop message return link in the same lane by establishing a link adaptive quality evaluation function, thus improving the user’s quality of server. The simulation results show that the link network delay of the CMAMR strategy is 14.6%, 42.86%, and 38.47% lower than those of the random with forwarding progress (RFP), most forward with fixed radius (MFR), and traditional vehicle-to-infrastructure and vehicle-to-vehicle cooperative communication (TVCC) message return strategies, respectively; correspondingly, the link reliability is at least 9.13% higher for the CMAMR strategy.

A novel security mechanism for software defined network based on Blockchain

The decoupling of the data plane and the control plane in the Software- Defined Network (SDN) can increase the flexibility of network management and operation. And it can reduce the network limitations caused by the hardware. However, the centralized scheme in SDN also can introduce some other security issues such as the single point of failure, the data consistency in multiple-controller environment and the spoofing attack initiated by a malicious device in the data plane. To solve these problems, a security framework for SDN based on Blockchain (BCSDN) is proposed in this paper. BCSDN adopts a physically distributed and logically centralized multi-controller architecture. LLDP protocol is periodically used to obtain the link state information of the network, and a Merkle tree is establised according to the collected link information and the signature is generate based on KSI for each link that submitted by a switch by the main controller selected by using the PoW mechanism. Such, the dynamic change of network topology is recorded on Blockchian and the consistency of the topology information among multiple controllers can be guaranteed. The main controller issues the signature to the corresponding switch and a controller checks the legitimate of a switch by verifying the signature when it requests the flow rule table from the controller later. The signature verification ensures the authenticated communication between a controller and a switch. Finally, the simulation of the new scheme is implemented in Mininet platform that is a network emulation platform and experiments are done to verify our novel solution in our simulation tool. And we also informally analysis the security attributes that provided by our BCSDN.

Routing in Delay-tolerant network Based on Nodes’ Sociality

To solve the problem of low ratio success delivery, long latency caused by the rapid dynamic change of network topology, limited node buffer, large amount of data transmission and large user population density in delay-tolerant network (DTN). This paper propose a routing algorithm in DTN based on nodes’ sociality. In this paper, contact history was used to determine the node link quality; Markov model was used to predict the node position based on the nodes’ movement track; according to nodes’ social similarity to divide community, then calculated node betweenness centrality. Finally, the best forwarding nodes for message transmission were determined. Besides, to increase the ratio of successful delivery, message delivery probability in the relay node’s buffer was calculated. Experimental results show that compared with traditional routing algorithm, the proposed algorithm performs better on the delivery ratio and average end-to-end delay.

Towards Optimal Dissemination of Emergency Messages in Internet of Vehicles: A Dynamic Clustering-Based Approach

In this paper, we target dissemination issues of emergency messages in a highly dynamic Internet of Vehicles (IoV) network. IoV is emerging as a new class of vehicular networks to optimize road safety as well as users’ comfort. In such a context, forwarding emergency messages through vehicle-to-vehicle communications (V2V) plays a vital role in enabling road safety-related applications. For instance, when an accident occurs, forwarding such information in real time will help to avoid other accidents in addition to avoiding congestion of network traffic. Thus, dissemination of emergency information is a major concern. However, on the one hand, vehicle density has increased in the last decade which may lead to several issues including message collisions, broadcast storm, and the problem of hidden nodes. On the other hand, high mobility of vehicles and hence dynamic changes of network topology result in failure of dissemination of emergency packets. To overcome these problems, we propose a new dissemination scheme of emergency packets by vehicles equipped with both DSRC and cellular LTE wireless communication capabilities. Our scheme is based on a dynamic clustering strategy, which includes a new cluster head selection algorithm to deal with the broadcast storm problem. Furthermore, our selection algorithm enables not only the election of the most stable vehicles as cluster heads, and hence their exploitation in forwarding the emergency information, but also the avoidance of packet collisions. We simulated our scheme in an urban environment and compared it with other data dissemination schemes. Obtained results show the efficiency of our scheme in minimizing collision and broadcast storm problems, while improving latency, packet delivery ratio and data throughput, as compared to other schemes.

Multi-Path Selection and Congestion Control for NDN: An Online Learning Approach

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path tag</i> to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

HDDS: Hierarchical Data Dissemination Strategy for energy optimization in dynamic wireless sensor network under harsh environments

The primary issue in Wireless Sensor Network (WSN) based monitoring applications is the preservation of the sensing coverage under the harsh physical environment. WSN forms the monitoring infrastructure, where a large set of energy-constrained sensor nodes are deployed to collect the physical parameters of the environment. The sensor nodes communicate their data to the nearby edge server (ES) where data is processed locally before it is forwarded to the distant cloud platform. Most of WSN’s existing routing algorithms aim to develop an energy-efficient data transmission route without recognizing the effect of harsh external environment, rendering it non-adaptive to environmental changes. In this paper, we have focused to develop an energy-efficient hierarchical data dissemination protocol for the sensor to ES communication. Along with energy optimization, the proposed protocol aims to preserve the network coverage and supports the dynamic changes in network topology like node failure, node addition, etc., due to harsh environmental interface. As the sensor nodes are required to operate autonomously, the proposed protocols are designed by considering only the local information of a sensor node. Simulation and experimental results show the efficiency of the proposed protocol over many state-of-the-art protocols with respect to several parameters such as energy consumption, lifetime of the network, coverage ratio, packet delivery ratio, latency, etc.

A Vehicular Service-Oriented Dynamical Routing Searching Algorithm in Software-Defined BusNet

With the development of the vehicular network, BusNet-based service is of great interest to both bus companies and public users. How to provide service and ensure service quality is considered as key points for the promotion of such kinds of applications. In this paper, a system architecture, which is designed based on multiaccess edge computing (MEC) architecture and software-defined network (SDN), is proposed. Moreover, considering the problem of the dynamicity of the network, a new routing path optimization algorithm, SPSO (serial particle swarm optimization), is proposed, while a dynamic weight matrix is used to improve routing performance. The comprehensive simulation results demonstrate that the proposed method can well adapt to the dynamic change of network topology and improve communication performance significantly.

Dynamic Orchestration of Brains and Instruments During Free Guitar Improvisation.

Playing music in ensemble requires enhanced sensorimotor coordination and the non-verbal communication of musicians that need to coordinate their actions precisely with those of others. As shown in our previous studies on guitar duets, and also on a guitar quartet, intra- and inter-brain synchronization plays an essential role during such interaction. At the same time, sensorimotor coordination as an essential part of this interaction requires being in sync with the auditory signals coming from the played instruments. In this study, using acoustic recordings of guitar playing and electroencephalographic (EEG) recordings of brain activity from guitarists playing in duet, we aimed to explore whether the musicians' brain activity synchronized with instrument sounds produced during guitar playing. To do so, we established an analytical method based on phase synchronization between time-frequency transformed guitar signals and raw EEG signals. Given phase synchronization, or coupling between guitar and brain signals, we constructed so-called extended hyper-brain networks comprising all possible interactions between two guitars and two brains. Applying a graph-theoretical approach to these networks assessed across time, we present dynamic changes of coupling strengths or dynamic orchestration of brains and instruments during free guitar improvisation for the first time. We also show that these dynamic network topology changes are oscillatory in nature and are characterized by specific spectral peaks, indicating the temporal structure in the synchronization patterns between guitars and brains. Moreover, extended hyper-brain networks exhibit specific modular organization varying in time, and binding each time, different parts of the network into the modules, which were mostly heterogeneous (i.e., comprising signals from different instruments and brains or parts of them). This suggests that the method capturing synchronization between instruments and brains when playing music provides crucial information about the underlying mechanisms. We conclude that this method may be an indispensable tool in the investigation of social interaction, music therapy, and rehabilitation dynamics.

Design and Development of Secured Framework for Efficient Routing in Vehicular Ad-Hoc Network

Due to many challenging issues in vehicular ad-hoc networks (VANETs), such as high mobility and network instability, this has led to insecurity and vulnerability to attacks. Due to dynamic network topology changes and frequent network re-configuration, security is a major target in VANET research domains. VANETs have gained significant attention in the current wireless network scenario, due to their exclusive characteristics which are different from other wireless networks such as rapid link failure and high vehicle mobility. In this are, the authors present a Secured and Safety Protocol for VANET (STVAN), as an intelligent Ad-Hoc On Demand Distance Vector (AODV)-based routing mechanism that prevents the Denial of Service attack (DoS) and improves the quality of service for secured communications in a VANET. In order to build a STVAN, the authors have considered a smart traffic environment in a smart city and introduced the concept of load balancing over VANET vehicles in a best effort manner. Simulation results reveal that the proposed STVAN accomplishes enhanced performance when compared with other similar protocols in terms of reduced delay, better packet delivery ratio, reasonable energy efficiency, increased network throughput and decreased data drop compared to other similar approach.

Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks

Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility characteristics of the nodes. According to the node prediction information, the metric value of the candidate node is calculated, and the node with the smaller metric value is selected as the data forwarding node, which can effectively reduce the packet loss rate and avoids excessive consumption. Simulation results show that compared with other algorithms, the proposed algorithm has higher data delivery ratio, and end-to-end data delay and routing overhead are significantly reduced.

Multi-Topology Routing Algorithms in SDN-Based Space Information Networks

Aiming at the complex structure of the space information networks (SIN) and the dynamic change of network topology, in order to design an efficient routing strategy, this paper establishes a SIN management architecture based on Software-defined Networking (SDN). A routing algorithm flow of the spatial information network based on a snapshot sequence is designed. For different spatial tasks with different Quality of Service (QoS) requirements, the concept of integrated link weight is proposed. The Warshall–Floyd algorithm is used to design the optimal routing strategy. A Task-oriented Bandwidth Resource Allocation (TBA) algorithm is proposed for multiple spatial tasks in the same link. Simulation results show that the algorithm can effectively guarantee the priority transmission of important tasks and avoid the unnecessary waste of bandwidth resources.

The Vehicle-to-Vehicle Link Duration Scheme Using Platoon-Optimized Clustering Algorithm

In the scenario of the vehicle network, the unstable vehicle to vehicle (V2V) connectivity caused by a high-speed vehicle movement is improved through efficient clustering algorithm to some extent. However, in view of the insufficient consideration of the variation of cluster stability in existing clustering algorithms, the V2V connectivity needs to be further enhanced. To this end, in this paper, a V2V link duration scheme using platoon-optimized clustering algorithm is proposed, which consists of three aspects: system model, a platoon-optimized clustering algorithm, and the analysis of link duration. Specifically, the second-order nonlinear dynamic system for platoon is analyzed to initialize the network scene. Then, a platoon-optimized clustering algorithm is performed, which combines the platoon leader (PL) selection based on motion consistency and the updated algorithm based on the dynamically stable cluster to reduce the randomness caused by the dynamic change of network topology. In addition, the V2V link duration based on the proposed scheme is quantitatively implemented through specific mathematical formulas. Finally, the extensive simulation results show that the proposed scheme can effectively guarantee the cluster stability and prolong the V2V link duration.